data management for undergraduate researchers
TRANSCRIPT
Data Management for Undergraduate
ResearchersOffice of Undergraduate Research Seminar and Workshop Series
Rebekah Cummings, Research Data Management LibrarianJ. Willard Marriott Library, University of Utah
September 21, 2015
• Introductions
• What are data?
• Why manage data?
• Data Management Plans
• Data Organization
• Metadata
• Storage and Archiving
• Questions
NameMajorResearch Project
What is data management?
The process of controlling the information (read: data) generated
during a research project.
https://www.libraries.psu.edu/psul/pubcur/what_is_dm.html
What are data?
“The recorded factual material commonly accepted in the research community as necessary to validate
research findings.”
- U.S. OMB Circular A-110
Data are diverse
Data are messy
Why manage data? • Save time and efficiency
• Meet grant requirements
• Promote reproducible research
• Enable new discoveries from your data
• Make the results of publicly funded research publicly available
We are trying to avoid this scenario…
Two bears data management problems
1. Didn’t know where he stored the data
2. Saved one copy of the data on a USB drive
3. Data was in a format that could only be read by outdated, proprietary software
4. No codebook to explain the variable names
5. Variable names were not descriptive
6. No contact information for the co-author Sam Lee
Scenario
You develop a research project during your undergraduate experience. You write up the results, which are accepted by a reputable journal. People start citing your work! Three years later someone accuses you of falsifying your work.
Scenario adapted from MANTRA training module
• Would you be able to prove you did the work as you described in the article?
• What would you need to prove you hadn’t falsified the data?
• What should you have done throughout your research study to be able to prove you did the work as described?
Data Management Plans
• What data are generated by your research?
• What is your plan for managing the data?
• How will your data be shared?
Research Data Lifecycle
Courtesy of the UK Data Archive http://www.data-
archive.ac.uk/create-manage/life-cycle
• Types of data
• Data description
• Data storage
• Data sharing
• Data archiving and
responsibility
• Data management costs
Data organization
File naming
MyData.xls
MeetingNotes.doc
Presentation.ppt
Assignment1.pdf
File naming best practices1. Be descriptive
2. Don’t be generic
3. Appropriate length
4. Be consistent
5. Think critically about your file names
File naming best practices• Files should include only letters,
numbers, and underscores/dashes.
• No special characters
• No spaces; Use dashes, underscores, or camel case (like-this or likeThis)
• Not all systems are case sensitive. Assume this, THIS, and tHiS are the same.
Version Control - Numbering
001002003009010099
Use leading zeros for scalability
Bonus Tip: Use ordinal numbers (v1,v2,v3) for major version changes and decimals for minor changes (v1.1, v2.6)
110239
99
Version Control - DatesIf using dates use YYYYMMDD
June2015 = BAD!
06-18-2015 = BAD!
20150618 = GREAT!
2015-06-18 = This is fine too
From a DMP…
“Each file name, for all types of data, will contain the project acronym PUCCUK; a reference to the file content (survey, interview, media) and the date of an event (such as the date of an interview).
• PLPP_EvaluationData_Workshop2_2014.xlsx
• MyData.xlsx
• publiclibrarypartnershipsprojectevaluationdataworkshop22014CummingsHelenaMontana.xlsx
Who filed better?
Who filed better? • July 24 2014_SoilSamples%_v6
• 20140724_NSF_SoilSamples_Cummings
• SoilSamples_FINAL
File organization best practices
• Top level folder should include project title and date.
• Sub-structure should have a clear and consistent naming convention.
• Document your structure in a README text file.
File organization exercise
Describing data
Research Documentation • Grant proposals and related reports
• Applications and approvals (e.g. IRB)
• Codebooks, data dictionaries
• Consent forms
• Surveys, questionnaires, interview protocols
• Transcripts, hard copies of audio and video files
• Any software or code you used (no matter how insignificant or buggy)
Three levels of documentation
• Project level – what the study set out to do, research questions, methods, sampling frames, instruments, protocols, members of the research team
• File or database level – How all the files relate to one another. A README file is a classic way of capturing this information.
• Variable or item level – Full label explaining the meaning of each variable.
http://datalib.edina.ac.uk/mantra/documentation_metadata_citation/
IJ?
XVAR?
FNAME?
http://www.icpsr.umich.edu/files/deposit/Guide-to-Codebooks_v1.pdf
MetadataUnstructured
Data
Structured Data
There was a study put out by Dr. Gary
Bradshaw from the University of
Nebraska Medical Center in 1982
called “ Growth of Rodent Kidney
Cells in Serum Media and the Effect of
Viral Transformation On Growth”. It
concerns the cytology of kidney cells.
Title Growth of rodent kidney cells in serum media and the effect of viral transformations on growth.
Author Gary Bradshaw
Date 1982
Publisher University of Nebraska Medical Center
Subject Kidney -- Cytology
Dublin Core
Disciplinary MetadataDigital Curation Centre’s list of subject-specific metadata schemas - http://www.dcc.ac.uk/resources/metadata-standards
LOCKSS (Lots of Copies Keeps
Stuff Safe)
Options for data storage
• Personal computers or laptops
• Networked drives
• External storage devices
Ubox – box.utah.edu
Language from a DMP“All data files will be stored on the University server that is backed up nightly. The University's computing network is protected from viruses by a firewall and anti-virus software. Digital recordings will be copied to the server each day after interviews.
Signed consent forms will be stored in a locked cabinet in the office. Interview recordings and transcripts, which may contain personal information, will be password protected at file-level and stored on the server.
Original versions of the files will always be kept on the server. If copies of files are held on a laptop and edits made, their file names will be changed.”
Thinking long-term
Archiving options
• Domain-specific repository
• General Purpose Data Repository
• Institutional repository
Major takeaways• Data management starts at the beginning of
a project
• Document your data so that someone else could understand it
• Have more than one copy of your data
• Consider archiving options when you are done with your project
