Data Management …
a “nuts-and-bolts” part of
Responsible Conduct of Research
March 21, 2015
Enid Karr, Sr. Bibliographer for Biology, Earth & Environmental Sciences,
Environmental Studies
enid.karry@bc.edu
Sally Wyman, Collection Development Librarian, Sr. Bibliographer for
Chemistry, Physics, Environmental Studies
sally.wyman@bc.edu
Barbara Mento, Data/GIS Librarian, Sr. Bibliographer for Computer
Science, Economics, Mathematics
barbara.mento@bc.edu
A “Typical”
Data Management Plan
1-2 pages describing the project and how data will be:
§
Collected (including formats, size, etc.) … Secured … Analyzed … Shared …
Preserved
Details about access/sharing
§
Potential audience(s) for the data
§
How access will be provided and how others will find it:
“Access” (freely-available) vs. “Sharing” (by request)
§
Stipulations for privacy, confidentiality, IP or other rights
§
Allowed re-use of the data, derivative products
Metadata standards to be used
How long data will be retained -- archiving, long-term preservation andformat
migration
File Formats
Whenever possible, save your data using open standards.
Avoid proprietary formats. Some examples:
§ TXT, PDF/PDF Archival, not Word (doc, docx)
§ ASCII, not Excel (xls, xlsx)
§ MPEG-4, not Quicktime (qtff)
§ TIFF or JPEG2000, not GIF or JPG
§ XML or RDF, not RDBMS
Ideally, save files in both original format AND one of the preferred ones
listed above.
Organization
File Naming Conventions/Best Practices
§
Consistent, descriptive, UNIQUE … avoid spaces and special characters
§
Use brief names
§
Can contain:
§
Project acronyms
§
Researchers’ initials
§
File type information
§
Version number
§
Date
§
File Status
IUS_v02_092011_final.csv
Internet Usage Study version 2, Sept 2011,
final draft, in csv format
Data Entry and Quality Control
§
Whatever you use, be consistent
§
Define abbreviations in readme.txt file or in a “codebook”
§
Record dates for best sorting (YYYYMMDD)
§
Check periodically for data corruption/integrity using checksum, for example §
Flag problematic data
§
Handling of null values: problematic in moving across software platforms
§
Consider using blanks: treated as null values by R, Python, Excel
§
Don’t use text (as in, “no data”) in a data column formatted for numbers
§
Avoid manual data entry whenever possible
§
Consider making your raw data files “read only”
Ci#ng Data Sets
Essen#al cita#on elements; style will vary: •
author or creator •
#tle or descrip#on •
year of publica#on •
publisher and/or the database/archive from which it was retrieved •
the URL or DOI if the data set is online
National Center for Biotechnology Information.
PubChem Compound Database; CID=5934766,
http://pubchem.ncbi.nlm.nih.gov/summary/
summary.cgi?cid=5934766 (accessed Feb. 22,
2011).
Mackey, R.A., Mackey, E.F., and O’Brien, B.A.
(1990). Lasting relationships research data archive
(eScholarship version) [Data file]. Boston
College School of Social Work.
http://hdl.handle.net/2345/2228
§
Additional Support
The Libraries
§
The Data Management LibGuide
libguides.bc.edu/dataplan
§
Subject Specialists
www.bc.edu/libraries/help/askalib.html
§
eScholarship@BC
escholarship.bc.edu
§ The Office for Sponsored Programs Research
http://www.bc.edu/research/osp.html
§ ITS/Research Services
http://www.bc.edu/offices/researchservices/
§ Office for Research Integrity and Compliance
http://www.bc.edu/research/oric/compliance.html
§ The Office for Technology Transfer and Licensing
http://www.bc.edu/research/ottl/
§
Some Useful Links
Data Management and Sharing Snafu in 3 Short Acts
(NYU Health Sciences Library)
https://www.youtube.com/watch?v=N2zK3sAtr-4
§ DataOne Best Practices
https//www.dataone.org/all-best-practices-download-pdf
§ DCC (Digital Curation Center) Disciplinary Metadata Standards
http://www.dcc.ac.uk//resources/metadata-standards
§ DCC Digital Curation Center Metadata Standards – Physical
Sciences
http://www.dcc.ac.uk/resources/subject-areas/physical-science
§ Guide to Writing “Readme” Style Metadata (Cornell)
http://data.research.cornell.edu/content/readme
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