### Research Data Services <p></p> <p> </p> Jan 24, 2020 Tim Dennis --- ## Research Data Task Force * See recommendations presented to Library Cabinet https://hackmd.io/@timdennis/HysFG_j3Q#/ * This is what we are working towards --- ## Recommendation #1 - Create a cross-library **Research Data Program** --- ### To meet this we established the **Library Data Science Center** in Fall 2018 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 in the Library ? * Campus is ready for the Library to lead in this area * Data Science Services is largely a **Blue Ocean** at UCLA * Library a common good and disciplinary agnostic service * Responsive to changes in research due to ubiquity of data, computing, & software * Act as a hub for connecting researchers to tools and methods --- ## DSC Service Objectives - Support all of campus - Support data & computationally intensive research - Provide data & coding support - Education on foundational coding and data science tools (Carpentries) - Community building around tools and practices - Provide leadership for data services in Library --- ## DSC Consulting * Added consultant from Digital Library Program * GIS specialist from DIIT * Redesigned data mgmt job into data science facilitation --- | Year | Consults[^first] | | ---- | -------- | | 2017 | 3 | | 2018 | 37 | | 2019 | 160 | [^first]: on our Calendly transactions, not walk-ins ---  ---  --- ### Recurring consulting topics: * Help with **R**, **Python**, **SQL** (getting started, improving or restructuring code) * Data acquisition, cleaning & integration * **GIS** & mapping support * Finding & access data (LARIAC, Twitter data, ICPSR, APIs, etc.) * Data analysis & basic statistics support * Open Science and computational reproducibility --- ## Instruction * Since 2017, offered over **80 distinct workshops & events** (as 2 day bootcamps, short courses, events, or lab-type sessions) * To over **1,300 learners** * 52% were graduate students, 24% staff, 8% undergraduate, 7% librarians, 6% faculty, and 3% community members. --- ## Train-the-Trainer Model Since 2017, we’ve added 12 instructors: * 2 from Ecology & Evolutionary Bio * 2 from IDRE (Stats consulting & HPC) * 1 from Cotsen * 2 from DSC * 4 from Digital Library Program * 1 from Institute of the Environment and Sustainability --- ## Next Steps Recommendation \#1. ### Build out 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 (and Campus) Research Data Programs ### Look like this --> ---  ---  ---  ---  --- ## Research Data Program * A vehicle for improving the quality of research output on campus * A program to increase the **data savviness** of the campus and library * Meet regularly for operations and education * Shared communication tools, tracking system for cross referral, etc. --- ## Recommendation #1 → Create an All Library Research Data Program * A cross-library Research Data Program led by the Data Science Center with responsibilities for consulting and outreach in support for open data science and data lifecycle * Composed of data specialists in major areas, such as life & physical sciences, humanities, and social sciences --- ## This would need to be comprised of these components / factors * Assessments/Quality Control * Principles * Marketing * Shared knowledge --- ## Purpose of Program defined * The program addresses current and emerging data support issues, compliance with policy requirements imposed by funders/journals and by the University, and reduction of risk associated with the challenges of data stewardship. * Develops educational curriculum that promotes leading practices in open data science, data management, publishing & curation. --- ## A program: * is critical for delivering the services, outreach and education to provide a accessible and approachable set of data services. * is an integrated a support service for all phases of the research process especially as it related to data and computationally intensive research. The program should have --- * has members with diverse expertise from different domains and disciplines. * directly supports researchers and encourages co-consulting and cross-pollination of skills. * that develops a sub-program for its own self-education. * has regular meetings that reflects a **learning and doing** perspective with alternating operational and educational focused meetings. --- ## Behaviors / characteristic of Program defined * Regular meetings (2x a month) * Shared communication and ticketing system (Slack Channel, Jira Service Desk, GDocs or Confluence Site) * Educational and operational - group must execute on consulting & education, but also continue to learn and grow as a team. --- ## Measurements of Program defined * Is there a RDS program? * Does it have members from life & physical sciences, humanities, and social sciences? * Are the members of this program assigned to the program at a significant portion of their job? * Does meet regularly for both operational, strategic, & education needs? --- * Does this group share responsibility for the program’s articulated services? * Is there a shared repository of all requests and their ultimate disposition? * Are there metrics in the way requests are managed? E.g. articulated service benchmarks, service time window. * If so, what is rate of response? * Are there metrics for quality of service (survey of user satisfaction with resolutions)? * And is this customer customer feedback systematized? --- ## Potential Membership * Director, Data Science Center * Data Science Facilitator * Digital Library Architect * Spatial Data Science Librarian * Science Data Informationist (User Experience) * Scholarly Communication Rep ---