cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26411668.v1
title: Supplementary Material for: Impact of the COVID-19 Pandemic on Acute Stroke Rehabilitation in Japanese Primary Stroke Centers: A Nationwide Cross-Sectional Study Using a Web-based Questionnaire Survey

description: Introduction: Acute stroke rehabilitation is crucial for achieving good functional recovery, even during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to clarify the impact of the COVID-19 pandemic on acute stroke rehabilitation and identify which components in the acute stroke rehabilitation provision system were susceptible to the pandemic.
Methods: A web-based questionnaire survey was conducted in all primary stroke centers (PSCs) in Japan between February 7 and April 21, 2022. The survey included questions about the current status of acute stroke rehabilitation and the influence of the pandemic. We classified the influences and investigated the relationship between the influence of the pandemic and the provision of rehabilitation. Additionally, we investigated a group of prefectures divided according to the grade of the pandemic.
Results: The survey response rate was 67% (639 of 959 PSCs). Among them, 387 PSCs (61%) reported that the COVID-19 pandemic affected acute stroke rehabilitation. In PSCs affected by the pandemic, more rehabilitation-related staff were working, and the proportion of weekend/holiday rehabilitation implementation was higher compared to those unaffected. In PSCs from highly infected prefectures, no significant relationship was observed between the influence of the pandemic and the rehabilitation provision system.
Conclusions: PSCs that provide denser acute stroke rehabilitation may be affected to a greater extent by the pandemic. We conclude that stroke hospitals should formulate infection control procedures for acute stroke rehabilitation in advance, depending on the conditions of the region and facility.
DOI: 10.15468/dl.d2nrjh
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Palaeomyia Poinar, 2004"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043175-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26405758
title: The Impact of Environmental Factors on Pedestrian Accident Severity Using Association Rules Mining: A Case Study From Qom, Iran

description: This research investigates the correlation between environmental factors such as light, precipitation, and temperature, and the severity of pedestrian accidents in Qom. By employing association rule mining, patterns between these factors and pedestrian injury severity were identified. The findings indicate that environmental factors have a significant impact on accident severity and can be utilized to enhance pedestrian safety.
DOI: 10.6084/m9.figshare.26405758.v2
title: The Impact of Environmental Factors on Pedestrian Accident Severity Using Association Rules Mining: A Case Study From Qom, Iran

description: This research investigates the correlation between environmental factors such as light, precipitation, and temperature, and the severity of pedestrian accidents in Qom. By employing association rule mining, patterns between these factors and pedestrian injury severity were identified. The findings indicate that environmental factors have a significant impact on accident severity and can be utilized to enhance pedestrian safety.
DOI: 10.6084/m9.figshare.26405758.v1
title: Untitled Item

description: This research investigates the correlation between environmental factors such as light, precipitation, and temperature, and the severity of pedestrian accidents in Qom. By employing association rule mining, patterns between these factors and pedestrian injury severity were identified. The findings indicate that environmental factors have a significant impact on accident severity and can be utilized to enhance pedestrian safety.
DOI: 10.15468/dl.s8qsk2
title: Occurrence Download

description: A dataset containing 777 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-11.36477 41.80214,-6.85340 41.80214,-6.85340 43.94759,-11.36477 43.94759,-11.36477 41.80214))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Acacia dealbata Link",
 "Year 2015-2023"
 ]
}

 The dataset includes 777 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043177-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c2pwbb
title: Occurrence Download

description: A dataset containing 14892 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Literature Occurrence, Living Specimen, Unknown evidence, Occurrence evidence, Specimen, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bryophyta",
 "TypeStatus is Unspecified type"
 ]
}

 The dataset includes 14892 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043176-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13134309
title: openwashdata/portawaterperu: portawaterperu: v0.0.1

description: Data about Peruvian Portable Water System
description: Visit the website of this dataset for instructions how to use it: https://openwashdata.github.io/portawaterperu/
DOI: 10.5281/zenodo.13134310
title: openwashdata/portawaterperu: portawaterperu: v0.0.1

description: Data about Peruvian Portable Water System
description: Visit the website of this dataset for instructions how to use it: https://openwashdata.github.io/portawaterperu/
DOI: 10.15468/dl.ru3znf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Cepaea hortensis (O.F.Müller, 1774), Cepaea nemoralis (Linnaeus, 1758))",
 "Continent is Europe",
 "StateProvince is one of (England, Wales, Scotland)",
 "DecimalLatitude is less than 50",
 "DecimalLatitude is greater than or equal to 51"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043178-240626123714530/datasets/export for details.
DOI: 10.15468/dl.dewveg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Andorra, Albania, Armenia, Austria, Azerbaijan, Bosnia and Herzegovina, Belgium, Bulgaria, Belarus, Switzerland, Cyprus, Czechia, Germany, Denmark, Algeria, Estonia, Egypt, Greece, Spain, Finland, Faroe Islands, France, Georgia, Guernsey, Gibraltar, Croatia, Hungary, Ireland, Israel, Isle of Man, Iceland, Italy, Jersey, Lebanon, Liechtenstein, Lithuania, Luxembourg, Latvia, Libya, Morocco, Monaco, Moldova, Republic of, Montenegro, North Macedonia, Malta, Netherlands, Norway, Poland, Palestine, State of, Portugal, Romania, Serbia, Russian Federation, Sweden, Slovenia, Svalbard and Jan Mayen, Slovakia, San Marino, Syrian Arab Republic, Tunisia, Türkiye, Ukraine, United Kingdom of Great Britain and Northern Ireland, French Guiana, Guadeloupe, Martinique, Mayotte, Réunion, Saint Martin (French part), Holy See)",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Machine Observation, Occurrence evidence, Observation)",
 "HasGeospatialIssue is false",
 "ScientificName is one of (Laserpitium siler, )"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043179-240626123714530/datasets/export for details.
DOI: 10.15468/dl.z2qrvn
title: Occurrence Download

description: A dataset containing 8876 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((44.45266 25.5812,46.09375 25.5812,46.09375 26.48853,44.45266 26.48853,44.45266 25.5812))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 8876 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043180-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13141952
title: CacheFSBasicInfo
DOI: 10.5281/zenodo.13141953
title: CacheFSBasicInfo
DOI: 10.5281/zenodo.13141219
title: MOH-AMM-SC-2024 ePoster ID 44: Mapping The Landscape of Precision Public Health: A Bibliometric Analysis of Influential Works and Key Contributors

description: Supplementary Material for Poster Presentation

MOH-AMM-SC-2024 ePoster ID 44: Mapping The Landscape of Precision Public Health: A Bibliometric Analysis of Influential Works and Key Contributors

Conference: 15th MOH-AMM Scientific Meeting 2024 in conjunction with 25th NIH Scientific Conference
DOI: 10.5281/zenodo.13141220
title: MOH-AMM-SC-2024 ePoster ID 44: Mapping The Landscape of Precision Public Health: A Bibliometric Analysis of Influential Works and Key Contributors

description: Supplementary Material for Poster Presentation

MOH-AMM-SC-2024 ePoster ID 44: Mapping The Landscape of Precision Public Health: A Bibliometric Analysis of Influential Works and Key Contributors

Conference: 15th MOH-AMM Scientific Meeting 2024 in conjunction with 25th NIH Scientific Conference
DOI: 10.15468/dl.xaxz54
title: Occurrence Download

description: A dataset containing 179 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((8.10224 49.55368,9.25892 49.55368,9.25892 50.46573,8.10224 50.46573,8.10224 49.55368))",
 "OccurrenceStatus is Present",
 "TaxonKey is Cerambyx cerdo Linnaeus, 1758"
 ]
}

 The dataset includes 179 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043181-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rz8suv
title: Occurrence Download

description: A dataset containing 48941 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ameiurus nebulosus (Lesueur, 1819)"
 ]
}

 The dataset includes 48941 records from 162 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043182-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ye2c7r
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phlebotomiella Meunier, 1906"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043183-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/jyrthk
title: A global database for the distributions of crop wild relatives

description: This dataset originally held 5 647 442 total records, where 34% of the records corresponded to germplasm accessions and 66% to herbarium samples. A total of 3 231 286 records had cross-checked coordinates (see Figure 2). 322 735 records were newly georeferenced using The Google Geocoding API and 15 713 new records were obtained after digitizing the information contained in herbaria specimens. Data was gathered from more than 100 data providers, including GBIF (a comprehensive list of institutions and individuals is available here: http://www.cwrdiversity.org/data-sources/ ).
The geographic coverage of the dataset includes 96% of the world countries and also includes records of cultivated plants (1/3 of the dataset). Records of the crop wild relatives of 80 crop gene pools can be queried and visualized in this interactive map: http://www.cwrdiversity.org/distribution-map/
This dataset was assembled as part of the project ‘Adapting Agriculture to Climate Change: Collecting, Protecting and Preparing Crop Wild Relatives’, which is supported by the Government of Norway. The project is managed by the Global Crop Diversity Trust and the Millennium Seed Bank of the Royal Botanic Gardens, Kew, and implemented in partnership with national and international genebanks and plant breeding institutes around the world. For further information, please refer to the project website: http://www.cwrdiversity.org/
For publication to GBIF, all records originally gathered from GBIF have been removed to avoid data duplication.
Citation: Crop Wild Relatives Occurrence data consortia ([year]). A global database for the distributions of crop wild relatives. Centro Internacional de Agricultura Tropical (CIAT). Occurrence dataset https://doi.org/10.15468/jyrthk accessed via GBIF.org on [date].
DOI: 10.17632/6jhv5dshgr.1
title: secondary marine aerosol growth study

description: aerosol measurement including aerosol mass spectrometer ptof data, and aerosol size distribution. cloud condensation nuclei number concentrations during the open ocean nucleation and growth events.
DOI: 10.17632/ggn44mb2tg.1
title: Long term observation of sub-saturation hygroscopicity in North Atlantic

description: This dataset reports the results of in situ measurements near 5 years aerosol hygroscopic growth factor from 2009 to 2014 at Mace Head Research Station(53.33° N, 9.90 °W; 21m a.m.s.l; 300m from the waterline). This dataset is discussed in a manuscript titled "Seasonal trends of aerosol hygroscopicity and mixing state of clean marine and polluted continental masse at Mace Head" by "Wei Xu, Jurgita Ovadnevaite, Kirsten. Fossum, Chunshui Lin, Ru-Jin Huang, Colin O’Dowd, Darius Ceburnis" which is submitted to Journal of Geophysics Research: Atmospheres.
DOI: 10.48668/uvvxjh
title: Seasonal effects and trophic pressure shape the responses of species interactions in a tropical seagrass meadow to marine heatwaves

description: Response variable data for epiphytes, leaves, rhizomes; snails; carbon, nitrogen
DOI: 10.5281/zenodo.10988280
title: Gapr for Large-scale Collaborative Single-Neuron Reconstruction: test data
DOI: 10.5281/zenodo.10988415
title: Gapr for Large-scale Collaborative Single-Neuron Reconstruction: reconstruction results
DOI: 10.6084/m9.figshare.23574813.v2
title: Full_Code_andData_CannibalismMesocosms

description: This repository contains an R markdown file with statistical code and associated datasets for Goodnight and McCoy cannibalism mesocosm project 2020. Submitted to Ecology 2023.
<br>
The Rmd file "Full_Code_CannibalismMesocosms" contains all statistical code, top models for each analysis, and base figure outputs for all analyses. File is separated into sections for each analysis: "Infection intensities and abundance models" ; "Cannibalism rate models" ; "Number of Nymphs surviving models" ; "Standard deviations of nymph body size by treatment" .
<br>
Contents of each dataset are provided here and also outlined in the first section of the R markdown file.
<br>
INITIAL_SIZES_1026.csv = initial size measurements of nymph hosts per mesocosm before the experiment
<br>
sizes.csv = full main dataset. Size measurements of nymph hosts post-exeriment, plus all infection intensity data (cyst counts) per parasite species per host. Tank ID numbers and other relevant data (experimental days of setup/breakdown, date of dissection for each nymph, etc.)
<br>
hali_only_withsizes.csv = subset of sizes.csv dataset with data from the tongueworm experiment only
<br>
haem_only_withsizes.csv = subset of sizes.csv dataset with data from the lungworm experiment only
<br>
varmean_all.csv = summed total parasite counts (abundance) by mesocosm for both experiments and all treatments, plus the size variance and mean metrics for nymph populations both at beginning and end of experiment
DOI: 10.5281/zenodo.3986171
title: Geostatistical Analysis of SARS-CoV-2 Positive Cases in the United States

description: Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com. This update of the Zenodo dataset (version 5) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. These datasets have been previously published in earlier versions of this archive (versions 2, 3 and 4): <strong>Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (May25-Aug2-v4).zip” </strong>– results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one week timespans (from version 4 of this Zenodo archive). Results are reported from the initial relaxation of distance constraints on Memorial Day weekend 2020 for ten consecutive 1-week intervals (May 25th through to August 2nd 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States. <strong>Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Jul13-v3).zip" </strong>– the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24<sup>th</sup> through July 13<sup>th</sup> 2020 (from version 3 of this Zenodo archive). <strong>Archive #3: "3.Initial Geostat. of SARS-CoV-2 in the US(v2).zip" </strong>– the results from the geostatistical analyses performed for version 2 of this Zenodo archive which analyzed COVID-19 case data prior to any case correction step. These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive week-long Space-Time Hot Spot analysis; ‘Archives #2 and #3’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archives #2 and #3’], Spatial Autocorrelation (Global Moran's I) [‘Archives #2 and #3’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’]. The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 5).docx") details the contents of each file and folder within these three archives, and gives general interpretations of these results.
DOI: 10.1285/i20705948v16n3p507
title: Classication of Breast Cancer Histopathological Images using Adaptive Penalized Logistic Regression with Wilcoxon Rank Sum Test

description: Classication of the histopathological image is an important problem indiagnosis and treatment. The problem of selecting the most useful fea-tures from thousands of candidates is a key problem in classication of thehistopathological image. In this paper, an adaptive penalized logistic regres-sion is proposed, with the aim of identication features, by combining thelogistic regression with the weighted L1-norm. Our proposed method is ex-perimentally tested and compared with state-of-the-art methods based on apublicly recent breast cancer histopathological image datasets. The resultsshow that the proposed method signicantly outperforms three competitormethods in terms of overall classication accuracy and the number of selectedfeatures.
DOI: 10.15468/6vsidh
title: Global Register of Introduced and Invasive Species - Eswatini (Swaziland)

description: The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified national checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level. Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness. Phase 1 of the project focused on developing validated and verified checklists of countries that are Party to the Convention on Biological Diversity (CBD). Phase 2 aimed to achieve global coverage including non-party countries and all overseas territories of countries, e.g. those of the Netherlands, France, and the United Kingdom. All kingdoms of organisms occurring in all environments and systems are covered. Checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records, as well as those under review, are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF Integrated Publishing Tool.
DOI: 10.15468/dl.4wnnka
title: Occurrence Download

description: A dataset containing 418 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Darevskia dahli (Darevsky, 1957)"
 ]
}

 The dataset includes 418 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005313-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.i83b94
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lacerta portschinskii Kessler, 1878"
 ]
}

 The dataset includes 14 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005319-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ml4da9
title: Occurrence Download

description: A dataset containing 86 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Darevskia mixta (Méhely, 1909)"
 ]
}

 The dataset includes 86 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005315-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.