cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.3hk6eu
title: Occurrence Download

description: A dataset containing 149 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Helichrysum keilii Moeser"
 ]
}

 The dataset includes 149 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0173375-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.skxkdt
title: Occurrence Download

description: A dataset containing 2292 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hemitriccus flammulatus Berlepsch, 1901"
 ]
}

 The dataset includes 2292 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0377440-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mgw9en
title: Occurrence Download

description: A dataset containing 82 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pycnocycla ledermannii H.Wolff"
 ]
}

 The dataset includes 82 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0099000-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9tr5pz
title: Occurrence Download

description: A dataset containing 817 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Diomedea sanfordi Murphy, 1917"
 ]
}

 The dataset includes 817 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0382808-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9wp9pp
title: Occurrence Download

description: A dataset containing 2158 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sagotia Baill."
 ]
}

 The dataset includes 2158 records from 89 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0203631-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.szc68n
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Rhipidura opistherythra",
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "Year 2011-2021",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 64 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083243-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rdtebd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Rhipidura hoedti",
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "Year 2011-2021",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083207-230530130749713/datasets/export for details.
DOI: 10.15468/dl.nxx87j
title: Occurrence Download

description: A dataset containing 9996 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Xyleborinus saxesenii (Ratzeburg, 1837) Ratzeburg, 1837, Xyleborus biographus LeConte, 1868)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 9996 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0331140-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.azudya
title: Occurrence Download

description: A dataset containing 488 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Searsia pallens (Eckl. & Zeyh.) Moffett"
 ]
}

 The dataset includes 488 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0178344-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7rnff9
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Rhipidura semirubra",
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "Year 2011-2021",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 13 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083262-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.azjt6y
title: Occurrence Download

description: A dataset containing 547740 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Harmonia axyridis (Pallas, 1773)",
 "Year 1900-2024"
 ]
}

 The dataset includes 547740 records from 1273 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0204767-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n9jybc
title: Occurrence Download

description: A dataset containing 486 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "MediaType is Image",
 "StateProvince is Indiana",
 "TaxonKey is Ficaria verna Huds."
 ]
}

 The dataset includes 486 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000289-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7944/00rk-3q93
title: Yukon River Inconnu biometric data

description: This is a tabular data set containing age, sex, and length measurements of Inconnu sampled from the Yukon River and tributaries in Alaska between 1990 and 2018.
DOI: 10.6084/m9.figshare.24969993.v1
title: PTM Splicing Analysis: Supplementary Data

description: This contains all raw data and analysis for the manuscript titled "A systematic analysis of the effects of splicing on the diversity of post-translational modifications in protein isoforms<b>"</b>. Folders contain data on PTMs projected onto alternative isoforms using our computational pipeline, as well as data generated from the analysis of this information. All data necessary to replicate the analysis and figures in this work can be found here, unless it can be downloaded elsewhere from an external database (TCGA, PhosphoSitePlus, etc.).
DOI: 10.15468/dl.pr8ppk
title: Occurrence Download

description: A dataset containing 374 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is USGS PWRC - Bird Phenology Program",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Selasphorus rufus (J.F.Gmelin, 1788)"
 ]
}

 The dataset includes 374 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000291-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25824/redu/c2ila2
title: Apologia da história em tempo digitais: redes americanistas nos Congressos Internacionais de História da América (1922-1937)

description: Metadados da Tese de Doutorado Intitulada: Apologia da História em tempos digitais: redes americanistas nos Congressos Internacionais de História da América (1922-1937). Os arquivos correspondem às bases de dados construídas a partir de acervos históricos digitalizados, como: Instituto Histórico e Geográfico Brasileiro Academia Nacional de História da Argentina Biblioteca Brasiliana da USP Biblioteca de Obras Raras da UFRJ Instituto Ibero-Americano de Berlim Hemeroteca Digital da Biblioteca Nacional Ferramentas e códigos desenvolvidos na tese estão disponíveis em: https://github.com/alessonrota/Ferramentas-tese-de-doutorado Resumo: A tese analisou a atuação da rede intelectual estabelecida a partir do I e II Congresso Internacional de História da América, realizados entre 1922 e 1937. A rede foi responsável pela produção de ideias americanistas com o objetivo de escrever a história do continente. Como resultado das conexões estabelecidas, foram realizadas conferências, publicações em revistas e traduções de livros que dialogaram internacionalmente com as configurações disciplinares da escrita da história e com as avaliações da política internacional. A política, entendida como produção cultural, foi uma das principais marcas da rede de intelectuais americanistas, que se engajou em projetos de relações exteriores, repercutindo diretamente nos países envolvidos e alterando a dinâmica dos atores participantes. O confronto entre o nacional e o internacional ocorreu nas políticas internas de cada país, em um cenário de crescente autoritarismo, que coexistia com dispositivos liberais e de soberania evocados nas Conferências Internacionais Americanas. O percurso analítico sobre o papel da intelectualidade na construção de uma transnacionalidade intercontinental mediante a escrita da história foi acompanhado por reflexões sobre o uso de tecnologias digitais na pesquisa histórica. Linguagens computacionais foram mobilizadas para explorar acervos digitais, desde mapeamentos simples até a construção de sentidos complexos, utilizando aprendizado de máquina. Esse caminho introduz uma nova prática de história digital. Ao comparar a configuração da disciplina da história, que adquiriu importantes características nas primeiras décadas do século XX, com o uso de linguagens computacionais para a escrita da história, a tese conclui que as transformações digitais ainda não são capazes de modificar a especificidade da escrita histórica.
DOI: 10.5281/zenodo.10157605
title: APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

description: Motivation: Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.
Results: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.
Availability and Implementation:
 APNet's R, Python scripts and Cytoscape methodologies are available at 
https://github.com/BiodataAnalysisGroup/APNet
DOI: 10.5281/zenodo.10400255
title: APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

description: Motivation: Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.
Results: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.
Availability and Implementation:
 APNet's R, Python scripts and Cytoscape methodologies are available at 
https://github.com/BiodataAnalysisGroup/APNet
DOI: 10.5281/zenodo.10409998
title: APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

description: Motivation: Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.
Results: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.
Availability and Implementation:
 APNet's R, Python scripts and Cytoscape methodologies are available at 
https://github.com/BiodataAnalysisGroup/APNet
DOI: 10.5281/zenodo.10438830
title: APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

description: Motivation: Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.
Results: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.
Availability and Implementation:
 APNet's R, Python scripts and Cytoscape methodologies are available at 
https://github.com/BiodataAnalysisGroup/APNet
DOI: 10.5281/zenodo.10157604
title: APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

description: Motivation: Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.
Results: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.
Availability and Implementation:
 APNet's R, Python scripts and Cytoscape methodologies are available at 
https://github.com/BiodataAnalysisGroup/APNet
DOI: 10.5281/zenodo.13143367
title: APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

description: Motivation: Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.
Results: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.
Availability and Implementation:
 APNet's R, Python scripts and Cytoscape methodologies are available at 
https://github.com/BiodataAnalysisGroup/APNet
DOI: 10.5281/zenodo.14680520
title: APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

description: Motivation: Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.
Results: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.
Availability and Implementation:
 APNet's R, Python scripts and Cytoscape methodologies are available at 
https://github.com/BiodataAnalysisGroup/APNet
DOI: 10.15468/dl.h4agbd
title: Occurrence Download

description: A dataset containing 1197 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "MediaType is Image",
 "StateProvince is INdiana",
 "TaxonKey is Alliaria petiolata (M.Bieb.) Cavara & Grande"
 ]
}

 The dataset includes 1197 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000294-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y5g42n
title: Occurrence Download

description: A dataset containing 49700 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cyprinus carpio Linnaeus, 1758",
 "Country is Australia",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 49700 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000295-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uhyhe5
title: Occurrence Download

description: A dataset containing 257284 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Anas undulata C.F.Dubois, 1838)",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 257284 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000290-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.