1. Is DaSCH suitable for my project?
We are a long-term archive for research data at PhD level and above, specialized in qualitative data from the humanities. Our services are available to all researchers at Swiss higher education institutions working in the humanities. Examples of qualitative data that we archive include data from archaeological excavations or critical editions. If you plan to use DaSCH to archive your data, please contact us in the planning phase of your project to see if we are the right partner for you. If DaSCH isn't suitable for your project, we will offer advice about alternatives. You can also use a repository registry service to gain an overview of different repositories, such as re3data.org, fairsharing.org or forschungsdaten.info.
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2. Can you help me write a Data Management Plan (DMP)?
Many researchers find it difficult to write a Data Management Plan (DMP), and we recommend that you ask your university's research IT support for help.
If you want to archive your data with DaSCH, please contact us when writing your DMP to ensure that we are the right partner for your kind of data. After assessing your DMP, we will issue a letter confirming that we will archive your data for the long term. We also offer free consulting during the first eight hours to help you plan your data archiving.
You can find best practices on how to write your DMP in our guide on archiving workflows.
Contact us at: info@dasch.swiss
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3. What is research data?
Humanities researchers often state that they do not produce research data. In this case, a simple thought experiment may help to understand the concept of research data: imagine that your computer is stolen the day before you submit your paper to the publisher, and the only back-up that you have is a Word document with the text of your article. All of the information that is now missing is your research data: Excel files, tables, photos, scans, personal notes, etc.
When archiving your research data, you will also need to create a data model that defines how your data is structured — establishing resource classes and their properties. This preparation step is an important part of making your data findable and reusable.
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