cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29278649.v1
title: Supplementary Material for: Functional non-coding variants in a TTTG microsatellite on chromosome 15q26.1 are a common genetic etiology of congenital hypothyroidism with thyroid gland in situ

description: Introduction: Variants affecting a microsatellite on the non-coding region of chromosome 15q26.1 are associated with familial non-autoimmune thyroid abnormalities characterized by mild congenital hypothyroidism (CH) with elevated thyroglobulin (Tg) levels. Some individuals who carry these variants may develop multinodular goiter (MNG) if left untreated. Although these variants have been recognized as genetic etiologies of CH, nongoitrous, 3 (CHNG3), the associated severity of CH remains unclear.
Methods: A cohort of 63 participants diagnosed with CH at Tohoku University underwent screening for genetic variants on 15q26.1. We then analyzed the clinical phenotypes of the variant-carrying participants.
Results: We identified five 15q26.1 variant carriers from four families among the cohort. Family histories of thyroid abnormalities were documented in three of these five cases. The variant carriers had mild CH phenotypes, with two discontinuing levothyroxine treatment and the others requiring relatively low doses (1.33–1.89 µg/kg/day) at their final visit. During levothyroxine treatment, serum thyroglobulin and thyroid-stimulating hormone (TSH) levels were within the reference ranges at the majority of the evaluation points. Three of the five participants continued treatment into adulthood, whereas the other two discontinued it and maintained serum TSH levels within the reference range. All five participants exhibited normal intellectual development and stature.
Conclusion: These findings provide further evidence supporting the role of 15q26.1 variants as a common genetic etiology of CH, with clinical phenotypes including transient CH. Early genetic evaluation may facilitate the identification of 15q26.1 variant carriers among patients who are diagnosed with CH.
DOI: 10.6084/m9.figshare.29278652
title: Supplementary Material for: Priming short children with sex steroids prior to growth hormone testing decreases the frequency of divergent results

description: Introduction
Diagnosing growth hormone (GH) deficiency (GHD) is challenging due to the low specificity of diagnostic tests, particularly in children during the prepubertal and early pubertal stages. Although sex steroid priming increases stimulated GH peak levels, its impact on spontaneous nocturnal GH values has not yet been reported. Priming may reduce discrepancies between spontaneous and stimulated GH testing, potentially improving diagnostic accuracy.

Aim
To assess the impact of priming on combined spontaneous and stimulated GH testing and the occurrence of divergent test results, as well as to evaluate short-term adverse events associated with priming.

Methods and Materials
This was a retrospective chart review of all 132 short children who underwent a nocturnal spontaneous GH secretion test followed by an arginine-insulin stimulation test over 30 years at the Department of Pediatrics, Örebro University Hospital, Sweden.

Results
Among the 132 children tested, 25 (19%) received priming prior to GH testing. Compared with nonprimed children, primed children presented higher peak and mean spontaneous GH values (14.2 µg/L (0.6–22.5) vs. 10.8 µg/L (0.2–27.0), p = 0.042 and 3.0 µg/L (0.3–6.9) vs. 2.3 µg/L (0.1–6.9), p = 0.007, respectively). Divergent results between the two GH tests were less common in primed children (4%) than in nonprimed children (23%, p = 0.027). Mild adverse events occurred in three (12%) of the primed children.

Conclusion
Sex steroid priming prior to GH testing is well tolerated, enhances spontaneous nocturnal GH secretion, and reduces the frequency of divergent results between spontaneous and stimulated values. We recommend incorporating priming when evaluating children in prepuberty or early puberty for suspected GHD.
DOI: 10.6084/m9.figshare.29278652.v1
title: Supplementary Material for: Priming short children with sex steroids prior to growth hormone testing decreases the frequency of divergent results

description: Introduction
Diagnosing growth hormone (GH) deficiency (GHD) is challenging due to the low specificity of diagnostic tests, particularly in children during the prepubertal and early pubertal stages. Although sex steroid priming increases stimulated GH peak levels, its impact on spontaneous nocturnal GH values has not yet been reported. Priming may reduce discrepancies between spontaneous and stimulated GH testing, potentially improving diagnostic accuracy.

Aim
To assess the impact of priming on combined spontaneous and stimulated GH testing and the occurrence of divergent test results, as well as to evaluate short-term adverse events associated with priming.

Methods and Materials
This was a retrospective chart review of all 132 short children who underwent a nocturnal spontaneous GH secretion test followed by an arginine-insulin stimulation test over 30 years at the Department of Pediatrics, Örebro University Hospital, Sweden.

Results
Among the 132 children tested, 25 (19%) received priming prior to GH testing. Compared with nonprimed children, primed children presented higher peak and mean spontaneous GH values (14.2 µg/L (0.6–22.5) vs. 10.8 µg/L (0.2–27.0), p = 0.042 and 3.0 µg/L (0.3–6.9) vs. 2.3 µg/L (0.1–6.9), p = 0.007, respectively). Divergent results between the two GH tests were less common in primed children (4%) than in nonprimed children (23%, p = 0.027). Mild adverse events occurred in three (12%) of the primed children.

Conclusion
Sex steroid priming prior to GH testing is well tolerated, enhances spontaneous nocturnal GH secretion, and reduces the frequency of divergent results between spontaneous and stimulated values. We recommend incorporating priming when evaluating children in prepuberty or early puberty for suspected GHD.
DOI: 10.15468/dl.wa2wmm
title: Occurrence Download

description: A dataset containing 647 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mandrillus sphinx (Linnaeus, 1758)"
 ]
}

 The dataset includes 647 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032929-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qkvva4
title: Occurrence Download

description: A dataset containing 1281 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 Geosciurus inauris (Zimmermann, 1780)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 1281 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032931-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qafjnk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hadromophryne natalensis (Hewitt, 1913)",
 "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 ((35.6306 -23.844,35.6007 -23.8125,35.6007 -24.0157,35.6306 -23.844))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032930-250525065834625/datasets/export for details.
DOI: 10.15468/dl.ez3c5c
title: Occurrence Download

description: A dataset containing 7313 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'YE'
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence."year" &gt;= 2000 AND occurrence."year" &lt;= 2025)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 7313 records were derived from 155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032920-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14466/cefasdatahub.37
title: Shelf Sea Biogeochemistry - CANDYFLOSS SmartBuoy

description: This data set consists of biogeochemical sensor data from SmartBuoy moorings (Mills et al, 2005). SmartBuoys consist of a 4 meter tall stainless steel frame with a fibreglass torroid for buoyancy. Instruments are attached at a nominal 1 meter depth to the frame. In-air sensors are mounted to the top of the torroid. The buoys feature a satellite  telemetry system providing near real-time telemetry. Temperature, salinity, dissolved oxygen, chlorophyll fluorescence, turbidity and photosynthetic active radiation (PAR) sensors were fitted to a Cefas ESM2 data logger. These instruments gathered data for 5 minutes every 30 minutes. Sensors were validated against in-situ samples during deployment and recovery. The buoy was also fitted with a water sampler which took periodic samples into pre-spiked (HgCl2) bags for later nutrient analysis. The in-situ salinity and nutrients samples are quality controlled via the Quality Assurance for Marine Environmental Measurements (QUASIMEME) scheme. PAR sensors were calibrated against a standard lamp. Fluorometers were standardised using fluorosphere beads. Turbidity meters were calibrated using Stabical (Hach, US). This mooring was also fitted with a thermistor string (multiple temperature sensors at various depth), this data is held by BODC. The data were collected as part of the Shelf Sea Biogeochemistry (SSB) research programme as part work package 1. SSB was co-funded by the Natural Environment Research Council (NERC) and the Department for Environment, Food and Rural Affairs (Defra). The programme took a holistic approach to the cycling of nutrients and carbon and the controls on primary and secondary production in UK and European shelf seas, to increase understanding of these processes and their role in wider biogeochemical cycles.
DOI: 10.15468/dl.ak6dug
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heleophryne depressa FitzSimons, 1946",
 "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 ((30.0251 -31.3959,30.5701 -30.7329,30.5701 -28.5786,25.5395 -28.5786,25.5395 -32.9007,28.2048 -32.9007,28.5753 -32.6569,29.4902 -31.7517,30.0251 -31.3959))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032933-250525065834625/datasets/export for details.
DOI: 10.15468/dl.tyh7hy
title: Occurrence Download

description: A dataset containing 20 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 Geosciurus princeps Thomas, 1929",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 20 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032934-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4vz2m5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heleophryne depressa FitzSimons, 1946",
 "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 ((30.5701 -24.0715,25.5395 -24.0715,25.5395 -28.5786,30.5701 -28.5786,30.5701 -24.0715))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032935-250525065834625/datasets/export for details.
DOI: 10.14466/cefasdatahub.38
title: Shelf Sea Biogeochemistry - Celtic Deep 2 Lander

description: This data set consists of biogeochemical sensor data from seabed moorings. The mooring consists of a 3 legged stainless steel "minilander" 1.5 meters high. The biogeochemical sensors are mounted at 1 meter above the seabed. Temperature, salinity, dissolved oxygen, chlorophyll fluorescence, turbidity where fitted to a Cefas ESM2 data logger. These instruments gathered data for 5 minutes every 30 minutes. Sensors were validated against in-situ samples during deployment and recovery. Fluorometers were standardised using fluorosphere beads. Turbidity meters were calibrated using Stabical (Hach, US). These moorings were also fitted with upward facing acoustic Doppler current profilers (ADCP), this data is held by BODC. The data were collected as part of the Shelf Sea Biogeochemistry (SSB) research programme as part work package 2. SSB was co-funded by the Natural Environment Research Council (NERC) and the Department for Environment, Food and Rural Affairs (Defra). The programme took a holistic approach to the cycling of nutrients and carbon and the controls on primary and secondary production in UK and European shelf seas, to increase understanding of these processes and their role in wider biogeochemical cycles.
DOI: 10.15468/dl.gbfgak
title: Occurrence Download

description: A dataset containing 50 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 Gerbilliscus afer (Gray, 1830)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 50 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032936-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12117933
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.15468/dl.94n5be
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heleophryne depressa FitzSimons, 1946",
 "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 ((27.1332 -20.158,25.5395 -23.2724,25.5395 -24.0715,30.5701 -24.0715,30.5701 -20.3186,29.6496 -19.6123,28.947 -19.4003,28.1821 -19.4609,27.6072 -19.7214,27.1332 -20.158))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032937-250525065834625/datasets/export for details.
DOI: 10.14279/depositonce-23834
title: Preprocessed Data of OSDaR23 Dataset for Publication "Metric Scaling and Extrinsic Calibration of Monocular Neural Network-Derived 3D Point Clouds in Railway Applications"

description: These are the preprocessed data from the OSDaR23 dataset (https://data.fid-move.de/dataset/osdar23), provided to support the reproduction of results from the publication "Metric Scaling and Extrinsic Calibration of Monocular Neural Network-Derived 3D Point Clouds in Railway Applications" (https://www.mdpi.com/2076-3417/15/10/5361). The file includes: - Preprocessed segmentations generated using InternImage with our pretrained model (https://github.com/OpenGVLab/InternImage/tree/master/segmentation) - Depth images generated using UniDepth v2 (https://github.com/lpiccinelli-eth/UniDepth) To reproduce the results, please follow the instructions on our GitHub page: https://github.com/dthomane/DispImgScaleCalib. Many thanks to: - Roman Tilly , Philipp Neumaier , Karsten Schwalbe , Pavel Klasek , Rustam Tagiew , Patrick Denzler , Tobias Klockau , Martin Boekhoff , Martin Köppel , (2023). Open Sensor Data for Rail 2023 [Data set]. TIB. https://doi.org/10.57806/9mv146r0 - Wang, Wenhai & Dai, Jifeng & Chen, Zhe & Zhenhang, Huang & Li, Zhiqi & Zhu, Xizhou & Hu, Xiaowei & Lu, Tong & Lu, Lewei & Li, Hongsheng & Wang, Xiaogang & Qiao, Yu. (2022). InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions. 10.48550/arXiv.2211.05778. - Piccinelli, Luigi & Yang, Yung-Hsu & Sakaridis, Christos & Segu, Mattia & Li, Siyuan & Gool, Luc & Yu, Fisher. (2024). UniDepth: Universal Monocular Metric Depth Estimation. 10106-10116. 10.1109/CVPR52733.2024.00963.
DOI: 10.15468/dl.uemhd8
title: Occurrence Download

description: A dataset containing 933373 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Observation, Material sample, Material citation, Specimen, Fossil, Living Specimen, Occurrence evidence)",
 "Continent is Asia",
 "Country is one of (China, Hong Kong, Macao)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (DD, LC, NT, VU, EN, CR, EW, EX)"
 ]
}

 The dataset includes 933373 records from 462 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032928-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8e8me7
title: Occurrence Download

description: A dataset containing 875594 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is AntWeb"
 ]
}

 The dataset includes 875594 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032932-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ef4xb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heleophryne depressa FitzSimons, 1946",
 "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 ((25.0214 -34.0501,25.5395 -34.1098,25.5395 -32.9007,20.509 -32.9007,20.509 -34.5676,20.524 -34.555,20.8859 -34.5419,20.9606 -34.4398,21.2995 -34.5131,21.5296 -34.4338,21.8696 -34.4439,22.0323 -34.2835,22.221 -34.2428,22.2566 -34.1304,22.5993 -34.0741,22.9652 -34.1674,23.4069 -34.1858,23.4955 -34.0792,23.6056 -34.0563,24.8471 -34.2877,24.9447 -34.2397,25.0214 -34.0501))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032940-250525065834625/datasets/export for details.
DOI: 10.14466/cefasdatahub.39
title: Shelf Sea Biogeochemistry - Celtic Deep 2 SmartBuoy

description: This data set consists of biogeochemical sensor data from SmartBuoy moorings (Mills et al, 2005). SmartBuoys consist of a 4 meter tall stainless steel frame with a fibreglass torroid for buoyancy. Instruments are attached at a nominal 1 meter depth to the frame. In-air sensors are mounted to the top of the torroid. The buoys feature a satellite telemetry system providing near real-time telemetry. Temperature, salinity, dissolved oxygen, chlorophyll fluorescence, turbidity and photosynthetic active radiation (PAR) sensors where fitted to a Cefas ESM2 data logger. These instruments gathered data for 5 minutes every 30 minutes. Sensors were validated against in-situ samples during deployment and recovery. The buoy was also fitted with a water sampler which took periodic samples into pre-spiked (MgCl2) bags for later nutrient analysis. The in-situ salinity and nutrients samples are quality controlled via the Quality Assurance for Marine Environmental Measurements (QUASIMEME) scheme. PAR sensors were calibrated against a standard lamp. Fluorometers were standardised using fluorosphere beads. Turbidity meters were calibrated using Stabical (Hach, US). This mooring was also fitted with a thermistor string (multiple temperature sensors at various depth), this data is held by BODC. The data were collected as part of the Shelf Sea Biogeochemistry (SSB) research programme as part work package 2. SSB was co-funded by the Natural Environment Research Council (NERC) and the Department for Environment, Food and Rural Affairs (Defra). The programme took a holistic approach to the cycling of nutrients and carbon and the controls on primary and secondary production in UK and European shelf seas, to increase understanding of these processes and their role in wider biogeochemical cycles. This data represents a snapshot of the wider Celtic Deep 2 SmartBuoy. The Celtic Deep SmartBuoy was first deployed in 2009 under the Defra E5304 project. From 2014 the mooring was maintained under the SSB project. This mooring was also fitted with a thermistor string (multiple temperature sensors at various depth), this data is held by BODC. The data were collected as part of the Shelf Sea Biogeochemistry (SSB) research programme as part work package 1. SSB was co-funded by the Natural Environment Research Council (NERC) and the Department for Environment, Food and Rural Affairs (Defra). The programme took a holistic approach to the cycling of nutrients and carbon and the controls on primary and secondary production in UK and European shelf seas, to increase understanding of these processes and their role in wider biogeochemical cycles.