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@ARTICLE{Abukaev:645945,
      author       = {Abukaev, Ainur and Voelter, Constantin and Romodin, Mikhail
                      and Schwartzkopff, Sebastian and Bertram, Florian and
                      Konovalov, Oleg and Hinderhofer, Alexander and Lapkin,
                      Dmitrii and Schreiber, Frank},
      title        = {pygid: a {P}ython package for fast data reduction in
                      grazing-incidence diffraction},
      journal      = {Journal of applied crystallography},
      volume       = {59},
      number       = {1},
      issn         = {0021-8898},
      address      = {Copenhagen},
      publisher    = {Munksgaard},
      reportid     = {PUBDB-2026-00701},
      pages        = {263 - 275},
      year         = {2026},
      abstract     = {Advances in X-ray and neutron sources, as well as in
                      area-detector technologies, enable the recording of several
                      terabytes of raw two-dimensional detector data in a single
                      experiment. While several efficient integration and
                      conversion tools are available for data collected in
                      transmission geometry, analogous solutions for
                      grazing-incidence diffraction (including grazing-incidence
                      X-ray diffraction and grazing-incidence wide-angle X-ray
                      scattering) experiments have not yet achieved the same level
                      of efficiency. The development of new data analysis tools,
                      including machine-learning-based software for X-ray data,
                      necessitates the establishment of a standardized format for
                      the converted data. To address these challenges, we have
                      developed a new Python library, pygid, which is designed to
                      facilitate fast data processing while providing
                      compatibility with various raw data formats, a standardized
                      data storage format and an intuitive interface for
                      straightforward use. pygid supports three types of
                      coordinate systems and both transmission and
                      grazing-incidence geometries. It is capable of handling
                      large datasets, performing one-dimensional line cuts and
                      simulating expected Bragg peak positions for given
                      structures. The package facilitates sample and experimental
                      metadata curation in accordance with the FAIR principles. As
                      an integral part of the broader mlgid pipeline, pygid serves
                      as the initial step linking raw scattering patterns with
                      machine learning tools for data analysis. The pygid package
                      is accessible at https://github.com/mlgid-project.},
      cin          = {DOOR ; HAS-User / FS-PETRA-D},
      ddc          = {540},
      cid          = {I:(DE-H253)HAS-User-20120731 /
                      I:(DE-H253)FS-PETRA-D-20210408},
      pnm          = {623 - Data Management and Analysis (POF4-623) / 6G3 - PETRA
                      III (DESY) (POF4-6G3) / DFG project G:(GEPRIS)460248799 -
                      DAPHNE4NFDI - DAten aus PHoton- und Neutronen Experimenten
                      für NFDI (460248799) / DFG project G:(GEPRIS)390727645 -
                      EXC 2064: Maschinelles Lernen: Neue Perspektiven für die
                      Wissenschaft (390727645)},
      pid          = {G:(DE-HGF)POF4-623 / G:(DE-HGF)POF4-6G3 /
                      G:(GEPRIS)460248799 / G:(GEPRIS)390727645},
      experiment   = {EXP:(DE-H253)P-P08-20150101},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1107/S1600576725010593},
      url          = {https://bib-pubdb1.desy.de/record/645945},
}