The published (or accepted) manuscript or publication, or a persistent link (such as the DOI) to an open access publication. If not already in the manuscript/publication, also include a brief description of the problem definition, research design, data collection (sampling, selection and representativeness of informants) and methods used.

The instructions, procedures, design of the experiment and stimulus materials (interview guide, questionnaires, surveys, tests) that can reasonably be deemed necessary in order to replicate the research. The materials must be available in the language in which the research was conducted. The publication package itself must be in English.

1. When you use primary data, the (de-identified) raw data files, providing the most direct registration of the behavior or reactions of participants. For example: an unfiltered survey export, raw time series for an EEG measurement, recordings, interview transcripts, or descriptions of observations.  
2. If the raw data files are accessible via an external archive (such as DANS), referring to the files in this archive will suffice. Such externally archived raw data may include primary or secondary data.
3. If applicable, documentation of the steps taken to de-identify the data.

Code describing the steps taken to process the raw data into processed/analysis data, for example Python or R scripts, Atlas.ti/SPSS/JASP syntax files, etc. Include brief explanations of the steps in English, for example through comments in the code or a description of the steps taken in the qualitative analysis of primary research data, i.e. themes, domains, taxonomies, components.  

The data files that were eventually analysed when preparing the manuscript (e.g. a data file after transforming variables, after applying selections, etc.). If the raw data was analysed directly, step 3 suffices.  

Code describing the steps taken to process the processed/analysis data into results in the manuscript, for example Python or R scripts, SPSS/JASP syntax files.  Include brief explanations of the steps in English, for example through comments in the code and/or a thorough methods section in the manuscript.

A copy of the most recent version of the data management plan.

A clear README.txt file that allows a fellow researcher to be able to replicate the results based on the components of the publication package.

1. Names of the persons involved in the project and their roles using the CRediT taxonomy (data collection, storage, analysis, manuscript writing, etc.). Provide at least one contact point.
2. Date on which the manuscript was accepted, including reference.
3. Date/period of data collection.
4. If relevant: locations of data collection and contact persons of those locations (if any).
5. Whether an ethical assessment took place, and, if so, the reference number.
6. A description of the publication package, and a “table of contents”, i.e. which files can be found where.
7. Prerequisites for using the dataset, such as specific software or hardware requirements.
8. Information about citation and reuse: the license for reuse and citation requirements. If the dataset is published under restricted access, a reference to a custom license text that explains this in more details will suffice as well.

Besides a README.txt file, it is recommended to add data-level documentation alongside the dataset, such as a data dictionary/codebook, lab notebook, or file overview sheet. Read more about metadata and documentation

Documents related to the ethical approval or a reference to such documents, for example the approval letter and a blank information letter and consent form.