[[!meta title="Collaborative data processing and documenting using org-babel"]] [[!meta copyright="Copyright © 2023 Jonathan Hartman, Lukas C. Bossert"]] [[!inline pages="internal(2023/info/collab-nav)" raw="yes"]] # Collaborative data processing and documenting using org-babel Jonathan Hartman (he/him), Lukas C. Bossert (he/him) - , , [[!inline pages="internal(2023/info/collab-before)" raw="yes"]] In our presentation we will show an efficient way of combining information and enriching it by retrieving data, processing it, and finally exporting it, all with org-mode. In this presentation, we will demonstrate not only org-mode, but also a few companion libraries that add functionality such as knowledge graph visualizations, literate programming, and collaborative editing to quickly create a deeply informative reference page. The starting point of our best practice is the National Research Data Infrastructure Germany (NFDI), about which we intend to retrieve and process certain information data gathered from wikidata. For this, we are additionally leveraging the "org-roam" emacs package, which provides functionality for quickly and simply linking together notes and ideas into a custom knowledge graph. Initially, we will write a short abstract about the NFDI and embed it into our existing knowledge graph by linking it to other existing nodes. In the visualized graph (using the “org-roam-ui” package), links and secondary connections to other existing nodes can now be revealed. Next, we would like to enrich the text about the NFDI by with data retrieved from the Wikidata API. A convenient way of creating self-documenting code is the approach called “literate programming”, which presents program logic embedded within human language text. In Emacs we achieve this by using the “org-babel” package. Perhaps now we find it is helpful to collaborate with a colleague in the document: while one is writing the code, the other can explain its use and interpret the results. We will do this simultaneously in the same document using a method called “crdt” (conflict-free replicated data type) and – of course – there is also an implementation of this in Emacs. The results of the code blocks can be used for further analysis and shared throughout the same document. Finally, for the sake of proper and barrier free documentation, we show how to export the document to various formats like pdf, html, txt etc. using either the built-in feature of org-mode or the implementation of pandoc. About the speakers: **Jonathan Hartman** is a trained data scientist and works at the IT Center of the RWTH Aachen University, Germany. **Lukas C. Bossert** is a trained classical archaeologist and is deputy head of the department "research process and data management" at the IT Center of the RWTH. Lukas, an intermediate Emacs user, is currently exploring how to optimize his daily workflow by leveraging various Emacs packages. On the other hand, Jonathan is a relative newcomer to this environment, encountering common pitfalls faced by beginners. Together, they explore the capabilities and functionalities of org-mode, discovering how it can enhance data management and presentation in their research processes. [[!inline pages="internal(2023/info/collab-after)" raw="yes"]] [[!inline pages="internal(2023/info/collab-nav)" raw="yes"]]