Preparing eScience Librarians for Managing Research Data - Jian Qin - RDAP12
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The document outlines initiatives to prepare eScience librarians for effective research data management through a structured curriculum. It emphasizes competencies in scientific data management, cyberinfrastructure technologies, and collaboration with various stakeholders to enhance data literacy. Various themes, including data analysis, collaboration, and communication, guide the curriculum development for fostering a skilled workforce in eScience librarianship.
Preparing eScience Librarians for Managing Research Data - Jian Qin - RDAP12
- 1. Preparing eScience Librarians for Managing Research Data RDAP 2012, New Orleans, LA Jian Qin School of InformaCon Studies Syracuse University
- 2. NoCons of eScience librarianship ProacCve training for data literacy ConsultaCve Leader in services for eScience data use and iniCaCves management AcCve players and contributors of data Part of team curaCon transcending disciplinary boundaries RDAP 2012, New Orleans 2
- 3. EducaCng the new type of workforce • ScienCfic data literacy (SDL) project (hNp://sdl.syr.edu), 2007-‐2009 • E-‐Science Librarianship Curriculum project (eSLib hNp://eslib.ischool.syr.edu), 2009-‐2012, in partnership with Cornell University Library RDAP 2012, New Orleans 3
- 4. A curriculum for eScience librarianship • Overall learning objecCves: – Ability to arCculate eScience and to plan and develop eScience librarianship projects – Competency in scienCfic data management – Competency in cyberinfrastructure technologies – Ability to collaborate, communicate, and lead in eScience librarianship projects RDAP 2012, New Orleans 4
- 5. Ability to • ArCculate eScience process and data lifecycle arCculate • IdenCfy user needs and translate eScience and to the needs into system requirements plan and develop • Make plans for eScience eScience librarianship project iniCaCon and librarianship implementaCon • Conduct research on data related projects issues such as insCtuConal data policy, support services, and technology adopCon • Write grant proposals for obtaining funding to support eScience librarianship projects RDAP 2012, New Orleans 5
- 6. • ArCculate data Competency in characterisCcs scien-fic data • Analyze domain data sets management and develop data models • Define metadata element sets • Develop specialized metadata for data curaCon, preservaCon, and access • Create metadata records for scienCfic data sets RDAP 2012, New Orleans 6
- 7. • Maintain informaCon Competency in retrieval interfaces cyberinfrastruct • Maintain informaCon ure technologies exchange networks • Program, write code, and manipulate scripts • Use content management systems • IdenCfy and model data/ work flows • Assess research needs for and performance of CI tools RDAP 2012, New Orleans 7
- 8. Ability to • Develop partnership with collaborate, internal and external communicate, and organizaConal units and lead in eScience collaborators librarianship • Communicate with projects administrators and researchers • Engage researchers in data management processes • IniCate and lead in eScience librarianship projects RDAP 2012, New Orleans 8
- 9. The curriculum Courses Primary learning outcomes in eScience librarianship projects Ability to collaborate, communicate, and lead ScienCfic Data Competency in scienCfic data Management (core) management Competency in Cyberinfrastructure (core) cyberinfrastructure technologies Ability to arCculate eScience and to Data services (capstone) plan and develop eScience librarianship projects Database systems (required elecCve) Metadata (elecCve) RDAP 2012, New Orleans 9
- 10. Theme 1: building fundamentals 1 2 Case studies that use Overview of scienCfic data pracCcal examples to guide management that covers students step-‐by-‐step in data and metadata data analysis and fundamentals management 3 Using scienCfic data, which involves discussions of data quality, data repositories and discovery, data analysis and presentaCon, and ethics and intellectual property issues RDAP 2012, New Orleans 10
- 11. Building fundamentals: data formats Overview of scienti.ic data management that covers data and metadata fundamentals Data NASA’s de-inition of data Processing level level processing levels Level 4 Self-‐descripCve informaCon existed as Level Reconstructed unprocessed instrument 0 data at full resolutions. Level 3 header of the data file Level Reconstructed, unprocessed instrument Level 2 1A data at full resolution, time referenced, and annotated with ancillary information, Common Data Format (CDF) Level 1B Flexible Image Transport System (FITS) but not applied to the Level 0 data. GRid In Binary (GRIB) Level Level 1A data that has been processed to Level 1A Hierarchical Data Format (HDF) 1B sensor units. Not all Network Common Data Format (netCDF) instruments will have a Level 1B Level 0 equivalent. Major scienCfic data format RDAP 2012, New Orleans 11
- 12. Building fundamentals: Understanding data and metadata Data formats Processing levels Data collecCons Some formats contain self-‐ Lineage vital to descripCve metadata assessing data Metadata standards need quality to be adjusted for local descripCon needs RDAP 2012, New Orleans 12
- 13. Building fundamentals: data literacy IL: ACRL. (2010). DL: Finn, Charles, W.P. (Tech & Learning, 2004) SDL: Qin, J. & J. D’Ignazio, (Journal of Library Metadata, 2010) RDAP 2012, New Orleans 13
- 14. Theme 2: Analysis and generalizaCon Analysis of data problems is an analysis of domain data, requirements, and workflows that will lead to the development of soluCons. RDAP 2012, New Orleans 14
- 15. Analysis and generalizaCon: engaging in real research projects • Engage students in research and service projects – Data policy analysis – Data management consultaCon – Interviews and survey design • Course projects – Real-‐world data management problems RDAP 2012, New Orleans 15
- 16. Theme 3: collaboraCon and communicaCon • Community of pracCce • InsCtuConalizaCon of data services – Data policies – Compliance to funding agency policies and mandates – Infrastructural data services at insCtuConal, community, and naConal levels • Awareness, incenCves, and training RDAP 2012, New Orleans 16
- 17. CollaboraCon and communicaCon • Mentoring by Cornell librarians, led by Gail Steinhart • Internships in academic libraries and/or research centers • Guest speakers to classes • Engaging students in research and service projects RDAP 2012, New Orleans 17
- 18. Evolving curriculum CAS in Data Science Required courses: • Database • Applied Data Science Data storage Data Data Systems and analyCcs visualizaCon management management RDAP 2012, New Orleans 18
- 19. eScience Librarianship Project Website: hNp://eslib.ischool.syr.edu/ RDAP 2012, New Orleans 19