# Welcome to Prometheus-X Building the Personal Data Space **Work in Progress:** this website is under construction. Our goal is to give you the most comprehensive and detailed pieces of information about our project. If you have any question, want to join or have a feedback, please contact us [here](/extras/contact). ## Overview We believe in a human-centric data ecosystem where people are in charge of their data and their destiny. For that we need to provide tools to people and organisations to empower them with their data and allow data to be shared and used in a trustworthy way. Check out how such an ecosystem brings value to people and organisations: {% embed url="" %} Prometheus-X is providing [building blocks](/overview/what-we-do) as digital commons to operate such an infrastructure at scale and thus paving the way for a next generation open and decentralized data sharing infrastructure for humanity. Prometheus-X is enabling an open ecosystem to share data and services with first use cases in the education & skills sector. It will provide a marketplace of interoperable technologies for data empowerment, intermediation, storage and processing. All participants of the ecosystem will be able to share data in a trustworthy way, without depending on any single player. All those services will respect same trust & interoperability specifications. Prometheus-X is building and providing open source building blocks that will allow anybody to run and operate these services, create open ecosystems, while respecting these specifications. This will guarantee no player in the ecosystem is unchangeable. Check out our [community](https://prometheus-x.org/?locale=en) and [use cases](/use-cases/bearers-of-fire). Here are what people are saying about Prometheus-X project: > « Prometheus-X is the first operational project to build the Data Space of Education and Skills (DASES) within the [Gaia-X](https://gaia-x.eu/) initiative. Their building blocks can unlock added-value use cases for education and skills, through a secured and ethical data circulation, within France and Europe. » > > — From GAIA-X Education & Skills Vertical coordination ([Claudio Cimelli,](https://www.linkedin.com/in/claudio-cimelli-81a29535/) [Federica Minichiello](https://www.linkedin.com/search/results/all/?heroEntityKey=urn%3Ali%3Afsd_profile%3AACoAAAXV_4kB-MdhtvPGt9Z5fRUp4oZABQSu7Jo\&keywords=federica%20minichiello%20degroote\&origin=RICH_QUERY_SUGGESTION\&position=0\&searchId=ef803f91-b4b1-4137-91f8-7ec8d2073a83\&sid=!3_)) > *Prometheus-X is a new generation of education-related services for students, but also unemployed and employed people.* > > — From [Jean-Noël Barrot](https://www.linkedin.com/in/jeannoelbarrot/), French Minister in charge of Digital Transition and Telecommunications. To learn more about the dataspaces emerging in Europe, see the video introduction below at the last [Gaia-X summit](https://gaia-x.eu/summit-2022/) 2022 by [Jean-Noël Barrot](https://www.linkedin.com/in/jeannoelbarrot/): {% embed url="" %} ## Quick links {% content-ref url="/pages/Kx4CbL9qGNdvdXdTQrTR" %} [What we do](/overview/what-we-do) {% endcontent-ref %} {% content-ref url="/pages/lxoDZHiMWBxHR1HZjcxi" %} [Vision](/fundamentals/vision) {% endcontent-ref %} # What we do We are developing building blocks to enable human-centric, ethical and trustworthy exchange of data with first applications in the sector of Education and Skills. Many organizations, public and private, in Europe have great innovations and datasets in the sector of Education and Skills. However, the exchange of data among these actors has been very limited due to several factors: * lack of existing, easily available and secure building blocks enabling data exchange, * the unilateral aggregation and centralization of personal data by a few dominant players, * the complexity of EU regulation about data (e.g. GDPR). In agreement with the EU data strategy and Gaia-X initiative, Prometheus-X objective is to fund the development of the [building blocks ](/building-blocks/catalog)required for building the first european Data Space of Education and Skills (DASES). ### Our building blocks are digital commons [Digital commons](https://en.wikipedia.org/wiki/Digital_commons_\(economics\)) are software or middleware components that are needed by group of actors in an industry and for which the development is mutualised in order to decrease costs and improve interoperability. > The digital commons are a form of [commons](https://en.wikipedia.org/wiki/Commons) involving the distribution and communal ownership of [informational resources](https://en.wikipedia.org/wiki/Information_technology) and [technology](https://en.wikipedia.org/wiki/Technology). Resources are typically designed to be used by the community by which they are created. - Wikipedia ### Our Building blocks Dataspace of Education and Skills (DASES) working groups started in march 2021. After many months of work and meetings between the particpants of the various [working groups](https://dataspace.prometheus-x.org/fundamentals/working-groups), a first list of building blocks was identified. This list was them submitted to the vote according to the [governance](https://dataspace.prometheus-x.org/fundamentals/governance) of the association. For now, these building blocks are: * [Identity](/building-blocks/identity) * [Contract](/building-blocks/contract) * [Consent](/building-blocks/consent) * [Traceability](/building-blocks/traceability) * Interoperability * ... of [skills data](/building-blocks/interoperability/skills-data) * ... of [learning records](/building-blocks/interoperability/learning-records) * ... of [impact studies](/building-blocks/interoperability/impact-study) * ... of [plugins](/building-blocks/interoperability/universal-plugin-sdk) * ... of [metadata](/building-blocks/interoperability/ai-metadata-enrichment) {% hint style="info" %} Additional building blocks may be required in the future. The list above is not exhaustive. This list describes the first and prioritized building blocks to be developed for the dataspace to launch. By taking part in Prometheus-X you can decide which building blocks to develop and fund next! {% endhint %} Check them out, use them and contribute to them! # Bearers of Fire You have an innovative skills data sharing use case ? You want to join the most innovative projects on the matter ? Bearers of Fire are the most innovative skills data ecosystems that shine the light and inspire others. They unite pioneering organisations that band together to make the future of learning and working happen! They are based on Prometheus-X's use cases: * [Personalised skills matching](/use-cases/skills/personalised-skills-matching) * [Decentralised skills analytics](/use-cases/skills/skills-analytics-dashboards) * [Personalised education](/use-cases/learning-traces/learning-analytics-to-personalize-education) **Sponsors** describe precise needs they have that a skills data ecosystem can solve. \ AI, service and data providers unite to answer that need and deploy the data ecosystem with the Sponsor towards its students, pupils, citizens, employees and partners. Bearers of Fire are selected by the Prometheus-X board to benefit from: * top expertise to build your data ecosystem, its business and governance model * credits to use the Prometheus-X services to operate your data ecosystem * unique visibility at international level across many education related events Join the program: * be matched with the most relevant ecosystems to provide your data and services * design the use case with the sponsor * define the business model to access your services and data * test the use case and start generating value Our sponsors (and the list is still growing):
Check out some quick descriptions of our sponsors' projects: {% content-ref url="/pages/IXiObVBkqEjh2V9jlc6m" %} [Grande Ecole du Numérique](/use-cases/bearers-of-fire/grande-ecole-du-numerique) {% endcontent-ref %} {% content-ref url="/pages/FJxG2fNvQ0KrszuP8znr" %} [Antares](/use-cases/bearers-of-fire/antares) {% endcontent-ref %} {% content-ref url="/pages/oEZRe3YDoahLerYwQwLC" %} [Ile-de-France Region](/use-cases/bearers-of-fire/ile-de-france-region) {% endcontent-ref %} {% content-ref url="/pages/M9fo0cleuuwgkicB6i23" %} [Sikt](/use-cases/bearers-of-fire/sikt) {% endcontent-ref %} {% content-ref url="/pages/o04VpZ2zpKtqhsQB99rd" %} [Institut Mines Telecom](/use-cases/bearers-of-fire/institut-mines-telecom) {% endcontent-ref %} {% content-ref url="/pages/7aotb8drMY2jXFAqjaar" %} [University of Lille](/use-cases/bearers-of-fire/university-of-lille) {% endcontent-ref %} {% content-ref url="/pages/dAg8RF6zfhnoPUmTUW2B" %} [Edunao](/use-cases/bearers-of-fire/edunao) {% endcontent-ref %} {% content-ref url="/pages/lfS7Zyw8EAvyHDUsiGgR" %} [Brest Business School](/use-cases/bearers-of-fire/brest-business-school) {% endcontent-ref %} {% content-ref url="/pages/4EXxGGulHWms3YhBbtJn" %} [Cabrilog](/use-cases/bearers-of-fire/cabrilog) {% endcontent-ref %} # Grande Ecole du Numérique **Data ecosystem project name:** Digital skills matching and analytics
**Sponsor name:** Grande Ecole du Numérique **Sponsor description:** GEN mission consists in promoting digital training opportunities in order to provide French companies with digital skilled workers and ensure their competitively, as France is facing a digital skills gap. **Project description:** The digital skills gap is not a French exception, Europe also faces a shortage of digital experts who can develop cutting-edge technologies for the benefit of all citizens. This issue has to be tackled at European level.
ExistingOpportunity / Use caseUsers interested Numbers

GEN succeeded in creating and implementing a detailed and exhaustive map API of existing digital jobs, already adopted by French digital ecosystem.


  • Implement a European digital mapping API to be enable countries to compare digital skills, trainings and jobs data
  • Add skills descriptions to the jobs
  • make the digital job opportunities and training offer more understandable

EU commission

Training organizations

People


This mapping allowed GEN to gathered data from every digital training provider to build a unique search engine helping people accessing to the whole training offer for each job (depending on current level, expected diploma, area…)

Enhance the search engine :

  • Add skills description to allow matching between people profiles, jobs and trainings
  • develop a search engine API to share with European ecosystem
  • add a job offers marching

European undergraduates, students

European people who need to reskill

European Edtechs



This API enabled GEN to classify job offers and publish statistics on French digital skills gap (GEN_SCAN). GEN also elaborated a stress index for every digital job depending on the area, to highlight current and upcoming digital skills gaps.

Expend this observatory throughout Europe in order to :

  • share a European unique dataset on digital skills gap
  • find solutions at European level
  • spot emergent digital job and skills needs and feed the digital job mapping
  • train the AI

Ministries of Education

Training funders

Training companies

Job boards

People






**Data provided:** * all training offers in the digital domain in France, * skills analytics in the digital domain in France. **Testers provided by the sponsor:** * training organisations in France, * students of GEN trainings. **Skills Datatypes needed:** * skills profiles of people working in digital domain * job and training offers in digital domain **Skills services needed:** * skills gap, * skills analytics, * skills identification, * skills matching services. **We are looking for: skills gap, skills analytics, skills identification and skills matching services, skills data sets, job offers and training offers data sets in the digital domain.**
# Sikt Norwegian Agency for shared services in education and research Sikt has for many years developed joint services for all higher education institutions in Norway, both for enrollment and student information. We are also providing the digital learning environment for the norwegian students. Norwegian higher education has not really started using AI to improve services supporting Lifelong learning, Learning analytic and skills matching, but a lot will start to happen in the years to come. All the higher education institutions in Norway with their students are potential users if we deploy the solutions. There are close to 300 000 students in Norway, but Sikt must cooperate with one or more universities to test the sollutions. We target to have between 2-5 test universities during the project.\ \ **We are looking for: learning analytics and adaptive learning solutions, skills analytics and matching services.**
# Ile-de-France Region The Ile-de-France region, through its Regional Public Orientation Service (SPRO) and its employment basin strategy, organizes actors in orientation, education, higher education, and employment into an ecosystem so that they can work together in a coordinated manner, particularly through the sharing of information. These actors include institutional actors such as the Academy, local missions, youth information networks, and universities, but also private actors such as training organizations, employers, and edtech companies. Each of these actors supports individuals in their orientation and education journey throughout their life, and therefore holds personal data about the individual that makes up part of their profile. This data may concern the individual's situation, professional experiences, education history, skills, interests, or career aspirations. No single actor has a complete profile, which prevents individuals from having a full understanding of themselves and sharing their complete profile to receive the best possible support or recommendation. The use case, therefore, consists of bringing together some of these actors within the same personal data network centered on the individual so that they can circulate their data between these different actors to receive a personalized and well-coordinated journey, as well as relevant recommendations for support, resources, education, and employment. **We are looking for:** **skills gap, skills analytics, skills identification and skills matching services, skills data sets, job offers and training offers data sets.** # Antares Antares Project is running the content distribution solutions for most German states. The reach of the solution is about 75% of schools in Germany. At the moment the usage is just in one direction: Presenting the content. There are no widely used solutions for publisher agnostic learning analytics. Not even a learning record store is known to German schools. Learning paths are not digitized. With this project we will introduce completely new possibilities to teachers and students on how to improve the learning experience. For this use case, it would be relevant to observe different data sets in order to progress in the creation of new learning paths. **We are looking for: learning traces data sets, learning analytics and adaptive learning solutions.**
# Institut Mines Telecom As a higher education institution, we are interested in: * Translating learning traces into skill data, and making them available both to our institution for validation and students for lifelong learning skills portfolio management. * Analyzing skills demand * Help people’s upskilling for continuing education. We have specific ongoing projects in Industry of the Future, Cybersecurity and Digital Health
IMT is the number one group of engineering and management graduate schools in France. IMT has more than 13,000 students in 8 engineering and management graduate schools. **We are looking for: learning analytics and adaptive learning solutions, skills analytics and matching services.**
# University of Lille As part of its transformation strategy, the University of Lille promotes the dissemination and reuse of open educational resources (OER). Several projects are underway, including a search engine for exam topics and educational resources called Ioké (). By allowing data circulation within a trusted framework, this diagram will enable the customization of searches in OER indexes to provide personalized recommendations for a given learning context (training course, teaching unit, etc.). This would involve cross-referencing with local learning traces to further personalize the student's path. The recommendation of resources will be cross-referenced between resources indexed by Ioké but also by resources indexed by other educational resource search engines such as the edtech company Inokufu's engine. The exchange of learning traces between the University of Lille and Inokufu would improve the relevance of recommendations for the learner by providing context for each query (training course, teaching unit, recent activities) in addition to the keywords searched. Other sources of learning traces, as well as student competence profiles or professional objectives, will also be added to the circulation diagram, such as the Orientoi, JobReady, and Campus Skills platforms. This will allow the interoperability service for learning traces from multiple entities with heterogeneous trace formats to be tested. The identification of competences, both of learners performing searches and educational resources, will also be carried out using the interoperability commons for competences developed by Prometheus-X. **We are looking for: learning traces data sets, learning analytics and adaptive learning solutions.** # Edunao Demo-ES PSL is a State-funded project of Université Paris Sciences & Lettres, aiming at recommending added training or education to alumni.
The detailed analysis of the students' knowledge vs Life-long learning offer helps alumni identify weak knowledge and ways to fill education gaps.
For this use case, it would be relevant to observe different data sets in order to progress in the creation of new learning paths **We are looking for: learning analytics and adaptive learning solutions, skills analytics and matching services.**
# Brest Business School The primary target will be to deploy the Prometheus project for undergraduate learners from Year 1 (Bachelor 1) to Year 5 (Master 2). This represents up to 500 learners with a diversity of backgrounds (arts & social sciences, scientific, business backgrounds, etc.) and nationalities (30% of international learners).
Most learners are apprentices and spend one to two years in a company at Bachelor or Master level. During the work-study part of the programme, they spend 3 weeks in their company and 2 weeks in class. From September 2023, they will follow all their courses in hybrid learning and will have the choice to come to class or attend it by distance to avoid unnecessary travel between the company and the business school which may be in different locations in France. \ The aim of the current project is to take a step further and test all along the undergraduate courses how the association of the various software interconnected with Oktonin will enable: * Learners to develop further specific skill sets through the online courses suggested by the AI based on their progression and improve their employability * Learners to find jobs related to the skills developed in Oktonine through the tailored job offers or through the suggested alumni network. * Corporate relations staff to gain information about the learners’ progression and better support to develop their skills, find jobs and successfully complete their apprenticeship. * To train the AI to identify the skills to be developed in a business school in order to match the market demand This should support learners to be better prepared for the world upon graduation, and beyond this case study, to provide useful information to the education system in the EU about the skills to develop and foster. **We are looking for: skills gap, skills analytics, skills identification and skills matching services.**
# Cabrilog **Use case 1 : Smart Enseigno AI** Smart Enseigno has an extensive collection of pedagogical resources covering 80 % of the cycle 2 mathematics program in France. To use this collection, teachers are assisted by an AI service: * in the fine analysis of students’ knowledge ; * in the construction of learning paths. The detailed analysis of the students' knowledge helps the teacher to identify weak knowledge or blocking difficulties for the student. The creation of learning paths is used for mediation, to consolidate certain knowledge and to achieve new learning objectives set by the teacher. This service is aimed directly at the teacher and gives them the freedom to personalize and adapt the suggestions of the IA service. The use case as described above has been deployed and used for more than 3 years by nearly 15 000 users in France. This use case is therefore operational and able to produce usable data. For this use case, it would be relevant to observe different data sets in order to progress in the creation of new learning paths.
In the current state, the AI service could be improved in the creation of paths to achieve a given learning objective. On a technical level as much as on a didactical level, it would be relevant to observe the choices made by teachers via various data sets. **We are looking for: learning traces data sets, learning analytics and adaptive learning solutions.** **Use case 2 AI to support the student** This use case is positioned in a perspective of autotome use by the student : * in a school context where the student will have access to a remedial service at home or in the school in total autonomy ; * in a context of lifelong training allowing access to new skills, with a view to returning to work, training or professional reorientation. The challenge is to be able to produce, from level diagnoses and learning objectives, learning paths covering various knowledge graphs. In this use case, the challenge is therefore the creation of a system capable of analyzing traces of learning such as typologies of errors, independently of the chosen knowledge graph.\ In this use case, the challenge is both didactic and technical. Research in didactic faces a conceptual obstacle to describe independently of the task carried out, an knowledge graph.
The technical aspect of the choice of feedback by the AI service is linked to the didactic work. The didactic work brings the knowledge graph which is treated by the AI system to produce the feedback. **We are looking for: learning traces data sets, learning analytics and adaptive learning solutions.**
**Use case 3 AI to support scientist** This use case is aimed at the researcher. Learning traces are a powerful tool in many research fields : didactics of mathematics, cognitive sciences, developmental psychology, neurosciences, etc. Learning traces are a powerful analysis tool. They allow the researcher to describe the interactions between a learner and mathematical concepts. In this case of use, the researcher is able to produce digital supports for his experiments and to choose the learning traces to be collected (mathematical validity of a result, manipulation carried out, strategies for exploring a figure, eye-tracking, etc.). In this case of use, the contribution of other AI systems is relevant and could allow the researcher access, for example, to new axes of analysis. **We are looking for: learning analytics and adaptive learning solutions.**
# Ecole Centrale Electronique The Ecole Centrale d'Electronique's initiative around the Valorization of Student Projects (VPE) was labeled in the spring of 2012 as part of the IDEFI call for projects. The principle of the VPE program is to encourage engineering students to undertake a project that goes beyond a simple pedagogical exercise by creating value that would be recognized outside of the ECE. Since October 2021, a team at ECE has been mobilized to implement a data visualization system that allows for the interrogation and analysis of impact data that is available. Objective: * Allow for the evaluation of digital solutions through harmonized impact measurements, identifying key criteria for their qualification. * Bring together learning data and traces, declarative questionnaires, and usage monitoring. We are looking for: learning traces data sets, learning analytics solutions. # Skills # Personalised skills matching This use case would allow to answer needs such as : * As a person, I would like to be able to easily interconnect the different competence actors that concern me, to pool data, to be able to visualize all of my competences and their level of validation and then to be able to easily valorize them externally and to be offered relevant training and jobs. * As a person, I would like to be able to value skills that are not only issued from the school or academic environment: jobs, professional, voluntary and artistic experiences also bring me skills that are useful in my personal and professional evolution. * As a training organization, I would like to be able to access the precise profile of a person's skills and to be able to trace this skill and its acquisition in order to offer the right training at the right time to the person. * As an employer, I want to be able to access a person's precise skill profile and be able to track that skill and its acquisition to offer the right job to the person. * As an organization (employer, training, community), I would like to be able to access aggregate skills data on a territory to establish precise statistics on the needs and prediction of skills in order to orient my recruitment policy, the financing of my training courses or the development of new training. Check out a demo of such a use case here: {% embed url="" %} This use case enables stakeholders to provide people and organizations with a portal to easily access all these services in an interconnected way: * People can use different app providers to define their profile (skills, hobbies, personality, preferences, etc) * They can share this data from multiple sources with app providers to help them identify the best career move (next title, job, sector) and the skills gap * They can identify which career move is best for them and share their skills gap with AI providers that match them with training catalogs, content providers to get the right learning recommendations * Recommendations of career moves and job / training offers can be easily integrated into any interface of the ecosystem, showing the relevant information to people where they are * People can share their full profile and plan with the relevant stakeholders to put it into place (coaches, managers, training providers, etc) * All the while they have full control over their data and who can access what These portals can be adapted and used by people in different situations: * high school pupils looking for what higher education to get / what sector to get into * jobseekers looking for an opportunity * employees looking to upskill or reskill The portal can be adapted to specific contexts, for instance: * to an employers skills ontology, organigram, learning content to match employees with career mobility options and upskill them accordingly * to a university / training provider training catalog to match students / alumni with particular trainings * to a region’s in demand jobs and skills to orient jobseekers towards recruiting jobs and industries ### Benefits * Allowing people to gather and share a full skills profile across all organisations of the ecosystem (edtechs, orientation services, higher ed, training orgs, employers, etc) * Pooling data to offer better services to people (employment, training, education, etc.) * Matching people with the right training opportunities * Creating ethical networks of personal data * Enabling the individual to receive personalized lifelong learning * Providing interoperability of skills data ### Roles *People:* * get innovative employment and orientation services * can share their full profile with relevant stakeholders *Universities / training providers:* * contribute their training catalog and skills ontology * match their offers with relevant profiles * provide students with innovative employment and orientation services * get precise student profiles *Employers:* * contribute their job offers/descriptions, skills ontology, learning content, organigram * match their offers with relevant profiles * provide employees with innovative upskilling and career mobility services * get precise employee profiles *High schools* * provide pupils with innovative employment and orientation services * get precise pupil profiles *Employment agencies:* * provide jobseekers with innovative employment and orientation services * get precise jobseeker profiles *Edtechs / AI Providers:* * provide their services and their data to the ecosystem * get more users and clients * provider better personalized services thanks to better data access *Infrastructure providers:* * provide services and building blocks to enable data sharing (consent, contract, interoperability, data visualization, decentralized processing, etc) * get organizations to use their services *Orchestrator:* * provide the ecosystem portal * coordinate governance, use cases and business model discussions in the ecosystem * get part of the value generated by the ecosystem through commissions, fees, etc Prometheus-X already unites an international ecosystem of stakeholders committed to implementing this use case through specific data ecosystem portals that serve their needs. ### **Partners involved:** *Training organisations / universities:* * [Institut Mines Telecom](https://www.imt.fr/) * [University of Koblenz](https://www.uni-koblenz-landau.de/en) * [Brest Business School](https://brest-bs.com/) * Training organizations and universities mobilized through Reskill 4 Employment Finland ([NOKIA](https://www.nokia.com/fr_int/)) * Training organizations and universities mobilized through [Grande Ecole du Numérique](https://www.grandeecolenumerique.fr/) * Training organizations and universities mobilized through [IMC](https://www.im-c.com/) (Germany) * Training organizations and universities mobilized through [Sikt](https://sikt.no/en/home) (Norway) * Edunao with possibly over 10 Higher Ed institutions and Corporate Training centers * Training organizations and universities mobilized through Reskill 4 Employment Sweden (Astra Zeneca) *Employers:* * NOKIA * [Orange](https://www.orange.fr/portail?gclid=Cj0KCQiA_P6dBhD1ARIsAAGI7HBR44wsvHebMxgjjPCkekqJcBiAS8zXxhxZhVM3C0O6rHKS6EWbqjEaAsEQEALw_wcB\&gclsrc=aw.ds) * Employers mobilized through [Digital Europe](https://www.digitaleurope.org/) * Employers mobilized through [France Digitale](https://francedigitale.org/) * Employers mobilize through Reskill 4 Employment Finland * Employers mobilize through Reskill 4 Employment Sweden * Employers mobilized through Grande École du Numérique *High Schools:* * [Académie de Nancy](https://www.ac-nancy-metz.fr/) (146 high schools) * Académie de Rennes (112 high schools) * High schools mobilized through [Antares](https://www.antares-geo.de/) (75% of german high school students) * Edunao with the French Ministry of Education *Employment agencies / orientation actors:* * Actors mobilized through [Visions’](https://visionspol.eu/) ambassador network (250+ orientation structures in France) * Actors mobilized through Grande Ecole du Numérique * Actors mobilized through the Paris Region * Actors mobilized through [Schueler Karriere](http://schuelerkarriere.de) * Actors mobilized through Antares * Actor mobilized through University of Koblenz *Edtechs / AI providers:* * [HeadAI](https://headai.com/) * [Mindmatcher](https://mindmatcher.org/) * [Trouve Ta Voie](https://www.trouvetavoie.io/) * [Inokufu](https://inokufu.com/) * [LA Group (PALM)](https://palm-app.ai/) * [Neobrain](https://www.neobrain.io/) * [Weanly (Oktonine)](https://oktonine.com/home) * [Schueler Karriere](http://schuelerkarriere.de) * [Aerowork](https://www.aerowork.fr/) * [Ikigai](https://ikigai.games/games/gamesList) - Games for citizens * [Maskott](https://www.maskott.com/) * 20+ edtechs interconnected through [VisionsGalaxy](https://visionsgalaxy.com/) *Infrastructure providers:* * Visions * HeadAI * Inokufu * Mindmatcher * [Cozy Cloud](https://cozy.io/fr/) * [PolyPoly](https://polypoly.net/en/) * Edunao *Orchestrator & portal provider:* * [VisionsGalaxy](https://visionsgalaxy.com/) ### Building blocks mobilized: In order to make such a use case a reality, different building blocks are needed. Prometheus-X is developing them: * [Consent](https://dataspace.prometheus-x.org/building-blocks/consent): to enable people to control their data between all parties in a human-centric way * consent agent: to suggest to people the apps / organisations that best fit their needs * [Contract](https://dataspace.prometheus-x.org/building-blocks/contract): to ensure trust and compliance between organisations sharing data * [Identity](https://dataspace.prometheus-x.org/building-blocks/identity): to ensure authentification of orgs and people sharing data * [Skills data interop: ](/building-blocks/interoperability/skills-data)to allow the translation of profiles into competences, to ensure interoperability between the different repositories and data models concerning competences, to allow the tracing of the recognition and validation of a competence. * [AI metadata enrichment: ](/building-blocks/interoperability/ai-metadata-enrichment)algorithms capable of extracting from raw data the activity that allowed the acquisition of competencies on the basis of common reference systems (ROME/ESCO/RECTEC...); * [inter-repository translators](/building-blocks/interoperability/skills-data): algorithms capable of semantically linking skills repositories to each other and to central repositories, thus making it possible to create a first ontology, making it possible to link sectorial skills (for example, to identify the skills of an individual using different repositories) * distributed data visualisation: to allow results of matchings (jobs, trainings, ressources) to be shown wherever the user is # Skills analytics dashboards This use case would allow to answer needs such as : * As an organization (employer, training, community), I would like to be able to access aggregate skills data on a territory to establish precise statistics on the needs and prediction of skills in order to orient my recruitment policy, the financing of my training courses or the development of new training. ### Functionality: This use case enables stakeholders to build Skill-Analytics Dashboards with underlying skill-analysis services to provide a flexible tool to support L\&D departments and other stakeholders. These Skill-analytics dashboard shall be able to aggregate and analyse skill-data coming from different sources within an organisation but also across different organisations and regions. The skill analytics dashboard service will be offered within the European Skills Dataspace. Organisations with the respective access rights will be able to book the service and feed the service with skill-data that match their needs and of course the access policies. Ideally, the tool proactively suggests to the organisation skill-datasets that match their requirements and the policies and rules. The user can then load the data, trigger analyses and drill-down the information to the respective level of granularity. Potential views of the dashboard - which need to be designed with stakeholders within the SIMPL project - are: * Skill-status analysis: Which skills and levels are available within an organisation by how many staff members in which department, region, company branch etc. * Skill-gap analysis: L\&D manager can feed into the tool known job profiles of the organisation required to be able to offer the services. The tool helps to find skill-gaps, viz. for which skills does the company lack sufficient staff members in a specific region. * Skill-status comparison: Compare the skill-status of different but related departments / organisations / regions. * General analyses: Show typical current and expected future job profiles of a selected industry sector These Dashboards will be useful in particular for larger multi-national companies or for public institutions responsible for skill and personnel planning in distributed organisations (across regions or even countries). The tool can also be used by educational training providers to analyse how they should update their educational offers to respond to skill-needs of the market. The implementation of such a service requires that (anonymized) skill-data and skill-models are shared on a broad basis by different stakeholders in different sectors and domains so that models can be trained to analyse current and predict future skill-needs. Relevant dataset concern e. g. skill-profiles for certain job roles, (anonymized) skill-profiles of individual users, organisational structures and groups, information about domains and sectors etc. For example, data from different sources, in different formats, following different skill-taxonomies need to be mutualized before they can be aggregated and analyzed in the skill-analytics dashboard. ### Building blocks mobilized: In order to make such a use case a reality, different building blocks are needed. Prometheus-X is developing them: * [Contract](https://dataspace.prometheus-x.org/building-blocks/contract): to ensure trust and compliance between organisations sharing data * [Identity](https://dataspace.prometheus-x.org/building-blocks/identity): to ensure authentification of orgs and people sharing data * [Skills data interop: ](/building-blocks/interoperability/skills-data)to allow the translation of profiles into competences, to ensure interoperability between the different repositories and data models concerning competences, to allow the tracing of the recognition and validation of a competence. * [AI metadata enrichment: ](/building-blocks/interoperability/ai-metadata-enrichment)algorithms capable of extracting from raw data the activity that allowed the acquisition of competencies on the basis of common reference systems (ROME/ESCO/RECTEC...); * [inter-repository translators](/building-blocks/interoperability/skills-data): algorithms capable of semantically linking skills repositories to each other and to central repositories, thus making it possible to create a first ontology, making it possible to link sectorial skills (for example, to identify the skills of an individual using different repositories) * distributed data visualisation: to allow results of matchings (jobs, trainings, ressources) to be shown wherever the user is and without * [anonymisation](https://dataspace.prometheus-x.org/building-blocks/anonymization-pseudonymization): to ensure all data used by the dashboards is not personal data Prometheus-X gathers numerous organisations, data and AI providers that can make these use cases happen: | **Dashboard users** | **AI Providers** | **Data providers** | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |

​​Grande Ecole du Numérique: national public body, addresses virtually all french citizens and french digital skills training orgs

Dashboard to show skills needed in the digital sector

| HeadAI: cognitive AI providing advanced analytics available via API, skills demand forecasting and simulations using a variety of sources | HEA: skills demand and offering in local, country, industry, or global level generated through analysis of 25 m job offers, 10m online content, 2 m books | |

SCHUELERKARRIERE: company, 4,5 million german pupils and a network of teacher and schools


Dashboard to show teachers skills of pupils

| MindMatcher: advanced analysis on the labour market, skills identification generated from semi-structured data sources (ex: CVs, skills needs in a territory), personalised analysis of skills and matching with career opportunities in a region, analysis of cross-functional skills in a geographical location or market |

MM

-4076 Semantic Entities for Ontologies building created through publicly available data (W3C, HRopen Standards, AFNOR,...)

-Jobs & Skills related Semantic Entities (50k+ entities)

-Semantic Relationships between jobs and skills in the Rome & Esco Graph (25k+ mappings)

| |

Antares: company, 75% of schools in Germany


Dashboard to show skills of pupils

| Neobrain (NEO): Skills management solution offering skills repository, analysis and identification of missing skills, visualisation of jobs accessible to employees based on competencies and matching skills with internal job offerings that correspond to the employee profiles in an organisation |

Neobrain:

-15.700 job titles entries, and around 64.000 skills associated with their corresponding type

| |

Univeristy of Koblenz (UoK): University, 10 000 students


Dashboard to show skills of students, predict needs and tailor trainings

| WEANLY (WEA) : AI specialising in defining skills through analysis of experiential learning activities |

TTV: samples ranging from 15 million to 20 million+ job offers from public job boards in France, key

information such as job titles, job descriptions, and publication date, location, salary, required

qualifications and skills.

| |

Institut Mines Telecom (IMT): University, 13000 students


Dashboard to show skills of students, predict needs and tailor trainings

| Inokufu: edtech company providing API access to learning object recommendation engine and competency matching. | Cozy Cloud: develops Cozy, an open-source and versatile personal data store to import, store and cross any kind of data from external providers, through connectors. The platform is used by more than 80K users | |

Games for Citizens (GC): Association of 30 universities


Dashboard to show skills of students, predict needs and tailor trainings

| Schuelerkarriere: AI enabling students to find suitable studies and careers based on their skills |

IMC: formalised job profiles (set of skills and levels for a specific job), user-specific skill profiles, skill-taxonomies with levels, training offer descriptions (metadata of different types of offers), (anonymized) user profiles and course learning histories, organisational data


Number of datasets depend on final deployment partners

| |

France Digitale (FD): association, 1500 employers in digital domain


Dashboard to analyze skills gaps for employers and help them recruit

| LA Group (PALM): skills gap prediction, career trajectory estimations, matching and scoring employees with available positions within the company |

GEN:

-15000 training offer descriptions

103 -Family/Job/descriptions in digital domain

-Skills analytics on job demand in the digital sector based on 15 to 20 million job offers

| |

Sikt (SIK): public body, enabling the innovation Norwegian higher ed


Dashboard to analyze skills and skills gaps for Norwegian citizens across universities

| TrouveTaVoie: comprehensive analysis of job postings data and analysis of trends in the job market, identifying on-demand skills and tracking the evolution of sectors over time |

ANT:

More than 27M xAPI learning traces generated from learner activity

| |

Le Cnam: high. Ed. organization dedicated to lifelong professional training, with more than 200 training centers in France


Dashboard to predict skills needed

|


|

SK:

-4.5 million orientation and skills profiles generated through user activity

| |

BME: Largest technical university in Hungary, 10000 students


Dashboard to show skills of students, predict needs and tailor trainings

|


|

WEANLY (Oktonine):

-10 000 learner profiles years of study, domain, specialization, activities, list of to-be-acquired competencies) list of performed activities, list of validated competencies

| |

Brest Business School (BBS): school, 500 learners


Dashboard to show skills of students, predict needs and tailor trainings

|


|

EDUNAO:

Edunao deliver managed Moodle environments to hundreds of organisations

| |


|


| Inokufu: dataset of learning traces (xAPI) for 2k users in transition to high demand job | |


|


| UoK: student data from 10 000 students | # Learning traces # Learning analytics to personalize education Personalized learning is considered to be the most efficient of all training and education approaches because it is tailored to the individual needs, interests and learning style of each student or learner. Personalized learning allows for the development of customized learning plans and activities that are based on the student's abilities, interests, and goals. This approach allows for a more efficient use of time and resources, as students are able to progress at their own pace, focusing on the areas where they need the most help and skipping over material they already know. However personalized education in current education and training organizations requires a high ratio of teacher/trainer per learner which makes it expensive and limits its democratization to a wider population. Additionally, the direct observation of a pedagogical activity in real time by a teacher alters the way a learner actually works and usually biases the way the activity is performed in terms of exercising different approaches and learning from errors that limits the impact of the activity. Recent advances in AI and automatic data collection techniques are paving the way for personalized learning at lower cost and thus large scale. Real time data collection is furthermore allowing a very flexible way to propose either “in activity” guidelines to the learner or provide delayed recommendations proposing a more global message from the whole educational activity. For this to happen, it is critical to have a trusted dataspace of services to collect, store and process the learning records of learners. These learning records are the elementary data that will fuel recommendation engines, dashboard visualizations, and other related services to provide more personalized and individualized learning experience to all learners within Europe, whether students in school or workers in SMEs needing reskilling. Many actors, both public and private (edtech tools, universities, schools, high-schools, employers, training organizations, edtech tools) are working to improve the collection, storing and processing of learning records and their uses in learning analytics. This must be done in a way that allows either to store their data in their own infrastructures or in a dedicated secured and sovereign cloud solution within a framework that guarantees their interoperability with other information systems. Indeed, it is very interesting to provide information beyond the pedagogical activity data to better guide learners: past curriculum is a tremendous source of information to understand the possible reasons of the deficiencies of a student and propose corrective activities, current curriculum and its description in terms of competences can be important to better associate a given activity output, possibly transversal to different educational programs such as a quiz application for instance, with the objectives of the current training of that learner, etc. All these stakeholders function in silo are currently with no efficient way of interconnecting their services and access each other’s data: * To this day, a EU citizen does not have an easy way to access all their own learning traces from school, middle school, high school, university, higher education or professional training. There could be no Lifelong Learning for EU citizens if there is no Lifelong availability and portability of learning data. * By lack of good practice or knowledge of regulations (GDPR), learning records may often contain personal data, their use and sharing among organizations has been limited. Still there are technical solutions for protecting the personal data in the learning records that could enable data exchange and full GDPR compliance. * While many training organizations are aware of the potential of personalized learning, most adaptive learning systems available today have been trained on limited data sets. Because these data sets are too small, they may contain bias and have limited range of application. By combining data sets of learning records of a few organizations, we could highly improve the efficiency and inclusivity of personalized learning systems. * Most training organizations are better at storing physical - paper and pen- learning records than digital learning records. This is quite paradoxical as collecting digital learning records is less expensive and easier to automatize than physical learning records. * While standards have been slowly emerging (i.e. xAPI), the data model of Learning records may widely differ in most organizations. This technical barrier comes in addition to the legal barrier. * The learning activities are evolving fast and have many new forms. Yesterday, learning was mainly happening in the physical classroom or with books. Now, learning activities encompass a wide array of physical activities outside the classroom (in museum, directly in the workplace, project-based learning, peer-to-peer learning) and also an even wider array of digital activities (in LMS/eLearning, videos, virtual classrooms, web browsing, apps, podcasts, chats, etc). Tomorrow, new learning activities will emerge such as virtual reality (VR), augmented reality (AR) or other immersive environments (metaverse). * Current Learning analytics approaches should evolve to be able to collect a wider array of learning records and to access and process them from a large number of organizations. A European dataspace as it will be established by this project will help to evolve from siloted learning analytics with limited impact to Distributed Learning Analytics with a much wider impact. This use case will provide people and organizations with a set of tools to easily implement Distributed Learning Analytics: * People will have Lifelong availability of their Learning Records, even if their data is held by many different training organizations or employers using many different data models. * People will be able to easily import, export, store, share, consent and control the access to their Learning Records. * AI providers will be able to train their models on larger amounts of Learning records, without contractual, legal or privacy issues and with a fair distribution of value. * Data providers of Learning records (e.g. training organizations or edtechs) will be able to easily share their data and improve their own services, without requiring expensive data preparation (data model alignment/mapping) or equally expensive ad-hoc contractualization. * Training organizations will be able to offer personalized learning at lower cost and thus better tailor their learning offer to the needs of their learners. * Training organizations will improve the inclusivity of their training offers. * Learning activities happening outside the classrooms will be better integrated and enable a 360° view of learning activities of each learner. ### Stakeholders In more detail, here is the value proposition of these tools for the different stakeholder *People:* * get personalized learning experiences * get lifelong access and portability of their learning records * get 360° view of all their learning activities (both physical and digital) * can share their full learning records with relevant stakeholders *Universities / training organizations:* * can contribute their learning records * can individualize their training offer for each learner * can personalize the learning experience of each learner * get value from their learning records instead of being a cost (hosting) *Employers:* * Get better return on investment (ROI) from their training spending * Get worker with the right skills required (instead of general training) * provide employees with innovative upskilling and career mobility services * get precise education and training background of employee *High schools* * Better detect students who need specific help (e.g. dyslexia) * get 360° view of each student learning activities (both physical and digital) *Edtechs / AI Providers:* * provide more easily their services and their data to the ecosystem * get more users and clients * provider better personalized services thanks to better data access *Infrastructure providers:* * provide services and building blocks to enable data sharing (consent, contract, interoperability, data visualization, decentralized processing, etc) * get organizations to use their services *Orchestrator:* * provide the ecosystem portal * coordinate governance, use cases and business model discussions in the ecosystem * get part of the value generated by the ecosystem through commissions, fees, etc
Prometheus-X gathers numerous data and AI providers that can make these use cases happen: | Users | AI Providers | Data providers | | ------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Académie de Nancy: public body managing 126 high school | Inokufu: edtech company providing API access to learning object recommendation engine and competency matching. |

ANT:

More than 27M xAPI learning traces generated from learner activity

| | Académie de Rennes: public body managing 112 high schools | Prof en poche: speech Recognition of children in classroom, Handwritten digit recognition, object detection and segmentation |


| | Maskott:Edtech company providing learning content for K12 schools with 20 000 schools, 106 000 teachers with learning traces of est. 1 million students | EvidenceB: provide an API to evaluate the level of student over a graph of activities, estimate attentional disorder or knowledge gap |

WEANLY (Oktonine):

-10 000 learner profiles years of study, domain, specialization, activities, list of to-be-acquired competencies) list of performed activities, list of validated competencies

| |

EvidenceB (EB): edtech company providing learning apps to K12 students, skills matching learning analytics. 500k users in 10 countries

| Cabrilog: adaptive learning solution for K-12 mathematics training courses |

EDUNAO:

Edunao deliver managed Moodle environments to hundreds of organisations

| |


Le Cnam: high. Ed. organization dedicated to lifelong professional training, with more than 200 training centers in France and 50k+users

| Maskott: providing an AI-based content recommendation system for teachers, encouraging students to form their communities with students that share similar interests | Inokufu: dataset of learning traces (xAPI) for 2k users in transition to high demand job | | Antares (ANT): company, providing 75% of high schools in Germany with LMS |


| UoK: student data from 10 000 students | ### Building blocks mobilized: In order to make such a use case a reality, different building blocks are needed. Prometheus-X is developing them: * [Consent](https://dataspace.prometheus-x.org/building-blocks/consent): to enable people to control their data between all parties in a human-centric way * consent agent: to suggest to people the apps / organisations that best fit their needs * [Contract](https://dataspace.prometheus-x.org/building-blocks/contract): to ensure trust and compliance between organisations sharing data * [Identity](https://dataspace.prometheus-x.org/building-blocks/identity): to ensure authentification of orgs and people sharing data * [Learning traces interop: ](/building-blocks/interoperability/learning-records)to allow the translation of learning profiles into xAPI, to ensure interoperability between the different repositories and data models concerning learning traces * [AI metadata enrichment: ](/building-blocks/interoperability/ai-metadata-enrichment)algorithms capable of extracting from raw data the activity that allowed the acquisition of competencies on the basis of common reference systems (ROME/ESCO/RECTEC...); * Learning Object Metadata crowd tagging: method of tagging and describing digital learning resources such as videos, presentations, and documents using a crowd of individuals * Web Analytics Learning Records Universal Connector: allows for the integration of web analytics data with a Learning Record Store (LRS) using the xAPI (Experience API) standard. It enables the conversion of analytics data, such as the data collected by Matomo, into an xAPI format that can be stored and tracked in an LRS * distributed data visualisation: to allow results of matchings (courses, exercises, ressources) to be shown wherever the user is * Personal learning record store: type of cloud-based service that allows individuals to store and manage their own learning records in a central location # Datasets This use case consists in allowing the pooling of aggregated data in order to train artificial intelligence algorithms. This is a necessity for the public and private sectors, as well as for research. This use case would allow to answer needs such as: * To cross-reference the usage data of digital readers and build a model that would allow to detect school dropout very early, or on the contrary an interest that is not reflected in the assessments * Facilitate the development of adaptive learning solutions, based on external data * Impact studies by cross-referencing data from individuals throughout their lives (school, orientation, training, skills, career) ### Benefits \- Allows actors to cross data sets, for instance learning traces, that are currently fragmented \- Limits cold start problems \- Exploit unused data in a trusted environment \- Larger datasets to open up training possibilities for Machine Learning models \- Interoperability of educational data ### Building blocks mobilized * [Contract:](/building-blocks/contract) which lowers the legal and contractual barrier and makes it accessible to actors of all sizes * [Learning traces interop and skills data interop:](/building-blocks/interoperability/learning-records) to build a coherent dataset from heterogeneous data by source and format * [Anonymization and pseudonymization:](/building-blocks/anonymization-pseudonymization) secure the supplier, allowing him to provide a dataset compatible with the regulations * [Consent:](/building-blocks/consent) easily integrated with the provider's services. The dataset produced is generated by users who are informed of the purpose and have given their consent. Among its early adopters and data providers,Prometheus -X brings together a set of stakeholders committed to implementing this use case. These organizations are: Pôle Emploi, INRIA, FUN MOOC, Insititut Mines Telecom, Université de Lille, MENJS, Openclassrooms, WebForce 3, Serious Factory, Weenoz, Digischool, numerous edtechs, ... # Impact study This use case allows to federate data and learning traces of students, declarative questionnaires, monitoring of the use of resources, across all solutions and services used, in a school-extraschool continuum, to analyze the impact of each solution and identify the margins of progress. This use case would address needs such as: * as a training organization, I want to identify a posteriori the resources that have proven to be the most adapted to the learners' profiles * as a producer of educational resources, I want to compare the results of learning paths on target populations with the skills of neutral panels * as a person, I need to be able to compare my skills with those of the society in which I live ### Benefits \- Allows stakeholders to easily access data that measures the impact of their solutions \- Ensures interoperability of datasets in order to compare over longer periods of time or with more data \- Interoperability of educational data ### Building blocks mobilized * [Skills data interoperability](/building-blocks/interoperability/skills-data): to enable the development of clusters between the different competencies of anonymized citizens. This strategy can then be used to situate the skills of an individual in relation to the group to which he belongs. * [AI metadata enrichment](/building-blocks/interoperability/ai-metadata-enrichment): highlight the impact of the platform's various services, both at the individual level and at the more global level of a population in need of attention (school dropouts, multilingualism, disability, digital insecurity, etc.) * [Consent](/building-blocks/consent): to define a regulatory and protective framework for the use of learning traces between different services Among its first users and data providers, Prometheus has brought together a group of players to contribute to the definition and implementation of this use case: Tralalere, Aren Education, Woonoz, Pitchboy, Webforce 3, Skills4all, my serious games, the ai institute, INRIA... [JOIN US!](/extras/contact) # Catalog Governance: Catalog ### In short * The catalog allows participants of the data space to find, search and register data or services on the data space * These assets are described through GAIA-X compliant self descriptions that can be easily shared with any other data space catalog * It interconnects with the [identity](/building-blocks/identity) of the participants and the [contract service](/building-blocks/contract) #### **Demo** ***This video shows how a catalog works, it is here operated by the Visions data intermediary. Through the Prometheus-X philosophy and open source building blocks, any other player can reuse the code and operate a similar service. Through this same approach, any service or dataset registered in one implementation of the catalog can easily be presented by another (see*** [***Architecture***](/fundamentals/architecture)***).*** {% embed url="" %} ### Timeline Start date: T0 (expected: Q1 2023) End date : T0 + 4 months Duration (in months): 4 #### **Where we are right now** * First version of such a catalog service implemented by Visions * code to be released in Q1 2023 Want to join the effort? See the [Working Groups](/fundamentals/working-groups). ### Objectives and Expected Outcomes The objective of this building block is to provide a cataloging service for the services and data available in the data network enabled by Prometheus. Each organization will be able to find the datasets it is interested in and the conditions of use of these datasets, it will then be able to choose the datasets it is interested in and launch a contracting process. The catalog will also include a search function. Each organization can also declare a data set on the catalog, describe it and its conditions. Prometheus will propose from its website an interface to interact with this catalog as well as an API to allow any other system to interact with this catalog. This catalog will be the showcase of all the data and services available on the network enabled by Prometheus. API and interface allowing : * Register, modify, delete a service or dataset to be made available in the catalog * Save, modify, delete the description and the conditions of use of a service or dataset in the catalog * Find a service or dataset in the catalog based on different criteria (type of data, type of data source, type of service, condition of use, price, etc) * Notify your interest in a service or dataset and notify the provider with your information * Ensure verification of the identities of organizations that interact with the catalog via interaction with the identity service (task 1.4) * Ensure access rights to the API functionalities according to the service governance decided by Prometheus The API will be developed based on existing code provided by one of the founding members of Prometheus, Visions, which is developing a similar service through VisionsTrust (visionstrust.com). ### Scope * State of the art of the latest developments in dataset and service description standards (GAIA X, IDSA, iShare, OASC) * Definition of the architecture of the API endpoints in accordance with the technical recommendations of GAIA-X and DSSC * Quantitative inventory of the main dataset and service description standards * Development of the endpoints necessary for the list of functionalities described above, starting from the available code made available by Visions * expression of data in JSON-LD format to ensure interoperability with other data spaces * API testing with model datasets provided by Prometheus volunteer partners (see support list) * integration with the contracting (task 1.3), consent (task 1.2), monitoring (task 1.6) and identity (task 1.4) services * Deployment of the service in a managed version in one of the partner cloud providers * Development of automated service deployment scripts for multi-cloud use (infrastructure as code e.g. Terraform) at partner cloud providers * Writing of public documentation, publication of the code according to the standards decided by Prometheus, hosting and uploading on the Prometheus platform ### Deliverables
#AvailabilityDeliverable
1.1.1T0 +1Documents : Specification, state of the art and quantitative study
1.1.2T0 + 2Development of the service and production launch in beta version (v0)
1.1.3T0 + 3QA test report + interconnection tests with contracting, consent and identity services
1.1.4T0 + 4Development of final version (v1) of the service + multi-cloud deployment scripts
# Consent Governance: Consent ### In short * The consent service allows participants of the data space to generate a consent towards an end user to share their data with an other participant of the data space * People can manage their consent from the services concerned in the data exchange or from their data intermediary where they can find and manage all their consents from a central place * The consent is generated from a data sharing agreement existing between the parties sharing the data; the consent triggers the real data exchange or access. #### **Demo** ***This video shows how a consent system works, it is here operated by the Visions data intermediary. Through the Prometheus-X philosophy and open source building blocks, any other player can reuse the code and operate a similar service. Through this same approach, people can not only share data between parties connected to their specific data intermediary but with all parties of the data space (see*** [***Architecture***](/fundamentals/architecture)***).*** {% embed url="" %} ## Timeline Start date: T0 + 2 months (T0 = expected: Q1 2023) End date : T0 + 9 months Duration (in months): 7 months #### **Where we are right now** * First version of such a consent service implemented by Visions, see [documentation](https://visionstrust.com/public/docs) here and [code](https://github.com/VisionsOfficial/visionstrust-api) here * Find the full decentralized protocol enabling data sharing between multiple contract services, consent services and personal data intermediaries [here](https://urlz.fr/kSCY) Want to join the effort? See the [Working Groups](/fundamentals/working-groups). ### Objectives and Expected Outcomes The objective of this ***building block*** is to provide a consent service to organizations wishing to exchange personal data. Indeed, in order to ensure compliance with the RGPD as well as ethical and person-centered principles in the flow and use of data, consent and information of the data subject are required. This service allows ecosystem members to present consent to the individual, store it, verify it and revoke it. This consent is generated based on an existing contract between the data user and the data source(s) (contracting service developed by Task 1.3) and will contain all the information needed to obtain it (purpose of the processing, parties of the exchange, data involved, duration of validity, way to revoke it). This service interacts with the data storage system on the blockchain, described in task 1.5, to guarantee the decentralized and unforgeable nature of the consent. The valid consent is then verified for each data exchange and processing that requires it. It also interacts with the monitoring system to be able to alert in case of suspected fraudulent use of the authorizations, see here. This will allow each data exchange to be linked to a consent when required and each member will be able to easily request a consent to exchange data with other members of the network. This service will enable the implementation of person-centric consent that links its different services together. This differs from internal consent services within each organization that only address the person's data and identity within that organization, here the person links their data and identities across different organizations through their consent. This allows one organization to request consent for data present in another. The service consists of an API allowing: * Retrieve information (processing, data types, organizations involved, description, identities, etc.) to generate consent based on a contract by interacting with the contracting service * Register consent on the blockchain by verifying the identity of all stakeholders by interacting with the identity systems of data providers and users as well as the identity system * Modify a consent * Notify stakeholders of the withdrawal or granting of consent to initiate or terminate a data exchange * Interacting with data users' data deletion systems when withdrawing consent * Ensure withdrawal of consent and notification when consent is revoked or expires * Verify a consent and its validity * Retrieve all consents for a stakeholder in a standard format * Interactions with the monitoring system to detect fraudulent use of consent (task 1.6) * Conduct accurate searches of a department's data as well as of all departments, data and processes * Limit and secure the various calls made to the API by a system of verification by IP / Service * Obtain detailed information about errors in a known error register The API will be developed based on existing code provided by one of the founding members of Prometheus, Visions, which is developing a similar service through VisionsTrust (visionstrust.com). ### Scope \- Definition of the architecture of the API endpoints in accordance with the technical recommendations of GAIA-X \- State of the art of the latest developments in consent standards (Kantara, ISO) \- Development of the endpoints necessary for the list of functionalities described above \- expression of data in JSON-LD format to ensure interoperability with other data spaces \- Integration with the blockchain storage system (task 1.5) \- API testing with use cases provided by Prometheus volunteer partners (see support list) \- Integration with contracting, identity, monitoring and interoperability services (see tasks 1.3, 1.4, 1.5 and 1.6) \- Deployment of the service in a managed version in one of the partner cloud providers \- Development of automated service deployment scripts for multi-cloud use (infrastructure as code e.g. Terraform) at partner cloud providers \- Writing of public documentation, publication of the code according to the standards decided by Prometheus, hosting and uploading on the Prometheus platform ### Sequence diagram consent and data sharing:
### Deliverables
#AvailabilityDeliverable
1.2.1T0 +3Documents : Specification, state of the art and quantitative study
1.2.2T0 + 5Development of the service and production launch in beta version (v0)
1.2.3T0 + 7Test report + interconnection tests with contracting, interoperability and identity services
1.2.4T0 + 9Development of final version (v1) of the service + multi-cloud deployment scripts
**Sequence flow:** # Contract Governance: Contracts ### In short * The contract service allows participants of the data space to automate the generation and verification of data sharing agreements * It allows data providers to set data Terms of Use, data users to agree to them as well as for both to define a Data Sharing Agreement and to sign it * The Data Sharing Agreement is checked before allowing any consent or data exchange to happen #### **Demo** ***This video shows how a contract system works, it is here operated by the Visions data intermediary. Through the Prometheus-X philosophy and open source building blocks, any other player can reuse the code and operate a similar service. Through this same approach, organizations can not only contractualize between parties connected to their specific data intermediary but with all parties of the data space (see*** [***Architecture***](/fundamentals/architecture)***).*** {% embed url="" %} ## Timeline Start date: T0 + 0 months (T0 = expected: Q1 2023) End date : T0 + 9 months Duration (in months): 9 #### **Where we are right now** * First version of the contract service developed, see [code and documentation here](https://github.com/Prometheus-X-association/Human-Centric-Rulebook) and architecture [here](https://drive.google.com/file/d/1W3LchNZhPlQhx_47PqFGDvJdmTP2Ixtk/view?usp=sharing) * First contract template developed on the basis of the Sitra Rulebook, see [contract here](https://docs.google.com/document/d/19Ft34NYAvFWui9-t9IrKV6znFoPi9jYD/edit?rtpof=true\&sd=true) * Find the full decentralized protocol enabling data sharing between multiple contract services, consent services and personal data intermediaries [here](https://urlz.fr/kSCY) Want to join the effort? See the [working groups](/fundamentals/working-groups)! ### Objectives and Expected Outcomes In order to produce a comprehensive European data portability infrastructure, a more precise legal framework is needed with a contractual framework that takes into account sector-specific regulations, ethics charters, codes of conduct and determines the responsibilities and obligations of each party in the transfer of data. This legal model must be supported by a robust legal infrastructure to automate the generation, signing, auditability and traceability of these contracts and governance frameworks. This legal infrastructure must be the collective work of the parties that contribute to and benefit from it. This contracting building block will provide an API for ecosystem members to use to automatically generate appropriate contracts for data transfers through open machine-readable contract clauses described as objects, published on a common open git, using W3C standards (data privacy and control vocabulary). The building block will use the blockchain to store contracts and ensure verification of their existence and terms through smart contracts directly available on the blockchain. This building block interacts with the identity system to validate the identity of each stakeholder and with the consent system to enable it to produce a consent that complies with the contract between the parties. It also interacts with the monitoring system to be able to alert in case of suspected fraudulent use of authorizations. The building block consists of an open source API allowing to: * Define and publish data use policies (DUPs): for each dataset, the data provider may specify conditions, obligations, restrictions, pricing, certifications, data security, rights, data protection, liability; relating to the use of the dataset; these provisions and obligations for accessing the dataset define the data policy, are made machine-readable using appropriate standards, and are made available (published) to the members of the data network. * Generate and manage Data Sharing Agreements (DSAs): When a service using data agrees to and complies with the data policy, a Data Sharing Agreement (DSA) is generated between the DUS and the DS and DI taking into account the provisions of the data policy. The DSA is generated in a human-readable and machine-readable format, using appropriate standards, and its metadata (parts, dataset, provisions, time) is made available to the parties to the DSA. * Prove data sharing agreements: a member of a data network can verify the existence of a data sharing agreement, obtain relevant metadata about the agreement and a token proving the validity of the agreement that can be used to enable data exchanges, each data sharing agreement will be stored with its conditions on the blockchain. In order to enable this service specific contractual clauses are needed taking into account the modalities and obligations of the sector, so this work also covers the legal work necessary for the elaboration of these clauses and contract models that will also be opened and used by the contractualization service. ### Scope \- State of the art and identification of model data sharing contracts and specific terms and regulations for education and skills data \- Definition of legal clauses useful for the first cases of use \- Definition of the architecture of the API endpoints in accordance with the technical recommendations of GAIA-X, IDSA and the DSSC \- State of the art of the latest developments in standards for describing contracts in machine-readable format (W3C, IEEE) \- Quantitative inventory of the main contract description standards \- Development of the endpoints necessary for the list of functionalities described above, starting from the available code made available by Visions \- expression of data in JSON-LD format to ensure interoperability with other data spaces \- Integration with the blockchain storage system (task 1.5) \- API testing with use cases provided by Prometheus volunteer partners (see support list) \- Integration with consent, identity, catalog, monitoring and interoperability services (see tasks 1.2, 1.4, 1.5, 1.6) \- Deployment of the service in a managed version in one of the partner cloud providers \- Development of automated service deployment scripts for multi-cloud use (infrastructure as code e.g. Terraform) at partner cloud providers \- Writing of public documentation, publication of the code according to the standards decided by Prometheus, hosting and uploading on the Prometheus platform ### Sequence diagram
### Deliverables
#AvailabilityDeliverable
1.3.1T0+2Documents : Identification and definition of appropriate legal clauses
1.3.1T0 +3Documents : Specification, state of the art and quantitative study
1.3.2T0 + 5Development of the service and production launch in beta version (v0)
1.3.3T0 + 6Test report + interconnection tests with consent, interoperability, catalog and identity services
1.3.4T0 + 7Development of final version (v1) of the service + multi-cloud deployment scripts
# Identity Governance: Identity ### In short * The identity service allows each participant to connect to the data space services through on single identity * This identity is decentralized and follows Self-Sovereign Identity principles * Each participant will have a easy to use Wallet to manage its identity #### **Demo** ***This video shows how though a decentralized identity wallet participants can log into the data spaces services (in this case the catalog). Through the Prometheus-X philosophy and open source building blocks, any other player can reuse the code and operate a similar service. Through this same approach, organizations are not tied to their identity provider and can easily change wallets (see*** [***Architecture***](/fundamentals/architecture)***).*** {% embed url="" %} ## Timeline Start date: T0 + 0 months (T0 = expected: Q1 2023) End date : T0 + 9 months Duration (in months): 9 #### **Where we are right now** * First implementation of an SSI wallet with the catalog service * At the conclusion of the NGI ESSIF Lab program, the mywallet.cloud academic wallet has been delivered and will be in production starting January 2023. It will serve as the technology base for the project. Want to join the effort? See the [working groups](/fundamentals/working-groups)! ### Objectives and Expected Outcomes The objective of this building block is to ensure proper and decentralized authentication of the members of the ecosystem exchanging data. This identity and authentication is absolutely necessary to guarantee the integrity and security of the exchanges on the network. The building block is based on a system allowing, via an "Identity Dashboard", to provide a Decentralized Identity (ESSIF - DID by expl.) to any internal or external entity (legal entities), but also to accept DIDs from reference ecosystems (EBSI, GAÏA-X) The proposed development uses the following technologies and knowledge: \- Self Sovereign Identity standards & implementations, ESSIF & W3C \- Wallets W3C standards & implementations \- Strong authentication & EIDAS \- Decentralized applications standards & implementations \- Elliptic curve cryptography \- Keystores. The interaction with this identity will be done via an "Enterprise Wallet". Once onboarded, the entity accesses its "Enterprise Wallet" allowing its identification and the authentication of the deposited data. The delegations of access to the Wallet for the administrators of the entities are carried out, with the choice, by strong authentication or by a "Citizen Wallet" which will be also provided by the service in open source (ESSIF Lab). The added value is significant: the service will allow to take advantage of the security and traceability of decentralized identities in a B2B context to authenticate data. This task will also allow to work on the UX of the described applications and to ensure the good integration of the identity service with the consent service, contractualization, cataloguing, monitoring and interoperability services. ### Scope * Writing of specifications and development of interface models, in coordination with the other software components of the project. Preparation of development environments. * Full development cycle, including a test environment, a beta version followed by a production version of the identity dashboard. * Full development cycle, including a test environment, a beta version followed by a deployed production version of the enterprise wallet. * UX development of the models * Integrations with other governance services (catalog, contractualization, consent) * Participation in the project management bodies and in the dedicated technical and functional groups. Constant verification, in agile method, of the good coherence of the developments. ### Sequence diagram
### Deliverables
#AvailabilityDeliverable
1.4.1T0+1Specifications
1.4.2T0+1Models
1.4.3T0+3Identity Dashboard SaaS instance operational for the ecosystem
1.4.4T0+4Web wallet service operational for ecosystem members
# Traceability Governance: Storage / certification of consent and contract data ## In short * This building block allows participants of the data space to store data in a decentralized way * It will be used by the Prometheus-X infrastructure to store consents and contracts in order to ensure trust and interoperability of these ## Timeline Start date: T0 + 8 months (T0 = expected: Q1 2023) End date : T0 + 12 months Duration (in months): 4 **Where we are now:** * The [BCdiploma](https://www.bcdiploma.com) technology is 100% operational and we will be able to start using it as soon as necessary for decentralized storage adapted to the needs of the ecosystem. ### Objectives and Expected Outcomes The different governance services and in particular the consent service and the contracting service need a decentralized storage space that guarantees the security of the consent and contract data, as well as their proof. This task aims to develop an operational space for decentralized evidential storage of data, evidence and documents. The service will implement a certification and data storage framework as part of the project. Specifically, owners of an "Enterprise Wallet" will have the ability to sign, encrypt; store data on a distributed storage instance. The data can then be shared with third parties, inside or outside the eco-system. The stored data can be of different nature: we can consider this storage mode as a data space in its own right, or use it as a record of proof of signatures or a record of encrypted storage of contracts. ### Scope * Deployment and configuration of the decentralized storage software ### Deliverables
#AvailabilityDeliverable
1.5.1T0+2An operational instance of EvidenZ to store data in a data space
# Monitoring Governance: Monitoring ## In short * The monitoring service of Prometheus-X will ensure trust in the infrastructure by allowing to detect and alert on any suspicious use of the data sharing infrastructure ## Timeline Start date: T0 + 0 months (T0 = expected: Q1 2023) End date : T0 + 4 months Duration (in months): 4 #### **Where we are now** * The work will start Q1 of 2023, want to learn more and join the effort: join the [Working Groups](/fundamentals/working-groups)! ### Objectives and Expected Outcomes Prometheus services are equipped with a system for monitoring the use of the infrastructure and compliance with legal rules, including consent and contract. Taking into account data from the catalog, contracts and consents as well as usage data and logs from the PROMETHEUS APIs, in order to be able to detect and alert if a service is bypassing the rules (too few or too many requests depending on what is declared). The system will alert PROMETHEUS governance and any other legitimate party of a possible infrastructure bypass so that appropriate action can be taken. The task will implement a dashboard (web/API) of aggregate usage of the platform services. ### Scope * Implementation of DevOps best practices * Deployment of monitoring tools in conjunction with the working groups concerned * Development of the dashboard and alert system ### Deliverables | # | Availability | Deliverable | | ----- | ------------ | --------------------------------------------------------------------- | | 1.6.1 | T0+2 | Unified CI/CD Pipeline | | 1.6.2 | T0+2 | Development of the monitoring system architecture | | 1.6.3 | T0+6 | Integration with relevant departments (consent, contracting, catalog) | | 1.6.4 | T0+3 | Dashboard with performance indicators | # Anonymization / Pseudonymization Governance: Pseudonymization / Anonymization ## In short * The anonymization / pseudonymization building block will allow participants of the data space to easily anonymize or pseudonymize their data sets before them being shared * This service will be interconnected with the contract service so it can be detected from the Data Sharing Agreement when a data set needs to be anonymized before being shared ## Timeline Start date: T0 + 6 months (T0 = expected: Q1 2023) End date : T0 + 36 months Duration (in months): 30 months #### Where we are now * The work will start Q1 of 2023. Want to learn more and join the effort: join [here](/fundamentals/working-groups)! ### Objectives and Expected Outcomes Provision of a suite of open source and managed tools that : * automate data anonymization * advise on good practices * validate the quality of the anonymization ### Scope * Development of the tools, availability in opensource for on-premise deployment or as a managed service within the platform * These tools can analyze the formats most used by users, and are kept up to date with the evolution of data formats, and new recommendations and best practices in the field. * They also perform statistical analysis to make recommendations on primary and secondary fields. ### Deliverables * Deliverable 1: Semester 2 * First release of the tool suite on a code sharing platform * Deliverable produced every 3 months, in the form of a new release of the suite.
#AvailabilityDeliverable
1.9.1T0+4First release of the tool suite on a code sharing platform
1.9.nT0+4+nNew release, new features and capabilities, bugfix
# Interoperability Interoperability is a key topic for the data exchange within the Education and Skills sectors. For now we have identified 5 digital commons that could greatly improve the interoperability: {% content-ref url="/pages/12n0dPRiFewOoDG1w7lP" %} [Learning records](/building-blocks/interoperability/learning-records) {% endcontent-ref %} {% content-ref url="/pages/zSyNqWKmlnBPPzGhLrVZ" %} [Skills data](/building-blocks/interoperability/skills-data) {% endcontent-ref %} {% content-ref url="/pages/N7iQwq0C7JUJlMh4KzHx" %} [Impact study](/building-blocks/interoperability/impact-study) {% endcontent-ref %} {% content-ref url="/pages/cl5BUyMNgkzEgau9qUNZ" %} [Universal plugin SDK](/building-blocks/interoperability/universal-plugin-sdk) {% endcontent-ref %} {% content-ref url="/pages/oQWTp7sD6dMuc6hOnRu6" %} [AI metadata enrichment](/building-blocks/interoperability/ai-metadata-enrichment) {% endcontent-ref %} # Learning records Interoperability: learning traces ## In short: * The learning traces interop building block is a parser translating datasets of learning traces into a common xAPI profile ## Timeline Start date: T0 (T0 = expected: Q1 2023) End date : T0 + 12 months Duration (in months): 12 #### Where we are now * A common xAPI Profile is currently being discussed with the member of the Learning Analytic working group * The work will start Q1 of 2023. Want to learn more and join the effort: join [here](/fundamentals/working-groups)! ### Objectives and Expected Outcomes The objective of this building block is to specify and develop APIs type "parser" to convert educational traces from one standard to another. Depending on the software and tools used in the field of education and training (LMS, LXP, ENT, etc.) several standards coexist in terms of data model of learning traces: [SCORM](https://scorm.com), [xAPI](https://xapi.com), [cmi5](https://aicc.github.io/CMI-5_Spec_Current/), [IMS Caliper](https://www.imsglobal.org/activity/caliper), etc. This task will allow to combine learning traces data sets expressed in different standards. This will allow, among other things, the aggregation of datasets (e.g. from LRS) in order to obtain either a larger volume of data or a follow-up of learning over a longer period of time and/or across several applications and educational platforms. These combined datasets will allow training of Machine Learning models on a scale that was previously impossible, except in situations of monopolistic data appropriation (e.g. Microsoft with Linkedin, Linkedin Learning and Teams). Note that by nature, the aggregation of learning traces could pose problems of identification of the individuals concerned (i.e. the learners). Therefore, in alignment with the Anonymization and Pseudonymization task, the APIs of this task will de facto integrate anonymization and pseudonymization functionalities, with a particular vigilance for geographical data which are generally the weak points allowing de-anonymization. ### Scope * Definition of the architecture of the API endpoints in accordance with the technical recommendations of GAIA-X * State of the art of the latest evolutions of learning traces standards * Quantitative inventory of the main software learning outcomes standards and tools used in the field of education and training in France and in Europe from the list identified in the working groups of the Data space Education & Skills (i.e. [SCORM](https://scorm.com), [xAPI](https://xapi.com), [cmi5](https://aicc.github.io/CMI-5_Spec_Current/), [IMS Caliper](https://www.imsglobal.org/activity/caliper)) * Development of the endpoints necessary for parsing the various priority standards identified above * Integration of anonymization/pseudonymization features * expression of data in JSON-LD format to ensure interoperability with other data spaces * API testing with model datasets provided by Prometheus volunteer partners (see support list) * De-anonymization tests of learning traces (in connection with the task Anonymization and Pseudonymization) * Deployment of the service in a managed version in one of the partner cloud providers * Development of automated service deployment scripts for multi-cloud use (infrastructure as code e.g. Terraform) at partner cloud providers * Drafting of the public documentation, hosting and putting it online ### Sequence diagram
### Deliverables
#AvailabilityDeliverable
2.1.1T0 + 4Documents : Specification, state of the art and quantitative study
2.1.2T0 + 8Development of the service and production launch in beta version (v0)
2.1.3T0 + 10QA test report + de-anonymization tests
2.1.4T0 + 12Development of final version (v1) of the service + multi-cloud deployment scripts
# Skills data Interoperability: skills data ## In short * The skills data interoperability service will allow participants of the data space to share skills profiles even when the data provider and data user use different skills ontologies and data models * This service allows skills data to be automatically translated to and from a common pivot ontology * This allows to not need all data space participants to apply the same data model and skills ontology #### **Demo** ***This video shows how a skills data interoperability system works, it is here demonstrated by the Visions data intermediary integrating an API provided by Mindmatcher that operates the interop system. Through the Prometheus-X philosophy and open source building blocks, any other player can reuse the code and operate a similar service.*** {% embed url="" %} ## Timeline Start date: T0 + 0 months (T0 = expected: Q1 2023) End date : T0 + 12 months Duration (in months): 12 #### **Where we are now** * The beta-version of Ismene - the ontology browser - is published and [open-sourced](https://gitlab.com/mmorg/ismene). * A first set of “well know” RDFs ontologies are made available through the Ismene browser. * A first version of Smart Orientation - the pivotal ontology - is [available on Ismene](https://ismene.competencies.be/ori/Ontologie/). * A scratch pad version of MMTranslator - the onto-terminology translator - is Set-up. * The “technology mix” of MMTranslator is validated in back-to-back HTTP context, but a java based component in this mix has to be more precisely evaluated. * MMTranslator actually run in a Test Environment with 3 repositories translated on the fly in Json-ld standard (demo video) ### Objectives and Expected Outcomes Competency data is described in numerous ontologies that differ from organization to organization. The purpose of this building block is to offer interoperability services to users/applications of the "Education & Skills Dataspace": * Creation of a Gaia X pivot ontology for a common model, * Bidirectional translators of partner data formats using the pivot model, * Management of business competency ontologies, * Alignment of the business skills ontologies with the institutional repositories (ROME, ESCO, etc.), * Management of training offers by competence and blocks of competence. The expected results of this task are as follows: * Translation module with respect to the pivot ontology, * Module for entering a skills repository, * IA modules for aligning a competency framework with ROME and ESCO, * Module for entering a training offer, * IA modules of suggested skills and blocks of skills, * API exchanges with these modules. ### Scope * Workshops with partners on the definition of the pivotal ontology, * Integration of the pivot ontology on the E\&S Dataspace platform instance, * Use of MMTranslator to integrate repositories on the E\&S Dataspace platform instance, * Automatic generation and validation of repository alignments with respect to the pivot model, * Functional design of a training offer entry, * UX design of the training offer management application, * Realization of the application for the management of the training offer. ### Deliverables
#AvailabilityDeliverable
2.2.1T0 + 3Document: RDFS semantic ontology of the pivot model
2.2.2T0 + 5Set-up: import of authority and partner repositories.
2.2.3T0 + 6

Document: RDFS Mapping of Partner Ontologies version 1

Document : Alignments referentials version 1

Service: Cross-format translation API version 1

2.2.4T0 + 10

Document: RDFS Mapping of Partner Ontologies version 2

Document : Alignments referentials version 2

2.2.5T0 + 12Service: Cross-format translation API version 2
2.2.6T0 + 4Document: specification application "training offer".
2.2.7T0 + 10Service: application "training offer" version 1
# Impact study Interoperability: Impact study ontology ## In short: * This building block will improve the evaluation of digital solutions through harmonized impact measurements, detection of key criteria allowing their qualification/recommendation, Federating data and learning traces of learners, pedagogical teams, declarative questionnaires to the monitoring of the use of resources. ## Timeline Start date: T0 (T0 = expected: Q1 2023) End date : T0 + 13 months Duration (in months): 12 #### Where we are now * We have developed a method called "impact stories", like the "user stories" known in UX/UI. To refine the development of tools, we had working sessions with different academic and private actors: LORIA, INSHEA, AVISE. * We have developed a dashboard prototype, in partnership with Latitudes - Tech For Good and ECE students, engineering school, Paris. * Read more on the progress [here](https://prometheus-x.org/rails/active_storage/disk/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdDRG9JYTJWNVNTSWhZMnR5WTNGNFkyUnplakE0ZG1nNWJISm5OV000TlRVMWRHWnpZd1k2QmtWVU9oQmthWE53YjNOcGRHbHZia2tpWldsdWJHbHVaVHNnWm1sc1pXNWhiV1U5SW0xbGMzVnlaUzFwYlhCaFkzUXRNakF5TWlCbGJpMVZVeTV3WkdZaU95Qm1hV3hsYm1GdFpTbzlWVlJHTFRnbkoyMWxjM1Z5WlMxcGJYQmhZM1F0TWpBeU1pVXlNR1Z1TFZWVExuQmtaZ1k3QmxRNkVXTnZiblJsYm5SZmRIbHdaVWtpRkdGd2NHeHBZMkYwYVc5dUwzQmtaZ1k3QmxRPSIsImV4cCI6IjIwMjItMTItMDhUMTg6MjQ6MzcuMzQ5WiIsInB1ciI6ImJsb2Jfa2V5In19--59d5e9f34fdb9548f4bd429fdff8881e47a350f5/mesure-impact-2022%20en-US.pdf?content_type=application%2Fpdf\&disposition=inline%3B+filename%3D%22mesure-impact-2022+en-US.pdf%22%3B+filename%2A%3DUTF-8%27%27mesure-impact-2022%2520en-US.pdf) * Want to learn more and join the effort: join [here](/fundamentals/working-groups)! ### Objectives and Expected Outcomes The objective of this building block is to implement the [impact study use case.](/use-cases/impact-study) This use case is used to improve the evaluation of digital solutions through harmonized impact measurements, detection of key criteria allowing their qualification/recommendation, Federating data and learning traces of learners, pedagogical teams, declarative questionnaires to the monitoring of the use of resources \- Benchmark data to measure the impact of Edtech solutions \- Multi-factor impact measurement \- Export data for analysis \- Visualization of harmonized impact data \- Facilitates the valuation of Edtech solutions and their relative positioning \- Classification and extrapolation of data ### Scope * State of the art of qualitative and quantitative measurement criteria and tools used in the field of education and training * Establishment of an interoperability framework, modeling of priority functionalities, definition of reference data, signs of change and their time scales, mapping - Development of a pilot dashboard template kit * Tests * Implementation of the interoperability plugin for the collection API and integration of different data sources ### Deliverables
#AvailabilityDeliverable
2.1.1T0 + 4Documents: state of the art, quantitative & qualitative study
2.1.2T0 + 8Specification and confrontation with ecosystem actors
2.1.4T0 + 12API for collecting and integrating different data sources (LRS, analytics, surveys, etc.)
# Universal plugin SDK Interoperability: universal plugin SDK ### In short: * The Universal plugin SDK is an open-source framework for developing edtech plugins that are compatible with all major educational software (build once, plug to many) * The Universal plugin SDK will allow data space participants to easily integrate any solution into any other without having to develop integrations each time ### Timeline Start date: T0 + 16 months (T0 = expected: Q1 2023) End date : T0 + 36 months Duration (in months): 20 #### Where we are now * The work will start Q2 of 2024. Want to learn more and join the effort: join [here](/fundamentals/working-groups)! ### Objectives and Expected Outcomes The objective of this building block is to specify and develop a software development kit (SDK) for the development of universal plugins, i.e. plugins that are compatible with the main softwares used in education and training. Most software such as Moodle, Blackboard, Canvas, Microsoft Teams for Education or Zoom allow the enrichment of their functionalities via plugins/APIs. Each of these platforms actually requires specific developments, according to different languages (php, C# .net, etc) and/or different data models. Each integration/plugin development represents a significant cost for edtech companies who usually only integrate or develop the plugin for the main softwares reinforcing data centralization. This task aims to develop an open-source framework for developing plugins that are compatible with all major educational software (build once, plug to many). This would simplify plugin development in a similar way to what React Native has been able to do for multi-OS (iOS/Android) mobile app deployment and what IMS has started to do with the Learning Interoperability Tool ([LTI](https://www.imsglobal.org/activity/learning-tools-interoperability)) standard. A framework that fills in the missing pieces of interoperability would therefore reinforce the dissemination of innovative products and services developed by the European edtech ecosystem. It should also be noted that this would allow for the operationalization of the right to data portability, which is supposed to be guaranteed by the RGPD but which in practice generally only translates into the possibility for users to recover their data in .zip format. With more interoperability between LMSs, the learner will be able to more easily allow the circulation of his data from one software to another without being locked in (vendor lock-in). ### Scope * State of the art of the plug-in frameworks of the main software and tools used in the field of education and training in France and in Europe from the list identified in the working groups of the Data space Education & Skills (i.e. Moodle, Teams, Zoom, LTI) * Modeling of priority functionalities * Design of the development kit (SDK: Software Development Kit) and generic and reusable modular elements (templates) * Development tests from the SDK * Drafting of public documentation, hosting and putting it online ### Deliverables
#AvailabilityDeliverable
3.1.1T0 + 16Documents : Specification and state of the art
3.1.2T0 + 24Development of the SDK in beta version (v0)
3.1.3T0 + 32QA test report
3.1.4T0 + 36Final version (v1) of the SDK and public documentation
# AI metadata enrichment Interoperability: AI-based automatic metadata enrichment ### In short: * The objective of this building block will be to develop a machine learning classification model capable of identifying the skills, professions or training areas associated with an educational resource, an educational activity or a training course. ### Timeline Start date: T0 + 12 months (T0 = expected: Q1 2023) End date : T0 + 36 months Duration (in months): 24 #### Where we are now * Inokufu has trained three prototype ML-models. They are available on huggingface [here](https://huggingface.co/inokufu). * The work will start Q1 of 2024. Want to learn more and join the effort: join [here](/fundamentals/working-groups)! ### Objectives and Expected Outcomes The objective of this building block is to improve the quality of educational resource, instructional activity, and training datasets by adding or correcting associated competency data (including job and/or domain). There are currently several standards for the expression of metadata associated with educational resources, pedagogical activities and training (e.g. LHEO, LOM, etc) as well as several competency frameworks (e.g. ROME code, formacode, ESCO, EQF, ECF, etc). The competency data in these datasets are not always well identified. This greatly limits the usefulness of these datasets, especially when these catalogs are used in search engines, which leads to results that are not considered relevant by users (e.g. MyTrainingCount). The objective of this common will be to develop a machine learning classification model capable of identifying the skills, professions or training areas associated with an educational resource, an educational activity or a training course. This will make datasets more interoperable for use in use cases related to jurisdictional data, combining datasets, and conducting impact studies. ### Scope * Identification and inventory of data sets that can be used to train the model * Development of automatic natural language processing (NLP) strategies based on existing models (BERT, OpenAI, etc.) and/or re-training of the last layers * Training tests and optimization of the model parameters by machine learning according to the different strategies identified * Deployment of the best model(s) to make them easily deployable and usable in API * Data expression in JSON-LD format to ensure interoperability with other data spaces * API testing with model datasets provided by Prometheus volunteer partners (see support list) * Deployment of the service in a managed version in one of the partner cloud providers * Development of automated service deployment scripts for multi-cloud use (infrastructure as code e.g. Terraform) at partner cloud providers * Drafting of public documentation, hosting and putting it online ### Deliverables | # | Availability | Deliverable | | ----- | ------------ | ---------------------------------------------------------- | | 3.2.1 | T0 + 16 | Documents : Specification and state of the art | | 3.2.2 | T0 + 24 | Development of the service in beta version (v0) | | 3.2.3 | T0 + 32 | QA test report | | 3.2.4 | T0 + 36 | Final version (v1) of the service and public documentation | # Decentralized AI # Decentralized AI training ### In short: * The Decentralised AI training building block allows data sources of the data space to enable their users to grant access to their data in order to train ML algorithms from participants of the data space. * The data never leaves the source and the owner of the algorithm never has access to it. ### Timeline Start date: T0 (expected: Q1 2024) End date : T0 + 12 months Duration (in months): 12 #### **Where we are right now** * First version of such a building block implemented by CozyCloud Want to join the effort? See the [Working Groups](/fundamentals/working-groups). ### Objectives and expected outcomes AI providers need user data to train their models, while the data providers need AI models to provide innovative features to their users. This building block is an answer to this need by making the link between AI providers and data providers through secure and trusted decentralized learning, notably in the fields of education and learning. Once a user gives consent to participate in an AI model, it becomes part of the contributors nodes. Once enough users give consent, an execution tree is computed, including contributors at and aggregators. Each contributor securely receives the relevant user data identified by the data provider, as well as the AI model to train including the weights, and the related algorithms. The computation is then made in a secure environment to guarantee the robustness and trustworthiness of the execution. Once the contribution is computed, the result is splitted in shares and a noise is added to each share to ensure the confidentiality of the contribution. The noise is computed in such a way that at the end of the execution, the aggregation of all the contributions removes the overall noise and produces the final trained model, which can be retrieved by the AI provider. During the process, no user data is exposed whatsoever, ensuring the security and the privacy of the users. In short the development phases of this building block are: * Conception and implementation of a decentralized federated AI protocol, to orchestrate node computations and communications. * Conception and implementation of an efficient and secure data retrieval process from nodes, including AI model, algorithms and user data. * Conception and implementation of a training computation model, to cope with the decentralized federated AI protocol properties, in the fields of education and learning. * Trustworthiness and explainability aspects of the solution: the computed AI models should be both trustworthy and explainable. The standards the building block will rely on: * The learning protocol will be based on the main federated learning principles: * Data between participants is not independent and identically distributed (i.i.d) * Participants do not have the same amount of data * Potentially many participants * Large models with limited communication * The nodes orchestration and computations roles will be based on the DISPERS concepts that describe how to securely build and execute a distributed computation tree * All network communications and encryption mechanisms will use state of the art standards ### Roles between partners: [Cozy Cloud](https://cozy.io/fr/) Cozy Cloud is conducting [a thesis](https://www.theses.fr/s252717#) in collaboration with an [Inria team](https://www.inria.fr/en/petrus) about distributed machine learning in the personal cloud. Preliminary results have been academically [published](https://hal.archives-ouvertes.fr/hal-03329878) and a proof-of-concept has been developed at Cozy Cloud based on this work. In this project, Cozy Cloud will bring its expertise to design and implement a decentralized federated learning protocol based on this preliminary work. This protocol should be able to create an aggregation tree of contributing nodes in a p2p fashion, and securely transmit data computations between nodes, to eventually produce trained AI models. [polypoly](https://polypoly.net/en/) polypoly will provide and further develop a privacy-preserving data repository and execution environment (so called polyPod) that can gather data from different sources. Other types of data will be collected and will be made available, upon user consent, for model learning and validation purposes in order to generate new knowledge and insights that can benefit users.
The polyPod is currently built by the polypoly cooperative, ownership of which is open to all European citizens, and lets users physically store and process data on their own devices in a fully distributed edge-based approach. The polyPod is an open, standardised and non-proprietary platform.
[Fraunhofer ISST](https://www.fraunhofer.de/en.html) / [Uni Koblenz](https://www.uni-koblenz-landau.de/en) Fraunhofer ISST / Uni Koblenz will establish the trustworthiness and explainability aspects of the solutions developed in this task.
[Loria](https://www.loria.fr/fr/) Machine-learning (ML) models need data to be trained, tested, and validated. For most real-world applications, the data is generated among thousands, millions, or more clients (devices). A relatively recent method of creating ML models is called federated learning, where each federated device (a client) communicates only the local model parameters rather than the local training dataset. This feature addresses the need for data privacy and confidentiality that are highly required in education. The way parameters are communicated depends on the federated learning topology, which is either centralized (using a central server to aggregate all the parameters) or decentralized (e.g peer-to-peer by sharing the parameters with a subset of parties). Even though, federated learning may be a viable solution for developing ML models that require large and dispersed data, it presents some challenges that Loria proposes to address: * Data heterogeneity: Data from federated parties can be highly heterogeneous in terms of quantity, quality, and variety of data. It is difficult to predict and quantify the effects of the heterogeneity of training data on the trained ML model. To mitigate the adverse effect of parties with poor data, it is essential to develop indicators that quantify the contribution of the parties involved in the training phase and, as a result, do not consider their parameters when building the global model. * Device heterogeneity: new ML models are required. The federated devices' computing capabilities are frequently heterogeneous. It is always difficult to ensure that training tasks will work across a diverse set of devices. As a result, there is a need to propose new machine learning algorithms that are tailored to Edge infrastructures and consider the computational heterogeneity. * Explainability of decentralized IA and interpretation. # Decentralized AI processing ### In short: * This building block objective is to implement decentralized AI treatments in data spaces, by enabling computations on edge nodes, without revealing individual contributions. ### Timeline Start date: T0 (expected: Q1 2024) End date : T0 + 12 months Duration (in months): 12 #### **Where we are right now** * Developments have yet to start Want to join the effort? See the [Working Groups](/fundamentals/working-groups). ### Objectives It aims at proposing a sovereign, open and ethical vision of educational data and its exploitation, based on a distributed approach to storage (edge computing) and processing (federated learning for example). It avoids the systematic collection of traces and the centralization of the models that process them by keeping the data and the processing as close as possible to the student or to the provider. The building block will allow data providers to easily allow their data to be processed by the AI providers without having to exchange the data. For instance: 1. I am a student and have identified my skills in a skills portfolio app A 2. from that app A, I can give my consent so that an AI provider B analyzes my skills and shows me in what industries I could work 3. on my consent, the algorithm of B is shared towards the dataset stored in A and executed on my data 4. I can see the result in my skills portfolio A 5. AI provider B never retrieved my data into their systems Infrastructure-related functionalities: The edge cloud infrastructure, built on an open-source platform, such as Kubernetes, aims at supporting the high level decentralized AI applications. Dedicated APIs (Application Programming Interfaces) will be exposed to the applications to enable given control on the operation. We plan to reuse the currently available features of e.g. Kubernetes as much as possible, however, several extensions will be required due to the special characteristics of the targeted decentralized AI applications. As a result, the implemented edge cloud infrastructure will provide the following functionalities: * data gathering/management functions to allow data providers to control the flow and storage of data coming from different sources (e.g. white lists of nodes can be defined where specific data can be stored and processed) * automatic, optimal control of data and function placement; AI providers can upload the AI processing logics as software artifacts, while the edge cloud platform deploys the functions to the optimal/closest/requested node having the data; available hardware accelerators (e.g. nodes with predefined GPU) can also be taken into account * dynamic placement optimization during the operation according to different quality constraints specified by the users or providers of the applications; e.g. data or functions can be migrated on-the-fly as a response to changes in network characteristics or the varying load on a given physical node * a dedicated Function as a Service (FaaS) API for AI applications supporting the on demand deployment and scaling of AI artifacts uploaded by AI providers taking the current location of data and hardware accelerators and also privacy requirements into consideration * novel horizontal and vertical resource scaling methods optimized for AI applications which can adjust the amount of allocated resources (compute, storage, network) and optimize the energy consumption of the overall infrastructure (e.g. artifacts running on CPU or GPU, performance vs. energy consumption) ### Roles between partners: [BME](https://www.bme.hu/?language=en) BME will lead the task addressing the design, development and operation of the edge computing infrastructure supporting decentralized AI applications. BME will contribute to the establishment of the infrastructure making use of open-source components, such as Kubernetes. Based on BME’s expertise and previous works, novel extensions and functionalities will also be proposed and implemented which enhance the edge infrastructure in terms of different aspects which are relevant to enable decentralized AI. Novel optimization algorithms will also be designed to control the energy consumption of the overall infrastructure (including compute, storage and network elements). BME will also focus on the software-related aspects and propose solutions fostering the development of upper level AI applications, such as novel FaaS APIs and related best practices which can be followed by developers. [polypoly](https://polypoly.net/en/) polypoly will provide and further develop a privacy-preserving data repository and execution environment (so called polyPod) that can gather data from different sources. Other types of data will be collected and will be made available, upon user consent, for model learning and validation purposes in order to generate new knowledge and insights that can benefit users. The polyPod is currently built by the polypoly cooperative, ownership of which is open to all European citizens, and lets users physically store and process data on their own devices in a fully distributed edge-based approach. The polyPod is an open, standardised and non-proprietary platform. [Uni Koblenz](https://www.uni-koblenz-landau.de/en) / Fraunhofer ISST Fraunhofer ISST / Uni Koblenz will provide Gaia-X based technology (such as the Eclipse Dataspace Connector) to support federation of edge clouds with data spaces in this context.
# Consent agent ### In short: * This building block provides a true human-centric data empowerment. Its objective is to help people manage their consents and find the services that are the closest to their needs. * Indeed in a data ecosystem with tens of different applications people will have to provide a lot of consents and will not have the time to fine tune each one. * The consent agent will match a person's “consent profile” with the terms and conditions of the services of the ecosytem to propose to the person the most suitable services according to their needs and to what they allow on their data. ### Timeline Start date: T0 (expected: Q1 2024) End date : T0 + 12 months Duration (in months): 12 #### **Where we are right now** * Developments have yet to start Want to join the effort? See the [Working Groups](/fundamentals/working-groups).
### Objective This would help prevent “consent fatigue” where people, for instance, always accept cookies because they don’t have the time to configure each parameter each time. The consent agent will rely on Prometheus-X’s [consent management building block ](https://dataspace.prometheus-x.org/building-blocks/consent)and enhance it with these matching functionalities. 1. As a person I determine my data ecosystem profile: what am I looking for, what I will accept or not on my data and under what conditions 2. As an organisation I also determine my profile: terms and conditions of my service 3. The consent agent compares these profiles and presents to the person the closest matching service, with explanations and a preconfigured consent 4. The person can choose if they provide it or not 5. The person can ask for a modification and the service can accept it or not (change the duration of conservation, provide less data than demanded, etc) 6. My agent learns from my preferences Several tasks are needed to operate this: 1. In depth legal analysis and a Privacy Impact Assessment of the functionality, ensuring its compliance with GDPR and the Data Governance Act 2. Terms and conditions and consents described in a standardized way, enabling their comparison 3. Modeling of the contractual relationship and of the negotiation 4. Machine learning algorithm to update the model based on user interaction The building block design and development will work closely with the stakeholders to achieve these tasks and objectives. ### Standards the building block will rely on: * ODRL: to describe consents, contracts and terms of conditions * Kantara consent receipt ### Roles between partners: [Visions](https://visionspol.eu/en/home/) will be leading the task as it already provides consent and contract management functionalities through its VisionsTrust data intermediary. Visions has developed the Prometheus-X consent and contract building blocks based on ODLR. Visions has already been working on a modeling of consent negotiating and prediction and will contribute it to the project as well as an anonymized datasets of over 50000 consents. Nomad Labs will provide decentralized data governance expertise. [IMC](https://www.im-c.com/) will ensure alignment with consent management development in GAIA-X and IDSA.
# Trustworthy AI assessment ### In short: * Allowing participants of the data space to provide trust in their profiling algorithms and users to trust the providers. * Easily audit profiling algorithms for the data space and compare them, ensuing a trust & ethical notation. * Clear communication of results and descriptions of algorithms to people. ### Timeline Start date: T0 (expected: Q1 2024) End date : T0 + 12 months Duration (in months): 12 #### **Where we are right now** * LORIA and Affectlog already have functioning versions of these platforms * Development to enhance their functionalities and connect them to the data spaces Want to join the effort? See the [Working Groups](/fundamentals/working-groups). ### Objectives: The objective is to deploy two operational platforms, each providing a different and complementary approach. They are presented below. 1. #### Audit Platform for Data and Algorithms General Framework For the past 3 years, LORIA, in collaboration with the French Ministry of Education, has been working on a proof of concept for a platform that allows both the secure sharing of educational data and the testing of AI algorithms on these data. Several use cases are proposed by the platform, and it is designed in order to be easily extended with nes use cases. This platform has been named LOLA for Open Laboratory for Learning Analytics. Each algorithm submitted to the platform receives a report in the form of an audit based on its own performance and a comparison with reference algorithms integrated into the platform, with regard to indicators related to the targeted use case. Thus, compared to other data sharing platforms, the LOLA platform offers the following advantages: 1. Security: Any data deposited in the platform is never accessible externally, neither for downloading nor for copying. 2. Control: Any data provider can configure the access rights to whoever he wants and keep an eye on the experiments that are done on it (especially the audit reports of new algorithms). 3. Transparency: Thanks to the edition of an associated audit report, each algorithm contributor can certify its performance according to use cases based on validated and sectioned indicators. 4. Extensibility: Each use case is declined in the form of a "scenario" which represents a central concept of LOLA. A use case provider always has the option of creating a new scenario that incorporates a description of the data, a specification of the algorithm's outputs, a set of indicators, and an experimental protocol for measuring performance. Trustworthy Artificial Intelligence In the short term, the platform aims to offer the EdTech community a space that would allow them to validate the choices of algorithms embedded in their applications and services. In the context of the measurement mentioned in the introduction of this document, the criteria concerned are obviously the following \- (2) transparency offered by an open and shared platform, \- (5) Privacy and data governance \- (6) Technical robustness and safety \- and (7) Accountability Infrastructure and Learning Analytics This platform is also a way to offer an infrastructure and a common standard for the output related to the providers of digital traces as well as the output related to use cases. Proposed Contributions Scalability (LORIA’s contribution) The LOLA platform is currently implemented in LORIA's LHS (high security laboratory), a private data center at the scale of a university. The current dedicated infrastructure is sized for a proof of concept with a few use cases, but it is essential to connect it to a common larger cloud of Prometheus-X for a scaling up. Complementary tools To facilitate its use, it is essential to add to the platform complementary tools, some of which will be developed by Loria within the framework of the present project, namely : * LORIA: The characterization of the population referenced by the data made available. This characterization could also be integrated into the performance indicators of the use cases * LORIA: The implementation of offline tuning tools (including the generation of public artificial data for testing purposes) * Fraunhofer ISST / Univ. Koblenz: The implementation of automated analysis of AI trustworthiness indicators (including explainability, data security, privacy, and fairness) Deliverables * Audit platform and associated tools * Methodological Framework 2. #### Platform for Security Evaluation AffectLog's risk assessment platform uses AI to extend the enterprise-grade Application Security Verification Standard (ASVS) to assess the security and privacy of applications both upstream and downstream of an application development lifecycle. It can help organizations identify areas of weakness in their AI models and prioritize efforts to improve their security. AffectLog's scoring matrix assesses the specific needs and risks of the AI models, and includes a dynamic rating scale with a numerical score, giving a snapshot of the overall security of the application. It also provides contextually relevant resources and a tool for tracking progress over time and identifying trends in security. It offers a way to create a Secure Coding Checklist specific to the application, platform or organization, aligned with industry security and privacy standards. AffectLog Basic (aligned with ASVS Level 1) - No access to source codebase/development lifecycle AffectLog Basic assessment determines compliance with the ASVS Level 1, by evaluating the application's protection against common vulnerabilities that are easy to discover, including those on OWASP's Top 10 list. This is the minimum level of security that should be achieved by all applications, especially those not handling sensitive data. The controls can be checked either automatically or manually without source code access. It is designed to evaluate an application's capabilities against simple, low-effort attacks and not against more determined attackers. However, if the application handles high value data, it is not advisable to rely solely on AffectLog Basic review. AffectLog Enhanced (aligned with ASVS Level 2) - Requires source code access An application that meets the requirements of AffectLog Enhanced audit (ASVS Level 2) is considered to have adequate protection against most common risks. It should have effective security controls in place and be using them appropriately. It is typically appropriate for applications that handle important business-to-business transactions, such as those that process healthcare (non-PII) information or sensitive assets like minors’ data, or those that implement business-critical or sensitive functions. This level also applies to industries where integrity is crucial to their business, to protect against nefarious agents. These applications may be targeted by skilled and motivated attackers using highly practiced and effective tools and techniques. AffectLog Ultimate (aligned with ASVS Level 3) - Requires full source code and development resource access AffectLog Ultimate assessment, aligned with ASVS Level 3, is the highest level of verification and is typically reserved for applications that require significant security measures, such as those in military, health and safety and critical infrastructure. Organizations may require AffectLog assessment for applications that perform critical functions, where failure could significantly impact the organization's operations and even its survivability. The assessment checks for advanced application security vulnerabilities and good security design. It requires in-depth analysis of architecture, coding and testing, and the application should be modularized in a meaningful way, with each module taking care of its own security responsibilities. AffectLog platform uses Machine Learning to bring attention to threats and vulnerabilities that may have been missed by manual checks, especially when dealing with large code bases. Relevant functionalities AffectLog risk assessment platform offers assessments bespoke not only to mobile apps, web apps and APIs but also specific to the emerging technologies like Machine Learning (ML) based on industry-standard methodologies. AffectLog leverages semi-supervised machine learning models (AffectLog ML360°) to build on industry-grade application risk assessment standards to assess the security of applications by: 1. Automating the assessment process: AffectLog ML360° is trained on the ASVS requirements and is used to automatically evaluate the security of a web application against those requirements. This helps organizations save time and resources by automating the assessment process. 2. Identifying security vulnerabilities: AffectLog ML360° is used to analyze the code and configuration of a web application and identify potential vulnerabilities or weaknesses. This helps AI driven organizations identify and address security issues before they become a problem. 3. Providing recommendations for improvement: AffectLog ML360° is used to analyze the security of an application and provide recommendations for improvement based on the ASVS requirements. This could help organizations identify areas where their security profile could be strengthened. 4. Continuous monitoring: AffectLog ML360° is used to continuously monitor the security of an application and alert organizations to any changes or issues that may arise. This helps organizations stay on top of their security posture and quickly address any issues that arise. ### Standards: * For LORIA’s platform The standards used are essentially: * Nextflow: for the description of the scenarios * Docker, docker-compose : for the integration of the algorithms * xAPi, xAPI Schema : for data and their documentation * For Affectlog’s platform AffectLog extends widely adopted industry standards for risk assessment, such as OWASP, UNESCO Recommendation on Ethics of AI, EU AI Act draft text, and NIST, to build its functionalities. AffectLog’s audit checklist encompasses Mobile Security Testing Guide, the Web Application Security Consortium (WASC) Threat Classification, Privacy Risk Assessment Methodology (PRAM) and the GDPR Data Protection Impact Assessment (DPIA) to assess the relevant security and data privacy risks of mobile and web applications. The platform aims to provide an auditable, explainable and transparent system to document the assessment checklist and the assessment scores. ### Roles between partners: Both LORIA (with the LOLA project) and Affectlog (with a maturing platform) have experience in designing, developing and implementing secure and specialized platforms for AI. Within the framework of this project, the deliverables will be two distinct platforms carried on the common infrastructure operated by Prometheus-X. The nature and objectives of these platforms are quite different and their development could be done quite independently. The Fraunhofer Institute contribution is quite naturally integrated in the first platform as a complementary tool. This requires a close collaboration especially in the integration phase
# Data value chain tracker ### In short: * The European dataspaces aim to guarantee users and organizations data sovereignty. * This means that in contrast to today´s closed digital platforms users and organizations may track and control the use of their personal or business data by other parties after making it available for sharing. * It is expected that on one hand this lowers the barriers for users and organizations to share sensitive data since the data remains to be protected by dataspace rules, consent and business contracts. On the other hand it may enable the creation of new incentives and business models that stimulate the exploitation of available data. * This building block allow to track who contributed what data in the value chain and how it was used to ensure fair retribution of all. ### Timeline Start date: T0 (expected: Q1 2024) End date : T0 + 12 months Duration (in months): 12 #### **Where we are right now** * Developments have yet to start Want to join the effort? See the [Working Groups](/fundamentals/working-groups). ### Objectives: For example, an operator of an AI-based recommender service for skill development requires data about training opportunities from training institutions as well as actual development paths from individuals. To motivate individuals to share their development path data for improving the AI-based recommendation engine (using machine learning techniques) the operator could distribute some of the value it generates when the recommendation engine is applied to the individuals who contributed their data by offering them premium services or even participation in the generated revenue.
This type of data-based business scenarios are expected to create substantial economic leverage in the context of dataspaces that goes beyond the one of closed digital platforms. Therefore the infrastructure of the European skill dataspace should facilitate the implementation of data-based business scenarios where data providers participate in the value created by services using their data.
Solution hypothesis Considering that the European Skills dataspace infrastructure will provide consent and contract tracking and management functions for individuals as well as for all organizations participating in a data value chain we assume that the implementation of participative data-driven business models as described above can be supported by a number of technical functions operating on the consent and contract tracking (meta-)data associated with the data value chain. These functions can be provided by the European Skills dataspace as a module to be integrated by service providers that wish to implement a participative data-driven business model, alternatively, as a SaaS solution provided by the federation operating the European Skills dataspace or by a participant of the dataspace.
Project goal (this building block) In this project we propose to 1. identify first use cases that can leverage this type of business model, e.g. in the context of AI-based training and career recommendations, AI-supported content creation or similar, 2. develop the concept and the functions to facilitate the implementation of participative data-driven business models based on the consent and contract tracking infrastructure in the European Skills dataspace, 3. adapt and augment the building blocks of the consent and contract tracking infrastructure if necessary, 4. implement first participative data-driven business model(s) using the defined concept and functions as a module or as a service, 5. augment the templates for European Skills dataspace-conform contracts and consent to cover the first participative data-driven business model(s) 6. test, iterate until identified use case(s) are covered
Describe how the roles between partners are divided and how each mobilize its expertise:
Nomad Labs
At Nomad Labs, we built a novel governance protocol for solving the problem of trustless governance in arbitrarily large permissioned networks (of people and/or data) - as the basis for the Nomad DAO project. The protocol enabled fully decentralized governance by properly incentivizing members to strengthen the network - and incorporates recruitment/permissioning (of addresses/IDs) in the same economic game as all other governance. The protocol itself is general enough to be utilized for most restricted-access DAOs, although the context in this paper is solving the stated problem.
The methodology used is just sound mathematics/game theory - building everything in Solidity. We also leverage existing open source code bases such as Compound Governance (), mirror.xyz () and OpenZeppelin (). The fundamental idea is to remove the need for both an external party in any transactions regarding all external actors of any permissioned organization, as well as all internal principal-agent problems.
Nomad Labs’ contribution of functionality to the building block Nomad governance offers four core innovations: a novel DAO governance protocol for independent decentralized restricted-access organizations, on-chain skill verification, a smart contract for employment, and various smart contracts for facilitating free internal trade. \ Our protocol can both help facilitate efficient data governance - in addition to facilitating free internal trade of data through our employment, auction, crowdfunding, and split smart contracts in addition to the governance protocol itself. \
Describe the standards you will rely on As stated above, we rely on open source code bases such as Compound Governance (), mirror.xyz () and OpenZeppelin () - and we build everything is Solidity at the moment (we plan to replicate the code in Rust over time). We also rely on standard Javascript libraries and agile software development principles - mostly Scrum.
Contributors: IMC, Visions, Nomad Labs
# Distributed data visualisation ### In short: * In the data space, AI providers will be able to interact with data providers in order to process their datasets and generate results (recommendations, analytics, predictions, etc). * These results need to be easily integrated into different apps and UIs. * Usually the output of a system is fed back to the system who provided the input. Our distributed approach will enable the output to be sent and shown to any other participant of the data space. ### Timeline Start date: T0 (expected: Q1 2024) End date : T0 + 12 months Duration (in months): 12 #### **Where we are right now** * Developments have yet to start Want to join the effort? See the [Working Groups](/fundamentals/working-groups).
### Objectives An example: 1. A person has skills data in a skills portfolio 2. they are looking to see what kind of job would be fitting for them 3. a service allows them to have their data processed by multiple AIs and compare the results 4. this service, instead of having to integrate a connector with the skills portfolio and with each of the AIs, only connects with the data space connector 5. all that can be analyzed on the edge, will be analyzed on the edge. What can not will be tokenized to ensure maximum privacy and personal data protection. 6. the service developer can design their ecosystem where the person can give their consent for their skills data to be accessed by the AIs from the skills portfolio and the results appear in the service’s UI
This means: 1. the data providers and AI providers have a way of identifying where to send the results 2. the UI provider has a single plugin to show results from any source ### Standards The idea is to reuse widely used assets, industry standards and standards that are already in use, eg. following: * standard data formats, like JSON-LD and xAPI * standard data visualization models like tables and graphs * open source frameworks, like D3.js and Chartist.js (standard way to use) * data storing technologies, like browser-cookies (industry standard) and data-api:s (standard way to use) ### Roles between partners: Headai: Use case definitions, Data model, visualization component, Institut Mines Telecom: Use case definitions, Data model, compatibility with other tools and systems Visions: Use case definitions, compatibility with other tools and systems, integration of component in VisionsTrust data intermediary and with other functionalities (consent, contract)
# Data veracity assurance **In short:** * Data spaces will lead to new levels and complexities of cross-party data sharing scenarios by standardising technical aspects of data federation as well as the contractual and consent aspects. * However, in these more dynamic and more complex networks of data interdepencies, the risks associated with incomplete or erroneous data are also magnified. * The data veracity building block will provide an integrated framework for the assurance of data quality of data space based data federation. \ Timeline Start date: T0 (expected: Q1 2024) End date : T0 + 12 months Duration (in months): 12 #### **Where we are right now** * Developments have yet to start Want to join the effort? See the [Working Groups](/fundamentals/working-groups). ### **Objectives:** * Agreements on data quality: lightweight, extendable semantic descriptions of the exchanged data and the constraints that have to be met on them (e.g., completeness, resolution, precision, internal linking, … properties). * Striking veracity level agreements: building on the consent management capabilities, facilities for striking data ‘veracity level agreements’ between the consenting parties. We plan to provide a decentralized implementation option for the agreement management. * Veracity evaluation modules: a range of veracity evaluation implementations for key data ensemble types and veracity requirements. These include * means for ensuring the veracity of properties which are easy to check (e.g., in data federation connectors); * facilities for weaving into the data transfer third-party data veracity assurance services (which may involve AI support); * checkable decentralized commitments (from simple hashes to zero-knowledge proofs); * decentralized agreement schemes for data where veracity evaluation is not feasible to automate and has to rely on either the judgement of third parties (e.g., in an audit role) or majority consensus. * Rule-based reconciliation: upon accepted/agreed on breaches of data veracity agreements, workflow/decision model based procedures for reconciliation. This reconciliation will leverage on fault-tolerant patterns to improve the trust in both the data sources and the AI components. We plan to provide a decentralized implementation option. ### Standards ISO8000 (Data Quality), emerging ISO/IEC CD 5259 family of standards (Data quality for AI) ### Roles between partners: BME will lead this task ,based on its solid background in fault tolerance, theory and practice of blockchain technologies, model-driven engineering and advanced data analysis. (The participating research group has been involved in 20+ EU projects.)
# Vision
[Prometheus-X](https://prometheus-x.org/?locale=en) Education & Skills is a non-profit association under the French law of 1901, uniting an international community to build and govern digital commons that allow every person and organization to control and freely share their data, especially in the education, training and employment sectors. These digital commons will allow the management of consent, identity, contracts, interoperability, anonymization, etc. We are working on this within the Data Space for Education & Skills of Gaia-X Association for Data and Cloud (AISBL) with many public and private organisations from multiple EU countries. We are working to personalize education, lifelong learning and employment in a human-centric way! We have working groups on technical and legal issues as well as on specific use cases of data circulation. Our vision is to build a technical infrastructure for education and skills data exchange and interoperability Our mission is to promote personalized learning, lifelong training and orientation, professional integration and research in these fields. Our ambition is to create and govern **infrastructure services for the circulation of education and skills data.** This infrastructure allows actors in these sectors to pool and share data to improve and customize their services. {% embed url="" %} # Architecture The architecture our digital commons enable are human-centric and rely on data intermediaries.
Data intermediary principles: * People manage their consents from a central place * Data intermediary do not store the personal data nor provide services on it: Separation of Powers Principle * Data intermediaries are interoperable * Data intermediaries rely on open governance and standards * Data intermediaries allow open and decentralized ecosystem The Prometheus-X building blocks allow anyone to build such data intermediaries and guarantee their interoperability. The Prometheus-X building blocks and governance also guarantee that organisations that are connected to one data intermediary can share data with organisations connected to another. Find the full decentralized protocol enabling data sharing between multiple personal data intermediaries [here](https://urlz.fr/kSCY). All building blocks respect GAIA-X, IDSA and Data Space Support Centre specifications.
High level Reference Architecture Model of the building blocks:
# Governance ### An open and representative governance of the industry To guarantee the adoption of this infrastructure, an open and legitimate governance is essential. This is why Prometheus-X was created as an association, **uniting different types of members who will govern this infrastructure together and decide on its development.** * Public and institutional members * Associative members or foundations * Corporate members {% hint style="info" %} Membership is exclusively reserved to legal entities {% endhint %} ### Members roles Member can take various roles within Prometheus-X, according to their nature and/or interest. #### Strategic actor Public, institutional or associative actors that are legitimate to represent the interests of the education/skills sector Ensures the legitimacy of the governance of the infrastructure, the confidence in its adoption and in its objective of general interest. #### Service Provider Organizations that develop and maintain infrastructure services Produce and make available the code for infrastructure services. They can also market them as managed services from the Prometheus-X platform for a fee. #### Data provider Organizations that make data available via infrastructure services Decide on the conditions of use and access to their data. #### Data consumer Organizations that use the data made available via infrastructure services to implement use cases Pilot the different use cases of the structure. Check out our community and member [here](https://prometheus-x.org/)! ### Board of Directors ![Logo Visions](https://visionspol.eu/wp-content/uploads/2021/10/logo-visions.png) ### Visions ### Matthias De Bièvre President ![Logo Inokufu](https://raw.githubusercontent.com/inokufu/logo/main/logo_inokufu_vector_full_square.png) ### Inokufu ### Matthieu Sonnati Treasurer ![Logo cstar-industries](https://visionspol.eu/wp-content/uploads/2021/12/cstar-industries.png) ### Cstar ### Eric Cestari Secretary General ![Logo Tralalere](https://visionspol.eu/wp-content/uploads/2021/12/tralalalere.png) ### Tralalère ### Déborah Elalouf VP Impact ![Logo Inria](https://visionspol.eu/wp-content/uploads/2021/12/Inria_logoWeb_50_red_FR_2x-1.png) ### Inria ### Muriel Brunet VP Research ![Logo Visions](https://edtechfrance.fr/wp-content/uploads/2019/09/Logo_EdTech_RVB.jpg) ### Edtech France ### Anne-Charlotte Monneret ![Logo editeurdeducation](https://visionspol.eu/wp-content/uploads/2021/12/editeurs-deducation.gif) ### Educational Publishers ### Valérie Barthez ![Logo Afinef](https://visionspol.eu/wp-content/uploads/2021/12/Afinef_logo1.png) ### AFINEF ### Marc Sagot ![Logo université de lille](https://upload.wikimedia.org/wikipedia/commons/thumb/0/02/Universit%C3%A9_de_Lille_logo.svg/1200px-Universit%C3%A9_de_Lille_logo.svg.png) ### University of Lille ### Pierre Boulet ![Logo Neobridge](https://visionspol.eu/wp-content/uploads/2021/12/neobridge.png) ### Neobridge ### Benoît Jaffeux # Business model Prometheus-X business model is based on royalties of the digital commons used by the participants of the dataspace. Prometheus-X will be the owners of the intelectual property (IP) associated to the difital common services for which it has funded the development. In practical terms, dataspace participants using PTX digital commons will pay Promteheus-x a variable fee (between 4 to 20%) based on the consumption of thoses services. In most use cases, Prometheus-X digitial commons will be used in three ways: * **Fully managed service**: As building blocks embbed in a fully managed service (*e.g.* SAAS). * **Multi cloud** **package**: As building blocks embbed in a self-managed Infrastructure as code package, ready to be deployed on multiple cloud providers. * **FOSS service**: As raw building blocks used in free and open-source software. ### **Fully managed service** This covers the use of Prometheus-X digitial commons as building blocks embbed in a fully managed service (*e.g.* SAAS).

Payments flow for managed services embedding PTX digital commons.

### **Multi-Cloud** **package** This covers the use of Prometheus-X digitial commons as building blocks embbed in a self-managed Infrastructure as code package, ready to be deployed on multiple cloud providers. In practice, the service willl be available as an Infrastructure as code package such as one prepared [Terraform](https://www.terraform.io/).

Payments flow for multi-cloud package and self managed services embedding PTX digital commons.

### **FOSS service** This covers the use of Prometheus-X digitial commons as raw building blocks used in free and open-source software.

Payments flow for freen open source service (FOSS) embedding PTX digital commons.

# Working Groups The work is divided among several working groups called Assemblies. An **Assembly** is a group of members who meet periodically to make decisions about a specific area or topic related to building the Education& Skills Dataspace (DASES). There are two types of **Assemblies** on this platform: * **DASES Working Groups** hold meetings, debates on various topics and create survey, which eventually lead to community consensus or to the need for developing new common services * **Prometheus-X General Assembly** holds meetings to vote governance rules and also to vote on proposals for funding the new common services identified within DASES Working Groups The work of these assembly is hosted on Prometheus-X collaborative platform based on Decidim and can be accessed [here](https://prometheus-x.org/assemblies). ### DASES - EU Vertical Education & Skills This assembly gathers documents and information exchanged amont **the Education and Skills vertical group, within GAIA-X initiative.** It is public and accessible to all vertical members for contribution. It is temporarely hosted on Prometheus-X platform (FR) on hold of a common collaborative solution from GAIA-X association {% embed url="" %} ### DASES - FR WG for a MVP of the Data Space This assembly works on the specifications of the Minimum Viable Portal of DASES (the catalog). {% embed url="" %} ### DASES - Legal sub-working Group A specific working group on the legal aspects, the contract template and the contract service: working on the lagal clauses for personal data sharing in the data space. {% embed url="" %} ### DASES - French WG Governance This assembly coordinates all the actions taken by the French WG to build the Data Space Education & Skills. This assembly has a European counterpart, the Vertical Education & Skills {% embed url="" %} ### Technical group This group focus on the these digital commons: catalog, consent, contract, identity and their compliance to GAIA-X specifications. {% embed url="" %} ### Skills Skills Working Group of the Education and Skills Data Space focusing on the skills data interop service and skills data sharing use cases. {% embed url="" %} ### Learning Analytics This group focuses on the subject of Learning Analytics, especially Learning records interoperability and their aggregation from several different datasets, in order to be able to exchange data between actors, in particular with a view to training machine learning models allowing the customization of learning path. {% embed url="" %} ### Virtual reality This assembly explores the use and exchange of data related to the use of virtual reality for learning. {% embed url="" %} ### Voice Datasets Voice Dataset Working Group of the Education and Skills Data Space working on mutualization of children voice data to train ML algorithms. {% embed url="" %} ### Impact Study Impact Study Working Group of the Education and Skills Data Space focusing {% embed url="" %} ## General assembly (Prometheus-X) This space is the digital twin of the General Assemblies of the Prometheus-X association. {% embed url="" %}
# Join us ### Why join us? #### Co-construction of rules and standards Take part in assemblies and working groups to provide ideas and opinions to create the governance of the Data Space Education & Skills. #### Play an active role in creating a common good Become an active member in the creation of a common good for the educational sector. A foundation on which all players in this field can build. To build the education of today and tomorrow, centered on the person and respectful of his or her rights. #### Exclusive access to the Prometheus-X platform Benefit from all the services of the Prometheus-X platform and participate in the development process. ### How to join? If you are intersted to particpate in our project, you are welcome! You can contact us [here](/extras/contact) or join our collaborative platform [here](https://prometheus-x.org/). # Events & Workshops # Market-X Workshop GAIA-X is organising [Market X](https://gaia-x.eu/market-x/), a great event in Vienna on the 14/15th of March.\ \ Dataspaces and solutions will be showcased and the whole dataspace ecosystem in the EU will meet!\ **Prometheus-X is a partner of the event and will have a booth there. Meet us!**\ \ Prometheus-X and [MERLOT](https://merlot-education.eu/) (also present at the event) will be co-organising a dedicated workshop during that event on our topics, on the **15th at 12.30.** **Venue (in person only):** Aula der Wissenschaften, Wollzeile 27A, 1010 Vienna, Austria The workshop will unite organisations commited to make the data space happen: 1. Edtechs/HRtechs 2. Universities 3. Employers 4. Training organisations 5. Employment agencies 6. Cloud and infrastructure providers We will: 1. Dive deeper into the business model(s) of our data space 1. We will take as a basis the concrete use cases and ecosystems defined by our community (human-centric skills matching, skills analytics, decentralized personalized learning and VR) 2. Clearly define the value and roles of these ecosystems 3. Produce the precise description of the value exchanges in the ecosystems 2. Dive deeper into the technical discussions around the building blocks 1. We will progress on specifications and formats for consent and contracts 2. We will progress on the architecture for skills and learning data interoperability 3. Test and use a first version of the Prometheus-X [data space marketplace](https://dataspace.prometheus-x.org/building-blocks/catalog) that will be available in demo mode 4. Define concrete implementation plans with the participants to make the data space real! 5. Get to know each other and discover Austrian specialities! Fill in this [form](https://docs.google.com/forms/d/e/1FAIpQLSfYJ85ITq_flAQbTDedptAMd2-QACGa6SW4H7URwzm-17M71w/viewform?usp=sf_link) ASAP to register (in person only) ! # Open letter Towards a human-centric and cross sectoral personal data space 20 top EU organisations join for a common effort and message towards the human-centric personal data space: > **The European strategy for data focuses on putting people first in developing technology, and defending and promoting European values and rights in the digital world**. - European Data Strategy
We believe that all sectors and data spaces are currently designing use cases that will require personal data in order to create more personalized products and services for people. However, we have noticed that no funds in the [Digital Europe Program](https://commission.europa.eu/funding-tenders/find-funding/eu-funding-programmes/digital-europe-programme_en) seem to address the importance of placing the individual at the center of the Data Spaces ecosystem. Therefore, we call for the EU to **focus on developing a human-centric approach to data**, where individuals are in control of their data and can securely share it with trusted organizations. We understand that **tremendous technical and financial efforts** are currently being applied to **connecting organizations across all sectors to data spaces** and building a data sharing infrastructure. However, we strongly advocate for this effort to also include the sharing of personal data. In order to build a strong digital economy, we need to **provide the human-centric infrastructure** to serve all stakeholders, to enable universities to offer better learning experiences to students, hospitals to engage with patients, governments with citizens, employers with employees, recruiters with job seekers, mobility providers with passengers and financial institutions to deliver better outcomes to their customers. To achieve this, we recommend including **Personal Data Intermediaries (PDIs)** in the development and design efforts of most, if not all, Data Spaces. PDIs act as trusted third parties that facilitate the exchange of personal data between individuals and organizations while ensuring data protection and privacy. By including PDIs, we can **provide users with transparency and control over their data sharing activities and help organizations access high-quality data** and provide better services, leading to increased innovation and competitiveness. We believe that the EU should **allocate part of the funds dedicated to the data space infrastructure towards building a data infrastructure that enables PDIs**. This will create a framework for secure data sharing that prioritizes the individual's rights and interests. Organizations working on the architecture, business models, and governance models of data spaces should also include personal data sharing in their plans to contribute to the development of a more transparent, fair, and secure data ecosystem that promotes innovation while safeguarding privacy and data protection. We are grateful to Prometheus-X for **bringing together a coalition of organizations**, some of whom have been working for more than 10 years on personal data sharing, to promote this message and help implement what is needed. Prometheus-X **has already developed the first building blocks for PDIs**, established its governance structure, and done first implementations in the education & skills data space. It now has **support from other data spaces (smart cities, tourism, mobility, media)** to reuse that work and build on top of it. We are committed to contributing to the design and deployment of data spaces to ensure a human-centric and cross-sectoral personal data space that will benefit all stakeholders.\ \ In conclusion, we strongly believe that personal data sharing is a critical component of the EU's data strategy, and **we call for Personal Data Intermediaries to be included in the development and design efforts of Data Spaces**. We urge the EU to allocate funds towards building a data infrastructure that enables PDIs and for organizations to consider personal data sharing when developing their data spaces to contribute to the development of a more transparent and fair data ecosystem.
# FAQ ### Work in progress # Contact Feel free to contact us if you have any question or if you want to join the association or the working groups. ✉️ Email: # Privacy Reading time: 10 min (i.e. the waiting time for the next bus) ### What is personal data? Any information that identifies me personally, directly (such as my name, login ID, email, etc.) or indirectly (my IP address, etc.). What data do we collect? What do we do with your personal data? Prometheus-X collects your data when you voluntarily share it either during my browsing, in particular through cookies. This collection of personal data and the various resulting processing operations are based on your consent, in accordance with a) of 1 of Article 6 of the GDPR. The general purpose of the service offered on Promehetus-X.org is to allow, through a participatory platform, all stakeholders to participate in the consultation implemented as part of the Data Education and Skills project. ### What information Prometheus-X collects and for what purpose? The Prometheus-X.org platform allows: To create an account; To react, participate, exchange content; To vote for the various proposals; To receive information and keep up to date with the news of the various working groups (via an alert system or subscription to a newsletter). 1\. Creation of an individual account and management of your account: Prometheus-X.org uses data about you for registration purposes and to manage your individual account. The key data we store may include: Your name Your first name The name of your company, institution… Your mail 2\. My Profile You can choose what information will appear on your profile: Your picture Last name First Name Name of your company, institution Your membership of the different working groups 3\. Contact form On our site, you have the possibility to contact us via a contact form. The information you provide via the contact form is generally: First name E-mail address 4\. Subscription to the various Newsletters If you subscribe to our various newsletters, we will use the following information to send periodic emails regarding general information about Prometheus-X. 5\. Content Sharing It is possible that you and other members of the site publish content including information about you (in the context of posts, comments) which will be publicly accessible on this platform. ### Who can have access to my data? The categories of recipients of personal data are: * authorized system administrators; * authorized functional administrators; * each of the identified users for their own personal information and productions; * the general public for all data made public by identified users. Some data collected, however, may be transmitted to subcontracting companies (within the limit necessary for the accomplishment of the missions that we entrust to them). We require our subcontractors to apply strict rules regarding the protection of personal data in accordance with applicable laws and regulations, both French and European. When you : Agree to subscribe to our various newsletters, Want to get a new password Your e-mail address is transmitted to the gandi.net service for sending e-mails to your address. Data retention period and security Cookies and trackers Maximum 6 months Data allowing the connection and concerning the profile of the user 2 months after the user unsubscribes ### How does Prometheus-X.org protect my data? Prometheus-X.org implements technical and organizational measures to secure your personal data and protect them against any destruction, loss, alteration or unauthorized access. Nevertheless, we recommend that you protect the password of your online account and not communicate it to anyone. If you suspect that your account has been the subject of usurpation, we ask that you notify us by email at as soon as possible. How does Prometheus-X.org contact me in the event of a data leak? In the event of a leak of personal data concerning you, we undertake to inform you as soon as possible. And to send you the procedures put in place in the event of a leak of this data. ### What are my rights, how can I exercise them? In accordance with the legal and regulatory provisions applicable to personal data, you have a right to limit the processing of your data, a right of opposition, access, portability, rectification and deletion of your data. You can exercise your rights by simple written request by email to . I want my data to be deleted ATTENTION: deletion of data is not possible if Prometheus-X.org has a legal obligation to keep my data. For other cases, I can ask Prometheus-X.org to delete my personal data at any time by email to . I correct my data I can ask Prometheus-X.org and the various administrators to rectify or complete my data so that they are accurate and up to date either: In my “profile/preferences” space. By sending us a request by email to I access my data I can easily obtain details of the data that Prometheus-x.org has on me or processes and request a copy by going to: " My account " " My data " Click on "Request data" I want to make a claim I can file a complaint with Prometheus-X.org if I believe that the use of my data does not comply with the regulations in force regarding the protection of personal data. If you believe, after contacting us, that your rights have not been respected or that this device does not comply with data protection rules, you can file a complaint with the National Commission for Computing and Freedoms ( CNIL). ### ARE COOKIES HIDING ON THIS PLATFORM? #### What is a cookie ? A cookie is a text file placed on your computer when visiting a site or consulting an advertisement. It is used to store user data in order to facilitate navigation and allow certain functionalities. Cookies are managed by your internet browser. #### Cookies on Prometheus-X.org? 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Including without limitations, the use of “we,” “us” or “our,” collectively throughout the entire document refers to the Prometheus-X’s team. Whereas the use of “you,” “your,” “they,” “them” or “their,” collectively refers to people and organizations outside Prometheus-X’s team.