Data Management and Sharing

Research data management includes the activities performed while handling data generated or gathered as part of a research project. Managing data is a crucial part of the research process. While good data management supports good research, poor data management can render data unusable and lead to the failure of a project. Data management includes activities relating to:

• how to generate or gather data
• where to store it
• how to document and describe it
• how to process it
• identifying your legal and ethical obligations for protecting and/or sharing it
• choosing what data to archive and discard
• where to share your data & how to license it
• how to cite your data in reports and publications

Data is a key piece of the scholarly record. This means that the way you manage your research data has an impact on the accuracy and integrity of the research record, especially if you publish your results in a journal article or present a poster at a conference. It also has an effect on the potential for data curation, sharing, and reuse or secondary analysis. This is recognized by the Office of Research Integrity, the National Academies of Science, federal funding agencies requiring data management plans, and initiatives like FORCE11. Kenneth Pimple describes data management as “the neglected, but essential, twin to the ‘scientific method.’”

Source: Coates, H. (2014). Ensuring research integrity The role of data management in current crises. College & Research Libraries News, 75(11), 598-601.

Data management is also related to the conversations about challenges across multiple disciplines - psychology, cancer research, cell biology, and more - in reproducing and/or replicating published results. Advancing our understanding of the world requires an accumulation of evidence from more than one study that supports theories about how things happen. Our theories are based on evidence, or data. Put simply, one study does not prove a theory. Several studies producing consistent data and results are necessary for a theory to be accepted as a likely explanation. In order to compare, aggregate, and analyze data across multiple studies, data need to be accessible, interoperable, defined, well-documented, and citable through the use of unique identifiers. Another approach to describing and making these ideas practical are the FAIR Guiding Principles, which are: Findable, Accessible, Interoperable, and Reusable.