By ensuring that your files are well structured; that data is categorized, version controlled, locatable and backed up; and that there is ample information about what software was used to analyze the data; you can more readily locate, access and analyze your research data.
While it may make more work for you at the front end of a research project, it can save you a lot of headaches later on.
Collecting data comes with a host of ethical requirements:
Brandon University has established a number of Regulatory Committees for Research, including the Brandon University Research Ethics Committee (BUREC) of Senate. Consult their respective pages to identify relevant policies.
Also, if you are receiving Tri-Agency Research Funding for research involving humans, you may need to complete the TCPS 2: CORE Tutorial on Research Ethics.
Funding Agencies, endowments, commercial funders or partners in research may have incorporated legal requirements around data security, patents, or data sharing. Similarly, there may be laws like FIPPA that need to be taken into account before data is shared.
DMPs help you to consider how your data will be managed according to any and all legal requirements associated with your research.
Data Management Plans are designed to make it easier for others to locate your data and use it after you have completed your study. File Versioning, Read Me Documents, Appropriate labeling of data fields, Good Metadata, Digital Object Identifiers, Housing Data in Appropriate Repositories that protect the data and Open Licensing make it easier for (1) search engines to locate data file sets for researchers and (2) for researchers to use the data without the need to contact you with questions around permissions, how the files are structured, etc.
Best practices around making data reusable are found in The FAIR Guiding Principles for scientific data management and stewardship.
Once you are done with your data, funders often want to make sure it is being properly preserved and made available for reuse.
By planning how you will (1) Share Data Ethically and Legally, (2) House Data in Appropriate Repositories with good preservation methods and (3) License Data, you will ensure your data is secure and accessible.
One of the biggest problems in research today is an ongoing difficulty in reproducing research findings.
Elements of a DMP that address Data Persistence, Structuring Data, Read Me Files with instructions around how the research was conducted, Access to Relevant Software (including Versioning Information), all work to better enable researchers to reproduce your findings.
The library has a collection of books about reproducible research.
Funders are making Data Management Plans an important component of grant applications. In Canada the Tri-Agency has implemented several documents and policies pertinent to data management. Some of these policies require Data Sharing; others are initial documents that will be enhanced and strengthened over time. In the interim, it is good practice to develop DMPs when seeking research funds.
To learn more about this Consult the Funder Requirements section of this Guide.
Many journals - especially journals with higher impact factors - or associated with major publishers - have instituted policies regarding the availability of research data underlying publications. In many cases, journals require that data are made openly available as a condition for publishing an article.
If you are starting a research project and intend for your research to be published in a specific journal, make sure to check into their policies around data management / sharing. Doing so will ensure you adopt practices essential for being published.
To learn more consult the Journal requirements section of this Guide.
There are many complications arising from poor data management as illustrated in the video below. Creating a DMP reduces the likelihood that you will be faced with many of them.
Source: How to avoid a data management nightmare, by Karen Hanson, Kevin Read & Alisa Surkis, New York University Health Sciences Library under a CC-BY license.
The Government of Canada’s Roadmap for Open Science highlights the benefits associated with Open Research. One of them is Creating Opportunities for Impact:
”Open Science accelerates the discovery process by allowing others to build on previously validated discoveries and research contributions and to create opportunities for innovation and prosperity.”
Rapid advancements in COVID 19 vaccine discovery occurred because scientists, publishers, companies and governments all agreed to openly license COVID 19 research findings. This should further the Government of Canada’s commitment Open Science - and hence Data Management as laid out in its Roadmap for Open Science:
“Federal departments and agencies should develop strategies and tools to implement FAIR data principles to ensure interoperability of scientific and research data* and metadata standards ...
This recommendation aims to ensure that the scientific information that is open is also “Findable, Accessible, Interoperable and Reusable” (or “FAIR”) ...
A pre-requisite for the implementation of FAIR data principles is strong data management practices...
Although it is recognized that NOT ALL DATA CAN OR SHOULD BE OPEN; it is also true that Open Data - that adheres to the FAIR Data Principles - will (1) be easier for machines and fellow researchers to use and (2) facilitate advances in knowledge.
The CARE Principles for Indigenous Data Governance are people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination. These principles complement the existing FAIR principles (www.go-fair.org) encouraging open and other data movements to consider both people and purpose in their advocacy and pursuits.
Collective Benefit:
Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.
Authority to Control:
Indigenous Peoples’ rights and interests in Indigenous data must be recognised and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data
Responsibility:
Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self-determination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples.
Ethics:
Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem.
Project team members can leave. New members can join. As written documents that list all practices around research data, DMPs provide a mechanism for continuity of practice despite changes in your team.
Studies demonstrate that anytime research is openly available that it has greater impact. Similarly, practices encouraged by DMPs such as incorporating DOIs with data - and better metadata - means that your data will be easier to cite.
Taken together the openness of data and DMP practices will work to ensure that your research has greater impact.