Plan & Design
Creating a DMP is considered good research practice. Decisions made early on in the research project helps researchers save time, consider the necessary resources and costs. These will be required for funding/grant applications
Why manage research data?
There are many reasons why research data should be managed
First and foremost, researchers need to plan, design & organise their research process:
plan what data you need to collect, and how to collect it
decide what data will be used in your work and how will it be represented
plan how and where your research data will be stored for preservation and reuse
decide how your data may be made discoverable, accessed, shared and terms of re-use
These are the core elements of Data Management Plans (DMPs) and as such, are to be initiated at the start of any research project/undertaking. Well-developed DMPs increases research efficiency.
Creating a DMP is considered good research practice. Decisions made early on in the research project helps researchers save time, consider the necessary resources and costs. These will be required for funding/grant applications
What is Data?
Data is a scholarly product (like journal articles and books)
Research data are not a mere by-product of scientific research, nor a simple means to (article) publication. They often have a much longer shelf life than the scientific publications they underpin:
They constitute the evidence needed to verify and validate published claims.
They can be reused for follow-up or new research, for teaching, etc.
Therefore, research data should be cared for as first-class research objects. RDM is about exactly that. Ghent University
Many kinds of data created as part of a research project are subject to the same rights as literary or artistic work. Such items acquire rights like copyright or more general Intellectual Property rights when they are created. This gives the rights owner control over the exploitation of their work, such as the right to copy and adapt the work, the right to rent or lend it, the right to communicate it to the public and the right to license and distribute. UK Data Service
Funder and Publisher requirements
Meet funders and publishers research data requirements
“Planning how your data will be looked after – many funders now require data plans as part of applications” University of Oxford
Save time and resources
Research data management saves time and resources
Researchers will spend less time on data management and more time on research by investing the time and energy at the start of the project. University of Oxford
Data preservation and data security
Data preservation and data security as data, especially digital data, is fragile and easily lost. University of Oxford
Risk protection
Protect institutions from reputation, financial and legal risk. University of Oxford
Data Analysis
Good management helps to prevent errors and increases the quality of data analysis
Data Sharing
Research data management facilitates sharing of research data and, when shared, data can lead to valuable discoveries by others outside of the original research team
Research integrity
Well-managed and accessible data allows others to validate and replicate findings, and to ensure research integrity.
RDM is a due diligence practice that ensures the researcher retains transparency and accountability.
MANTRA - Jeff Haywood - Role of data management for a new researcher
30 November 2011
MANTRA - Jeff Haywood - Advice to PhD students and early career researchers
30 January 2012
MANTRA - Professor Lynn Jamieson - Importance of data management
4 April 2014
MANTRA - Ellie Bates - Advice on managing research data
4 May 2012
Data Management Plans (DMPs)
An important first step in managing your research data is planning
MANTRA - Lynn Jamieson. Challenges for PhD students working with research data
4 May 2012
In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.
Findable: The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
Accessible: Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.
Interoperable: The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
Reusable: The ultimate goal of FAIR is to optimise 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.
GOFAIR's RDM Starter Kit lists resources designed to help researchers get started to organize their data.
Many researchers do not manage their data. It is usually an ad hoc store of physical and digital information and data.
It makes it difficult to find.
As pioneering research data management experts, this section provides best practice guidance, as well as more resources, to data managers and those looking to learn more about how to manage their research data.
The Digital Curation Centre (DCC) is a world-leading centre of expertise in digital information curation with a focus on building capacity, capability and skills for research data management.
The DCC provides expert advice and practical help on how to store, manage, protect and share digital research data. We provide a broad range of resources including online tools, guidance and training. We also provides consultancy services on issues such as policy development and data management planning.
Services are targeted primarily at the higher education community, both in the UK and internationally, but our resources are of benefit to the commercial sector too.
The Curation Lifecycle Model helps you to define research data management (RDM) workflows and associated roles and responsibilities within your organisation. The data-centric model supports a holistic approach to RDM infrastructure development and optimisation and can be used to help organisations map research data management activities and support across functional and operational units.
The digital curation lifecycle comprises the following steps:
Conceptualise: conceive and plan the creation of digital objects, including data capture methods and storage options.
Create: produce digital objects and assign administrative, descriptive, structural and technical archival metadata.
Access and use: ensure that designated users can easily access digital objects on a day-to-day basis. Some digital objects may be publicly available, whilst others may be password protected.
Appraise and select: evaluate digital objects and select those requiring long-term curation and preservation. Adhere to documented guidance, policies and legal requirements.
Dispose: rid systems of digital objects not selected for long-term curation and preservation. Documented guidance, policies and legal requirements may require the secure destruction of these objects.
Ingest: transfer digital objects to an archive, trusted digital repository, data centre or similar, again adhering to documented guidance, policies and legal requirements.
Preservation action: undertake actions to ensure the long-term preservation and retention of the authoritative nature of digital objects.
Reappraise: return digital objects that fail validation procedures for further appraisal and reselection.
Store: keep the data in a secure manner as outlined by relevant standards.
Access and reuse: ensure that data are accessible to designated users for first time use and reuse. Some material may be publicly available, whilst other data may be password protected.
Transform: create new digital objects from the original, for example, by migration into a different form.”
Research Data Management (RDM) lifecycle
The research data lifecycle is a key concept within RDM. It describes the different stages research data go through before, during, and after a research project. Each stage of the research data lifecycle entails various data management activities, and the choices made in one phase influence the next one. Source: Ghent University
Research Data Lifecycle. Adapted from UK Data Service Model 2017. Source: University of Tasmania
Research data lifecycle
The research data lifecycle is a model that illustrates the stages of data management and describes how data flow through a research project from start to finish. Data management refers to the process of deciding and documenting how data will be collected, organized, stored and shared.
For more information: Lauren Brochu & Jane Burns (2018): Librarians and Research Data Management–A Literature Review: Commentary from a Senior Professional and a New Professional Librarian, New Review of Academic Librarianship, DOI: 10.1080/13614533.2018.1501715
The research data lifecycle is a key concept within RDM. It describes the different stages research data go through before, during, and after a research project. Each stage of the research data lifecycle entails various data management activities, and the choices made in one phase influence the next one. Ghent University
Source: Research Data Lifecycle visual, Created by Helen Morgan and Nadine Davidson-Wall, University of Queensland. Available online: http://libguides.library.cqu.edu.au/researchdatamanagement. This item is listed as Creative Commons.
MANTRA Research Data Management courses
The aim of this unit is to introduce you to the concepts of research data organisation, explain why it is important, and what constitutes good data file management.
After completing this unit you will:
▪ Appreciate why research data organisation is important as your project grows.
▪ Understand data file naming, re-naming and versioning conventions.
▪ Be prepared to manage your code and track workflows to make them shareable and reproducable.
▪ See how electronic lab notebooks can support the collaborative research process.
The aim of this unit is to help you to think through how you will collect, store and share the wealth of research data you will collect during your research project.
After completing this unit you will:
▪ Understand the basic principles of research data management and the key role that data management plays in the responsible conduct of research.
▪ Consider your research in terms of the research data lifecycle and be able to plan ahead to prepare for potential data management pitfalls.
▪ Know about the data management planning requirements of different research funders.
▪ Be aware of data management planning tools, support and guidance which are available to academic researchers.
▪ Be able to use the information in this unit to develop a data and software management plan, and to maintain it through the course of your research.