Manage Your Data: Navigating Data Services at the UW Libraries
1 like107 views
This document summarizes a presentation about managing research data at the University of Washington Libraries. It discusses considerations for data management like storage, backup, organization, documentation, and sharing. Attendees are encouraged to ask questions and discuss how the topics relate to their own research. The presentation provides an overview of data services available through the libraries and other campus resources. It aims to help researchers navigate requirements and best practices for responsible data management.
Manage Your Data: Navigating Data Services at the UW Libraries
- 1. Welcome! As you come in, please: 1) Sign in with your barcode 2) Take an index card and write down one question you hope will be answered during this presentation.
- 2. Manage Your Data: Navigating Data Services at the UW Libraries Jenny Muilenburg & Liz Bedford September 20, 2016 TA/RA Conference Slideshare link
- 3. Today’s Session ● Introductions ● Considerations for managing research data ● Data services @ UW Libraries ● Discussions: How does this fit with your research? ● Tips: Seattle Secrets Questions are welcome anytime!
- 4. ● Database management ○ eScience Institute ○ http://escience.washington.edu/ ● Data analysis ○ CSSCR ○ http://csscr.washington.edu/courses.html ○ UW-IT ○ http://itconnect.uw.edu/learn/workshops/ Not Today’s Session
- 5. The Data Lifecycle Image credit: UK Data Archive
- 6. The Data Lifecycle Image credit: UK Data Archive
- 7. Research Data Services Guide
- 8. Group Discussion: What are you working with? Turn to your neighbor(s) and ask: ● Will you be creating data in the course of your research, or using someone else’s? Or both? ● What does your data look like? What formats will you use? How much will there be?
- 9. Report Out ● Observational Data ○ Think about what this might mean for how long you should keep your data ● Experimental Data ○ Metadata and documentation will be particularly important ● Computational Data ○ Will your outputs be useful to others, or is it more important to keep the algorithms and initial variables?
- 10. Tip #1: Seattle Food Trucks! ● All over, nearby and on Red Square ● Many, many options www.seattlefoodtruck.com
- 12. ● Funders ○ http://datasharing.sparcopen.org/ ● Publishers ○ Example from Nature: http://www.nature.com/authors/policies/availabil ity.html ● Human Subjects/IRB ○ http://www.washington.edu/research/hsd/ ● Intellectual Property ○ http://comotion.uw.edu/ External Requirements
- 14. Group Discussion: What are your external considerations? Turn to your neighbor(s) and ask: ● Who are your most likely Funders and Publishers? ● Will you need to work with UW’s IRB? ● Will your research bring up intellectual property issues?
- 15. Report Out ● Human Subjects? Personally Identifiable Information? ○ Security and sharing will pose unique challenges ● Intellectual Property issues? ○ Learning about licenses will be important
- 16. Tip #2: UW Cherry Blossoms ● Bloom in March/ April each year ● http://depts.washi ngton.edu/grounds /arboriculture/qua d_cherries.php ● Located on the quad
- 18. 3-2-1 Rule ● Three copies, on two different storage media, at least one of which is in a separate location PCs and external devices (hard drives and USBs)? Shared servers? Remote storage and cloud services?
- 19. Storage and Backup Options @ UW ● Free Online storage: Google drive and U Drive ○ Google servers vs UW servers ○ Unlimited storage vs 30GB ○ FERPA compatible, but NOT for HIPAA/PHI data ● Other UW-IT servers and storage services ● Departmental offerings ○ Medicine, Computer Science & Engineering, College of Engineering
- 20. Sharing and Storage Guide
- 21. Group Discussion: What is your role, and who else is involved? Turn to your neighbor(s) and ask: ● Who collects the data? Who generates the metadata? ● Who knows where all of the data is stored and how it’s organized? ● Who is responsible for the backup schedule?
- 22. Report Out ● Sole responsibility? ○ There’s a lot on your plate, but you have complete control ● Otherwise, did you know the answer to all of those questions? ○ You are not alone! You’ll need to make explicit agreements about responsibility
- 23. Tip #3: Bowling! ● The HUB has a bowling alley ● 12 lanes, is reservable for parties, and now has state-of-the-art bowling kiosks and screens. ● Food (and socks!) available for purchase
- 25. Organization ● Naming conventions ○ Brief but descriptive names ○ Include info about dates and versioning ● Folder organization ○ Organize by data type, subject, project stage, etc. ○ Consistency is key! ● Regular cleanup
- 26. Documentation ● Metadata is your friend! ○ Varies by discipline - find what works for you ● Document data AND process ○ Software to the rescue ○ Here’s a great example readme file ● Check out the eScience Institute’s Reproducibility and Open Science Working Group
- 27. Organization and Format Guide
- 28. Group Discussion: Open Data Turn to your neighbor(s) and ask: ● What is your field’s general reaction to the idea that data should be shared openly? ● Do you support Open Data? Why or why not?
- 29. Index Cards: On the second side of your card please write down the most important issue that you would like to know more about. Pass the index cards to the center.
- 30. Report Out ● Have you ever used data gathered by someone else? ○ Think about how you plan to give them credit ● Do you plan to share your data? ○ Deciding this before you begin collecting will help guide your choices for documentation and organization
- 31. Tip #4: Free Art Across the City ● Henry Art Gallery and Burke Museum ● Olympic Park ● Frye Museum ● First Thursdays in Pioneer Square
- 33. Sharing and Licensing ● Where to share? ○ NIH has a list of approved sharing sites ○ DIY Sharing: Roberts Lab website ○ Community websites like the Neuroscience Information Network ● Attaching a license ○ Options: Creative Commons, Open Data Commons ○ UW CoMotion has more info
- 34. Data Preservation and Archiving ● Sharing != Archiving ○ You are looking for long-term preservation and accessibility ○ Sometimes a repository is good for both, often not ● What to preserve? ○ Raw data, final data, code, software? ● Where to preserve? ○ UW ResearchWorks, funder repository, Registry of Research Data Repositories
- 37. Ask Us! Jenny Muilenburg, jmuil@uw.edu Liz Bedford, ebedford@uw.edu UW Libraries Data Services Team, uwlibs-data@uw.edu