Data Sharing in Psychology

by

Joy Kennedy

• LAST REVIEWED: 24 February 2021
• LAST MODIFIED: 24 February 2021
• DOI: 10.1093/obo/9780199828340-0272

Introduction

Narrowly defined, data sharing is the practice of making scientific research data available to other researchers. However, the term is often used to include a variety of open-science practices, including making data, methodology (e.g., coding scheme), analytic syntax, and other research materials available to other researchers, as well as the reuse of those resources by others. There are multiple avenues for data sharing, for example data repositories (either subscription-based or free) or direct request to the researcher. Data sharing is a fairly common practice in the life and earth sciences. Excepting a handful of longitudinal projects, psychology lacks this robust historical precedent for sharing data. In fact, in the not-so-distant past, institutional review boards typically required that data be destroyed after a preset period in order to protect participants’ privacy—and some still do. And many researchers still do not take the first step—modifying their informed consent procedures to include explicit consent to share. Although still not frequent, data sharing in psychology is becoming more common. In part, this trend is being driven by the requirements set by publications and funding agencies. For publications, data sharing is intrinsic to transparency and replication of study findings. For funders, data sharing ensures greater return on investment—that expensive and time-consuming primary data collection does not wind up sitting on a dusty shelf, but rather can be reused for secondary data analysis to answer new questions. In psychology as in other fields, technological improvements in storage capacity and computing power have also facilitated data sharing and reuse. While many psychologists are still concerned that data sharing will result in being “scooped” or found in error, there is increasing recognition of the benefits of data sharing. First, data repositories ensure that data are archived, and that the burden of preservation does not fall on the researcher or the researcher’s institution. Sharing also increases the pace of scientific progress, as researchers can build on each other’s work. For example, researchers can learn how other experts approached measurement or coding of a given outcome. In replication studies, inconsistent findings can point to contextual variations in the construct under study, rather than researcher error. And in a field where null findings are often difficult to publish, sharing allows these data to be included in meta-analyses across studies to examine broader impacts. Most importantly, data sharing enhances transparency, a key ingredient in the scientific process.

Data Sharing Overviews

The articles in this section provide a broad overview of data sharing in psychology. Ceci and Walker 1983 presents one of the first calls for data sharing in an “open data bank.” Decades later, both Houtkoop, et al. 2018 and Martone, et al. 2018 report on the lack of frequency of data sharing in psychology, although Martone, et al. 2018 talks briefly about the tradition of public longitudinal studies in developmental psychology, such as the US’s National Longitudinal Study of Youth. Houtkoop, et al. 2018 presents the results of a survey of researchers on barriers to sharing, including the fear of being scooped and the fear that data will be misused or mis-represented. Both Houtkoop, et al. 2018 and Martone, et al. 2018 talk about solutions and best practices for data sharing. Pampel and Dallmeier-Tiessen 2014 presents similar overviews, but from the European Union, and The Royal Society from the United Kingdom. Johnson 2001 discusses how data sharing facilitates meta-analysis, while Gilmore 2016 talks about links between data sharing and big data approaches to developmental science. Finally, Fane, et al. 2019 provides a potential forecast of where researchers in psychology will be situated once data sharing becomes a more accepted practice.

• Ceci, S. J., and E. Walker. 1983. Private archives and public needs. American Psychologist 38:414–423.

  DOI: 10.1037/0003-066X.38.4.414

  One of the first calls for sharing of research data in an “open data bank,” potentially after an embargo period to protect intellectual rights. Discusses who owns research data, and the legal, ethical, and pragmatic barriers to sharing, as well as the technical and ethical costs and benefits. Recognized that an open bank was unlikely, so recommended that federal funding agencies begin to encourage data sharing.

• Fane, B., P. Ayris, M. Hahnel, I. Hrynaszkiewicz, G. Baynes, and E. Farrell. 2019. The state of open data report 2019 (Version 2). Digital Science, figshare.

  DOI: 10.6084/m9.figshare.9980783.v2

  The latest installment of an annual survey of researchers, conducted by Figshare, Digital Science, and Springer Nature. While not focused on psychology, the report provides interesting perspectives from fields where data sharing is accepted practice, as researchers are now interested in enforcement of existing mandates, and penalties for lack of compliance. The report perhaps foreshadows what is to come in psychology as data sharing becomes more requisite.

• Gilmore, R. O. 2016. From big data to deep insight in developmental science. Wiley Interdisciplinary Reviews Cognitive Science 7.2: 112–126.

  DOI: 10.1002/wcs.1379

  Links data sharing to big data approaches to developmental science. Specific references to several big developmental datasets, including those from governmental, researcher-based, measure-specific (e.g., the MacArthur Communicative Inventory), and commercial sources. Discusses technical issues surrounding data storage, sharing, and retrieval.

• Houtkoop, B. L., C. Chambers, M. Macleod, D. V. M. Bishop, T. E. Nichols, and E.-J. Wagenmakers. 2018. Data sharing in psychology: A survey on barriers and preconditions. Advances in Methods and Practices in Psychological Science 1.1: 70–85.

  DOI: 10.1177/2515245917751886

  Results of a survey of six hundred authors in psychology regarding attitudes toward and perceived barriers to data sharing. Houtkoop and colleagues found that sharing is uncommon, but valued. Barriers to sharing include a belief that sharing is uncommon, that it is time-consuming, and a lack of training and information. The authors conclude that encouragement from institutions, funders, and publishers, along with better training, will facilitate sharing.

• Johnson, D. 2001. Sharing data: It’s time to end psychology’s guild approach. APS Observer 14.8: 1–3.

  Links data sharing practices to meta-analysis, as well as traditions in the field that resemble guild practices.

• Martone, M. E., A. Garcia-Castro, and G. R. VandenBos. 2018. Data sharing in psychology. American Psychologist 73.2: 111–125.

  DOI: 10.1037/amp0000242

  The history of data sharing in psychology, the evolution of open science, and the benefit of persistent identifiers (e.g., ORCID, DOI). The perceived negatives to sharing are examined, as is refuting evidence. The benefits and incentives for sharing are presented. Best practices for sharing are discussed in the context of the FAIR principles for data sharing (see Wilkinson, et al. 2016).

• Pampel, H., and S. Dallmeier-Tiessen. 2014. Open research data: From vision to practice. In Opening science. Edited by S. Bartling and S. Friesike, 213–224. Cham, Switzerland: Springer International.

  DOI: 10.1007/978-3-319-00026-8_14

  Focus on open science (including data sharing) in the European Union, and the infrastructure demanded by data sharing, including training, metadata, and long-term sustainability of repositories. Discussion of how to incentivize scientists to share, primarily through increases in citations.

• The Royal Society. 2012. Science as an open enterprise: The Royal Society Science Policy Centre report 02/12

  A comprehensive review of open science and data sharing, with a focus on the United Kingdom. Presented are ten recommendations to promote data sharing, primarily at the institutional level (e.g., universities, professional organizations, governments, journals, and funders). Also discussed are effective communication with the general public, and the boundaries of what can and should be shared.