Ce jeu de données liste les jeux de données publiés dans data.gouv.nc.

The data.gouv.fr platform is the single interministerial portal intended to gather and freely make available all public information of the State, its public administrative bodies, local and regional authorities and persons governed by public or private law entrusted with a public service mission. This dataset contains information about the entire data catalogue of data.gouv.fr: La liste des jeux de données publiés sur data.gouv.fr : for each published dataset, its identification number, its title, its url, its affiliation to an organisation (where it exists), its description, its update frequency, its associated license, its temporal and spatial coverage, its creation and last update dates, its publication status (private or public), its assigned tags and some audience indicators are indicated. La liste des ressources publiées sur data.gouv.fr : for each published resource, it shall include its attachment to a dataset, its title, description, url, type, format, size, creation and last update dates and number of downloads. La liste des réutilisations publiées sur data.gouv.fr : for each re-use published, its identification number, its title, its url, its type, its description, its attachment to an organisation, its creation and last update dates, its assigned tags, its attachment to the re-used dataset and some audience indicators are indicated. La liste des organisations créées sur data.gouv.fr : for each organisation created, it includes its identification, name, url, description, logo, dates of creation and last modification and some audience indicators. La liste des tags créés sur data.gouv.fr : for each tag created is indicated the number of times it has been assigned to a dataset and reuse. La liste des discussions ouvertes sur data.gouv.fr : for each open discussion, it includes its identification number, the name of its linked user, its title, its number of messages, the content of its messages, its creation and closing dates. La liste des moissoneurs sur data.gouv.fr : for each harvester, it is indicated in particular its status (validated or pending), the name of the organization and the technology concerned. The data is collected from a script that automatically extracts the data from the database. They are published under an open license and are updated weekly.

1. Purpose of the database: This database was collected in order to organize the TissueNet Data Challenge. This dataset consists of high resolution images of microscopic slides created from cervical biopsies and surgical specimens. Additionally, the competitors were given slide metadata as well as annotations for the training set that outlined some (but not necessarily all) of the lesions present on a slide. The database shared on data.gouv.fr is a portion of the database used for the data challenge. This database contains the slides coming from the pathological centers that have agreed to share the data openly. This database includes 1272 microscopic slides of uterine cervical tissue from medical centers across France. The slides are distributed in the following datasets: diagnosed_biopsies = 443 slides diagnosed_conizations = 21 slides annotated_biopsies = 295 slides undiagnosed_biopsies = 217 slides undiagnosed_conizations = 296 slides Diagnosed_biopsies & diagnosed_conizations: Pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization (5th edition): 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level). Fully_annotated_biopsies: Pathologists have labeled and annotated these images to point out regions that represent lesions. When working with the annotations, it's important to keep in mind the following points: -- The annotated regions do not necessarily include all lesioned tissue in the slide. An unannotated region is not necessarily normal tissue. -- The whole image class label and the annotation class label do not necessarily match. The annotated regions may be the image's labeled class or below. For instance, an image labeled as a class 2 lesion could have annotations representing class 0, 1, or 2. At least some of the annotated regions will represent the most severe/labeled class. All annotations on a slide with label 0 will be normal tissue. -- The lesion may fall entirely within the square, or may extend beyond the annotation boundaries. -- All annotations are a fixed size of 300x300 micrometers. As images have different resolutions in pixels/micrometer, annotations will have different dimensions in terms of pixels. -- When using the geometries, it is important to know the origin of the coordinate system. Image processing software may assume the image origin is either the bottom left or the top left. The WKT shapes that we provide as annotations (geometry column in train_annotations.csv) are relative to the bottom left being the origin (0, 0). Undiagnosed_biopsies & undiagnosed_conizations: There are no labels for these images or corresponding annotations All images are standardized in pyramidal TIF format. These images are compressed using JPEG Q=75. The pyramidal TIF format maintains a sufficient level of detail for pathologists to perform diagnoses while enabling smaller file sizes and easier loading with actively developed Python libraries such as PyVips. 2. Context of creation of the database: This database was created as part of the TissueNet Data Challenge. This challenge began in 2019 when the French Society of Pathology (SFP) and the Health Data Hub (HDH) decided to build a challenge using a data bank of whole slide images (WSIs). Nineteen public and private pathology departments across France contributed more than 5,000 WSIs as data for the challenge. These slides are often difficult for pathologists themselves to diagnose, and expert eyes may be required. All labeled images included in the challenge were reviewed twice by expert pathologists. The database shared on data.gouv.fr is a portion of the database used for the data challenge. This database contains the slides coming from the pathological centers that have agreed to share the data openly. This database includes 1272 microscopic slides of uterine cervical tissue from medical centers across France. 3. Target: Data challenges are global competitions aimed at solving specific problems within a given time frame using highly anonymized data. Thus, these challenges are intended for data scientists (researcher, industrials, students etc.) from all around the world. The objective of the challenge was to classify each image according to the most severe category of epithelial lesion present in the sample. The classes are defined as follows: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) 4. Results obtained from the database: In the TissueNet competition, participants were tasked with building machine learning models that could predict the most severe lesions in each digital biopsy slide. What's more, participants needed to submit code for executing their solution on test data in the cloud, ensuring that the model could run fast enough on this large scale data to be useful in practice. This setup rewards models that perform well on unseen images and brings these innovations one step closer to impact. Global performance of each algorithm was evaluated according to a custom metric devised by a panel of expert pathologists. The score for each prediction equals 1 minus the error, where the error weighting for misclassification has been set by an expert consensus within the scientific council as defined in the table below. The total error is the average error across all predictions. Note that the metric is symmetric, e.g., predicting class 3 when it is actually class 0 produces the same error as predicting class 0 when it is actually class 3. Error table of misclassification: The winning solutions used clever approaches to prioritize the parts of each slide to analyze further, and built computer vision pipelines to determine the most appropriate diagnosis for the selected tissue. Models were scored not just on their accuracy, but also on the impact of their errors (providing a large penalty for mistakes that have worse consequences in practice). The top-performing model achieved over 76% accuracy in predicting the exact severity label of each slide across 4 ranked classes, including 95% accuracy for the most severe class of cancerous tissue. In addition, the top 3 solutions achieved >98% on-or-adjacent accuracy, meaning they reduced the more costly misclassifications that erred by more than one class to less than 2% of the 1,500+ slide test set! All prize-winning solutions are available under an open source license for ongoing use and learning. For more details : winning models on GitHub 5. Other informations: Here are some resources you can use in order to work with the data : -OpenSlide supports all native whole slide image formats, including: .mrxs (MIRAX) .svs (Aperio) .ndpi (Hamamatsu) -PyVips is a Python binding for libvips, a low-level library for working with large images. PyVips can be used to read and manipulate the pyramidal TIF formats. -Cytomine allows you to display and explore native whole slide images and pyramidal TIF formats in a web browser. It also supports adding annotations and executing scripts from inside Cytomine or from any computing server using the dedicated Cytomine Python client. Cytomine can be installed locally or on any Linux server. The Cytomine GitHub repository includes examples of Python scripts demonstrating how to interact with your Cytomine instance, as well as examples of ready-to-use machine learning scripts (all S_ prefixed repos, such as S_CellDetect_Stardist_HE_ROI). Here are a few papers and tutorials that talk about machine learning with WSI that you may find helpful: Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge? Le premier data challenge organisé par la Société Française de Pathologie : une compétition internationale en 2020, un outil de recherche en intelligence artificielle pour l’avenir ?The first data challenge of the french society of pathology: An international competition in 2020, a research tool in A.I. for the future? Whole slide image preprocessing in Python Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions - PubMed Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children 6. Licences: Creative Commons Attribution (CC BY 3.0) Licence Ouverte/Open Licence 2.0 (Etalab 2.0) 7. User form: USER FORM The purpose of the user form is to track who (in terms of individuals and institutions) is using the data and potentially for what purposes. This form is not restrictive in the sense that access requests will never be denied. 7. Cite: For any reuse of this database, use the DOI provided below: https://doi.org/10.60597/eaqa-k904

This dataset presents the impact indicators of the data.gouv.fr platform. The mission of data.gouv.fr is to ensure the provision of quality open data to promote transparency and efficiency of public action while facilitating the creation of new services. These indicators aim to monitor the extent to which data.gouv.fr meets its objectives. Objective 1: data.gouv.fr promotes the discoverability of open data The aim here is to measure the extent to which users find the data they need. Indicator: percentage of users who answered positively to the question "Did you find what you were looking for?" Objective 2: data.gouv.fr promotes open data quality This is to measure whether data.gouv.fr makes it easy to publish and reference quality data. Indicator: Average quality score of the 1000 most viewed datasets on the platform. Objective 3: data.gouv.fr promotes the reuse of open data The aim here is to measure the extent to which data.gouv.fr facilitates interactions between data producers and re-users. Indicator: average time for a "legitimate" response to discussions on datasets (legitimate: reply by a member of the organisation publishing the dataset or by a member of the data.gouv.fr team.) Objective 4: data.gouv.fr facilitates access to information of the most important datasets This is to measure the extent to which data.gouv.fr participates in access to information. Indicator: number of datasets in the top 100 associated with "quality" reuse (quality reuse is an editorial choice of the data.gouv.fr. team) ## Data format This dataset shall comply with the "impact of a digital public service" data scheme aimed at ensuring a smooth publication of the impact statistics of digital public services. Its use makes it possible to compare and centralize data from different products, in order to facilitate their understanding and reuse. Read more ### Description of columns - "administration_rattachement" : aadministration to which the digital public service is attached. - public_numeric_service_name: name of the digital public service - "indicator": Name of indicator. - "value" : vvalue of the indicator, as determined on the date indicated in the field 'date'. - "unite_measure" : unity of the indicator - “is_target” : Indicates whether the value is a target value (projected to a future date) or whether it is an actual value (measured to a past date). - frequency_monitoring: frequency with which the indicator is consulted and used by the service. - "date": date when the indicator was measured, or when the target value is desired if it is a target. - est_periode: Boolean indicating whether the measurement is made over a period (true) or whether it is a stock (false). - date_start: date of the start of the measurement period, if the indicator covers a period of time. - “is_automated“: specifies whether data collection is automated (true) or manual (false). - "source_collection": specify how the collection is carried out: script, survey, manual collection... - insee_code : if the indicator is calculated at a certain geographical scale, specify the INSEE code of that scale. - dataviz_wish: indication for visualization producers of the appropriate type of dataviz for this indicator. - "comments": specify the known limitations and biases and justify the choice of the indicator despite its limitations.

Ce jeu de données technique est utilisé pour afficher la liste des actualités et événements de la plateforme data.gouv.nc en rapport avec la data.

Le baromètre COVID19 de Datacovid, grâce à un partenariat avec IPSOS, recueille des données précises permettant de renseigner les comportements des Français et leurs impacts sur la dynamique de l’épidémie pendant la phase de lutte contre le COVID 19, pour les offrir en open-data à la communauté scientifique, aux administrations publiques, aux entreprises et à l’ensemble des citoyens. L'enjeu est de répondre rapidement au déficit d’information des dispositifs de gestion de l’épidémie, tant dans la période de confinement actuelle que dans la période qui suivra. Le baromètre COVID-19 de Datacovid est constitué de trois catégories d’informations sur un panel non biaisé représentatif de la population : des informations relatives aux symptômes de l’infection et aux antécédents médicaux ; des paramètres comportementaux sur le suivi des règles de confinement et le respect des gestes barrières ; des caractéristiques sociodémographiques, économiques et psychologiques des personnes répondant. CONDITIONS D'UTILISATION DES JEUX DE DONNÉES ISSUS DU BAROMÈTRE COVID-19 Les jeux de données publiés par datacovid.org sur son site datacovid.org/data, datacovid.org/api et sur data.gouv.fr, sont issus du Baromètre Covid-19 opéré par IPSOS en partenariat avec datacovid.org, association à but non lucratif régie par la loi française de 1901. Ces jeux de données: sont régis par le droit français et par les conditions d'utilisation du site datacovid.org, sont publiés sur Internet dans un objectif non lucratif, scientifique et citoyen pour combler le déficit d’information sur les dispositifs de gestion sociétale des épidémies, ont été préalablement expurgés de toute donnée permettant d'identifier une personne ayant répondu au Baromètre Covid-19, sont ouverts, c’est-à-dire qu'ils peuvent être consultés, utilisés et partagés par tous, en particulier à des fins de recherche, notamment scientifique, historique et statistique, ne doivent pas donner lieu à un usage commercial, sauf à ce que les résultats dérivés de ces jeux de données, directement ou indirectement, puissent être ouverts eux aussi, au sens défini ci-dessus et portés à la connaissance de datacovid.org pour en assurer l'ouverture, ne doivent pas être rapprochés avec d'autres jeux de données ou d'autres ressources dans des conditions qui permettraient à un tiers, par corrélation, par inférence ou par quelque moyen que ce soit, d'identifier une personne ayant répondu au Baromètre Covid-19. En conséquence, toute utilisation de ces jeux de données et de tout ou partie de leurs éléments constitutifs qui ne respecterait pas chacune des conditions énumérées ci-dessus est interdite, en particulier: tout usage commercial non ouvert au sens défini ci-dessus, est interdit et serait passible de poursuites et de sanctions civiles, administratives et pénales en application de la réglementation française en vigueur, toute corrélation ou toute inférence entre les éléments constitutifs de ces jeux de données et d'autres sources de données, qui permettrait à un tiers d'identifier une personne ayant répondu au Baromètre Covid-19, engagerait la responsabilité d'un tel tiers à l'égard de datacovid.org et de toute personne concernée et serait passible de poursuites et de sanctions civiles, administratives et pénales en application de la réglementation française en vigueur. En téléchargeant ou en rendant accessibles à un tiers des données issues du Baromètre Covid-19, je m'engage à respecter les objectifs ci-dessus et les conditions d'utilisation des jeux de données publiés par datacovid.org. Si j'ai des questions ou des doutes, j'interroge contact@datacovid.org tout en restant responsable de mes actes ou de ceux de mes préposés et prestataires. NB : Les données de trafic relatives à l'accès au site datacovid.org sont traitées par datacovid.org et ses prestataires afin d'en mesurer la fréquentation et d'assurer la disponibilité, l'intégrité et la sécurité du site et de ses contenus, dans des conditions et selon des durées de conservation conformes à la réglementation française. Toute personne physique justifiant de son identité peut écrire à contact@datacovid.org pour exercer les droits qui lui sont garantis par la réglementation française et européenne en vigueur relative à la protection des données personnelles et de la vie privée, notamment les droits d'accès, d'opposition ou de suppression des données personnelles la concernant traitées par datacovid.org.

This dataset collects the acronyms entered in the data catalog.gouv.fr. The aim is to be able to feed the dictionary of abbreviations.

This dataset will no longer be maintained as of 16 November 2023. Please refer to the documentation by following this link The Order of 22 March 2019 on essential data provides for the obligation for all French public buyers (territorial authorities, ministries, public hospitals, public institutions, etc.) to publish the essential data of their public procurement and concession contracts on their buyer profile for a period of five years. It is also possible to publish them on the national open data portal (data.gouv) In order to facilitate the consumption of this data on a national scale, we have developed scripts bringing together, removing and enriching this data from data.gouv.fr, in a single file bringing together markets and concession contracts.This unique file is made available on data.gouv.fr at the [next] link(https://www.data.gouv.fr/fr/datasets/donnees-essentielles-de-la-commande-publique-fichiers-consolides/). The data sources used are as follows: * data from the PES Market of DGFiP * data collected by AIFE’s DUME API * data from buyer profile Achatpublic.com put to * layout via AIFE’s DUME API * data from the buyer profile Dematis facilitating the * download customer data (e-marchespublics.com) * data published on the Open Data portal of Greater Lyon * data published on AWS buyer profile (Marches-publics.info), extracted and published manually by Colin Maudry on data.gouv.fr If you are aware of any data sources that may be aggregated to this dataset, please contact us at demat.daj@finances.gouv.fr.Please refer to the documentation by following this link The Order of 22 March 2019 on essential data provides for the obligation for all French public buyers (territorial authorities, ministries, public hospitals, public institutions, etc.) to publish the essential data of their public procurement and concession contracts on their buyer profile for a period of five years. It is also possible to publish them on the national open data portal (data.gouv) In order to facilitate the consumption of this data on a national scale, we have developed scripts bringing together, removing and enriching this data from data.gouv.fr, in a single file bringing together markets and concession contracts. This unique file is made available on data.gouv.fr at the [next] link(https://www.data.gouv.fr/fr/datasets/donnees-essentielles-de-la-commande-publique-fichiers-consolides/). The data sources used are as follows: * data from the PES Market of DGFiP * data collected by AIFE’s DUME API * data from buyer profile Achatpublic.com put to * layout via AIFE’s DUME API * data from the buyer profile Dematis facilitating the * download customer data (e-marchespublics.com) * data published on the Open Data portal of Greater Lyon * data published on AWS buyer profile (Marches-publics.info), extracted and published manually by Colin Maudry on data.gouv.fr If you are aware of any data sources that may be aggregated to this dataset, please contact us at demat.daj@finances.gouv.fr.

Analysis of ‘Statistiques de consultation de data.gouv.fr’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/5a22644f88ee3848529af925 on 13 January 2022.

--- Dataset description provided by original source is as follows ---

Ces jeux de données correspondent aux statistiques journalières de consultation du site data.gouv.fr découpée par années. Les données proviennent de stats.data.gouv.fr et sont compilées à la fin de chaque année.

⚠️ A partir de 2020, les statistiques du site et de l'API sont désormais séparées. Ce jeu de données concerne uniquement le site à partir de 2020. Les données avant 2020 et à partir de 2020 ne sont pas comparables.

La documentation des différentes colonnes est disponible ici.

--- Original source retains full ownership of the source dataset ---

These datasets correspond to the daily statistics of the website data.gouv.fr cut out by year. The data comes from stats.data.gouv.fr and is compiled at the end of each year. Starting in 2020, the statistics of the site and the API are now separated. This dataset only applies to the site from 2020. Data before 2020 and from 2020 are not comparable. Documentation of the different columns is available here.

This dataset lists the organisations of data.gouv.fr linked to the database Wikidata.org. The data is being consolidated and should be used with caution. As an indication, the reconciliation of the list of organisations of data.gouv.fr and Wikidata can be used to: * analyse the nature of the organisations (public administration, joint, undertaking, association, etc.); * have additional information about these same organisations (e.g. Github account or Twitter account); * obtain alternative labels to data.gouv.fr labels; * display links to an organisation’s data.gouv.fr page from the corresponding Wikipedia article; * have a vision of the hierarchy of organisations (knowing that one organisation is a subsidiary of another).

Ce jeu de données présente toute les réutilisations référencées sur data.gouv.nc.

976 -Nb exploi. agri. par communes. Cadastre_Commune - Linguistique Données spatiales Mayotte. Mayotte_IRCOM 2016 et 2017 Nb d'exploi. végétales par communes.

The Order of 22 March 2019 on essential data provides for the obligation for all French public buyers (territorial authorities, ministries, public hospitals, public institutions, etc.) to publish the essential data of their public procurement and concession contracts on their buyer profile for a period of five years. It is also possible to publish them on the national open data portal (data.gouv.fr) In order to facilitate the consumption of this data nationwide, we have developed scripts gathering, removing and enriching the data on concession contracts from data.gouv.fr, The resulting unique file includes exclusively data from concession contracts that meet the main regulatory requirements in terms of formats, variables and schemas. This unique file is made available on data.gouv.fr at the [next] link(https://www.data.gouv.fr/fr/datasets/decp-v3-concessions-valides/). ** ** For more information click here.

This dataset will no longer be maintained as of 16 November 2023. Please refer to the documentation by following this link

The Order of 22 March 2019 on essential data provides for the obligation for all French public buyers (territorial authorities, ministries, public hospitals, public institutions, etc.) to publish the essential data of their public procurement and concession contracts on their buyer profile for a period of five years.

It is also possible to publish them on the national open data portal (data.gouv)

In order to facilitate the consumption of this data on a national scale, we have developed scripts bringing together, removing and enriching this data from data.gouv.fr, in a single file bringing together markets and concession contracts.This unique file is made available on data.gouv.fr at the [next] link(https://www.data.gouv.fr/fr/datasets/donnees-essentielles-de-la-commande-publique-fichiers-consolides/).

The data sources used are as follows: * data from the PES Market of DGFiP * data collected by AIFE’s DUME API * data from buyer profile Achatpublic.com put to * layout via AIFE’s DUME API

• data from the buyer profile Dematis facilitating the

• download customer data (e-marchespublics.com)

• data published on the Open Data portal of Greater Lyon

• data published on AWS buyer profile (Marches-publics.info), extracted and published manually by Colin Maudry on data.gouv.fr

If you are aware of any data sources that may be aggregated to this dataset, please contact us at demat.daj@finances.gouv.fr.

Text extracted from pdfs found on data.gouv.fr ## Description This dataset contains text extracted from 6602 files that have the ‘pdf’ extension in the resource catalog of data.gouv.fr. The dataset contains only the pdfs of 20 Mb or less and which are always available on the URL indicated. The extraction was done with PDFBox via its Python wrapper python-PDFBox. PDFs that are images (scans, maps, etc.) are detected with a simple heuristic: if after converting to text with ‘PDFBox’, the file size is less than 20 bytes, it is considered to be an image. In this case, OCRisation is carried out. This one is made with Tesseract via its Python wrapper pyocr. The result is ‘txt’ files from ‘pdfs’ sorted by organisation (the organisation that published the resource). There are 175 organisations in this dataset, so 175 files. The name of each file corresponds to the string ‘{id-du-dataset}--{id-de-la-resource}.txt’. #### Input Catalogue of data.gouv.fr resources. #### Output Text files of each ‘pdf’ resource found in the catalogue that was successfully converted and satisfied the above constraints. The tree is as follows: Bash . ACTION_Nogent-sur-Marne 53ba55c4a3a729219b7beae2--0cf9f9cd-e398-4512-80de-5fd0e2d1cb0a.txt 53ba55c4a3a729219b7beae2--1ffcb2cb-2355-4426-b74a-946dadeba7f1.txt 53ba55c4a3a729219b7beae2--297a0466-daaa-47f4-972a-0d5bea2ab180.txt 53ba55c4a3a729219b7beae2--3ac0a881-181f-499e-8b3f-c2b0ddd528f7.txt 53ba55c4a3a729219b7beae2--3ca6bd8f-05a6-469a-a36b-afda5a7444a4.txt |... Aeroport_La_Rochelle-Ile_de_Re Agency_de_services_and_payment_ASP Agency_du_Numerique ... “'” ## Distribution of texts [as of 20 May 2020] The top 10 organisations with the largest number of documents is: Python [(‘Les_Lilas’, 1294), (‘Ville_de_Pirae’, 1099), (‘Region_Hauts-de-France’, 592), (‘Ressourcerie_datalocale’, 297), (‘NA’, 268), (‘CORBION’, 244), (‘Education_Nationale’, 189), (‘Incubator_of_Services_Numeriques’, 157), (‘Ministere_des_Solidarites_and_de_la_Sante’, 148), (‘Communaute_dAgglomeration_Plaine_Vallee’, 142)] “'” And their preview in 2D is (HashFeatures+TruncatedSVD+[t-SNE]): Plot t-SNE of DGF texts ## Code The Python scripts used to do this extraction are here. ## Remarks Due to the quality of the original pdfs (low resolution scans, non-aligned pdfs,...) and the performance of the pdf->txt transformation methods, the results can be very loud.

On the occasion of April 1, data.gouv.fr gave itself the mission of Rickroll a significant number of users. For the sake of transparency, the indicators relating to this mission are published here as open data as a CSV file.

The data are from stats.data.gouv.fr.

Monthly statistics on the number of downloads of datasets from the open data platform of the Ministry for Higher Education and Research and the open data platform data.gouv.fr.

The data sets provided by Île-de-France Open Data for data.gouv.fr.

Jeu de données technique pour la publication des 3 derniers jeux de données à la une de la page d'accueil de data.gouv.nc.