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Types of research data

Research data is any information that has been collected, observed, generated or created to support, validate or reproduce research findings.

There are many different types of research data. The data you collect will depend on your chosen methodology as well as your discipline. Some common examples include:

• Documents, spreadsheets
• Laboratory notebooks, field notebooks, diaries
• Questionnaires, interview transcripts, survey responses
• Audio recordings, videos, photographs
• Specimens, samples, instrument measurements
• Database contents
• Models, algorithms, scripts, codebooks
• Contents of an application (input, output, log files for analysis software, simulation software, schemas)

Not all materials will necessarily require long-term retention. Researchers should identify which data are of continuing value and necessary to validate research findings.

Data reuse

Data reuse, also known as secondary analysis, occurs when a researcher conducts their own analysis of data collected by others. Reusing existing data can increase the efficiency of your research by cutting down on time spent generating, processing and preserving data that you collect.

Data sources

To find relevant data repositories, you can use the database which provides information on over 2,000 repositories from different academic disciplines. To search repositories for individual datasets, you can use the web search interface to search records which have been assigned a DOI.

UK-based researchers working in life and health sciences, for example, might be able to make use of the database which contains anonymised health information of 500,000 volunteer participants.

When searching for data, you should always assess the quality of the data source as you would for any other source of research information.

Considerations

• Does the licence permit you to access, reuse, adapt, and redistribute the data?
• Have the original participants given consent for the data to be reused?
• Is metadata and documentation available? Is it sufficiently detailed to support understanding and reuse of the data?
• What data formats have been used? Is the data available in a common, open file format?

Methodology

Your DMP should include a summary of your research methodology. This should address the goals of data collection and how you plan to collect data. Include information on how the consistency, quality and integrity of data collection will be maintained throughout the project.

Where appropriate, you should adopt recognised disciplinary standards, protocols, or controlled vocabularies when collecting data. Using established standards improves data quality, interoperability, and opportunities for future reuse.

Example

An interview methodology will be employed. Semi-structured interviews and focus groups will be digitally recorded and subsequently transcribed into anonymised transcripts.

Interview recordings will be high quality by ensuring recording equipment is properly calibrated ahead of each interview. Interviewers will be trained to ensure standardised approaches to engaging with participants. A single protocol for transcription will be used for all interviews.

File formats

You should identify the most appropriate file formats for the data that you plan to collect. Your chosen file formats should enable long-term preservation and facilitate reuse where you've chosen to share your data. Make sure to also check whether your intended data repository recommends or requires particular file formats.

Decisions may be based on the expertise of project members, preference for open formats, or existing conventions within a discipline.

Recommended formats for preservation:

• Tabular (e.g. spreadsheets): CSV, TAB
• Textual: TXT, XML, RTF, PDF/A, HTML
• Audio: FLAC, WAV, MP3
• Video: MP4, OGV, MJ2
• Image: TIF (preferred), DCM, JPEG
• Geospatial: ESRI Shapefile, DWG, Geo-referenced TIF

The UK Data Service provides more detailed information on .

Data volume

It's important to estimate the overall volume of data you'll collect as part of your DMP. Consider the quantity of your data separated out by each data type (e.g. quantity of audio files, quantity of transcripts). You can use tools such as the to help estimate the size of the files you'll generate.

Precise calculations are not required. The aim is to provide a realistic estimate of anticipated storage requirements.

Example

The researchers will be collecting audio and textual data. Below is an estimated breakdown of the maximum volume of each type of data collected by this research study.

Interview recordings (15 1-hour WAV files, 600 MB in size each; total volume: 9 GB)

Focus group recordings (3 120-minute WAV files, 1 GB in size each; total volume: 3 GB)

Estimated data volume: 12 GB

File naming conventions

Effective file naming and organisation can: 

• improve the efficiency of searching
• enable logical sorting
• allow you to quickly distinguish data. 

When developing a naming convention, bear these points in mind:

Consistency. Establish rules for your naming convention early in the project which are consistent and logical. You can use different conventions for different file sets.

Organisation. Order the elements within file names logically to enable sorting. You should put the most important information first. YYYYMMDD is the preferred format for dates and allows you to sort chronologically.

Context. File names should be short but sufficiently descriptive. You should identify what metadata are needed to easily locate files, such as experiment conditions, type of data, or researcher initials. Take care not to identify any research participants in your file names.

If files are likely to undergo multiple revisions, a version numbering convention should also be adopted (e.g. v01, v02, v03).