Themes

Theme	DCC & UC3 Guidance
DATA DESCRIPTION	Give a summary of the data you will collect or create, noting the content, coverage and data type, e.g., tabular data, survey data, experimental measurements, models, software, audiovisual data, physical samples, etc. Consider how your data could complement and integrate with existing data, or whether there are any existing data or methods that you could reuse. Indicate which data are of long-term value and should be shared and/or preserved. If purchasing or reusing existing data, explain how issues such as copyright and IPR have been addressed. You should aim to minimise any restrictions on the reuse (and subsequent sharing) of third-party data.
DATA FORMAT	Clearly note what format(s) your data will be in, e.g., plain text (.txt), comma-separated values (.csv), geo-referenced TIFF (.tif, .tfw). Explain why you have chosen certain formats. Decisions may be based on staff expertise, a preference for open formats, the standards accepted by data centres or widespread usage within a given community. Using standardised, interchangeable or open formats ensures the long-term usability of data; these are recommended for sharing and archiving. See UK Data Service guidance on recommended formats or DataONE Best Practices for file formats.
DATA VOLUME	Note what volume of data you will create in MB/GB/TB. Indicate the proportions of raw data, processed data, and other secondary outputs (e.g., reports). Consider the implications of data volumes in terms of storage, access and preservation. Do you need to include additional costs? Consider whether the scale of the data will pose challenges when sharing or transferring data between sites; if so, how will you address these challenges?
DATA COLLECTION	Outline how the data will be collected and processed. This should cover relevant standards or methods, quality assurance and data organisation. Indicate how the data will be organised during the project, mentioning, e.g., naming conventions, version control and folder structures. Consistent, well-ordered research data will be easier to find, understand and reuse. Explain how the consistency and quality of data collection will be controlled and documented. This may include processes such as calibration, repeat samples or measurements, standardised data capture, data entry validation, peer review of data or representation with controlled vocabularies. See the DataOne Best Practices for data quality.
METADATA & DOCUMENTATION	What metadata will be provided to help others identify and discover the data? Researchers are strongly encouraged to use community metadata standards where these are in place. The Research Data Alliance offers a Directory of Metadata Standards. Data repositories may also provide guidance about appropriate metadata standards. Consider what other documentation is needed to enable reuse. This may include information on the methodology used to collect the data, analytical and procedural information, definitions of variables, units of measurement, any assumptions made, the format and file type of the data and software used to collect and/or process the data. Consider how you will capture this information and where it will be recorded, e.g., in a database with links to each item, in a ‘readme’ text file, in file headers, etc.
ETHICS & PRIVACY	Investigators carrying out research involving human participants should request consent to preserve and share the data. Do not just ask for permission to use the data in your study or make unnecessary promises to delete it at the end. Consider how you will protect the identity of participants, e.g., via anonymisation or using managed access procedures. Ethical issues may affect how you store and transfer data, who can see/use it and how long it is kept. You should demonstrate that you are aware of this and have planned accordingly. See UK Data Service guidance on consent for data sharing. See ICPSR approach to confidentiality and Health Insurance Portability and Accountability Act (HIPAA) regulations for health research.