Work in data curation and data management is for data of all kinds but is often focused on scientific research data. This may include (1) research data collections, such as data created during a laboratory or research project; (2) community and reference collections, like genome databases such as MGI-Mouse, or the Protein Data Bank; and (3) other data collections of scientific merit and interest comprised of genetic, biological, climate, census and other scientific and research data (Oliver & Harvey, 2016, p. 48). Responsibilities for curating or managing data may include developing data management plans, describing data using metadata schema, assuring the quality of the data, and preparing data to be used in visualizations, modeling and secondary use. Other essential tasks include policy development and compliance, preservation planning and facilitation, and advocacy and training. Information professionals working in data curation and data management, including data librarians, work closely with data creators and data scientists to ensure that research data remains findable, accessible, interoperable, and reusable(FAIR) across the data lifecycle (Wilkinson et al., 2018).

➡️ View Application Requirements

➡️ Explore Our MSIS Program

Professional Work in Data Curation and Data Management: Position Titles and Descriptions

Titles and responsibilities for information professionals working in data curation and/or data management vary. A sample of titles includes: data curator; data librarian; data manager; data quality engineer; data specialist; data analyst; and eScience specialist. Below, three positions titles are expanded upon, building from real-world job vacancy postings, to provide insight into responsibilities and qualifications. For more position titles and job requirements, consult job posting services, including the ALA JobList and I Need a Library Job.

• Data Curators play a leading role in identifying, implementing and assessing the technologies, policies and procedures that support qualitative and quantitative data gathering, discovery, manipulation, transformation, visualization and analysis. They may also be expected to advise on the development of infrastructure supporting data repositories that ensure interoperability and reuse of data sets. Data curators are typically responsible for assigning appropriate metadata and archiving data sets into digital repositories.
• Data Librarians often work in specific disciplines, (e.g., eScience, Social Science, Geography), sharing expertise with data producers and aggregators on issues related to data management planning, data archiving, data use and re-use, and other aspects of scholarly communication, such as open access publishing, copyright, licensing and data citation. They also develop and provide consultation services for data consumers of the, including assistance with data discovery, analysis, visualization, and re-use.
• Data Quality Engineers often work in corporate settings, such as private companies. Their chief responsibility is to maintain the integrity and quality of large data sets through the development of tools, scripts, and tests which detect data anomalies, and creation of new data collections and curation of existing data collections. They are responsible for ensuring data quality. This kind of position requires programming skills, including the ability to write scripts in several computer languages such as Python, PHP, Perl, Ruby, etc.

---

Preparing to Enter the Professional World of Science:

Professional Development:

Suggested Associations

• • Association of Information Science and Technology (ASIS&T), particularly Scientific and Technical Information Interest Group (SIG-STI)
  • Committee on Data of the International Science Council (CODATA)
  • Digital Curation Centre
  • Research Data Alliance
  • Transforming Data with Intelligence (TDWI)

Suggested Conferences:

• • International Digital Curation Conference
  • Various CODATA conferences (held annually and biennially)
  • Research Data Alliance Plenary Meetings

Suggested Serials:

• • Data Science Journal
  • International Journal of Digital Curation
  • Journal of eScience Librarianship
  • Scientific Data

Required Courses:

• • 511 Information Concepts and Foundations
  • 512 Information Organization and Retrieval
  • 514 Information Technology and Foundations

Recommended Courses (in SIS):

In addition to completing the courses required for the MSIS degree, students with an interest in data curation and data management might consider enrolling in some of these courses (please note that this listing is not a substitute for consulting with the MSIS Program Advisor).

Essential:

• 524 Metadata
• 562 Digital Curation
• 563 Data Management

Recommended:

• 516 Geospatial Technologies
• 543 Spatial Data Management
• 545 Scientific and Technical Communications
• 592 Introduction to Data Analytics and Visualization
• 597 Information Architecture
• 599 Practicum

Real-World Potential Experience:

Practicum Settings:

Work in data curation and data management takes place in a variety of professional settings, including businesses, government agencies, and academic, research and special libraries. See the SIS Practicum Webpage for more information on practicum opportunities. Examples of recent practicum placements in data curation and data management include:

• Information International Associates, Oak Ridge TN
• Oak Ridge National Laboratory
• Office of Scientific and Technical Information (OSTI), U.S. Department of Energy
• U.S.Geological Survey (USGS)
• National Center for Atmospheric Research (NCAR)
• National Oceanic and Atmospheric Administration (NOAA)

Recent Placements of UTK-SIS Alumni:

• • Earth Resources Technology, Inc. (ERT)
  • U.S.Geological Survey (USGS)
  • National Oceanic and Atmospheric Administration (NOAA)
  • University Libraries, University of Tennessee
  • Cornell University Library

References: Oliver, G, &Harvey, R. (2016). Digital curation(2nd ed.). Chicago: Neal-Schuman; Wilkinson, M., Sansone, S., Schultes, E.et al (2018).A design framework and exemplar metrics for FAIRness.Scientific Data,5. doi: 10.1038/sdata.2018.118