Make your data available within three months of your award ending
ReShare is the UK Data Service’s online data repository, where researchers can archive, publish and share research data, as open or safeguarded data.
Collections of data and accompanying documentation can be submitted after registering with the UK Data Service.
ESRC grant holders are expected to make the research data that result from their grant available for reuse, as set out in the ESRC research data policy. The data can be made available via ReShare or an appropriate responsible digital repository within three months of the end of their grant. ESRC grant holders must provide a metadata record for resource discovery via the UK Data Service’s self-deposit repository ReShare to maximise the discoverability of ESRC data assets.
Datasets described in a data paper or data descriptor in a peer-reviewed journal, such as Scientific Data and the Research Data Journal, can be deposited directly into ReShare.
We also provide information on managing and depositing data relating to your award.
How to submit data to ReShare
We have also produced two PDF documents to guide you through the process of depositing your data with ReShare.
- Publishing and Sharing Data Using the ReShare Repository (PDF).
- ReShare: Re-Shaping the Landscape of Research Data Repositories (PDF).
Before you start to deposit a new data collection, check these guidelines on how to prepare your data files and documentation:
- Group your data files in zip bundles (max 4gb) according to their content or file format, to make upload and download easier, e.g. a zip bundle for transcripts, a zip bundle for the documentation (for large collections, keep a folder structure for the files in your zip bundle).
- Check our recommended file formats before uploading files.
- Give files meaningful names that reflect the file content, avoiding spaces and special characters.
- Check that files contain no disclosive information (beware of hidden tracked changes in text or table files and remove names and disclosive info from ‘file properties’); to anonymise data consider techniques such as:
- Removing direct identifiers e.g. respondents’ names, addresses (physical, email and IP), institution name, telephone numbers etc.; respondents’ names can be replaced by pseudonyms.
- Aggregating variables, restricting the upper or lower ranges of continuous variables, reducing the precision of a variable or textual information by replacing potentially disclosive free-text responses with more general text.
- Create a ReadMe file for your data collection, with:
- for each filename a short description of what it includes
- any relationships between the data files.
- Prepare essential documentation to upload with your data:
- your ReadMe file (see above)
- topic list for interviews
- clear variable descriptions and code labels in each data file
- questionnaire(s) or data dictionary for surveys
- blank copies of consent form and information sheet used
- methods description
- data list of interviews
- PDF of website materials.
For advice at any stage of your project: