Amanda Whitmire Maura Valentino OSU Libraries OPP Workshop Series 5 December 2012.
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Transcript of Amanda Whitmire Maura Valentino OSU Libraries OPP Workshop Series 5 December 2012.
Where’s Your Data?
Amanda WhitmireMaura Valentino
OSU Libraries
OPP Workshop Series5 December 2012
Why is a Librarian asking?
We are curious.
We manage information.
Data are a kind of
information.
TAKING CARE OF YOUR DATA
What’s your plan?
GOAL:
Achievable habits for implementing
data management best practices into your workflow
“…the recorded factual material commonly accepted in the scientific community
as necessary tovalidate research findings.”
Research data is:
U.S. Office of Management and Budget, Circular A-110
“…management activities required to maintain research
data long-termsuch that it is available for reuse and preservation.”
Data curation is:
Wikipedia
CURATION ≠ ARCHIVAL
“It is obvious that making data widely
available is an essential element of scientific research.”
Science editorial, “Making Data Maximally
Available,”11 Feb 2011
The case for data managementstewardship
curationetc.
$
Common missteps
“Why can’t I open this WordPerfect document?”“I think those data are on a ZipDisk somewhere…”“Oh, that dataset is on our group server…” “I never actually gave my advisor the final dataset…”“My laptop got stolen, so I lost the data…”“It was so long ago, I can’t remember …”
Research data lifecycle
New research question
posedResearch
planning & design
Data collection & description
Data processing &
analysisDissemination &
publication of findings
Data archiving
Accessible data located
Data transformed / repurposed
Research Cycle
How can we help?
New research question
posedResearch
planning & design
Data collection & description
Data processing &
analysisDissemination &
publication of findings
Data archiving
Accessible data located
Data transformed / repurposed
Research Cycle
Where to start?
How much data?
Resources needed
Roles & responsibilities
Metadata
Data formats
Data storage
Ethics & consent
Copyright (open data)
Sharing
Make a plan. Consider:
A fewtidbits
Data storage & curation
Anticipate: Volume/File type(s) Raw data vs. processed/analyzed data File Naming Conventions Privacy Concerns Storage practice Backup plans (LOCKSS, checksums)
File naming conventions
1. Be consistent• Have conventions for naming: (1) Directory structure
(2) Folder names(3) File names
• Always include the same information (e.g. date and time)• Retain the order of information (e.g. YYYYMMDD, not
MMDDYYY )
2. Be descriptive• Try to keep file and folder names under 32 characters
example: Project_instrument_location_YYYYMMDDhhmmss_extra.ext
SG157_20100426_001.raw (raw data)
SG157_20100426_001.mat (working data)
ESPOMZ_SG157_20100426_001.txt
(shareable)
Legal and ethical considerations
Intellectual property• Office for Commercialization & Corporate Development (OCCD)• Copyright
LicensingCharging for data?Data attribution & citation
Human subjects? Informed consent & anonymization prior to publishingResources @ OSU:• Office of Research Integrity, Institutional
Review Board (IRB)• Responsible Conduct of Research (RCR)
Program
Archiving and preservation
PoliciesPreservation optionsTypes of repositoriesCosts and benefits
University of SouthamptonSchool of Electronics & Computer ScienceSouthampton, UK, 2005
A word about backups…
Metadata
“The metadata accompanying your data should be written for a user 20 years into the future -- what does that person need to know to use your data properly? Prepare the metadata for a user who is unfamiliar with your project, methods, or observations.”
Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical
Dynamics(ORNL DAAC)
What is Metadata?
Metadata is “data about data”
WHO created the data? WHAT is the content of the data? WHEN were the data created? WHERE is it geographically? HOW were the data developed? WHY were the data developed?
Metadata schemes
Dublin Core (DC), Darwin Core (DwC), EML, DDI, NBII,
FGDC/CSDGM, ISO 19139,
ISO 19115, DIF, LDIF, e-GMS,
AGLS, METS, MODS, PREMIS,
OAI-PMH, MARC, CDWA, CIDOC/CRM, DACS, DIG35,
GILS, GML, ISBD, LCSH, KML,
MARCXML, MEI, MODS, MIX,
OAIS, ANSI/NISO Z39.88, PB
Core, PRISM, QDC, RDF, SGML, VSO, XML, XMP
X
Metadata schemes
“Metadata schemes are like toothbrushes – everybody agrees that you should use one, but nobody wants to use someone else’s.”
You already use metadata…
-23
87
48
Metadata in use
State City Location Date Time Temperature (F)
Alaska Anchorage City Hall 2/12/2010 1400 -23
Florida Miami Weather Center 2/12/2010 1400 87
New York New York Empire State Building 2/12/2010 1400 48
Metadata in real life
You use it all the time…
Darwin Core | biological diversity, taxonomy
Dublin Core | general
DDI (Data Documentation Initiative) | social and
behavioral sciences data
DIF (Directory Interchange Format) |
environmental sciences
EML (Ecological Metadata Language) | ecology
FGDC/CSDGM (Federal Geographic Data
Committee/Content Standard for Digital
Geospatial Metadata) | geographic data
NBII (National Biological Information
Infrastructure) | biology
Major metadata standards
http://sbc.lternet.edu/cgi-bin/showDataset.cgi?docid=knb-lter-sbc.10
Metadata activity!
Take it away, Maura…
Let’s Describe this Dataset
Bright orange Garibaldi fishHypsypops rubicundusCalifornia, USA
Ornate Butterfly fishChaetodon ornatissimusIndo-Pacific
Scenario 1
Research for preschoolers to see if they learn colors and
patterns better from real life examples
Scenario 2
Research on what fish are local to a particular area. The
photos are the data
Scenario 3
Research into specific details of specific types of fish
File/Folder Organization
You have monitors attached to 18 athletes (6 tennis players, 6 golfers, 6 rowers) for 7 days. Each day you get 2 readouts for each athlete, 1 for heart rate and 1 for body temperature. You transfer the data to Excel. Name and organize the files for this experiment.
Think about your own data– What types of data need to be described?
– What are the relationships between them?
– What descriptive metadata can you find?
– What metadata is being captured automatically?
– What other descriptive metadata do you need to help users find your data?
– What metadata do you need to help other scientists reproduce your data or use it for comparison?
– What events has/will the data undergo?
– For how long do you want to retain the data?
– How intensive are your preservation needs?
– How diverse is your user base? Does this influence your preservation needs?
Data Management Plans
Data Management Plans
The types of dataData & metadata standards | format
and content
Policies for access and sharingPolicies and provisions for re-usePlans for archiving data{Budget} $$$
Use available resources
http://www.dataone.org/data-management-planning
https://dmp.cdlib.org/
Contact information
Amanda Whitmire | Data Management
Specialist
Maura Valentino| Metadata Librarian
fin