DATA CURATION NETWORK

Data Curation Workshop

Timothy Norris - tnorris@miami.edu
James Sobczak - jsobczak@miami.edu
Cameron Riopelle - criopelle@miami.edu

University of Miami Libraries - July 2 2019

- this work is licenced under a creative commons attribution 4.0 international license.

Adapted from Data Curation Network Workshop, Johns Hopkins University, April 17-18, 2019.

Learning Outcomes
  • Increase understanding of data curation practices and tools in various disciplines, data types, and formats.
  • Share expertise and enhance curation capacity for librarians at the University of Miami.
  • Meet like-minded colleagues who are interested in building and extending curation practices at UM.
Research data have value beyond their original purpose, but…
  • There is a lot of data.
  • Data can be messy and incomprehensible (lack context!)
Research data have value beyond their original purpose, but…
  • There is a lot of data.
  • Data can be messy and incomprehensible (lack context!)
  • Digital file formats are constantly at risk
Research data have value beyond their original purpose, but…
  • There is a lot of data.
  • Data can be messy and incomprehensible (lack context!)
  • Digital file formats are constantly at risk
  • Most data never leaves their author’s laptop ⇒ benign neglect
Incentives for sharing data strengthen the need for better curation
  • Funding mandates
  • Publisher data sharing policies (PLoS and Nature)
  • Reproducibility => standardized practices around data pipelines and replication studies
  • Retraction Watch (stick) and Open Data movement (carrot) helps safeguard against scientific fraud or the dissemination of erroneous results
What is data curation?
“The encompassing work and actions taken by curators of a data repository in order to provide meaningful and enduring access to data.”
Data Curation Network: Data Curation Terms & Activities. http://hdl.handle.net/11299/188638
Remember… data curation is just one piece of the broader data services landscape
Kouper, I., Fear, K., Ishida, M., Kollen, CC., and Williams, S. (2017). Research Data Services Maturity in Academic Libraries. In Curating Research Data: Practical Strategies for Your Digital Repository. Ed. Lisa R. Johnston. Chicago: Association of College and Research Libraries: pp 153-170.
Well-curated data are...
  • Easier for fellow scholars and future collaborators to understand
  • More likely to be trusted
  • The research they represent are more likely to be reproducible
  • More likely to be properly cited
  • Represent potential cost-savings
  • Findable, accessible, interoperable, and reusable, or FAIR (Wilkinson et. al, 2016)
Wilkinson, M. D., M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, P. E. Bourne, J. Bouwman, A. J. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C. T. Evelo, R. Finkers, A. Gonzalez-Beltran, A. J. G. Gray, P. Groth, C. Goble, J. S. Grethe, J. Heringa, P. A. C. ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S. J. Lusher, M. E. Martone, A. Mons, A. L. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S.-A. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M. A. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao and B. Mons (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. doi: 10.1038/sdata.2016.18.
FAIR Data
FFindable rich metadata with assigned DOI
AAccessible Metadata and data available across open protocols
IInteroperable Data and metadata represented with standard or easily understood data structures
RReusable metadata includes provenance and uses community accepted descriptions
Wilkinson, M. D., M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, P. E. Bourne, J. Bouwman, A. J. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C. T. Evelo, R. Finkers, A. Gonzalez-Beltran, A. J. G. Gray, P. Groth, C. Goble, J. S. Grethe, J. Heringa, P. A. C. ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S. J. Lusher, M. E. Martone, A. Mons, A. L. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S.-A. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M. A. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao and B. Mons (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. doi: 10.1038/sdata.2016.18.
Role of libraries in data curation
Libraries and academic-based data repositories are just one piece of the data repository landscape.
Kind of Repository Examples
Federally Funded Data Centers NASA Distributed Active Archives (DAAC). NOAA National Centers for Environmental Information (NCEI). National Snow and Ice Data Center (NSIDC}. USGS Earth Resources Observation Systems (EROS) Data Center (EDC)
Federally Funded Research and Development Centers National Center for Atmospheric Research (NCAR), Jet Propulsion Lab (JPL), Oak Ridge National Laboratory (ORNL)
National Libraries National Library of Medicine (NLM), National Agricultural Library (NAL). Library of Congress (LOC)
State and local Agencies State geological surveys. County planning offices
Subject Long Term Ecological Research Network Information System (LTER NIS). Andrews Forest LTER (AND. National Snow and Ice Data Center (NSIDC). Maria Rogers Oral History Program
Domain Repository Global Biodiversity Information Facility (GBIF). Inter-university Consortium for Political and Social Research (ICPSR). DataOne. Interdisciplinary Earth Data Alliance (IEDA)
Institutional Repository Purdue University Research Repository (PURR). Data Repository for the University of Minnesota (DRUM)
Replication Repository Dryad Digital Repository, Pangea Data Library
Software Repository GitHub, SourceForge
Commercial Archives Digital Globe, Aerial photography companies, Resource exploration companies. Figshare
Private Archives Huntington Library, Getty Research Institute
More info at: https://www.nature.com/sdata/policies/repositories
Elevator Speech
Exercise: Choose an audience (e.g, someone from your library administration, faculty members, university admin, etc.) and take 3-5 minutes to draft a short statement explaining the value of data curation and why it merits increased consideration or investment.

Practice your elevator speech with a neighbor.

Data Curation Activities
Research performed by the DCN
Data Curation Network
  • Collaborative staffing model for curating research data across academic and general data repositories
  • Funded by Alfred P. Sloan Foundation
  • Implementation phase (2018-2021) is piloting the model with nine partner institutions
  • Goal is to expand to all users in 2020
Data Curation Activities

Code review
Contextualize
Documentation
Embargo
File Format Transformations
Persistent Identifier
Quality Assurance
Use Analytics

File renaming
File Inventory or Manifest
File validation
Metadata
Metadata Brokerage
Rights Management
Risk Management
…...more

47 Data Curation Activities Identified
Full list of activities: http://bit.ly/DCNcurationActivities
DCN Researcher Study 2018 (n=91)
Johnston, L. R., Carlson, J., Hudson-Vitale, C., Imker, H., Kozlowski, W., Olendorf, R., & Stewart, C. (2018). How Important is Data Curation? Gaps and Opportunities for Academic Libraries. Journal of Librarianship and Scholarly Communication, 6(1), eP2198. DOI: https://doi.org/10.7710/2162-3309.2198

Documentation
Chain of custody
Emulation
Restricted Access
Contact Information
Full-Text Indexing
DCN Researcher Study 2016 (n=91)
Most Important Activities* (4 out of 5)
  • (Create) Documentation (4.6)
  • Secure Storage (4.4)
  • Quality Assurance (4.3)
  • Persistent Identifier (4.3)
  • Software Registry (4.1)
  • Data Visualization (4.0)
  • File Audit (4.0)
  • (Create) Metadata (4.0)
  • Versioning (3.9)
  • Contextualization (3.9)
  • Code Review (3.9)
  • File Format Transformations (3.9)
Not Happening for Majority of Researchers
  • Persistent Identifier (37% happens)
  • Software Registry (41% happens)
  • File Audit (16% happens)
  • Contextualization (38% happens)
  • Code Review (38% happens)
Happening, but not satisfactorily
  • Documentation (26% satisfied)
  • Secure storage (38% satisfied)
  • Quality Assurance (14% satisfied)
  • Data Visualization (12.5% satisfied)
  • Metadata (29% satisfied)
  • Versioning (13% Satisfied)
  • File Format Transformations (29% satisfied)
Johnston, L. R., Carlson, J., Hudson-Vitale, C., Imker, H., Kozlowski, W., Olendorf, R., & Stewart, C. (2018). How Important is Data Curation? Gaps and Opportunities for Academic Libraries. Journal of Librarianship and Scholarly Communication, 6(1), eP2198. DOI: https://doi.org/10.7710/2162-3309.2198
DCN Researcher Study 2016 (n=91)
Most Important Activities* (4 out of 5)
  • (Create) Documentation (4.6)
  • Secure Storage (4.4)
  • Quality Assurance (4.3)
  • Persistent Identifier (4.3)
  • Software Registry (4.1)
  • Data Visualization (4.0)
  • File Audit (4.0)
  • (Create) Metadata (4.0)
  • Versioning (3.9)
  • Contextualization (3.9)
  • Code Review (3.9)
  • File Format Transformations (3.9)
Not Happening for Majority of Researchers
  • Persistent Identifier (37% happens)
  • Software Registry (41% happens)
  • File Audit (16% happens)
  • Contextualization (38% happens)
  • Code Review (38% happens)
Happening, but not satisfactorily
  • Documentation (26% satisfied)
  • Secure storage (38% satisfied)
  • Quality Assurance (14% satisfied)
  • Data Visualization (12.5% satisfied)
  • Metadata (29% satisfied)
  • Versioning (13% Satisfied)
  • File Format Transformations (29% satisfied)
Johnston, L. R., Carlson, J., Hudson-Vitale, C., Imker, H., Kozlowski, W., Olendorf, R., & Stewart, C. (2018). How Important is Data Curation? Gaps and Opportunities for Academic Libraries. Journal of Librarianship and Scholarly Communication, 6(1), eP2198. DOI: https://doi.org/10.7710/2162-3309.2198
DCN Researcher Study 2016 (n=91)
Most Important Activities* (4 out of 5)
  • (Create) Documentation (4.6)
  • Secure Storage (4.4)
  • Quality Assurance (4.3)
  • Persistent Identifier (4.3)
  • Software Registry (4.1)
  • Data Visualization (4.0)
  • File Audit (4.0)
  • (Create) Metadata (4.0)
  • Versioning (3.9)
  • Contextualization (3.9)
  • Code Review (3.9)
  • File Format Transformations (3.9)
Not Happening for Majority of Researchers
  • Persistent Identifier (37% happens)
  • Software Registry (41% happens)
  • File Audit (16% happens)
  • Contextualization (38% happens)
  • Code Review (38% happens)
Happening, but not satisfactorily
  • Documentation (26% satisfied)
  • Secure storage (38% satisfied)
  • Quality Assurance (14% satisfied)
  • Data Visualization (12.5% satisfied)
  • Metadata (29% satisfied)
  • Versioning (13% Satisfied)
  • File Format Transformations (29% satisfied)
Johnston, L. R., Carlson, J., Hudson-Vitale, C., Imker, H., Kozlowski, W., Olendorf, R., & Stewart, C. (2018). How Important is Data Curation? Gaps and Opportunities for Academic Libraries. Journal of Librarianship and Scholarly Communication, 6(1), eP2198. DOI: https://doi.org/10.7710/2162-3309.2198
Key Takeaways
  • We need a shared language when discussing data curation activities
  • Researchers are actively engaged in data curation activities for their data
  • Many activities may not be happening in a satisfactory way
  • Libraries will most benefit from emphasizing, investing in, and/or heavily promoting the services that researchers value (rather than what we value)
  • Gaps in satisfaction for highly-valued data curation activities provide opportunity for partnership
CURATE(D) MODEL
Tangible Actions
Data Curation Network - Mnemonic for Curation
CCheck files and read documentation (risk mitigation, file inventory, appraisal/selection)
UUnderstand the data, or try to ... (run files/environment, QA/QC issues, readmes)
RRequest missing information or changes (tracking provenance of processes and changes)
AAugment metadata for findability (DOIs, metadata standards, discoverability)
TTransform file formats for reuse (data preservation, conversion tools, data viz)
EEvaluate for FAIRness (licenses, responsibility standarads, metrics for tracking)
DDocument the curation process
Data Curation Network (2018). "Checklist of CURATED Steps Performed by the Data Curation Network." https://datacurationnetowrk.org
C ➾ U ➾ R ➾ A ➾ T ➾ E ➾ (D)
⇓
CHECK
CHECK
CHECK step - Actions
  • Review the content of the data files (e.g., open and run the files or code).
  • Verify all metadata provided by the author and review the available documentation.
CHECK
CHECK step - Checklist
  • ☐ Files open as expected
    • ☐ Issues __________
  • ☐ Code runs as expected
    • ☐ Produces minor errors
    • ☐ Does not run and/or produces many errors
    • ☐ Did not try to run the code
  • ☐ Metadata quality is rich, accurate, and complete
    • ☐ Metadata has issues _________
  • ☐ Documentation Type (circle)
    Readme / Codebook / Data Dictionary / Other: ________________________
    • ☐ Missing/None
    • ☐ Needs work
  • ☐ Human subjects data present? Ask for consent form.
CHECK
Software
Specialty Tools (Processing and Review)
  • notepad++, sublime (text editors)
  • ArcGIS, QGIS, GeoNetwork (GIS)
  • RStudio (stat)
  • Matlab (stat and models)
  • AliView (genomics data viewer)
  • Omero (microscopic images)
  • ChemDraw (structure)
  • MZmine - mass spec data
  • Jena - 3d molecular structure/Crystallographic files
Discussion
C ➾ U ➾ R ➾ A ➾ T ➾ E ➾ (D)
⇓
UNDERSTAND
UNDERSTAND
UNDERSTAND step - Actions
  • Check for quality assurance and usability issues such as missing data, ambiguous headings, code execution failures, and data presentation concerns.
  • Try to detect and extract any “hidden documentation” inherent to the data files that may facilitate reuse.
  • Determine if the documentation of the data is sufficient for a user with similar qualifications to the author’s to understand and reuse the data
UNDERSTAND
UNDERSTAND step - Checklist
  • ☐ Organization of data well-structured
    • ☐ Not rectangular
    • ☐ Split tables into separate tabs
  • ☐ Headers/codes clearly defined
    • ☐ Define headers
    • ☐ Clarify codes used ________
    • ☐ Clarify use of "blanks"
    • ☐ Clarify units of measurement
  • ☐ Quality control clearly defined
    • ☐ Unclear quality control
    • ☐ Update/add Methodology
This is an example for microsoft excel. Other data would have different checklists.
UNDERSTAND
UNDERSTAND step - Documentation
  • Assessing data for reuse
  • Assistance in actual reuse
  • Explaining and/or maintaining consistency of data
  • Training new people
  • Efficiency in archiving
UNDERSTAND
Data Documentation Continuum
C ➾ U ➾ R ➾ A ➾ T ➾ E ➾ (D)
⇓
REQUEST
REQUEST
REQUEST step
!?

What information do you need to address the gaps that you have identified?

How will you get this information?

REQUEST
REQUEST Goals

Photo by rawpixel on Unsplash

  • Goal #1 - Get a Response
  • Goal #2 - Establish a Rapport
  • Goal #3 - Get the Information that you Need
REQUEST
REQUEST approach
  • Be as specific as possible
  • Prioritize your needs
    • Essential
    • Important
    • Supplemental
  • Do you need information or permission to take action?
REQUEST
REQUEST advice - make it easy
  • Limit to 4 asks
  • Be specific, but try to keep it short
  • Keep it as simple as possible (Ideally, response could be “yes or no”)
  • Provide resources where useful
  • If possible, do the work yourself and ask for approval
C ➾ U ➾ R ➾ A ➾ T ➾ E ➾ (D)
⇓
AUGMENT
C ➾ U ➾ R ➾ A ➾ T ➾ E ➾ (D)
⇓
TRANSFORM
C ➾ U ➾ R ➾ A ➾ T ➾ E ➾ (D)
⇓
EVALUATE
C ➾ U ➾ R ➾ A ➾ T ➾ E ➾ (D)
⇓
DOCUMENT
Thanks!