Data Services: Research Data Managment

• Best Practices Primer 

  A few fundamental suggestions to get started; from DataOne

• Best Practices in Data Management 

  Comprehensive suggestions for more than just DMPs; from DataOne

• DCC Curation Reference Manual 

  Advice, in-depth information and criticism on current digital curation techniques and best practice.
  The Manual is an ongoing, community-driven project,

• Guide to Social Science Data Preparation and Archiving 

  From the ICPSR (Inter-University Consortium for Political and Social Research) at University of Michigan. Best practices for dataset preparation are applicable to all disciplines.

• Best Practices Across the Data Life Cycle 

  From the Data Documentation Initiative (DDI), an international collaboration to support sharing of social science datasets.

Organizing and Structuring Data

Metadata standards different across the disciplines and varies often by research project.  If your are not familiar with the various metadata definitions (data/text used to describe data) used by your discipline/union OR you are creating and collecting new types of data you might want to consider using some of the guidelines best practices for data schemas below.

Metadata is foundational to any data project so spending time mapping out what data points you'll be recording and collecting and the process to create these data points and datum is well worth the time.

Metadata best practices include.

• Consider the purpose and use of the data before finalizing metadata requirements for your project.Look at the various metadata schemas identified by disciplines/unions/projects - Comprehensive Disciplinary Metadata Resource Guide
• If your data will be deposited in a repository, use their recommended metadata schema 
• Look at metadata schemas used for similar projects; especially if data will be contributed to another project

If you need assistance mapping out your data collection, tracking down metadata standards or best metadata practices for you project please contact kara.kugelmeyer@colby.edu.

Below are example metadata schemas used for data projects and more about metadata.

• Data Documentation Initiative

  metadata for social and behavioral sciences

• Dublin Core

  Used in Colby's DigitalCommons and in harvesting data via OAI (Open Access Initiative)

• METS - Metadata Encoding & Transmission Standard

  designed to accommodate non-book objects and relationships of objects on the Web.

• TEI - Text Encoding Initiative

  used for digital texts, useful for manipulating full-text content (humanities, social sciences, linguistics)

• Metadata: Archival, Automated, Ontologies, Preservation

  Many chapters from the DCC Manual explaining aspects of metadata

• Science Data Literacy Project

  Lists several metadata schemas for science

• ICPSR Data Management & Curation

  Lists important metadata elements

• ISO Standards

  Federal agencies are transitioning over to ISO metadata standards. An older standard may now be superceded. Check out ISO's site for the type of data and the agency's guidelines.

• DataCite Metadata Schema

  A list of core metadata properties chosen for the accurate and consistent identification of data for citation and retrieval purposes, along with recommended use instructions.

• Data Dictionary - Technical Metadata for Digital Still Images

  NISO

• Metadata for Images in XML Schema

• Federal Agencies Digitization Guidelines Initiative

  Includes file format comparisons, TIFF Image Metadata

Data Management Plans

A data management plan (DMP) will help you manage your data, meet funder requirements, and help others use your data if shared.

You can use the questions below and any specific data management requirements from your funding agency to write your data management plan. 

Resources for Creating Plans

• The DMPTool is a web-based tool that helps you construct data management plans using templates that address specific funder requirements. 
• Guidelines for Effective Data Management Plans and Data Management Plan Resources and Examples (ICPSR): Framework for creating a plan and links to examples of data management plans in various scientific disciplines
• Example Plans (University of Minnesota)
• See other guides to data management from MIT for additional guidance on managing data and select information related to particular formats or disciplines.

Funder Requirements

If you have a funder for your research many of them require you to address certain aspects of data management in your grant or funding proposal. Below are links to some of the most popular funders and their requirements. Also links to RI's DMP sites.

• Comprehensive list of funder requirements - for all disciplines and major funders
• National Science Foundation (NSF) Data Management Plan Requirements: As an extension of the NSF Data Sharing Policy, grant proposals must include a data management plan of no more than 2 pages describing how all data resulting from the research will be managed and deposited in a repository. Data underlying research papers must also comply with publisher data sharing policies. Note: requirements may evolve in line with the NSF Public Access Plan.
• National Institutes of Health: Data Sharing Policy: Supports the sharing of research data and expects 
• National Endowment for the Humanities
• MIT Funders DMP List
• UMass Amherst Funders DMP List

QUESTIONS FOR DMP (From MIT DM Team)

Project, Experiment, and Data description

• What’s the purpose of the research?
• What is the data? How and in what format will the data be collected? Is it numerical data, image data, text sequences, or modeling data?
• How much data will be generated for this research?
• How long will the data be collected and how often will it change?
• Are you using data that someone else produced? If so, where is it from?
• Who is responsible for managing the data? Who will ensure that the data management plan is carried out?

Documentation, organization, and storage

• What documentation will you be creating in order to make the data understandable by other researchers?
• Are you using metadata that is standard to your field? How will the metadata be managed and stored?
• What file formats will be used? Do these formats conform to an open standard and/or are they proprietary?
• Are you using a file format that is standard to your field? If not, how will you document the alternative you are using?
• What directory and file naming convention will be used?
• What are your local storage and backup procedures? Will this data require secure storage?
• What tools or software are required to read or view the data?

Access, Sharing, and Re-use

• Who has the right to manage this data? 
• What data will be shared, when, and how?
• Does sharing the data raise privacy, ethical, or confidentiality concerns?  Do you have a plan to protect or anonymize data, if needed?
• Who holds intellectual property rights for the data and other information created by the project? Will any copyrighted or licensed material be used? Do you have permission to use/disseminate this material?
• Are there any patent- or technology-licensing-related restrictions on data sharing associated with this grant? 
• Will this research be published in a journal that requires the underlying data to accompany articles?
• Will there be any embargoes on the data?
• Will you permit re-use, redistribution, or the creation of new tools, services, data sets, or products (derivatives)? Will commercial use be allowed?

Archiving

• How will you be archiving the data? Will you be storing it in an archive or repository for long-term access? If not, how will you preserve access to the data?
• Is a discipline-specific repository available? If not, you could consider depositing your data into Colby Digital Commons
• How will you prepare data for preservation or data sharing? Will the data need to be anonymized or converted to more stable file formats?
• Are software or tools needed to use the data? Will these be archived?
• How long should the data be retained? 3-5 years, 10 years, or forever?