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NISO Training Thursday Crafting a Scientific Data Management Plan

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Feb 26 NISO Training Thursday
Crafting a Scientific Data Management Plan
About the Training

Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors.

This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth.

About the Instructors

Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries

Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries

Published in: Education
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NISO Training Thursday Crafting a Scientific Data Management Plan

  1. 1. NISO Training Thursday Crafting a Scientific Data Management Plan Thursday, February 26, 2015 Instructors: Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries http://www.niso.org/news/events/2015/training_Thursdays/TT_crafting/
  2. 2. Crafting a Scientific Data Management Plan Thursday, February 26, 2015
  3. 3. Instructors Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska- Lincoln Libraries, kdeards2@unl.edu Jenny Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries, jthoegersen2@unl.edu
  4. 4. Training overview  Introduction to data management plan requirements  Data Management Plan Checklist  Review good and bad data management plan excerpts
  5. 5. Introduction to data management plans  Follow guidelines provided by granting agency, directorate, and solicitation  Keep the plan clear, complete, and concise  Refer back to the project proposal, if necessary  Recheck requirements for changes
  6. 6. Data Management Checklist 1. What type of data are being produced and what are the file formats? 2. How much data are being produced, and at what growth rate? Will the data change? 3. How long should the data be retained? 4. What directory and file naming conventions will be used? 5. Do you need data identifiers? 6. Are there tools and software needed to render the data? 7. Who will be responsible for data management? 8. Are there privacy, legal, ethical, or security requirements? 9. Does the funder require a data sharing policy, data management plan, or other information? 10. Are the data properly described (metadata) and the overall project documented? 11. How will you store and backup the data? 12. Do you need to publish the data in a repository?
  7. 7. Data types & file formats What types of data file formats have you encountered?
  8. 8. Data types & file formats  Match data types to file formats  Favor open source and widely used formats  Consider data repository requirements
  9. 9. Quantity of data A LOT? A little data Or…
  10. 10. Retention of data Time
  11. 11. Directory and file naming conventions  Avoid special characters ("/ : * ? " < > [ ] & $)  Use underscores, not spaces  Avoid names longer than 25 characters  Use consistent versioning identification (DM_Guide_v03)  Use the ISO 6801 standards for date formats (YYYY-MM-DD)  Use names that describe the content
  12. 12. Directory and file naming conventions “…the PIs, senior personnel, technician and students on the project will convene a dedicated data management meeting. At this time, the PIs will set out naming, processing and storage conventions for all data collected at the experimental and observational sites…training will be reiterated at a yearly data management and analysis meeting to remind participants of the conventions and train any new participants.” From Elsa Cleland's proposal The influence of plant functional types on ecosystem responses to altered rainfall. Available at http://idi.ucsd.edu/data- curation/examples.html
  13. 13. Data identifiers “Message error 404” by Roberto Zingales, https://www.flickr.com/photos/filicudi/2891898817 (CC BY 2.0)
  14. 14. Rendering data By Images courtesy of http://abstrusegoose.com/ under a Creative Commons license via Wikimedia Commons, http://www.ccc.uga.edu/summer/programs/comic2.png (CC BY-SA 3.0) In 30 years, how will you access your data?
  15. 15. Who is responsible? From “Lease”, by Randall Monroe http://xkcd.com/616/ (CC BY-NC 2.5)
  16. 16. Privacy, legal, ethical, or security requirements “Speak no evil, See no evil, Hear no evil” by Rose Davies, https://www.flickr.com/photos/rosedavies/110850792/ (CC BY 2.0 )
  17. 17. Publishing, Preserving, & Rights Determine where data will be preserved and shared after the conclusion of a project Outline the rights associated with the data “Cat #24 - Mummy Cat” by Marty Omnitarian, https://www.flickr.com/photos/omnitarian/4300610111/ (CC BY-NC-ND 2.0)
  18. 18. Funder requirements Funder guidelines can be very simple or very complex
  19. 19. NSF Basic DMP Requirements 1. the types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of the project; 2. the standards to be used for data and metadata format and content (where existing standards are absent or deemed inadequate, this should be documented along with any proposed solutions or remedies); 3. policies for access and sharing including provisions for appropriate protection of privacy, confidentiality, security, intellectual property, or other rights or requirements; 4. policies and provisions for re-use, re-distribution, and the production of derivatives; and 5. plans for archiving data, samples, and other research products, and for preservation of access to them. From the Grant Proposal Guide (http://www.nsf.gov/pubs/policydocs/pappguide/nsf13001/gpg_2.jsp)
  20. 20. Description & Documentation U.S. National Archives and Records Administration [Public domain], via Wikimedia Commons, http://commons.wikimedia.org/ wiki/File%3ADon't_kill_your_reputation%2C_organize_your_information_-_NARA_-_518156.jpg
  21. 21. Storage & backup Maintain 3 copies of data-- one remotely
  22. 22. Storage & backup Where do you store and back up your data?
  23. 23. Storage & backup Storage Option The Good The Bad Personal computer/laptop Convenient for active data Lost/stolen; fail; responsible for backups Network/departmen t drives Automatic backup & security Access/capacity limitations External devices Low cost; portable; easy use Lost/stolen; fail Holland Computing Center Automatic backup & security Cost for storage Box Global access; collaboration Security/privacy limitations Physical (e.g. notebook) Convenient; tangible Manual backup
  24. 24. Data management plan excerpts All sample data will be collected and organized using [Specialty Software Name]. The files will contain information about sample characteristics and the conditions under which these characteristics were measured. Approximately 1-2 Gb of data will be generated. What’s wrong with this example?
  25. 25. Data management plan excerpts All files will be stored on the PI’s secure computer. All laboratory notebooks will be stored in the PI’s office. What’s wrong with this example?
  26. 26. Data management plan excerpts Data will be available to anyone who desires access to our data. When possible, data will be made available online. What’s wrong with this example?
  27. 27. Data management plan excerpts This DMP covers the data which will be This study will only collect non-sensitive data. No personal identifiers will be recorded or retained by the researchers in any form. What’s right with this example?
  28. 28. Data management plan excerpts The project will leverage existing metadata standards currently stored in Ecological Metadata Language (EML) format. We chose EML format for our metadata since it allows integration with existing NutNet data housed in the Knowledge Network for Biocomplexity (KNB) data repository. What’s right with this example?
  29. 29. Questions?
  30. 30. Resources & References Basics of Data Management: http://unl.libguides.com/datamanagement UNL Libraries Data Management Services: http://libraries.unl.edu/data-management Example NSF DMPs from UC San Diego: http://idi.ucsd.edu/data- curation/examples.html
  31. 31. NISO Training Thursday • February 26, 2015 Questions? All questions will be posted with presenter answers on the NISO website following the webinar: http://www.niso.org/news/events/2015/training_Thursdays/TT_crafting/ NISO Training Thursday Crafting a Scientific Data Management Plan
  32. 32. Thank you for joining us today. Please take a moment to fill out the brief online survey. We look forward to hearing from you! THANK YOU

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