cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31360851
title: Additional file 2 of Dual RNA-seq analysis reveals differences in defensive lncRNA expression in Pinus spp. with varying susceptibility to Fusarium circinatum

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.31360851.v1
title: Additional file 2 of Dual RNA-seq analysis reveals differences in defensive lncRNA expression in Pinus spp. with varying susceptibility to Fusarium circinatum

description: Supplementary Material 2.
DOI: 10.15468/dl.grku97
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Papua New Guinea",
 "OccurrenceStatus is Present",
 "TaxonKey is Hephaestus adamsoni"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026473-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31360918
title: Additional file 4 of A scoping review of the use of generative artificial intelligence tools in health profession education

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.31360918.v1
title: Additional file 4 of A scoping review of the use of generative artificial intelligence tools in health profession education

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.31360921
title: Additional file 5 of A scoping review of the use of generative artificial intelligence tools in health profession education

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.31360921.v1
title: Additional file 5 of A scoping review of the use of generative artificial intelligence tools in health profession education

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.31360919
title: Additional file 2 of Deciphering the shared genetic architecture between female reproductive disorders and psychiatric disorders

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.31360919.v1
title: Additional file 2 of Deciphering the shared genetic architecture between female reproductive disorders and psychiatric disorders

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.31360922
title: Additional file 3 of Deciphering the shared genetic architecture between female reproductive disorders and psychiatric disorders

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.31360922.v1
title: Additional file 3 of Deciphering the shared genetic architecture between female reproductive disorders and psychiatric disorders

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.31360925
title: Additional file 4 of Deciphering the shared genetic architecture between female reproductive disorders and psychiatric disorders

description: Supplementary Material 4.
DOI: 10.6084/m9.figshare.31360925.v1
title: Additional file 4 of Deciphering the shared genetic architecture between female reproductive disorders and psychiatric disorders

description: Supplementary Material 4.
DOI: 10.6084/m9.figshare.31360928
title: Additional file 5 of Deciphering the shared genetic architecture between female reproductive disorders and psychiatric disorders

description: Supplementary Material 5.
DOI: 10.6084/m9.figshare.31360928.v1
title: Additional file 5 of Deciphering the shared genetic architecture between female reproductive disorders and psychiatric disorders

description: Supplementary Material 5.
DOI: 10.6084/m9.figshare.31360943
title: Additional file 1 of Analysis of LRP1 gene mutation in developmental dysplasia of the hip: a case series

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.31360943.v1
title: Additional file 1 of Analysis of LRP1 gene mutation in developmental dysplasia of the hip: a case series

description: Supplementary Material 1.
DOI: 10.15468/dl.d9ashv
title: Occurrence Download

description: A dataset containing 786 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "GadmGid is USA.45_1",
 "TaxonKey is Asclepias asperula"
 ]
}

 The dataset includes 786 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026474-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31360967
title: Additional file 5 of Population structure and zoonotic potential of Cryptosporidium parvum in Italy inferred using a multi-locus sequence typing scheme

description: Additional File 5: Table S2. List of the alleles found in the 72 samples from Italy at the eight markers comprised in the MLST scheme
DOI: 10.6084/m9.figshare.31360967.v1
title: Additional file 5 of Population structure and zoonotic potential of Cryptosporidium parvum in Italy inferred using a multi-locus sequence typing scheme

description: Additional File 5: Table S2. List of the alleles found in the 72 samples from Italy at the eight markers comprised in the MLST scheme
DOI: 10.6084/m9.figshare.31360973
title: Additional file 2 of Auxin dysregulation: a key early event in sugarcane susceptibility to Sporisorium scitamineum

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.31360973.v1
title: Additional file 2 of Auxin dysregulation: a key early event in sugarcane susceptibility to Sporisorium scitamineum

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.31360982
title: Additional file 2 of The continuous net benefit: assessing the clinical utility of prediction models when informing a continuum of decisions

description: Additional file 2. (.R): Code (in R) showcasing the use of continuous net benefit
DOI: 10.6084/m9.figshare.31360982.v1
title: Additional file 2 of The continuous net benefit: assessing the clinical utility of prediction models when informing a continuum of decisions

description: Additional file 2. (.R): Code (in R) showcasing the use of continuous net benefit
DOI: 10.6084/m9.figshare.31360985
title: Additional file 3 of The continuous net benefit: assessing the clinical utility of prediction models when informing a continuum of decisions

description: Additional file 3. Supplementary containing additional information of the model development and validation presented in the Results section
DOI: 10.6084/m9.figshare.31360985.v1
title: Additional file 3 of The continuous net benefit: assessing the clinical utility of prediction models when informing a continuum of decisions

description: Additional file 3. Supplementary containing additional information of the model development and validation presented in the Results section
DOI: 10.6084/m9.figshare.31361000
title: Additional file 2 of Genome-wide association study of exotic Fragaria germplasm accessions for resistance to Phytophthora crown rot in strawberry

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.31361000.v1
title: Additional file 2 of Genome-wide association study of exotic Fragaria germplasm accessions for resistance to Phytophthora crown rot in strawberry

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.31361003
title: Additional file 3 of Genome-wide association study of exotic Fragaria germplasm accessions for resistance to Phytophthora crown rot in strawberry

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.31361003.v1
title: Additional file 3 of Genome-wide association study of exotic Fragaria germplasm accessions for resistance to Phytophthora crown rot in strawberry

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.31361006.v1
title: Additional file 4 of Genome-wide association study of exotic Fragaria germplasm accessions for resistance to Phytophthora crown rot in strawberry

description: Supplementary Material 4.
DOI: 10.6084/m9.figshare.31361006
title: Additional file 4 of Genome-wide association study of exotic Fragaria germplasm accessions for resistance to Phytophthora crown rot in strawberry

description: Supplementary Material 4.
DOI: 10.5281/zenodo.18676930
title: Dataset: Air Quality Dataset for Shijiazhuang (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Shijiazhuang 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 Shijiazhuang, compare Shijiazhuang 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.18676931
title: Dataset: Air Quality Dataset for Shijiazhuang (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Shijiazhuang 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 Shijiazhuang, compare Shijiazhuang 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: 10.15468/dl.mywr6r
title: Occurrence Download

description: A dataset containing 15 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)"
 ]
 }
 },
 "Year is greater than or equal to 1900",
 "TaxonKey is Salsola ikonnikovii"
 ]
}

 The dataset includes 15 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026475-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18676941
title: Dataset: Air Quality Dataset for Chattogram (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Chattogram 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 Chattogram, compare Chattogram 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: 10.15468/3wb428
title: National Comprehensive Oceanographic Survey (NCOS) - Polychaeta

description: The Database of NCOS-China includes records of specimen collected in the Marine Biological Museum of Chinese Academy of Sciences during the National Comprehensive Oceanographic Survey (NCOS) from 1958 to 1960. The surveyed area covered the coast of China Seas, including the Bohai Sea, the Yellow Sea, the East China Sea and the South China Sea, almost 1200 sampling stations. Most taxa of marine invertebrates were obtained in this survey.
DOI: 10.5281/zenodo.18676940
title: Dataset: Air Quality Dataset for Chattogram (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Chattogram 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 Chattogram, compare Chattogram 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: 10.15468/dl.v4mx7u
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Papua New Guinea",
 "OccurrenceStatus is Present",
 "TaxonKey is Melanotaenia monticola"
 ]
}

 The dataset includes 7 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026476-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18676948
title: Dataset: Air Quality Dataset for Addis Ababa (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Addis Ababa 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 Addis Ababa, compare Addis Ababa 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.18676947
title: Dataset: Air Quality Dataset for Addis Ababa (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Addis Ababa 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 Addis Ababa, compare Addis Ababa 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.18676957
title: Dataset: Air Quality Dataset for Kunming (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Kunming 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 Kunming, compare Kunming 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.18676956
title: Dataset: Air Quality Dataset for Kunming (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Kunming 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 Kunming, compare Kunming 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: 10.34740/kaggle/dsv/14874321
title: SEC EDGAR Filings Index
DOI: 10.5281/zenodo.18676966
title: Dataset: Air Quality Dataset for Zhongshan (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Zhongshan 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 Zhongshan, compare Zhongshan 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.18676965
title: Dataset: Air Quality Dataset for Zhongshan (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Zhongshan 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 Zhongshan, compare Zhongshan 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: 10.15468/eruneg
title: NCOS1959_Mollusca

description: The Database of NCOS1959_Mollusca includes 16008 records of Mollusca specimen collected in the Marine Biological Museum of Chinese Academy of Sciences during the National Comprehensive Oceanographic Survey (NCOS) from 1958 to1960. The surveyed area covered the coast of China Seas, including the Bohai Sea, the Yellow Sea, the East China Sea and the South China Sea, almost 1200 sampling stations. Most taxa of marine invertebrates were obtained in this survey.
DOI: 10.5281/zenodo.18676975
title: Dataset: Air Quality Dataset for Nanning (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Nanning 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 Nanning, compare Nanning 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.18676976
title: Dataset: Air Quality Dataset for Nanning (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Nanning 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 Nanning, compare Nanning 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: 10.6084/m9.figshare.31361041
title: Supplementary Material for: A Rare Case of Concurrent Small Intestinal Neuroendocrine Neoplasms and Acromegaly: A Coincidence or a Clinical Association?

description: Introduction: Neuroendocrine neoplasms of the small intestine (SI-NENs), although rare, are the most prevalent primary neoplasms of this organ. Acromegaly is characterized by excessive secretion of growth hormone (GH), typically due to a pituitary neuroendocrine tumor (PitNET) but, in rare cases, it can result from ectopic secretion of growth hormone-releasing hormone (GHRH) by non-pituitary tumors, particularly neuroendocrine neoplasms (NENs) of the lung or pancreas. We reported a case of concurrent acromegaly and SI-NENs.
Case Presentation: A 68-year-old male with a history of severe restrictive pulmonary syndrome secondary to thoracic kyphoscoliosis was referred for evaluation of iron-deficiency anemia and colorectal polyps. Video capsule endoscopy revealed an ulcerative-vegetative lesion in the distal ileum. CT enterography demonstrated multiple hypervascular lesions in the ileum, consistent with NENs. A 68Ga-DOTA-NOC PET/CT scan identified several foci of abnormal uptake, consistent with multiple intestinal NENs and suspected lymph node metastasis. Increased uptake was also observed in the pituitary gland. The patient was referred for endocrinology evaluation, where an acromegaloid phenotype was noted. Laboratory findings showed elevated levels of insulin-like growth factor 1 (IGF1) (454 ng/mL; reference range (RR) &lt;225), growth hormone (GH) (66 µg/L; RR ≤3) and chromogranin A (625 ng/mL; RR≤ 102). Brain computed tomography (CT) scan suggested a PitNET (the patient could not tolerate MRI). No pathogenic variants were detected in the MEN1 and CDKN1B genes. Given the concurrent diagnoses of acromegaly and SI-NENs, the extent of the disease, the high surgical risk, and the patient's preference, long-acting octreotide therapy was initiated.
Conclusion: The association between SI-NENs and acromegaly is rarely reported in the literature and remains unclear. Proposed mechanisms include the coexistence of independent tumors, the development of NENs in response to excessive IGF1 stimulation, and induction of acromegaly through ectopic GHRH secretion by NENs.
DOI: 10.6084/m9.figshare.31361041.v1
title: Supplementary Material for: A Rare Case of Concurrent Small Intestinal Neuroendocrine Neoplasms and Acromegaly: A Coincidence or a Clinical Association?

description: Introduction: Neuroendocrine neoplasms of the small intestine (SI-NENs), although rare, are the most prevalent primary neoplasms of this organ. Acromegaly is characterized by excessive secretion of growth hormone (GH), typically due to a pituitary neuroendocrine tumor (PitNET) but, in rare cases, it can result from ectopic secretion of growth hormone-releasing hormone (GHRH) by non-pituitary tumors, particularly neuroendocrine neoplasms (NENs) of the lung or pancreas. We reported a case of concurrent acromegaly and SI-NENs.
Case Presentation: A 68-year-old male with a history of severe restrictive pulmonary syndrome secondary to thoracic kyphoscoliosis was referred for evaluation of iron-deficiency anemia and colorectal polyps. Video capsule endoscopy revealed an ulcerative-vegetative lesion in the distal ileum. CT enterography demonstrated multiple hypervascular lesions in the ileum, consistent with NENs. A 68Ga-DOTA-NOC PET/CT scan identified several foci of abnormal uptake, consistent with multiple intestinal NENs and suspected lymph node metastasis. Increased uptake was also observed in the pituitary gland. The patient was referred for endocrinology evaluation, where an acromegaloid phenotype was noted. Laboratory findings showed elevated levels of insulin-like growth factor 1 (IGF1) (454 ng/mL; reference range (RR) &lt;225), growth hormone (GH) (66 µg/L; RR ≤3) and chromogranin A (625 ng/mL; RR≤ 102). Brain computed tomography (CT) scan suggested a PitNET (the patient could not tolerate MRI). No pathogenic variants were detected in the MEN1 and CDKN1B genes. Given the concurrent diagnoses of acromegaly and SI-NENs, the extent of the disease, the high surgical risk, and the patient's preference, long-acting octreotide therapy was initiated.
Conclusion: The association between SI-NENs and acromegaly is rarely reported in the literature and remains unclear. Proposed mechanisms include the coexistence of independent tumors, the development of NENs in response to excessive IGF1 stimulation, and induction of acromegaly through ectopic GHRH secretion by NENs.