Research information management: making sense of it all
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"Research information management: making sense of it all" - Julia Hawks, VP North America, Symplectic Slides from Shaking It Up: Challenges and Solutions in Scholarly Information Management, San Francisco, April 22, 2015
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Research information management: making sense of it all
- 1. Research Information Management: Making Sense of it All Julia Hawks, VP North America April 2015
- 2. • We are a software development and service company serving researchers and research administration. • We work in partnership with our clients to develop new features. • We specialise in integrating systems, data and institutional platforms. • We are vendor, open source community and data source agnostic About Symplectic
- 3. – Research Information Management systems (RIMs) • What are they/why are they needed • Primary use cases for adoption – How do RIMs differ to traditional management information databases? • Automated data capture • Disambiguation • Joining the dots • Focus on reuse Overview
- 4. RIM is used to refer to the integrated management of information about the research life-cycle, and about the entities which are party to it (e.g. researchers, research outputs, organizations, grants, facilities etc). The aim is to synchronise data across parts of the university, reducing the burden to all involved of collecting and managing data about the research process. An outcome is to provide greater visibility onto institutional research activity. - source: OCLC Research Information Management
- 5. Collecting data about researchers and their activities is difficult – Disambiguation – Data is in multiple places – Duplication – Assignment to a department – Researchers are busy people! Why a RIM system? The real challenge here is translation of information already in existence in scattered sources “ Annon: SCITS Conference, Chicago 2013
- 6. A centralised system dedicated to supporting the capture, linking and dissemination of information associated with research and teaching activity within an institution: • Person data • Research Outputs and Datasets • Grants • Professional & teaching activities • Equipment • & more… A means of populating institutional repositories A tool that supports Open Access Policies A flexible reporting framework Symplectic Elements
- 7. • Collecting and managing publication information • Evaluating institutional research activity • Responding to funder requests • Carrying out government assessment/returns • Growing institutional repositories • Populating public researcher profiles with up-to-date information • Supporting the generation of researcher CV’s and other internal reports Key RIMs use cases
- 8. RIM versus database – a move away from form filling!
- 9. Far greater focus on the user experience
- 11. Both internal and external systems Searched by DOI Profile/Expertise Systems Symplectic Elements VIVO Scival Researcher Identifier Systems ORCiD Researcher ID Bibliographic Aggregators* Scopus Web of Science Disciplinary Article Repositories PubMed arXiv E-PMC Datasets figshare CINii RePEc DBLP Academic (or their proxy) selects external data sources most relevant to them Symplectic Reporting Database Funding Data Bibliometrics altmetric TR Impact Factors Media Data Reference Data Org IDs Sherpa FundRef SSRN CVCV & Biosketch Data Warehouse / BI Tool ETL Research Output Sources Open Access Repository Profile and external research output data CMS / Profile Tool (e.g. VIVO or Profiles RNS) RefMgr BibTeX Teaching Database HR Award Mgnt Institutional Systems of Record Equipment Other Data Sources API API Funder and Govt Systems XML APIAPI Legend Symplectic Digital ScienceOpen DataInstitution Journal dataCrossRef Patent Data DOAJ Awards Data API .doc & pdf Google Books Dimensions
- 12. Data captured from 7 different sources to make this single record Citations counts from the major citations engines in the same place Integration with Altmetric Article associated with 3 authors within the institution Journal-level metrics Direct integration with digital research repository Relationships to grants, equipment used, etc. Result = richer contextualized data
- 13. The fun bit: Analytics