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STRATEGIES FOR DISCUSSING AND COMMUNICATING DATA SERVICES
“Don’t Hate the Player, Hate the Game” Joel Herndon, Duke University Robert O’ Reilly, Emory University
Notable Library US RDS Initiatives
Library Research Data Services
Defining Research Data Services “services that a library offers to researchers in relation to managing data and can include information services (consulting on data management, metadata standards, reference support for finding data sets, and web guides) as well as technical services (providing technical support for data repositories and preparing data for the repository.” – Tenopir et al. 2012
Defining Research Data Services
“In most cases services are evolving ahead of evidence which models and strategies will prove most effective or successful” – Fearon et. al. 2013
Research Data Services 2011-2014 • The strongest driver in the majority of libraries providing
RDS are funder mandates and response to government sharing policies. (Tenopir et al. 2015)
• Library data services have not expanded at the rate suggested by Tenopir’s 2011 survey on RDS
• Most libraries that do engage in RDS work have tended to focus more on consulting services (data sources, consulting on data management plans, creating guides) and less on the technical work of data archiving, creating metadata
Academic Perspectives on Data Management • Akers (2014), “Going Beyond Data Management Planning:” • Many, if not most, academics are not funded by the likes
of the NIH or NSF, so matters of funder mandates are not necessarily of concern to them
• “We should keep in mind that DMP requirements impact only a small proportion of academic researchers and, furthermore, that such requirements are only one factor motivating researchers to share their data.”
• Academic discussions of data management strongly support Akers’ position
Academic Perspectives on Data Management • Academics – discussions of data management focus on different matters: • Transparency • Replication/Reproducibility of findings in empirical
analyses • Funder mandates – often not a major focus • Discussions and debates on how to promote transparency and replication span fields and go back decades
Academic Perspectives on Data Management
Evolving Notions of Transparency
• Discussions have grown in specificity with time: • Initial views – data sharing and availability • With time – availability of code
• Notions of transparency and reproducibility have gotten more rigorous – data alone are not enough
• Data management also involves all the work it takes to get data and clean them up
• “80% perspiration, 10% great idea, 10% output”
Gaps in Methods Curricula • Cleaning up and processing data is often most of the work, but it’s not woven into the curricula of methods training
• Thomas Carsey (UNC): methods courses “often devote little or not time to broader issues of data management, data access, and the generation of transparent research replication materials.”
• Even experienced researchers may have trouble meeting such expectations
Re-framing Data Services
“libraries may not see these scholarly communication issues as being connected to e-science, when, in fact, the connection is closer than is realized.” -Soehner et al, 2010
Services Related to Publication and Access
• Assisting with journal data policies
• Assisting with data citation/DOIs
• Licensing and copyright issues • Instruction on sharing and citing data
Services Related to Data Cleaning
• Consulting on transparent workflows
• Consulting on writing “sharable code”
• Consulting on documenting data
Conclusions
• Academic literature on data sharing and data management suggests a path for developing/ expanding research data services
• Additionally, this literature suggests more libraries could offer these services than currently do so.
• The heavy bias toward sharing/replication of quantitative data suggests further research is needed for scoping data services for qualitative and geospatial data.
