cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.v6regp
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 (Gopherus agassizii)",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080087-250717081556266/datasets/export for details.
DOI: 10.15468/dl.dx6wg6
title: Occurrence Download

description: A dataset containing 62477 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Euphorbia esula"
 ]
}

 The dataset includes 62477 records from 627 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079976-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2cxe38
title: Occurrence Download

description: A dataset containing 660350 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Epipactis Zinn"
 ]
}

 The dataset includes 660350 records from 1787 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032872-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qcwrfr
title: Occurrence Download

description: A dataset containing 94 species occurrences available in GBIF matching the query:
{
 "and" : [
 "IucnRedListCategory is CR",
 "TaxonKey is Erodium astragaloides"
 ]
}

 The dataset includes 94 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006715-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18680338
title: POLYCYSTIC OVARY SYNDROME: ETIOLOGY, CLINICAL SIGNIFICANCE AND COMPLICATIONS

description: Polycystic ovary syndrome (PCOS) is a widely prevalent endocrine-metabolic disorder among women of reproductive age, characterized by hormonal imbalance, anovulation, and insulin resistance. The aim of this study was to investigate the clinical course of PCOS, its metabolic and oncological complications, and to evaluate the effectiveness of preventive measures.

The study included 60 patients aged 18-35 years diagnosed with PCOS. Clinical, laboratory, and ultrasound examinations were performed. Hormonal parameters (LH, FSH, testosterone), glucose metabolism, and body mass index were assessed. The results showed that 70% of patients had menstrual cycle disorders, 65% demonstrated signs of hyperandrogenism, and 55% had insulin resistance. Endometrial hyperplasia was detected in 18% of cases. Improvement in metabolic and hormonal parameters was observed in patients who followed rational nutrition and weight control measures.
DOI: 10.5281/zenodo.18680337
title: POLYCYSTIC OVARY SYNDROME: ETIOLOGY, CLINICAL SIGNIFICANCE AND COMPLICATIONS

description: Polycystic ovary syndrome (PCOS) is a widely prevalent endocrine-metabolic disorder among women of reproductive age, characterized by hormonal imbalance, anovulation, and insulin resistance. The aim of this study was to investigate the clinical course of PCOS, its metabolic and oncological complications, and to evaluate the effectiveness of preventive measures.

The study included 60 patients aged 18-35 years diagnosed with PCOS. Clinical, laboratory, and ultrasound examinations were performed. Hormonal parameters (LH, FSH, testosterone), glucose metabolism, and body mass index were assessed. The results showed that 70% of patients had menstrual cycle disorders, 65% demonstrated signs of hyperandrogenism, and 55% had insulin resistance. Endometrial hyperplasia was detected in 18% of cases. Improvement in metabolic and hormonal parameters was observed in patients who followed rational nutrition and weight control measures.
DOI: 10.5281/zenodo.18680361
title: Dataset: Air Quality Dataset for Algiers (2013-01-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Algiers covering 2013-01-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Algiers, compare Algiers with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_europe domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.18680360
title: Dataset: Air Quality Dataset for Algiers (2013-01-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Algiers covering 2013-01-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Algiers, compare Algiers with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_europe domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.25934/pr00004643.0
title: Available datapackage for study 'A Randomized Double-Blind Placebo Controlled Trial to Investigate the Efficacy and Tolerability of Topiramate in the Prophylaxis of Chronic Migraine'

description: The purpose of this study is to evaluate the safety and effectiveness of individualized doses (50 to 200 milligrams) of topiramate tablets compared against placebo for the prevention of chronic migraine headaches over a period of 16 weeks.
DOI: 10.5281/zenodo.18680369
title: Dataset: Air Quality Dataset for Nanchang (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Nanchang covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Nanchang, compare Nanchang with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.14026160
title: Dataset for Hybrid Approach to Reconstruct Nanoscale Grating Dimensions using Scattering and Fluorescence with Soft X-rays

description: This dataset accompanies the paper titled "Hybrid approach to reconstruct nanoscale grating dimensions using scattering and fluorescence with soft X-rays" in Nanoscale, 2025,17, 6017-6029 (https://doi.org/10.1039/D4NR04580G). It includes measurement data, optimization results and fit data collected from a nanoscale grating with a soft X-ray fluorescence scatterometer, a model of the experiment and weighted, combined regression.
DOI: 10.5281/zenodo.14028657
title: Dataset for Hybrid Approach to Reconstruct Nanoscale Grating Dimensions using Scattering and Fluorescence with Soft X-rays

description: This dataset accompanies the paper titled "Hybrid approach to reconstruct nanoscale grating dimensions using scattering and fluorescence with soft X-rays" in Nanoscale, 2025,17, 6017-6029 (https://doi.org/10.1039/D4NR04580G). It includes measurement data, optimization results and fit data collected from a nanoscale grating with a soft X-ray fluorescence scatterometer, a model of the experiment and weighted, combined regression.
DOI: 10.5281/zenodo.18680368
title: Dataset: Air Quality Dataset for Nanchang (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Nanchang covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Nanchang, compare Nanchang with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.18680366
title: Lightning
DOI: 10.5281/zenodo.18680367
title: Lightning
DOI: 10.5281/zenodo.18680377
title: Dataset: Air Quality Dataset for Hohhot (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Hohhot covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Hohhot, compare Hohhot with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.18680376
title: Dataset: Air Quality Dataset for Hohhot (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Hohhot covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Hohhot, compare Hohhot with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.18680384
title: Dataset: Air Quality Dataset for Nagoya (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Nagoya covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Nagoya, compare Nagoya with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.18680385
title: Dataset: Air Quality Dataset for Nagoya (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Nagoya covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Nagoya, compare Nagoya with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.18680380
title: LEGAL REGULATION OF PRIVACY AND PERSONAL DATA PROTECTION IN ELECTRONIC GOVERNMENT SYSTEMS

description: Protection of personal privacy and personal data security in digital government administration systems represents a comprehensive legal problem at the intersection of private rights and administrative efficiency. The research analyzes the main legal frameworks for data protection, including international standards and national legislative acts that form the contemporary system of privacy protection in electronic government. The work examines consent mechanisms, privacy guarantees, and the evolving legal environment for data protection in the context of digital administration. Particular attention is given to constitutional foundations, sectoral privacy regulations, and international data protection standards. The research identifies key privacy issues in digital administrative relations, including questions of data minimization, consent, cross-border transfer, and technical protection measures. The results demonstrate the need to create a balanced approach that ensures protection of fundamental citizens' rights while maintaining government administration efficiency under conditions of digital transformation.
DOI: 10.5281/zenodo.18680381
title: LEGAL REGULATION OF PRIVACY AND PERSONAL DATA PROTECTION IN ELECTRONIC GOVERNMENT SYSTEMS

description: Protection of personal privacy and personal data security in digital government administration systems represents a comprehensive legal problem at the intersection of private rights and administrative efficiency. The research analyzes the main legal frameworks for data protection, including international standards and national legislative acts that form the contemporary system of privacy protection in electronic government. The work examines consent mechanisms, privacy guarantees, and the evolving legal environment for data protection in the context of digital administration. Particular attention is given to constitutional foundations, sectoral privacy regulations, and international data protection standards. The research identifies key privacy issues in digital administrative relations, including questions of data minimization, consent, cross-border transfer, and technical protection measures. The results demonstrate the need to create a balanced approach that ensures protection of fundamental citizens' rights while maintaining government administration efficiency under conditions of digital transformation.
DOI: 10.34669/wi.wd/1
title: Umfrage zu Forschungsdatenmanagement am Weizenbaum-Institut (2021)
DOI: 10.15468/dl.mvucqu
title: Occurrence Download

description: A dataset containing 96259 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ajuga reptans",
 "Continent is Europe",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 100",
 "Year is greater than or equal to 1970",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Material citation"
 ]
 }
 }
 ]
}

 The dataset includes 96259 records from 286 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027031-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18680412
title: Dataset: Air Quality Dataset for Rome (2013-01-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Rome covering 2013-01-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Rome, compare Rome with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_europe domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.18680411
title: Dataset: Air Quality Dataset for Rome (2013-01-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Rome covering 2013-01-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Rome, compare Rome with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_europe domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.6084/m9.figshare.31353343
title: Data for: Turner, R.S., Lasne, O.J.D., Lv, L., Osmond, H.L., Cockburn, A., Kruuk, L.E.B., & Youngentob, K.N. (2026). Using Landsat satellite imagery to investigate spatial and temporal variation in avian life history traits in a long-term study population of superb fairy-wrens <i>Malurus cyaneus </i>in Australia

description: Data for: Turner, R.S., Lasne, O.J.D., Lv, L., Osmond, H.L., Cockburn, A., Kruuk, L.E.B., & Youngentob, K.N. (2026). Using Landsat satellite imagery to investigate spatial and temporal variation in avian life history traits in a long-term study population of superb fairy-wrens <i>Malurus cyaneus </i>in Australia
DOI: 10.6084/m9.figshare.31353343.v1
title: Data for: Turner, R.S., Lasne, O.J.D., Lv, L., Osmond, H.L., Cockburn, A., Kruuk, L.E.B., & Youngentob, K.N. (2026). Using Landsat satellite imagery to investigate spatial and temporal variation in avian life history traits in a long-term study population of superb fairy-wrens <i>Malurus cyaneus </i>in Australia

description: Data for: Turner, R.S., Lasne, O.J.D., Lv, L., Osmond, H.L., Cockburn, A., Kruuk, L.E.B., & Youngentob, K.N. (2026). Using Landsat satellite imagery to investigate spatial and temporal variation in avian life history traits in a long-term study population of superb fairy-wrens <i>Malurus cyaneus </i>in Australia
DOI: 10.15468/dl.wxch7b
title: Occurrence Download

description: A dataset containing 914704 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Aetobatus ocellatus",
 "TaxonKey is Alopias pelagicus",
 "TaxonKey is Alopias superciliosus",
 "TaxonKey is Alopias vulpinus",
 "TaxonKey is Anoxypristis cuspidata",
 "TaxonKey is Carcharhinus albimarginatus",
 "TaxonKey is Carcharhinus amblyrhynchoides",
 "TaxonKey is Carcharhinus amblyrhynchos",
 "TaxonKey is Carcharhinus amboinensis",
 "TaxonKey is Carcharhinus brevipinna",
 "TaxonKey is Carcharhinus falciformis",
 "TaxonKey is Carcharhinus galapagensis",
 "TaxonKey is Carcharhinus leucas",
 "TaxonKey is Carcharhinus longimanus",
 "TaxonKey is Carcharhinus macloti",
 "TaxonKey is Carcharhinus melanopterus",
 "TaxonKey is Carcharhinus sorrah",
 "TaxonKey is Carcharodon carcharias",
 "TaxonKey is Chiloscyllium punctatum",
 "TaxonKey is Eucrossorhinus dasypogon",
 "TaxonKey is Eusphyra blochii",
 "TaxonKey is Galeocerdo cuvier",
 "TaxonKey is Glaucostegus typus",
 "TaxonKey is Gymnura australis",
 "TaxonKey is Hemipristis elongata",
 "TaxonKey is Hemiscyllium ocellatum",
 "TaxonKey is Hemiscyllium trispeculare",
 "TaxonKey is Hexanchus griseus",
 "TaxonKey is Himantura leoparda",
 "TaxonKey is Lamna nasus",
 "TaxonKey is Loxodon macrorhinus",
 "TaxonKey is Mobula alfredi",
 "TaxonKey is Mobula birostris",
 "TaxonKey is Mobula eregoodoo",
 "TaxonKey is Nebrius ferrugineus",
 "TaxonKey is Negaprion acutidens",
 "TaxonKey is Pastinachus ater",
 "TaxonKey is Pateobatis fai",
 "TaxonKey is Pateobatis jenkinsii",
 "TaxonKey is Prionace glauca",
 "TaxonKey is Pristis clavata",
 "TaxonKey is Pristis pristis",
 "TaxonKey is Pristis zijsron",
 "TaxonKey is Pseudocarcharias kamoharai",
 "TaxonKey is Rhina ancylostoma",
 "TaxonKey is Rhincodon typus",
 "TaxonKey is Rhizoprionodon acutus",
 "TaxonKey is Rhynchobatus australiae",
 "TaxonKey is Stegostoma tigrinus",
 "TaxonKey is Taeniura lymma",
 "TaxonKey is Taeniurops meyeni",
 "TaxonKey is Triaenodon obesus",
 "TaxonKey is Urogymnus asperrimus",
 "TaxonKey is Urogymnus granulatus"
 ]
 },
 "HasCoordinate is true",
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Living Specimen",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Material citation",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Observation",
 "BasisOfRecord is Occurrence evidence",
 "BasisOfRecord is Specimen"
 ]
 },
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 914704 records from 679 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027002-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18680422
title: Dataset: Air Quality Dataset for Queens (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Queens covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Queens, compare Queens with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.18680421
title: Dataset: Air Quality Dataset for Queens (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Queens covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Queens, compare Queens with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.18680434
title: Dataset: Air Quality Dataset for Houston (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Houston covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Houston, compare Houston with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.5281/zenodo.18680433
title: Dataset: Air Quality Dataset for Houston (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Houston covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Houston, compare Houston with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni