🟣Open Science - Use Cases
Open Science - Use Cases
Below are tailored definitions of the eight LERU pillars of Open Science, contextualized specifically for reNEW Copenhagen and its mission in stem cell–driven biomedical research. Each pillar is interpreted through the lens of translational medicine, regenerative therapies, and responsible data-intensive science to support reNEW’s strategic objectives.
🔬 FAIR Data (Findable, Accessible, Interoperable, Reusable)
In the context of stem cell and biomedical research, FAIR data ensures that raw and derived data—from confocal microscopy, omics, and clinical-grade assays—are meticulously documented, stored, and shared using globally recognized metadata standards (e.g., MIAME, MIACARM, OME). For reNEW, this pillar underpins reproducibility, regulatory readiness, and collaboration across international sites. Adherence to FAIR principles enables cross-study data integration, downstream reuse in disease modeling or therapeutic design, and supports alignment with the European Open Science Cloud (EOSC) infrastructure. → Use case: A high-content screening dataset is FAIR through OMERO + metadata templates, indexed with persistent identifiers, and deposited in a community repository (e.g. BioStudies, EMBL-EBI).
🧬 Research Integrity
Research integrity at reNEW means upholding the highest ethical, scientific, and clinical standards throughout the stem cell therapy development pipeline. This includes transparent protocol reporting, rigor in experimental design, open error correction, and documentation of provenance across imaging, sequencing, and differentiation workflows. Given reNEW’s proximity to clinical translation, this pillar also entails full compliance with GDPR, ethical approvals, and institutional biosafety governance. Integrity ensures public trust, ethical legitimacy, and basic and translational research replicability. → Use case: A CRISPR-edited iPSC line includes full metadata traceability, version control, and conflict-of-interest disclosure in the publication and registry record.
📊 Next Generation Metrics
Rather than relying solely on high-impact publications, reNEW evaluates scientific contributions based on meaningful impact—including dataset reuse, software adoption, reproducibility, and patient/public benefit. This pillar calls for acknowledging open datasets, tools (e.g., CellProfiler pipelines), preprints, and negative results in staff evaluations and funding proposals. In the biomedical domain, next-generation metrics must also capture translational readiness (e.g, protocol standardization, GMP documentation, assay robustness). → Use case: A researcher’s contribution to a stem cell protocol repository (with >1,000 downloads) is formally recognized in their reNEW career development plan.
📚 Future of Scholarly Communication
Biomedical research at reNEW must embrace emerging models of open and responsible dissemination. This includes preprints (e.g., bioRxiv), open-access publishing, protocol repositories (e.g., protocols.io), and open peer review. Future communication practices also mean rapid sharing of datasets that support urgent therapeutic needs and new forms of community feedback. For stem cell research, clarity in methods, ethical sourcing, and data transparency are essential for reproducibility and trust. → Use case: A collaborative differentiation protocol is published as a preprint and simultaneously shared via a live protocol repository, allowing early feedback and reproducibility.
👥 Citizen Science
Although traditionally less emphasized in biomedical research, reNEW can engage patients and citizens in ethical governance, donor consent processes, and data donation (e.g., patient registries, biobanks). With increasing focus on societal impact and inclusiveness, reNEW’s projects may benefit from public involvement in priority setting, co-designed research questions, or longitudinal data collection. Trust and transparency are critical when dealing with human biological materials and potential therapies. → Use case: A rare disease patient group collaborates with reNEW to define outcome measures and prioritize tissue samples for organoid development.
🎓 Education and Skills
To deliver cutting-edge therapies responsibly, reNEW researchers must be trained not only in stem cell biology but also in open data stewardship, metadata annotation, code versioning (e.g., Git), and reproducible computational analysis (e.g., Jupyter, R). reNEW’s infrastructure should embed Open Science into PhD and postdoc curricula, including licensing, reproducibility, and data ethics sessions. Support staff must also be equipped to guide imaging data curation, dataset deposition, and ELN compliance. → Use case: All reNEW PhD students complete an Open Science module covering FAIR data, reproducible pipelines, and licensing strategies for biomedical data.
🏆 Rewards and Incentives
reNEW must formally recognize non-traditional research outputs—datasets, software, metadata standards, infrastructure contributions, citizen engagement, and negative results—within hiring, evaluation, and promotion criteria. Aligning with LERU, this pillar enables long-term cultural change and supports early-career researchers committed to openness. Biomedical research must balance innovation with transparency and reward collaborative rather than competitive behaviors. → Use case: A data steward who leads reNEW's implementation of dataset versioning and curation protocols receives co-authorship and evaluation credit in funding applications.
☁️ European Open Science Cloud (EOSC)
For a data-intensive node like reNEW Copenhagen, EOSC provides access to scalable computing, cross-border data sharing, and federated infrastructure for imaging, omics, and metadata harmonization. Participation in EOSC allows reNEW to integrate with European standards, submit metadata to EOSC-compatible registries, and leverage services like B2SHARE, ELIXIR, and BioImage Archive. → Use case: Imaging datasets from reNEW’s high-throughput screen are stored in an EOSC-aligned repository, with metadata synchronized to the BioImage Archive and DOI minting via DataCite.
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