"tardis": {
    "tags": [
        "2014MNRAS.440..387K",
        "2019A&A...621A..29V",
        "kerzendorf_wolfgang_2019_2590539"
    ],
    "logo": "img/tardis.png",
    "language": "Python",
    "category": "Transients",
    "keywords": [
        "supernova",
        "spectral modelling",
        "radiative transfer. supernovae"
    ],
    "description": "An open-science software to simulate and analyse supernovae and other transients",
    "link": "https://tardis-sn.github.io/",
    "attribution_link": "https://tardis-sn.github.io/credits/",
    "zenodo_doi": "10.5281/zenodo.592480",
    "custom_citation": "This research made use of \\\\textsc{Tardis}, a community-developed software package for spectral synthesis in supernovae \\\\citep{2014MNRAS.440..387K, 10.5281/zenodo.592480}. The development of \\\\textsc{Tardis} received support from the Google Summer of Code initiative and from ESA’s Summer of Code in Space program. \\\\textsc{Tardis} makes extensive use of Astropy \\\\citep{astropy:2013, astropy:2018, astropy:2022} and PyNE \\\\url{https://pyne.io/} . The Spectral modeling of type II supernovae is from \\\\citet{2019A&A...621A..29V}."
}

@ARTICLE{2014MNRAS.440..387K,
           author = {{Kerzendorf}, W.~E. and {Sim}, S.~A.},
            title = "{A spectral synthesis code for rapid modelling of supernovae}",
          journal = {\mnras},
    archivePrefix = "arXiv",
           eprint = {1401.5469},
     primaryClass = "astro-ph.SR",
         keywords = {radiative transfer, methods: numerical, supernovae: general},
             year = 2014,
            month = may,
           volume = 440,
            pages = {387-404},
              doi = {10.1093/mnras/stu055},
           adsurl = {http://adsabs.harvard.edu/abs/2014MNRAS.440..387K},
          adsnote = {Provided by the SAO/NASA Astrophysics Data System}
    }
    @ARTICLE{2019A&A...621A..29V,
           author = {{Vogl}, C. and {Sim}, S.~A. and {Noebauer}, U.~M. and {Kerzendorf}, W.~E. and {Hillebrandt}, W.},
            title = "{Spectral modeling of type II supernovae. I. Dilution factors}",
          journal = {\aap},
         keywords = {radiative transfer, methods: numerical, stars: distances, supernovae: general, supernovae: individual: SN1999em, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics},
             year = "2019",
            month = "Jan",
           volume = {621},
              eid = {A29},
            pages = {A29},
              doi = {10.1051/0004-6361/201833701},
    archivePrefix = {arXiv},
           eprint = {1811.02543},
     primaryClass = {astro-ph.HE},
           adsurl = {https://ui.adsabs.harvard.edu/abs/2019A&A...621A..29V},
          adsnote = {Provided by the SAO/NASA Astrophysics Data System}
    }
    @software{kerzendorf_wolfgang_2019_2590539,
      author       = {Kerzendorf, Wolfgang and
                      Nöbauer, Ulrich and
                      Sim, Stuart and
                      Lietzau, Stefan and
                      Jančauskas, Vytautas and
                      Vogl, Christian and
                      Mishin, Mikhail and
                      Tsamis, Fotis and
                      Boyle, Aoife and
                      Gupta, Vaibhav and
                      Desai, Karan and
                      Klauser, Michael and
                      Beaujean, Frederik and
                      Suban-Loewen, Adam and
                      Heringer, Epson and
                      Shingles, Luke and
                      Barna, Barnabas and
                      Gautam, Gaurav and
                      Patel, Maryam and
                      Barbosa, Talytha and
                      Varanasi, Kaushik and
                      Reinecke, Martin and
                      Bylund, Tomas and
                      Bentil, Laud and
                      Rajagopalan, Srinath and
                      Jain, Rinkle and
                      Singh, Sourav and
                      Talegaonkar, Chinmay and
                      Sofiatti, Caroline and
                      Patel, Pratik and
                      Yap, Kevin and
                      Wahi, Ujjwal and
                      Gupta, Suyash},
      title        = {tardis-sn/tardis: TARDIS v3.0 alpha2},
      month        = mar,
      year         = 2019,
      doi          = {10.5281/zenodo.2590539},
      url          = {https://doi.org/10.5281/zenodo.2590539}
    }

"RAMSES": {
    "tags": [
        "2002A&A...385..337T"
    ],
![full_project_logo](https://github.com/TomWagg/software-citation-station/assets/4170009/c7586898-adc2-4e4f-b55a-e15a17803f33)

    "logo": "img/RAMSES.png",
    "language": "Fortran",
    "category": "Galactic Dynamics",
    "keywords": [],
    "description": "Ramses is an open source code to model astrophysical systems, featuring self-gravitating, magnetised, compressible, radiative fluid flows. It is based on the Adaptive Mesh Refinement (AMR) technique.",
    "link": "https://ramses-organisation.readthedocs.io/en/latest/",
    "attribution_link": "https://github.com/ramses-organisation/ramses",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": []
}

@ARTICLE{2002A&A...385..337T,
       author = {{Teyssier}, R.},
        title = "{Cosmological hydrodynamics with adaptive mesh refinement. A new high resolution code called RAMSES}",
      journal = {\aap},
     keywords = {GRAVITATION, HYDRODYNAMICS, METHODS: NUMERICAL, COSMOLOGY: THEORY, COSMOLOGY: LARGE-SCALE STRUCTURE OF UNIVERSE, Astrophysics},
         year = 2002,
        month = apr,
       volume = {385},
        pages = {337-364},
          doi = {10.1051/0004-6361:20011817},
archivePrefix = {arXiv},
       eprint = {astro-ph/0111367},
 primaryClass = {astro-ph},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2002A&A...385..337T},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"sunpy": {
    "tags": [
        "sunpy_community2020"
    ],
    "logo": "img/sunpy.png",
    "language": "Python",
    "category": "Solar Physics",
    "keywords": [
        "solar physics",
        "solar",
        "science",
        "sun",
        "wcs",
        "coordinates"
    ],
    "description": "sunpy is a Python software package that provides fundamental tools for accessing, loading and interacting with solar physics data in Python.",
    "link": "https://docs.sunpy.org/en/stable/",
    "attribution_link": "https://docs.sunpy.org/en/stable/citation.html",
    "zenodo_doi": "10.5281/zenodo.591887",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "scipy"
    ]
}

@ARTICLE{sunpy_community2020,
  doi = {10.3847/1538-4357/ab4f7a},
  url = {https://iopscience.iop.org/article/10.3847/1538-4357/ab4f7a},
  author = {{The SunPy Community} and Barnes, Will T. and Bobra, Monica G. and Christe, Steven D. and Freij, Nabil and Hayes, Laura A. and Ireland, Jack and Mumford, Stuart and Perez-Suarez, David and Ryan, Daniel F. and Shih, Albert Y. and Chanda, Prateek and Glogowski, Kolja and Hewett, Russell and Hughitt, V. Keith and Hill, Andrew and Hiware, Kaustubh and Inglis, Andrew and Kirk, Michael S. F. and Konge, Sudarshan and Mason, James Paul and Maloney, Shane Anthony and Murray, Sophie A. and Panda, Asish and Park, Jongyeob and Pereira, Tiago M. D. and Reardon, Kevin and Savage, Sabrina and Sipőcz, Brigitta M. and Stansby, David and Jain, Yash and Taylor, Garrison and Yadav, Tannmay and Rajul and Dang, Trung Kien},
  title = {The SunPy Project: Open Source Development and Status of the Version 1.0 Core Package},
  journal = {The Astrophysical Journal},
  volume = {890},
  issue = {1},
  pages = {68-},
  publisher = {American Astronomical Society},
  year = {2020}
}

"Inseq": {
    "tags": [
        "sarti-etal-2023-inseq"
    ],
    "logo": "https://inseq.org/en/latest/_static/inseq_logo.png",
    "language": "Python",
    "category": "Machine Learning",
    "keywords": [
        "interpretability",
        "explainable ai",
        "transformers",
        "language models"
    ],
    "description": "Inseq is a Pytorch-based hackable toolkit to democratize the study of interpretability for sequence generation models.",
    "link": "https://inseq.org/en/latest/",
    "attribution_link": "https://github.com/inseq-team/inseq#citing-inseq",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "Jupyter",
        "matplotlib",
        "numpy",
        "python",
        "transformers"
    ]
}

@inproceedings{sarti-etal-2023-inseq,
    title = "Inseq: An Interpretability Toolkit for Sequence Generation Models",
    author = "Sarti, Gabriele  and
      Feldhus, Nils  and
      Sickert, Ludwig  and
      van der Wal, Oskar and
      Nissim, Malvina and
      Bisazza, Arianna",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.40",
    doi = "10.18653/v1/2023.acl-demo.40",
    pages = "421--435",
}

"'ELK'": {
    "tags": [
        "Wainer_JOSS",
        "Wainer_AJ"
    ],
    "logo": "img/'ELK'.png",
    "language": "Python",
    "category": "Stars",
    "keywords": [],
    "description": "This package allows you to download, correct, analyze and visualize integrated light curves from TESS FFI data.",
    "link": "https://elk.readthedocs.io/en/latest/",
    "attribution_link": "https://elk.readthedocs.io/en/latest/pages/cite.html",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "Lightkurve"
    ]
}

@ARTICLE{Wainer_JOSS,
      author = {{Wainer}, Tobin M. and {Wagg}, Tom and {Poovelil}, Vijith Jacob and {Zasowski}, Gail},
        title = "{ELK: A python package for correcting, analyzing, and diagnosing TESS integrated light curves}",
      journal = {The Journal of Open Source Software},
    keywords = {astronomy, variability, blending, star clusters, Python, Jupyter Notebook},
        year = 2023,
        month = oct,
      volume = {8},
      number = {90},
          eid = {5605},
        pages = {5605},
          doi = {10.21105/joss.05605},
      adsurl = {https://ui.adsabs.harvard.edu/abs/2023JOSS....8.5605W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
} 

@ARTICLE{Wainer_AJ,
    author = {{Wainer}, Tobin M. and {Zasowski}, Gail and {Pepper}, Joshua and {Wagg}, Tom and {Hedges}, Christina L. and {Poovelil}, Vijith Jacob and {Fetherolf}, Tara and {Davenport}, James R.~A. and {Christodoulou}, P. Marios and {Dinsmore}, Jack T. and {Patel}, Avi and {Goold}, Kameron and {Gibson}, Benjamin J.},
     title = "{Catalog of Integrated-light Star Cluster Light Curves in TESS}",
   journal = {\aj},
  keywords = {Star clusters, Time series analysis, Light curves, Variable stars, 1567, 1916, 918, 1761, Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics},
      year = 2023,
     month = sep,
    volume = {166},
    number = {3},
       eid = {106},
     pages = {106},
       doi = {10.3847/1538-3881/ace960},
    adsurl = {https://ui.adsabs.harvard.edu/abs/2023AJ....166..106W},
   adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"PetroFit": {
    "tags": [
        "2022AJ....163..202G",
    ],
    "logo": "img/PetroFit.svg",
    "language": "Python",
    "category": "Imaging",
    "keywords": [
        "sersic",
        "petrosian",
        "petrofit"
    ],
    "description": "Python package for calculating Petrosian properties and fitting galaxy light profiles.  PetroFit includes tools for Petrosian profiling and image model  fitting (Sérsic etc.).",
    "link": "https://petrofit.readthedocs.io/en/latest/",
    "attribution_link": "https://petrofit.readthedocs.io/en/latest/citing.html",
    "zenodo_doi": "10.5281/zenodo.6040780",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "numpy",
        "python",
        "scipy"
    ]
}

@ARTICLE{2022AJ....163..202G,
       author = {{Geda}, Robel and {Crawford}, Steven M. and {Hunt}, Lucas and {Bershady}, Matthew and {Tollerud}, Erik and {Randriamampandry}, Solohery},
        title = "{PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles}",
      journal = {\aj},
     keywords = {Astronomy software, CCD photometry, Regression, Galaxies, 1855, 208, 1914, 573, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies},
         year = 2022,
        month = may,
       volume = {163},
       number = {5},
          eid = {202},
        pages = {202},
          doi = {10.3847/1538-3881/ac5908},
archivePrefix = {arXiv},
       eprint = {2202.13493},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022AJ....163..202G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"MOSFiT": {
    "tags": [
        "2018ApJS..236....6G",
        "2017ascl.soft10006G"
    ],
    "logo": "img/MOSFiT.png",
    "language": "Python",
    "category": "Transients",
    "keywords": [
        "Transients",
        "Light Curves",
        "Supernovae",
        "Tidal Disruption Events"
    ],
    "description": "A package for fitting, sharing, and estimating the parameters of transients via user-contributed transient models.",
    "link": "https://mosfit.readthedocs.io/en/latest/#",
    "attribution_link": "https://github.com/guillochon/MOSFiT",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "scipy",
        "emcee",
        "schwimmbad",
        "seaborn"
    ]
}

@ARTICLE{2018ApJS..236....6G,
       author = {{Guillochon}, James and {Nicholl}, Matt and {Villar}, V. Ashley and {Mockler}, Brenna and {Narayan}, Gautham and {Mandel}, Kaisey S. and {Berger}, Edo and {Williams}, Peter K.~G.},
        title = "{MOSFiT: Modular Open Source Fitter for Transients}",
      journal = {\apjs},
     keywords = {catalogs, methods: data analysis, methods: numerical, methods: statistical, supernovae: general, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena},
         year = 2018,
        month = may,
       volume = {236},
       number = {1},
          eid = {6},
        pages = {6},
          doi = {10.3847/1538-4365/aab761},
archivePrefix = {arXiv},
       eprint = {1710.02145},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2018ApJS..236....6G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@software{2017ascl.soft10006G,
       author = {{Guillochon}, James and {Nicholl}, Matt and {Villar}, V. Ashley and {Mockler}, Brenna and {Narayan}, Gautham and {Mandel}, Kaisey S. and {Berger}, Edo and {Williams}, Peter K.~G.},
        title = "{MOSFiT: Modular Open-Source Fitter for Transients}",
 howpublished = {Astrophysics Source Code Library, record ascl:1710.006},
         year = 2017,
        month = oct,
          eid = {ascl:1710.006},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2017ascl.soft10006G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"Lightkurve": {
    "tags": [
        "2018ascl.soft12013L"
    ],
    "logo": "",
    "language": "Python",
    "category": "Data Handling",
    "keywords": [
        "photometry",
        " Kepler",
        " TESS"
    ],
    "description": "Lightkurve offers a user-friendly way to analyze time series data on the brightness of planets, stars, and galaxies. The package is focused on supporting science with NASA’s Kepler and TESS.",
    "link": "https://docs.lightkurve.org/",
    "attribution_link": "https://docs.lightkurve.org/about/citing.html",
    "zenodo_doi": "10.5281/zenodo.1181928",
    "custom_citation": ""
}

@MISC{2018ascl.soft12013L,
   author = {{Lightkurve Collaboration} and {Cardoso}, J.~V.~d.~M. and
             {Hedges}, C. and {Gully-Santiago}, M. and {Saunders}, N. and
             {Cody}, A.~M. and {Barclay}, T. and {Hall}, O. and
             {Sagear}, S. and {Turtelboom}, E. and {Zhang}, J. and
             {Tzanidakis}, A. and {Mighell}, K. and {Coughlin}, J. and
             {Bell}, K. and {Berta-Thompson}, Z. and {Williams}, P. and
             {Dotson}, J. and {Barentsen}, G.},
    title = "{Lightkurve: Kepler and TESS time series analysis in Python}",
 keywords = {Software, NASA},
howpublished = {Astrophysics Source Code Library},
     year = 2018,
    month = dec,
archivePrefix = "ascl",
   eprint = {1812.013},
   adsurl = {http://adsabs.harvard.edu/abs/2018ascl.soft12013L},
}

"spindler": {
    "tags": [
        "valli_longterm_2024"
    ],
   "logo": "",
    "language": "Python",
    "category": "General",
    "keywords": [
        "circumbinary disk",
        "supermassive black hole binary",
        "binary star formation",
        "post common envelope"
    ],
    "description": "A python package to compute the long-term orbital evolution of a binary interacting with a circumbinary disk",
    "link": "https://spindler.readthedocs.io/en/latest/",
    "attribution_link": "https://github.com/ruggero-valli/spindler/blob/master/README.md#citing-spindler",
    "zenodo_doi": "10.5281/zenodo.10529059",
    "custom_citation": "",
    "dependencies": [
        "numpy",
        "pandas",
        "python",
        "scipy"
    ]
}

@ARTICLE{valli_longterm_2024,
       author = {{Valli}, Ruggero and {Tiede}, Christopher and {Vigna-G{\'o}mez}, Alejandro and {Cuadra}, Jorge and {Siwek}, Magdalena and {Ma}, Jing-Ze and {D'Orazio}, Daniel J. and {Zrake}, Jonathan and {de Mink}, Selma E.},
        title = "{Long-term Evolution of Binary Orbits Induced by Circumbinary Disks}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics},
         year = 2024,
        month = jan,
          eid = {arXiv:2401.17355},
        pages = {arXiv:2401.17355},
          doi = {10.48550/arXiv.2401.17355},
archivePrefix = {arXiv},
       eprint = {2401.17355},
 primaryClass = {astro-ph.HE},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240117355V},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"galpy": {
    "tags": [
        "2015ApJS..216...29B"
    ],
    "logo": "img/galpy.png",
    "language": "Python",
    "category": "Galactic Dynamics",
    "keywords": [
        "galaxies",
        "galactic dynamics",
        "dynamics",
        "orbits",
        "actions",
        "distribution functions"
    ],
    "description": "galpy is a Python package for galactic dynamics. It supports orbit integration in a variety of potentials, various distribution functions, and the calculation of action-angle coordinates.",
    "link": "https://docs.galpy.org/",
    "attribution_link": "https://docs.galpy.org/en/latest/#acknowledging-galpy",
    "zenodo_doi": "",
    "custom_citation": "This work made use of \\texttt{galpy}\\footnote{Available at \\url{http://github.com/jobovy/galpy}~.} \\citep{2015ApJS..216...29B}.",
    "dependencies": [
        "matplotlib",
        "numpy",
        "scipy"
    ]
}

@ARTICLE{2015ApJS..216...29B,
       author = {{Bovy}, Jo},
        title = "{galpy: A python Library for Galactic Dynamics}",
      journal = {\apjs},
     keywords = {galaxies: general, galaxies: kinematics and dynamics, Galaxy: fundamental parameters, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2015,
        month = feb,
       volume = {216},
       number = {2},
          eid = {29},
        pages = {29},
          doi = {10.1088/0067-0049/216/2/29},
archivePrefix = {arXiv},
       eprint = {1412.3451},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2015ApJS..216...29B},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"numpyro": {
    "tags": [
        "phan2019composable",
        "bingham2019pyro"
    ],
    "logo": "img/numpyro.png",
    "language": "Python",
    "category": "Library",
    "keywords": [],
    "description": "\"NumPyro is a lightweight probabilistic programming library that provides a NumPy backend for Pyro. We rely on JAX for automatic differentiation and JIT compilation to GPU / CPU.\"",
    "link": "https://num.pyro.ai/en/stable/",
    "attribution_link": "https://num.pyro.ai/en/stable/getting_started.html#citing-numpyro",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": []
}

@article{phan2019composable,
  title={Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro},
  author={Phan, Du and Pradhan, Neeraj and Jankowiak, Martin},
  journal={arXiv preprint. arXiv:1912.11554},
  year={2019}
}

@article{bingham2019pyro,
  author    = {Eli Bingham and
               Jonathan P. Chen and
               Martin Jankowiak and
               Fritz Obermeyer and
               Neeraj Pradhan and
               Theofanis Karaletsos and
               Rohit Singh and
               Paul A. Szerlip and
               Paul Horsfall and
               Noah D. Goodman},
  title     = {Pyro: Deep Universal Probabilistic Programming},
  journal   = {J. Mach. Learn. Res.},
  volume    = {20},
  pages     = {28:1--28:6},
  year      = {2019},
  url       = {http://jmlr.org/papers/v20/18-403.html}
}

"WebbPSF": {
    "tags": [
        "2012SPIE.8442E..3DP",
        "2014SPIE.9143E..3XP"
    ],
    "logo": "img/webbpsf.png",
    "language": "Python",
    "category": "Imaging",
    "keywords": [
        "JWST",
        "Roman",
        "PSF"
    ],
    "description": "WebbPSF computes simulated point spread functions (PSFs) for NASA’s James Webb Space Telescope (JWST) and Nancy Grace Roman Space Telescope.",
    "link": "https://webbpsf.readthedocs.io/en/stable/index.html",
    "attribution_link": "https://webbpsf.readthedocs.io/en/stable/index.html",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "scipy",
        "astroquery"
    ]
}

@INPROCEEDINGS{2012SPIE.8442E..3DP,
       author = {{Perrin}, Marshall D. and {Soummer}, R{\'e}mi and {Elliott}, Erin M. and {Lallo}, Matthew D. and {Sivaramakrishnan}, Anand},
        title = "{Simulating point spread functions for the James Webb Space Telescope with WebbPSF}",
    booktitle = {Space Telescopes and Instrumentation 2012: Optical, Infrared, and Millimeter Wave},
         year = 2012,
       editor = {{Clampin}, Mark C. and {Fazio}, Giovanni G. and {MacEwen}, Howard A. and {Oschmann}, Jacobus M., Jr.},
       series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series},
       volume = {8442},
        month = sep,
          eid = {84423D},
        pages = {84423D},
          doi = {10.1117/12.925230},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2012SPIE.8442E..3DP},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@INPROCEEDINGS{2014SPIE.9143E..3XP,
       author = {{Perrin}, Marshall D. and {Sivaramakrishnan}, Anand and {Lajoie}, Charles-Philippe and {Elliott}, Erin and {Pueyo}, Laurent and {Ravindranath}, Swara and {Albert}, Lo{\"\i}c.},
        title = "{Updated point spread function simulations for JWST with WebbPSF}",
    booktitle = {Space Telescopes and Instrumentation 2014: Optical, Infrared, and Millimeter Wave},
         year = 2014,
       editor = {{Oschmann}, Jacobus M., Jr. and {Clampin}, Mark and {Fazio}, Giovanni G. and {MacEwen}, Howard A.},
       series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series},
       volume = {9143},
        month = aug,
          eid = {91433X},
        pages = {91433X},
          doi = {10.1117/12.2056689},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2014SPIE.9143E..3XP},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"Gammapy": {
    "tags": [
        "gammapy:2023"
    ],
    "logo": "img/Gammapy.png",
    "language": "Python",
    "category": "Gamma-ray astrophysics",
    "keywords": [
        "Astronomy",
        "Gamma-rays",
        "Data analysis"
    ],
    "description": "A Python package for gamma-ray astronomy: https://gammapy.org/",
    "link": "https://docs.gammapy.org/",
    "attribution_link": "https://gammapy.org/acknowledging.html",
    "zenodo_doi": "10.5281/zenodo.4701488",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "scipy",
        "regions",
        "pyyaml",
        "click",
        "pydantic",
        "iminuit",
        "naima",
        "healpy",
        "requests",
        "tqm",
        "ipywidgets",
    ]
}

@article{gammapy:2023,
    author = {{Donath}, Axel and {Terrier}, R\'egis and {Remy}, Quentin and {Sinha}, Atreyee and {Nigro}, Cosimo and
        {Pintore}, Fabio and {Kh\'elifi}, Bruno and {Olivera-Nieto}, Laura and {Ruiz}, Jose Enrique and
        {Br\"ugge}, Kai and {Linhoff}, Maximilian and {Contreras}, Jose Luis and {Acero}, Fabio and
        {Aguasca-Cabot}, Arnau and {Berge}, David and {Bhattacharjee}, Pooja and {Buchner}, Johannes and
        {Boisson}, Catherine and {Carreto Fidalgo}, David and {Chen}, Andrew and {de Bony de Lavergne}, Mathieu and
        {de Miranda Cardoso}, Jos\'e Vinicius and {Deil}, Christoph and {F\"u\ss{}ling}, Matthias and
        {Funk}, Stefan and {Giunti}, Luca and {Hinton}, Jim and {Jouvin}, L\'ea and {King}, Johannes and
        {Lefaucheur}, Julien and {Lemoine-Goumard}, Marianne and {Lenain}, Jean-Philippe and {L\'opez-Coto}, Rub\'en
        and {Mohrmann}, Lars and {Morcuende}, Daniel and {Panny}, Sebastian and {Regeard}, Maxime and {Saha}, Lab
        and {Siejkowski}, Hubert and {Siemiginowska}, Aneta and {Sip"ocz}, Brigitta M. and {Unbehaun}, Tim
        and {van Eldik}, Christopher and {Vuillaume}, Thomas and {Zanin}, Roberta},
    title = {Gammapy: A Python package for gamma-ray astronomy},
    DOI= "10.1051/0004-6361/202346488",
    url= "https://doi.org/10.1051/0004-6361/202346488",
    journal = {A&A},
    year = 2023,
    volume = 678,
    pages = "A157",
}

"X-ray: Generate and Analyse (XGA)": {
    "tags": [
        "2022arXiv220201236T",
        "2023ascl.soft01012T"
    ],
    "logo": "img/X-ray: Generate and Analyse (XGA).png",
    "language": "Python",
    "category": "X-ray Astrophysics",
    "keywords": [
        "astronomy",
        "sas",
        "astrophysics",
        "galaxy-clusters",
        "x-ray",
        "xspec",
        "heasoft",
        "x-ray-astronomy",
        "xmm",
        "xmm-science-analysis",
        "xmm-observation",
        "erosita",
        "esass"
    ],
    "description": "A generalised X-ray astronomy analysis package; given an archive of data it will identify observation relevant to your sources, generate products (with convenient interfaces) and perform analyses.",
    "link": "https://xga.readthedocs.io/",
    "attribution_link": "https://xga.readthedocs.io/en/latest/",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "pandas",
        "python",
        "scipy",
        "emcee"
    ]
}

@ARTICLE{2022arXiv220201236T,
       author = {{Turner}, D.~J. and {Giles}, P.~A. and {Romer}, A.~K. and {Korbina}, V.},
        title = "{XGA: A module for the large-scale scientific exploitation of archival X-ray astronomy data}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - High Energy Astrophysical Phenomena},
         year = 2022,
        month = feb,
          eid = {arXiv:2202.01236},
        pages = {arXiv:2202.01236},
archivePrefix = {arXiv},
       eprint = {2202.01236},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220201236T},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@software{2023ascl.soft01012T,
       author = {{Turner}, D.~J. and {Giles}, P.~A. and {Romer}, A.~K. and {Korbina}, V.},
        title = "{XGA: Efficient analysis of XMM observations}",
 howpublished = {Astrophysics Source Code Library, record ascl:2301.012},
         year = 2023,
        month = jan,
          eid = {ascl:2301.012},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023ascl.soft01012T},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"pysersic": {
    "tags": [
        "Pasha2023"
    ],
    "logo": "https://github.com/pysersic/pysersic/blob/main/misc/pysersic.png",
    "language": "Python",
    "category": "General",
    "keywords": [
        "profile fitting",
        "galaxy morphology",
        "sersic",
        "bayesian",
        ""
    ],
    "description": "A Python package for determining galaxy structural properties via Bayesian inference, accelerated with jax.",
    "link": "https://pysersic.readthedocs.io",
    "attribution_link": "https://github.com/pysersic/pysersic",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "JAX"
    ]
}

@article{Pasha2023,
        doi = {10.21105/joss.05703},
        url = {https://doi.org/10.21105/joss.05703},
        year = {2023},
        publisher = {The Open Journal},
        volume = {8},
        number = {89},
        pages = {5703},
        author = {Imad Pasha and Tim B. Miller},
        title = {pysersic: A Python package for determining galaxy structural properties via Bayesian inference, accelerated with jax},
        journal = {Journal of Open Source Software} }

"Agama": {
    "tags": [
        "2019MNRAS.482.1525V"
    ],
    "logo": "img/agama.png",
    "language": "C++",
    "category": "Galactic Dynamics",
    "description": "C++ package for galactic dynamics, with Python & Julia interfaces, plugins for NEMO & AMUSE, and interoperability with Gala & Galpy",
    "link": "http://arxiv.org/abs/1802.08255"
}

@ARTICLE{2019MNRAS.482.1525V,
       author = {{Vasiliev}, Eugene},
        title = "{AGAMA: action-based galaxy modelling architecture}",
      journal = {\mnras},
         year = 2019,
        month = jan,
       volume = {482},
       number = {2},
        pages = {1525-1544},
          doi = {10.1093/mnras/sty2672},
archivePrefix = {arXiv},
       eprint = {1802.08239},
 primaryClass = {astro-ph.GA}
}

"FLEET": {
    "tags": [
        "2020ApJ...904...74G",
        "2020zndo...4013965G"
    ],
    "logo": "img/FLEET.png",
    "language": "Python",
    "category": "Transients",
    "keywords": [
        "Transients",
        "Supernovae",
        "Machine Learning",
        "Tidal Disruption Events"
    ],
    "description": "A machine learning pipeline designed to predict the probability of transients to be either a superluminous supernova or a tidal disruption event.",
    "link": "https://github.com/gmzsebastian/FLEET/blob/master/README.md",
    "attribution_link": "https://github.com/gmzsebastian/FLEET",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "scipy",
        "astroquery"
    ]
}

@ARTICLE{2020ApJ...904...74G,
       author = {{Gomez}, Sebastian and {Berger}, Edo and {Blanchard}, Peter K. and {Hosseinzadeh}, Griffin and {Nicholl}, Matt and {Villar}, V. Ashley and {Yin}, Yao},
        title = "{FLEET: A Redshift-agnostic Machine Learning Pipeline to Rapidly Identify Hydrogen-poor Superluminous Supernovae}",
      journal = {\apj},
     keywords = {Supernovae, Core-collapse supernovae, Surveys, 1668, 304, 1671, Astrophysics - High Energy Astrophysical Phenomena},
         year = 2020,
        month = nov,
       volume = {904},
       number = {1},
          eid = {74},
        pages = {74},
          doi = {10.3847/1538-4357/abbf49},
archivePrefix = {arXiv},
       eprint = {2009.01853},
 primaryClass = {astro-ph.HE},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020ApJ...904...74G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@software{2020zndo...4013965G,
       author = {{Gomez}, Sebastian and {Berger}, Edo and {Blanchard}, Peter K. and {Hosseinzadeh}, Griffin and {Nicholl}, Matt and {Villar}, V. Ashley and {Yin}, Yao},
        title = "{FLEET Finding Luminous and Exotic Extragalactic Transients}",
         year = 2020,
        month = sep,
          eid = {10.5281/zenodo.4013965},
          doi = {10.5281/zenodo.4013965},
      version = {1.0.0},
    publisher = {Zenodo},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020zndo...4013965G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"Redback": {
    "tags": [
        "sarin_redback"
    ],
    "logo": "img/Redback.png",
    "language": "Python",
    "category": "Transients",
    "keywords": [],
    "description": "A package for simulating, modelling, and inference of all electromagnetic transients and multi-messenger inference.",
    "link": "https://redback.readthedocs.io/en/latest/",
    "attribution_link": "https://redback.readthedocs.io/en/latest/acknowledgements.html#general-citation-text",
    "zenodo_doi": "",
    "custom_citation": "Instructions available at https://redback.readthedocs.io/en/latest/acknowledgements.html#general-citation-text",
    "dependencies": [
        "numpy",
        "scipy"
    ]
}

@ARTICLE{sarin_redback,
       author = {{Sarin}, Nikhil and {H{\"u}bner}, Moritz and {Omand}, Conor M.~B. and {Setzer}, Christian N. and et al.},
        title = "{Redback: A Bayesian inference software package for electromagnetic transients}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - High Energy Astrophysical Phenomena},
         year = 2023,
        month = aug,
          eid = {arXiv:2308.12806},
        pages = {arXiv:2308.12806},
archivePrefix = {arXiv},
       eprint = {2308.12806},
 primaryClass = {astro-ph.HE},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230812806S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"VISTA": {
    "tags": [
        "1988igbo.conf..443S"
    ],
    "logo": "",
    "language": "Fortran",
    "category": "Photometry",
    "keywords": [
        "old school",
        "photometric modeling",
        "photometric profiles"
    ],
    "description": "VISTA is an astronomical CCD data reduction and analysis software package",
    "link": "http://ganymede.nmsu.edu/holtz/xvista/help/man/index.html",
    "attribution_link": "http://ganymede.nmsu.edu/holtz/xvista/",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": []
}

@INPROCEEDINGS{1988igbo.conf..443S,
       author = {{Stover}, R.~J.},
        title = "{The Vista Astronomical CCD Data Reduction and Analysis Software}",
    booktitle = {Instrumentation for Ground-Based Optical Astronomy},
         year = 1988,
        month = jan,
        pages = {443},
       adsurl = {https://ui.adsabs.harvard.edu/abs/1988igbo.conf..443S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"Rapster": {
    "tags": [
        "Kritos:2022ggc"
    ],
    "logo": "img/Rapster.png",
    "language": "Python",
    "category": "Population Synthesis",
    "keywords": [
        "binary black hole mergers",
        "dynamical formation",
        "star clusters"
    ],
    "description": "Rapid population synthesis code for binary black hole mergers in dynamical environments.",
    "link": "https://github.com/Kkritos/Rapster",
    "attribution_link": "https://github.com/Kkritos/Rapster?tab=readme-ov-file#citingthiswork",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": []
}

@article{Kritos:2022ggc,
    author = "Kritos, Konstantinos and Strokov, Vladimir and Baibhav, Vishal and Berti, Emanuele",
    title = "{Dynamical formation of black hole binaries in dense star clusters: the Rapster code}",
    eprint = "2210.10055",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.HE",
    month = "10",
    year = "2022"
}

"zeus": {
    "tags": [
        "karamanis2021zeus",
        "karamanis2020ensemble"
    ],
    "logo": "img/zeus.png",
    "language": "Python",
    "category": "Statistics",
    "keywords": [
        "mcmc",
        "Bayesian inference",
        "data analysis",
        "sampling",
        "markov chain monte carlo",
        "astronomy",
        "cosmology",
        "posterior"
    ],
    "description": "Lightning-fast MCMC",
    "link": "https://zeus-mcmc.readthedocs.io",
    "attribution_link": "https://github.com/minaskar/zeus",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "numpy",
        "scipy",
        "scikit-learn"
    ]
}

@article{karamanis2021zeus,
  title={zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference},
  author={Karamanis, Minas and Beutler, Florian and Peacock, John A},
  journal={arXiv preprint arXiv:2105.03468},
  year={2021}
}

@article{karamanis2020ensemble,
    title = {Ensemble slice sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions},
    author = {Karamanis, Minas and Beutler, Florian},
    journal = {arXiv preprint arXiv: 2002.06212},
    year = {2020}
}

"Pydantic": {
    "tags": [],
    "logo": "img/pydantic_logo.png",
    "language": "Python",
    "category": "Data Handling",
    "keywords": [
        "python",
        "validation",
        "parsing",
        "json-schema",
        "hints",
        "typing"
    ],
    "description": "Pydantic is the most widely used data validation library for Python.",
    "link": "https://docs.pydantic.dev/latest/",
    "attribution_link": "https://github.com/pydantic/pydantic/blob/main/CITATION.cff",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": []
}

"ligo-python": {
    "tags": [],
    "logo": "",
    "language": "Python",
    "category": "Gravitational Waves",
    "keywords": [
        "Gamma-Ray Bursts"
    ],
    "description": "A Python software package designed for low-latency coincidence searches between external triggers and gravitational wave (GW) candidates",
    "link": "https://ligo-raven.readthedocs.io/en/latest/index.html",
    "attribution_link": "https://pypi.org/project/ligo-raven/",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "numpy",
        "scipy",
        "h5py",
        "graced-sdk",
        "hpmoc",
        "ligo-gracedb",
        "matplotlib",
        "astropy-healpix",
        "ligo.skymap"
    ]
}

"REBOUND": {
    "tags": [
        "2012A&A...537A.128R"
    ],
    "logo": "img/REBOUND.png",
    "language": "Python",
    "category": "N-body",
    "keywords": [
        "whfast",
        "ias15",
        "nbody",
        "planetary dynamics"
    ],
    "description": "REBOUND is an N-body integrator, i.e. a software package that can integrate the motion of particles under the influence of gravity.",
    "link": "https://rebound.readthedocs.io/en/latest/",
    "attribution_link": "https://rebound.readthedocs.io/en/latest/#acknowledgements",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": []
}

@ARTICLE{2012A&A...537A.128R,
       author = {{Rein}, H. and {Liu}, S. -F.},
        title = "{REBOUND: an open-source multi-purpose N-body code for collisional dynamics}",
      journal = {\aap},
     keywords = {methods: numerical, planets and satellites: rings, protoplanetary disks, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Mathematics - Dynamical Systems, Physics - Computational Physics},
         year = 2012,
        month = jan,
       volume = {537},
          eid = {A128},
        pages = {A128},
          doi = {10.1051/0004-6361/201118085},
archivePrefix = {arXiv},
       eprint = {1110.4876},
 primaryClass = {astro-ph.EP},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2012A&A...537A.128R},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"Arepo": {
    "tags": [
        "2010MNRAS.401..791S",
        "2011MNRAS.418.1392P",
        "2013MNRAS.432..176P",
        "2016MNRAS.455.1134P",
        "2020ApJS..248...32W"
    ],
    "logo": "img/Arepo.png",
    "language": "C",
    "category": "N-body",
    "keywords": [],
    "description": "AREPO is a massively parallel code for gravitational n-body systems and hydrodynamics, both on Newtonian as well as cosmological background.",
    "link": "https://arepo-code.org/",
    "attribution_link": "https://gitlab.mpcdf.mpg.de/vrs/arepo",
    "zenodo_doi": "",
    "custom_citation": "A description of the numerical algorithms employed by the Arepo code is given in the original code papers \\citep{2010MNRAS.401..791S,2011MNRAS.418.1392P,2013MNRAS.432..176P,2016MNRAS.455.1134P} and the release paper of the public version \\citep{2013MNRAS.432..176P}.",
    "dependencies": []
}

@ARTICLE{2010MNRAS.401..791S,
       author = {{Springel}, Volker},
        title = "{E pur si muove: Galilean-invariant cosmological hydrodynamical simulations on a moving mesh}",
      journal = {\mnras},
     keywords = {methods: numerical, galaxies: interactions, cosmology: dark matter, Astrophysics - Cosmology and Nongalactic Astrophysics},
         year = 2010,
        month = jan,
       volume = {401},
       number = {2},
        pages = {791-851},
          doi = {10.1111/j.1365-2966.2009.15715.x},
archivePrefix = {arXiv},
       eprint = {0901.4107},
 primaryClass = {astro-ph.CO},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2010MNRAS.401..791S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{2011MNRAS.418.1392P,
       author = {{Pakmor}, Ruediger and {Bauer}, Andreas and {Springel}, Volker},
        title = "{Magnetohydrodynamics on an unstructured moving grid}",
      journal = {\mnras},
     keywords = {MHD, turbulence, methods: numerical, stars: formation, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2011,
        month = dec,
       volume = {418},
       number = {2},
        pages = {1392-1401},
          doi = {10.1111/j.1365-2966.2011.19591.x},
archivePrefix = {arXiv},
       eprint = {1108.1792},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2011MNRAS.418.1392P},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{2013MNRAS.432..176P,
       author = {{Pakmor}, R{\"u}diger and {Springel}, Volker},
        title = "{Simulations of magnetic fields in isolated disc galaxies}",
      journal = {\mnras},
     keywords = {MHD, methods: numerical, galaxies: formation, Astrophysics - Cosmology and Extragalactic Astrophysics},
         year = 2013,
        month = jun,
       volume = {432},
       number = {1},
        pages = {176-193},
          doi = {10.1093/mnras/stt428},
archivePrefix = {arXiv},
       eprint = {1212.1452},
 primaryClass = {astro-ph.CO},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2013MNRAS.432..176P},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{2016MNRAS.455.1134P,
       author = {{Pakmor}, R{\"u}diger and {Springel}, Volker and {Bauer}, Andreas and {Mocz}, Philip and {Munoz}, Diego J. and {Ohlmann}, Sebastian T. and {Schaal}, Kevin and {Zhu}, Chenchong},
        title = "{Improving the convergence properties of the moving-mesh code AREPO}",
      journal = {\mnras},
     keywords = {hydrodynamics, methods: numerical, galaxy: formation, Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2016,
        month = jan,
       volume = {455},
       number = {1},
        pages = {1134-1143},
          doi = {10.1093/mnras/stv2380},
archivePrefix = {arXiv},
       eprint = {1503.00562},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2016MNRAS.455.1134P},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{2020ApJS..248...32W,
       author = {{Weinberger}, Rainer and {Springel}, Volker and {Pakmor}, R{\"u}diger},
        title = "{The AREPO Public Code Release}",
      journal = {\apjs},
     keywords = {Computational methods, Computational astronomy, Magnetohydrodynamics, Astrophysical fluid dynamics, Gravitation, Galaxy formation, Hydrodynamics, Large-scale structure of the universe, 1965, 293, 1964, 101, 661, 595, 767, 902, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Physics - Computational Physics},
         year = 2020,
        month = jun,
       volume = {248},
       number = {2},
          eid = {32},
        pages = {32},
          doi = {10.3847/1538-4365/ab908c},
archivePrefix = {arXiv},
       eprint = {1909.04667},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020ApJS..248...32W},
       adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"Photutils": {
    "tags": [],
    "logo": "img/Photutils.svg",
    "language": "Python",
    "category": "Imaging",
    "keywords": [
        "python",
        "astronomy",
        "photometry",
        "astropy",
        "astropy-affiliated",
        "source-detection"
    ],
    "description": "Photutils is an affiliated package of Astropy that primarily provides tools for detecting and performing photometry of astronomical sources.",
    "link": "https://photutils.readthedocs.io/",
    "attribution_link": "https://photutils.readthedocs.io/en/stable/citation.html",
    "zenodo_doi": "10.5281/zenodo.596036",
    "custom_citation": "This research made use of Photutils, an Astropy package for\ndetection and photometry of astronomical sources (Bradley et al.\n<YEAR>).",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "scipy",
        "scikit-image"
    ]
}

@software{larry_bradley_2024_10967176,
  author       = {Larry Bradley and
                  Brigitta Sipőcz and
                  Thomas Robitaille and
                  Erik Tollerud and
                  Zé Vinícius and
                  Christoph Deil and
                  Kyle Barbary and
                  Tom J Wilson and
                  Ivo Busko and
                  Axel Donath and
                  Hans Moritz Günther and
                  Mihai Cara and
                  P. L. Lim and
                  Sebastian Meßlinger and
                  Zach Burnett and
                  Simon Conseil and
                  Michael Droettboom and
                  Azalee Bostroem and
                  E. M. Bray and
                  Lars Andersen Bratholm and
                  William Jamieson and
                  Adam Ginsburg and
                  Geert Barentsen and
                  Matt Craig and
                  Sergio Pascual and
                  Shivangee Rathi and
                  Marshall Perrin and
                  Brett M. Morris and
                  Gabriel Perren},
  title        = {astropy/photutils: 1.12.0},
  month        = apr,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {1.12.0},
  doi          = {10.5281/zenodo.10967176},
  url          = {https://doi.org/10.5281/zenodo.10967176}
}

"umap-learn": {
    "tags": [
        "mcinnes2018umap-software",
        "2018arXivUMAP"
    ],
    "logo": "img/umap-learn.png",
    "language": "Python",
    "category": "Visualisation",
    "keywords": [
        "visualization",
        "machine-learning",
        "dimensionality-reduction",
        "umap",
        "topological-data-analysis"
    ],
    "description": "Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction.",
    "link": "https://umap-learn.readthedocs.io/",
    "attribution_link": "https://github.com/lmcinnes/umap?tab=readme-ov-file#citation",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "numpy",
        "scipy",
        "Numba",
        "scikit-learn",
        "tqdm"
    ]
}

@article{mcinnes2018umap-software,
  title={UMAP: Uniform Manifold Approximation and Projection},
  author={McInnes, Leland and Healy, John and Saul, Nathaniel and Grossberger, Lukas},
  journal={The Journal of Open Source Software},
  volume={3},
  number={29},
  pages={861},
  year={2018}
}

@article{2018arXivUMAP,
     author = {{McInnes}, L. and {Healy}, J. and {Melville}, J.},
     title = "{UMAP: Uniform Manifold Approximation
     and Projection for Dimension Reduction}",
     journal = {ArXiv e-prints},
     archivePrefix = "arXiv",
     eprint = {1802.03426},
     primaryClass = "stat.ML",
     keywords = {Statistics - Machine Learning,
                 Computer Science - Computational Geometry,
                 Computer Science - Learning},
     year = 2018,
     month = feb,
}

"Paicos": {
    "tags": [
        "Berlok_Paicos_A_Python_2024"
    ],
    "logo": "",
    "language": "Python",
    "category": "Visualisation",
    "keywords": [],
    "description": "An object-oriented Python package for analysis of (cosmological) simulations performed with Arepo.",
    "link": "https://paicos.readthedocs.io/",
    "attribution_link": "https://github.com/tberlok/paicos",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "numpy",
        "python",
        "scipy",
        "Cython",
        "h5py",
        "Numba"
    ]
}

@article{Berlok_Paicos_A_Python_2024,
author = {Berlok, Thomas and Jlassi, Léna and Puchwein, Ewald and Haugbølle, Troels},
doi = {10.21105/joss.06296},
journal = {Journal of Open Source Software},
month = apr,
number = {96},
pages = {6296},
title = {{Paicos: A Python package for analysis of (cosmological) simulations performed with Arepo}},
url = {https://joss.theoj.org/papers/10.21105/joss.06296},
volume = {9},
year = {2024}
}

"transformers": {
    "tags": [
        "wolf-etal-2020-transformers"
    ],
    "logo": "img/transformers.png",
    "language": "Python",
    "category": "Machine Learning",
    "keywords": [
        "python",
        "nlp",
        "machine-learning",
        "natural-language-processing",
        "deep-learning",
        "tensorflow",
        "pytorch",
        "transformer",
        "speech-recognition",
        "seq2seq",
        "flax",
        "pretrained-models",
        "language-models",
        "nlp-library",
        "language-model",
        "hacktoberfest",
        "bert",
        "jax",
        "pytorch-transformers",
        "model-hub"
    ],
    "description": "Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.",
    "link": "https://huggingface.co/docs/transformers/index",
    "attribution_link": "https://github.com/huggingface/transformers?tab=readme-ov-file#citation",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": []
}

@inproceedings{wolf-etal-2020-transformers,
    title = "Transformers: State-of-the-Art Natural Language Processing",
    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
    pages = "38--45"
}

"PyNeb": {
    "tags": [
        "2015Luridiana_aap573"
    ],
    "logo": "img/PyNeb.png",
    "language": "Python",
    "category": "Interstellar Medium",
    "keywords": [],
    "description": "PyNeb (Luridiana V., Morisset C. and Shaw, R. A 2013) is a modern python tool to compute emission line emissivities (recombination and collisionally excited lines).",
    "link": "https://github.com/Morisset/PyNeb_devel/tree/master/docs",
    "attribution_link": "https://github.com/Morisset/PyNeb_devel",
    "zenodo_doi": "10.5281/zenodo.1246922",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "scipy"
    ]
}

@article{2015Luridiana_aap573,
	author = {{Luridiana}, V. and {Morisset}, C. and {Shaw}, R.~A.},
	journal = {\aap},
	month = jan,
	pages = {A42},
	title = {{PyNeb: a new tool for analyzing emission lines. I. Code description and validation of results}},
	volume = 573,
	year = 2015}

"Bilby": {
    "tags": [
        "bilby_paper"
    ],
    "logo": "img/Bilby.jpeg",
    "language": "Python",
    "category": "Gravitational Waves",
    "keywords": [],
    "description": "Bilby: a user-friendly Bayesian inference library.",
    "link": "https://lscsoft.docs.ligo.org/bilby/index.html",
    "attribution_link": "https://lscsoft.docs.ligo.org/bilby/citing-bilby.html",
    "zenodo_doi": "10.5281/zenodo.2602177",
    "custom_citation": "",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "scipy",
        "corner.py"
    ]
}

@article{bilby_paper,
    author = "Ashton, Gregory and others",
    title = "{BILBY: A user-friendly Bayesian inference library for gravitational-wave astronomy}",
    eprint = "1811.02042",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.IM",
    doi = "10.3847/1538-4365/ab06fc",
    journal = "Astrophys. J. Suppl.",
    volume = "241",
    number = "2",
    pages = "27",
    year = "2019"
}

"Rosetta": {
    "tags": [
        "leaver-fay_rosetta3_2011"
    ],
    "logo": "img/Rosetta.png",
    "language": "C++",
    "category": "Biochemistry",
    "keywords": [
        "proteins",
        "protein",
        "peptides",
        "peptide",
        "nucleic acids",
        "nucleic acid",
        "DNA",
        "RNA",
        "amino acid",
        "structure prediction",
        "design",
        "Monte Carlo",
        "simulated annealing",
        "docking"
    ],
    "description": "A suite of C++ libraries and applications for designing, docking, and predicting structures of proteins, peptides, nucleic acids, glycans, and other biomolecules.",
    "link": "https://new.rosettacommons.org/docs/latest/Home",
    "attribution_link": "https://new.rosettacommons.org/docs/latest/getting_started/FAQ#frequently-asked-questions_how-to-cite-rosetta-in-papers",
    "zenodo_doi": "",
    "custom_citation": "See additional output from Rosetta runs for particular papers to cite for specific Rosetta applications or modules.",
    "dependencies": [
        "python",
        "tensorflow"
    ]
}

@incollection{leaver-fay_rosetta3_2011,
        title = {Rosetta3},
        volume = {487},
        isbn = {978-0-12-381270-4},
        language = {en},
        booktitle = {Methods in {Enzymology}}, 
        publisher = {Elsevier},
        author = {Leaver-Fay, Andrew and Tyka, Michael and Lewis, Steven M. and Lange, Oliver F. and Thompson, James and Jacak, Ron and Kaufman, Kristian W. and Renfrew, P. Douglas and Smith, Colin A. and Sheffler, Will and Davis, Ian W. and Cooper, Seth and Treuille, Adrien and Mandell, Daniel J. and Richter, Florian and Ban, Yih-En Andrew and Fleishman, Sarel J. and Corn, Jacob E. and Kim, David E. and Lyskov, Sergey and Berrondo, Monica and Mentzer, Stuart and Popović, Zoran and Havranek, James J. and Karanicolas, John and Das, Rhiju and Meiler, Jens and Kortemme, Tanja and Gray, Jeffrey J. and Kuhlman, Brian and Baker, David and Bradley, Philip}, 
        year = {2011},
        doi = {10.1016/B978-0-12-381270-4.00019-6},
        pages = {545--574},
}

"healpy": {
    "tags": [
        "Zonca2019",
        "2005ApJ...622..759G"
    ],
    "logo": "img/healpy.jpeg",
    "language": "Python",
    "category": "Data Handling",
    "keywords": [],
    "description": "healpy is a Python package to handle pixelated data on the sphere.",
    "link": "http://healpy.readthedocs.org/",
    "attribution_link": "https://github.com/healpy/healpy/blob/main/CITATION",
    "zenodo_doi": "",
    "custom_citation": "Use of the HEALPix/healpy software package should be explicitly acknowledged in all publications in the following form:\n* an acknowledgment statement – \"Some of the results in this paper have been derived using the healpy and HEALPix package\"\n* at the first use of the HEALPix acronym, a footnote placed in the main body of the paper referring to the HEALPix website – currently http://healpix.sourceforge.net",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python"
    ]
}

@article{Zonca2019,
  doi = {10.21105/joss.01298},
  url = {https://doi.org/10.21105/joss.01298},
  year = {2019},
  month = mar,
  publisher = {The Open Journal},
  volume = {4},
  number = {35},
  pages = {1298},
  author = {Andrea Zonca and Leo Singer and Daniel Lenz and Martin Reinecke and Cyrille Rosset and Eric Hivon and Krzysztof Gorski},
  title = {healpy: equal area pixelization and spherical harmonics transforms for data on the sphere in Python},
  journal = {Journal of Open Source Software}
}

@ARTICLE{2005ApJ...622..759G,
   author = {{G{\'o}rski}, K.~M. and {Hivon}, E. and {Banday}, A.~J. and 
	{Wandelt}, B.~D. and {Hansen}, F.~K. and {Reinecke}, M. and 
	{Bartelmann}, M.},
    title = "{HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere}",
  journal = {\apj},
   eprint = {arXiv:astro-ph/0409513},
 keywords = {Cosmology: Cosmic Microwave Background, Cosmology: Observations, Methods: Statistical},
     year = 2005,
    month = apr,
   volume = 622,
    pages = {759-771},
      doi = {10.1086/427976},
   adsurl = {http://adsabs.harvard.edu/abs/2005ApJ...622..759G},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

"R": {
    "tags": [
        ""
    ],
    "logo": "img/R.png",
    "language": "R",
    "category": "Statistics",
    "keywords": [
        "r"
    ],
    "description": "The R statistical programming language.",
    "link": "https://cran.r-project.org/manuals.html",
    "attribution_link": "https://cran.r-project.org/doc/FAQ/R-FAQ.html#Citing-R",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": []
}

@Manual{,
  title        = {R: A Language and Environment for Statistical
                  Computing},
  author       = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address      = {Vienna, Austria},
  year         = 2024,
  url          = {https://www.R-project.org}
}

"Photutils": {
    "tags": [],
    "logo": "img/Photutils.png",
    "language": "Python",
    "category": "Imaging",
    "keywords": [
        "python",
        "astronomy",
        "photometry",
        "astropy",
        "astropy-affiliated",
        "source-detection"
    ],
    "description": "Photutils is an affiliated package of Astropy that primarily provides tools for detecting and performing photometry of astronomical sources.",
    "link": "https://photutils.readthedocs.io/",
    "attribution_link": "https://photutils.readthedocs.io/en/stable/citation.html",
    "zenodo_doi": "10.5281/zenodo.596036",
    "custom_citation": "This research made use of Photutils, an Astropy package for\ndetection and photometry of astronomical sources (Bradley et al.\n<YEAR>).",
    "dependencies": [
        "astropy",
        "matplotlib",
        "numpy",
        "python",
        "scipy",
        "scikit-image"
    ]
}

"Colossus": {
    "tags": [
        "2018ApJS..239...35D"
    ],
    "logo": "img/Colossus.png",
    "language": "Python",
    "category": "General",
    "keywords": [
        "cosmology",
        "dark matter",
        "halo",
        "large-scale structure"
    ],
    "description": "Colossus is a python toolkit for calculations pertaining to cosmology, the large-scale structure of the universe, and the properties of dark matter halos.",
    "link": "https://bdiemer.bitbucket.io/colossus/",
    "attribution_link": "https://bdiemer.bitbucket.io/colossus/",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": []
}

@ARTICLE{2018ApJS..239...35D,
       author = {{Diemer}, Benedikt},
        title = "{COLOSSUS: A Python Toolkit for Cosmology, Large-scale Structure, and Dark Matter Halos}",
      journal = {\apjs},
     keywords = {cosmology: theory, methods: numerical, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2018,
        month = dec,
       volume = {239},
       number = {2},
          eid = {35},
        pages = {35},
          doi = {10.3847/1538-4365/aaee8c},
archivePrefix = {arXiv},
       eprint = {1712.04512},
 primaryClass = {astro-ph.CO},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2018ApJS..239...35D},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

- regions
- iminuit
- naima

"pocoMC": {
    "tags": [
        "karamanis2022accelerating",
        "karamanis2022pocomc"
    ],
    "logo": "img/pocoMC.png",
    "language": "Python",
    "category": "Statistics",
    "keywords": [
        "Bayesian inference",
        "sampling",
        "model comparison",
        "evidence",
        "data analysis",
        "mcmc",
        "Markov chain Monte Carlo",
        "Preconditioned Monte Carlo",
        "posterior",
        "likelihood"
    ],
    "description": "A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation",
    "link": "https://pocomc.readthedocs.io",
    "attribution_link": "https://github.com/minaskar/pocomc",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "numpy",
        "scipy"
    ]
}

@article{karamanis2022accelerating,
    title={Accelerating astronomical and cosmological inference with preconditioned Monte Carlo},
    author={Karamanis, Minas and Beutler, Florian and Peacock, John A and Nabergoj, David and Seljak, Uro{\v{s}}},
    journal={Monthly Notices of the Royal Astronomical Society},
    volume={516},
    number={2},
    pages={1644--1653},
    year={2022},
    publisher={Oxford University Press}
}

@article{karamanis2022pocomc,
    title={pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology},
    author={Karamanis, Minas and Nabergoj, David and Beutler, Florian and Peacock, John A and Seljak, Uros},
    journal={arXiv preprint arXiv:2207.05660},
    year={2022}
}