This document discusses strategies for libraries to provide research data services. It defines research data services and notes that while funder mandates are a driver, academics' focus is more on transparency and reproducibility. The document suggests libraries expand services to assist with publishing data and ensuring transparent workflows, such as consulting on data cleaning and documentation. It concludes that the academic focus on sharing quantitative data means further research is needed to scope qualitative and geospatial data services.

1. STRATEGIES FOR DISCUSSING AND
COMMUNICATING DATA SERVICES
“Don’t Hate the Player, Hate the Game”
Joel Herndon, Duke University
Robert O’ Reilly, Emory University

3. Notable Library US RDS Initiatives

5. Library Research Data Services

6. 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

7. 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

8. 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

9. 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

10. 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

11. Academic Perspectives on Data
Management

12. 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”

13. 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

14. 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

15. 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

16. Services Related to Data Cleaning
• Consulting on transparent workflows
• Consulting on writing “sharable code”
• Consulting on documenting data

17. 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.

18. Thank you!
Joel Herndon
joel.herndon@duke.edu
@jherndon01
Rob O’Reilly
roreill@emory.edu