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Scholarly Communication Guide: Research Data Management

This guide provides information on the support services available at the library to help with all stages of your research, from planning your research, to measuring the impact of your research.

Research Data Management

"Research Data Management refers to the storage, access and preservation of data produced from a given investigation. Data management practices cover the entire data lifecycle, from planning the investigation to conducting it, and backing up data as it is created and used to the long-term preservation of data deliverables after the research investigation has concluded..."

Research data can include everything that researchers create, collect, produce, generate, or (re)use in their research initiatives, such as:
• measuring outcomes, statistics, and observations.
• interviews and surveys (such as transcriptions, survey questionnaires, and responses).
• audio-visual materials (such as recordings and films).
• Images and photos • Research diaries, notes, and lab books • Software and source code • Physical or digital source material (e.g., biological samples, objects, literature excerpts, etc.)
Research data and materials can be quantitative or qualitative, digital or physical. 

Why is it important to manage research data?

• Organizes, stores, and manages research data for long-term use.

• Saves time and resources.

• Reduces the risk of data loss.

• Promotes transparency, validity, reliability, and quality of research.

• Allows for long-term storage and reuse of data.

• Helps researchers meet funder requirements.

• Complies with publisher data policies.

• Allows for publication of data.

FAIR stands for Findable, Accessible, Interoperable and Reusable. The FAIR Data Principles were developed and endorsed by researchers, publishers, funding agencies and industry partners in 2016 and are designed to enhance the value of all digital resources.

  • Findable – It should be possible for others to discover your data. Rich metadata should be available online in a searchable resource, and the data should be assigned a persistent identifier.
  • Accessible – It should be possible for humans and machines to gain access to your data, under specific conditions or restrictions where appropriate. FAIR does not mean that data need to be open! There should be metadata, even if the data aren’t accessible.
  • Interoperable – Data and metadata should conform to recognised formats and standards to allow them to be combined and exchanged.
  • Reusable – Lots of documentation is needed to support data interpretation and reuse. The data should conform to community norms and be clearly licensed so others know what kinds of reuse are permitted.

If your goal is to make your data FAIR you should build this into your research plan from the start.

What is a Data Management Plan?

  • Data Management Plan, or a DMP, is a formal document that outlines how a researcher will handle data both during research, and after the project is completed.
    • The creation of such a plan is increasingly being requested by research funders. 
    • A DMP outlines the practices for collecting, organizing, backing up, and storing the data that will be generated.
    • There are many templates to assist with the creation of a plan are available, but the basic design is similar. 

Creating a Data Management Plan

Research is all about discovery, and doing research sometimes requires you to shift gears and revise your intended path. Your DMP is a living document that you may need to alter as the course of your research changes. Remember that any time your research plans change, you should review your DMP to ensure it meets your needs.

What should be covered in the Data Management Plan?

Element Description
Data Description What are your data about? What do they look like? Who is the audience of users or community types for the data? Survey the existing data. What other existing data are relevant to what you have collected? These questions may help you decide where to archive your data set.
Access and Sharing How will you archive and share your data, and why have you chosen this method? What are the terms of use, if any? Indicate the timeliness of dissemination.
Metadata What are your data about? What do they look like? Who is the audience of users or community types for the data? Survey the existing data. What other existing data are relevant to what you have collected? These questions may help you decide where to archive your data set.
Intellectual Property Rights Be clear about who owns the data and how intellectual property will be protected if needed. Who is responsible for personnel with access to data? Any copyright restrictions must be noted. Are there any legal requirements? If so, provide a list of all relevant federal and funder requirements.
Ethics and Privacy Describe how informed consent is handled and privacy protected. How will the data be protected during the project?
Format    Describe how the data were generated and how they will be maintained and shared - including a rationale for the process and archiving suggested formats.
Archiving and Preservation What procedures are in place, or envisioned, for long-term archiving and preservation, including succession plans if transfer is needed? Include budget costs of preparing data and documentation. Funding requests may be included as well.
Storage and Backup Consider storage methods and backup procedures - both cyber and physical resources for practical preservation and storage   (several copies are recommended). What are the different levels of data retention from short-term to long-term preservation depending on the data types? Another aspect is data organization, particularly for dynamic data. How will data be managed during the project? Provide information about the version.

Data Sharing Repositories

How to choose a suitable repository

How to evaluate a data repository

Below is the checklist and some sample questions provided by Digital Curation Centre for data repository evaluations.

 1. Is a reputable repository available?

  • Is the repository listed in repository registries or broadly recognised in the research domain?
  • Is the repository supported by any institution?
  • Is the repository endorsed by a relevant funder, journal, or learned/professional society?
  • Does this data repository have a local, national, or international user base?
  • Is the repository certified to an appropriate international standard?

2. Will the repository take the data you want to deposit?

  • What data types or domains will the repository accept?
  • Does it focus on data types or domains similar to that which you have to deposit?

3. Will the data be safe in legal terms?

  • By agreeing to the terms and conditions of the repository, will you be breaching other data protection principles or confidentiality agreement with data subjects or owners? Will you be in breach of copyright, or any contract terms covering intellectual property in the research?
  • Does the repository allow you to assign rights or license to your data or collections? Does it allow you to apply bespoke access conditions or your own license where appropriate?
  • Was your data collected or created in accordance with legal and ethical criteria prevailing in the data producer's geographical location or discipline? Does the repository review and manage disclosure risks in the data you deposit with them?
  • Does the repository allow you to define access rights to specified user groups and permissions to access, edit, move or delete files/objects?

4. Will the repository sustain the data value?

  • Does the repository publish and catalogue comprehensive metadata?
  • Is the metadata discoverable? Is the repository able to harvest data or be harvested by third-party sources, e.g. other repositories, author IDs, search engines, etc.?
  • Is a persistent identifier such as DOI generated for the data?
  • Does the repository have clear guidelines of preservation to ensure data reusability, e.g. file checking and preservation planning, data integrity & fixity, continuity strategy, version control?

5. Will the repository support analysis and track data usage?

  • Does the repository provide citation and usage tracking?
  • Does the repository support comprehensive search and browse options?
  • Does it support data mining and visualisation?

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