#### Recommendations of the #### Research Data Services Task Force <p></p> <p> </p> Presentation to Cabinet November 6, 2018 Tim Dennis --- ### Research Data Services Task Force Report #### Team: * Tim Dennis, Chair * Claudia Horning, Library Metadata Team * Jamie Jamison, Data Collections Mgr * Lisa McAulay, Head, Digital Library * Jessica Mentesoglu, Head, DIIT Operations and Services * Rikke Ogawa, Director Biomed Library --- ### Methodology We adopted the process of Peer Benchmarking to: * Select peer institutions * Conduct a targeted environmental scan * Rank peers into "Tiers" of excellence --- ### Our Peers * Cornell * New York University * TU-Delft * UC Berkeley * UC San Francisco * University of Michigan * UNC Chapel Hill * University of Southern California * University of Virginia --- ### Benchmarking Criteria * Identity * Community building * Data Management Service * Data Integrity and Reproducible Research * Program Maturity --- ### Identity * **Research Data Services** is part of a coherent, documented, and integrated program supporting full data life cycle. --- ### Community Building * The Research Data Services program includes training, consulting and outreach programs. --- ### Data Management Services * Program includes actual services a researcher can leverage to * share, publish, and store their data in a repository * A locally managed repository service exists * which represents a local investment in software development or purchasing licensed services, or both. --- ### Data Integrity and Reproducible Research * The program offers consulting specifically about * researchers' methods to promote reusable data/software, and * researchers' options for preserving their data --- ### Program Maturity * The program is clearly well established and perceived as reliable * Characteristics include * dedicated staff * proven track record of quality services. --- ### Results: --- | Tier | Synopsis | Description | Institutions | | ---- | -------- | -----------| ------------ | | 1 | Leader |Robust across all bench-marking criteria | NYU, Cornell | | 2 | Excellent |Strong in most benchmarking criteria, very strong in some areas and weaker in others | UNC, UC Berkeley, TU-Delft, UCSF | --- | Tier | Synopsis | Description | Institutions | | ---- | -------- | -----------| ------------ | | 3 | Developing |Mixed assessment | UVA, UMich, USC | | 4 | Lagging |Weak assessment in all or most areas | **_UCLA_** | --- ## UCLA is behind in developing research data services, and we must take targeted action to improve --- ## Recommendations from the [Task Force Report](https://docs.google.com/document/d/18ogKqtKXZE7W7kj8Sxh1e_9tWqBckeXiQvfxhTB9NPo/export?format=pdf) --- ### We focus on moving from Tier 4 to Tier 3 today, because: * We can accomplish this by ourselves * Moving to Tiers 2 and 1 will require campus partnerships * This **first step** is a prerequisite for moving to 2 and 1 --- #### To move from Tier 4 to Tier 3: 7 Concrete Actions 1. Create a cross-library **Research Data Program** 2. Fill **existing provisions** and add **new roles** (recognize the specialized & technical nature of data) 3. Implement a **data sharing/publishing repository** and related data reuse tools 4. Redesign, unify, and heighten the Library's **research data support web representation** --- #### 7 Concrete Actions (cont.) 6. Provide a **data preservation infrastructure** 7. Develop an external-facing **training, consulting, and outreach program** 8. Create an internal-facing **community-of-practice based training program** --- ## Things we are already working on: --- ## Recommendation #1 We established the Library Data Science Center Note: * science of learning from data; * it studies the methods involved in the analysis and processing of data and proposes technology to improve methods in an evidence-based manner. * Help researchers improve their practices using applications from Data Science research * Provide infrastructural access to an array of tools in Data Science * Build community and data science curricula around these emerging practices --- ## Why Data Science and Why in the Library ? * Campus is ready for the Library to lead in this area * Data Science Services is largely a *Blue Ocean* at UCLA --> There are no competitors in the field with the reach and mandate that the Library has * The Library is open to partnering and has wide array of use cases and engagements * These concerns are interdisciplinary in nature that deal with heterogeneous data at scale * Driven by ubiquity of computing, software, and data - necessarily technical --- ## Things we are working on now (in part) *2. Fill existing provisions* *3. Implement a data sharing/publishing repository* *4. Develop an external-facing training, consulting, and outreach program* *7. Create an internal-facing community of practice-based training program* Note: * Some numbers: * We have started a number of efforts * Under-resourced, need capacity building * need more curators * more trainers * more specialized fte with technical capacity --- ## Recommendation \#2 ### Fill existing provisions * Spatial Data Sciences Librarian (SDSL) * active recruitment * Sciences Data Informationist (SDI) * awaiting posting Note: * Search committee meeting on SDSL and we have viable pool, if candidates still interested * SDI in queue to be posted * New jobs - * Data Science Center responsible for data repository and archival infrastructure * plus building pieces of Data Science and Research Data Management infrastructure (clients to a repo for injest, reuse, visualizing tools (gis, 3d objects) * analytic tools (juypter notebooks, R tools)) * Reproducibility Librarian - a focus on best practices in computational repro including standards for fields, instruction and building research tools to help --- ## Recommendation \#3 ### Implement a data sharing & publishing repository Note: * **Dataverse** will support self-service deposit plus sub-dvns (this is non intelligible but leaving it here as a note to you) * **Dataverse** has been implemented with existing DSC archival staff (Jamie and me) * Caveat -- Will need more trained staff to be effective for campus --- ## Recommendation \#6 ### Develop a campus-facing training, consulting, and outreach program --- ## Recommendation \#7 ### Create an internal-facing community of practice-based training program ### Develop a train-the-trainer methodology Note: * to the campus community in order to teach foundational coding and fundamental data skills needed to conduct research, * meet funder/journal mandates, * and support emerging expectations for transparency and reproducibility in research. * for public-facing librarians and staff to develop skills, knowledge, and confidence to work effectively with researchers about their data. * and an experience-based teaching continuum in which actual research consulting and/or reference encounters are used as case studies or practicums. Document expectations for public facing librarians regarding data consultations and hand-off procedures. * Carpentries membership and capacity building. --- ## One Year Horizon ### Near-term things we are poised to address --- ## More work on Recommendation \#1. ### Create an All-Library Research Data Program Note: * a precondition recommendation for rest of our library-oriented recommendation - led by dsc * responsibilities for consulting and outreach in support for the data life cycle * responsible for continuing to educational curriculum * needs to be unified and share communication * meets reg. for operational & ed., self-improvement, shares ticketing --- ## Exemplary All-Library Research Data Programs ### Look like this --> ---  ---  ---  --- ## Research Data Program * A requirement for moving up our Tiers is to have a RDS program * A vehicle for improving the data savviness of the campus and library * Needs to have at least 20% or more of job dedicated to data * Jobs need to have explicit relationship with RDP * Meet regularly for operations and education * Shared communication tools, tracking system, etc. --- ## Recommendations that Need More Time --- ## Recommendation #4 ### Redesign, unify, and heighten the Library's research data support web representation Note: services and information about data life cycle. Current offerings are misleading, outdated, and scattered. --- ### Examples we liked: ---  ---  --- ## And:  --- ## Needs to: * Be backed-up by real services (IT, mediated, or high touch) * With staff who have computing, software and data skills * And are engaged in a community-of-practice process of self-improvement and growth --- ## Design-wise needs to: * Be dynamic, filterable, and holistic * Allow for inclusion of campus partners * Should be high level (library.ucla.edu/data or research-data.ucla.edu ) * This will probably not be doable in LibGuides, but that could serve as backend --- ## Recommendation #5 ### Provide a data preservation infrastructure Note: * for locally stored research data that includes versioning, replicas and fixity/authenticity checks, including documentation that is maintained and publicly available. --- ### Summary * Research Data Services (RDS) are integral to continued academic and research excellence * Our peers are more advanced in this arena than we are * The Library needs to invest in Research Data Services to achieve the Strategic Plan Goal of being the "Heart of Research" * And we have to _assess_ our services regularly, working with the Assessment for Change Team is necessary