AndreaBozzo/ceres-open-data-index huggingface dataset:
Modeled density map of Syringammina fragillissima at a fine scale from the Rockall Bank in North East Atlantic between 1981-2010

[Lien vers la fiche source] Fine scale density map of the xenophyophores Syringammina fragillissima (ind. m-2) at a 1200m deep station off Rockall Bank (North East Atlantic) by The University of Plymouth for the Deeplinks project. The drivers of the distribution of this species are poorly known despise its Vulnerable Marine Ecosystems (VME) status and more knowledge on its biology is needed to efficiently manage its conservation. A Convolutional Neural Network was used to measure its density in 58,147 images collected by the AUV autosub6000 during the JC136 cruise in May 2016. Several environmental data layers (topographic and oceanographic) at 2.5 x 2.5 m resolution were collected by the AUV during the same cruise and used in the modelling. Random Forest regression was used to predict the density of the target species in the extent of the environmental layer representing around 3 km2.
Replication Data for  :  "Dynamic light scattering unveils stochastic degradation in large-pore mesoporous silica nanoparticles"

[Lien vers la fiche source] Replication data for the article "Dynamic light scattering unveils stochastic degradation in large-pore mesoporous silica nanoparticles" The article studies the mechanism of hydrolysis of large-pores mesoporous silica nanoparticles. These nanoparticles can be very useful for the encapsulation and the release of biomolecules. Understanding their dissolution is therefore important. DLS techniques, in millimetric and micrometric volumes, are used in this study as a new tool to monitor the diameter and concentration of nanoparticles in the colloids. Unexpectedly, we found out that the diameter of the nanoparticles remained constant throughout the study, whereas the concentration decreased following a mono-exponential decay law. These observation led us to conclude that the nanoparticles stochastically fragilize and completely vanish, whereas intact nanoparticles can be found until the end of the process.
Loi Croissance Notaire - Zones d'installation

Le jeu de données présente la liste des zones d'installations pour la profession de notaires sur les périodes 2021-2023 et 2023-2025 - 3ème & 4ème vagues. Comme pour les précédentes vagues, seule l’Alsace-Moselle n’est pas concernée (information vide). Source : Arrêté du 28/02/24 au JO [CLIQUER : DATA VISUALISATION ]
Pendlermatrix: Erwerbstätige mit Wohngemeinde im Kanton Schwyz

Dieser Datensatz gibt Auskunft über den Arbeitsort der Erwerbstätigen, die in einer Gemeinde im Kanton Schwyz wohnen. Hinweise: * Relationen mit weniger als 5 Erwerbstätigen wurden gruppiert zu «Andere Gemeinden» (Gemeindenummer: 7777) und «andere Kantone» (Kantonskürzel: ZZ, Kantonsnummer 77) * Als Erwerbstätige gelten Personen, die eine oder auch mehrere Beschäftigungen mit einem AHV-pflichtigen Lohn (d.h. mindestens 2300 Fr. pro Beschäftigung und Jahr) aufweisen. Die Matrix berücksichtigt dabei alle Personen, die im Dezember des jeweiligen Jahres erwerbstätig waren. * Die Daten basieren auf einer Registerverknüpfung aus AHV, STATPOP (Statistik der Bevölkerung) und STATENT (Statistik der Unternehmensstruktur).
Pendlermatrix: Erwerbstätige mit Arbeitsgemeinde im Kanton Schwyz

Dieser Datensatz gibt Auskunft über den Wohnort der Erwerbstätigen, die in einer Gemeinde im Kanton Schwyz arbeiten. Hinweise: * Relationen mit weniger als 5 Erwerbstätigen wurden gruppiert zu «Andere Gemeinden» (Gemeindenummer: 7777) und «andere Kantone» (Kantonskürzel: ZZ, Kantonsnummer 77) * Als Erwerbstätige gelten Personen, die eine oder auch mehrere Beschäftigungen mit einem AHV-pflichtigen Lohn (d.h. mindestens 2300 Fr. pro Beschäftigung und Jahr) aufweisen. Die Matrix berücksichtigt dabei alle Personen, die im Dezember des jeweiligen Jahres erwerbstätig waren. * Die Daten basieren auf einer Registerverknüpfung aus AHV, STATPOP (Statistik der Bevölkerung) und STATENT (Statistik der Unternehmensstruktur).
Modeled distribution map of Kophobelemnon stelliferum assemblage in the North East Atlantic in 2021

[Lien vers la fiche source] Modelled density of the seapen Kophobelemnon stelliferum in the North East Atlantic. The Random Forest density model trained on data collected by an ROV was constrained by an ensemble of Maxent and Random Forest presence-absence model trained on a larger dataset also collected by an ROV. This species provides structural complexity in an environment where it is lacking and, thus, promotes higher biodiversity where they settle. They are vulnerable to mechanical disturbance of the sediment by fishing gear and a better understanding of their distribution will lead to better management of their population. This work was performed at the University of Plymouth in 2021.
RMQS1 Microbial gene abundances (16S and 18S) and F:B ratio

[Lien vers la fiche source] RMQS: The French Soil Quality Monitoring Network (RMQS) is a national program for the assessment and long-term monitoring of the quality of French soils. This network is based on the monitoring of 2240 sites representative of French soils and their land use. These sites are spread over the whole French territory (metropolitan and overseas) along a systematic square grid of 16 km x 16 km cells. The network covers a broad spectrum of climatic, soil and land-use conditions (croplands, permanent grasslands, woodlands, orchards and vineyards, natural or scarcely anthropogenic land and urban parkland). The first sampling campaign in metropolitan France took place from 2000 to 2009. Dataset: This dataset contains 16S (Bacteria) and 18S (Fungi microbial gene abundances (number of rDNA copies per gram of soil) from 2,073 points with the 18S/16S ratio. Real-time PCR was used to quantify archeal and bacterial (16S) densities and fungal (18S) gene abundances. The 18S on 16S was then computed and explored in associated article. To enhance the robustness of data comparison, a post-processing treatment was performed by calibration using the master curve method (see the ct values file for data, and [Sivaganesan et.al. 2008] See associated articles for details. File structure: * rmqs1_ratio_18s_16s.tsv: four columns file with 16S, 18S and 18S/16S adjusted number of rDNA copies per gram of soil (or density) for each site * rmqs1_ratio_18s_16s_ct_values.tsv: seven columns file with Ct (qPCR threshold cycles), adjusted Ct and qPCR plate for 16S and 18S values, for each site. * Metadata used in the associated study are available at [10.57745/AKSR1A]. * General RMQS1 environmental metadata are available at [10.15454/QSXKGA] (physico-chemical property, landuse, coordinates). * Sites with ID longer than 4 number are supplementary sites that are not in the center of the cells (e.g. 10797 and 20797 that came from cell 797).
Circalittoral faunal turf occurrences on the Azores shelves (central north Atlantic) from 2005 to 2018

[Lien vers la fiche source] Circalittoral faunal turfs are animal-dominated shelf assemblages that are found on hard substrata between the lower limit of the infralittoral zone and the shelf edge. Benthic organisms generally form dense intricate mixes that may include tall alcyonarians, sponges, hydroids, scleractinians and gorgonians, as well as less prominent fauna such as encrusting sponges, bryozoans, ascidians, bivalves, solitary cup corals, serpulid worms and vermetid gastropods.
Flux tower by Eddy Covariance and InfraRed scintillometry on ORACLE observatory

[Lien vers la fiche source] Observational data from flux tower and infrared scintillometry. Data are associated with micro-meteorological data. All data is measured at high-frequency. Data have been collected during CRITEX/EQUIPEX project on the ORACLE observatory (INRAE). These data are free available on the BDOH database (https://doi.org/10.17180/obs.oracle).
Behavior x Heat Tolerance

[Lien vers la fiche source] This dataset contains the predicted posture of 22 pigs under two contrasted conditions: Heat Stress (HS) corresponding to the tropical climate (between 20.3°C and 27.9°C) and Thermoneutral (TN) consisting of an indoor temperature-controlled room at 22°C. At the beginning of the monitoring, animals were 17 weeks of age and were placed in individual metal-slatted pens (1.60 × 0.84 m) equipped with a stainless steel feeder and a nipple water drinker. The energy and protein ration were distributed throughout the day in three meals : between 6:00 and 8:00 (half of the soybean meal ration), between 9:00 and 11:00 (the totality of the green bananas) and between 12:00 and 14:00 (the other half of the soybean meal). Water was available to all pigs ad libitum from a nipple drinker designed to limit waste. The daily feed intake (ADFI, in g/day) was calculated as the difference between the amount of feed provided on day n and the amount of feed left over the morning of day n+1. Average Daily Gain (ADG, in g/day) was calculated as the difference between pig BW at the end and at the beginning of the trial divided by the number of days of the trial. Feed Efficiency (FE) was calculated as ADG divided by ADFI. We used 8 Time-Lapse cameras (TLC2000 Pro, manufactured in 2018, by the brand Brinno) to monitor the animal at 20-second intervals, from 8:00 to 18:00. Each camera capturing a single animal at a time, cameras were switched every 2 days to allow monitoring of the 11 animals of each trial. On average each animal was monitored for 4.3 days (SD = 1.33 days, min=2, max=8). More details are available in the associated publication. The dataset is a table containing 157,279 rows and 8 columns. One row corresponds to one observation, i.e. the posture of the animal at a given time, with the associated meta-data. The first column, date, provides the date and time of the day at which the observation was made. The column anim provides the animal number. Note that this is a fake id and does not correspond to the national identification number. But this can be used to differentiate the observation from the different animals. The column posture provides the estimated posture of the animal. Five postures were considered. Standing: Body in upright position, with extended legs, and when only hooves are in contact with the floor. Sitting: Animal is partly rested on stretched front legs with caudal end of body contacting the floor. Sternal: The animal is lying on abdomen/sternum with the front legs folded under the body. The hind legs folded invisible or stretched out visible. Abdomen is totally/partially obscured. Lateral: The animal is lying on either side with all four legs visible and abdomen is totally visible. The posture was estimated using a neural network. The column muscle_temp provides the muscle temperature, while the column ambient-temp provides the ambiant temperature. The column condition provides the condition at which the animal was housed, either HS or TN. The column adg provides the ADG and the column feed_efficiency provides de FE.
Populations légales des communes de SQY en vigueur au 1er janvier 2024

Populations légales des communes de Saint-Quentin-en-Yvelines en vigueur au 1er janvier 2024 Ces données sont produites à partir des données de population au 1er janvier 2021 dans les limites territoriales des communes au 1er janvier 2023 sont officielles et authentifiées par le décret du 26 décembre 2023. Elles sont rentrées en vigueur au 1er janvier 2024. La population municipale comprend les personnes ayant leur résidence habituelle sur le territoire de la commune, dans un logement ou une communauté, les personnes détenues dans les établissements pénitentiaires de la commune, les personnes sans abri recensées sur le territoire de la commune et les personnes résidant habituellement dans une habitation mobile recensées sur le territoire de la commune. La population comptée à part comprend certaines personnes dont la résidence habituelle est dans une autre commune mais qui ont conservé une résidence sur le territoire de la commune : - les personnes mineures dont la résidence familiale est dans une autre commune mais qui résident, du fait de leurs études, dans la commune ; - les personnes ayant une résidence familiale sur le territoire de la commune et résidant dans une communauté d’une autre commune ; la communauté faisant partie de la liste suivante : * services de moyen ou de long séjour des établissements publics ou privés de santé, établissements sociaux de moyen ou de long séjour, maisons de retraite, foyers et résidences sociales ; * commaunautés religieuses ; * casernes ou établissements militaires ; - les personnes majeures âgées de moins de 25 ans ayant leur résidence familiale sur le territoire de la commune et qui résident dans une autre commune pour leurs études ; - les personnes sans domicile fixe rattachées à la commune au sens de la loi du 3 janvier 1969 et non recensées dans la commune. La population totale est la somme de la population municipale et de la population comptée à part.
Raw bulk density and coarse fragment data of the second campaign of the French Soil Quality Monitoring Network

[Lien vers la fiche source] The French Soil Quality Monitoring Network (RMQS) is a national program for the assessment and long-term monitoring of the quality of French soils. This network is based on the monitoring of 2240 sites representative of French soils and their land use. These sites are spread over the whole French territory (metropolitan and overseas) along a systematic square grid of 16 km x 16 km cells. The network covers a broad spectrum of climatic, soil and land-use conditions (croplands, permanent grasslands, woodlands, orchards and vineyards, natural or scarcely anthropogenic land and urban parkland). The physical, chemical and biological properties of the soil are measured on each site, during the first campaign and presently on the second campaign. The spatial and temporal variability of soil properties are explained by biophysical variables, sources of contamination, history of land-use and management practices on each plot. The first sampling campaign in metropolitan France took place from 2000 to 2009. This campaign focused on soil contamination assessment and made it possible to map key soil parameters (28 variables) as well as 12 trace metal elements and 70 persistent organic pollutants. The second on-going campaign started in 2016 and should last 12 years. The sampling plot includes a sampling area and a pit at a distance of 5 m. This dataset includes physical data from plots set from the beginning of the second RMQS campaign. Data come from measurements on dedicated samples from the pit. These measurements are made at Conservatoire Européen des Sols INRAE site d’Orléans. They are : mass of the sample at field, mass of the sample after 105°C drying, mass of coarse fragments after washing and sieving. Volumes are measured at field, according to different methods adapted to field constraints (cylinder method or excavation method). These measurements allow to calculate bulk density, soil moisture and coarse fragment content for each sample. Composite samples were collected with an auger at two sampling layers: 0-30 cm or cultivated layer named “upper layer” or “layer 1”, and 30-50 cm named “subsoil layer” or “layer 2”. Each composite sample was made up of 25 individual sample cores taken on the sampling area of 400 m², using an unaligned systematic sampling design. In some cases, a third layer was made up of holorganic layers in forests or meadows, corresponding to pedological horizons OF and OH, when these layers were sufficiently thick, (at least 1 cm) and continuous over the sampling area. This composite sample was used also as a volumetric sample. Samples were distributed along the soil profile to match each composite layer thickness and according to three pseudo-replicates by layer, in general case. The sampling method, measurements and observations on each site are described in the “RMQS guidelines” (reference below).
Weighted average of bulk density, coarse fragment by layer of the first and second campaign of the French Soil Quality Monitoring Network

[Lien vers la fiche source] The French Soil Quality Monitoring Network (RMQS) is a national program for the assessment and long-term monitoring of the quality of French soils. This network is based on the monitoring of 2240 sites representative of French soils and their land use. These sites are spread over the whole French territory (metropolitan and overseas) along a systematic square grid of 16 km x 16 km cells. The network covers a broad spectrum of climatic, soil and land-use conditions (croplands, permanent grasslands, woodlands, orchards and vineyards, natural or scarcely anthropogenic land and urban parkland). The physical, chemical and biological properties of the soil are measured on each site, during the first campaign and presently on the second campaign. The spatial and temporal variability of soil properties are explained by biophysical variables, sources of contamination, history of land-use and management practices on each plot. The first sampling campaign in metropolitan France took place from 2000 to 2009. This campaign focused on soil contamination assessment and made it possible to map key soil parameters (28 variables) as well as 12 trace metal elements and 70 persistent organic pollutants. The sampling method, measurements and observations on each site are described in the RMQS guidelines. The second campaign started in 2016 and should last 12 years. The soil tests were carried out on composite samples collected with an auger at two sampling layers: 0-30 cm or cultivated layer named “upper layer” or “layer 1” and 30-50 cm named “subsoil layer” or “layer 2”. Each composite sample was made up of 25 individual sample cores taken on a sampling area of 400 m², using an unaligned systematic sampling design (see description in the RMQS guidelines). In some cases, a third layer was made up of holorganic layers in forests or meadows, corresponding to pedological horizons OF and OH, when these layers were sufficiently thick, (at least 1 cm) and continuous over the sampling area. On each plot, were performed, on a pit, volumetric samples to measure bulk density and coarse fragment content. Usually three pseudo-replicates corresponding to both layer 1 and layer 2 and covering the thickness of each layer were sampled. For holorganic layers, bulk density was calculated from the composite sample, for which each elementary volume was measured. The dataset contains the results of bulk density and coarse fragment data from 2168 plots of the first campaign and their available layers and from 2137 plots of the 2nd campaign until 2022. The raw values of bulk density and coarse fragment content are not available here but corrected by layer by applying a weighting taking in account overlapping of samples.
Évolution des naissances et décès entre 2013 et 2022 - SQY

Évolution des naissances et décès sur les 12 communes de Saint-Quentin-en-Yvelines entre 202414 et 2021. Cette donnée est produite à partir des données du recensement de la population de 2021 selon la géographie en vigueur au 1er janvier 2024. Le recensement de la population permet de connaître la diversité et l'évolution de la population de la France. L’INSEE fournit ainsi des statistiques sur les habitants et les logements, leur nombre et leurs caractéristiques : répartition par sexe et âge, professions, conditions de logement, modes de transport, déplacements domicile-travail, etc. La dernière actualisation de cette donnée date du 7 novembre 2023Elle concerne spécifiquement : - Les enfants nés vivants ainsi que les jugements déclaratifs de naissance. Le lieu retenu pour la naissance est le domicile de la mère et non pas le lieu de naissance - Les statistiques annuelles concernent les décès domiciliés ainsi que les jugements déclaratifs de décès. Le lieu retenu pour la naissance est le domicile de la mère et non pas le lieu du décès Pour plus de renseignements sur les variables, vous pouvez consulter les définitions en cliquant sur ce [lien] Pour plus d'information méthodologique, vous pouvez consulter les fiches "Conseils pour l'utilisation des résultats du recensement" en cliquant sur le lien Pour plus de renseignements sur l'utilisation des données, vous pouvez consulter ce [document]
Adresse et géolocalisation des établissements gersois d'enseignement du premier et second degrés

Liste géolocalisée des établissements d'enseignement des premier et second degrés (tous ministères de tutelle, secteurs public et privé) situés en France et dans les collectivités d'outre-mer. Les établissements concernés sont déterminés selon leur code nature dans la nomenclature de l'éducation nationale. Les codes entre 100 et 199 (premier degré) et entre 300 et 399 (second degré) sont retenus. Les données sont issues de RAMSESE (répertoire académique et ministériel sur les établissements du système éducatif). Le référentiel national des bâtiments a été ajouté à ce jeu de données améliorant l'information sur l'établissement.
RMQS1 16S operational taxonomic units

[Lien vers la fiche source] RMQS: The French Soil Quality Monitoring Network (RMQS) is a national program for the assessment and long-term monitoring of the quality of French soils. This network is based on the monitoring of 2,240 sites representative of French soils and their land use. These sites are spread over the whole French territory (metropolitan and overseas) along a systematic square grid of 16 km x 16 km cells. The network covers a broad spectrum of climatic, soil and land-use conditions (croplands, permanent grasslands, woodlands, orchards and vineyards, natural or scarcely anthropogenic land and urban parkland). The first sampling campaign in metropolitan France took place from 2000 to 2009. Dataset: This dataset contains 16S (Archaea and Bacteria) operational taxonomic units (OTU) abundance table of 1,842 sites of the RMQS, as well as their core sequences. Soil 16S rDNA gene was sequenced using pyrosequecing (GS FLX Titanium - Roche 454) at Genosocope. Bioinformatics analysis was performed using [BIOCOM-PIPE] (previously named GNS-PIPE) metabarcoding pipeline. OTUs were clustered at 95% using a post-clustering strategy [Terrat et.al. (2019)] across the whole dataset, producing 188,030 robust OTUs. See associated articles for details (keep in mind that first results were obtained without post-clustering, richness is therefore higher after post-clustering for each sample). Raw sequencing data are available at EBI under project [PRJEB21351]. File structure: * rmqs1_16S_otu_abundance.tsv: OTU abundance per site (each OTUs name “DB” + number, arranged by their global abundance). * Each line sums to 10,000 (rarefaction chosen threshold). * otu_reference_sequence.fasta.gz: OTU core sequence. * rmqs1_16S_otu_taxonomy.tsv: Tabulated file giving the complete taxonomy for each OTU. * SEQUENCE: Name of the considered OTU * KINGDOM: Taxonomy at the kingdom level * PHYLUM: Taxonomy at the phylum level * CLASS: Taxonomy at the class level * ORDER: Taxonomy at the order level * FAMILY: Taxonomy at the family level * GENUS: Taxonomy at the genus level Details: * Some sites sample could not be collected, they do not appear in dataset. * Some sites did not pass laboratory or bioinformatics step to attain 10,000 sequences before post-clustering, they dot not appear in the dataset. * One can link this dataset with [10.15454/QSXKGA] to get each sample physico-chemical property, landuse, coordinates, or filtering sites using its site_officiel column. * Sites with ID longer than 4 number are supplementary sites that are not in the center of the cells (e.g. 10797 and 20797 that came from cell 797). * The taxonomy was defined based on the SILVA R132 database using only the core sequence of each OTU. This can be different to the approach of BIOCOM-PIPE, affiliating all sequences, and not only OTU core sequences.