Welcome to the Digital Database of the Islamic Cultural Archive (ICA) Project

This digital archive is part of the joint database of the Africa Multiple – Cluster of Excellence at the University of Bayreuth. The database serves as a platform where all digital data collected by the members of the Cluster-funded projects will be stored, tagged, and connected by metadata, thus building a flexible relational taxonomy that can be expanded in any direction. The taxonomy of the ICA is a descriptive ontology developed by the research team to approach the field of Islamic learning from a variety of research themes and perspectives. The categories used in this taxonomy are designed to break with closed and static systems of classification. Instead, we try to categorize data in a way that allows us to integrate a multiplicity of research interests based on a broad approach to data collection. By creating as many nodes as possible where we can connect our data, the database will help us to develop and explore new research questions in a creative manner.

There are various levels of data descriptions included in the ICA database. The first is a general description indicating the type of data, the person who created the entry, and the time and place of creation. Such information is collected in various types of templates that describe the data as it is stored and tagged. As the types of data we collect within this project are of a great variety, we organize them along the category of media that they represent:

  1. Text (Book; Book chapter; Journal article; Newspaper article; Thesis; Interview; Manuscript; Report; Field note)
  2. Audio (Recording; Interview; Radio Broadcast)
  3. Video (Film; Interview; Video Recording; TV Broadcast)
  4. Web Content (Blog; Social Media; Website; Podcast)
  5. Image (Person; Building; Place; Artifact; Poster; Sticker; Drawing)
  6. Map

Further, each data entry can be connected with research notes added by each team member by entering the edit mode of the data entry and writing a note to the entry. This allows us to highlight specific content-related interests and, in the long run, to benefit from each other’s data collection. Ultimately, we will create a vast archive that covers a wide range of research interests.

The second level of description concerns the content of the data. Here, our taxonomy maps our primary field of interest: Islamic learning from various angles. We employ three different angles to organize our data: a contextual description, a physical description and a meta-description. These descriptions are informed by questions we can pose to the data from these three angles. Again, our categories are not intended as a fixed structure that assigns our data to static categories, but are designed to generate as many nodes as possible in order to connect and relate diverse sets of data to each other. These nodes are created through tags, which are based on the underlying taxonomy that maps the field of Islamic learning from multiple perspectives. Our taxonomy is structured in an open and flexible way that allows us to accommodate new findings and research questions that we generate over the course of our work in the project. Each member is invited to add tags whenever he or she misses any. Just send a request to add a tag by contacting the administrator. The administrator will ensure that the new tags are made available in all languages and that the extension conforms with the underlying taxonomy and remains coherent.

If you wish to learn more about the taxonomy that structures our tagging system, please click here for further explanation.