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Ten Simple Rules For Creating A Good Data Management Plan

How to Create a Data Management Plan

22Mar

Research sponsors are increasingly recognizing the value in research studies and data, and are therefore more open to funding different projects. They do, however, often require a detailed data management plan, which will outline what will happen, once the research is completed, to the data that has been collected. Those sponsoring your efforts want to know that the information collected will be safe and easily sharable, so it can help contribute to future research and education. Following these 10 rules for creating a good data management plan will help you demonstrate how you will achieve these things.

Rule 1: Figure out the what the research sponsor needs

Each research sponsor will have their own requirements that they'll want to see outlined within a data management plan. The same realm of research will require varying outlines of how data will be managed and shared, depending on the expectations of each individual sponsor.

When you understand the things an organization wants to see set out in a proposal, you'll save significant time and work while you're composing it. Get information about specific funding agencies and their requirements for a data management plan using the DMP Tool, for agencies in the US, and DMP Online, for those in the UK. You can also find a number of sample plans here, that have been created by other users. Also, keep in mind that some sponsors want you to limit the length of your plan to two pages. For help in keeping to this length, the word count tool can keep you on track.

Rule 2: Make sure roles are clearly defined

Within your overall plan should be an outline of each individual involved and their role in the process. Whether that role involves data collection or the distribution of that information, it has to be clearly defined who will be executing these things. Update your data management plan as needed to reflect changes that have happened over time. Within your defined roles, there should be an individual assigned to produce these updates. Rule 10: Create a sensible budget that you can stick to

There are so many costs involved in the collection, storage and sharing of research data, so outlining a realistic budget is an essential part of your data management plan. If you're limited to a strict budget, consider referencing Dryad Digital Repository for free and easily discovered information underlying scientific publications.

Rule 3: Outline what kind of data will be collected

Your entire data management plan will hinge on what type of data is being collected. When you're detailing this information, keep in mind these factors regarding the data:

  • Type: Start with an outline of the type of data you'll be collecting or generating.
  • Sources: Clearly define where this data will come from, whether it's from first-hand research or from a secondary source.
  • Volume: The amount of data being collected has an impact on everything from time involved, to budget.
  • Data and file formats: It's preferred to use formatting that is widely used and adopted by the scientific community, which are uncompressed, unencrypted and use standard character encodings in storage.

Online writing tools and resources, such as Essayroo are also trained to assist with various types of data collection, if you are in need of additional assistance.

Rule 4: Detail how data documentation will happen

Outlining the metadata gives others the big picture when it comes to questions about data collection, processing, and interpretation that has taken place. It's best to delegate one person with maintaining the project details in an electronic notebook that should be regularly checked by another member of the team. Work in collaboration with others and develop your own software to utilize in research and data collection through GitHub, an open source network for building software.

Rule 5: Lay out the plan for how data quality is assured

Depending on the type of study or the research sponsor, there may be specific quality assurance and quality control measures that need to be followed. But, even if there are no steadfast rules in place for your research, it is always advisable to outline the details of the quality assurance and quality control processes that will be used. A large part of maintaining the quality and integrity of your data is ensuring that no plagiarism of any type is taking place, and all sources are clearly and properly cited. The plagiarism guides at Academized can help direct you towards avoiding this mistake.

Rule 6: Outline how information will be shared

Specifics about when, how and what information will be shared is an essential component in your data management plan. Whether you're planning a passive dissemination plan, which includes sending out information upon request, or you are taking a more active approach to sharing information, including submitting your research as an appendix to a journal article, you'll need to outline your plan for research sponsors. A more active approach is generally preferred, as it can cut down on costs and time. Check out Creative Commons for a supportive online community where you can share your work.

While you're sharing your collected data, remember that most publications want to see proper citations throughout you work, and need to have the necessary information so that others can properly cite your work. Cite It In can help you produce perfectly composed citations.

Rule 7: Explain how data will be stored and preserved

Mechanical devices fail all of the time, so it's vital to have a plan for safe and secure data storage and preservation. A plan for archiving data for the long-term helps keep it safe from computer crashes, media degradation and other problems that can occur. Determine both how the data will be stored soundly over the course of the research project, as well as how it will be preserved for future use. For help getting started on the data storage and preservation portion of your data management plan, Ukwritings can pair you with a professional writer to assist in your plan creation.

Rule 8: Clearly set out any data policies

A plan for protecting the work in a research study and the information that's collected within that study needs to be clearly outlined within your proposal. There is an obligation for researchers and everyone else involved in the study to promote ethically responsible behavior. Not having a plan set out for who this will be achieved and enforced could compromise any work done or data collected.

There are three steps to follow when setting out your policies. First, identify the preexisting data or materials that will be utilized, and describe licensing arrangements that have been made for that data. Second, outline how and when collected data will be made available, and include information about licenses that will help guide later use of this data. And, third, set out your plan for how sensitive information, such as personal data regarding human subjects, will be treated and kept secure.

Rule 9: Communicate how data organization will take place

When putting together your data management plan, a key component that needs to be detailed is the organization plan for everything you'll be collecting. Of course, this plan may evolve as the project does, so it may need to be updated as things progress. Determine the software that will be used, and in what format all of the collected data will stored.

Rule 10: Create a sensible budget that you can stick to

There are so many costs involved in the collection, storage, and sharing of research data, so outlining a realistic budget is an essential part of your data management plan. If you're limited to a strict budget, consider referencing Dryad Digital Repository for free and easily discovered information underlying scientific publications.

Taking the time to careful detail your data management plan helps you set the foundation for a successful research project, and gives your research sponsors a good vision of what they are to expect from start to finish and beyond.

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