Research Data Archiving Checklist
In accordance with the TU Delft Research Data Framework Policy, all doctoral candidates who started on, or after 1 January 2019, are required to upload all research data and code underlying completed PhD theses to 4TU.ResearchData (or another suitable data archive) before graduating, unless there are valid reasons not to do so.
This document should be used by PhD supervisors as a final checklist about the activities and plans regarding archiving of the research data and/or code from a PhD project before signing Form B. Please only sign Form B after going through this checklist with your PhD candidate and you are satisfied that the research data and/or code is archived in accordance with the guidelines given here and the best practices in your research domain, if applicable.
Please note that:
- Data and/or code underlying scientific papers should have been archived at the time of the publication of the corresponding papers. This guidance document can be used for such data and/or code that has not been archived yet or for any other remaining data and/or code.
- Issues around research data management and data archiving should have been already discussed and addressed throughout the PhD project and the corresponding decisions should have been recorded in a Data Management Plan in consultation with the faculty Data Steward.
- This checklist can also be used for archiving research code/software, whenever applicable. For more detailed guidance, please check the FAIR software checklist by the TU Delft Digital Competence Center.
Explanations
Research Data
Research data is the evidence that underpins answers to research questions, and which is necessary to validate research findings. Data can come in various forms and types, characteristic to specific disciplines of research. For example, data can be quantitative information or qualitative statements collected by researchers in the course of their work by experimentation, observation, modelling, interview or other methods, or information derived from existing evidence. Examples of research data include:
- Documents (text, Word), spreadsheets
- Laboratory notebooks, field notebooks, diaries
- Questionnaires, transcripts, codebooks
- Audiotapes, videotapes
- Photographs, films
- Protein or genetic sequences
- Test responses
- Slides, artifacts, specimens, samples
- Collection of digital objects acquired and generated during the process of research
- Database contents (video, audio, text, images)
- Models, algorithms, scripts
- Contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
- Methodologies and workflows
- Standard operating procedures and protocols
Trusted data repository
4TU.ResearchData, zenodo and figshare are all trusted repositories that archive data according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles by:
- making research data accessible, discoverable and available for the long term,
- providing persistent and unique identifiers like DOI to make data findable and citable
- offering standard licenses that determines terms and conditions regarding sharing and reuse
- using common metadata standards to help others identify and discover the data.
Yet, if there are other disciplinary or domain repositories that are commonly used and endorsed by your research community, those might be more suitable to archive the data resulting from the project. Re3data.org offers an overview of data repositories.
README file
A README file provides information about a dataset and is intended to help ensure that the data can be correctly interpreted, by yourself at a later date or by others when sharing or publishing data. A README file must be submitted along with the dataset file(s).
Personal data
Personal data – all information about an identified or identifiable natural person (the data subject). A person is considered identifiable if he or she can be identified directly or indirectly based on one or more items of personal data, for example, name and address, ethnicity, date of birth and IP-address. In general, it can be assumed that personal data include all data relating to a living person that makes it possible to identify this person or to distinguish him or her uniquely from other persons.
Confidential data
A few examples of confidential data:
- national security data (e.g. nuclear research)
- data falling under export control regulations
- confidential data received from commercial, or other external partners
- data related to competitive advantage (e.g. patent, IP)
- data which could lead to reputation/brand damage (e.g. animal research, climate change, personal data)
- politically-sensitive data (e.g. research commissioned by public authorities, research in social issues)
