data management for undergraduate research
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
June 18, 2015
• Introductions
•What are data?
•Why manage data?
•Data Management Plans
• File Naming
•Metadata
•Storage and Archiving
•Questions
NameMajorResearch Project
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?
Your best collaborator is yourself six months from now, and your past self doesn’t answer emails.
Why else 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
Two bears data management
problems1. 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 Data was in a format that could only be read by outdated, proprietary software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
Data Management Plan
PLANNINGPLANNING
Courtesy of the UK Data Archive http://www.data-
archive.ac.uk/create-manage/life-cycle
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?
Elements of a DMP•Types of data, including file formats
•Data description
•Data storage
•Data sharing, including confidentiality or security restrictions
•Data archiving and responsibility
•Data management costs
File naming
File naming best practices
•Be descriptive
•Don’t be generic
•Appropriate length
•Be consistent
•PLPP_EvaluationData_Workshop2_2014.xlsx
•MyData.xlsx
•publiclibrarypartnershipsprojectevaluationdataworkshop22014CummingsHelenaMontana.xlsx
Who filed better?
File naming best practices
•Files should include only letters, numbers, and underscores.
•No special characters (%@#*?!)
•No spaces
•Lowercase or camel case (LikeThis)
•Not all systems are case sensitive. Assume this, THIS, and tHiS are the same.
Dates and numbering…
1. Use leading zeros for scalability
001
002
009
019
999
2. If using dates use YYYYMMDD
June2015 = BAD!
06-18-2015 = BAD!
20150618 = GREAT!
2015-06-18 = This is fine too
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
MetadataUnstructure
d 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
Why create metadata?
IJ?
XVAR?
FNAME
?
Data documentation includes…
•Questionnaires
•Interview protocols
•Lab notebooks
•Code or scripts
•Consent forms
•Samples, weights, methods
•Read me files
LOCKSS (Lots of Copies
Keeps Stuff Safe)
Options for data storage
•Personal computers or laptops
•Networked drives
•External storage devices
Storing sensitive data
•If possible, collect the necessary data without using direct identifiers
•Otherwise, de-identify your data upon collection or immediately afterwards
•Do not store or share sensitive data on unencrypted devices
•Talk to IRB
Thinking long-term
Archiving options
•Public repository – FigShare
•Domain-specific 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
