AndreaBozzo/ceres-open-data-index huggingface dataset:
ABS - Census of Population and Housing - Ancestry by Country of Birth of Parents  - Suburb Level - G08

ABS Census data extract - G08 ANCESTRY BY COUNTRY OF BIRTH OF PARENTS providing a breakdown of population at Suburb level and by: * ancestry(a) * birthplace not stated(b) * total responses(c) and * other(d) This data is based on place of usual residence. (a) This list of ancestries consists of the most common 30 Ancestry responses reported in the 2016 and 2011 Census. (b) Includes birthplace for either or both parents not stated. (c) This table is a multi-response table and therefore the total responses count will not equal the total persons count. (d) If two responses from one person are categorised in the 'Other' category only one response is counted. Includes ancestries not identified individually and 'Inadequately described'. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
ABS - Census of Population and Housing - Ancestry by Country of Birth of Parents - Suburb Level - G08

ABS Census data extract - G08 ANCESTRY BY COUNTRY OF BIRTH OF PARENTS providing a breakdown of population at Suburb level and by: * ancestry(a) * birthplace not stated(b) * total responses(c) and * other(d) This data is based on place of usual residence. (a) This list of ancestries consists of the most common 30 Ancestry responses reported in the 2016 and 2011 Census. (b) Includes birthplace for either or both parents not stated. (c) This table is a multi-response table and therefore the total responses count will not equal the total persons count. (d) If two responses from one person are categorised in the 'Other' category only one response is counted. Includes ancestries not identified individually and 'Inadequately described'. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
ABS - Census of Population and Housing - Country of birth of person by age - LGA Level - G09

ABS Census data extract - G09 COUNTRY OF BIRTH OF PERSON BY AGE providing a breakdown of population at LGA level and by: * age groups * country of birth of person(a) * Australia(b) * China (excludes SARs and Taiwan)(c) * Hong Kong (SAR of China)(c) * Born elsewhere(d) This data is based on place of usual residence. (a) This list consists of the most common 50 Country of Birth responses reported in the 2016 Census and 2011 Census. (b) Includes 'Australia', 'Australia (includes External Territories), nfd', 'Norfolk Island' and 'Australian External Territories, nec'. (c) Special Administrative Regions (SARs) comprise 'Hong Kong (SAR of China)' and 'Macau (SAR of China)'. (d) Includes countries not identified individually, 'Inadequately described', and 'At sea'. Excludes not stated. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
ABS - Census of Population and Housing - Country of birth of person by age - Suburb Level - G09

ABS Census data extract - G09 COUNTRY OF BIRTH OF PERSON BY AGE providing a breakdown of population at Suburb level and by: * age groups * country of birth of person(a) * Australia(b) * China (excludes SARs and Taiwan)(c) * Hong Kong (SAR of China)(c) * Born elsewhere(d) This data is based on place of usual residence. (a) This list consists of the most common 50 Country of Birth responses reported in the 2016 Census and 2011 Census. (b) Includes 'Australia', 'Australia (includes External Territories), nfd', 'Norfolk Island' and 'Australian External Territories, nec'. (c) Special Administrative Regions (SARs) comprise 'Hong Kong (SAR of China)' and 'Macau (SAR of China)'. (d) Includes countries not identified individually, 'Inadequately described', and 'At sea'. Excludes not stated. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
SHIFDR: Sub-metered HVAC Implemented For Demand Response

The Sub-metered HVAC Implemented for Demand Response (SHIFDR) dataset is a massive dataset that captures the response of individual commercial building HVAC system components to demand response events. The dataset includes device-level power consumption during demand response events as well as during normal operation. We have organized the data into subsets, with each subset containing data from buildings in different parts of the world. Kindly refer to the README file within each subsection for specific information about how data is organized. Please reach out if you have data that you would like to share, find any mistakes in the data, or have any questions. We are always trying to improve SHIFDR.
Benthic habitats using EUNIS classification for the Brazilian EEZ

[Lien vers la fiche source] The dataset contains the shapefile of marine benthic broad habitats classified according to EUNIS classification for the Brazilian Exclusive Economic Zone region. It was developed using open access databases and GIS operations to develop two intermediate layers: (i) biological zones and (ii) substrate type. Both layers were combined to form the first continuous map of marine habitats for the entire region.
ABS - Census of Population and Housing - Language used at home by proficiency in spoken English - Suburb Level - G13

ABS Census data extract - G13 LANGUAGE USED AT HOME BY PROFICIENCY IN SPOKEN ENGLISH providing a breakdown of population at Suburb level and by: * speaks English only * other language(a) * not stated(b) * other Chinese languages(c) * other Indo-Aryan languages(d) * other Southeast Asian Austronesian languages(e) * other(f) This data is based on place of usual residence. (a) This list of languages consists of the most common Language used at home responses reported in the 2016 Census and 2011 Census. (b) Includes the categories 'Proficiency in English not stated' and 'Language and proficiency in English not stated'. (c) Comprises 'Chinese, nfd', 'Hakka', 'Wu', 'Min Nan' and 'Chinese, nec'. (d) Comprises' Indo-Aryan, nfd', 'Konkani', 'Marathi', 'Sindhi', 'Assamese', 'Dhivehi', 'Kashmiri', 'Oriya', 'Fijian Hindustani' and 'Indo-Aryan, nec'. (e) Comprises 'Bikol', 'Bisaya', 'Cebuano', 'Ilokano', 'Ilonggo (Hiligaynon)', 'Pampangan', 'Malay', 'Tetum', 'Timorese', 'Acehnese', 'Balinese', 'Iban', 'Javanese', 'Southeast Asian Austronesian Languages, nec' and 'Southeast Asian Austronesian Languages, nfd'. (f) Includes languages not identified individually, 'Inadequately described' and 'Non-verbal, so described'. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
ABS - Census of Population and Housing - Language used at home by proficiency in spoken English by sex - Suburb Level - G13

ABS Census data extract - G13 LANGUAGE USED AT HOME BY PROFICIENCY IN SPOKEN ENGLISH providing a breakdown of population at Suburb level and by: * speaks English only * other language(a) * not stated(b) * other Chinese languages(c) * other Indo-Aryan languages(d) * other Southeast Asian Austronesian languages(e) * other(f) This data is based on place of usual residence. (a) This list of languages consists of the most common Language used at home responses reported in the 2016 Census and 2011 Census. (b) Includes the categories 'Proficiency in English not stated' and 'Language and proficiency in English not stated'. (c) Comprises 'Chinese, nfd', 'Hakka', 'Wu', 'Min Nan' and 'Chinese, nec'. (d) Comprises' Indo-Aryan, nfd', 'Konkani', 'Marathi', 'Sindhi', 'Assamese', 'Dhivehi', 'Kashmiri', 'Oriya', 'Fijian Hindustani' and 'Indo-Aryan, nec'. (e) Comprises 'Bikol', 'Bisaya', 'Cebuano', 'Ilokano', 'Ilonggo (Hiligaynon)', 'Pampangan', 'Malay', 'Tetum', 'Timorese', 'Acehnese', 'Balinese', 'Iban', 'Javanese', 'Southeast Asian Austronesian Languages, nec' and 'Southeast Asian Austronesian Languages, nfd'. (f) Includes languages not identified individually, 'Inadequately described' and 'Non-verbal, so described'. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
ABS - Census of Population and Housing - Language used at home by proficiency in spoken English by sex - LGA Level - G13

ABS Census data extract - G13 LANGUAGE USED AT HOME BY PROFICIENCY IN SPOKEN ENGLISH providing a breakdown of population at LGA level and by: * speaks English only * other language(a) * not stated(b) * other Chinese languages(c) * other Indo-Aryan languages(d) * other Southeast Asian Austronesian languages(e) * other(f) This data is based on place of usual residence. (a) This list of languages consists of the most common Language used at home responses reported in the 2016 Census and 2011 Census. (b) Includes the categories 'Proficiency in English not stated' and 'Language and proficiency in English not stated'. (c) Comprises 'Chinese, nfd', 'Hakka', 'Wu', 'Min Nan' and 'Chinese, nec'. (d) Comprises' Indo-Aryan, nfd', 'Konkani', 'Marathi', 'Sindhi', 'Assamese', 'Dhivehi', 'Kashmiri', 'Oriya', 'Fijian Hindustani' and 'Indo-Aryan, nec'. (e) Comprises 'Bikol', 'Bisaya', 'Cebuano', 'Ilokano', 'Ilonggo (Hiligaynon)', 'Pampangan', 'Malay', 'Tetum', 'Timorese', 'Acehnese', 'Balinese', 'Iban', 'Javanese', 'Southeast Asian Austronesian Languages, nec' and 'Southeast Asian Austronesian Languages, nfd'. (f) Includes languages not identified individually, 'Inadequately described' and 'Non-verbal, so described'. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
ABS - Census of Population and Housing - Language used at home by proficiency in spoken English - LGA Level - G13

ABS Census data extract - G13 LANGUAGE USED AT HOME BY PROFICIENCY IN SPOKEN ENGLISH providing a breakdown of population at LGA level and by: * speaks English only * other language(a) * not stated(b) * other Chinese languages(c) * other Indo-Aryan languages(d) * other Southeast Asian Austronesian languages(e) * other(f) This data is based on place of usual residence. (a) This list of languages consists of the most common Language used at home responses reported in the 2016 Census and 2011 Census. (b) Includes the categories 'Proficiency in English not stated' and 'Language and proficiency in English not stated'. (c) Comprises 'Chinese, nfd', 'Hakka', 'Wu', 'Min Nan' and 'Chinese, nec'. (d) Comprises' Indo-Aryan, nfd', 'Konkani', 'Marathi', 'Sindhi', 'Assamese', 'Dhivehi', 'Kashmiri', 'Oriya', 'Fijian Hindustani' and 'Indo-Aryan, nec'. (e) Comprises 'Bikol', 'Bisaya', 'Cebuano', 'Ilokano', 'Ilonggo (Hiligaynon)', 'Pampangan', 'Malay', 'Tetum', 'Timorese', 'Acehnese', 'Balinese', 'Iban', 'Javanese', 'Southeast Asian Austronesian Languages, nec' and 'Southeast Asian Austronesian Languages, nfd'. (f) Includes languages not identified individually, 'Inadequately described' and 'Non-verbal, so described'. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
RSA - Répartition des allocataires selon le type de RSA [Commune]

Bénéficiaires au titre de décembre du revenu de solidarité active (RSA) à l'échelle communale depuis 2020 selon le type de RSA. Ce jeu présente les foyers allocataires, les personnes couvertes et le montant total de prestation versée. Sous conditions de ressources, le revenu de solidarité active assure un revenu minimum aux personnes les plus démunies ou à celles ayant des ressources faibles ([https://caf.fr/allocataires/aides-et-demarches/droits-et-prestations/vie-professionnelle/le-revenu-de-solidarite-active-rsa]). Dans ce jeu de données, les nombres de foyers et de personnes sont arrondis à 5. A titre d'exemple, 7 est arrondi à 5 et 12 est arrondi à 10. Ainsi, pour un département donné, la somme des communes ne donne pas le total exact départemental.
Apprentissage et Assurance chômage : éléments de suivi - juillet 2024

Ce fichier Excel contient les graphiques et les tableaux accompagnant la publication "[Apprentissage et Assurance chômage : éléments de suivi]", diffusée par l'Unédic en juillet 2024. Ces données portent sur les sortants d'apprentissage qui ouvrent des droits à l'Assurance chômage.
High-resolution land use and land cover maps for the greater Mariño watershed in 2019

[Lien vers la fiche source] This repository contains the land use land cover maps, as well as the field database, generated in Vallet et al. " Generating high-resolution land use and land cover maps for the greater Mariño watershed in 2019 with machine learning" (https://doi.org/xxxxx). The land use land cover maps are provided in raster and vectorial format, for the 3 levels of the land use land cover nomenclature. At the finest level of classification, we distinguish between 21 different land use and land cover classes. All datasets can be visualized at https://idg-tetis.teledetection.fr/geonetwork/srv/fre/catalog.search#/metadata/1aca752e-ad4b-4b50-9281-f79df08232b13
Supplementary data for: Transpiration efficiency variations in the pearl millet reference collection PMiGAP

[Lien vers la fiche source] Transpiration efficiency (TE), the biomass produced per unit of water transpired, is a key trait for crop performance under limited water. As water becomes scarce, increasing TE would contribute to increase crop drought tolerance. This study is a first step to explore pearl millet genotypic variability for TE on a large and representative diversity panel. We analyzed TE on 537 pearl millet genotypes, including inbred lines, test-cross hybrids, and hybrids bred for different agroecological zones. Three lysimeter trials were conducted in 2012, 2013 and 2015, to assess TE both under well-watered and terminal-water stress conditions. We recorded grain yield to assess its relationship with TE. Up to two-fold variation for TE was observed over the accessions used. Mean TE varied between inbred and testcross hybrids, across years and was slightly higher under water stress. TE also differed among hybrids developed for three agroecological zones, being higher in hybrids bred for the wetter zone, underlining the importance of selecting germplasm according to the target area. Environmental conditions triggered large Genotype x Environment (GxE) interactions, although TE showed some high heritability. Transpiration efficiency was the second contributor to grain yield after harvest index, highlighting the importance of integrating it into pearl millet breeding programs. Future research on TE in pearl millet should focus (i) on investigating the causes of its plasticity i.e. the GxE interaction (ii) on studying its genetic basis and its association with other important physiological traits. This dataset contains raw data of experiment on the transpiration efficiency variations in the pearl millet reference collection PMiGAP.
Dataset linked to the publication of McLeod et al. (2023): "Multi-environment association study highlights candidate genes for robust agronomic quantitative trait loci in a novel worldwide Capsicum core collection"

[Lien vers la fiche source] The dataset contains raw data associated to the publication “ Multi-environment association study highlights candidate genes for robust agronomic QTLs in a novel worldwide Capsicum core collection “, McLeod et al. (2023). It contains two files. G2P-SOL_Pepper_CC_Pheno_data_2023.csv: raw phenotypic data of 350 accessions constituting the worldwide core collection of Capsicum annuum constructed within the European Union’s Horizon 2020 ‘G2P-SOL’ project (Linking genetic resources, genomes, and phenotypes of Solanaceous crops), grown in 6 environment-trials (INRAE, France in 2019 and 2020, CREA, Italy in 2019, ARO, Israel in 2020, BATEM, Türkiye in 2020, WorldVeg, Taiwan in 2020 ), within a complete block design. It contains assessments of 23 phenotypic agronomic traits related to fruit and plant development traits. Accessions are named based on their G2P-SOL code. G2P-SOL_Pepper_CC_Geno_data_2023.gz: genotypic data (VCF) of the 350 accessions constituting the worldwide core collection of Capsicum annuum constructed within the European Union’s Horizon 2020 ‘G2P-SOL’ project (Linking genetic resources, genomes, and phenotypes of Solanaceous crops), characterized for 10,195 SNP/indels-derived markers obtained by Genotyping-by-Sequencing and after filtering to retain bi-allelic SNPs, with a minor allele frequency (MAF) greater than 0.01, a mean read depth greater than 8, less than 30% of missing data and heterozygous for less than 20% of the accessions
Multiple deletions of candidate effector genes lead to the breakdown of partial grapevine resistance to downy mildew

[Lien vers la fiche source] Grapevine downy mildew, caused by the oomycete Plasmopara viticola, is a globally destructive disease that particularly affect the Eurasian wine grape V. vinifera. While genetically resis- tant varieties are becoming more accessible, populations of P. viticola are demonstrating rapid adaptability, successfully over- coming these resistances. Here we aimed to identify the aviru- lence genes involved in the interaction with the Rpv3.1-mediated resistance in grapevine. We sequenced the full genome of 136 P. viticola strains sampled in a natural population of Bordeaux (France) and characterized their development on both resis- tant and sensitive cultivars. The genome-wide association study allowed the identification of a genomic region associated with the breakdown of Rpv3.1 grapevine resistance (avrRpv3.1 lo- cus). A diploid-aware reassembly of the P. viticola INRA-Pv221 genome allowed to detect structural variations in this locus, in- cluding a major 30 Kbp deletion. At the avrRpv3.1 locus, vir- ulent P. viticola strains presented deletion on both haplotypes indicating that avirulence is recessive. The deletion involves two closely-related genes that encode proteins containing 800- 900 amino acids with a signal peptide. The structure of the predicted proteins contains repeats of the LWY-fold structural modules, typical of oomycete effectors. Moreover, when these proteins were transiently expressed, they induced cell death in grapevines carrying Rpv3.1 resistance, confirming their aviru- lence nature. The first description of candidate effectors of P. viticola involved in the interaction with resistance gene provides valuable insights into the genetic mechanisms that enable P. viti- cola to adapt to grapevine resistance, laying a foundation for developing strategies to manage this damaging crop pathogen.
Production routes and properties of various plant powders intended for the manufacture of green products: Spectra and Images

[Lien vers la fiche source] This dataset includes images of the powders, SEM images, as well as various spectra and curves acquired (particle size, FTIR spectra, and Spectrofluorimetry spectra, thermogravimetric measurements), both in the form of txt or csv files and as images. All the files are organized into folders, with one folder for each powder. The dataset also includes an additional folder containing a general overview of the biochemical composition of the ten powders (table) and several graphs (bar and pie charts) for comparing global and monosaccharide contents.
Production routes and properties of various plant powders intended for the manufacture of green products: raw and calculated analytical data

[Lien vers la fiche source] This Dataset includes several characterizations of different biomass powders. For each analysis performed on the powders, this dataset includes a table with the raw data (analysis raw), and in cases where multiple observations of the same parameter were made, there is also a calculated table (analysis calc) that includes the average and standard deviation values for the several observations. The data set also includes the requests SPARQL queries used to generate the tables from the online tools SPO2Q. Queries names and table names have the same name, only the extension differs replacing the string ‘tsv’ by ‘sparql’. By example ‘Table 2.1 monosaccharides raw.sparql’ is the name of the query associated with table ‘Table 2.1 monosaccharides raw.tsv’
Résultats des élections européennes 2024 - version détaillée

Données concernant le résultat des élections européennes de 2024 à Strasbourg. Les données sont issues des décomptes des bureaux de vote et fiables à 100%. Une ligne du tableau correspond au résultat d'un candidat sur un bureau de vote. Le jeu de données intègre la cartographie des secteurs de vote. Données disponibles à partir du dimanche 9 juin après 20 heures et au fur et à mesure du dépouillement
Effects of genotype (Duroc or Pietrain crossbreds) of non castrated male pigs in organic farming on welfare indicators, growth performance, carcass traits and meat quality

[Lien vers la fiche source] These data present the effects of genotype (Duroc or Pietrain crossbreds) of non castrated male pigs in organic farming on welfare indicators, growth performance, carcass traits and meat quality. Data are issued from animal experiment was carried out at the INRAE Porganic (certified organic) experimental farm (INRAE GenESI, 86480 Rouillé, France; doi:10.15454/1.5572415481185847E12)
Population densities of the Pseudomonas syringae species complex in the Durance river in 2016-2017

[Lien vers la fiche source] These data represent estimations of densities of naturally-occurring populations of the species complex “Pseudomonas syringae” in surface water that flows through the Durance watershed. Population densities were quantified in samples obtained at 21 sites along the river during 2 years, based on a combination of platings on microbiological media to count and isolate colonies of putative P. syringae and validation of their identities via phylogenetic analyses of the sequences of a conserved housekeeping gene determined with NGS technology.
Electricity Supply & Demand (ES&D)

NERC collects, maintains, and annually publishes the Electricity Supply and Demand (ES&D) database, which includes 10-year projections for the North American bulk power system (BPS). Data are validated by the NERC Regions, NERC staff, and the Reliability Assessment Subcommittee (RAS) during NERC’s annual development of the Long-Term Reliability Assessment (LTRA). Data are presented for each NERC Region (and subregions where applicable) for years 1990-2010. For years 2011 to present, data are presented for each NERC Assessment Area. Some information in the ES&D has been aggregated to protect sensitive information. Provided data is from 2023.
POSTER Foss-AI, an exploratory project using deep learning to extract paleobiological and paleoenvironmental data from fossil woods.

[Lien vers la fiche source] Contributed poster, XXth International Botanical Congress, Madrid, Spain, July 2024. Decombeix A-L, Verlingue K. 2024. Foss-AI, an exploratory project using deep learning to extract paleobiological and paleoenvironmental data from fossil woods.
Customer Outage History (Michigan)

When a utility is experiencing outages beyond a threshold level, they report their status and estimated number of customers affected to the MPSC. The MPSC uses this information to monitor restoration activities and coordinate with utilities and local emergency responders when help is needed. For Consumers Energy and DTE Electric, events greater than 20,000 customers are listed. For all other utilities, events greater than 7,500 are listed. This data will be updated as final numbers are received from the utility reports submitted in U-21122.