cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.wg9c77
title: Occurrence Download

description: A dataset containing 178971 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Geometry POLYGON((-99.65102 18.87547,-98.86 18.87547,-98.86 19.67529,-99.65102 19.67529,-99.65102 18.87547))",
 "Geometry POLYGON((-99.57686 19.67529,-99.57549 19.65589,-99.63454 19.63132,-99.61394 19.61579,-99.63728 19.57957,-99.60982 19.54204,-99.65102 19.52651,-99.60432 19.50839,-99.57411 19.44624,-99.47112 19.43459,-99.50819 19.42293,-99.46425 19.41775,-99.49309 19.36594,-99.43816 19.27133,-99.38322 19.25578,-99.40932 19.19613,-99.38185 19.20391,-99.35988 19.17148,-99.39696 19.10402,-99.37086 19.00537,-99.38872 18.96511,-99.42442 18.95732,-99.43266 18.88587,-99.36812 18.87547,-99.35851 18.92225,-99.31731 18.95862,-99.29396 18.92095,-99.261 18.93654,-99.25826 18.98589,-99.18684 18.9742,-99.15388 18.88717,-99.1017 18.88587,-99.07149 18.91445,-99.09209 18.95212,-99.06462 18.92225,-99.02205 18.94433,-98.97673 18.92485,-99.01106 18.98589,-98.92043 19.02225,-98.96025 19.07157,-98.90257 19.1131,-98.95888 19.09494,-98.99458 19.14684,-98.89433 19.13257,-98.86 19.14943,-98.86549 19.18445,-98.99184 19.21428,-98.99046 19.1624,-99.12779 19.17278,-99.14015 19.14424,-99.15251 19.15981,-99.14839 19.1144,-99.17586 19.10532,-99.19096 19.12348,-99.17174 19.18056,-99.19096 19.18964,-99.17036 19.20132,-99.22942 19.18056,-99.20882 19.20002,-99.24864 19.23892,-99.18959 19.23503,-99.17586 19.26874,-99.24178 19.27133,-99.26924 19.32707,-99.29533 19.31929,-99.28435 19.34909,-99.31456 19.34909,-99.25139 19.42811,-99.29671 19.43977,-99.27199 19.46955,-99.29671 19.49156,-99.23903 19.54334,-99.27199 19.55369,-99.261 19.58216,-99.33104 19.58863,-99.33379 19.61579,-99.38048 19.62485,-99.38048 19.65072,-99.43678 19.60415,-99.41756 19.64037,-99.4464 19.65718,-99.4615 19.63649,-99.47524 19.65848,-99.50133 19.64554,-99.49446 19.67012,-99.52467 19.65589,-99.57686 19.67529))"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 178971 records from 282 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002586-250221090833381/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.27328358
title: <i>In silico</i> aquatic toxicity prediction of chemicals toward <i>Daphnia magna</i> and fathead minnow using Monte Carlo approaches

description: The fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. <i>Daphnia magna</i> and <i>Fathead minnow</i> are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants. Herein, global quantitative structure–toxicity relationship (QSTR) models have been developed to predict the toxicity (pEC(LC)50) of a large dataset comprising 2106 chemicals toward <i>Daphnia magna</i> and <i>Fathead minnow</i>. The optimal descriptor of correlation weights (DCWs) is calculated using the notation of simplified molecular input line entry system (SMILES) and is used to construct QSTR models. Three target functions, TF<sub>1</sub>, TF<sub>2</sub>, and TF<sub>3</sub> are utilized to generate 12 QSTR models from four splits, and their statistical characteristics are also compared. The designed QSTR models are validated using both internal and external validation criteria and are found to be reliable, robust, and excellently predictive. Among the models, those generated using the TF<sub>3</sub> demonstrate the best statistical quality with <i>R</i><sup>2</sup> values ranging from 0.9467 to 0.9607, <i>Q</i><sup>2</sup> values ranging from 0.9462 to 0.9603 and RMSE values ranging from 0.3764 to 0.4413 for the validation set. The applicability domain and the mechanistic interpretations of generated models were also discussed.
DOI: 10.6084/m9.figshare.27328358.v1
title: <i>In silico</i> aquatic toxicity prediction of chemicals toward <i>Daphnia magna</i> and fathead minnow using Monte Carlo approaches

description: The fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. <i>Daphnia magna</i> and <i>Fathead minnow</i> are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants. Herein, global quantitative structure–toxicity relationship (QSTR) models have been developed to predict the toxicity (pEC(LC)50) of a large dataset comprising 2106 chemicals toward <i>Daphnia magna</i> and <i>Fathead minnow</i>. The optimal descriptor of correlation weights (DCWs) is calculated using the notation of simplified molecular input line entry system (SMILES) and is used to construct QSTR models. Three target functions, TF<sub>1</sub>, TF<sub>2</sub>, and TF<sub>3</sub> are utilized to generate 12 QSTR models from four splits, and their statistical characteristics are also compared. The designed QSTR models are validated using both internal and external validation criteria and are found to be reliable, robust, and excellently predictive. Among the models, those generated using the TF<sub>3</sub> demonstrate the best statistical quality with <i>R</i><sup>2</sup> values ranging from 0.9467 to 0.9607, <i>Q</i><sup>2</sup> values ranging from 0.9462 to 0.9603 and RMSE values ranging from 0.3764 to 0.4413 for the validation set. The applicability domain and the mechanistic interpretations of generated models were also discussed.
DOI: 10.6084/m9.figshare.27636421
title: Disposable electronic cigarettes – chemical composition and health effects of their use. A systematic review

description: Despite the rising popularity of disposable e-cigarettes, little is known about their chemical characteristics, or their impact on users’ health. This work attempts to summarize current knowledge about chemical composition and known health effects of disposable e-cigarettes. The literature search was performed in February and March 2024 in Pub Med and Science Direct databases (no time range) by the terms ‘disposable electronic cigarette’, ‘disposable e-cigarette’, ‘disposable e-cigs’, ‘cig-a-like e-cigarette’, ‘cig-a-like electronic cigarette’. Disposable e-cigarettes contain: nicotine, humectants (propylene glycol, glycerin), flavoring agents (diacetyl, acetoin, triacetin, p-menthone, triethyl citrate, ethyl maltol, 3-hexen-1-ol, methyl anthranilate, α-terpineol, perillartine, benzyl alcohol, vanillin, melonal, methyl dihydrojasmonate, and γ-decalactone), cooling agents (WS-3, WS-23, menthol), carbonyl compounds (acetaldehyde, formaldehyde, propionaldehyde, acetone, acrolein) volatile organic compounds (VOCs) (benzene, ethanol, methanol, styrene, acetylpirazine and 2,3,5-trimethylpyrazine), metals and inorganic compounds (chromium, nickel, manganese, lead, aluminum, and zinc) and reactive oxygen species. Furthermore, there was some evidence of nicotine dependence, risk of cancer and adverse respiratory effects of using disposable e-cigarettes. Despite the fact that disposable e-cigarettes contain significantly less toxins compared to combustible cigarettes, they include compounds that are absent in such products that may provide health risk in prolonged usage. In addition, there is a limited number of data on the health effect of disposable e-cigarettes, especially in long time period, for never-smokers. Therefore, due to growing popularity of disposable e-cigarettes among young people, who choose them when initiating nicotine use, further research on their long-term impact of on the users’ health is necessary.
DOI: 10.6084/m9.figshare.27636421.v1
title: Disposable electronic cigarettes – chemical composition and health effects of their use. A systematic review

description: Despite the rising popularity of disposable e-cigarettes, little is known about their chemical characteristics, or their impact on users’ health. This work attempts to summarize current knowledge about chemical composition and known health effects of disposable e-cigarettes. The literature search was performed in February and March 2024 in Pub Med and Science Direct databases (no time range) by the terms ‘disposable electronic cigarette’, ‘disposable e-cigarette’, ‘disposable e-cigs’, ‘cig-a-like e-cigarette’, ‘cig-a-like electronic cigarette’. Disposable e-cigarettes contain: nicotine, humectants (propylene glycol, glycerin), flavoring agents (diacetyl, acetoin, triacetin, p-menthone, triethyl citrate, ethyl maltol, 3-hexen-1-ol, methyl anthranilate, α-terpineol, perillartine, benzyl alcohol, vanillin, melonal, methyl dihydrojasmonate, and γ-decalactone), cooling agents (WS-3, WS-23, menthol), carbonyl compounds (acetaldehyde, formaldehyde, propionaldehyde, acetone, acrolein) volatile organic compounds (VOCs) (benzene, ethanol, methanol, styrene, acetylpirazine and 2,3,5-trimethylpyrazine), metals and inorganic compounds (chromium, nickel, manganese, lead, aluminum, and zinc) and reactive oxygen species. Furthermore, there was some evidence of nicotine dependence, risk of cancer and adverse respiratory effects of using disposable e-cigarettes. Despite the fact that disposable e-cigarettes contain significantly less toxins compared to combustible cigarettes, they include compounds that are absent in such products that may provide health risk in prolonged usage. In addition, there is a limited number of data on the health effect of disposable e-cigarettes, especially in long time period, for never-smokers. Therefore, due to growing popularity of disposable e-cigarettes among young people, who choose them when initiating nicotine use, further research on their long-term impact of on the users’ health is necessary.
DOI: 10.15468/dl.fu49w2
title: Occurrence Download

description: A dataset containing 1686 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Avena ludoviciana Durieu"
 ]
}

 The dataset includes 1686 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002589-250221090833381/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.25911682
title: Competitive ability underpins the effect of spatial aggregation on plant performance

description: Data and R codes that support the results of the manuscript "A phylogenetic signal in the effect of spatial aggregation on plant performance" published from <i>Ecology</i>, by Naoto Shinohara and Haruna Ohsaki.
DOI: 10.15468/dl.hfxvuf
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations, INSDC Sequences)",
 "GadmGid is CHN.27_1",
 "Month is one of (January, February, March, April, May, June, July, August, September, October, November, December)",
 "TaxonKey is Egretta garzetta (Linnaeus, 1766)",
 "Year 2014-2014"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002590-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25959/jnm2-dt34
title: Material from PhD thesis: Appendix A-1 List of samples from live Tasmanian devils and spotted-tail quolls.

description: This dataset contains supplemental material as part of a PhD thesis: Development of Novel Diagnostic Tools and Antigen Discovery for Devil Facial Tumour Disease. The material includes a list of samples collected from wild and live Tasmanian devils (Sarcophilus harrisii) and spotted-tail quolls (Dasyurus maculatus) which were taken as part of an annual monitoring field trapping trip in 2023 at Narawntapu National Park. The list also contains name, sex, net weight, estimated birth year, microchip number, and whether there was clinical signs of devil facial tumour disease.
DOI: 10.15468/dl.jnv8wv
title: Occurrence Download

description: A dataset containing 102 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lycalopex culpaeus reissii (Hilzheimer, 1906)"
 ]
}

 The dataset includes 102 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002591-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uwymxs
title: Occurrence Download

description: A dataset containing 636 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Sigmodon alstoni (Thomas, 1881)"
 ]
}

 The dataset includes 636 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002592-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rxwrqc
title: Occurrence Download

description: A dataset containing 281821 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cerastium glomeratum Thuill.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 281821 records from 2247 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002588-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0214982.10fu
title: SH0214982.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0214983.10fu
title: SH0214983.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0214984.10fu
title: SH0214984.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0214985.10fu
title: SH0214985.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0214986.10fu
title: SH0214986.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0214987.10fu
title: SH0214987.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0214988.10fu
title: SH0214988.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0214989.10fu
title: SH0214989.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.