AndreaBozzo/ceres-open-data-index huggingface dataset:
RPG-CP; France Métropolitaine (2019)

[Lien vers la fiche source] ### Description Ce jeu de données fournit, pour chaque parcelle agricole du millésime 2019 du Registre Parcellaire Graphique (RPG), une estimation du code postal du siège de l'exploitation à laquelle elle appartient. Il permet ainsi de relier spatialement les parcelles cultivées à la localisation administrative de leur exploitant, offrant une information complémentaire au RPG habituellement disponible. Les données sont fournies à l'échelle des départements français au format GeoPackage (MultiPolygon, système de projection RGF93 / Lambert-93, EPSG:2154) et peuvent être ouverte avec le logiciel libre QGIS. Elles comportent les attributs suivants pour chaque parcelle : id_parc : identifiant de la parcelle dans le RPG. cp_siege : code postal estimé de la commune du siège de l'exploitation à laquelle la parcelle est rattachée. cp_parcel : code postal de la commune dans laquelle la parcelle se situe géographiquement. cp_matching : indicateur booléen signalant si le code postal du siège et le code postal géographique de la parcelle sont identiques (1) ou différents (0). surf_parc : surface administrative de la parcelle (en hectares). code_cultu / cultu_lib : code de la culture principale déclarée et libellé correspondant. code_group / group_lib : code du groupe de cultures et libellé correspondant. culture_d1 / culture_d2 : codes des cultures dérobées éventuelles. annee : année de la campagne culturale concernée. ### Méthode d'estimation du code postal du siège L'estimation du code postal du siège d'exploitation repose sur un appariement entre le RPG de Niveau 2 et le Recensement Général Agricole (RGA, millésimes 2010 et 2020) via le numéro PACAGE des exploitations. Le RGA fournit la commune de l'adresse du siège des exploitations recensées. Pour les parcelles dont l'exploitation ne figure pas dans le RGA, le code postal est estimé à partir de la répartition spatiale du parcellaire de l'exploitation : les parcelles d'une même exploitation (identifiées grâce au numéro PACAGE dans le RPG Niveau 2) sont associées au code postal de la commune dans laquelle cette exploitation possède la plus grande surface agricole cultivée pour l'année considérée. Cette méthode respecte scrupuleusement les règles du secret statistique. Les données rattachées à un code postal donné sont considérées conformes à ces règles lorsque les deux conditions suivantes sont simultanément remplies : a) Au moins trois numéros PACAGE distincts sont rattachés à ce code postal (règle de fréquence) ; b) Aucune exploitation agricole ne concentre plus de 85 % de la surface totale qui lui est attribuée (règle de dominance). Toute parcelle agricole initialement associée à un code postal ne satisfaisant pas ces deux conditions est finalement rattachée au code postal de la commune dans laquelle elle se situe géographiquement. Système de référence spatiale : RGF93 / Lambert-93 (EPSG:2154) Format : GeoPackage (.gpkg) Sources utilisées : - Registre Parcellaire Graphique, Niveau 2 (IGN / ASP) - Recensement Général Agricole 2010 et 2020 (SSP / Agreste)
Straßenwidmungen Potsdam

Straßenwidmungen von Potsdam. Enthält straßenrechtliche Inhalte gemäß: - Brandenburgisches Straßengesetz (BbgStrG) - Bundesfernstraßengesetz (FStrG) - Straßenverzeichnisverordnung (StrVerzV), wie z.B. Widmung, Widmungsdatum, Einstufung/Funktion, ggf. Amtsblätter, Straßenname, amtl. Straßenschlüssel
Eastern Power Networks (EPN) Vectorisation Delivery Plan

******** Please note, the vectorisation programme for Eastern Power Networks (EPN) has concluded. As such, this dataset has expired but the catalogue card will remain. ## Introduction The GIS (Geographic Information System) vectorisation project will deliver the incremental digital conversion of our legacy geospatial network records. This dataset defines the sub-areas which will be incrementally delivered, detailing corresponding current status and planned completion dates. This allows users to understand the current and future coverage of digital geospatial network records as the project progresses. ## Methodological Approach * Progress against a defined project plan is captured and updated throughout the day. * A script is run to convert into a shapefile. * This shapefile is then uploaded onto the Open Data Portal. ## Quality Control Statement This dataset is provided "as is". ## Assurance Statement The Open Data Team has checked outputs to validate. ## 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/]
GUYACAM : Effect of anthropogenic activities on wildlife diversity in French Guiana

[Lien vers la fiche source] Ecological interactions are being affected at unprecedented rates by human activities in tropical forests. Yet, the continuity of environmental functions provided by animals, such as seed dispersal, is crucial for forest regeneration and species resilience to anthropogenic pressures. The construction of new roads in tropical forests is one of the main boosters of habitat destruction. It facilitates human access to previously isolated areas and increases defaunation and loss of ecological functions. Therefore, it becomes increasingly urgent to assess how recently opened roads and associated anthropogenic activities rapidly affect ecological processes in natural habitats so that appropriate management measures can be taken to conserve diversity. In this project, we aimed to evaluate the effects of anthropogenic pressures on the health status of a mature rainforest crossed by a newly opened road in French Guiana. We assessed the activity of frugivores using camera traps deployed in forest patches located near (<1 km) ecological corridors preserved as canopy bridges over the road during the fruiting periods of four animal-dispersed tree species. The overall protocol allows us to compare the presence and frequency of terrestrial and arboreal vertebrates living in three tropical forest ecosystems of French Guiana with different levels of human pressures. The study is also conducted at two mature forests away from the main road, one with (Montagne Tortue-Bélizon forest) and the other without (Pararé, Nouragues National Natural Reserve) anthropogenic pressures. Camera traps are set in the understory and the canopy of targeted fruiting trees.
Data on the genetic and morphological diversity in spontaneous populations of Brassica rapa: how do feral populations differ from wild ones

[Lien vers la fiche source] Phenotypic dataset for 117 Brassica rapa populations, including landraces from farms or genebanks and spontaneous populations sampled in the wild across the Mediterranean basin, analysed in a manuscript currently under review for the journal Molecular Ecology, entitled "Genetic and morphological diversity in spontaneous populations of Brassica rapa: how do feral populations differ from wild ones". This data was collected as part of the BrasExplor project (https://brasexplor.hub.inrae.fr/), which aimed to explore the genetic diversity of Brassica species to promote sustainable crop production. The project sought to identify relevant material and their adaptive traits in order to help the Mediterranean area face upcoming climate change and contribute to biodiversity-based agriculture. The data was measured in a common garden experiment conducted at Le Rheu (France, 48°06’N 1°47’O). The 117 populations were sown in mid-September 2022 in an alpha lattice design with three repetitions, using 150 seeds per plot of 1 m x 1.5 m.
Untargeted metabolomic dataset of leaves from twenty four accessions of wild and cultivated tomato plants

[Lien vers la fiche source] This dataset contains LC-HRMS data from leaf hydro-methanolic extracts of twenty-four accessions of wild and cultivated tomato plants. The accessions included in this study were selected to represent a broad spectrum of genetic diversity (for more detail see Amegan et al., 2024, https://dx.doi.org/10.1127/entomologia/2024/2399). These comprised 6 Solanum lycopersicum accessions, in which both large-fruited varieties and cherry-type (var. cerasiforme) were considered together, as well as wild species such as Solanum cheesmaniae (10), Solanum habrochaites (6), and Solanum pennellii (2). All the accessions were provided by the CRB-Leg, INRAE Avignon. Seeds were pre-germinated in a controlled environment chamber at 24 ± 1°C, with 65 ± 5% humidity and a 16-hour light cycle. For S. cheesmaniae, seeds were previously soaked in bleach (2°4) for 30 min, to accelerate the degradation of the outer seed coat and, thereby, to promote germination. The germinated seedlings were grown in small cubic pots filled with a mixture of potting soil and perlite. Seedlings were placed in a climatic chamber under the following conditions : 24 ± 1 °C, 60–70% RH, 16:8h [light: dark] photoperiod. Two fresh leaflets from 4- to 6-week-old plants of each accession were harvested to prepare methanolic extracts according to Royer et al., 2013, https://doi.org/10.1016/j.phytochem.2012.12.003). LC-HRMS analyses were acquired on an Orbitrap IDXTM (ThermoFisher Scientific, Bremen, Germany) mass spectrometer in positive and negative electrospray ionization (ESI) modes. MS1 acquisition allowed the detection and quantification of 3764 features accross the 217 samples (24 accessions x 9-13 replicates). MS2 analysis was realized on a mixture of all samples which allows to obtain fragmentation data on 3412 features. The MS1 quantification on all samples is given as a spreadsheet. MS2 data is contained in a .mgf file. Details on the sample preparation and acquisition are also described in a txt.file. This dataset allows characterizing the intra- and inter-specific leaf metabolic diversity on cultivated and wil d tomato plants.
Production, Trade and Supply of Brown Coal and Oil Shale

This dataset contains Production, Trade, and Supply of Brown Coal and Oil Shale 1990-2024. Data from United Nations Statistics Division. Follow [datasource.kapsarc.org] for timely data to advance energy economics research. Notes: - Refer back to the original source for numbers estimated by the United Nations Statistics Division (numbers with * symbol). - Data refer to brown coal and oil shale.
Operational Data Connectivity

This dataset details the connectivity between Grid Supply Points (GSPs), Bulk Supply Points (BSPs), and their connected 132kV and 66kV feeders. There is also a limited sample of 33kV feeders and primary substations included, which should be completed in the future. For each asset (either a feeder or a substation), there are upstream and downstream connections listed. Each asset and its connected asset are also linked to a respective dataset ID which should allow cross-referencing between live operational datasets which contain half-hourly metered data for our assets. 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 require a login to view. Any data caveats for the dataset are listed below: – Currently only contains the connections to 132kV and 66kV feeders (with a small sample of 33kV connections). This will be updated soon.
History of electricity consumption data in Québec by industry sector (AR, month)

This Hydro Québec data represents electricity consumption for all consumption locations in Québec by industry sector. The data is updated once a year and has been available since 2016. Consumption data is provided by industry sector (agricultural, residential, commercial, institutional and industrial) for an administrative region (AR) in the province of Québec. ## Useful information for interpreting the data Data from municipal systems (Alma, Amos, Baie-Comeau, Coaticook, Joliette, Magog, Saguenay, Sherbrooke and Westmount) and from the systems of the Coopérative régionale d’électricité de Saint-Jean-Baptiste-de-Rouville are compiled in the “Institutional” industry sector. Given the sensitivity of electricity consumption data regarding privacy concerns and commercial interests, such data may not be available for some of the smaller municipalities. ## Additional information * Initial distribution: 2022‑10‑07 * Notices and conditions of use: The information provided represents raw data. It is without a guarantee of quality and subject to change without notice.
3D skeletal muscle model recapitulating the myostatin KO phenotypic and mitochondrial metabolic features

[Lien vers la fiche source] S1-S3 supplemental Tables: S1 supplemental Table: Immunofluorescence solutions S2 supplemental Table: List of antibodies used for immunofluorescence assays (IF) and western blotting S3 supplemental table. Primers for gene expression in 3D muscle /// S4 Supplemental Data: A Z-stack video /// S5 Supplemental Data: Video records of microtissues contractility ///
Vegetation control planning (distribution system) - Shapefile

Ensemble de données d’Hydro-Québec sur la planification des travaux de maîtrise de la végétation pour dégager les fils moyenne tension du réseau de distribution. Ces travaux sont nécessaires pour assurer la sécurité du réseau et la qualité du service d’électricité. Ils sont réalisés de façon cyclique sur plus de 100 000 km de lignes, à raison de quelque 17 000 km par année. À noter que le calendrier des travaux peut changer sans préavis en raison de la météo ou à la suite d’événements climatiques. ## Information utile pour interpréter les données Les données proviennent des calendriers de planification des travaux de maîtrise de la végétation sur le réseau de distribution d’Hydro‑Québec. * La base de données regroupe les informations d’un fichier Excel sur les zones du réseau de distribution à entretenir et celles d’un fichier numérique sur l’historique des zones de végétation. * Aucune mesure géométrique ne doit être prise à partir de ces données (précision moyenne estimée). ## Renseignements additionnels * Couverture géographique : province du Québec, sauf Alma, Amos, Baie‑Comeau, Coaticook, Joliette, Magog, Saguenay, Sherbrooke et Westmount (réseaux municipaux), le territoire desservi par la Coopérative régionale d’électricité de Saint‑Jean‑Baptiste‑de‑Rouville (réseau coopératif) ainsi que les régions alimentées par des réseaux autonomes. * Couverture temporelle : 2025-2026 * Diffusion initiale : 2017-12-18 * Notices et conditions d’utilisation : Les travaux de maîtrise de la végétation sont réalisés de façon cyclique. Le calendrier peut changer sans préavis en raison des conditions météorologiques ou à la suite d’événements climatiques. Aucune mesure géométrique ne doit être prise à partir de ces données (précision moyenne estimée).
Kindergarten Readiness - Bexar County Campuses

The Texas Academic Performance Reports (TAPR) pull together a wide range of information on the performance of students in each school and district in Texas every year. Performance is shown disaggregated by student groups, including ethnicity and socioeconomic status. The reports also provide extensive information on school and district staff, programs, and student demographics. The Texas Education Agency (TEA) bases Kindergarten Readiness on a literacy assessment focused on vocabulary, letter names, and spelling. A student is "Kindergarten Ready" if they meet the passing/"on track" benchmark for all three literacy assessment subtests: vocabulary, letter names, and spelling. Benchmark criteria for kindergarten readiness are determined by the TEA. TAPR are available for the state and every public school, district, or region of Texas. Download site: [https://rptsvr1.tea.texas.gov/perfreport/tapr/tapr_dd_download.html?year=2024] Data Dictionary [https://rptsvr1.tea.texas.gov/cgi/sas/broker?_service=marykay&_program=perfrept.perfmast.sas&_debug=0&ccyy=2025&sumlev=C&dsname=KG&dd=kg&asvab=&prgopt=reports/tapr/dd/dd_tapr_dictionary.sas] Symbols Used for Masking: ─────────────┬─────────┬───────────┬──────┬──────────────────────────────┬──────────────────────────────────────┬────────────────────────────────────── Examples │Numerator│Denominator│Rate │What is Shown on │What is Stored on │Meaning │ │ │ │Report - Rate │Data Download - Rate │ ─────────────┼─────────┼───────────┼──────┼──────────────────────────────┼──────────────────────────────────────┼────────────────────────────────────── Small Group │1 │4 │25.0% │* │-1 │Denominator is 1, 2, 3, or 4. ├─────────┼───────────┼──────┼──────────────────────────────┼──────────────────────────────────────┤ │0 │2 │0.0% │* │-1 │ ├─────────┼───────────┼──────┼──────────────────────────────┼──────────────────────────────────────┤ │3 │3 │100.0%│* │-1 │ ─────────────┼─────────┼───────────┼──────┼──────────────────────────────┼──────────────────────────────────────┼────────────────────────────────────── No Students │0 │0 │• │– │• │Denominator is 0. ├─────────┼───────────┼──────┼──────────────────────────────┼──────────────────────────────────────┼────────────────────────────────────── │n/a │n/a │n/a │n/a │n/a │Data reporting is not applicable. ─────────────┼─────────┼───────────┼──────┼──────────────────────────────┼──────────────────────────────────────┼────────────────────────────────────── Abnormal Data│> 0 │0 │• │? │-2 │Numerator is greater than denominator. ├─────────┼───────────┼──────┼──────────────────────────────┼──────────────────────────────────────┤ │8 │6 │133.0%│? │-2 │ ─────────────┴─────────┴───────────┴──────┴──────────────────────────────┴──────────────────────────────────────┴────────────────────────────────────── Note: Due to changes in the TX-KEA Kindergarten Readiness scoring methodology for the 2022-2023 school year, kindergarten readiness scores may not be directly comparable to prior years.
Utilisation de l'IA pour un positionnement amélioré des landmarks 3D : corriger les biais des méthodes de registration traditionnelles

[Lien vers la fiche source] In this study, we developed a 2D DL architecture to automate the placement of 10 selected landmarks on 463 mouse skulls. These landmarks were chosen for their diverse locations and definitions, and, for each of them, the predicted landmark positions was compared to predictions of deformable registration approaches such as ALPACA. Euclidean distances to ground truth and individual shape conservation were compared between the two approaches. Images .tif files and associated manual landmarks are provided with this metadata file, along with DL model weights and R codes used to perform the statistics in the associated publication.
ECOSCOPA network : high frequency environmental database

[Lien vers la fiche source] Based on the consolidation of the Ifremer networks RESCO (https://doi.org/10.17882/53007) and VELYGER (https://doi.org/10.17882/41888), the general objective of the ECOSCOPA project is to analyze the causes of spatio-temporal variability of the main life traits (Larval stage - Recruitment - Reproduction - Growth – Survival – Cytogenetic anomalies) of the Pacific oyster in France and follow their evolution over the long term in the context of climate change. The high frequency environmental data are monitored since 2010 at several stations next to oyster farm areas in eight bays of the French coast (from south to north): Thau Lagoon and bays of Arcachon, Marennes Oléron, Bourgneuf, Vilaine, Brest, Mont Saint-Michel and Veys (see map below). Sea temperature and practical salinity are recorded at 15-mins frequency. For several sites, fluorescence and turbidity data are also available. Data are acquired with automatic probes directly put in oyster bags or fixed on metallic structure at 50 cm over the sediment bottom, except for Thau Lagoon whose probes are deployed at 2m below sea surface. Since 2010, several types of probes were used: STP2, STPS, SMATCH or WiSens CTD from NKE (www.nke-instrumentation.fr) and recently ECO FLNTU (www.seabird.com). The probes are regularly qualified by calibrations in the Ifremer coastal laboratories. Precision estimated of the complete data collection process is: temperature (±0.1°C), salinity (±0.5psu), in vivo fluorescence (±10%), turbidity (±10%). The data are qualified into several levels: 0-No Quality Check performed, 1-Good data, 2-Probably good data, 3-Probably bad data, 4-Bad data, 5-Value changed, 7-Nominal value, 8-Interpolated value, 9-Missing value.
Soil health data (soil characteristics, fauna, microbiome) from Legincrop project (ACTAE) 2017-2018

[Lien vers la fiche source] Legincrop project was conducted in Vietnam (former Yen Bai, now re-named Lao Cai province) in 2017 and 2018. Intercropping system (cassava-cowpea) was compared to monocropping cassava system and the effects of legume addition to the main cassava crop were assessed on soil physico-chemical characteristics and soil biodiversity (including soil macro-, meso- and microfauna and microbiomes). This dataset includes the results of the soil physico-chemical analyses, the raw counts of soil fauna and the rawdata of amplicon sequencing (16S and ITS) that were used to prepare the manuscript entitled "Cassava-cowpea intercropping system rapidly enhances soil health in mountainous regions of Northern Vietnam".
NiTi-Like SMAs transformation temperatures dataset

[Lien vers la fiche source] This dataset provides the martensite transformation temperatures for various "NiTi-like" alloys containing 1, 2, 3, and more of the most employed elements that are thermodynamically closest to Ni and Ti. The elements (Ni, Fe, Co, Cu, Pd, and Pt) are classified as Ni-like elements, while (Ti, Zr and Hf ) are considered as Ti-like elements and Nb and Ta are the Nb-like elements. This is an extension of the first version, which was published in October 2023 and focused solely on martensite start transformation temperature (Ms).
Results of 3D fibre orientation in 3D around a Douglas knot and Initial data of the self-developed laboratory method

[Lien vers la fiche source] Since laser dot scanning only gives reliable information about the fibre orientation in the plane of board surfaces, simple assumptions are usually made to define the inner fibre orientation to model timber boards. Those models would be improved by better insight into real fibre deviation around knots. The present data is extracted from the laboratory method developed to evaluate growth layers geometries and fibre orientation, through successive planing, colour and laser dot scanning. The method is applied to a Douglas fir timber specimen. The proposed method provides data on fibre orientation in 3D with an accuracy that is relevant for the calibration of detailed models, more simply and feasibly than previously proposed methods.
An image processing method to recognize position of sawn boards within the log

[Lien vers la fiche source] This dataset includes source images, codes, and results used in a paper that addresses the problem of timber board positioning within the log they were sawn from. The method takes as input images of the end cross-section of logs and boards that can be obtained in a sawmill. It uses a two-step image matching method based on scale invariant feature transform (SIFT) and normalized correlation coefficient (NCC). In the first step, the scale factor and rotation angle of board end images are estimated from the board images that are correctly identified on the log end image by SIFT. Then, the accurate position of each board within the log end image is achieved by the NCC method. The method has been tested on 70 different log images and the 798 corresponding board images of various visual aspects and coming from three different species (Douglas fir, Norway spruce, and oak). The results fully demonstrate that the proposed method is not only rotation and scale invariant, but also has high accuracy properties.