1. Library Data Services Functions and activities

2. Library functions Is providing research data management infrastructure outside the purpose, mission, mandate, or functions of the library?

3. Library functions The naysayers would argument that one could fit any function within the library, that is, with RDMI we are just shoehorning another set of services that don’t necessarily belong in the library.

4. Library functions Research data are recognized as a digital resource having teaching and research value and, as such, data are increasingly being seen as a resource that should be managed within the library. Collection services, user services, access services, and preservations services are all functions supporting the management of data.

5. Service functions and activities Eight Activity Categories Function Function Function Function Eight horizontal activity categories have been identified that cut across these four service functions.

6. Why eight activity categories? These eight categories represent aggregations of activities for which we have experience in providing data services over the years. These are based on practice, not on theory.

7. Governance framework for data Policies, procedures, and guidelines • The rules around which research data are administered and managed, just like other resources supported within the library. • Examples: collection development, data deposit agreements, data license

8. Process and technical description Metadata and standards • Information across the lifecycle describing the process and context of data and documenting the technical nature of the data. Producing such metadata using standards increases management functionality (such as, discovery, access, and preservation, etc.) and interoperability. • Examples: DDI, EML, use of DOI’s

9. Long tail of data Data products • The large variety of research data and accompanying metadata distributed across the long tail of data from “big data” to a plethora of smaller data. • Examples: Neptune’s continuous monitoring of the ocean floor to the few million time series in CANSIM

10. Tools to manage and process data Data management tools and standards • The technology and standards supporting the curation, management, manipulation, integration, visualization, and interoperability of research data. • Examples: Dataverse, SPSS, CSV

11. Data professionalism Expertise, skills, and staffing • The knowledge and skills to curate and manage data, to consult on best practices, standards, and metadata across the data lifecycle, to perform data stewardship responsibilities, to teach data literacy skills, and to promote data culture. • Examples: OAIS processing, subset data, produce metadata

12. Building national infrastructure Collaboration and partnerships • The management of research data is too great for any single institution to conduct on its own. Full data stewardship requires partners locally, regionally, nationally, and internationally. • Examples: Odesi, DLI, DDI

13. Community in the data ecosystem Designated user communities • Much of research data is produced in the context of domain-based, project-level research. Working with specific user communities focuses service provision by identifying data needs and requirments for curation support. • Examples: IPY researchers, social science graduate students, Nursing researchers

14. The sustainability of data Business case • Libraries have developed models business models for sustaining collections over generations, including inter-institutional sharing arrangements, consortial licensing, national platforms, etc. Libraries are now challenged to build sustainable national research data infrastructure. • Examples: DLI, DMTI, ICPSR federations, CPDN

15. The sustainability of sustainability xkcd

16. The task ahead for you Instructors will review with you these eight categories of activities for each of the four functions and with each function, you will work through a small group exercise identifying relevant activities.