cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.spdjw8
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-9.99447 36.42709,-6.34635 36.42709,-6.34635 42.27379,-9.99447 42.27379,-9.99447 36.42709))",
 "TaxonKey is Schoenoplectus erectus (Poir.) Palla ex J.Raynal",
 "Year 1975-2025"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008140-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uakh46
title: Occurrence Download

description: A dataset containing 12233 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((11.74482 -6.644871, 12.77453 -6.644871, 12.77453 -5.202203, 11.74482 -5.202203, 11.74482 -6.644871))"
 ]
}

 The dataset includes 12233 records from 145 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008139-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.atjb4x
title: Occurrence Download

description: A dataset containing 177 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 Nothopegia Blume"
 ]
}

 The dataset includes 177 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008141-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4up94x
title: Occurrence Download

description: A dataset containing 3484460 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plantae)",
 "Country is New Zealand",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 0"
 ]
}

 The dataset includes 3484460 records from 395 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008115-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z6x4m4
title: Occurrence Download

description: A dataset listing the 179 species recorded in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "TaxonKey is one of (Plantae)",
 "GadmGid is one of (COL.2.46_2)",
 "PublishingOrg is one of (bad9baef-be05-4410-83c8-31060a9bafb7)",
 "DatasetKey is one of (Registros de flora y fauna asociados al estudio de diagnóstico para la declaratoria del Área de Recreación Humedal El Trianón – La Heliodora)"
 ]
 }
 ]
}

 The dataset's 179 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008131-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kd3wuc
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Pseudaptinus dorsalis (Brullé, 1834)",
 "Year is greater than or equal to 1950",
 {
 "or" : [
 "GadmGid is USA",
 "GadmGid is CAN",
 "GadmGid is MEX"
 ]
 }
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008142-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.emvp84
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-9.57994 36.53151,-7.02746 36.53151,-7.02746 42.05356,-9.57994 42.05356,-9.57994 36.53151))",
 "TaxonKey is Schoenoplectus litoralis (Schrad.) Palla",
 "Year 1975-2025"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008143-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sgzufy
title: Occurrence Download

description: A dataset containing 196 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Solanum subsylvestre L.B.Sm. & Downs"
 ]
}

 The dataset includes 196 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008144-250515123054153/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/11875788
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.15467662
title: Use of Artificial Intelligences during WebAcademy
DOI: 10.5281/zenodo.15467663
title: Use of Artificial Intelligences during WebAcademy
DOI: 10.6084/m9.figshare.29104646
title: Dataset for "Negative CO2 emissions to mitigate extremes in land hydrological cycle"

description: This dataset comprises the post‑processed data files and analysis scripts used in the study "Negative CO2 emissions to mitigate extremes in land hydrological cycle" by Shin et al. It includes model outputs from a nine‑member CESM2 ensemble simulation under both zero‑ and negative‑emissions scenarios. The full raw dataset is available upon request due to its large size.
DOI: 10.15468/dl.th3b5b
title: Occurrence Download

description: A dataset containing 193 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acropora globiceps (Dana, 1846)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 193 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008145-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.82r93s
title: Occurrence Download

description: A dataset containing 1441 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pinus resinosa Aiton"
 ]
}

 The dataset includes 1441 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008147-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hh88wn
title: Occurrence Download

description: A dataset containing 12988158 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Peronospora erythraeae J.G.Kühn ex Gäum., Anisopteromalus calandrae (Howard, 1881), Corythucha morrilli Osborn & Drake, 1917, Zacosmia maculata (Cresson, 1879), Neaspilota signifera (Coquillett, 1894), Tetropismenus hirtus Loew, 1876, Anthophora curta Provancher, 1895, Diadasia enavata (Cresson, 1872), Synanthedon albicornis (Edwards, 1881), Bicyrtes capnopterus (Handlirsch, 1889), Erynnis zarucco (Lucas, 1857), Automeris zugana Druce, 1886, Caryocolum blandelloides Karsholt, 1981, Eupithecia pygmaeata (Hübner), Coleophora chalcogrammella Zeller, 1839, Hypera diversipunctata (F.P.Schrank, 1798), Melampsorella caryophyllacearum J.Schröt., Leptotrochila cerastiorum (Wallr. ex Fr.) Schüepp, Dacnusa faeroeensis (Roman, 1917), Coleophora striatipennella Nylander & Tengström, Didymella cerastii Gucevič, Peronospora conferta (Unger) Unger, Peronospora paula A.Gustavsson, Peritrechus lundi (Gmelin, 1790), Septoria cerastii Roberge ex Desm., Peronospora tomentosa Fuckel, Caryocolum blandulella (Tutt, 1887), Procas granulicollis Walton, 1848, Sirocalodes mixtus (Mulsant & Rey, 1858), Rattus rattus (Linnaeus, 1758), Mussidia nigrivenella Ragonot, 1888, Psalmocharias plagifer (Schumacher, 1922), Eublemma rufimixta Hampson, 1918, Luciobarbus guiraonis (Steindachner, 1866), Coptodon rendalli (Boulenger, 1897), Anas fulvigula Ridgway, 1874, Mareca strepera (Linnaeus, 1758), Anas undulata C.F.Dubois, 1838, Thalassornis leuconotus Eyton, 1838, Gallinula chloropus galeata (Lichtenstein, 1818), Aythya fuligula (Linnaeus, 1758), Anser indicus (Latham, 1790), Spatula querquedula (Linnaeus, 1758), Phaedon armoraciae (Linnaeus, 1758), Callopistria floridensis Guenée, 1852, Aleuroduplidens carverae Martin, 1999, Euploea phaenareta (Schaller, 1785), Euploea camaralzeman Butler, 1866, Jodis argutaria (Walker, 1866), Asaphes pubescens Kamijo & Takada, 1973)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 12988158 records from 3052 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008114-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13932633
title: ProteinGym

description: Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins to address our most pressing challenges in climate, agriculture and healthcare. Despite an increase in machine learning-based protein modeling methods, assessing their effectiveness is problematic due to the use of distinct, often contrived, experimental datasets and variable performance across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym v1.0, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 40 high-performing models from various subfields (eg., mutation effects, inverse folding) into a unified benchmark. We open source the corresponding codebase, datasets, MSAs, structures, predictions and develop a user-friendly website that facilitates comparisons across all settings.
DOI: 10.5281/zenodo.13932632
title: ProteinGym

description: Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins to address our most pressing challenges in climate, agriculture and healthcare. Despite an increase in machine learning-based protein modeling methods, assessing their effectiveness is problematic due to the use of distinct, often contrived, experimental datasets and variable performance across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym v1.0, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 40 high-performing models from various subfields (eg., mutation effects, inverse folding) into a unified benchmark. We open source the corresponding codebase, datasets, MSAs, structures, predictions and develop a user-friendly website that facilitates comparisons across all settings.
DOI: 10.5281/zenodo.13936340
title: ProteinGym

description: Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins to address our most pressing challenges in climate, agriculture and healthcare. Despite an increase in machine learning-based protein modeling methods, assessing their effectiveness is problematic due to the use of distinct, often contrived, experimental datasets and variable performance across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym v1.0, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 40 high-performing models from various subfields (eg., mutation effects, inverse folding) into a unified benchmark. We open source the corresponding codebase, datasets, MSAs, structures, predictions and develop a user-friendly website that facilitates comparisons across all settings.
DOI: 10.5281/zenodo.14997691
title: ProteinGym

description: Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins to address our most pressing challenges in climate, agriculture and healthcare. Despite an increase in machine learning-based protein modeling methods, assessing their effectiveness is problematic due to the use of distinct, often contrived, experimental datasets and variable performance across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym v1.0, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 40 high-performing models from various subfields (eg., mutation effects, inverse folding) into a unified benchmark. We open source the corresponding codebase, datasets, MSAs, structures, predictions and develop a user-friendly website that facilitates comparisons across all settings.
DOI: 10.5281/zenodo.15293562
title: ProteinGym

description: Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins to address our most pressing challenges in climate, agriculture and healthcare. Despite an increase in machine learning-based protein modeling methods, assessing their effectiveness is problematic due to the use of distinct, often contrived, experimental datasets and variable performance across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym v1.0, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 40 high-performing models from various subfields (eg., mutation effects, inverse folding) into a unified benchmark. We open source the corresponding codebase, datasets, MSAs, structures, predictions and develop a user-friendly website that facilitates comparisons across all settings.
DOI: 10.15468/dl.txcvrw
title: Occurrence Download

description: A dataset containing 861521 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2010",
 "Month is May",
 "Day is greater than or equal to 22",
 "Day is less than or equal to 28"
 ]
}

 The dataset includes 861521 records from 383 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000633-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nzadc4
title: Occurrence Download

description: A dataset containing 7929 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Monaco",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 7929 records from 210 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013147-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.va84jv
title: Occurrence Download

description: A dataset containing 1128 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eriphia gonagra"
 ]
}

 The dataset includes 1128 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040329-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j5j3sq
title: Occurrence Download

description: A dataset containing 647 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Weinmannia trichosperma",
 "Country is Chile",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 647 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040330-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wzsp43
title: Occurrence Download

description: A dataset containing 276 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Darwinia diosmoides",
 "HasCoordinate is true"
 ]
}

 The dataset includes 276 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040331-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hejz8z
title: Occurrence Download

description: A dataset containing 862 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Glyptocephalus zachirus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1970",
 "Year is less than or equal to 1979"
 ]
}

 The dataset includes 862 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040332-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.33r6ps
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudaphycus malinus"
 ]
}

 The dataset includes 17 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040335-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.34ecz3
title: Occurrence Download

description: A dataset containing 22225 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Glyptocephalus zachirus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 1989"
 ]
}

 The dataset includes 22225 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040334-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mrup3p
title: Occurrence Download

description: A dataset containing 1499341 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dactylis glomerata",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1499341 records from 4784 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040309-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k38v4y
title: Occurrence Download

description: A dataset containing 4492 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((33.2554 35.06913,33.48344 35.06913,33.48344 35.28011,33.2554 35.28011,33.2554 35.06913))",
 "TaxonKey is Passeriformes"
 ]
}

 The dataset includes 4492 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014829-250525065834625/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.29209525
title: Time series quantile regression kink with an unknown threshold

description: This article studies a time series quantile regression kink model with an unknown threshold over certain quantile levels in the distribution. We propose to estimate the threshold parameter and regression parameters using a two-stage method. We also propose a weighted CUSUM test for threshold effect at both a given quantile level and multiple quantile levels based on the subgradient of the quantile loss function. In addition, we consider a likelihood-ratio-type test for the presence of a common threshold value across different quantile levels. Excellent finite sample performance of the proposed method is demonstrated by simulation studies. We further apply our proposed method to the S & P500 index data to explore the possible nonlinearity and heteroscedasticity of the return autocorrelations in the S & P500 index.