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Data Services: Research Data Managment

What is Research Data Managment (RDM)

Research data management (or RDM) is a term that describes the organization, storage, preservation, and sharing of data collected and used in a research project. It involves the everyday management of research data during the lifetime of a research project (for example, using consistent file naming conventions). It also involves decisions about how data will be preserved and shared after the project is completed (for example, depositing the data in a repository for long-term archiving and access).

How we can help:

Assistance anywhere in your data's life cycle 
From planning, collecting, preserving, and analysis we can provide support for students and faculty.

Help creating data management plans
Many research funders have requirements for data sharing and data management plans. We can help you to create these plans, assess the data management needs of your project, and help to identify data management solutions.

Individual consultation
We are available to help you identify your data management needs and recommend best practices for keeping your data usable, now and into the future. We also collaborate with Academic ITS and Colby Grants Office.

Workshops
We have done numerus each you how to manage your data more efficiently and help you to share your data with others. 

Contact us
For help with your data management needs, email us at kara.kugelmeyer@colby.edu

Best Practices for RDM

Organizing and Structuring Data

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 Managment Plans for Grants

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

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.

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?
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