The following is a guide to the UEL Data Management Plan template. Some funders have their own templates which must be completed and submitted at either grant application stage or within a specified period during the research project: contact us for assistance with funder data management requirements.
Research data is information collected, generated, observed or created during a research project, to underpin and validate the content of the final research output. It may vary in nature according to the discipline and can be both digital and non-digital in format. Examples include audio/video recordings, spreadsheets, transcripts, software code, prototypes, photographs, models, experimental measurements, physical specimens, and observations.
List and describe each type of data you will collect, create, or use to answer your research questions, whether they are physical or digital, including raw, processed, and published data. If using secondary data, provide a reference for the data including a DOI where possible.
State what formats each data type will be stored in: to enable wider re-use and long-term preservation, consider whether data collected or created in proprietary formats will be saved to an ‘open’ format later, e.g. .xls spreadsheets stored as .csv files. The UK Data Service has a guide to recommended file formats. For each data type estimate the volume of data you expect to create or collect: this could be, for example, in numbers of experiments or files generated, or size in bytes (MB, GB, TB).
The type, scope, and volume of data will determine appropriate methods of storage, transfer, and long-term preservation. It can also help identify and mitigate the risk of data loss, disclosure of personal and sensitive information, and identify actions enabling future data use, such as migration of proprietary file formats to open formats.
Spreadsheets of Patient Health Questionnaire (PHQ-9) responses (50 participants) created in .xlsx and converted to .csv format, contains personal and special category data related to mental health.
Interview recordings in .mp4 format (20 files, approx. 8GB total) and transcripts in .docx
Describe for each data type your data collection methods and any software, devices, and instruments used. Outline when, how, and to where your data will be transferred or exported.
Describe how you will organise your data, including a logical, meaningful folder & file structure and file-naming conventions.
State what data quality or assurance methods you will use (e.g. calibration of instruments, standardised protocols, controlled vocabularies for data entry, etc.)
Potential risks may be identified, such as data loss, security, or quality issues, with tools, systems, or methodology. Your data collection methods may identify additional resources that might be required, such as specialist software or equipment, to avoid potential delays in starting a project due to their lack.
Interviews will be conducted and recorded remotely using Microsoft Teams installed on the interviewer’s UEL-managed laptop, with the resulting .mp4 files transferred to OneDrive. Recordings will be stored following the file-naming convention: [ProjectCode]-[InterviewerInitials]-[ParticipantNumber]-[Location]-[Date].Ext . An interview schedule will be developed so that a standard format is followed.
EEG recordings will be captured in .bdf format directly onto PC from electrode headcaps using the BioSemi ActiveTwo system. This data will be imported to MATLAB for analysis. A standardised protocol will be established to ensure data quality.
Which data are of long-term value and should be retained, shared, and/or preserved?
What should I include here?
You will not be likely to need or be able to keep all the data you collect or create, so will have to decide which is of value and which can be destroyed. Some considerations when deciding data to select: what data will be needed to validate your research findings; potential purposes for re-use; can data be easily reproduced; are the data unique; are there legal, ethical, or contractual reasons to keep the data; are the data personal or sensitive.
Why is this important?
Appraisal can take place during the project as well as at completion, so planning for how you will select data can help ensure you retain data of value and keep only what is necessary. It is also important to plan for how you will meet funder expectations on retention and preservation if you are bidding for, or have been awarded, a grant.
Preservation of data involves more than just long-term storage, it includes activities and processes necessary to enable data to be available and usable into the future, such as file format migration and performing quality checks. Not all data needs to undergo preservation but may still need to be retained.
UEL has a digital archiving service, Arkivum, which is suitable for long-term secure safeguarding of data and includes data integrity checks: access is limited to system administrators at Arkivum and in Library, Archives, and Learning Services at UEL, so would not be suitable for data you require regular access to. We can provide more information about this service and whether it would be appropriate.
Funders may have specific retention periods relating to research data: UKRI’s Concordat on Open Research Data states that data underlying publications should be retained for 10 years from the date of publication. UEL’s Research Data Management Policy is that, “Data must be appraised (reviewed) at the end of the research project and every 5 years thereafter, unless another timescale is specified by the research funder, until the data are transferred or destroyed.”
Why is this important?
Planning for retention and/or preservation helps ensure that necessary data are accessible and usable into the future, as well as to meet any funder, legal, or ethical conditions. Some processes or technologies may require additional resources, which may be easier to obtain when planned for.