The following aspects should be considered before you deposit your data.
When you deposit data, you enter into a deposit agreement with DANS. For more information, see the Legal information page.
Personal data within the meaning of the General Data Protection Regulation (GDPR) is data by which a living person can be identified, either directly or indirectly. Examples include names, identification numbers, location data, online identifiers, or elements that are characteristic of a person’s physical, physiological, genetic, mental, economic, cultural or social identity. Anonymous data is not considered personal data. Pseudonymized data, however, is considered personal data.
- When you deposit personal data, you, or your organisation, are a data controller under the GDPR. You and DANS must conclude a processing agreement. A processing agreement needs to be concluded with DANS. For more information, see the Legal information page. A processing agreement does not apply if your dataset only contains personal data to account for the dataset, such as the ‘Creator’ (one of the metadata fields) or citations.
- As a data controller under the GDPR, you or your organisation are responsible for the correct processing of personal data in your data set. This includes anonymising, pseudonymisation and/or encrypting personal data. It is important that you store the key securely and permanently elsewhere.
- If your dataset contains personal data (and these are not used to justify the dataset), DANS always archives these personal data with the access category ‘Restricted Access’. This way, you know who uses your dataset and you have the possibility to impose appropriate conditions.
- Research practice information:
- One way of anonymizing data is by recording, for example date of birth to year of birth, postal code to numerals only, occupation to standard classification (in Dutch). The appropriate anonymization method will always be context-dependent.
- SURF Wikiwijs e-learning module ‘Privacy in research’
- CESSDA Data Management Expert Guide – section ‘Protect’
- National Coordination Point for Research Data Management (LCRDM): Guidelines: Privacy
- VSNU:Code of conduct for using personal data in research (currently being revised: last consultation version, 2017 (in Dutch))
- European Data Protection Board (EDPB): GDPR: Guidelines, Recommendations, Best Practices
Not all file formats ensure long-term usability, accessibility and preservation of data. DANS works with preferred formats. For more information, see the File formats page.
For the purpose of sustainable archiving, DANS will convert audio-visual files to a preferred format if they are supplied in a different format. In the archive, these conversions will be considered the original files.
Preparing documentation and files
- Which files are you going to deposit? Not all data needs to be preserved for the long term. More information can be found on the webpage ‘Selecting research data’ on the Research Data Netherlands website.
- Provide the relevant documentation: how were the data collected and what is the meaning of variables, abbreviations and terminology? Relevant information includes codebooks and the dataset structure.
- Are there many files in your dataset? If you deliver many files at once, please provide a file list, i.e. a list of file names, descriptions of the content and of any connections between the files.
- Does your dataset include personal data? DANS does not disclose personal data. File names can be viewed by anyone. That is why file names may not contain personal data, with the exception of data required to account for the dataset (such as the creator or citations). You cannot therefore include data about your research subjects in file names.
Discipline-specific deposit requirements
Specific deposit requirements apply to the following disciplines:
- Historical sciences:
- a description of the archival and other sources,
- the selection procedure used,
- the way in which the sources were used, and
- which standards or classification systems (such as HISCO) were applied.
- Social and behavioural sciences:
- the variable labels and value labels,
- the questionnaires and/or other research tools,
- the fieldwork report (if available), and
- a codebook (description of variables and information about population, types of data (units of observation/analysis), sample procedure, response/non-response, data collection method, weighting variables, constructed and/or derived variables), and
- the language of the variable labels and value labels must correspond to the language of the rest of the dataset.
- Language and literature studies:
- CLARIN-compliant delivery means that your dataset shall contain one or more metadata files in the Component MetaData Infrastructure format.
- Would you like to know more about the E-depot for Dutch Archaeology? Please visit this page.
- Projects that have been described using the archaeological exchange protocol (SIKB0102 standard) must be submitted via the ArcheoDepot. The dataset files deposited with the provinces must be supplied in Preferred Formats.
- Via the ArchaeoDepot, datasets are automatically sent from the provincial depot to the DANS archive.
- During the startup phase of the ArcheoDepot, datasets can still be deposited directly at DANS if the province is not yet connected to the ArcheoDepot.
- With extensive data sets, users must be able to gain a thorough understanding of the contents of the various files. The Archaeological Metadata manual (in Dutch) will assist in preparing the required documentation.
- Personal contact details of field staff or clients must be removed from the supplied documents. This includes the personnel overview in the plan of action or the administrative data in the final report.
- Oral history and audiovisual sources:
- With a view to reusing the data, including a transcript of the interview is highly desirable. DANS has a template (in Dutch) available for this purpose.
- The viewer of the Data Station allows audiovisual materials to be played. Please note that in this case, each interview (video- or audio file) requires its own dataset.
- Historical sciences:
More information can be found on this page.
© DANS. Version 1.0, June 17, 2022