AndreaBozzo/ceres-open-data-index huggingface dataset:
CMEMS L3S Baltic Sea - Sea Surface Temperature Reprocessed

[Lien vers la fiche source] This dataset provides a time series of daily multi-sensor supercollated fields (referred to as L3S) of Sea Surface Temperature (SST) at a 0.02 x 0.02 degree grid for the Baltic Sea and North Sea. The processing combines night-time satellite data from infrared radiometers. The data is inter-calibrated and bias corrected before being combined to provide daily L3S fields. In each L3 grid point, the L3S SST estimate is generated through a weighted average of the available L3 satellite observations in the particular grid points. The weights are determined by the associated uncertainty estimates on the available SST observations and the resulting uncertainty is the average uncertainty, divided by the square root of the number of observations under the assumption that the uncertainty estimates are uncorrelated. The L3S SST and uncertainty fields are written to the output netCDF files in addition to auxiliary information, such as number of observations in the grid and the source of the satellite observations. The product is produced in the frame of Copernicus Marine Environment Monitoring Service (CMEMS) and the data available through various tools and protocols with a simple user registration from this service (product identifier: SST_BAL_PHY_L3S_MY_010_040)
CMEMS L4 Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis

[Lien vers la fiche source] This product is a diurnal sub-skin Sea Surface Temperature Analysis available hourly for the Baltic Sea and North Sea, produced by the Danish Meteorological Institute (DMI). The DMI L4 Diurnal Sub-skin Sea Surface Temperature Analysis consist of hourly, gap-free (L4) fields of sea surface temperature and sea ice area fraction at 0.02 x 0.02degree horizontal resolution, using infra-red satellite observations and high resolution sea ice information. Hourly temporal availability can allow for monitoring of the diurnal variability, which can be large in the Baltic Sea and North Sea during spring and summer. The increased spatial resolution (compared to the global products) gives a better representation of small islands and complex coastal regions, and a better representation of the sea surface temperature fronts and near coastal effects. The product is produced in the frame of Copernicus Marine Environment Monitoring Service (CMEMS) and the data available through various tools and protocols with a simple user registration from this service (product identifier: SST_BAL_PHY_SUBSKIN_L4_NRT_010_034).
CMEMS L4 Baltic Sea - Sea Surface Temperature Reprocessed

[Lien vers la fiche source] This dataset provides time series of daily multi-sensor gap-free fields (referred to as L4) of foundation Sea Surface Temperature (SST) at a 0.02 x 0.02 degree grid for the Baltic Sea and North Sea. Whereas along swath observation data essentially represent the skin or sub-skin SST, the L4 SST product is defined to represent the foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines night-time satellite data from infrared radiometers. The data is inter-calibrated and bias corrected before being merged and interpolated using an optimal interpolation scheme to provide daily gap-free fields. The optimal interpolation scheme uses the previous days analysis as a background field. The product is produced in the frame of Copernicus Marine Environment Monitoring Service (CMEMS) and the data available through various tools and protocols with a simple user registration from this service (product identifier: SST_BAL_SST_L4_REP_OBSERVATIONS_010_016): https://data.marine.copernicus.eu/product/SST_BAL_SST_L4_REP_OBSERVATIONS_010_016/description
CMEMS L3S Arctic Ocean - Sea and Ice Surface Temperature Reprocessed

[Lien vers la fiche source] This product provides a time series of daily fields of multi-sensor gridded "super-collated" (L3S) fields of Arctic Sea Surface Temperature (SST) and Sea Ice Surface Temperature (IST) at a 0.05 x 0.05 degree grid of the open ocean, sea ice and the marginal ice zone. The IST represents the skin temperature, while the SST represents the daily mean at 20 cm depth. The dataset is based on reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The data is inter-calibrated and bias corrected before being merged to provide the super-collated L3S fields. The product is produced in the frame of Copernicus Marine Environment Monitoring Service (CMEMS) and the data available through various tools and protocols with a simple user registration from this service (product identifier: SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021).
CMEMS L4 Baltic Sea - Sea Surface Temperature Analysis

[Lien vers la fiche source] This dataset provides near-real-time (NRT) daily multi-sensor gap-free fields (referred to as L4) of foundation Sea Surface Temperature (SST) at a 0.02 x 0.02 degree grid for the Baltic Sea and North Sea. Whereas along swath observation data essentially represent the skin or sub-skin SST, the L4 SST product is defined to represent the foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines night-time satellite data from infrared radiometers. The data is inter-calibrated and bias corrected before being merged and interpolated using an optimal interpolation scheme to provide daily gap-free fields. The optimal interpolation scheme uses the previous days analysis as a background field. The product is produced in the frame of Copernicus Marine Environment Monitoring Service (CMEMS) and the data available through various tools and protocols with a simple user registration from this service (product identifier: SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b).
CMEMS L4 Arctic Ocean - Sea and Ice Surface Temperature Analysis

[Lien vers la fiche source] This dataset provides near-real-time daily multi-sensor gap-free fields (referred to as L4) of Arctic Sea Surface Temperature (SST) and Sea Ice Surface Temperature (IST) at a 0.05 x 0.05 degree grid of the open ocean, sea ice and the marginal ice zone. The IST represents the skin temperature, while the SST represents the daily mean at 20 cm depth. The dataset is based on reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The data is inter-calibrated and bias corrected before being merged and interpolated using an optimal interpolation scheme to provide daily gap-free fields. The optimal interpolation scheme uses the previous days analysis as a background field. The product is produced in the frame of Copernicus Marine Environment Monitoring Service (CMEMS) and the data available through various tools and protocols with a simple user registration from this service (product identifier: SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008).
CMEMS L3S Baltic Sea - Sea Surface Temperature Analysis

[Lien vers la fiche source] This dataset provides daily multi-sensor supercollated fields (referred to as L3S) of Sea Surface Temperature (SST) at a 0.02 x 0.02 degree grid for the Baltic Sea and North Sea. The processing combines night-time satellite data from infrared radiometers. The data is inter-calibrated and bias corrected before being combined to provide daily L3S fields. In each L3 grid point, the L3S SST estimate is generated through a weighted average of the available L3 satellite observations in the particular grid points. The weights are determined by the associated uncertainty estimates on the available SST observations and the resulting uncertainty is the average uncertainty, divided by the square root of the number of observations under the assumption that the uncertainty estimates are uncorrelated. The L3S SST and uncertainty fields are written to the output netCDF files in addition to auxiliary information, such as number of observations in the grid and the source of the satellite observations. The product is produced in the frame of Copernicus Marine Environment Monitoring Service (CMEMS) and the data available through various tools and protocols with a simple user registration from this service (product identifier: SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032)
Retour d’expérience technique de la crue du  2 octobre 2020 dans la vallée de la Vésubie - Volet torrentiel

[Lien vers la fiche source] Ce dataset regroupe le rapport de retour d'expérience et ses annexes. Il a été rédigé par l'ONF-RTM, l'ONF-DRN et INRAE ETNA, appuyés de chercheurs ayant contribué à l'analyse. Il visait à documenter et analyser les crues torrentielles qui ont touché le bassin versant de la Vésubie pendant la tempête Alex. Ce retour d’expérience technique a été réalisé à la demande de la Direction Départementale des Territoires et de la Mer des Alpes-Maritimes ainsi que du Ministère de la Transition Ecologique, Direction Générale de la Pré-vention des Risques, Service des Risques Naturels et Hydrauliques, qui en a confié le pilotage et la rédaction à l’ONF-RTM en collaboration avec l’INRAE dans le cadre de la mission d’intérêt général « Prévention des risques naturels en montagne » réalisée par l’ONF au bénéfice du MTE.
Statistiques des documents numérisés des bibliothèques patrimoniales

Les documents d'archives des bibliothèques patrimoniales de la Ville de Paris ont été numérisées et continuent à l'être. Des dessins, estampes, catalogues commerciaux, d'exposition, cartes postales mais aussi des 78 tours sont ainsi disponibles en consultation sur le portail des bibliothèques spécialisées. Le tableau statistique répertorie les + de 1 millions d'images par type de document.
GHRSST Level 4 DMI_OI Global Foundation Sea Surface Temperature Analysis

[Lien vers la fiche source] A Group for High Resolution Sea Surface Temperature (GHRSST) gap-free (L4) Sea Surface Temperature (SST) analysis produced daily on an operational basis by the Danish Meteorological Institute (DMI) using an optimal interpolation (OI) approach on a global 0.05 degree grid. Whereas along swath observation data essentially represent the skin or sub-skin SST, the L4 SST product is defined to represent the foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines night-time GHRSST L2P skin and subskin SST observations from several satellites. The data is inter-calibrated and bias corrected before being merged and interpolated using an optimal interpolation scheme to provide daily gap-free fields. The optimal interpolation scheme uses the previous days analysis as a background field. An ice field from the EUMETSAT OSI-SAF is used to mask out areas with ice.
Résidences étudiantes 2023

L’Agence de Développement et d’Urbanisme de l’agglomération Strasbourgeoise (ADEUS) réalise, chaque année, un recensement des résidences étudiantes sur l’ensemble de l’Eurométropole de Strasbourg. Ce recensement repose sur la collaboration et la déclaration des résidences lorsqu’elle est consentie ou le cas échéant sur la collecte d’information via les sites internet. Il permet d’obtenir, lorsque cela est possible, des informations sur le nombre de places, le type de logements ou encore le niveau des redevances. Le dernier recensement date de 2023. [Consulter la page dédiée]
Elections municipales 2020 - Résultats du 1er tour - CCPHVA

Résultats du premier tour des élections municipales du 15 mars 2020 Ces données sont publiées sous réserve : * des recours formés devant le tribunal administratif ; * d'éventuelles rectifications (erreurs matérielles, utilisation par les candidats de leur droit de rectification des données personnelles les concernant) Enrichissement * * Ajout des Geo Points des bureaux de vote. * Ajout des noms/codes des régions. Traitement * Réorganisation des données afin de permettre le calcul des résultats par ville. Il y a des erreurs de géolocalisation sur les données.
Annuaire des professionnels de santé à Roubaix

Ce jeu de données est issu du traitement des données de l'annuaire santé publié par la Caisse nationale d'assurance maladie ([https://data.opendatasoft.com/explore/dataset/mede...] : Chaque enregistrement correspond à un professionnel de santé exerçant en France et détaille : - La profession du professionnel de santé - Sa localisation et ses coordonnées - La nature de son activité et la convention sous laquelle il exerce - Les actes techniques qu'il réalise Ce jeu de données est mis à jour de manière mensuelle.
Biochemical data documenting variations in mucilage polysaccharide amount and macromolecular properties in a range of glycosyltransferase mutants

[Lien vers la fiche source] During Arabidopsis seed coat development, copious amounts of mucilage polysaccharides are produced in the epidermal cells. When hydrated on imbibition, these polysaccharides expand and are released to encapsulate the seed as a two layered hydrogel. Polysaccharides are synthesized from UDP-sugars by glycosyltransferases (GTs) and several GTs, with differing activities, have been identified that contribute to mucilage polysaccharide synthesis How these GTs orchestrate production of the complex polysaccharides found in mucilage remains to be determined. In this study, we generated a range of multiple GT mutants using either CRISPR/Cas9 targeted mutation or genetic crosses of existing T-DNA insertion mutants. Mucilage amounts and macromolecular properties (4 traits) were examined for four replicates from 31 different GT mutant combinations.
Elections municipales 2020 - Résultats du 2ème tour - CCPHVA

Résultats du second tour des élections municipales du 28 juin 2020 Ces données sont publiées sous réserve : * des recours formés devant le tribunal administratif ; * d'éventuelles rectifications (erreurs matérielles, utilisation par les candidats de leur droit de rectification des données personnelles les concernant) Enrichissement * Ajout des Géo Points des bureaux de vote (il y a des erreurs de géolocalisation sur les données). * Ajout des noms/codes des régions.
Législatives 2022 - 1er tour - par bureau de vote - CCPHVA

Ce jeu de données contient les résultats du 1er tour des élections législatives 2022. Ces données sont publiées sous réserve des recours formés devant le tribunal administratif et d'éventuelles rectifications (erreurs matérielles, utilisation par les candidats de leur droit de rectification des données les concernant). Enrichissement * Ajout des noms de nuances. * Ajout des noms/codes des départements et régions. * Ajout des coordonnées des bureaux de vote (voir [dataset])
Présidentielles 2022 - 2nd tour par bureau de vote - CCPHVA

Ce jeu de données contient les résultats du 2nd tour des élections présidentielles 2022. Ces données sont publiées sous réserve des recours formés devant le tribunal administratif et d'éventuelles rectifications (erreurs matérielles, utilisation par les candidats de leur droit de rectification des données les concernant). Enrichissement * Ajout des noms/codes des départements et régions. * Ajout des coordonnées des bureaux de vote (voir [dataset])
A catalog of genes and species of the human skin microbiota

[Lien vers la fiche source] # Dataset overview This dataset provides: * a non-redundant high-quality catalog of 2.9 million genes * 392 Metagenomic Species Pangenomes (MSPs) This dataset can be used to analyze shotgun sequencing data of the human skin microbiota. # How to use this dataset 1. Create a gene abundance table by aligning reads from each sample against the catalog. For this purpose, you can use [Meteor] or [NGLess]. Then, normalize raw counts by gene length. 2. Taxonomic profiling: the abundance of each species can be estimated as the average abundance of its 100 first core genes. To reduce the false positive rate, only consider that a species is present if at least 10/100 marker genes are detected. # Methods ## Data sources This dataset was built using the following data sources: * 118 isolate-derived genomes from the [HMRGD] * 246 isolate-derived genomes from the [Skin Microbial Genome Collection (SMGC)] * 1,407 skin metagenome assemblies from the [Skin Microbial Genome Collection (SMGC)] ## Non-redundant gene catalog After filtering out short contigs (<1500 bp), genes were predicted with [Prodigal] on genomes (mode: single) and metagenome assemblies (mode: meta). Complete genes (partial=00) were pooled and clustered with [cd-hit-est] (parameters -c 0.95 -aS 0.90 -G 0 -d 0 -M 0 -T 0) by choosing those from the longest contigs as representatives. ## Functional annotation KOs assignments were obtained with [KofamScan] using the KEGG 107 database. ## MSPs recovery Reads from the 1,120 skin metagenomes available in the bioproject [PRJNA46333] were aligned against the non-redundant gene catalog with the [Meteor software suite] to produce a raw gene abundance table (2.9M genes quantified in 1,120samples). Then, co-abundant genes were binned in 392 Metagenomic Species Pan-genomes (MSPs, i.e. clusters of co-abundant genes that likely belong to the same microbial species) using [MSPminer]. ## MSPs taxonomic annotation Taxonomic annotation was performed by alignment of all core and accessory genes against representative genomes of the [GTDB] database (release r214) using blastn (version 2.7.1, task = megablast, word_size = 16). A species-level assignment was given if > 50% of the genes matched the representative genome of a given species, with a mean nucleotide identity ≥ 95% and mean gene length coverage ≥ 90%. The remaining MSPs were assigned to a higher taxonomic level (genus to superkingdom), if more than 50% of their genes had the same annotation. ## Construction of the phylogenetic tree 39 universal phylogenetic markers genes were extracted from the MSPs (or the corresponding genome if available) with [fetchMGs]. Then, the markers were separately aligned with [MUSCLE]. The 40 alignments were merged and trimmed with [trimAl] (parameters: -automated1). Finally, the phylogenetic tree was computed with [FastTreeMP] (parameters: -gamma -pseudo -spr -mlacc 3 -slownni).
A dataset of physico-chemical properties, extractable organic N, N mineralization and physical organic matter fractionation of soils developed on loess silts, crystalline rocks and sedimentary rocks

[Lien vers la fiche source] A network of 137 cultivated fields covering the wide diversity of soils, crop rotations and cropping practices throughout the region of Brittany (France) was monitored to collect data on soil organic nitrogen (SON) mineralization and to identify the factors that explain the observed variability. The dataset presented in this article contains all of the information about the soils, which were subjected to pedological description and in-depth analysis of their topsoil properties. The topsoil (0-30 cm) was sampled by mixing 30 samples to obtain one composite per field, which was divided into one sub-sample sieved at 5 mm to analyze soil microbial biomass (SMB) and SON mineralization via anaerobic incubation, and one subsample dried at 40°C and sieved at 2 mm. The physico-chemical analyses included the particle-size distribution of five fractions; organic matter (OM); organic C; organic N; pH; pH KCl; CEC (Metson); CEC (cobaltihexammine); Al, Ca, Fe, K, Mg, Mn, Na échangeables à la cobaltihexammine; Olsen P; Dyer P; and total Al, Ca, Fe, K, Mg, Mn, Na and P. Physical OM fractionation was used to characterize the 200-2000 µm and 50-200 µm fractions of particulate organic matter (POM). Finally, three chemical methods were used to determine extractable organic nitrogen (EON): hot KCl, hot water and phosphate buffer tests. This dataset covers a wide range of pedological situations and cropping systems, and is of great interest to scientists searching for soil properties that can explain SON mineralization. It provides original data on EON indices, SMB and multiple forms of P