🟣Open Science - LERU
What is Open Science?
FAIR Data
Data that are Findable, Accessible, Interoperable, and Reusable: implies use of persistent identifiers, rich metadata, standard formats, open access (as much as possible), and clear licensing or usage conditions.
Ensuring metadata quality, balancing openness vs privacy / confidentiality, establishing domain standards, avoiding “dark data” or data silos, data stewardship capacity.
Research Integrity
Upholding ethical, transparent, and accountable conduct in all stages of research: e.g. avoidance of misconduct, reproducibility, clear authorship, data provenance, and conflict of interest handling.
Reconciling openness with confidentiality (e.g. sensitive human data), ensuring truthful reporting and correction, embedding integrity training, handling errors transparently.
Next Generation Metrics
Alternative or expanded metrics beyond traditional bibliometrics — incorporating open science contributions (data, software, altmetrics, societal impact) to assess research quality more holistically.
Avoiding misuse or overemphasis on metrics, discipline differences, gaming risks, ensuring fairness, integration into evaluation and promotion systems.
Future of Scholarly Communication
Transitioning the modes of producing, reviewing, disseminating, and archiving research outputs: e.g. open access publishing, preprints, open peer review, interoperable repositories, new publishing models.
Economic sustainability (publishing costs), publisher business models, copyright and licensing, discipline norms, repository interoperability.
Citizen Science
Involving members of the public (non-specialists) in scientific research (data collection, analysis, idea generation), thereby enhancing engagement, inclusivity, and societal relevance of research.
Quality control, training, ethical considerations, data validation, incentives, governance of citizen contributions, diversity and equity in participation.
Education and Skills
Ensuring that researchers, students, and support staff have the competencies to deploy Open Science practices: e.g. training in FAIR data, open publishing, reproducible workflows, integrity, and metadata standards.
Resource allocation, embedding in curricula, continuous updating (keeping pace with evolving tools and standards), motivating uptake among established researchers.
LERU “Implementing Open Science” (leru.org); UCL summary of 8 Pillars (University College London)
Rewards and Incentives
Reforming academic recognition and reward systems to value open science practices (data sharing, reproducibility, code, public engagement) on par with traditional outputs like papers.
Institutional inertia in promotion criteria, resistance from researchers accustomed to classical metrics, aligning incentives with quality rather than quantity, balancing discipline differences.
European Open Science Cloud (EOSC)
A federated digital ecosystem that enables seamless access, management, analysis, and reuse of research data across Europe, connecting institutional infrastructures, services, and standards.
Technical interoperability, governance across institutions/countries, sustainability, data sovereignty, legal and policy harmonization, adoption and integration with institutional systems.
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