Qualitative data sharing

The Center for Qualitative and Mixed Methods Inquiry at Syracuse University (only a few hours away from McGill!) hosts a well known Summer Institute for Qualitative and Multi-Method Research. I recently found out that the center also houses a qualitative data repository (QDR). The topic of whether or not to share qualitative data has come up in brownbag discussions in the past. Undoubtedly there are drawbacks to sharing qualitative data. But the website of the QDR outlines some interesting rationales for qualitative data sharing. It states:

QDR provides leadership and training in—and works to develop and publicize common standards and practices for—managing, archiving, sharing, reusing, and citing qualitative data. QDR hopes to expand and improve the use of qualitative data in the evaluation of research, in scholarly production, and in teaching.

Qualitative data are used by social scientists to advance a range of analytical, interpretive, and inferential goals. Yet in the United States, traditionally such data have been used only once: social scientists collect them for a particular research purpose, and then discard them. The lack of a data-sharing custom is due in part to an infrastructure gap – the absence of a suitable venue for storing and sharing qualitative data.

QDR hopes to help to fill this gap. First, the repository expands and eases access to qualitative social science data. This access empowers research that otherwise would not be conducted, and promotes teaching and learning about generating, sharing, analyzing, and reusing qualitative data. Further, the repository contributes to making the process and products of qualitative research more transparent. This increased openness facilitates the replication, reproduction, and assessment of empirically based qualitative analysis. Finally, by increasing researcher visibility, the repository induces intellectual exchange, promoting the formation of epistemic communities and serving as a platform for research networks and partnerships.

It will be interesting to see if the data sharing in qualitative research will become seen as a best practice as it increasingly is in quantitative research.

Teaching Good Research Practice

I attended a webinar on how to teach students to document empirical research by Richard Ball and Norm Medeiros from Havorford College and hosted by the Interuniversity Consortium for Political and Social Research (ICPSR). This idea aims to counter current norms, policies and practices in teaching empirical research by having students submit all their statistical analyses with their final project. This should include all the necessary documentation to allow a third-party to replicate all statistical results, what Ball and Medeiros call “a soup-to-nuts approach”. This approach in turn enhances professional norms and practices through a trickle-up effect, students actually understand what they are doing, and students know they are being held accountable. The webinar used an example from an economics course, but it is easy to imagine the potential for social work education and research.

The slides are available on their YouTube channel. It’s worth checking out and rethinking how we can use this in our classrooms and research.

Amsterdam Manifesto on data citation and sharing

Below you can find the Amsterdam Manifesto on data citation and sharing. These principles allign well with the philsophy of replication and reproducible research that we’ve discussed so much at the brownbag.

For those who expressed concern about data sharing in social work HERE is a post by @carlystrasser who tackles many of the arguments against data sharing, including “my data is embarrasingly bad”.
Unfortunately, she does not discuss sensitive data topics with vulnerable populations.

The Amsterdam Manifesto on Data Citation Principles
Preface:
We wish to promote best practices in data citation to facilitate access to data sets and to enable attribution and reward for those who publish data. Through formal data citation, the contributions to science by those that share their data will be recognized and potentially rewarded. To that end, we propose that:
1. Data should be considered citable products of research.
2. Such data should be held in persistent public repositories.
3. If a publication is based on data not included with the article, those data should be cited in the publication.
4. A data citation in a publication should resemble a bibliographic citation and be located in the publication’s reference list.
5. Such a data citation should include a unique persistent identifier (a DataCite DOI recommended, or other persistent identifiers already in use within the community).
6. The identifier should resolve to a page that either provides direct access to the data or information concerning its accessibility. Ideally, that landing page should be machine-actionable to promote interoperability of the data.
7. If the data are available in different versions, the identifier should provide a method to access the previous or related versions.
8. Data citation should facilitate attribution of credit to all contributors

About
This Manifesto was created during the Beyond the PDF 2 Conference in Amsterdam, 20 March 2013.
Original authors are Mercè Crosas, Todd Carpenter, David Shotton and Christine Borgman.

See more on the Amsterdam Manifesto HERE

Blog authors are solely responsible for the content of the blogs listed in the directory. Neither the content of these blogs, nor the links to other web sites, are screened, approved, reviewed or endorsed by McGill University. The text and other material on these blogs are the opinion of the specific author and are not statements of advice, opinion, or information of McGill.