cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26778148
title: Lachlan River ecosystem metabolism data

description: River ecosystem metabolism and ancillary data accompanying Giling <i>et al. </i>(in press). This data is used to perform the Bayesian spatio-temporal model (Santos-Fernandez <i>et al. </i>2022).The dataset includes:<b>'lsn.ssn'. </b>ssn object containing spatial information on the stream network (Ver Hoef <i>et al. </i>2006, Peterson and Ver Hoef 2014).<b>'sites_df_with_obs.csv' and associated metadata</b>. The metabolism estimates were modelled from diel dissolved oxygen, water temperature, and PAR data collected as part of the Commonwealth Environmental Water Holder’s flow monitoring programs. Raw dissolved oxygen data are publically available (CEWH 2023). Discharge, water level, and water temperature data were sourced from the NSW Department of Planning, Industry and Environment (http://www.bom.gov.au/waterdata/, accessed 29/07/2021). Gridded daily solar exposure data were sourced from the Bureau of Meteorology. Note that the ecosystem metabolism rates presented here are not identical to the rates published from the broader Flow-MER program (available at https://data.gov.au/data/dataset/flow-mer-metabolism-base-model) due to the setting of site-specific constraints on reaeration rates in the current work, as described in the Supplementary Information of Giling <i>et al. </i>(under review).<b>'preds_spatial_df.csv' and associated metadata</b><b>. </b>Data containing prediction coordinates and ancillary data for predicting ecosystem metabolism rates at new sites.<br><br><b>References</b>CEWH. 2023. Metabolism oxygen logger data. Flow-MER Program. Commonwealth Environmental Water Holder, Australian Government Department of Climate Change, Energy, the Environment and Water. Sourced from https://data.gov.au/data/dataset/flow-mer-oxygen-logger. Last accessed 23/08/2023).Peterson EE, Ver Hoef JM. 2014. Stars: An ArcGIS toolset used to calculate the spatial information needed to fit spatial statistical models to stream network data. Journal of Statistical Software 56:1–17.Santos-Fernandez E, Ver Hoef JM, Peterson EE, McGree J, Isaak DJ, Mengersen K. 2022. Bayesian spatio-temporal models for stream networks. Computational Statistics & Data Analysis 170:107446.Ver Hoef JM, Peterson E, Theobald D, Hoef JMV, Peterson E, Theobald D. 2006. Spatial statistical models that use flow and stream distance. Environ Ecol Stat 13:449–64.<br><br><br>
DOI: 10.6084/m9.figshare.26778148.v1
title: Lachlan River ecosystem metabolism data

description: River ecosystem metabolism and ancillary data accompanying Giling <i>et al. </i>(in press). This data is used to perform the Bayesian spatio-temporal model (Santos-Fernandez <i>et al. </i>2022).The dataset includes:<b>'lsn.ssn'. </b>ssn object containing spatial information on the stream network (Ver Hoef <i>et al. </i>2006, Peterson and Ver Hoef 2014).<b>'sites_df_with_obs.csv' and associated metadata</b>. The metabolism estimates were modelled from diel dissolved oxygen, water temperature, and PAR data collected as part of the Commonwealth Environmental Water Holder’s flow monitoring programs. Raw dissolved oxygen data are publically available (CEWH 2023). Discharge, water level, and water temperature data were sourced from the NSW Department of Planning, Industry and Environment (http://www.bom.gov.au/waterdata/, accessed 29/07/2021). Gridded daily solar exposure data were sourced from the Bureau of Meteorology. Note that the ecosystem metabolism rates presented here are not identical to the rates published from the broader Flow-MER program (available at https://data.gov.au/data/dataset/flow-mer-metabolism-base-model) due to the setting of site-specific constraints on reaeration rates in the current work, as described in the Supplementary Information of Giling <i>et al. </i>(under review).<b>'preds_spatial_df.csv' and associated metadata</b><b>. </b>Data containing prediction coordinates and ancillary data for predicting ecosystem metabolism rates at new sites.<br><br><b>References</b>CEWH. 2023. Metabolism oxygen logger data. Flow-MER Program. Commonwealth Environmental Water Holder, Australian Government Department of Climate Change, Energy, the Environment and Water. Sourced from https://data.gov.au/data/dataset/flow-mer-oxygen-logger. Last accessed 23/08/2023).Peterson EE, Ver Hoef JM. 2014. Stars: An ArcGIS toolset used to calculate the spatial information needed to fit spatial statistical models to stream network data. Journal of Statistical Software 56:1–17.Santos-Fernandez E, Ver Hoef JM, Peterson EE, McGree J, Isaak DJ, Mengersen K. 2022. Bayesian spatio-temporal models for stream networks. Computational Statistics & Data Analysis 170:107446.Ver Hoef JM, Peterson E, Theobald D, Hoef JMV, Peterson E, Theobald D. 2006. Spatial statistical models that use flow and stream distance. Environ Ecol Stat 13:449–64.<br><br><br>
DOI: 10.15468/dl.rry9rf
title: Occurrence Download

description: A dataset containing 1075744 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Belgium, Germany, Netherlands)",
 "TaxonKey is one of (Ondatra zibethicus (Linnaeus, 1766), Myocastor coypus (Molina, 1782))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1075744 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047076-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b3kj3z
title: Occurrence Download

description: A dataset containing 315098 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hordeum vulgare L."
 ]
}

 The dataset includes 315098 records from 821 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047087-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kwsnvt
title: Occurrence Download

description: A dataset containing 501 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Daphne pontica L., Phyllosticta daphnes-ponticae Siemaszko, Daphne pontica subsp. haematocarpa Woronow, Daphne pontica subsp. melanocarpa Woronow, Daphne pontica subsp. pontica, Daphne pontica var. szovitsii K.Koch)"
 ]
}

 The dataset includes 501 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070822-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11608162
title: CTO Dataset: A Clinical Trial Outcome Benchmark

description: Supplementary files and predicted labels for https://github.com/chufangao/CTOD/

Please see the github for additional information.
DOI: 10.34740/kaggle/dsv/8671002
title: Contrastive Language Image Pretraining by openai
DOI: 10.5281/zenodo.11584380
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0005

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0005. The model includes the event CME-0005a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.5281/zenodo.11584381
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0005

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0005. The model includes the event CME-0005a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.60840/nhrsp-a355
title: Leveraging ABSHER Platform to Assess NCDs Burden in Saudi Arabia

description: Non-communicable diseases (NCDs) are a significant threat to population health. In Saudi Arabia, the burden of NCDs is higher than in developed countries. One of the primary goals for the Saudi Vision 2030 is to increase life expectancy from 74 to 80 by the year 2030. In order to increase life expectancy, much investment is needed to reduce significant threats to mortality such as NCDs. The increasing availability of digital data from different sources opens a new frontier for healthcare research. This study assesses the distribution and the frequency of major NCDs and their risk factors to understand their burden on population health, such as diabetes, hypertension, and asthma and some of their risk factors, and more importantly this study includes a validation analysis looking at a different sampling approach. Data were collected by an online survey through Absher platform over a period of 11 months. To create a representative survey sample, we constructed sample weights using the Saudi population's regions, gender, nationality, and age group distributions. Weights were used to sample from the survey data randomly. Next, a stratified systematic random sampling was applied with probability proportional to size. Two strata were created, one for males and the other for females. This approach resulted in a random subsample that match the Saudi population. The prevalence of diabetes, hypertension, high cholesterol and asthma was found to be 14.40%, 15.65%, 16.25% & 6.29% respectively. The results suggest that NCDs remain a high burden of population health in Saudi Arabia. As the Kingdom move further to achieve Vision 2030, continuous monitoring of disease prevalence is warranted.
DOI: 10.5281/zenodo.11584470
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0014

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0014. The model includes the event CME-0014b. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.5281/zenodo.11584471
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0014

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0014. The model includes the event CME-0014b. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.5281/zenodo.11584572
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0017

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0017. The model includes the event CME-0017a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.5281/zenodo.11584573
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0017

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0017. The model includes the event CME-0017a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.5281/zenodo.11584650
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0020

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0020. The model includes the event CME-0020a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.5281/zenodo.11584651
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0020

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0020. The model includes the event CME-0020a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.5281/zenodo.11584756
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0021

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0021. The model includes the event CME-0021b. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.5281/zenodo.11584757
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0021

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0021. The model includes the event CME-0021b. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.60840/nhrsp-mk13
title: Non-communicable Diseases Among Non-Saudi Population: Results from a National Database
DOI: 10.5281/zenodo.11584875
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0028

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0028. The model includes the event CME-0028a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.5281/zenodo.11584874
title: EUHFORIA modeling result of SERPENTINE event SEP-C25-0028

description: This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the SERPENTINE event SEP-C25-0028. The model includes the event CME-0028a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:



Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: 10.1051/swsc/2018020)
DOI: 10.5281/zenodo.11608675
title: INNOVATSION YONDASHUV ASOSIDA O'QUVCHILARDA TEXNOLOGIK KOMPETENSIYALARNI SHAKLLANTIRISH
DOI: 10.5281/zenodo.11608676
title: INNOVATSION YONDASHUV ASOSIDA O'QUVCHILARDA TEXNOLOGIK KOMPETENSIYALARNI SHAKLLANTIRISH
DOI: 10.15468/dl.3wnezz
title: Occurrence Download

description: A dataset containing 5898 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ireland, Svalbard and Jan Mayen, Norway, Sweden, Finland, Estonia, Latvia, Denmark, Portugal, Spain, France, Germany, Netherlands, Switzerland, Austria, Italy, Greece, Türkiye, Russian Federation, Belarus, Ukraine, Poland, Czechia, Slovakia, Hungary, Romania, Bulgaria, Albania, Serbia, Slovenia, Bosnia and Herzegovina)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cassiope tetragona (L.) D.Don"
 ]
}

 The dataset includes 5898 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070824-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ymkrwg
title: Occurrence Download

description: A dataset containing 1999145 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Asia",
 "Country is Sri Lanka",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year 1048-2024"
 ]
}

 The dataset includes 1999145 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070813-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25377/sussex.22795940
title: Data for 'Risk factors for severe COVID-19 disease increase SARS CoV-2 infectivity of endothelial cells and pericytes'

description: Data for paper published in <i>Open Biology</i> (12/06/2024)Each .csv file is the data for a different figure or sub-figure, as indicated in the file name. All data is viral titre in different cells in response to incubation with SARS CoV-2 pseudo-typed virus (PV). The cells, genotype, type of PV and other experimental variables are listed in column headers.Matching R markdown code to use these data to generate the figures in the linked manuscript are provided. These can be used by saving all the data files into a folder, from which there is a subfolder called "Analysis". The R code will then generate the figures and save analyses into that subfolder.AbstractCOVID-19 was initially considered a primarily respiratory disease but is now known to affect other organs including the heart and brain. A major route by which COVID-19 impacts different organs is via the vascular system. We studied the impact of apolipoprotein E (APOE) genotype and inflammation on vascular infectivity by pseudo-typed SARS-CoV-2 viruses in mouse and human cultured endothelial cells and pericytes. Possessing the APOE4 allele or having existing systemic inflammation is known to enhance the severity of COVID-19. Using targeted replacement human APOE3 and APOE4 mice, and inflammation induced by bacterial lipopolysaccharide (LPS) we investigated infection by SARS-CoV-2. Here, we show that infectivity was higher in murine cerebrovascular pericytes compared to endothelial cells, and higher in cultures expressing APOE4. Furthermore, increasing the inflammatory state of the cells by prior incubation with LPS increased infectivity into human and mouse pericytes, and human endothelial cells. Our findings provide insights into the mechanisms underlying severe COVID-19 infection, highlighting how risk factors such as APOE4 genotype and prior inflammation may exacerbate disease severity by augmenting the virus's ability to infect vascular cells.
DOI: 10.25377/sussex.22795940.v1
title: Data for 'Risk factors for severe COVID-19 disease increase SARS CoV-2 infectivity of endothelial cells and pericytes'

description: Data for paper published in <i>Open Biology</i> (12/06/2024)Each .csv file is the data for a different figure or sub-figure, as indicated in the file name. All data is viral titre in different cells in response to incubation with SARS CoV-2 pseudo-typed virus (PV). The cells, genotype, type of PV and other experimental variables are listed in column headers.Matching R markdown code to use these data to generate the figures in the linked manuscript are provided. These can be used by saving all the data files into a folder, from which there is a subfolder called "Analysis". The R code will then generate the figures and save analyses into that subfolder.AbstractCOVID-19 was initially considered a primarily respiratory disease but is now known to affect other organs including the heart and brain. A major route by which COVID-19 impacts different organs is via the vascular system. We studied the impact of apolipoprotein E (APOE) genotype and inflammation on vascular infectivity by pseudo-typed SARS-CoV-2 viruses in mouse and human cultured endothelial cells and pericytes. Possessing the APOE4 allele or having existing systemic inflammation is known to enhance the severity of COVID-19. Using targeted replacement human APOE3 and APOE4 mice, and inflammation induced by bacterial lipopolysaccharide (LPS) we investigated infection by SARS-CoV-2. Here, we show that infectivity was higher in murine cerebrovascular pericytes compared to endothelial cells, and higher in cultures expressing APOE4. Furthermore, increasing the inflammatory state of the cells by prior incubation with LPS increased infectivity into human and mouse pericytes, and human endothelial cells. Our findings provide insights into the mechanisms underlying severe COVID-19 infection, highlighting how risk factors such as APOE4 genotype and prior inflammation may exacerbate disease severity by augmenting the virus's ability to infect vascular cells.
DOI: 10.5281/zenodo.11608455
title: Demo upload for alzheimer stall catcher

description: This is a demo upload.
DOI: 10.15468/dl.76b898
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Thailand",
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acer calcaratum Gagnep.",
 "Year 2000-2024"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070825-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/5hzc-5p73
title: WildScenes Dataset

description: WildScenes is a large-scale 2D and 3D semantic segmentation dataset containing both labelled images and lidar point clouds, in natural environments. The data was collected from two natural environments in Brisbane, Australia across multiple revisits. Our release includes 2D images, 2D annotated images, 3D point cloud submaps, 3D annotated point cloud submaps, alongside accurate 6-DoF poses.
DOI: 10.15468/dl.d7bbre
title: Occurrence Download

description: A dataset containing 2080 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ireland, Svalbard and Jan Mayen, Norway, Sweden, Finland, Estonia, Latvia, Denmark, Portugal, Spain, France, Germany, Netherlands, Switzerland, Austria, Italy, Greece, Türkiye, Russian Federation, Belarus, Ukraine, Poland, Czechia, Slovakia, Hungary, Romania, Bulgaria, Albania, Serbia, Slovenia, Bosnia and Herzegovina)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cerastium arcticum Lange"
 ]
}

 The dataset includes 2080 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070826-240506114902167/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.25845352
title: <b>Relationship between Executive Attention, Fine Motor Performance, and the Risk of Fall in Elderly Individuals</b>

description: <b></b>Executive attention refers to a supervisory system that suppresses inappropriate responses and triggers appropriate ones. It may be summoned to compensate for age-related declines in motor skills. The study aims to investigate the connection between executive attention, fine motor performance, and the risk of falls in elderly individuals.