Amanda L. Whitmire

PDFI2 | These guidelines are for using the analytic rubric developed as part of the IMLS-funded DART Project for
reviewing and analyzing NSF data management plans (DMP). Included in the guidelines are examples of
how we rated the criteria of an NSF DMP, as well as a set of additional questions that we used when
reviewing plans. All documents related to this project are available at Whitmire, Amanda L, Jake
Carlson, Brian Westra, Patricia M. Hswe, and Susan Wells Parham. 2016. “The DART Project: Using Data
Management Plans as a Research Tool.” Open Science Framework. http://osf.io/kh2y6 | DOI 10.17605/OSF.IO/KH2Y6 |

Table of Contents
Introduction ....... 3
Rubric Section 1: Types of data produced ...... 4
ALL DIRECTORATES ...... 4
SPECIFIC DIRECTORATES/DIVISIONS ..... 5
Rubric Section 2: Standards for data and metadata ... 7
ALL DIRECTORATES ....... 7
SPECIFIC DIRECTORATES ......... 9
Rubric Section 3: Policies for access and sharing ..... 11
ALL DIRECTORATES ....... 11
SPECIFIC DIRECTORATES ...... 13
Rubric Section 4: Policies and provisions for re-use and redistribution ..... 16
ALL DIRECTORATES ..... 16
SPECIFIC DIRECTORATES ...... 18
Rubric Section 5: Plans for data archiving and preservation of access ..... 19
ALL DIRECTORATES ..... 19
SPECIFIC DIRECTORATES ....... 19
Additional Questions of Interest ..... 24

SA - https://www.librarything.com/work/32118813/book/262385548 | https://www.librarything.com/work/32114324/book/262338546 | https://www.librarything.com/work/31655380/book/257745777 | https://www.librarything.com/work/31612433/book/257258692 | https://www.librarything.com/work/31591416/book/256946007 | https://www.librarything.com/work/31507626/book/255801976 |
RT - Evaluation
BT - Review
NT - Assessment/Guidance
UF - This document provides guidelines for using the DART Rubric to assess NSF data management plans. The DART Rubric is based upon published NSF and directorate guidelines dated 2011 – 2013, or undated. Note that as NSF and directorate guidance evolves, it will be important to correlate the date of a proposal submission with the guidance and criteria that were in effect at the time. Also note that the most general guidance (NSF-wide materials) is superseded by more specific guidance if provided by the Directorate, Program, or Funding
Opportunity Announcement (FOA).
SN - This PDF was downloaded from the internet server/database in which the journal is stored. (This entry does not reference a hierarchical list)… (meer)

PPPW | To provide research data management (RDM) support services, libraries need to develop expertise in data curation and management within the library. Many academic libraries are reorganizing to initiate RDM service structures but may lack staff expertise in this area. Funding agencies increasingly require a data management plan (DMP) with funding proposals; they describe how data generated in the proposed work will be managed, preserved, and shared. We have developed an analytic rubric for assessing DMPs. An analysis of DMPs can identify common gaps in researcher understanding of RDM principles and practices, and identify barriers for researchers in applying best practices. Our rubric allows librarians to utilize DMPs as a research tool that can inform decisions about which research data services they should provide. This tool enables librarians who may have no direct experience in applied research or RDM to become better informed about researchers' data practices and how library services can support them. This panel will consist of five data specialists from academic libraries who will introduce the rubric, share the results of our analyses, and describe how the results informed the evolution of services at our respective libraries | PowerPoint Presentation | https://ir.library.oregonstate.edu/concern/defaults/fn107276t?locale=en |

Contents
1. Science
2. Levels of Data Services Slide 9
3. Informed data services development Slide 10
4. Question Slide 14
5. DART Premise Slide 15
6i. We Need a Tool Slide 17
7. Solution: an analytic rubric Slide 18
8. Mini-Reviews 1&2 Slide 23
9. Inter-Rater Reliability Slide 25
10. A Primer on Scoring Slide 26

SA - https://www.librarything.com/work/32118813/book/262385548 | https://www.librarything.com/work/31655380/book/257745777 | https://www.librarything.com/work/31468613/book/255246347 |
RT - Evaluation
BT - Assessments
NT - Rubric
UF - An introduction to the DART evaluation instrument for data management plans.
SN - Powerpoint Presentation Slides (This entry does not reference a hierarchical list)… (meer)

PDFR50 | As data-driven research becomes the norm, practical knowledge in data stewardship is critical for researchers (Jahnke, Asher, & Keralis, 2012; Ogburn, 2010). Carlson, Fosmire, Miller, and Nelson (2011) capture the sentiment nicely by saying, “ …it is not simply enough to teach students about handling incoming data, they must know, and practice, how to develop and manage their data with an eye toward the next scientist down the line.” In a series of semi-structured Data Curation Profile interviews with faculty from a broad range of disciplines, Witt, Carlson, Brandt, and Cragin (2009) found that faculty often felt that graduate students lacked data management and curation skills. Just as importantly, they also observed that the faculty interviewees often admitted that they were unprepared to provide adequate training for their students in RDM because they lacked knowledge and skills. Despite its growing importance and a clear need for training, however, credit-bearing coursework for graduate students in research data management (RDM) is still rare at the university level |

Contents
1. Introduction pg. 2
2. Literature Review pg. 3
3. Description of Program pg. 3
4. Instructional Approach pg. 5
5. Assessment pg. 6
6. Table 1 Examples of how DIL core competencies and learning outcomes can be linked with various teaching strategies. pg. 7
7. Lessons Learned pg. 8
8. Next Steps pg. 10
9. Conclusion pg. 11
10. Acknowledgments pg. 12
11. References pg. 12
12. Appendix 1 Developing learning outcomes for a graduate-level research data management course by merging existing outcomes and creating new ones.
13. Appendix 2 Research Data Management, Winter 2014 meeting schedule with source materials for lectures and laboratory exercises
14. Works Cited pg. 23

SA - ACRL Framework
RT - Course Design
BT - Library Services
NT - Data Management Instruction
UF - Digital Hygiene
SN - This paper describes the development and implementation of a new course for graduate students in selected aspects of data information literacy. (This entry does not reference a hierarchical list)… (meer)