On the Reuse of Scientific Data

Abstract

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.

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

“…Although information retrieval (IR) has been extensively studied for over sixty years (Sanderson & Croft, 2012), data retrieval is a nascent field. Recent studies surrounding the issue examine how data are made available via data sharing (Tenopir et al, 2011(Tenopir et al, , 2015, how researchers reuse data (Faniel, Kriesberg, & Yakel, 2016;Pasquetto, Randles, & Borgman, 2017), and how systems are designed to optimize data discoverability and retrieval (Pallickara, Pallickara, & Zupanski, 2012). Information documenting data retrieval behaviors is buried throughout other disciplinary and data-related literature and is not easy to identify (Gregory et al, 2018).…”

Community Standards for Open Cell Migration Data

“…Combining harmonised data (e.g. from the same cell culture model retrieved from different studies [31][32][33][34] ) will facilitate data analysis and mining across imaging acquisition techniques, setups and cell lines, with applications such as: (i) comparison of results from 2D and 3D culture environments that make use of the same cell model; (ii) validation of in vitro results from against datasets from in vivo experiments in model organisms (e.g. zebrafish embryos, mouse models of disease) to ascertain in vivo relevance; or (iii) systematic comparison of the relative doseeffects of growth factors or chemical compounds on migration behaviors across cell models.…”

“…As an important further opportunity, the reuse of existing primary image data with new analyses can reveal previously unexplored patterns contained in such complex data [31][32][33][34] . Typical examples for secondary reuse 7 of multiparametric imaging datasets results from applying novel computational algorithms to derive kinetic shape features (e.g.…”

ACCESS climate data management

“…References to the scientific work of others are included using literature only. Adding data publishing to the process means that data collected can be reused (and referenced), considerably increasing the benefits of public investment in science (Nosek et al 2015; Wilkinson et al 2016; Pasquetto et al 2017). …”