Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/115759
Title: | Best practices for data management and sharing in experimental biomedical research | Authors: | Cunha-Oliveira, Teresa Ioannidis, John P. A. Oliveira, Paulo J. |
Keywords: | biomedicine; data management; metadata; raw data; reporting guidelines; reproducibility | Issue Date: | Mar-2024 | Publisher: | American Physiological Society | Project: | info:eu-repo/grantAgreement/EC/HE/101087416/EU/EXCELlent Science with Impact, Open and Reliable PTDC/ BTM-SAL/29297/2017 POCI-01-0145- FEDER-029297 UIDB/04539/2020 info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP/04539/2020 LA/P/0058/2020 DL57/2016/CP1448/CT0016 |
Serial title, monograph or event: | Physiological Reviews | Volume: | 104 | Abstract: | Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers’ careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers. | URI: | https://hdl.handle.net/10316/115759 | DOI: | 10.1152/physrev.00043.2023 | Rights: | embargoedAccess |
Appears in Collections: | I&D CIBB - Artigos em Revistas Internacionais |
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