On the Reuse of Scientific Data

By Irene V. Pasquetto, Bernadette M. Randles, Christine L. Borgman. 2017

202 citations recorded by the source corpus.

While science policy promotes data sharing and open data, these are not ends in themselves. Arguments for data sharing are to reproduce research, to make public assets available to the public, to leverage investments in research, and to advance research and innovation. To achieve these expected benefits of data sharing, data must actually be reused by others. Data sharing practices, especially motivations and incentives, have received far more study than has data reuse, perhaps because of the array of contested concepts on which reuse rests and the disparate contexts in which it occurs. Here we explicate concepts of data, sharing, and open data as a means to examine data reuse. We explore distinctions between use and reuse of data. Lastly we propose six research questions on data reuse worthy of pursuit by the community: How can uses of data be distinguished from reuses? When is reproducibility an essential goal? When is data integration an essential goal? What are the tradeoffs between collecting new data and reusing existing data? How do motivations for data collection influence the ability to reuse data? How do standards and formats for data release influence reuse opportunities? We conclude by summarizing the implications of these questions for science policy and for investments in data reuse.

Subfields: Information Systems, Information Systems and Management, Management Science and Operations Research

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Citations in the corpus

Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science (2017, background): “This often arises very starkly when data collected for one purpose is applied to another purpose, an act of translation that has become a signature move in data science.(24) A case in point is when administrative and transactional records from the public sector are made available for ‘‘social good’’”
Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness (2020, background): “42 Most major funding sources now require applicants to outline a data management and data sharing plan; this can entail (among other things) storing the data in a convenient format along with a data dictionary, a long term archiving plan, and providing an independent access mechanism (eg, a…”
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development (2021, unknown): “For example, only five publications provided any demographic information regarding who annotated the data instances.”
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2020, background): “ing fields may view the same data in considerably different ways” [29], see also [108]), creating detailed accounts of assumptions and decisions is critical to the dataset design phase.”