cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.9zxx6b
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Poa nemoralis alexeenkoi, Poa nemoralis caespitosa, Poa nemoralis debilis, Poa nemoralis firmula, Poa nemoralis firmula, Poa nemoralis hylaea, Poa nemoralis hypanica, Poa nemoralis juncoides, Poa nemoralis lapponica, Poa nemoralis montana)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021469-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.973375
title: Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2018T53

description: The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2018T53 (a.k.a. FMI_0503) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Fedorov Transdrift XXIV (TICE) in 2018) as part of the project FMI. Its thermistor chain is 5 m long, and equipped with 241 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2018-09-12T01:00:13 and 2019-09-06T01:00:14. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt.
DOI: 10.1594/pangaea.973379
title: Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2018T54

description: The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2018T54 (a.k.a. FMI_0504r) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Fedorov Transdrift XXIV (TICE) in 2018) as part of the project FMI. Its thermistor chain is 5 m long, and equipped with 241 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2018-09-11T10:00:14 and 2020-03-11T10:00:15. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt.
DOI: 10.1594/pangaea.973382
title: Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2018T52

description: The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2018T52 (a.k.a. FMI_0502) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Fedorov Transdrift XXIV (TICE) in 2018) as part of the project FMI. Its thermistor chain is 5 m long, and equipped with 241 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2018-09-14T13:00:14 and 2020-04-01T01:00:14. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt.
DOI: 10.5281/zenodo.15955398
title: Data and Code Supporting: 'Prevalence of Chromosomal Abnormalities and Factors Associated with Prenatal Cell-Free DNA (cfDNA) Screening Success in Indonesia: A Retrospective Cross-Sectional Study'

description: This dataset contains:



Anonymized prenatal cfDNA screening result data in .csv format

R scripts used for data analysis and visualization in .pdf format

Study protocol in .pdf format


The data were collected as part of a retrospective cross-sectional study evaluating the performance of prenatal cfDNA screening in detecting chromosomal abnormalities among pregnant women in Indonesia. The dataset includes maternal age, fetal fraction values, gestational age, maternal body mass index, type of pregnancy, and the prenatal cfDNA screening outcomes.

Sensitive patient identifiers have been removed to ensure participant confidentiality. This repository is intended to promote transparency and the reproducibility of research findings.
DOI: 10.1594/pangaea.973317
title: Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2014T13

description: The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2014T13 (a.k.a. Awi_54) is an autonomous instrument that was installed on drifting sea ice in the Central Arctic Ocean (Polarstern PS87 (ARK28/4, ALEX) in 2014) as part of the project Sea Ice Physics @ AWI (AWI_SeaIce). Its thermistor chain is 5 m long, and equipped with 240 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2014-08-30T16:00:39 and 2014-09-23T04:00:39. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt.
DOI: 10.1594/pangaea.973388
title: Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2018T46

description: The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2018T46 (a.k.a. FMI_0404) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Fedorov Transdrift XXIV (TICE) in 2018) as part of the project FMI. Its thermistor chain is 5 m long, and equipped with 241 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2018-09-16T00:00:39 and 2020-02-18T12:00:39. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt.
DOI: 10.1594/pangaea.973389
title: Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2018T35

description: The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2018T35 (a.k.a. Awi_81) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Fedorov Transdrift XXIV (TICE) in 2018) as part of the project Sea Ice Physics @ AWI (AWI_SeaIce). Its thermistor chain is 5 m long, and equipped with 240 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2018-09-14T02:00:39 and 2019-12-26T14:33:02. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt.
DOI: 10.15468/dl.wqvgua
title: Occurrence Download

description: A dataset containing 2621 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dendropanax trifidus"
 ]
}

 The dataset includes 2621 records from 138 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021470-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t3cgvd
title: Occurrence Download

description: A dataset containing 499653 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Myadestes townsendi)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 499653 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021448-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.785322
title: (Table 2-2) Distribution of total population of microorganisms and δ¹³C-Corg values in the most surface layer of bottom sediments collected by Niemistoe corers in the White Sea in August 2006
DOI: 10.15468/dl.mxw59e
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAX2873"
 ]
}

 The dataset includes 23 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021471-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4dz4mj
title: Occurrence Download

description: A dataset containing 18090 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ageratina adenophora)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 18090 records from 179 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021473-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ka72ec
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mylopharyngodon piceus",
 "Country is India",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021472-250711103210423/datasets/export for details.
DOI: 10.15468/dl.pxum7k
title: Occurrence Download

description: A dataset containing 72 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mollugo snodgrassii"
 ]
}

 The dataset includes 72 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021475-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/z7gsky
title: CIM Project Codes for Numerical Simulation

description: Monte Carlo simulation of a multimode Coherent Ising Machine (CIM) using quantum jump (MCWF) methods in Python using QuTiP and in Matlab using xSPDE.
Features:
- Simulates coherent coupling between multiple DOPO modes
- Compares vacuum vs coherent superposition initial states
- Supports time-dependent parameters
- Coupling matrix J determines the Ising Problem
- Computes success rate and time/sampling error estimates
DOI: 10.15468/dl.g5rh34
title: Occurrence Download

description: A dataset containing 9867 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Castilleja foliolosa"
 ]
}

 The dataset includes 9867 records from 123 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021474-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6htzcu
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Draba werffii"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021476-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gxj2wn
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cynodon dactylon",
 "Country is Cuba",
 "HasCoordinate is true"
 ]
}

 The dataset includes 37 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021478-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f4u89h
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hypothenemus hampei",
 "Country is India",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021477-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4q7yha
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Poa nemoralis montana, Poa nemoralis parca, Poa nemoralis parnellii, Poa nemoralis rigidula, Poa nemoralis subpolaris, Poa nemoralis uniflora, Poa nemoralis vulgaris, Poa nemoralis acuta, Poa nemoralis adjarica, Poa nemoralis agrostoides)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 18 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021479-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fq6s2u
title: Occurrence Download

description: A dataset containing 663 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dendrophthora clavata"
 ]
}

 The dataset includes 663 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021480-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e28rwp
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",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Myadestes townsendi)",
 "Country is one of (Åland Islands, Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Republic of, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021481-250711103210423/datasets/export for details.
DOI: 10.15468/dl.mjcc9b
title: Occurrence Download

description: A dataset containing 923 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Physaria recurvata"
 ]
}

 The dataset includes 923 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021482-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.78kejp
title: Occurrence Download

description: A dataset containing 181 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mylossoma duriventris"
 ]
}

 The dataset includes 181 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021483-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.