AndreaBozzo/ceres-open-data-index huggingface dataset:
Données de rendement et de valeur alimentaire des cultures fourragères estivales acquises dans le cadre de l'essai Cap Proteines volet élevage.

[Lien vers la fiche source] Ce jeu de donnée contient les données brutes de l'essai Cap Proteines volet elevage_Cultures Fourragères Estivales (CFE). Cet essai en bloc randomisé, a été répété deux années (2021 et 2022) sur quatre sites expérimentaux français : ARVALIS – Ferme Expérimentale des Bordes 36120 JEU LES BOIS, INRAE – UE 1297, Unité Expérimentale Physiologique Animale de l’Orfrasière 37380 NOUZILLY, CIIRPO – Ferme Expérimentale du Mourier 87800 SAINT PRIEST LIGOURE, IDELE – 42 rue Georges Morel – CS 60057 49071 BEAUCOUZE
UK Power Networks Grid Substation Distribution Areas

## Introduction This dataset is a geospatial view of the areas fed by grid substations. The aim is to create an indicative map showing the extent to which individual grid substations feed areas based on MPAN data. ## Methodology * Data Extraction and Cleaning: MPAN data is queried from SQL Server and saved as a CSV. Invalid values and incorrectly formatted postcodes are removed using a Test Filter in FME. * Data Filtering and Assignment: MPAN data is categorized into EPN, LPN, and SPN based on the first two digits. Postcodes are assigned a Primary based on the highest number of MPANs fed from different Primary Sites. * Polygon Creation and Cleaning: Primary Feed Polygons are created and cleaned to remove holes and inclusions. Donut Polygons (holes) are identified, assigned to the nearest Primary, and merged. * Grid Supply Point Integration: Primaries are merged into larger polygons based on Grid Site relationships. ny Primaries not fed from a Grid Site are marked as NULL and labeled. * Functional Location Codes (FLOC) Matching: FLOC codes are extracted and matched to Primaries, Grid Sites and Grid Supply Points. Confirmed FLOCs are used to ensure accuracy, with any unmatched sites reviewed by the Open Data Team. ## Quality Control Statement Quality Control Measures include: * Verification steps to match features only with confirmed functional locations. * Manual review and correct of data inconsistencies * Use of additional verification steps to ensure accuracy in the methodology * Regular updates and reviews documented in the version history ## Assurance Statement The Open Data Team and Network Data Team worked with the Geospatial Data Engineering Team to ensure data accuracy and consistency. ## Other Download dataset information: [Metadata (JSON)] Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: [https://ukpowernetworks.opendatasoft.com/pages/glossary/] To view this data please register and login.
UK Power Networks Licence Area 66kV Underground Cables

## Introduction Shapefile showing UK Power Networks' 66kV underground cables. ## Methodological Approach * Data Extraction: Shapefiles are extracted from our geospatial mapping system - NetMap. * Vectorisation: Shapefiles will be regularly updated to account for vectorisation updates. ## Quality Control Statement The data is provided "as is". Please be aware that we have not published the dataset as we are currently vectorising LPN. Please keep an eye on our Data Roadmap for further updates on delivery. Refer to our [LPN Vectorisation Delivery dataset.] ## Assurance Statement The Open Data Team checked to ensure data accuracy and consistency. ## Other Download dataset information: [Metadata (JSON)] Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: [https://ukpowernetworks.opendatasoft.com/pages/glossary/] To request access to this dataset, please fill out the [shared data request form.] To view this data please register and login.
Landscape heterogeneity and pesticide reduction favor predation, but also grape infestation by <i> Lobesia botrana </i>

[Lien vers la fiche source] The data deposited were collected at the BACCHUS workshop site, located in Gironde in south-west France. It covers the Libournais and Entre-Deux-Mers wine-growing regions. The data were acquired as part of the SECBIVIT project and OPERA project. The data were collected in 19 paired landscapes, one in a conventional vineyard and one in an organic vineyard. Different landscape characteristics were recorded and farmers were interviewed to collect data on farming practices. In these landscapes, predation rates were measured on different development stages of the pest, the grape berry moth Lobesia botrana, as well as the damage caused to the vines corresponding to the 1st and 2nd generation of the pest. Data were collected in 2019.
Dataset for : "A new dataset to elucidate inaccurate temperature thresholds masking a hidden source of risk uncertainties in commun wheat"

[Lien vers la fiche source] Database of thermal stress thresholds for common wheat and associated metadata (including geographic location, wheat type, targeted physiological processes, and references) used in the article "A New Dataset to Uncover Inaccurate Temperature Thresholds That Mask Hidden Sources of Risk and Uncertainty".
Stationnement vélo sécurisé en gare

Le jeu de données précise le nombre de stationnements vélos sécurisés par gare et les objectifs d'installations demandés par le décret relatif aux stationnements sécurisé (loi LOM). 1075 gares SNCF Gares & Connexions sont concernées par ce décret. Les données affichées sont les suivantes : - Code gare : Clé permettant d’identifiant fonctionnel de la gare - Nom de la gare : Nom de la gare (libellé noté dans le décret, peut être différent de celui du jeu de données des gares publié par SNCF Gares et Connexions) - Région: Région administrative de la gare - Gare LOM / HORS LOM : Gare concernée par une installation des stationnements vélos sécurisés demandée par le décret de la loi LOM. 2 valeurs : LOM / Hors LOM - Objectif décret LOM : Objectif de nombre de places de stationnement sécurisées à installer au regard du décret d'application de la loi LOM - Nombre de places sécurisées existantes au 30.06.24 : Nombre de places sécurisées installées à date. - Nombre de places sécurisées existantes au 30.12.24 : Nombre de places sécurisées installées à date.
Orthomosaïque d'une parcelle à Saint-Laurent de La Prée (2025)

[Lien vers la fiche source] Orthomosaïque de la zone d'étude survolée par drone le 4 février 2025. Elle a été générée, à partir de 928 images RGB acquises avec le drone Mavic 3E RTK connecté au réseau collaboratif de bases GNSS ouvertes CentipedeRTK, avec une résolution de 2,6 cm/pixel. Le vol a été réalisé par Natacha Volto de la plateforme Drones de l’UMR LIENSs le 4 février 2025 pour Lilia Mzalia de l'INRAE. Les traitements photogrammétriques ont été réalisés par Natacha Volto du LIENSs à l'aide du logiciel Agisoft Metashape.
Netbeheerder en gas energie-inhoud per gemeente

Overzicht van de netbeheerders en hun activiteiten actief per gemeente. Disclaimer: Fluvius spant zich in om volledige en actuele informatie aan te bieden. Niettemin kan Fluvius niet garanderen dat alle weergegeven informatie en data volledig of correct is. De informatie en data kunnen onvolledig zijn en fouten, vergissingen of omissies bevatten.
Localisation des défibrillateurs

Localisation des DAE (défibrillateurs) en libre service sur l'Agglomération Melun Val de Seine. **** Informations sur la base nationale des défibrillateurs **** Géo'DAE est la base de données nationale des défibrillateurs automatisés externes (DAE), recensés en France. A peine 1 citoyen sur 10 survit à un arrêt cardiaque faute d'avoir bénéficié au bon moment de l'intervention d'une personne. Le plan national de santé publique prévoit de former 80% de la population aux gestes de premiers secours et d'améliorer l'accès aux défibrillateurs automatisés externes sur le territoire national, en favorisant leur géolocalisation et leur maintenance. La création d'une base de données nationale, disposition de la loi du 28 juin 2018 relative au défibrillateur cardiaque, s'inscrit dans cette ambition. Tous les exploitants de DAE doivent désormais déclarer les données de leurs défibrillateurs et leurs caractéristiques dans la base nationale. Le portail de déclaration est disponible sur le lien suivant : [https://geodae.atlasante.fr/apropos].
Zone à Trafic Limité (ZTL)

Le jeu de données représente le périmètre de la Zone à Trafic Limité (ZTL), mise en œuvre à Paris Centre à compter du 5 novembre 2024 Le périmètre correspond au secteur de Paris Centre (les 1er, 2e, 3e et 4e arrondissements) sans les Grands Boulevards au nord, les îles de la Cité et de Saint-Louis ni les quais bas et hauts rive droite dans le sens ouest-est. Seul le trafic ayant pour origine ou destination les rues de ce périmètre est autorisé. À contrario, le trafic de transit (traverser la zone) y est interdit, sauf exceptions. La Zone à Trafic Limité concerne seulement les véhicules motorisés (les voitures, les véhicules utilitaires légers, les poids lourds et les 2-roues motorisés). Les modes actifs (vélos, trottinettes, etc.) ne sont pas concernés. Pour en savoir plus sur la ZTL : [Paris.fr] [Consulter l’arrêté instaurant la ZTL dans le centre de Paris]
Historical Peak Demand

Our Historical Peak Demand dataset provides information on substation peak demand seen in an annual year, back to 2019/2020. This information is provided for substations on the Northern Powergrid EHV network. We have also included percentage growth columns to show the change of max demand between years. If you are unsure of any of the column meanings, please hover over the column heading for a description. Related Feature Pages [Regional Demand and Generation Tracker] - Our Regional Demand and Generation Tracker provides near real time data for the generation mix of our network over the last 30 days. Glossary of Terms Our [Glossary] dataset provides definitions for various terms and acronyms found throughout our data. If the term you are searching for isn't in our list, please let us know via our [Open Data team] and we will get it added! Data Governance If you wish to view the data triage document for any dataset, they can be found in our [Data Triage Assessment] dataset. This dataset does NOT require a login to view Any data caveats for the dataset are listed below - None
Flow intermittence indicators from the projection simulations (1985-2100) in the Albarine DRN (France)

[Lien vers la fiche source] Statistical monthly and yearly flow intermittence indicators calculated from the simulated daily state of flow in the Albarine DRN (France). Indicators are provided for 3 SSP scenarios and 5 climate models on the projection periods (1985-2014 and 2015-2100).
Hydrological projections (1985-2100) of the Bükkösdi DRN (Hungary)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the projection simulations (1985-2100) in the Bükkösdi DRN (Hungary). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale. Hydrological projections were produced for 3 SSP scenarios and 5 climate models.
Hydrological projections (1985-2100) of the Velicka DRN (Czechia)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the projection simulations (1985-2100) in the Velicka DRN (Czechia). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale. Hydrological projections were produced for 3 SSP scenarios and 5 climate models.
Flow intermittence indicators from the projection simulations (1985-2100) in the Butiznica DRN (Croatia)

[Lien vers la fiche source] Statistical monthly and yearly flow intermittence indicators calculated from the simulated daily state of flow in the Butiznica DRN (Croatia). Indicators are provided for 3 SSP scenarios and 5 climate models on the projection periods (1985-2014 and 2015-2100).
Flow intermittence indicators from the projection simulations (1985-2100) in the Genal DRN (Spain)

[Lien vers la fiche source] Statistical monthly and yearly flow intermittence indicators calculated from the simulated daily state of flow in the Genal DRN (Spain). Indicators are provided for 3 SSP scenarios and 5 climate models on the projection periods (1985-2014 and 2015-2100).
Hydrological reconstruction (1960-2021) of the Albarine DRN (France)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the reconstruction period (1960-2021) in the Albarine DRN (France). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale.
Flow intermittence indicators from the projection simulations (1985-2100) in the Bükkösdi DRN (Hungary)

[Lien vers la fiche source] Statistical monthly and yearly flow intermittence indicators calculated from the simulated daily state of flow in the Bükkösdi DRN (Hungary). Indicators are provided for 3 SSP scenarios and 5 climate models on the projection periods (1985-2014 and 2015-2100).
Hydrological reconstruction (1960-2021) of the Bükkösdi DRN (Hungary)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the reconstruction period (1960-2021) in the Bükkösdi DRN (Hungary). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale.
Hydrological reconstruction (1960-2021) of the Lepsämänjoki DRN (Finland)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the reconstruction period (1960-2021) in the Lepsämänjoki DRN (Finland). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale.
Hydrological reconstruction (1960-2021) of the Butiznica DRN (Croatia)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the reconstruction period (1960-2021) in the Butiznica DRN (Croatia). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale.
Hydrological reconstruction (1960-2021) of the Genal DRN (Spain)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the reconstruction period (1960-2021) in the Genal DRN (Spain). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale.
Hydrological reconstruction (1960-2021) of the Velicka DRN (Czechia)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the reconstruction period (1960-2021) in the Velicka DRN (Czechia). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale.
Hydrological projections (1985-2100) of the Albarine DRN (France)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the projection simulations (1985-2100) in the Albarine DRN (France). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale. Hydrological projections were produced for 3 SSP scenarios and 5 climate models.
Downscaled climate projections (1971-2100) for the Vantaanjoki-Lepsämänjoki river basin (Finland)

[Lien vers la fiche source] Downscaled projections of temperature, precipitation, and reference evapotranspiration for the Vantaanjoki river basin (which includes the Lepsämänjoki DRN). 3 SSP scenarios and 5 climate models.
Hydrological projections (1985-2100) of the Butiznica DRN (Croatia)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the projection simulations (1985-2100) in the Butiznica DRN (Croatia). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale. Hydrological projections were produced for 3 SSP scenarios and 5 climate models.
Hydrological projections (1985-2100) of the Lepsämänjoki DRN (Finland)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the projection simulations (1985-2100) in the Lepsämänjoki DRN (France). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale. Hydrological projections were produced for 3 SSP scenarios and 5 climate models.
Hydrological projections (1985-2100) of the Genal DRN (Spain)

[Lien vers la fiche source] Hydrological model outputs at daily time step for the projection simulations (1985-2100) in the Genal DRN (Spain). Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale. Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale. Hydrological projections were produced for 3 SSP scenarios and 5 climate models.
Flow intermittence indicators from the projection simulations (1985-2100) in the Lepsämänjoki DRN (Finland)

[Lien vers la fiche source] Statistical monthly and yearly flow intermittence indicators calculated from the simulated daily state of flow in the Albarine DRN (France). Indicators are provided for 3 SSP scenarios and 5 climate models on the projection periods (1985-2014 and 2015-2100).
Flow intermittence indicators from the projection simulations (1985-2100) in the Velicka DRN (Czechia)

[Lien vers la fiche source] Statistical monthly and yearly flow intermittence indicators calculated from the simulated daily state of flow in the Velicka DRN (Czechia). Indicators are provided for 3 SSP scenarios and 5 climate models on the projection periods (1985-2014 and 2015-2100).
GraviMob-1 campaign: measurement data and processing results

[Lien vers la fiche source] GraviMob-1 campaign is an underwater gravimetric survey conducted near the southern coast of France in the Mediterranean Sea in March 2016. The AUV Asterx was used to carry the GraviMob system. The operation was performed on board the vessel Europe. The data and processing results of this campaign are supporting the paper "High-resolution gravity measurement on board an Autonomous Underwater Vehicle: Data reduction and accuracy assessment". The dataset thus contains the following elements: 1. The raw data exported from GraviMob 2. The shipborne gravity data provided by SHOM 3. The processing results
Très Haut Débit : Calendrier de déploiement des prises par commune en février 2025

La convention de délégation de service public relative à la conception, construction, exploitation du réseau de communications électroniques très haut débit (FTTH) a été signée avec la société SFR Collectivités (puis SFR FTTH, nommée aujourd’hui XpFibre) le 16 octobre 2018, pour une durée de 30 ans. La filiale dédiée a été créée sous le nom de Corsica Fibra. Le tableau joint présente le calendrier de déploiement par communes. Il expose pour chaque commune un état du déploiement arrêté au 1^{er} février 2025.
Specific timepoints derived from axial dynamic MRI of healthy participants

[Lien vers la fiche source] This dataset provides the specific time-points (beginning, maximum of exercise, end) of the different repetition of each exercise. This dataset was derived from the dynamic MRI of twenty healthy participants performing three different exercises: breathing, coughing and the Valsalva maneuver (corresponding dataset: https://doi.org/10.57745/CTM9BO). It also contributed to derive different abdominal metrics (corresponding dataset: https://doi.org/10.57745/R5AQRS). Please look at the README file for more informations.
Interruptions Incentive Scheme

## Introduction The dataset provides detailed information about UK Power Networks' unplanned fault reporting under the Ofgem Interruptions Incentive Scheme (IIS). It includes key characteristics such as: * Date/time fault start/end * Number of customers affected * Restoration stages * Cause and main equipment involved * Status of fault in terms of its exclusion from the IIS scheme due to it being a one-off /severe weather fault * The upstream primary substation to the fault More information about the IIS can be found on Ofgem's website or this document: [RIIO-ED2 Regulatory Instructions and Guidance – Interruptions] ## Methodological Approach * Source: IIS returns post application of Ofgem decision one status of fault in terms of its exclusion from the IIS scheme due to it being a one-off /severe weather fault * Manipulation: Includes all rows from unplanned fault reporting IIS returns with calculation of CI/CML removed, leaving source data only i.e. no calculated fields * Addition: Inclusion of the sitefunctional location of the upstream Primary substation and its geopoint. ## Quality Control Statement The data is provided "as is". ## Assurance Statement The Regulation Team checked to ensure data accuracy and consistency. ## Other Download dataset information: [Metadata (JSON)] Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: [https://ukpowernetworks.opendatasoft.com/pages/glossary/] To view this data please register and login.