EOSC-Life Report on reproducibility
Creators: name: Fratelli, Maddalena
name: Saunders, Gary
name: Rybina, Arina
name: Tang, Jing
Publisher: name: Zenodo
Year: 2023
Subjects: Life Science Research Infrastructures
Reproducibility
FAIR principles
data sharing
workflow standardisation
quality control

description: This deliverable highlight EOSC-Life’s contributions to enhancing research reproducibility. The EOSC-Life project focuses on improving research reproducibility through various strategies including data sharing, standardization of workflows, promotion of FAIR principles, quality control, community engagement, long-term data preservation and training. The Deliverable assesses the impact of EOSC-Life resources on reproducibility using various Key Performance Indicators (KPIs): Usage Statistics: Tracks visits, page views, submissions, and downloads to gauge adoption and practical relevance. Citations: Measures resource impact through citations, recognizing that citation patterns vary based on factors like field and utility. Challenges in interpreting citation data are acknowledged, including varying citation practices, niche audiences, and lag between publication and citation. Not all users cite resources, impacting their recognition. However, the Deliverable highlights evidence of community use of the EOSC-Life resources and related initiatives, that were designed to improve reproducibility. Data re-use is a pivotal factor in advancing reproducibility within life science research, allowing for independent validation of findings. Re-using data from past or external studies permits the replication of analyses and experiments using the same dataset, affirming the reliability of original results beyond specific analysis methods. The practice enables researchers to contrast their outcomes with prior studies employing similar datasets, unveiling consistent patterns or divergences across analyses, which deepens comprehension of the studied phenomenon. Pooling data from multiple sources augments statistical power, offering larger sample sizes for more robust conclusions. Data re-use empowers researchers to explore fresh research questions using pre-existing datasets before embarking on new data generation, fostering novel discoveries and insights overlooked by the original authors. This approach saves time, resources, and costs by capitalizing on pre-collected data, allowing researchers to prioritize analysis and interpretation, thus accelerating scientific progress. The text highlights a dedicated effort within WP8 (Work Package 8) concerning data re-use assessment and discusses factors impacting this practice. The text presents findings from a literature review on data re-use in ovarian cancer pharmacology and outcomes of a round-table discussion during the final AGM of the project. Barriers to data re-use are discussed, including awareness, technical complexities, lack of bioinformatics expertise, data quality concerns, publication incentives, and data attribution. The importance of aggregating data in trusted resources for enhanced discoverability, consolidation, standardization, integration, and preservation is emphasized. Challenges in tracking data re-use and strategies for improvement are outlined, along with recommendations for policies that support data re-use, such as promoting training, fostering collaborations, standardizing metadata, recognizing data usage, and facilitating open sharing of unpublished and negative results data. In conclusion, the EOSC-Life project significantly contributes to enhancing reproducibility in life science research by fostering open collaboration, standardization, and integration. Its impact is measured through usage statistics and citations, reflecting increased adoption and practical relevance. Collaboration with various data-related initiatives amplifies the project's impact, leading to a connected ecosystem for reproducible and collaborative research in the life sciences. Moreover, the Deliverable underscores the critical role of data re-use in bolstering reproducibility in life science research. The text emphasizes the benefits of leveraging existing datasets, calls for overcoming barriers to data re-use, and proposes measures to create a supportive environment for fostering data sharing and re-use. These recommendations could be the basis for future projects’ activities.