reference.bib

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title     = "Coding for Economists",
author    = "Turrell, Arthur, and contributors",
year      = 2021,
publisher = "Online",
url       = "https://aeturrell.github.io/coding-for-economists"
}

@book{geron2022hands,
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@book{wickham2023r,
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@misc{samuel2023computational,
      title={Computational reproducibility of Jupyter notebooks from biomedical publications}, 
      author={Sheeba Samuel and Daniel Mietchen},
      year={2023},
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      archivePrefix={arXiv},
      primaryClass={cs.DL}
}

@book{vanderplas2016python,
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@article{galianafuzzy,
  title={Fuzzy matching on big-data An illustration with scanner data and crowd-sourced nutritional data},
  author={Galiana, Lino and Castillo, Milena Suarez},
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  publisher={Proceedings of the 2022 "Journées de Méthodologie Statistiques"}
}

@book{dale2022data,
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@article{palsky2017semiologie,
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@book{wilke2019fundamentals,
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@misc{izsak2021train,
      title={How to Train BERT with an Academic Budget}, 
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      year={2021},
      eprint={2104.07705},
      archivePrefix={arXiv},
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}


@book{mckinney2012python,
  title={Python for data analysis: Data wrangling with Pandas, NumPy, and IPython},
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@article{galiana2020segregation,
  title={Residential segregation, daytime segregation and spatial frictions: an analysis from mobile phone data },
  author={Galiana, Lino and S{\'e}m{\'e}curbe, Fran{\c{c}}ois and Sakarovitch, Benjamin and Smoreda, Zbigniew},
  year={2020},
  publisher={Insee Working Paper}
}

@inproceedings{galiana2022,
  author = {Galiana, Lino and Suarez Castillo, Milena},
  title = {Fuzzy Matching on Big-Data: An Illustration with Scanner and Crowd-Sourced Nutritional Datasets},
  year = {2022},
  isbn = {9781450392846},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3524458.3547244},
  doi = {10.1145/3524458.3547244},
  abstract = {Food retailers’ scanner data provide unprecedented details on local consumption, provided that product identifiers allow a linkage with features of interest, such as nutritional information. In this paper, we enrich a large retailer dataset with nutritional information extracted from crowd-sourced and administrative nutritional datasets. To compensate for imperfect matching through the barcode, we develop a methodology to efficiently match short textual descriptions. After a preprocessing step to normalize short labels, we resort to fuzzy matching based on several tokenizers (including n-grams) by querying an ElasticSearch customized index and validate candidates echos as matches with a Levensthein edit-distance and an embedding-based similarity measure created from a siamese neural network model. The pipeline is composed of several steps successively relaxing constraints to find relevant matching candidates.},
  booktitle = {Proceedings of the 2022 ACM Conference on Information Technology for Social Good},
  pages = {331–337},
  numpages = {7},
  keywords = {ElasticSearch, Fuzzy matching, Siamese neural networks, Natural language processing, Word embeddings},
  location = {Limassol, Cyprus},
  series = {GoodIT '22}
}

@article{inseeSemiologie,
  title={Guide de sémiologie cartographique},
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@misc{strubell2019energy,
      title={Energy and Policy Considerations for Deep Learning in NLP}, 
      author={Emma Strubell and Ananya Ganesh and Andrew McCallum},
      year={2019},
      eprint={1906.02243},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@InProceedings{Rombach_2022_CVPR,
    author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
    title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {10684-10695}
}

@article{mckinney2017apache,
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  author={McKinney, Wes},
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  year={2017}
}


@article{gabelica2022many,
  title={Many researchers were not compliant with their published data sharing statement: mixed-methods study},
  author={Gabelica, Mirko and Boj{\v{c}}i{\'c}, Ru{\v{z}}ica and Puljak, Livia},
  journal={Journal of Clinical Epidemiology},
  year={2022},
  publisher={Elsevier}
}


@article{hurley2016credit,
  title={Credit scoring in the era of big data},
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  pages={148},
  year={2016},
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}

@article{charpentier2018econometrics,
  title={Econometrics and machine learning},
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  volume={505},
  number={1},
  pages={147--169},
  year={2018},
  publisher={Pers{\'e}e-Portail des revues scientifiques en SHS}
}


@article{MullainathanJEP,
Author = {Mullainathan, Sendhil and Spiess, Jann},
Title = {Machine Learning: An Applied Econometric Approach},
Journal = {Journal of Economic Perspectives},
Volume = {31},
Number = {2},
Year = {2017},
Month = {May},
Pages = {87-106},
DOI = {10.1257/jep.31.2.87},
URL = {https://www.aeaweb.org/articles?id=10.1257/jep.31.2.87}}

@article{salmon2010probleme,
  title={Le probl{\`e}me du r{\'e}alisme des hypoth{\`e}ses en {\'e}conomie politique},
  author={Salmon, Pierre},
  year={2010}
}

@incollection{friedman1953methodology,
  title={The methodology of positive economics},
  author={Friedman, Milton},
  booktitle={Essays in Positive Economics},
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  year={1953},
  address={Chicago}
}


@techreport{arcep2019,
  title={L'empreinte carbone du numérique},
  author={Arcep},
  journal={Rapport de l'Arcep},
  year={2019}
}

@misc{Reproducibilitycrisis,
  doi = {10.48550/ARXIV.2207.07048},
  url = {https://arxiv.org/abs/2207.07048},
  author = {Kapoor, Sayash and Narayanan, Arvind},
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Methodology (stat.ME), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Leakage and the Reproducibility Crisis in ML-based Science},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{davenport2012data,
  title={Data scientist, the sexiest job of the 21st century},
  author={Davenport, Thomas H and Patil, DJ},
  journal={Harvard business review},
  volume={90},
  number={5},
  pages={70--76},
  year={2012},
  url={https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century}
}

@article{davenport2022data,
  title={Is Data Scientist Still the Sexiest Job of the 21st Century?},
  author={Davenport, Thomas H and Patil, DJ},
  journal={Harvard business review},
  volume={90},
  number={5},
  pages={70--76},
  year={2012},
  url={https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century}
}