SlideShare a Scribd company logo

IASSIST 2014: Building Support for Research Data Management

•
•
N
Natsuko Nicholls

This document summarizes a study that interviewed representatives from eight universities about their research data management (RDM) services. It found both similarities and differences in how universities developed these services. Key similarities included being motivated by federal funding requirements, conducting needs assessments, and providing training and resources. Outreach methods like workshops were common. Differences included the timeline of developing services and approaches to staffing and skills development. The conclusion discusses ongoing challenges like effective outreach strategies and learning from peers' experiences.

1 of 18
Download to read offline
Building Support for Research Data Management: Knitting Disparate Narratives of Eight Institutions Natsuko Nicholls, CLIR/DLF Data Curation Fellow University of Michigan Paper co-authors: Katherine Akers, Fe Sferdean and Jennifer Green IASSIST40,Toronto, Canada June 3-6, 2014
RESEARCH DESIGN
Goals  To respond to the emerging and prominent role of the library in assisting researchers with research data management (RDM)  To conduct an environmental scan:Where does the U of M stand?  To identify similarities and dissimilarities among universities in motivation, approach, and method of data service development  To apply research findings to practice at U-M
Methods  Sample Selection ◦ Both public and private research universities ◦ Sample variation: Being at different stages of RDM development and implementation ◦ Sample commonality:All employed at least one staff/librarian fully dedicated to RDM  Data collection ◦ Semi-structured phone interviews with representatives of selected institutions ◦ Interviews took place between Oct – Dec 2012 (follow-up in Dec 2013)
Eight Institutions  Cornell University  Emory University  Johns Hopkins University  Pennsylvania State University  Purdue University  University of Illinois at Urbana-Champaign  University of Michigan  University ofVirginia
Interview Questions  Four categories 1. Context e.g. historical origin, current state, assessment 2. Content e.g. types of services and repository systems, university policies 3. Infrastructure e.g. funding models, campus partnership, IT, supercomputing facilities 4. Challenges and opportunities e.g. staffing, outreach strategies, disciplinary-specific or interdisciplinary needs
A list of interview questions are available at: https://docs.google.com/spreadsheet/ccc?key= 0AsWH4EdAxHD4dHVUTnlEZDFkX2NSbU EtR05kQkVUV1E&usp=sharing
FINDINGS Similarities and Dissimilarities Where does the U of Michigan stand?
Key Milestones  Environmental scan  Data service needs assessment  Education: from awareness-building to data management training  Tool and infrastructure development  Policy formation  Data service evaluation
1997 1999 2001 2003 2005 2007 2009 2011 20131984 Data IRData Services IR Assessment RDM Services NSF DMP requirement 1984 19841996 2014201220102008200620042002 Year Emory University Cornell University University of Michigan Purdue University Johns Hopkins University University of Virginia University of Illinois Penn State University InstitutionalTimelines of Building RDM Support
Motivation  Federal funding agency requirements  Comprehensive research support ◦ Focusing on data, research and grant lifecycles ◦ Focusing on e-Science and e-Research ◦ Multi-disciplinary focus  Cross-institutional collaboration ◦ ARL/DLF/DuraSpace E-Science Institute ◦ Grant-based library projects ◦ CLIR/DLF E-Research Peer Networking and Mentoring Group Program
Outreach  Campus partnership ◦ Buy-in from other campus units ◦ Buy-in from librarians ◦ Working with faculty data champions  Outreach methods ◦ Resource development: Website, LibGuide ◦ Data ManagementWorkshops: Designed for librarians, faculty and graduate students
Outreach at UM  Data Education for Librarians 1. Basic Training: Research Data Concepts for Librarians  Working with data  Sharing and preserving data 2. AdvancedTraining: Deep Dive into Data  Deep Dive into Ecology Data  Deep Dive into Psychology Data  Deep Dive into Clinical Data  Deep Dive into Arts and Humanities Data  Deep Dive into International Data  Data ManagementWorkshops for Engineering Faculty (as part of a data support pilot)
Staffing, Re-skilling and Changes in Job Responsibilities  Changes in staffing  Changes in skill-sets  Changes in subject specialists’ levels of engagement associated with research data ◦ Ability to understand the ‘data landscape’ for a discipline or area-responsibility as a basic data- expectation ◦ Ability to advertise library data initiatives and provide data reference services (and appropriate referral) ◦ Ability to provide data management consultations
Staffing, Re-skilling and Changes in Job Responsibilities at UM  New library leadership ◦ Associate University Librarian for Research ◦ Director of Research Data Services ◦ Research Data Services Manager  New data responsibilities ◦ Changes in subject specialists’ levels of engagement ◦ Changes in job descriptions: Currently, the University leadership is drafting the ‘data expectations’ language to go into all subject specialists’ job descriptions
CONCLUSION
Challenges and Opportunities  Grappling with outreach strategy: How to reach out to and interest researchers in improving their data management, i.e., how to move from ‘nice-to-have’ to ‘must-have’  Identifying the best target despite the marketing pitch of ‘multi-disciplinary focus’  Learning from peers: Institutional contexts differ and matter, but peers’ trials and errors will help to avoid unnecessary duplication of effort and maximize efficiency and effectiveness
Thank you! Questions?

More Related Content

IASSIST 2014: Building Support for Research Data Management

  • 1. Building Support for Research Data Management: Knitting Disparate Narratives of Eight Institutions Natsuko Nicholls, CLIR/DLF Data Curation Fellow University of Michigan Paper co-authors: Katherine Akers, Fe Sferdean and Jennifer Green IASSIST40,Toronto, Canada June 3-6, 2014
  • 3. Goals  To respond to the emerging and prominent role of the library in assisting researchers with research data management (RDM)  To conduct an environmental scan:Where does the U of M stand?  To identify similarities and dissimilarities among universities in motivation, approach, and method of data service development  To apply research findings to practice at U-M
  • 4. Methods  Sample Selection ◦ Both public and private research universities ◦ Sample variation: Being at different stages of RDM development and implementation ◦ Sample commonality:All employed at least one staff/librarian fully dedicated to RDM  Data collection ◦ Semi-structured phone interviews with representatives of selected institutions ◦ Interviews took place between Oct – Dec 2012 (follow-up in Dec 2013)
  • 5. Eight Institutions  Cornell University  Emory University  Johns Hopkins University  Pennsylvania State University  Purdue University  University of Illinois at Urbana-Champaign  University of Michigan  University ofVirginia
  • 6. Interview Questions  Four categories 1. Context e.g. historical origin, current state, assessment 2. Content e.g. types of services and repository systems, university policies 3. Infrastructure e.g. funding models, campus partnership, IT, supercomputing facilities 4. Challenges and opportunities e.g. staffing, outreach strategies, disciplinary-specific or interdisciplinary needs
  • 7. A list of interview questions are available at: https://docs.google.com/spreadsheet/ccc?key= 0AsWH4EdAxHD4dHVUTnlEZDFkX2NSbU EtR05kQkVUV1E&usp=sharing
  • 8. FINDINGS Similarities and Dissimilarities Where does the U of Michigan stand?
  • 9. Key Milestones  Environmental scan  Data service needs assessment  Education: from awareness-building to data management training  Tool and infrastructure development  Policy formation  Data service evaluation
  • 10. 1997 1999 2001 2003 2005 2007 2009 2011 20131984 Data IRData Services IR Assessment RDM Services NSF DMP requirement 1984 19841996 2014201220102008200620042002 Year Emory University Cornell University University of Michigan Purdue University Johns Hopkins University University of Virginia University of Illinois Penn State University InstitutionalTimelines of Building RDM Support
  • 11. Motivation  Federal funding agency requirements  Comprehensive research support ◦ Focusing on data, research and grant lifecycles ◦ Focusing on e-Science and e-Research ◦ Multi-disciplinary focus  Cross-institutional collaboration ◦ ARL/DLF/DuraSpace E-Science Institute ◦ Grant-based library projects ◦ CLIR/DLF E-Research Peer Networking and Mentoring Group Program
  • 12. Outreach  Campus partnership ◦ Buy-in from other campus units ◦ Buy-in from librarians ◦ Working with faculty data champions  Outreach methods ◦ Resource development: Website, LibGuide ◦ Data ManagementWorkshops: Designed for librarians, faculty and graduate students
  • 13. Outreach at UM  Data Education for Librarians 1. Basic Training: Research Data Concepts for Librarians  Working with data  Sharing and preserving data 2. AdvancedTraining: Deep Dive into Data  Deep Dive into Ecology Data  Deep Dive into Psychology Data  Deep Dive into Clinical Data  Deep Dive into Arts and Humanities Data  Deep Dive into International Data  Data ManagementWorkshops for Engineering Faculty (as part of a data support pilot)
  • 14. Staffing, Re-skilling and Changes in Job Responsibilities  Changes in staffing  Changes in skill-sets  Changes in subject specialists’ levels of engagement associated with research data ◦ Ability to understand the ‘data landscape’ for a discipline or area-responsibility as a basic data- expectation ◦ Ability to advertise library data initiatives and provide data reference services (and appropriate referral) ◦ Ability to provide data management consultations
  • 15. Staffing, Re-skilling and Changes in Job Responsibilities at UM  New library leadership ◦ Associate University Librarian for Research ◦ Director of Research Data Services ◦ Research Data Services Manager  New data responsibilities ◦ Changes in subject specialists’ levels of engagement ◦ Changes in job descriptions: Currently, the University leadership is drafting the ‘data expectations’ language to go into all subject specialists’ job descriptions
  • 17. Challenges and Opportunities  Grappling with outreach strategy: How to reach out to and interest researchers in improving their data management, i.e., how to move from ‘nice-to-have’ to ‘must-have’  Identifying the best target despite the marketing pitch of ‘multi-disciplinary focus’  Learning from peers: Institutional contexts differ and matter, but peers’ trials and errors will help to avoid unnecessary duplication of effort and maximize efficiency and effectiveness