cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/lkkw3o
title: Extracted Data from: Health Professions Training Programs

description: This submission includes publicly available data extracted in its original form.
&lt;br&gt;
If you have questions about the underlying data stored here, please contact HRSA Contact Center (Phone: 877-464-4772 (TTY: 877-897-9910)). If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu.”
&lt;br&gt;
&lt;br&gt;
"This dashboard contains aggregated performance data for HRSA-awarded health professions training grants from Academic Year 2012-2013 to present. The user may view demographic summaries of health workforce trainees across programs, view the locations and types of clinical training sites, and the types of curricula developed for trainees, faculty training, and continuing education."
[Quote from: https://data.hrsa.gov/data/download?data=HSCD#HSCD]
description: The terms and conditions were not captured from the source dataset, which was publicly available to download. If you have additional information about terms of use, please contact us.
DOI: 10.48472/deposita/gp3pdk
title: Diagnóstico sobre o uso de repositórios digitais na região Norte

description: Este conjunto de dados resulta de uma pesquisa conduzida pelo Núcleo de Dados de Pesquisa (NDP), sob a coordenação da Rede Brasileira de Repositórios Digitais (RBRD), sobre a utilização de repositórios digitais para publicações e dados científicos. O estudo analisou a realidade das instituições de ensino e pesquisa quanto ao uso dessas plataformas, visando estimular a implementação de repositórios de dados de pesquisa nas instituições da região Norte.
description: O Núcleo de Dados de Pesquisa realizou este estudo junto às instituições que fazem parte da Rede Brasileira de Repositórios, organizada por regiões. Dessa forma, os conjuntos de dados são disponibilizados conforme a região correspondente, sendo este referente à Região Norte.
DOI: 10.15468/dl.g4pmm8
title: Occurrence Download

description: A dataset containing 8943 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Macroramphosus scolopax"
 ]
}

 The dataset includes 8943 records from 172 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000966-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4tthva
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Spelaeornis kinneari",
 "Country is Chinese Taipei",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000967-250827131500795/datasets/export for details.
DOI: 10.15468/dl.qneupc
title: Occurrence Download

description: A dataset containing 1056584 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PhylumKey is one of (Arthropoda)",
 "OccurrenceStatus is Present",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON ((-114.607 48.99865, -96.60328 48.99865, -96.60328 54.12891, -114.607 54.12891, -114.607 48.99865))",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 1056584 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000935-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.as542y
title: Occurrence Download

description: A dataset containing 83588 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coscoroba coscoroba"
 ]
}

 The dataset includes 83588 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000928-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16968613
title: Leopard-EM paper review data

description: Subset of data in the Leopard-EM paper used to generate figures. Details of file contents are listed below.

Images

xenon_252_000_0.0_DWS.mrc - Image for the 40S and 60S searches

150_Mar12_12.28.45_165_0.mrc - Image for the 20S core proteasome search

Match template results files

xenon_252_000_0_output*_60S.mrc - Results for the 60S search on the xenon_252 image. Exact contents of these results (based on name) are discussed on the online documentation. 

xenon_252_000_0_output*_40S.mrc - Results for the 40S 2DTM search on the xenon_252 image. Exact contents of these results (based on name) are discussed on the online documentation.

150_Mar12_12.28.45_165_0_output*.mrc - Results for the 20S proteasome 2DTM search on the 150_Mar12 image. Exact contents of these results (based on name) are discussed on the online documentation.

Refine template results CSVs

xenon_252_000_0.0_DWS_refined_results_60S.csv - Refined results (post-match template, post-refine template) CSV per particle for the xenon_252 image results.

xenon_252_000_0.0_DWS_refined_results_40S.csv - Refine results (post-match template, post-refine template) CSV per particle for the xenon_252 image results.

Simulated maps

60S_map_px0.936_bscale0.5.mrc - Large ribosomal subunit map used for the 2DTM analysis

SSU-body_map_px0.936_bscale0.5.mrc - Small ribosomal subunit (body only) map used for the 2DTM analysis

1ryp_map_px1.059_k3dqe.mrc - Core proteasome map used for the 2DTM analysis

Configuration files for these data

xenon_252_000_0.0_DWS_config_60S.yaml - Match template configuration file for 60S search

xenon_252_000_0.0_DWS_config_40S.yaml - Match template configuration file for 20S search

150_Mar12_12.28.45_165_0_match_template_config.yaml - Match template configuration file for core proteasome 20S search
DOI: 10.5281/zenodo.16968612
title: Leopard-EM paper review data

description: Subset of data in the Leopard-EM paper used to generate figures. Details of file contents are listed below.

Images

xenon_252_000_0.0_DWS.mrc - Image for the 40S and 60S searches

150_Mar12_12.28.45_165_0.mrc - Image for the 20S core proteasome search

Match template results files

xenon_252_000_0_output*_60S.mrc - Results for the 60S search on the xenon_252 image. Exact contents of these results (based on name) are discussed on the online documentation. 

xenon_252_000_0_output*_40S.mrc - Results for the 40S 2DTM search on the xenon_252 image. Exact contents of these results (based on name) are discussed on the online documentation.

150_Mar12_12.28.45_165_0_output*.mrc - Results for the 20S proteasome 2DTM search on the 150_Mar12 image. Exact contents of these results (based on name) are discussed on the online documentation.

Refine template results CSVs

xenon_252_000_0.0_DWS_refined_results_60S.csv - Refined results (post-match template, post-refine template) CSV per particle for the xenon_252 image results.

xenon_252_000_0.0_DWS_refined_results_40S.csv - Refine results (post-match template, post-refine template) CSV per particle for the xenon_252 image results.

Simulated maps

60S_map_px0.936_bscale0.5.mrc - Large ribosomal subunit map used for the 2DTM analysis

SSU-body_map_px0.936_bscale0.5.mrc - Small ribosomal subunit (body only) map used for the 2DTM analysis

1ryp_map_px1.059_k3dqe.mrc - Core proteasome map used for the 2DTM analysis

Configuration files for these data

xenon_252_000_0.0_DWS_config_60S.yaml - Match template configuration file for 60S search

xenon_252_000_0.0_DWS_config_40S.yaml - Match template configuration file for 20S search

150_Mar12_12.28.45_165_0_match_template_config.yaml - Match template configuration file for core proteasome 20S search
DOI: 10.5281/zenodo.16969429
title: THE CREDIT-MODULE SYSTEM FOR STUDENTS

description:  The credit-module system has become a global standard in higher education, promoting student-centered learning, flexibility, and independence. This paper examines strategies to enhance students' competencies for effectively working within CMS frameworks. Through a comparative analysis of international practices and an exploration of innovative pedagogies, the study highlights how CMS can transform education by equipping students with critical skills such as time management, independent learning, and problem-solving.
DOI: 10.5281/zenodo.16969430
title: THE CREDIT-MODULE SYSTEM FOR STUDENTS

description:  The credit-module system has become a global standard in higher education, promoting student-centered learning, flexibility, and independence. This paper examines strategies to enhance students' competencies for effectively working within CMS frameworks. Through a comparative analysis of international practices and an exploration of innovative pedagogies, the study highlights how CMS can transform education by equipping students with critical skills such as time management, independent learning, and problem-solving.
DOI: 10.5281/zenodo.15797111
title: Soluble endoglin reflects endothelial dysfunction in myocardial infarction patients: a retrospective observational study

description: source data and PDF of publication

Abstract

Acute manifestations of ischemic heart disease are among the most serious and fatal consequences of atherosclerotic processes. In this study, we hypothesized that a soluble proprotein convertase subtilisin/kexin type 9 (PCSK9), soluble bone morphogenetic protein 4 (BMP-4), soluble E-selectin (sE-selectin), soluble endoglin (sENG) and soluble endocan (Endocan) would differ from healthy controls in myocardial infarction (MI) patients admitted to the hospital without any previous history of cardiovascular disease and with no cardioprotective drugs taken before admission. The study was conducted using a cross-sectional design. We analyzed data from 79 patients (mean age 54.1 ± 8.9, 18% of women) admitted for the first manifestation of MI and with no history of cardioprotective treatment use before the event. As a control group, we analyzed 17 age-matched healthy volunteers (mean age 51.5 ± 8.6, 47% of women). In addition to routinely obtaining clinical and laboratory data, we analyzed plasma concentrations of the aforementioned biomarkers using ELISA and Luminex analyses. Patients with MI did not differ from healthy controls in total cholesterol, LDL, non-HDL, and triglyceride levels. PCSK9, BMP-4, and sE-selectin levels did not differ significantly between the MI and the control group. Patients with MI had significantly higher sENG and Endocan levels than the control group. In addition, levels of sENG were significantly higher in patients with higher body mass index (BMI) and in smokers. We demonstrated that sENG could serve as a biomarker reflecting endothelial dysfunction in MI patients without prior treatment for cardiovascular risk factors.
DOI: 10.5281/zenodo.15797110
title: Soluble endoglin reflects endothelial dysfunction in myocardial infarction patients: a retrospective observational study

description: source data and PDF of publication

Abstract

Acute manifestations of ischemic heart disease are among the most serious and fatal consequences of atherosclerotic processes. In this study, we hypothesized that a soluble proprotein convertase subtilisin/kexin type 9 (PCSK9), soluble bone morphogenetic protein 4 (BMP-4), soluble E-selectin (sE-selectin), soluble endoglin (sENG) and soluble endocan (Endocan) would differ from healthy controls in myocardial infarction (MI) patients admitted to the hospital without any previous history of cardiovascular disease and with no cardioprotective drugs taken before admission. The study was conducted using a cross-sectional design. We analyzed data from 79 patients (mean age 54.1 ± 8.9, 18% of women) admitted for the first manifestation of MI and with no history of cardioprotective treatment use before the event. As a control group, we analyzed 17 age-matched healthy volunteers (mean age 51.5 ± 8.6, 47% of women). In addition to routinely obtaining clinical and laboratory data, we analyzed plasma concentrations of the aforementioned biomarkers using ELISA and Luminex analyses. Patients with MI did not differ from healthy controls in total cholesterol, LDL, non-HDL, and triglyceride levels. PCSK9, BMP-4, and sE-selectin levels did not differ significantly between the MI and the control group. Patients with MI had significantly higher sENG and Endocan levels than the control group. In addition, levels of sENG were significantly higher in patients with higher body mass index (BMI) and in smokers. We demonstrated that sENG could serve as a biomarker reflecting endothelial dysfunction in MI patients without prior treatment for cardiovascular risk factors.
DOI: 10.15468/dl.44rumg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Leptachirus alleni)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000968-250827131500795/datasets/export for details.
DOI: 10.15468/dl.hh9ps8
title: Occurrence Download

description: A dataset containing 83588 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coscoroba coscoroba"
 ]
}

 The dataset includes 83588 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000931-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ycgvs2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Spelaeornis reptatus",
 "Country is Chinese Taipei",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000971-250827131500795/datasets/export for details.
DOI: 10.15468/dl.upq9zk
title: Occurrence Download

description: A dataset containing 55603 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Salix caprea caprea"
 ]
}

 The dataset includes 55603 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000910-250827131500795/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.29876786
title: <b>Dataset for: Redefining Multi-Target Weather Forecasting with a novel Deep Learning model: Hierarchical Temporal Convolutional Long Short-Term Memory with Attention (HTC-LSTM-Attn) in Bangladesh</b>

description: <br>The climate data set was compiled monthly for Bangladesh, from January 1961 to December 2022; it was generated from the Central Climate Information Management System of the BARC. The initial data consisted of measurements from 35 weather stations, covering a multitude of weather parameters that include solar radiation, <b>potential evaporation (PE), </b><b>evapotranspiration</b><b> (</b><b>ETo)</b>, <b>maximum temperature, rainfall, humidity, wind speed, cloud cover, and sunshine duration</b>. In the scope of the research titled "Redefining Multi-Target Weather Forecasting with a Novel Deep Learning Model: HTC-LSTM-Attn in Bangladesh," the dataset underwent several pre-processing steps to ensure its quality and suitability for deep learning-based forecasting. Some of these were:<b>Data consolidation</b>: The merging of multiple CSV files (solar radiation, PET, sunshine, wind speed, cloud coverage, humidity, rainfall, and temperature) into one dataset keyed by station code, year, and month.<b>Station filtering</b>: Eleven stations were excluded due to incomplete or unreliable records, retaining 24 stations representing various climate regions.<b>Outlier treatment</b>: Anomalies are detected by the Interquartile range (IQR) method, and such values are replaced with the closest nearest valid value for the same station.<b>Missing value imputation</b>: For gap-filling, k-nearest neighbours (k=5) are applied.<b>Feature engineering</b>: Added seasonal indicators, lag features, and rolling averages to account for temporal dependencies.<b>Feature Selection</b>: By removing highly correlated variables (Pearson's r &gt; 0.9), redundancy was reduced.<b>Normalization</b>: Normalize numerical columns between 0 and 1 scaling using statistics calculated over training sets.<b>Usage:</b><br>The processed dataset is optimized for deep learning–based weather forecasting models such as HTC-LSTM-Attn, but can also be used for climate trend analysis, seasonal prediction, and meteorological research.
DOI: 10.6084/m9.figshare.29876786.v1
title: <b>Dataset for: Redefining Multi-Target Weather Forecasting with a novel Deep Learning model: Hierarchical Temporal Convolutional Long Short-Term Memory with Attention (HTC-LSTM-Attn) in Bangladesh</b>

description: <br>The climate data set was compiled monthly for Bangladesh, from January 1961 to December 2022; it was generated from the Central Climate Information Management System of the BARC. The initial data consisted of measurements from 35 weather stations, covering a multitude of weather parameters that include solar radiation, <b>potential evaporation (PE), </b><b>evapotranspiration</b><b> (</b><b>ETo)</b>, <b>maximum temperature, rainfall, humidity, wind speed, cloud cover, and sunshine duration</b>. In the scope of the research titled "Redefining Multi-Target Weather Forecasting with a Novel Deep Learning Model: HTC-LSTM-Attn in Bangladesh," the dataset underwent several pre-processing steps to ensure its quality and suitability for deep learning-based forecasting. Some of these were:<b>Data consolidation</b>: The merging of multiple CSV files (solar radiation, PET, sunshine, wind speed, cloud coverage, humidity, rainfall, and temperature) into one dataset keyed by station code, year, and month.<b>Station filtering</b>: Eleven stations were excluded due to incomplete or unreliable records, retaining 24 stations representing various climate regions.<b>Outlier treatment</b>: Anomalies are detected by the Interquartile range (IQR) method, and such values are replaced with the closest nearest valid value for the same station.<b>Missing value imputation</b>: For gap-filling, k-nearest neighbours (k=5) are applied.<b>Feature engineering</b>: Added seasonal indicators, lag features, and rolling averages to account for temporal dependencies.<b>Feature Selection</b>: By removing highly correlated variables (Pearson's r &gt; 0.9), redundancy was reduced.<b>Normalization</b>: Normalize numerical columns between 0 and 1 scaling using statistics calculated over training sets.<b>Usage:</b><br>The processed dataset is optimized for deep learning–based weather forecasting models such as HTC-LSTM-Attn, but can also be used for climate trend analysis, seasonal prediction, and meteorological research.
DOI: 10.6084/m9.figshare.29876786.v2
title: <b>Dataset for: Redefining Multi-Target Weather Forecasting with a novel Deep Learning model: Hierarchical Temporal Convolutional Long Short-Term Memory with Attention (HTC-LSTM-Attn) in Bangladesh</b>

description: <br>The climate data set was compiled monthly for Bangladesh, from January 1961 to December 2022; it was generated from the Central Climate Information Management System of the BARC. The initial data consisted of measurements from 35 weather stations, covering a multitude of weather parameters that include solar radiation, <b>potential evaporation (PE), </b><b>evapotranspiration</b><b> (</b><b>ETo)</b>, <b>maximum temperature, rainfall, humidity, wind speed, cloud cover, and sunshine duration</b>. In the scope of the research titled "Redefining Multi-Target Weather Forecasting with a Novel Deep Learning Model: HTC-LSTM-Attn in Bangladesh," the dataset underwent several pre-processing steps to ensure its quality and suitability for deep learning-based forecasting. Some of these were:<b>Data consolidation</b>: The merging of multiple CSV files (solar radiation, PET, sunshine, wind speed, cloud coverage, humidity, rainfall, and temperature) into one dataset keyed by station code, year, and month.<b>Station filtering</b>: Eleven stations were excluded due to incomplete or unreliable records, retaining 24 stations representing various climate regions.<b>Outlier treatment</b>: Anomalies are detected by the Interquartile range (IQR) method, and such values are replaced with the closest nearest valid value for the same station.<b>Missing value imputation</b>: For gap-filling, k-nearest neighbours (k=5) are applied.<b>Feature engineering</b>: Added seasonal indicators, lag features, and rolling averages to account for temporal dependencies.<b>Feature Selection</b>: By removing highly correlated variables (Pearson's r &gt; 0.9), redundancy was reduced.<b>Normalization</b>: Normalize numerical columns between 0 and 1 scaling using statistics calculated over training sets.<b>Usage:</b><br>The processed dataset is optimized for deep learning–based weather forecasting models such as HTC-LSTM-Attn, but can also be used for climate trend analysis, seasonal prediction, and meteorological research.
DOI: 10.48472/deposita/ghcvn5
title: Dados de replicação para: Gestão estratégica de patentes em instituições públicas a partir da análise pelo Índice Interno de Esforço (IIE)

description: Dados coletados junto ao Site INPI, da Espacenet e do INPA sobre os depósitos de pedidos de patentes e de patentes concedidas visando aplicar o Índice Interno de Esforço (IIE) para mensurar sua manutenção.
description: Introdução: A gestão eficaz de ativos intangíveis, como patentes, é um desafio comum para as instituições públicas e privadas. A tomada de decisão sobre quais ativos manter no portfólio é fundamental para garantir a otimização dos recursos e o alinhamento com os objetivos estratégicos da organização. Objetivo: Avaliar se a métrica Índice Interno de Esforço (IIE) é confiável para definição de manutenção ou abandono de pedidos de patente e patentes, tomando como estudo de caso o Instituto Nacional de Pesquisas da Amazônia (INPA). Método: Foram utilizadas as informações de 54 pedidos de patente e patente do INPA, coletadas no site do Instituto Nacional da Propriedade Industrial (INPI), sendo usados como dataset para testes empíricos 9 variáveis, constituindo como principais as que (i) descrevem a importância das criações, o (ii) número de citações e se a patente foi (iii) concedida ou não. Resultado: Os resultados indicam que o IIEa permite uma avaliação estatística de peso mediano para amparar a decisão sobre manter ou não o pagamento das anuidades das patentes, baseada no seu potencial de citações. Conclusão: O IIEa se apresentou com um parâmetro viável para uma padronização de um indicador que possa ser utilizado pelas instituições públicas para obtenção de resultados mais eficientes e eficazes no gerenciamento dos recursos aplicados às patentes.