AndreaBozzo/ceres-open-data-index huggingface dataset:
NIRS Calibration Database on fresh grated cassava for DM at NaCRRI Uganda

[Lien vers la fiche source] Cassava utilization for food and/or industrial products depends on inherent root dry matter content (DMC). Accordingly, there is need to develop NIRS models to aid breeding and selection of dry matter content as a critical industrial trait taking care of root sample preparation and cassava germplasm diversity available in Uganda. For calibration development (300 clones) were used. Grating was the main sample preparation method. Reference DMC data was obtained by oven-drying. DMC in calibration samples ranged from 20 to 45%. Modified Partial least square (MPLS) regression model was used with the pretreatment carried out by SNVD over the segment 1100-2500nm at a 2nm step. The number of factors used was 8 while a total number of 13 outliers were eliminated. The regression coefficients (R2) of 0.997 were obtained while the R2 for cross validation was 0.993. In addition, the RMSEC was 0.28 while the RMSECV was 0.43. These findings show the robustness of NIRS in estimation of dry matter content and thus justify its use in routine cassava breeding operations.
DigiEye image reference base on raw intact sweetpotato in Uganda

[Lien vers la fiche source] The images were collected from 3 raw intact roots. To take the images, 3 raw roots were peeled, washed and cut cross-sectionally across the mid-section. For each half of the root, 2 images were taken; (i) cross-sectionally and (ii) at the surface, under the peel. Sensory data was collected by 12 trained panellists, who consumed small cubes of each cooked sweetpotato genotype and rated the overall liking of the samples on a 10-point hedonic scale ranging from 1 (dislike extremely) to 10 (like extremely), for each sensory trait per genotypes.
DigiEye image reference base on boiled sweetpotato in Uganda

[Lien vers la fiche source] The images were collected from 3 cooked roots. To take the images, 3 raw roots were peeled, washed, cut cross-sectionally across the mid-section and cooked immediately. For each half of the root, 2 images were taken; (i) cross-sectionally and (ii) at the surface, under the peel. Sensory data was collected by 12 trained panellists, who consumed small cubes of each cooked sweetpotato genotype and rated the overall liking of the samples on a 10-point hedonic scale ranging from 1 (dislike extremely) to 10 (like extremely), for each sensory trait per genotypes.
The 'Plantain-Optim' dataset: growth, development and yield of 405 plantains

[Lien vers la fiche source] The 'Plantain-Optim' dataset contains growth, development and yield data of 405 plantain every 15 days from planting to harvest. These data represent 9 plantain varieties, included 5 natural plantains (Essong, Batard, French clair, Mbouroukou n°3 and Big Ebanga) and 4 plantain-like hybrids (CRBP39, FHIA21, DD248, DC535). These data come from the 'Optim' experiment conducted at CARBAP station (Cameroon) from 2009 to 2011.
Données de réplication pour deep sequencing of two mock phage communities

[Lien vers la fiche source] Fastq files contain Illumina pair end reads (2x150 nt). The first set deals with a synthetic mix of 5 phage genomes. Files have "Acc", "G" or "T7" prefixes. Acc stands for xGen kit, G for genomiphi pre-treatment, T7 for T7pol pre-treatment. 1 and 2 are repeats ot the low ssDNA mix, 3 and 4 are repeats of the highssDNA mix. The second set deals with two natural and complex human gut viromes. It has a "Stool" prefix.
City and County Energy Profiles

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and complements the wealth of data, maps, and charts on the State and Local Planning for Energy (SLOPE) platform, available at the "Explore State and Local Energy Data on SLOPE" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link.
Caractérisation des activités économiques des bassins de vie en 2007 et 2014

[Lien vers la fiche source] Données à l'échelle des bassins de vie 2012 utilisées dans les chapitres 6 et 7 de l'ouvrage "Métamorphose du monde rural" corédigé par Hervé Le Bras et Bertrand Schmitt en 2020.
Elections - Européennes 2024

Résultats des élections européennes du 9 juin 2024 par bureau de vote, quartier, arrondissement et circonscription. Le fichier des résultats comprend : * Nombre de procurations * Nombre d’inscrits * Nombre d’émargements * Nombre de votants * Nombre de blancs * Nombre de nuls * Nombre d’exprimés * Les résultats pour chaque candidat Conformément à l’article R 40-1 du code électoral, les résultats du bureau de vote à caractère dérogatoire sont disponibles dans l’Open-data sous la référence 4-99. Ces résultats correspondent aux extraits le soir de scrutin du Système d'information élection de la Ville de Paris ( "Paris Vote" à partir de 2017). Nous attirons l'attention des utilisateurs sur le fait que des écarts peuvent exister entre les résultats de ces fichiers et ceux publiés par le Ministère de l’Intérieur. L’open data recense les chiffres transmis par la Ville à la préfecture de Paris le soir des scrutins. Ceux-ci sont susceptibles d’être corrigés par la commission de recensement des votes. Ces résultats ne prennent pas en compte d’éventuelles rectifications effectuées par les organismes de contrôle ou le juge de l’élection.
PROTEKER : un observatoire sous-marin côtier aux Iles Kerguelen pour le suivi des habitats benthiques

[Lien vers la fiche source] PROTEKER is a pilot program that aims at establishing a baseline for assessing the impact of climate change on near-shore marine ecosystems of the Kerguelen Islands by implementing an ecological and genetic monitoring at reference sites. The high diversity of coastal marine ecosystems is usually strongly impacted by environmental changes over the planet. In the Kerguelen Islands, such environments have been little investigated compared to open sea areas, and are still poorly known. The present project should provide stake holders and decision makers with scientific criteria for protection and conservation of Kerguelen coastal marine ecosystems. The PROTEKER first phase (2011-2014) aimed at assembling together and merging all available data from previous programs, selecting, and setting up monitoring stations for completion of the second phase of the program (2015-2018). During this second, operational phase, scientific investigations will integrate all levels of marine biodiversity, from species to community levels and will consist in a pluri-disciplinary approach including monitoring of abiotic parameters, habitat mapping, population genetics, genomics, functional ecology (physiological/trophic analyses), and macroecological analyses. Expected results should provide with integrative models of Kerguelen coastal marine life distribution and sensitivity to environmental changes.
Descripteurs de centres d'action atmosphériques associés à des types de temps autour de la Nouvelle-Zélande

[Lien vers la fiche source] Here we introduce a set of descriptors applied to weather regimes that allow for a detailed monitoring of the location and intensity of their atmospheric centers of action (e.g., troughs and ridges) and the gradients between them, when applicable. Descriptors are designed to document the effect of climate variability and change in modulating the character of daily weather regimes, rather than merely their occurrence statistics. As a case study, the methodology is applied to Aotearoa New Zealand (ANZ), using ERA5 ensemble reanalysis data for the period 1979–2019.
Horodateurs sur Rennes

Cette couche localise les horodateurs sur le territoire de la Ville de Rennes. Parmi les attributs disponibles : - etat : état de l'horodateur - version : version de l'horodateur - zone_tarif : zone tarifaire de l'horodateur (verte / orange / rouge) - zone_resid : zone résidentielle de l'horodateur En juin 2024, l'identifiant de l'horodateur est passé de 6 à 8 chiffres.
Monitoring photosynthetically active radiation on Mont Ventoux, 2016-2017

[Lien vers la fiche source] This publication presents the following data obtained on the northern face of Mont Ventoux (France) at seven mini-weather stations from June 2016 to July 2017: The stations were installed at different strategic elevations and locations (see SitesInfo.tab), in clearings, more than 30 metres from forest edges, and the sensors were fixed on a vertical pole and positioned horizontally using a spirit level. Data were recorded at one-hour intervals. Data provided by URFM, INRAE, Avignon, France.
Land use of the Laksip village south of the National Road 13, Northern Lao PDR (2016)

[Lien vers la fiche source] This dataset has been collected thanks to the long-term partnership with Department of Agricultural Land Management (DALaM), Lao PDR, which granted the permission for field access, and to the financial, scientific, technical, and/or logistical support of the Multiscale TROPIcal CatchmentS (SNO M-TROPICS; https://mtropics.obs-mip.fr/) critical zone observatory (previously MSEC). This dataset made of a .shp with associated .dbf, .prj, and .shx files (Coordinate Reference System: EPSG:4326 - WGS 84) includes land-use information of the Laksip village, Northern Lao PDR, upstream the station BLS (LAT: 19.848421; LONG: 102.168295). Land-use survey was performed in August 2016. The authors acknowledge the financial support of the Institut de Recherche pour le Développement (IRD), including for the postdoctoral grant 2016-2017 of Laurie Boithias (Accueil de post-doctorants / Campagne 2015), and of the French National Research Agency (TecItEasy project ANR-13-AGRO-0007).
Population 2019 - Communes - France

Jeu de données géoréférencés des communes française et de leurs populations en 2019. Issu de la jointure entre les jeux de données suivant : - https://www.datajoule.fr/explore/dataset/communes-2019-france - https://www.datajoule.fr/explore/dataset/populations-legales-communes-et-arrondissements-municipaux-2019-france
Hourly Consumption by Postal Code (1000 to 2000)

This DataSet is built based on load diagrams collected by smart meters. In cases where there are still no meters capable of carrying out these collections, or there is difficulty in communicating with them, a profile of consumption is carried out. Consult here the document that systematizes the estimation methodology of the different profiles. (https://www.e-redes.pt/sites/eredes/files/2021-08/DocMetodologico_Perfis.pdf) Due to the high volume of data, this dataset presents information regarding a period of thirty days, in a sliding window model, with no history conservation. For a longer temporal analysis, regular data extractions are recommended. In accordance with the General Data Protection Regulation (GDPR), it is necessary to aggregate postal codes, which are not available individually, into the "OUTROS" set. 1. The information made available by E-REDES constitutes an approximation to the values taken from the system and is based on the moment in which it is collected. Given that the connection points, the electricity distribution network, and the consumption and production values themselves are naturally very dynamic, it is safeguarded that the information made available may be subject to subsequent changes and updates, with the exception of any omissions and/or occasional inaccuracies of location that the information may contain. 2. In this way, E-REDES is not liable to third parties, namely, partners, service providers, contractors, users and customers, for damages that may arise as a result, direct or indirect, of the use of this Information, in particular when carrying out interventions, calculations and/or estimates, without confirming the accuracy and updating of the data, whereby it is duly noted that the consultation of this information does not affect the duty to promote a direct consultation with E-REDES to obtain updated information.
Hourly Consumption by Postal Code (2501 to 2695)

This DataSet is built based on load diagrams collected by smart meters. In cases where there are still no meters capable of carrying out these collections, or there is difficulty in communicating with them, a profile of consumption is carried out. Consult here the document that systematizes the estimation methodology of the different profiles. (https://www.e-redes.pt/sites/eredes/files/2021-08/DocMetodologico_Perfis.pdf) Due to the high volume of data, this dataset presents information regarding a period of thirty days, in a sliding window model, with no history conservation. For a longer temporal analysis, regular data extractions are recommended. In accordance with the General Data Protection Regulation (GDPR), it is necessary to aggregate postal codes, which are not available individually, into the "OUTROS" set. 1. The information made available by E-REDES constitutes an approximation to the values taken from the system and is based on the moment in which it is collected. Given that the connection points, the electricity distribution network, and the consumption and production values themselves are naturally very dynamic, it is safeguarded that the information made available may be subject to subsequent changes and updates, with the exception of any omissions and/or occasional inaccuracies of location that the information may contain. 2. In this way, E-REDES is not liable to third parties, namely, partners, service providers, contractors, users and customers, for damages that may arise as a result, direct or indirect, of the use of this Information, in particular when carrying out interventions, calculations and/or estimates, without confirming the accuracy and updating of the data, whereby it is duly noted that the consultation of this information does not affect the duty to promote a direct consultation with E-REDES to obtain updated information.
Hourly Consumption by Postal Code (2696 to 2820)

This DataSet is built based on load diagrams collected by smart meters. In cases where there are still no meters capable of carrying out these collections, or there is difficulty in communicating with them, a profile of consumption is carried out. Consult here the document that systematizes the estimation methodology of the different profiles. (https://www.e-redes.pt/sites/eredes/files/2021-08/DocMetodologico_Perfis.pdf) Due to the high volume of data, this dataset presents information regarding a period of thirty days, in a sliding window model, with no history conservation. For a longer temporal analysis, regular data extractions are recommended. In accordance with the General Data Protection Regulation (GDPR), it is necessary to aggregate postal codes, which are not available individually, into the "OUTROS" set. 1. The information made available by E-REDES constitutes an approximation to the values taken from the system and is based on the moment in which it is collected. Given that the connection points, the electricity distribution network, and the consumption and production values themselves are naturally very dynamic, it is safeguarded that the information made available may be subject to subsequent changes and updates, with the exception of any omissions and/or occasional inaccuracies of location that the information may contain. 2. In this way, E-REDES is not liable to third parties, namely, partners, service providers, contractors, users and customers, for damages that may arise as a result, direct or indirect, of the use of this Information, in particular when carrying out interventions, calculations and/or estimates, without confirming the accuracy and updating of the data, whereby it is duly noted that the consultation of this information does not affect the duty to promote a direct consultation with E-REDES to obtain updated information.
Hourly Consumption by Postal Code (2821 to 3080)

This DataSet is built based on load diagrams collected by smart meters. In cases where there are still no meters capable of carrying out these collections, or there is difficulty in communicating with them, a profile of consumption is carried out. Consult here the document that systematizes the estimation methodology of the different profiles. (https://www.e-redes.pt/sites/eredes/files/2021-08/DocMetodologico_Perfis.pdf) Due to the high volume of data, this dataset presents information regarding a period of thirty days, in a sliding window model, with no history conservation. For a longer temporal analysis, regular data extractions are recommended. In accordance with the General Data Protection Regulation (GDPR), it is necessary to aggregate postal codes, which are not available individually, into the "OUTROS" set. 1. The information made available by E-REDES constitutes an approximation to the values taken from the system and is based on the moment in which it is collected. Given that the connection points, the electricity distribution network, and the consumption and production values themselves are naturally very dynamic, it is safeguarded that the information made available may be subject to subsequent changes and updates, with the exception of any omissions and/or occasional inaccuracies of location that the information may contain. 2. In this way, E-REDES is not liable to third parties, namely, partners, service providers, contractors, users and customers, for damages that may arise as a result, direct or indirect, of the use of this Information, in particular when carrying out interventions, calculations and/or estimates, without confirming the accuracy and updating of the data, whereby it is duly noted that the consultation of this information does not affect the duty to promote a direct consultation with E-REDES to obtain updated information.
Hourly Consumption by Postal Code (3781 to 4420)

This DataSet is built based on load diagrams collected by smart meters. In cases where there are still no meters capable of carrying out these collections, or there is difficulty in communicating with them, a profile of consumption is carried out. Consult here the document that systematizes the estimation methodology of the different profiles. (https://www.e-redes.pt/sites/eredes/files/2021-08/DocMetodologico_Perfis.pdf) Due to the high volume of data, this dataset presents information regarding a period of thirty days, in a sliding window model, with no history conservation. For a longer temporal analysis, regular data extractions are recommended. In accordance with the General Data Protection Regulation (GDPR), it is necessary to aggregate postal codes, which are not available individually, into the "OUTROS" set. 1. The information made available by E-REDES constitutes an approximation to the values taken from the system and is based on the moment in which it is collected. Given that the connection points, the electricity distribution network, and the consumption and production values themselves are naturally very dynamic, it is safeguarded that the information made available may be subject to subsequent changes and updates, with the exception of any omissions and/or occasional inaccuracies of location that the information may contain. 2. In this way, E-REDES is not liable to third parties, namely, partners, service providers, contractors, users and customers, for damages that may arise as a result, direct or indirect, of the use of this Information, in particular when carrying out interventions, calculations and/or estimates, without confirming the accuracy and updating of the data, whereby it is duly noted that the consultation of this information does not affect the duty to promote a direct consultation with E-REDES to obtain updated information.
Hourly Consumption by Postal Code (3081 to 3780)

This DataSet is built based on load diagrams collected by smart meters. In cases where there are still no meters capable of carrying out these collections, or there is difficulty in communicating with them, a profile of consumption is carried out. Consult here the document that systematizes the estimation methodology of the different profiles. (https://www.e-redes.pt/sites/eredes/files/2021-08/DocMetodologico_Perfis.pdf) Due to the high volume of data, this dataset presents information regarding a period of thirty days, in a sliding window model, with no history conservation. For a longer temporal analysis, regular data extractions are recommended. In accordance with the General Data Protection Regulation (GDPR), it is necessary to aggregate postal codes, which are not available individually, into the "OUTROS" set. 1. The information made available by E-REDES constitutes an approximation to the values taken from the system and is based on the moment in which it is collected. Given that the connection points, the electricity distribution network, and the consumption and production values themselves are naturally very dynamic, it is safeguarded that the information made available may be subject to subsequent changes and updates, with the exception of any omissions and/or occasional inaccuracies of location that the information may contain. 2. In this way, E-REDES is not liable to third parties, namely, partners, service providers, contractors, users and customers, for damages that may arise as a result, direct or indirect, of the use of this Information, in particular when carrying out interventions, calculations and/or estimates, without confirming the accuracy and updating of the data, whereby it is duly noted that the consultation of this information does not affect the duty to promote a direct consultation with E-REDES to obtain updated information.
Hourly Consumption by Postal Code (4421 to 4550)

This DataSet is built based on load diagrams collected by smart meters. In cases where there are still no meters capable of carrying out these collections, or there is difficulty in communicating with them, a profile of consumption is carried out. Consult here the document that systematizes the estimation methodology of the different profiles. (https://www.e-redes.pt/sites/eredes/files/2021-08/DocMetodologico_Perfis.pdf) Due to the high volume of data, this dataset presents information regarding a period of thirty days, in a sliding window model, with no history conservation. For a longer temporal analysis, regular data extractions are recommended. In accordance with the General Data Protection Regulation (GDPR), it is necessary to aggregate postal codes, which are not available individually, into the "OUTROS" set. 1. The information made available by E-REDES constitutes an approximation to the values taken from the system and is based on the moment in which it is collected. Given that the connection points, the electricity distribution network, and the consumption and production values themselves are naturally very dynamic, it is safeguarded that the information made available may be subject to subsequent changes and updates, with the exception of any omissions and/or occasional inaccuracies of location that the information may contain. 2. In this way, E-REDES is not liable to third parties, namely, partners, service providers, contractors, users and customers, for damages that may arise as a result, direct or indirect, of the use of this Information, in particular when carrying out interventions, calculations and/or estimates, without confirming the accuracy and updating of the data, whereby it is duly noted that the consultation of this information does not affect the duty to promote a direct consultation with E-REDES to obtain updated information.