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Approach to Getting Data Ready for Data Management Solutions](https://arxiv.org/abs/2409.07083).\nThe manuscript illustrates how [echemdb's](https://github.com/echemdb/) toolbox can be used for local research data management.\nIt provides information on file naming convention (FNC), automatic/programmatic metadata acquisition, and converting data into [frictionless Data Packages](https://datapackage.org/).\nFurthermore, the [unitpackages](https://echemdb.github.io/unitpackage/) API is introduced (based on the [frictionless Python framework](https://framework.frictionlessdata.io/)), which allows creating, interacting, and exploring these Data Packages.\n\nThe examples from the manuscript and the usage of our tools for RDM applications are also illustrated in a [specific documentation](https://echemdb.github.io/rawtofigure/intro.html).\n\n## Structure\n\nThe doc folder contains Jupyter notebooks on\n\n* [how ro get started with local data management](./doc/getting_started.md)\n* [illustrating automatic annotation of metadata with watchdog](./doc/tag_data.ipynb)\n* [providing usage examples for the unitpackage API](./doc/unitpackage_demo.ipynb)\n\nand a folder with\n\n* [data used in the notebooks or shown in the manuscript](./doc/data/)\n* [metadata templates for the fileobserver](./doc/files/)\n\n## Installation\n\nThe required packages can be found in the [environment.yml](environment.yml), which can be installed manually, with pip, or conda/mamba in a specific environment.\n\n```sh\nconda env create -f environment.yml\n```\n\nMore details on requirements and installation instructions can be found in the [Getting started](getting_started.md) 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