FAIRCORE4EOSC Deliverable D1.5 Strategic Alignment and Contribution to the EOSC Partnership
Bibliographic record
Abstract
The FAIRCORE4EOSC is a European Open Science Cloud (EOSC) project, funded from Horizon Europe programme, that is tasked to develop nine new EOSC-Core services to support a FAIR EOSC as well as to enhance interoperability and discoverability of an increased amount of research outputs. The services are developed by leveraging existing technologies and services. Furthermore, the project has ensured the user centricity of the component development by identifying case studies, represented by partners coming from different scientific communities. The case studies are early adopters of the services within these communities. The purpose of this report is to present the strategic alignment activities carried out during the FAIRCORE4EOSC project and to outline the contribution to the EOSC partnership that the project has built during the project and that continues to produce impact after the project. This report is also aimed to outline the lessons that the project has learned by conducting the alignment activities during the project. Strategic alignment can take different forms, and the level of engagement in alignment activities can vary. Different forms of activities can be defined at the project planning phase, or maybe alignment can be identified only at the project implementation phase, in more or less formalized forms. A classification for planning alignment activities is discussed in the introduction of this report. Some of the planned activities turned out to be as useful, some even more useful than foreseen. Some planned actions turned out to be of low relevance. There were also unforeseen collaborations that proved to be highly synergistic. The level of engagement in alignment activities can depend on resources allocated for such activities, and the maturity of the project outputs. First, it is crucial for projects to share knowledge on different developments, and to find synergies so that the work does not take place in silos. Sharing information may prevent misalignment of efforts between actors and prevent duplication of work. Second, raising awareness for project outputs helps to ensure the developed services match the needs of users through engaging identified actors in requirement elicitation and validation of developed features. Third, both broad and targeted dissemination help in finding interested users and increasing adoption of the developed services. These three things are major facets in creating the projects impact. However, sometimes expectations for alignment activities are simply too high and the alignment does not take place at an expected level. Reasons for this may be for example that the services developed in FAIRCORE4EOSC are mostly mature enough for adoption only in the end of the project. Expectation management plays a relevant role here. We have classified the alignment activities into groups by target actor type: EOSC partnership, INFRAEOSC projects, strategic communities, and other stakeholders. The alignment activities are be outlined in this report alongside the challenges that the project has overcome to improve the sustainability of the project contributions in the EOSC partnership. The overall rationale for alignment has been to maximise the impact of the services developed for the past three years to make them relevant to research communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.108 | 0.052 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".