cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.cusf5z
title: Occurrence Download

description: A dataset containing 9761 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chasmanthium latifolium (Michx.) H.O.Yates",
 "HasCoordinate is true",
 "Year is greater than or equal to 1900",
 "BasisOfRecord is one of (Human Observation, Specimen, Unknown evidence)"
 ]
}

 The dataset includes 9761 records from 101 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055589-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.haqs59
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Litoria elkeae Günther & Richards, 2000, Litoria elkae Günther & Richards, 2000)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055640-230224095556074/datasets/export for details.
DOI: 10.15468/dl.62xgjs
title: Occurrence Download

description: A dataset containing 72 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Base de datos de Rana catesbeiana."
 ]
}

 The dataset includes 72 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0266797-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dn8w43
title: Occurrence Download

description: A dataset listing the 1017772 species recorded in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset's 1017772 records were derived from 10932 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016223-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mzbzxm
title: Occurrence Download

description: A dataset listing the 94 species recorded in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((-43.9323 -19.82227,-43.88177 -19.82227,-43.88177 -19.78975,-43.9323 -19.78975,-43.9323 -19.82227))"
 ]
}

 The dataset's 94 records were derived from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001520-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hxqtt3
title: Occurrence Download

description: A dataset containing 167 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Costa Rica",
 "DatasetKey is one of (Insecta of Costa Rica (INBio), International Barcode of Life project (iBOL), Entomology Division, Yale Peabody Museum, Observation.org, Nature data from around the World, Triplehorn Insect Collection, The Ohio State University, Auckland Museum Entomology Collection, Lund University Biological Museum - Insect collections Specimens, eButterfly Surveys)",
 "Geometry POLYGON((-84.29447 10.10497,-84.29447 9.92478,-84.11429 9.92478,-84.11429 10.10497,-84.29447 10.10497))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Nymphalidae"
 ]
}

 The dataset includes 167 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001542-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zndcz4
title: Occurrence Download

description: A dataset containing 6154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is North America",
 "OccurrenceStatus is Present",
 "TaxonKey is Haliotis rufescens Swainson, 1822"
 ]
}

 The dataset includes 6154 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001543-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ugf3bh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2020",
 "Geometry POLYGON ((4.52063 10.47711, 4.52063 10.5675, 4.606812 10.5675, 4.606812 10.47711, 4.52063 10.47711))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001544-250121130708018/datasets/export for details.
DOI: 10.48544/66a31457-2dfb-43fb-9007-c960bfcf449b
title: Leren en welzijn verbonden.
DOI: 10.6084/m9.figshare.28216202
title: Prospective contingency explains behavior and dopamine signals during associative learning, Qian et al., 2025

description: Code and Data for Prospective contingency explains behavior and dopamine signals during associative learningAbstract: <b>Abstract</b>Associative learning depends on contingency, the degree to which a stimulus predicts an outcome. Despite its importance, the neural mechanisms linking contingency to behavior remain elusive. Here we examined the dopamine activity in the ventral striatum – a signal implicated in associative learning – in a Pavlovian contingency degradation task in mice. We show that both anticipatory licking and dopamine responses to a conditioned stimulus decreased when additional rewards were delivered uncued, but remained unchanged if additional rewards were cued. These results conflict with contingency-based accounts using a traditional definition of contingency or a novel causal learning model (ANCCR), but can be explained by temporal difference (TD) learning models equipped with an appropriate inter-trial-interval (ITI) state representation. Recurrent neural networks trained within a TD framework develop state representations like our best ‘handcrafted’ model. Our findings suggest that the TD error can be a measure that describes both contingency and dopaminergic activity.
DOI: 10.5281/zenodo.14720516
title: DATA_SI3_ISBAES_VALIDATION_AT_SHEBA

description: Contains the datasets for the five simulations used in the Brivoal et al., 2025 paper on SI3+ISBAES validation at SHEBA
DOI: 10.5281/zenodo.14720517
title: DATA_SI3_ISBAES_VALIDATION_AT_SHEBA

description: Contains the datasets for the five simulations used in the Brivoal et al., 2025 paper on SI3+ISBAES validation at SHEBA
DOI: 10.15468/dl.4ydse3
title: Occurrence Download

description: A dataset containing 27313 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Material citation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Centruroides Marx, 1890"
 ]
}

 The dataset includes 27313 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001545-250121130708018/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.28255967
title: Bone Forcep Compression Data.xlsx

description: This project was intended to measure compression generated by varying bone forceps when using for reduction of lateral condylar fractures in immature canine patients.
DOI: 10.15468/dl.yye2ed
title: Occurrence Download

description: A dataset containing 1963 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2020",
 "Geometry POLYGON ((5.140802 8.629255, 5.140802 9.162283, 6.316484 9.162283, 6.316484 8.629255, 5.140802 8.629255))"
 ]
}

 The dataset includes 1963 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001546-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10182973
title: Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes

description: NOTE for users: please use the latest version of the product. Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2022, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2022 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively. OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar. The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Ocean Heat Uptake is also calculated from the monthly OHCA and then averaged as described above to produce a yearly time series and included in the file.

For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a  confidence level of 68% or 95% respectively). These standard deviations in the file are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. 

Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.
DOI: 10.5281/zenodo.10572322
title: Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes

description: NOTE for users: please use the latest version of the product. Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2023, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2023 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively  (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.

For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a  confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. 

Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.
DOI: 10.5281/zenodo.10645137
title: Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes

description: Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2023, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2023 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively  (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.

For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a  confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. 

Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.
DOI: 10.5281/zenodo.14720478
title: Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes

description: Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2024, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2024 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively  (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.

For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a  confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. 

Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.
DOI: 10.15468/dl.a6sagb
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Liolaemus famatinae Cei, 1980",
 "Year is greater than or equal to 2020"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001547-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e6sfgj
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2020",
 "Geometry POLYGON ((6.774468 5.693019, 6.774468 5.715195, 6.84377 5.715195, 6.84377 5.693019, 6.774468 5.693019))"
 ]
}

 The dataset includes 26 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001548-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xnf92g
title: Occurrence Download

description: A dataset containing 21812822 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Bombus Latreille, 1802, Solitary bee, Diptera, Apis mellifera Linnaeus, 1758, Lepidoptera, Vespidae, Meligethes Stephens, 1830)",
 "Year 2014-2024"
 ]
}

 The dataset includes 21812822 records from 234 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001527-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zuwyg4
title: Occurrence Download

description: A dataset containing 74 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2020",
 "Geometry POLYGON ((8.896151 8.62718, 8.896151 8.784664, 9.073208 8.784664, 9.073208 8.62718, 8.896151 8.62718))"
 ]
}

 The dataset includes 74 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001549-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.djd4ut
title: Occurrence Download

description: A dataset containing 90 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2020",
 "Geometry POLYGON ((6.432451 4.776697, 6.432451 4.998901, 6.603816 4.998901, 6.603816 4.776697, 6.432451 4.776697))"
 ]
}

 The dataset includes 90 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001550-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/hhk5ptrhsg
title: Influence of the boundary between embedded words on accessing compounds and pseudo-compounds.

description: Data from four experiments that examine the effect of transposed and replaced letter primes in compounds and pseudo-compounds.
DOI: 10.17632/hhk5ptrhsg.1
title: Influence of the boundary between embedded words on accessing compounds and pseudo-compounds.

description: Data from four experiments that examine the effect of transposed and replaced letter primes in compounds and pseudo-compounds.
DOI: 10.17632/pt3b52483t.1
title: Baicalin alleviates lipid accumulation in adipocytes via inducing metabolic reprogramming and targeting Adenosine A1 receptor （raw data）

description: raw data
DOI: 10.17632/pt3b52483t.2
title: Baicalin alleviates lipid accumulation in adipocytes via inducing metabolic reprogramming and targeting Adenosine A1 receptor （raw data）

description: raw data