cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35078/gwgtpj
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Teófilo Otoni | Município: Paulistas

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.25318/1410029901-eng
title: Usual hours worked by occupation, annual, inactive

description: Number of employed persons by usual hours worked, National Occupational Classification (NOC) and sex, last 5 years.
DOI: 10.25318/1410029901-fra
title: Heures habituellement travaillées selon la profession, données annuelles, inactif

description: Nombre de personnes occupées selon les heures habituellement travaillées, la Classification nationale des professions (CNP) et le sexe, 5 dernières années.
DOI: 10.25810/fj1y-8c74.48
title: International Wire Services Coverage of Climate Change or Global Warming, 2004-2022 - December 2022

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.25318/1410030101-eng
title: Actual hours worked by occupation, annual, inactive

description: Number of employed persons by actual hours worked, National Occupational Classification (NOC) and sex, last 5 years.
DOI: 10.25318/1410030101-fra
title: Heures effectivement travaillées selon la profession, données annuelles, inactif

description: Nombre de personnes occupées selon les heures effectivement travaillées, la Classification nationale des professions (CNP) et le sexe, 5 dernières années.
DOI: 10.5281/zenodo.6635684
title: Comprehensive Automatic Processing and Analysis of Adaptive Optics Flood Illumination Retinal Images

description: A collaborative research group has established this database to support AO-FIO image utilization and evaluation of photoreceptor detection. <br> Please cite the following publication when using the database: Eva Valterova, Jan D. Unterlauft, Mike Francke, Toralf Kirsten, Radim Kolar, and Franziska G. Rauscher, "Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects," Biomed. Opt. Express <strong>14</strong>, 945-970 (2023)<br> <br> The database can be utilized in connection with our application MATADOR for AO-FIO image registration and analysis, which is freely available on: https://github.com/evavalterova/MATADOR.git The database includes over 200 flood illumination adaptive optics images of 10 normal healthy subjects. Each folder includes 10 images of the right eye (denoted by OD) and 10 images of the left eye (denoted by OS) with their preliminary determined retinal position during image acquisition. foveal and peripheral patches. Each consists of 40 cropped regions from the set of 200 images. In each cropped region are manually labeled positions of photoreceptors by three evaluators. axial lengths of 10 subjects in ".xlsx" file<br>
DOI: 10.35078/kkad08
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Teófilo Otoni | Município: Pavão

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.5281/zenodo.7576904
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images</strong></em> These Residual-UNet model data are based on Chesapeake Land Cover images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://lila.science/datasets/chesapeakelandcover Image size used by model: 512 x 512 x 3 pixels classes:<br> water<br> tree_canopy_forest<br> low_vegetation_field<br> barren land<br> impervious_other<br> impervious_road<br> no_data <br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References<br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Robinson C, Hou L, Malkin K, Soobitsky R, Czawlytko J, Dilkina B, Jojic N. Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data. Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019)
DOI: 10.5281/zenodo.7576903
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images</strong></em> These Residual-UNet model data are based on Chesapeake Land Cover images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://lila.science/datasets/chesapeakelandcover Image size used by model: 512 x 512 x 3 pixels classes:<br> water<br> tree_canopy_forest<br> low_vegetation_field<br> barren land<br> impervious_other<br> impervious_road<br> no_data <br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References<br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Robinson C, Hou L, Malkin K, Soobitsky R, Czawlytko J, Dilkina B, Jojic N. Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data. Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019)
DOI: 10.25318/1410030301-eng
title: Multiple jobholders by occupation, annual, inactive

description: Number of multiple jobholders by National Occupational Classification (NOC), sex and age group, last 5 years.
DOI: 10.25318/1410030301-fra
title: Personnes ayant plus d'un emploi selon la profession, données annuelles, inactif

description: Nombre de personnes ayant plus d'un emploi selon la Classification nationale des professions (CNP), le sexe et le groupe d'âge, 5 dernières années.
DOI: 10.25318/1410030501-eng
title: Job tenure by occupation, annual, inactive

description: Number of employed persons by job tenure, National Occupational Classification (NOC) and sex, last 5 years.
DOI: 10.5281/zenodo.7603481
title: List of non-naturalized plant species present in France extracted from Pl@ntNet data (exotic ornamental and cultivated plants in particular).

description: This dataset contains the list of plant species that have been observed on the French territory using the Pl@ntNet application and that are NOT know as being either native or naturalized according to Kew's Plants of the World Online repository (POWO). Such species are typically exotic species managed by humans in anthropized environments such as guardens, houses or cultivated areas. This includes commercialized plants for various usage such as ornemental plants, eatable plants, phytotherapy, etc. The list contains 5,589 species, each associated with its scientific name and the number of valid Pl@ntNet observations of that species geo-localized in the metropolitan French territory.
DOI: 10.5281/zenodo.7603480
title: List of non-naturalized plant species present in France extracted from Pl@ntNet data (exotic ornamental and cultivated plants in particular).

description: This dataset contains the list of plant species that have been observed on the French territory using the Pl@ntNet application and that are NOT know as being either native or naturalized according to Kew's Plants of the World Online repository (POWO). Such species are typically exotic species managed by humans in anthropized environments such as guardens, houses or cultivated areas. This includes commercialized plants for various usage such as ornemental plants, eatable plants, phytotherapy, etc. The list contains 5,589 species, each associated with its scientific name and the number of valid Pl@ntNet observations of that species geo-localized in the metropolitan French territory.
DOI: 10.25318/1410030501-fra
title: Durée de l'emploi selon la profession, données annuelles, inactif

description: Nombre de personnes occupées selon la durée de l'emploi, la Classification nationale des professions (CNP) et le sexe, 5 dernières années.
DOI: 10.25318/1410030901-eng
title: Employees working overtime (weekly) by occupation, annual, inactive

description: Number of employees working overtime (weekly) and average overtime hours by National Occupational Classification (NOC), sex and age group, last 5 years.
DOI: 10.25318/1410030901-fra
title: Employés travaillant à temps supplémentaire (hebdomadaire) selon la profession, données annuelles, inactif

description: Nombre d'employés travaillant à temps supplémentaire (hebdomadaire) et moyenne d'heures supplémentaires selon la Classification nationale des professions (CNP), le sexe et le groupe d'âge, 5 dernières années.
DOI: 10.25318/1410031901-eng
title: Union status by occupation, inactive

description: Number of employees by union status and National Occupational Classification (NOC), last 5 years.
DOI: 10.25318/1410031901-fra
title: Situation syndicale selon la profession, inactif

description: Nombre d'employés selon la situation syndicale et la Classification nationale des professions (CNP), 5 dernières années.
DOI: 10.25318/1410033501-eng
title: Labour force characteristics by occupation, annual, 1987 to 2022, inactive

description: Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by National Occupational Classification (NOC) and sex, 1987 to 2022.
DOI: 10.17876/plate/dr.3/plates/202_52100
title: Digital object identifier (DOI) for the plate 52100 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52101
title: Digital object identifier (DOI) for the plate 52101 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52102
title: Digital object identifier (DOI) for the plate 52102 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52103
title: Digital object identifier (DOI) for the plate 52103 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52104
title: Digital object identifier (DOI) for the plate 52104 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52105
title: Digital object identifier (DOI) for the plate 52105 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52106
title: Digital object identifier (DOI) for the plate 52106 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52107
title: Digital object identifier (DOI) for the plate 52107 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52108
title: Digital object identifier (DOI) for the plate 52108 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52109
title: Digital object identifier (DOI) for the plate 52109 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52110
title: Digital object identifier (DOI) for the plate 52110 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52111
title: Digital object identifier (DOI) for the plate 52111 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52112
title: Digital object identifier (DOI) for the plate 52112 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52113
title: Digital object identifier (DOI) for the plate 52113 in archive 202 from the APPLAUSE DR3
DOI: 10.17189/1519594
title: CASSINI ORBITER SAT/SW CAPS DERIVED ELECTRON MOMENTS V1.0
DOI: 10.17189/1520241
title: LRO MOON LROC 3 CDR V1.0
DOI: 10.17632/f3mdswct65.2
title: Data for Constraining re-equilibration timescales of contact thermal metamorphism on the Moon 

description: Supplementary materials for manuscript 'Constraining re- equilibration timescales of contact thermal metamorphism on the Moon'. JPG files are high resolution photomicrographs of all samples investigated in this study. Excel file 'SOM tables_V1' contains all mineral data collected for this study. Therms32.exe is the THERMOS programme use for the thermal modeling of the lunar crust (see manuscript for details).
DOI: 10.17876/plate/dr.3/plates/202_52114
title: Digital object identifier (DOI) for the plate 52114 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52115
title: Digital object identifier (DOI) for the plate 52115 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52116
title: Digital object identifier (DOI) for the plate 52116 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52117
title: Digital object identifier (DOI) for the plate 52117 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52118
title: Digital object identifier (DOI) for the plate 52118 in archive 202 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/202_52119
title: Digital object identifier (DOI) for the plate 52119 in archive 202 from the APPLAUSE DR3
DOI: 10.26302/sshade/experiment_mb_20230522_0001
title: VNIR and SWIR spectra of terrestrial feldspathic rocks and powders (sample NR1 as an example)

description: Visible Near-Infrared spectra.
DOI: 10.5066/f7cj8bhf
title: Mojave Region Water-Level Study, 2012

description: Regional water-table maps of the Mojave River and Morongo ground-water basins have been published in reports by the U.S. Geological Survey (USGS) every two years since 1992. The water-level reports for the years 1992-2004 were published as USGS Water-Resources Investigations Reports or Scientific Investigations Reports and are available online as separate Portable Document Fomat (pdf) formatted reports, which can be accessed on the &quot;References&quot; page on this website; the water-level contours from most of the water-level maps are available also on this website: data page. Beginning in 2006, the biannually scheduled water-level reports and maps will be published on this interactive mapping website; any future water-level studies also will be available on this website..\r\n\r\nThe water-level studies include water-level contour maps drawn from data measured from wells during each study. Most of the studies demonstrate water-level changes by hydrographs that show long-term and short-term water-level changes, and by maps that compare water levels at individual wells between two consecutively published reports.
DOI: 10.5281/zenodo.4982672
title: Dataset of the European Light Microscopy Initiative 2019 Core Facility Large Survey

description: In occasion of the 2019 ELMI meeting held in Brno, Czech Republic the organizers of the Core Facility day (Laurent Gelman, Marco Marcello, Roland Nitschke, Laure Plantard, Stefan Terjung) launched a Large Core Facility Survey.<br> This repository contains the screenshots of the original survey, a formatted list of questions, and the anonymised data from this survey, a total of 226 responses from 25 countries distributed over 5 continents. Results are subdivided in 5 Areas, Staff, Finances, Equipment, Quality control, IT and Data Management.
DOI: 10.5281/zenodo.6497221
title: Data from 'Deoxygenation lowers thermal threshold of coral bleaching.'

description: Increasing exposure to deoxygenation as a result of climate warming and localised pollution is emerging as an important contributing factor for widespread coral bleaching and mortality. Some corals have been observed to thrive under multiple extreme conditions including very low O<sub>2</sub> levels and high temperatures yet the combined effects of heating and deoxygenation on coral bleaching susceptibility remain unknown. Here, we incorporated deoxygenated seawater into short-term heat assays (CBASS) to show that deoxygenation can lower the thermal limit of an <em>Acropora</em> coral species. Data files provided: 'CBASS_deoxy_ref_transcriptome.fasta' contains the reference transcriptome assembled <em>de novo</em> using SOAPdenovo-Trans using non-bleached samples of the CBASS assay (i.e., 30°C and 33°C) and then filtered to keep only reads ≥ 500 bp in length (20,115 contigs). 'CBASS_deoxy_BIS_photos_Initial_and_poststress.pdf' contains images of coral fragments alongside a colour reference chart for estimating bleaching index scores (BIS) before experiment starts (initial) and post 6hr stress phase. Sample IDs are denoted as: H0/HH = normoxia/deoxygenation treatment, 30/33/36/39 = temperature in °C, T1 = time point 1, R1/2/3/4 = replicate 1/2/3/4. 'CBASS_deoxy_SymPortal_output_files.zip' contains all SymPortal (https://symportal.org) output files.
DOI: 10.5281/zenodo.7829838
title: MRAMS mesoscale model dataset, tools and animations for Jezero crater nighttime turbulence analysis

description: This material constitutes the software tools to generate MRAMS data used in the analysis submitted for publication in JGR:Planets "Nocturnal turbulence at Jezero crater as determined from MEDA measurements and modeling"<br> by Pla-Garcia et al. The manuscript was submitted to JGR: Planets on 2 October, 2022
DOI: 10.5281/zenodo.7930128
title: Can Archean Impact Structures be Discovered? A Case Study from Earth's Largest, Most Deeply Eroded Impact Structure. Additional Data.

description: Modeling inputs: Input files for iSALE. Code: Files for Matlab. Out: Graphs and tabulated results.
DOI: 10.5281/zenodo.8002376
title: Mars thermal+non-thermal H Modeling

description: This folder contains the RT model output for thermal H modeling and non-thermal H modeling. These values have been used to determine the line of sight modeled intensity for HST observations corresponding to campaign GO-15097.
DOI: 10.5281/zenodo.8176255
title: Mars Thermal + Non-Thermal Modeling Data from figures

description: This folder contains all the values from all the figures present in the paper titled "Evidence of Non-thermal Hydrogen in the Exosphere of Mars Resulting in Enhanced Water Loss", by Bhattacharyya et al.
DOI: 10.17189/1519593
title: CASSINI E/J/S/SW CAPS CALIBRATED V1.0
DOI: 10.17876/plate/dr.3/plates/202_52120
title: Digital object identifier (DOI) for the plate 52120 in archive 202 from the APPLAUSE DR3