Sharing qualitative data: the launch of the Qualitative Data Repository

February 25

This is a guest post by Colin Elman (Associate Professor of Political Science, Maxwell School of Citizenship and Public Affairs, Syracuse University, celman@maxwell.syr.edu) and Diana Kapiszewski (Assistant Professor of Government, Georgetown University, dk784@georgetown.edu), who co-direct the  Qualitative Data Repository (QDR).

QDR
Debates about why and how to make academic research more open and data more accessible are emerging all around us. In June 2013, the Inter-university Consortium for Political and Social Research (ICPSR) convened a meeting on the topic, and the American Political Science Association (APSA) recently amended its standards for transparency in published research (outlined in its Guide to Professional Ethics in Political Science). As part of that broader conversation, discussion is heating up over the practice and promise of replication (as reflected in recent posts here). Meanwhile, Congress continues to consider the “Digital Accountability and Transparency Act.” And a benign (if perhaps somewhat fanciful) interpretation of recent efforts to de-fund (or significantly reduce the latitude of) the National Science Foundation’s Political Science Program might attribute those attempts to the opacity of the discipline’s research, which compromises its perceived relevance and value.

While data sharing, research transparency, and replication have customarily been prominent concerns for quantitative researchers, they are increasingly being seen as relevant for the qualitative tradition. Proponents of openness argue that it strengthens and improves all social science. It allows for the careful assessment of evidence-based claims. Further, secondary analysis can only occur when data are shared, enabling research that might otherwise not be possible, as well as avoiding redundant and costly data generation. Finally, teaching approaches that use active learning in combination with applications drawn from the real world are only feasible when scholars share their data.

The Qualitative Data Repository (QDR), which recently came online at Syracuse University, is consistent with this position. QDR aims to provide an approachable venue for storing and sharing digital data generated through qualitative and multi-method research in the social sciences. It is hosted by the Center for Qualitative and Multi-Method Inquiry (a unit of Syracuse University’s Maxwell School of Citizenship and Public Affairs), and funded by the National Science Foundation.

Why deposit data with QDR?

Sharing data through QDR makes them more visible and easier for other scholars to discover, access, use, and cite – thus amplifying their potential impact. QDR offers authenticated online access to data, and is fully searchable. It provides study documentation and other materials needed to facilitate data re-use. Depositors can establish user-access controls for their data when these are necessary to protect confidentiality and personal privacy as required by law and the ethical standards of the research community. QDR will assign unique persistent DOIs (Digital Object Identifiers) to data and ensure long-term preservation of digital assets. Storing data with QDR also helps scholars to meet data-management requirements being established by funding institutions and by journals and other publishing venues.

QDR is also building a library of guidance and resources to help scholars manage qualitative data, prepare them for sharing, reuse them, and cite them. In addition, QDR’s governance includes Research and Technical Advisory Boards consisting of prominent qualitative researchers and leaders in the data-management field.

What kinds of data does QDR accept?

QDR accepts many types of qualitative data including (but not limited to) unpublished primary sources, published primary sources, primary sources cited in secondary sources, secondary sources, and other research materials. Qualitative data projects (aggregates of qualitative data) might be of three types: active citation compilations, data collections, and topic clusters.

Active citation compilations – “Active citation,” originally developed by Andrew Moravcsik (2010, 2012, 2014), entails digitally enhancing static citations in scholarship based on qualitative or multi-method research by hyperlinking them to a Transparency Appendix (TRAX) containing enriched citation which, in turn, can be hyperlinked to the actual sources (when these can be legally and ethically shared). A Guide to Active Citation offers instructions for using this technique, and two tools QDR is creating — the ACE (Active Citation Editor), which helps scholars to create a TRAX for an existing research publication, and the LACE (Live Active Citation Editor), which helps scholars develop a TRAX while writing — facilitate its use.

Data collections – A data collection is a coherent group of data with an organizational logic that relates the data to each other in identifiable, describable ways. Data collections vary widely in content and structure. They may contain many different types of data, may include formalized information gathered in the context of pre-set categories and come in the form of a preconfigured database (i.e., may have rows and columns), or may be a particular group of documents or a specific set of interview transcripts.

Topic clusters – A topic cluster is an unstructured amalgamation of materials on a particular issue or subject. When conducting research, qualitative political scientists invariably gather considerably more information than they ever carefully organize and analyze. The materials in the “everything else” box can be a windfall for other scholars who are addressing the same or a similar topic or searching for relevant background information.

Please visit QDR and check out our pilot projects in progress. If you have data arising from qualitative or multi-method inquiry that you would like to share, we would welcome the opportunity to discuss your depositing them with QDR. Also, we are always looking for collaborators to help us develop training and other materials. Please contact us (qdr@syr.edu) if you would like to share your data, or get involved with QDR.

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