Environmental Observation and Informatics (EOI) Library Resources : Data Management

UW-Madison Research Data Services (RDS) is an interdisciplinary organization that provides students and researchers with the tools and resources that support their efforts to store, analyze, and share data.

Curious about a particular aspect of data management? Browse their list of data management core concepts to learn more.

• Data Management Essentials
  • Store and back up.
    • UW-Madison Data Storage Finder
  • Keep data/digital materials in sustainable formats.
  • Include metadata to preserve contextual information about who collected/created it, the date, instrument settings, etc.
    • Ecology Metadata Language (EML): A metadata standard developed for the earth, environmental and ecological sciences.
    • ISO 19115: An internationally-adopted schema for describing geographic information and services.

    • More metadata standards by discipline: From the Digital Curation Centre (DCC), searchable list of disciplinary metadata standards including those for biology, Earth science, physical science, social science & humanities and general research data.

  • Organize and structure them using file naming/versioning conventions, ontologies/vocabularies, spreadsheets, and/or databases.
  • Keep data secure and implement procedures for keeping sensitive data private.
  • Include explanations about how data may be re-used and how the source of the data should be acknowledged.
• Data Sharing & Data Repositories
• Finding, Reusing, and Citing Data
  • DataOne: A community-driven project providing access to data across multiple member repositories, supporting enhanced search and discovery of Earth and environmental data.
  • Environmental Data Initiative Data Portal: Digital archive of environmental and ecological data, emphasizing data from projects funded by the NSF Division of Environmental Biology. 
  • Long Term Ecological Research (LTER) Network Data Portal: Digital archive of ecological data contributed by past and present LTER sites.

Don’t see the information you’re looking for above? You can always contact them with specific questions about your data.

Tidy Data (open access article)

Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59(10), 1 - 23. doi:http://dx.doi.org/10.18637/jss.v059.i10

Tidy data is a standard way of mapping the meaning of a dataset to its structure. A dataset is messy or tidy depending on how rows, columns and tables are matched up with observations, variables and types. In tidy data:

1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.

Ecological Data Sharing (open access article)

Michener, W. K. (2015). Ecological data sharing. Ecological Informatics, 29, 33-44. doi:https://doi.org/10.1016/j.ecoinf.2015.06.010

Highlights

• Data sharing has evolved slowly and unevenly due to incentives and disincentives.
• "Big ecology” policies have pioneered the initial movement to open data.
• Research sponsors, publishers and scientific societies drive sociocultural change.
• Information technologies like metadata tools and repositories promulgate sharing.
• Emerging best practices support data openness and sharing in ecology.

Practical Guidance for Integrating Data Management into Long-Term Ecological Monitoring Projects (open access article)

Sutter, R. D., Wainscott, S. B., Boetsch, J. R., Palmer, C. J., & Rugg, D. J. (2015). Practical guidance for integrating data management into long‐term ecological monitoring projects. Wildlife Society Bulletin, 39(3), 451-463. doi:https://doi.org/10.1002/wsb.548

Long‐term monitoring and research projects are essential to understand ecological change and the effectiveness of management activities... Recent papers have provided broad recommendations for data management; however, practitioners need more detailed guidance and examples. We present general yet detailed guidance for the development of comprehensive, concise, and effective data management for monitoring projects...

Explore tools and techniques to organize, analyze, and visualize your data, as well as track your research impact, through these campus and online resources.

On Campus

• Data Science Hub: Workshops, consultations, and more to help researchers learn the skills they need to reproducibly write code and analyze data. 
• Social Science Computing Cooperative (SSCC) statistical software classes: Free workshops on R, Stata, NVivo, and more.
• UW-Madison Information Technology Software Training for Students: A variety of free technology classes designed to meet the needs of students.

• Graduate Support Series: Free workshops that cover tools and techniques for data management, grant funding, and research impact.

Online

• UW-Madison Libraries Researcher Support: A portal to many of the resources covered in this course guide as well as information on patents, copyright, and managing your sources.
• Library Micro-courses: Short, nongraded courses focused on a particular topic that you complete at your own pace. Topics include: research data management, grants & funding, copyright & fair use, and more.
• ORCiD: This free program provides a persistent digital identifier that distinguishes you from every other researcher and helps you track your published research.
• LinkedIn Learning: Free tutorials for R, SQL, Tidy Data, Python and more.
• DMPtool: Free templates and examples of data management plans. After clicking on the "Get started" button, use the UW-Madison institutional affiliation (Option 1) and log in with your NetID and password.
• OpenRefine: Free download and short tutorials about this tool that cleans your data quickly and efficiently.
• Open Science Framework: Not just for science, but a free workflow and collaboration tool for research and scholarship in all disciplines.
• Tabula: Free download and instructions for this tool that extracts data are locked inside PDFs.