MTU recognises that research data is a valuable institutional asset and expects, as per the University's Research Data Management (RDM) policy, that all researchers manage their data to the highest standards throughout the research lifecycle, as part of the University’s commitment to research excellence.
This page contains guidance on RDM, completing a data management plan, how to apply the FAIR data principles of Findability, Accessibility, Interoperability and Reusability, and how to choose a data repository to store your data.
A Data Management Plan (DMP) is a document that explains how collection and management of the data utilised or generated throughout the lifecycle of research project are expected to occur. Many research funders require a DMP to be created, with any changes updated and notified to ensure compliance with funding requirements.
MTU's Research Data Management Policy states that "All new research proposals should include a research data management plan (DMP) or protocols that explicitly address data capture, management, integrity, confidentiality, retention, sharing and publication".
MTU Library subscribes to DMPonline, which helps researchers to create, review, and share data management plans that meet institutional and funder requirements.
Create your MTU DMPonline account here (ensuring to input Munster Technological University as your organisation) for access to funder DMP templates, completed DMPs and guides to completing your own.
The below video shows the features of DMPonline from creating an account, beginning a DMP based on a funder template, adding contributors to the DMP, and accessing a completed live example of that template.
MTU’s research data management policy states (with reference to the European Code of Conduct for Research) that “Research data should be compliant to the principles of FAIR data (Findable, Accessible, Interoperable, Re-Useable)”
These principles, “allow findings to be verified, reproduced and digitally preserved; and therefore, are fundamental to high quality research outputs and research integrity.” Most funder DMPs will require you to follow these principles also.
They are explained by Foster Science as follows:
The first thing to be in place to make data reusable is the possibility to find them. It should be easy to find the data and the metadata for both humans and computers. Automatic and reliable discovery of datasets and services depends on machine-readable persistent identifiers (PIDs) and metadata.
The (meta)data should be retrievable by their identifier using a standardized and open communications protocol, possibly including authentication and authorisation. Also, metadata should be available even when the data are no longer available.
The data should be able to be combined with and used with other data or tools. The format of the data should therefore be open and interpretable for various tools, including other data records. The concept of interoperability applies both at the data and metadata level. For instance, the (meta)data should use vocabularies that follow FAIR principles.
Ultimately, FAIR aims at optimizing the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. Also, the reuse of the (meta)data should be stated with (a) clear and accessible license(s).
In summary, uploading well managed research data to a recognised data repository (see guidance below) will provide PID’s, such as a DOI, along with recognised metadata, language standards and licensing best suited to the data to make it FAIR.
In deciding where to store your research data, base your choice in the following order of preference:
1. Check your funder requirements: your funder(s) may mandate which repository you should use and they may also have other criteria about the period of storage or the use of embargoes.
2. Use an external data archive or repository already established for your research domain to preserve the data according to recognised standards in your discipline. Such a repository can be searched for at re3data.org. On top of specific research disciplines you can filter on access categories, data usage licenses, trustworthy data repositories (with a certificate or explicitly adhering to archival standards) and whether a repository gives the data a persistent identifier.
3. If your discipline does not have a recognised data repository, deposit in a cost-free, established, trusted, general data repository such as Zenodo or Figshare
- Gives your submitted dataset a persistent and unique identifier. This is essential for sustainable citations (both for data and publications) and to make sure that research outputs in disparate repositories can be linked back to researchers and grants.
- Provides a landing page for each dataset, with metadata that helps others find it, tell what it is, relate it to publications, and cite it. This makes your research more visible and stimulates reuse of the data.
- Helps you to track how the data has been used by providing access and download statistics.
- Responds to community needs and is preferably certified as a ‘trustworthy data repository’, with an explicit ambition to keep the data available in the long term.
- Matches your particular data needs (e.g. formats accepted; access, back-up and recovery, and sustainability of the service). Most of this information should be contained within the data repository’s policy pages.
- Offers clear terms and conditions that meet legal requirements (e.g. for data protection) and allows reuse without unnecessary licensing conditions.
- Provides guidance on how to cite the data that has been deposited.
- Is there a service cost involved?
