19. Do I need in-depth IT knowledge to use the DaSCH archiving software (DSP)?
This depends on your project. We have a customer-friendly user interface (DSP-APP) in which you can build a data model and manually add single data points, which simply requires following some instructions and a bit of practice. However, if you have a large dataset that you want to archive at once (mass data import), you either need some IT knowledge in order to convert your data into our DSP-conforming XML format, or we can offer this as a paid service.
In any case, you should take some time to acquaint yourself with some core concepts such as data models, resources and properties – this is even more important for a successful collaboration than IT knowledge in terms of preventing misunderstandings.
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20. Can I archive datasets containing sensitive data?
For legal reasons, we don't archive sensitive data. It is the researcher's responsibility to anonymize all data before archiving, ensuring that it does not allow any conclusions to be made about living persons. Sensitive data includes information on health, privacy, ethnic origin, social welfare needs, religious, ideological or political views, as well as criminal punishments and measures. Parts of the data that cannot be fully anonymized can be marked as not publicly accessible.
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21. Why can't I archive my dataset with just a few clicks?
DaSCH is a curated repository for research data from the arts and humanities. If you have just a few files to deposit, please contact us at info@dasch.swiss. After a check whether your data fall within the scope of our repository, we will create a project for you, you may use a simple standard data model, adapt it to your needs if necessary, and proceed on your own.
If you want your data to be directly accessible and visible, we offer a platform that presents your data in a highly structured, richly annotated and consistent manner. In this case it is necessary to create a custom data model that exactly defines the inner structure of your data, described by resource classes (e.g. "book", "author") and properties (e.g. a "book" has a year of publication, a certain number of pages, a cover image, etc.). Afterwards, you either add data manually on our platform - e.g. with 100 books all with a year of publication, etc. - or your data has to be cleaned in order to comply with the data model and to be imported. While this approach involves a lot of work, it makes your data cleaner and more understandable for other users, while significantly increasing its findability when searching.
DaSCH provides consulting support for data model creation to help you through this process.
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