Data Management Plans - content

Site: Postgraduate online research training
Course: Module 1: Introduction
Book: Data Management Plans - content
Printed by: Guest user
Date: Thursday, 27 January 2022, 8:18 PM

Description

Data Management Plans - content

1. Learning Outcomes

Let's start with an outline of why the Data Management Plan is useful to historians. At the first workshop for the History DMT Project John Nicholls outlined his views of why it is so important.

It is very easy to simply focus on the research itself without considering how you will manage the resulting data as the project expands.  Data gathered during the research often has an enduring value beyond the initial purposes.  Research for a doctorate will seem at the time to be an enclosed piece of work, but after this is finished you will most likely find that you will need to revisit elements of this research for future projects some of which will be small, others might be large.  Without thought into data management it is likely you will have to retrace your steps, perhaps even recreate the original research from scratch.  Therefore a data management plan is important to safeguarding yourself from duplicating your efforts and does not take too long to complete.  The exercise is largely one of consideration about the managerial aspects of collecting and making data, but it is also one with which you can go away with a research plan in hand. 

Ask yourself this:

What is needed to validate the results of your research? 

If you were to produce an article researching, for example, the criminal underclass in early-twentieth century New York, what data would you need to include for someone else to replicate your results?  Think about it in terms of your own research.

A bibliography would be the most immediate and obvious starting point, revealing to the reader all the sources that you have used to base your research.  But what of the gathering mechanisms you used?  Did you create a database or undertake statistical analysis?  If so you need to make the database and statistics available.  This doesn’t just mean providing the files in a readable format, but to provide documentation and to make sure that the data is clearly identified with explicit headings, well-structured, and easily identified.

Focusing on what is needed for validation and re-use, rather than the obvious attributes of research data, is useful.  It helps you to think through the process of research from a different perspective and what it is you have actually done to come to your conclusions.  It also allows you to show the process you have undertaken; revealing how valuable your approach might be and making the data and research clearer for others to follow or replicate.

Therefore, it doesn’t necessarily matter if you plan to share your data with other scholars, what matters is considering this prospect as you work out how you are going to go about your research.  It will help you to understand what it is you are doing more clearly and give you the basis to share that data later on if you so wish.   

By the end of this book you will be able to:

  • Recognise a data management plan and its constituent parts
  • Explain the purpose of a data management plan and how you can apply it to your research
  • Started to construct your own data management plan

2. What is a data management plan?

A data management plan (DMP) can perform a number of roles over the course of a research project.

  • A checklist – a DMP acts a means of checking that everything that needs to be done to effectively manage the data you are working with is being done.  It can be particularly useful at the start of a project to ensure you get up and running smoothly, but can also be applied at different stages of the project to check everything is proceeding as it should be.
  • A manual – a DMP can go beyond a checklist and be used as a manual to guide you through different aspects of managing your data when needed.  Establishing how different aspects of data management can or should be undertaken as part of setting up your research will enable you to confidently address data management steps and issues as they arise.
  • A record – whilst a DMP is predominantly used for the purposes described above, it can also be used as a record of the data management activity you have undertaken.  This can then act as a demonstration of good research practice, and also be part of the overall project documentation and output.

The Digital Curation Centre recommends three stages of a DMP, which emphasise it being a living, working document. 

  • Minimal plan – at the conceptualisation or grant application stage
  • Core plan – once the funding is in place or when everything is ready to go with the research, covering data management issues up to the point of long-term management and preservation (usually the end of the research)
  • Full plan – that adds issues of longer term data management (post-research)

This practical approach can help focus the data management activity on what you need to do at each stage.  Note, though, that data management actions at the start of a project may have an impact on longer-term management, and it is of benefit to take the long view in completing all stages of the DMP.

In conclusion, a DMP is what you need it to be to aid your research and work with the data within this.  This section of the course will describe more fully the detail of a DMP for history research, based on a prepared template.  Subsequent sections will invite you to complete relevant parts of the DMP and by the end of the course you should have been able to complete a basic DMP for your research project.  It is important to take account of local guidance and practice in creating your DMP, but the general structure of the document will be the same across any research topic.

 

3. Why have a data management plan?

Having a document that assists with the management of data within your research, as described above, is a substantial reason on its own for having a DMP.  Nevertheless, there are also a number of broader reasons why data management planning can be a valuable component of your research process.

  • Helping to flag up where assistance may be required and is available
  • Ensuring research data and records are accurate, complete, authentic and reliable
  • Avoiding unnecessary data duplication
  • Increasing research efficiency
  • Communicating research data practice to others (enabling continuity if staff change)
  • Saving time and resources in the long run
  • Enhancing data security and minimising the risk of data loss
  • Ensuring research integrity and reproducibility

There are also professional reasons why data management should be an integral part of research.

  • Funders increasingly require a data management plan.  Guidance on funder requirements can be found through the SHERPA JULIET service, though it is also advisable to check details with individual funders when applying.  Funders are requesting this because of the reasons listed above.
  • Data management is becoming an acknowledged research skill.  Demonstrating good practice in this area through good planning will stand the test of time in supporting future research activity.

Good data management can lead to data publication, an alternative and additional form of research dissemination that provides credit and citation for the effort in creating and implementing a DMP.

4. What does a data management plan cover?

There are nine key sections to include within a DMP.  These are:

1)     Project information and context

This section simply describes the project being undertaken, who is involved, when the research will take place, and which body or bodies are funding it: essentially, information that is used for a variety of purposes.  Specific to the DMP is a note of whether the funder or your associated institution has any specific data management requirements that need attention.

2)     Data, materials, resource collection information

It is important to be clear about what data you will be collecting, and how you will go about doing this.  Additional considerations relate to whether the data is unique or derived, in digital or non-digital form, and how the data will be quality assured and demonstrate value.  Information on any intended associated documentation that helps to explain the data can also be captured here.

3)     Ethics and intellectual property

If data is being collected involving people it is important to be clear about the ethics and privacy involved.  It is also important to check compliance with other areas of relevant legislation.  If working in partnership, clarity of practice across the partners will be important to establish.

4)     Access and use of information

Whether required by a funder or not, considering how your data may by accessed and used by others (with acknowledgement) will be important to make sure it can be shared effectively.

5)     Storage and backup of data

Storing data can seem straightforward in this computing age.  However, it is important to consider how you will store the data during and after the research, how it will be backed-up, and how you will manage different versions as these evolve.

6)     Archiving and future proofing of information

Specific consideration will need to be given about what happens to the data after the research is completed and how it continues to be managed, if appropriate.  Key to this is also how the data should be cited.

7)     Resourcing of data management

This section is used to formally list those people who have responsibility for managing the data or assisting with this in some way.  It is also where specific funding issues relating to data management can be held.

8)     Review of data management process

A place to note how the DMP will be put into action, and how the actions described will be adhered to.

9)     Statement(s) of agreement and expertise

Noting the responsibilities described in section 7 and the review process in section 8, the final section comprises a set of signed agreements from the individuals concerned to give backing to the actions in the DMP.  It is also a space to list known expertise in different areas of data management so these can be referenced as required.

A DMP template comprising these sections, and with attached guidance notes, is available here or from the left-hand menu.  This is not intended to be any straightjacket in formulating your own plan, but is provided as an aid in ensuring all the appropriate areas are properly covered. 

An online version of the plan is also available as part of the DMPOnline tool provided by the Digital Curation Centre.  This is a general tool, but a history-specific template has been applied.  It is also possible to apply funder-specific templates to ensure you are addressing the particular requirements they have.

5. Getting started

DMP

To kick start the development of your own DMP, you should complete the following exercise before moving onto the next module of the tutorial.  These exercises are based on the DMP template described in the previous section.

  1. Complete section 7, outlining your initial thinking about how data management will be resourced
  2. Complete section 8, stating up front how the DMP will be adhered to in its lifetime
  3. Complete section 9.2, listing known expertise that can be called upon.  You should also use this to flag up known gaps that will require attention.

As you proceed through the subsequent modules of this tutorial, you will be prompted to complete further sections of your DMP, based on the specific topics that have been covered.  As the DMP develops, it is also advised that you re-visit completed sections and add to these as your knowledge and understanding of how the DMP can be used develops.