Jeu de données payant contenant les noms de villes, coordonnées, régions et divisions administratives des villes du monde. Disponible aux formats Excel (.xlsx), CSV, JSON, XML et SQL après achat.

The list of communes of France contains 38 data allowing to identify the communes and to link the French communes with files from the Open Data, using the Insee code of the commune, or the postal code(s), codes of departments, regions, cantons or academy. The files also contain data on population, area, density, coordinates (of the town hall and geographical center), altitude (average, minimum and maximum) and various information. The simplified geography of the territory of the communes is present in the files marked "with geography" or "with polygon". The names of the cities are offered in 5 different formats (with or without article and/or preposition, in lower or upper case...). The municipalities of the overseas departments, regions and collectivities (DROM-COM) are included in the files but some data may be missing. ### Available file formats The files are available in csv, csv.gz and json on data.gouv.fr. Files in Excel (xlsx), Parquet (.parquet) and Feather (.feather) formats are not accepted on data.gouv.fr but are freely available on villedereve.fr/open-data-donnees-libres-sur-les-communes. ### Years Available The files are available for the years 2022, 2023, 2024 and 2025. The geographies used are those of year N-1 (e.g. 1 January 2024 for file 2025). The differences between the files from one year to the next mainly concern the population as well as administrative changes (groupings or deletions of municipalities, mainly). ### List of data available in files - insee_code: Common code, INSEE code, code assigned by INSEE to the municipality - standard_name: Standard name of the municipality, with its article (e.g.: Le Havre) - name_without_pronoun: Name of the municipality, without its article if applicable (e.g. Havre) - name_a: Name of the municipality, preceded by the preposition to, to or from and article of the municipality, if applicable (e.g.: Le Havre) - name of: Name of the municipality, preceded by the preposition of the municipality's article(s), if any (e.g. Le Havre) - name_without_accent: Name of the municipality without accent, special characters or spaces - Standard_name: Name of municipality in capital letters (e.g.: THE HAVRE) - typecom: Type of municipality in abbreviated version (COM, COMA, COMD, ARM) - typecom_text: Type of municipality in text version - reg_code: Region code assigned by INSEE to the region of the municipality - reg_name: Name of the region where the municipality is located - dep_code: Department code assigned by INSEE to the department of the commune - dep_nom: Name of the department where the municipality is located - canton_code: Canton code of the commune - canton_name: Name of the canton of the municipality - epci_code: EPCI code (public institutions of inter-municipal cooperation) assigned by INSEE to the region of the municipality - epci_name: Name of the EPCI where the municipality is located - postal_code: Main postal code of the municipality - postal_codes: Postal codes attached to the municipality - academie_code: Code of the academy of attachment of the schools of the commune - academie_nom: Name of the home academy - employment_zone: Area of use of the municipality, defined by INSEE - code_insee_centre_zone_emploi: INSEE code of the municipality centre of the area of employment - population: Municipal population - area_hectare: Area of the municipality, in hectare - area_km2: Area of the municipality, in km2 - density: Density of the municipality, inhabitant per km2 - average_altitude: Average altitude, m - minimum_altitude: Minimum altitude, m - maximum_altitude: Maximum altitude, m - latitude_mairie: Latitude of the town hall - longitude_mairie: Longitude of the town hall - latitude_centre: Latitude of the centroid of the communal territory - longitude_centre: Longitude of the centroid of the communal territory - densite_grid: Communal grid of density at 7 levels, according to INSEE - nice: Gentile (names of inhabitants) - url_wikipedia: URL of the wikipedia page of the municipality - url_villedereve: URL of the page City of dream of the municipality ### Data source - INSEE - geo.api.gouv.fr - Ministry of Education - La Poste

The dataset comprises responses to 116 questions, with contributions from both human and AI sources. The data is organized into a single folder called "AI classifier dataset," containing 100 Excel files and one JSON list file named "dataset.jsonl." Each Excel file contains three attributes: "Question", "Human", and "AI" except one file, 457c895.xlsx, which has columns "Question", "Answer," and "AI or Human."The JSON file includes four attributes for each entry: an ID, the original question, the answer, and Is_it_AI. In total, the JSON list file contains 4,231 rows of data. The source code folder contains the website design code for the question distribution and data collection website.

This repository contains the datasets and data sources, analysis code, and workflow associated with the manuscript "Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution". The following resources are provided:

• Data Files:

  • time_series_data.csv: A curated time series dataset with dates as rows and NUTS 2 regions as columns. Each column is labeled using a 4-letter abbreviation format "CC.RR", where "CC" represents the country code and "RR" represents the region code. This same abbreviation is also included in the accompanying GeoJSON file.
  • geometry_data.geojson: A GeoJSON file representing the spatial boundaries of the NUTS 2 regions, with the same 4-letter abbreviations used in the CSV file. EPSG:4326.
  • COVID19_data_sources.xlsx: This Excel file contains important metadata regarding the sources of COVID-19 data used in this study. It includes:
    • Source of the data for each country
    • Official website(s)
    • The agency responsible for the data
    • Description of the processing steps used to curate the data into the final time series.

• Code:

  • analysis.py: A Python script used to process and analyze the data. This code can be run using Python 3.x. The libraries required to run this script are listed in the first lines of the code. The code is organized in different numbered sections (1), (2), ... and sub-sections (1a), (1b) ... Make sure to run the script one (sub-)section at a time, so that everything stays overviewable and you don't get all the output at once.

• Workflow:

  • workflow.png : A detailed workflow according to the Knowledge Discovery in Databases (KDD) process, outlining the steps involved in processing and analyzing the data, including the methods used. This workflow provides a comprehensive guide to reproducing the analysis presented in the paper.

Evolution of large-scale land use 2009/2017 by municipality on metropolis Aix-Marseille Provence Data produced by TTI Production on behalf of the metropolis Aix-Marseille-Provence. Evolution of large-scale vector land occupations between 2017 and 2009 in the territory of the Aix-Marseille Provence metropolis divided by municipality. Nomenclature of 96 posts on 4 levels compatible with the CRIGE PACA nomenclature. Projection: Lambert 93 (compliant with the EGR). Attention: the cutting of polygons at the strict perimeter of municipalities has generated UMCs and LMCs that no longer comply with the nomenclature. The accuracy of the databases produced allows optimal use between the 1/5 000th and 1/10 000th. The reliability rate after external quality control and in the order of 95 %. Polygonal vector layer, topological evolutions 2009-2017. UMC from 500 to 1 000 m² in urban areas. UMC from 1000 to 2 500 m² in the farm. UMC from 500 (water) to 2 500 m² in natural spaces. The production methodology is computer-assisted photo-interpretation (PIAO) on Ortho-photo natural color and Infra-Rouge from 2009 and 2017. The evolution of MOS from 2009 to 2017 was produced by crossing the two respective soil occupations. To download the MOS In CSV, Excel, JSON or GeoJson: in the Export tab. Please note that downloading the entire MOS can take several minutes. In SHP: in the attachments of the Information tab. SHP download from the Export tab is limited to 25000 records (instead of the 50000 shown) due to the complexity of the geographic areas.

Dataset AssociationThis dataset belongs to the project "PPG Signals and Cholesterol Data: Repository for the Validation of Total Blood Cholesterol Estimation Methods" where different PPG signals are presented together with cholesterol information of the subjects. This is done with the objective of validating tools or methods for estimating the total blood cholesterol level from the PPG signal.Dataset DescriptionThis dataset contains files in. json (JavaScript Object Notation) format, corresponding to the PPG signal of 46 subjects. Subject data such as age, sex, and cholesterol are not found in the files presented here. If these data are needed in the records, they can be located in the following dataset within this project "PPG Signals & Cholesterol Data Subject Files (Format: .json)". Other data such as weight, height and whether the subject is on medication can be found in the excel document included in the project.Dataset format.json (JavaScript Object Notation)Other formats available in the project:.txt (Text file).csv (Comma-separated values).mat (MATLAB file)

Temporary art exhibitions and installations in New York City Department of Parks & Recreation properties since 2000. To convert the JSON feed to CSV (or excel), use: https://json-csv.com/ Data Dictionary: https://docs.google.com/spreadsheets/d/1tDKjYnYG1xPkMhBPr8vPKWVSoEhSBxYWkXsCDkgpBcE/edit?usp=sharing

• Description: Notifications from citizens in public space. The shape and geojson files contain the notifications whose location is indicated using the nearest address, whose coordinates are known. This concerns about half of the total number of reports. The reports go back to about 1 year. The CSV and Excel files contain all reports, including those without an exact location indication, and date back to January 1, 2013 * Source: management system of the municipality of The Hague * Purpose of registration: reports of citizens in public space * ** Restrictions:** This dataset is not suitable for legal or surveying purposes * Features: This dataset is suitable for analysis and providing insight into the location on the map * Coordinate system: WGS84

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

The ZIP file comprises all files (except ACTS) required for reevaluating the Ontology Browser in combination with the combinatorial testing tool ACTS. In particular, it contains three ontologies used in several research publications, the testing results obtained, and a spreadsheet summarizing the results.

experiment_results.xlsx ... An EXCEL file comprising the experimental results obtained
OntologyBrowser-1.4 ... Ontology browser

+ 3 subdirectories comprising the ontologies and their corresponding information

SUBDIRECTORIES:

Each subdirectory comprises a doc.txt file containing the ACTS tool's output for generating test cases. The CSV files contain the generated test cases in comma-separated-value format of the given strength, ranging from 2 to 4 or 6. The *.txt file comprises the test input model, and the *.json file the ontology that can be loaded into the browser.

Experimental setup:

We conducted the experimental evaluation using an Apple MacBook Pro M1, 16 GB RAM, macOS 15.4, the presented tool for ontology development, and ACTS in version 2.8. During the experiment, the computer ran other software like a Web browser, an email client, etc. For running ACTS, we used the following prompt in the command line shell:

java -Doutput=csv -Ddoi=

In this prompt, we set the strength \verb+