cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30620715
title: Use of a machine learning approach to estimate pathobiological effects of crack cocaine administration in rats

description: This study aimed to apply a machine learning workflow to identify the most relevant biological parameters for predicting both the dose and the route of crack cocaine administration.Seventeen variables were evaluated in rats exposed to different doses of crack cocaine, either intraperitoneally (18 or 36 mg/kg, i.p.) or <i>via</i> passive inhalation (25, 50, or 100 mg). Random forest (RF) analysis was used to build predictive models, feature importance analysis to identify key variables, and interaction dependence analysis to explore relationships among variables.Eighty percent of the data was used for training and 20% for testing. The model achieved 85% accuracy in the training phase and 100% in the test phase. During training, the highest accuracy was observed for the 100 mg inhaled group, while the lowest was for the 50 mg inhaled group. Notably, 20% of the 50 mg i.p. cases were misclassified as 36 mg i.p. Feature importance analysis highlighted four key predictors: liver karyolysis, kidney Ki-67 expression, liver binucleation, and escape behaviour.These findings demonstrate that machine learning (ML) can accurately predict both dose and route of crack cocaine exposure, and can highlight biologically relevant parameters involved in the drug’s systemic and behavioural effects. This study aimed to apply a machine learning workflow to identify the most relevant biological parameters for predicting both the dose and the route of crack cocaine administration. Seventeen variables were evaluated in rats exposed to different doses of crack cocaine, either intraperitoneally (18 or 36 mg/kg, i.p.) or <i>via</i> passive inhalation (25, 50, or 100 mg). Random forest (RF) analysis was used to build predictive models, feature importance analysis to identify key variables, and interaction dependence analysis to explore relationships among variables. Eighty percent of the data was used for training and 20% for testing. The model achieved 85% accuracy in the training phase and 100% in the test phase. During training, the highest accuracy was observed for the 100 mg inhaled group, while the lowest was for the 50 mg inhaled group. Notably, 20% of the 50 mg i.p. cases were misclassified as 36 mg i.p. Feature importance analysis highlighted four key predictors: liver karyolysis, kidney Ki-67 expression, liver binucleation, and escape behaviour. These findings demonstrate that machine learning (ML) can accurately predict both dose and route of crack cocaine exposure, and can highlight biologically relevant parameters involved in the drug’s systemic and behavioural effects.
DOI: 10.6084/m9.figshare.30620715.v1
title: Use of a machine learning approach to estimate pathobiological effects of crack cocaine administration in rats

description: This study aimed to apply a machine learning workflow to identify the most relevant biological parameters for predicting both the dose and the route of crack cocaine administration.Seventeen variables were evaluated in rats exposed to different doses of crack cocaine, either intraperitoneally (18 or 36 mg/kg, i.p.) or <i>via</i> passive inhalation (25, 50, or 100 mg). Random forest (RF) analysis was used to build predictive models, feature importance analysis to identify key variables, and interaction dependence analysis to explore relationships among variables.Eighty percent of the data was used for training and 20% for testing. The model achieved 85% accuracy in the training phase and 100% in the test phase. During training, the highest accuracy was observed for the 100 mg inhaled group, while the lowest was for the 50 mg inhaled group. Notably, 20% of the 50 mg i.p. cases were misclassified as 36 mg i.p. Feature importance analysis highlighted four key predictors: liver karyolysis, kidney Ki-67 expression, liver binucleation, and escape behaviour.These findings demonstrate that machine learning (ML) can accurately predict both dose and route of crack cocaine exposure, and can highlight biologically relevant parameters involved in the drug’s systemic and behavioural effects. This study aimed to apply a machine learning workflow to identify the most relevant biological parameters for predicting both the dose and the route of crack cocaine administration. Seventeen variables were evaluated in rats exposed to different doses of crack cocaine, either intraperitoneally (18 or 36 mg/kg, i.p.) or <i>via</i> passive inhalation (25, 50, or 100 mg). Random forest (RF) analysis was used to build predictive models, feature importance analysis to identify key variables, and interaction dependence analysis to explore relationships among variables. Eighty percent of the data was used for training and 20% for testing. The model achieved 85% accuracy in the training phase and 100% in the test phase. During training, the highest accuracy was observed for the 100 mg inhaled group, while the lowest was for the 50 mg inhaled group. Notably, 20% of the 50 mg i.p. cases were misclassified as 36 mg i.p. Feature importance analysis highlighted four key predictors: liver karyolysis, kidney Ki-67 expression, liver binucleation, and escape behaviour. These findings demonstrate that machine learning (ML) can accurately predict both dose and route of crack cocaine exposure, and can highlight biologically relevant parameters involved in the drug’s systemic and behavioural effects.
DOI: 10.6084/m9.figshare.28783172
title: Observation and data fusion for soil moisture dynamics of three LULCs in the Yangtze River Delta region

description: We have observed soil moisture for three different land use and cover types. Then we examined the soil moisture dynamics to rainstorms. Hierarchical Bayesian Network (HBN) and improved HBN (iHBN) algorithms are used to enhance the performance of SMAP by merging in situ measurements. Here is all data necessary to reproduce the results of this work and codes generated during this study.
DOI: 10.6084/m9.figshare.28783172.v1
title: Observation and data fusion for soil moisture dynamics of three LULCs

description: We have observed soil moisture for three different land use and cover types. Then we examined the soil moisture dynamics to rainstorms. Hierarchical Bayesian Network (HBN) and improved HBN (iHBN) algorithms are used to enhance the performance of SMAP by merging in situ measurements.
DOI: 10.6084/m9.figshare.28783172.v2
title: Observation and data fusion for soil moisture dynamics of three LULCs in the Yangtze River Delta region

description: We have observed soil moisture for three different land use and cover types. Then we examined the soil moisture dynamics to rainstorms. Hierarchical Bayesian Network (HBN) and improved HBN (iHBN) algorithms are used to enhance the performance of SMAP by merging in situ measurements. Here is all data necessary to reproduce the results of this work and codes generated during this study.
DOI: 10.15468/dl.w8yedd
title: Occurrence Download

description: A dataset containing 940 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Zabrotes subfasciatus"
 ]
}

 The dataset includes 940 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055728-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/370exj
title: PAN-00182076 - Fede ring

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00182076&lt;/p&gt;
description: Modified: 2025-11-03T14:15:10+01:00
DOI: 10.5281/zenodo.17866630
title: Supporting datasets for "Post-disturbance ice-wedge degradation in Alaskan tundra fire scars using space-for-time substitution remote sensing"

description: This collection contains datasets related to the publication "Post-disturbance ice-wedge degradation in Alaskan tundra fire scars using space-for-time substitution remote sensing" by T Rettelbach, J Bader, B Groenke, V Helm, M Langer, J-C Freytag, and G Grosse.

The datasets describe:



all_106_aois_550m.gpkg: Geopackage with outlines of all analyzed areas of interest (AOIs)

aois_and_fires_good.csv: Table with all information on fire behavior and derived graph metrics for all analyzed AOIs 

csv: Output folder for graph metrics from Nextflow script "networkAnalysis.nf"

dicts_avg: Output folder for pkl-dictionaries describing edge parameters of the graphs. Output of Nextflow script "transectAnalysis.nf".

dicts_fitted: Output folder for nested pkl-dictionaries describing transect properties within edges. Output of Nextflow script "networkAnalysis.nf".

shp: Output folder for graph shapefiles. Edges and nodes are separate. Output of Nextflow script "graphToShapfile.nf".

tifs: Output folder for detrended DEMs and skeletons of AOIs. Output of Nextflow script "demToGraph.nf".
DOI: 10.5281/zenodo.17866631
title: Supporting datasets for "Post-disturbance ice-wedge degradation in Alaskan tundra fire scars using space-for-time substitution remote sensing"

description: This collection contains datasets related to the publication "Post-disturbance ice-wedge degradation in Alaskan tundra fire scars using space-for-time substitution remote sensing" by T Rettelbach, J Bader, B Groenke, V Helm, M Langer, J-C Freytag, and G Grosse.

The datasets describe:



all_106_aois_550m.gpkg: Geopackage with outlines of all analyzed areas of interest (AOIs)

aois_and_fires_good.csv: Table with all information on fire behavior and derived graph metrics for all analyzed AOIs 

csv: Output folder for graph metrics from Nextflow script "networkAnalysis.nf"

dicts_avg: Output folder for pkl-dictionaries describing edge parameters of the graphs. Output of Nextflow script "transectAnalysis.nf".

dicts_fitted: Output folder for nested pkl-dictionaries describing transect properties within edges. Output of Nextflow script "networkAnalysis.nf".

shp: Output folder for graph shapefiles. Edges and nodes are separate. Output of Nextflow script "graphToShapfile.nf".

tifs: Output folder for detrended DEMs and skeletons of AOIs. Output of Nextflow script "demToGraph.nf".
DOI: 10.15468/dl.8z3csp
title: Occurrence Download

description: A dataset containing 395468 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Castanea",
 "HasCoordinate is true"
 ]
}

 The dataset includes 395468 records from 2102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055714-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wmuwmw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heleophryne regis Hewitt, 1910",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.5395 -28.5786,20.509 -28.5786,20.509 -32.9007,25.5395 -32.9007,25.5395 -28.5786))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033193-250525065834625/datasets/export for details.
DOI: 10.3334/cdiac/otg.tsm_coastalms_88w_30n
title: High-resolution ocean and atmosphere pCO2 time-series measurements from mooring CoastalMS_88W_30N in the Coastal Waters of Louisiana and Gulf of Mexico (NCEI Accession 0100068)

description: This dataset 0100068 includes chemical, meteorological, physical and time series data collected from Mooring CoastalMS_88W_30N in the Coastal Waters of Louisiana and Gulf of Mexico from 2009-05-12 to 2017-05-29. These data include air-sea difference of partial pressure of carbon dioxide (pCO2), barometric pressure, oxygen, pCO2 in atmosphere, pCO2 in water, sea surface salinity, sea surface temperature and pH on total scale. The Moored Autonomous pCO2 (MAPCO2®) instruments used to collect these data include Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer, Humidity Sensor, and oxygen meter. The Global CO2 Time-series and Moorings Project involves international groups from 18 countries who have mounted sensors on moored buoys to provide high resolution time-series measurements of atmospheric boundary layer and surface ocean CO2 partial pressure (pCO2). The CO2 Time-series and Moorings Project is coordinated by UNESCO International Ocean Carbon Coordination Project (IOCCP).
DOI: 10.3334/cdiac/otg.tsm_crimp1_158w_21n
title: High-resolution ocean and atmosphere pCO2 time-series measurements from mooring CRIMP1_158W_21N in the North Pacific Ocean (NCEI Accession 0100069)

description: This dataset includes chemical, meteorological, physical and time series data collected from Mooring CRIMP1_158W_21N in the North Pacific Ocean from 2005-12-01 to 2008-05-30. These data include air-sea difference - partial pressure of carbon dioxide (pCO2), barometric pressure, oxygen, pCO2 - atmosphere, pCO2 - water, pH on the total scale, sea surface salinity and sea surface temperature. The Moored Autonomous pCO2 (MAPCO2®) instruments used to collect these data include Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer, Humidity Sensor, and oxygen meter. The Global CO2 Time-series and Moorings Project involves international groups from 18 countries who have mounted sensors on moored buoys to provide high resolution time-series measurements of atmospheric boundary layer and surface ocean CO2 partial pressure (pCO2). The CO2 Time-series and Moorings Project is coordinated by UNESCO International Ocean Carbon Coordination Project (IOCCP).
DOI: 10.15468/dl.v399m6
title: Occurrence Download

description: A dataset containing 365 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Caulerpa cupressoides var. lycopodium Weber Bosse"
 ]
}

 The dataset includes 365 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033192-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3334/cdiac/otg.tsm_lapush_125w_48n
title: High-resolution ocean and atmosphere pCO2 time-series measurements from mooring Chá Bă (LaPush_125W_48N) in the Pacific Ocean (NCEI Accession 0100072)

description: This dataset includes chemical, physical and time series data collected from Mooring Chá Bă (LaPush_125W_48N) in the North Pacific Ocean from 2010-07-16 to 2023-05-04. These data include air-sea difference of partial pressure carbon dioxide, oxygen, partial pressure of carbon dioxide - atmosphere, partial pressure of carbon dioxide - water, salinity, sea surface temperature, pH on total scale, total chlorophyll, nephelometric turbidity and salinity-compensated dissolved oxygen. The Moored Autonomous pCO2 (MAPCO2®) instruments used to collect these data include Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer, Humidity Sensor, and oxygen meter. The Global CO2 Time-series and Moorings Project involves international groups from 18 countries who have mounted sensors on moored buoys to provide high resolution time-series measurements of atmospheric boundary layer and surface ocean CO2 partial pressure (pCO2). The CO2 Time-series and Moorings Project is coordinated by UNESCO International Ocean Carbon Coordination Project (IOCCP).
DOI: 10.15468/dl.hshu4v
title: Occurrence Download

description: A dataset containing 332 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Grammomys ibeanus (Osgood, 1910)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 332 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033195-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3334/cdiac/otg.tsm_tao125w
title: High-resolution ocean and atmosphere pCO2 time-series measurements from mooring TAO125W_0N in the Equatorial Pacific Ocean (NCEI Accession 0100076)

description: This dataset includes chemical, meteorological, physical time series data collected from TAO125W_0N in the Equatorial Pacific Ocean from 2004-05-08 to 2024-01-22. These data include air-sea difference of partial pressure of carbon dioxide (pCO2), barometric pressure, oxygen, pCO2 in atmosphere, pCO2 in water, sea surface salinity, sea surface temperature and pH on total scale. The Moored Autonomous pCO2 (MAPCO2®) instruments used to collect these data include Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer, Humidity Sensor, and oxygen meter. The Global CO2 Time-series and Moorings Project involves international groups from 18 countries who have mounted sensors on moored buoys to provide high resolution time-series measurements of atmospheric boundary layer and surface ocean CO2 partial pressure (pCO2). The CO2 Time-series and Moorings Project is coordinated by International Ocean Carbon Coordination Project (IOCCP).
DOI: 10.15468/dl.bn9m4b
title: Occurrence Download

description: A dataset containing 112475 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is COL.10_2",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 112475 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033178-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mxeq83
title: Occurrence Download

description: A dataset containing 3739 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "TaxonKey is Parkinsonia aculeata L."
 ]
}

 The dataset includes 3739 records from 197 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033194-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3334/cdiac/otg.clivar_a21_2006
title: Partial pressure (or fugacity) of carbon dioxide, dissolved inorganic carbon, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, Carbon dioxide (CO2) gas analyzer and other instruments from LAURENCE M. GOULD in the South Atlantic Ocean and Southern Oceans from 2006-03-21 to 2006-04-04 (NCEI Accession 0108070)

description: This dataset includes chemical, discrete sample, physical and profile data collected from LAURENCE M. GOULD in the South Atlantic Ocean and Southern Oceans (&gt; 60 degrees South) from 2006-03-21 to 2006-04-04. These data include DELTA CARBON-13, DELTA CARBON-14, DISSOLVED INORGANIC CARBON (DIC), DISSOLVED OXYGEN, HYDROSTATIC PRESSURE, NITRATE, NITRITE, Partial pressure (or fugacity) of carbon dioxide - water, Potential temperature (theta), SALINITY, WATER TEMPERATURE, phosphate and silicate. The instruments used to collect these data include CTD, Carbon dioxide (CO2) gas analyzer, Coulometer for DIC measurement, Flask with closed headspace for discrete carbon dioxide (CO2) measurement and bottle.

These data were collected by Colm Sweeney of NOAA Earth System Research Laboratory (ESRL) as part of the CLIVAR_A21_2006 dataset. CDIAC associated the following cruise ID(s) with this dataset: CLIVAR_A21_2006

The International CLIVAR Global Ocean Carbon and Repeat Hydrography Program carries out a systematic and global re-occupation of select WOCE/JGOFS hydrographic sections to quantify changes in storage and transport of heat, fresh water, carbon dioxide (CO2), and related parameters.
DOI: 10.15151/esrf-dc-2183326285
title: Synchrotron X-ray micro Computed Tomography of a fossil caridean shrimp MNHN.F.A58277 from the La Voulte-sur-Rhône Konservat-Lagerstätte (ca. 165 Ma)

description: The specimen MNHN.F.A58277 is housed in the palaeontological collections of Muséum national d’Histoire naturelle, Paris. It corresponds to a caridean shrimp preserved within a sideritic nodule from the Callovian (Middle Jurassic, ca 165 Ma) La Voulte-sur-Rhône Konservat-Lagerstätte. The specimen was historically mechanically prepared to reveal its dorsal side., the appendages remaining inside the nodule. 
The dataset presented here result from the acquisition performed at the BM18 beamline of the European Synchrotron Radiation Facility (Grenoble, France) using propagation phase contrast synchrotron X-ray micro-computed tomography. The acquisition comprise most of the nodule in which the specimen is enclosed. The beamline was setup for filtered white beam using the 1.56 T central pole of the tri-pole wiggler, with 0.44 mm of Molybdenum, 15 mm of SiO2, 59.3 mm of glassy carbon and 7.5 mm of Sapphire, resulting in a total integrated detected energy of 96.7 keV. The sample detector distance was set to 4 m. The indirect detector comprised a LuAG:Ce 250 µm scintillator, visible light lenses set for 1x magnification, and an Iris 15 sCMOS camera (Teledyne Photometrics, Tucson, AZ, USA), resulting in a measured isotropic pixel size of 4.007 µm. The usable field of view was (vertical x horizontal): 1856 x 4944 pixels, corresponding to 7.424 x 19.776 mm2. To compensate for the limited vertical field of view, 8 acquisitions were performed, moving the sample by 3.712 mm (50% of the vertical field of view) between each acquisition. Each acquisition consisted of 6000 projections recorded over a 360° rotation, each saved projection having an exposure time of 120 msec., resulting from the accumulation of 3 images of 40 msec.
DOI: 10.15468/dl.a6eq6e
title: Occurrence Download

description: A dataset containing 6249 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Caulerpa racemosa (Forssk.) J.Agardh"
 ]
}

 The dataset includes 6249 records from 190 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033196-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.