# What is kiara? *Kiara* is a **data orchestration tool** designed for digital humanities researchers who want to maintain a critically aware relationship with their sources and data. Developed by the **DHARPA project** (Digital History Advanced Research Projects Accelerator) at the Luxembourg Centre for Contemporary and Digital History, kiara addresses a fundamental challenge in digital humanities: how to preserve scholarly agency and critical engagement when using computational methods. Unlike conventional data processing tools that often function as "black boxes," kiara is built to illuminate the research process from start to finish. It's a **Python-based** tool that combines the technical power of data [pipeline](https://docs.dharpa.org/key-concepts#pipeline) frameworks with a strong emphasis on transparency, documentation, and critical reflection. At its core, kiara is **modular and data-centric**, allowing researchers to document their engagement with sources at every step through [lineages](https://docs.dharpa.org/key-concepts#lineage) of their [workflow](https://docs.dharpa.org/key-concepts#workflow). Whether you're analyzing texts, building network graphs, or processing historical data, kiara helps you maintain awareness of how your sources are transformed into computer-legible data and how your methodological choices shape your research outcomes. The tool operates via command line or Jupyter notebooks, making it accessible to researchers with basic Python familiarity while offering the depth and flexibility needed for advanced digital humanities projects. By developing additional [plugins](https://docs.dharpa.org/key-concepts#plugin), more competent users can extend and customise kiara. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.dharpa.org/before-you-begin/readme.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.