This presentation was given by myself and Brad Houston (http://www.slideshare.net/herodotusjr), for UWM's Responsible Conduct of Research (RCR) series in Fall of 2013. It covers data management plans and practical data management tips. The corresponding handout is also available on Slideshare: http://www.slideshare.net/kbriney/rcr-data-management-handout
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Responsible Conduct of Research: Data Management
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What if your hard drive crashes?
What if you are accused of fraud?
What if your collaborator abruptly quits?
What if the building burns down?
What if you need to use your old data?
What if your backup fails?
What if your computer gets stolen?
What if…
Do You Still Have Your Data?
2. Data Management &
Data Management Plans
Responsible Conduct of Research
22 November 2013
Kristin Briney & Brad Houston
3. Why Data Management?
• Don’t lose data
• Find data more easily
– Especially if you need older data
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Easier to analyze organized, documented data
Avoid accusations of fraud & misconduct
Get credit for your data
Don’t drown in irrelevant data
4. For each minute of planning at
beginning of a project, you will save
10 minutes of headache later
6. What Are Data?
• “Research data is defined as the recorded
factual material commonly accepted in the
scientific community as necessary to validate
research findings”
– OMB Circular A-110
http://www.whitehouse.gov/omb/circulars_a110
7. What Are Data?
• Observational
– Sensor data, telemetry, survey data, sample
data, images
• Experimental
– Gene sequences, chromatograms, toroid magnetic
field data
• Simulation
– Climate models, economic models
• Derived or compiled
– Text and data mining, compiled database, 3D
models, data gathered from public documents
10.
Your Data
Management Plan
should come *last*.
First consider:
◦ Information about
your data
◦ Information about
your audience
◦ Obligations to
funders and others
Source: Sam Howzit
11.
What kind of data is it?
◦ (See Kristin’s slide on the 4 categories)
What are the key characteristics of the data?
◦ (File Format? Size? Programs needed to access it?)
Can I recreate the data, if needed?
What infrastructure is available to manage it?
◦ On-campus and off-campus– don’t limit yourself
Is the data intelligible to people other than
me?
◦ If the answer to this one is “no”, that’s something
you should probably fix
12.
In order of amount of documentation you’ll
need:
◦ Future You (reference use only)
◦ Colleagues within your discipline, in your lab or
elsewhere
◦ Colleagues in related disciplines
◦ General Public/The World!
The question to ask: is my data described
well enough to be usable by my audience?
13.
Rights shared with
collaborators
◦ Decide who’s
responsible for the
official copy of data
Information Security
Access Provisions
◦ NIH: Public Access
policy
◦ NSF: Directorate
access policy
◦ Others? (OMB A-110)
Often attached to funding.
14.
Your data management plan (DMP) should
contain 5 key components:
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Expected Data
Standards for format and content
Policies for Access and sharing
Policies for Reuse and distribution
Plans for archiving data and preserving access
Note: These are minimum requirements.
◦ Specific agencies or directorates may ask for more–
check their application sites!
15.
In short: What kind of data will be produced
by your research processes?
Keep in mind:
◦ File formats of complete data sets
◦ Any software or code that will be needed/produced
◦ Physical samples or other individual data points
Some divisions require retention of physical samples;
consult your Program Officer
16.
In short: how will you organize your data
within datasets to make it widely
accessible, and how will you make data sets
identifiable?
Keep in mind:
◦ Any data formatting standards for your particular
discipline
◦ Any metadata (author, date, subject, etc.) that your
program attaches automatically, and what you will
need to attach manually
◦ How will you find your data for later consultation?
How will others find it?
17.
In short: How will you
allow other
researchers to find
and use your data?
Keep in mind:
◦ How will other
researchers find your
data?
◦ How will you provide
access to your data?
◦ How will you prepare
your data for sharing?
18.
In short: How will
researchers obtain
permission to use
your data?
Keep in mind:
◦ Will you grant blanket
permission or case-bycase?
◦ What responsibilities
will users of your data
have re:
privacy, intellectual
property, etc.?
◦ What if a provision is
violated?
19.
In short: How will you
make sure your data
stays available?
Keep in Mind:
◦ What are your retention
requirements? Is this a
permanent data set?
◦ What storage media
will you use? Are you
prepared to migrate as
needed?
◦ Do you have a data
backup plan?
Above: Not A Good Way to archive
your data.
20.
You also need to keep track of supplementary
research records:
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Documentation on funding/expenditures
Copies of IRB/Animal Care research protocols
Hazardous Materials documentation
Invention Disclosure/Tech Transfer documentation
Conflict of Interest reports
Every institution has a different retention
requirement– ask your records officer!
◦ For UWM: almost all of this is “End of Grant + 3
years”
21.
Document Everything!
◦ Information about the data and your methods
◦ Information about where/how you’re keeping the
data (short-term and long-term)
◦ What is needed to access the data
◦ What security/privacy policies apply
◦ Any collaborators outside the institution and their
rights
◦ Any supplementary files or forms needed to
document use of funding
24. Storage and Backups
• Library motto: Lots of Copies Keeps Stuff Safe!
• Rule of 3: 2 onsite, 1 offsite
• Any backup is better than none
• Automatic backup is better than manual
• Your research is only as safe as your backup
plan
– Periodically test restore from backup!
25. Storage and Backups
• Library motto: Lots of Copies Keeps Stuff Safe!
• Rule of 3: 2 onsite, 1 offsite
• Any backup is better than none
• Automatic backup is better than manual
• Your research is only as safe as your backup
plan
– Periodically test restore from backup!
26. Example
• I keep my data
– On my computer
– Backed up manually on shared drive
• I set a weekly reminder to do this
– Backed up automatically via SpiderOak cloud
storage
• A note on cloud storage…
28. Consistency
• Consistent file naming
– Make it easier to find files
– Avoid many duplicates
– Make it easier to wrap up a project
• Names descriptive but short (<25 characters)
• Avoid “ / : * ? ‘ < > [ ] & $ and spaces
• Date convention: YYYY-MM-DD
30. Consistency
• Consistent documentation
– Record all necessary information
– Keep information in one place
– Easier to search and use later
• Take 5 minutes before starting a project
• Create a list of information to record
– Don’t forget to record the units!
31. Example
• For my experiment, I need to collect:
– Date
– Experiment
– Scan number
– Powers
– Wavelengths
– Concentration (or sample weight)
– Calibration factors, like timing and beam size
33. Recording Your Conventions
• What if someone needs to find your data?
• Eventually will hand off data to your PI
• Record your naming conventions
• Record your documentation schemes
• Record overall project information
– Contact info, grant #, project summary, etc.
34. Examples
• Print out near computer/experiment area
– Document conventions
• In front of research/lab notebook
– Page 1: Project information
– Page 2: Conventions and abbreviations
– Page 3-X: Index of experiments
• README.txt in data folder
– Top-level folder: project information
– Lower-level folder: what’s in this folder?
35. Planning for the Future
http://www.flickr.com/photos/bonedaddy/2791636546/ (CC BY-SA)
36. Planning for the Future
• Get help for sensitive data!
– HIPAA, FERPA, FISMA, IRB, etc.
• UWM Information Security Office
– Visit: www.uwm.edu/itsecurity/
– Email: infosec@uwm.edu
• Policy pages
– www.uwm.edu/legal/hipaa/index.cfm
– www.uwm.edu/academics/ferpa.cfm
37. Planning for the Future
• We can’t open files from 10 years ago
• Proprietary file types
– Convert to open file format
• .doc .txt
• .xls .csv
• .jpg .tif
– Preserve software if no open file format
• Periodically move data to new media
38. Don’t Stress Over Data
http://www.flickr.com/photos/72775875@N06/7729764370/ (CC BY-NC-SA)
39. More Data Management
• Data Services
– www.uwm.edu/libraries/dataservices/
• Data Management Plans
– dataplan.uwm.edu
• Kristin Briney, Data Services Librarian
• Brad Houston, University Records Officer
40. Thank You
• The content of this presentation is licensed
under a Creative Commons Attribution 3.0
Unported License (CC BY)
– Image licenses as marked