cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.35trzh
title: Occurrence Download

description: A dataset containing 194 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Polyphagotarsonemus latus"
 ]
}

 The dataset includes 194 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039087-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17092252
title: Atmospheric deposition of polycyclic aromatic hydrocarbons in southwestern German soils: sources, cancer risks and policy implications

description: Dataset associated with the article entitled:  Atmospheric deposition of polycyclic aromatic hydrocarbons in southwestern German soils: sources, cancer risks and policy implications
DOI: 10.15468/dl.psdmra
title: Occurrence Download

description: A dataset containing 120 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Urophycis tenuis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1920",
 "Year is less than or equal to 1949"
 ]
}

 The dataset includes 120 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039086-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/f0qd41
title: Inventario Español de Especies Terrestres (MAGRAMA)

description: El Inventario Español de Especies Terrestres recoge la distribución, abundancia y estado de conservación de la fauna y flora terrestre española. Se desarrolla como requerimiento del Real Decreto 556/2011, de 20 de abril, para el desarrollo del Inventario Español del Patrimonio Natural y la Biodiversidad.
Este juego de datos se adscribe actualmente al Ministerio para la Transición Ecológica y Reto Demográfico (MITERD) pero conserva la denominación del organismo que lo publicó en origen (Ministerio de Agricultura, Alimentación y Medio Ambiente).
DOI: 10.1285/i20705948v18n2p238
title: Balancing performance and environmental efficiency: a multiclass classification study of textual data

description: This study evaluates Multiclass classification (MCC) strategies -- One-Vs-Rest (OVA), One-Vs-One (OVO), Best-of-Best (BOB), and Error-Correcting-Output-Codes (ECOC) -- using classifiers like Naïve Bayes, Random Forest, Linear Discriminant Analysis, Logistic Regression, Neural Networks, Support Vector Machine, and Threshold-based Naïve Bayes on the 20NewsGroup text dataset, well known in literature for its complexity. Findings shows that the choice of classifier significantly affects accuracy and computational effort. Threshold-based Naïve Bayes excels with OVO, OVA, and BOB but declines with ECOC. Artificial Neural Network and Random Forest, which are slowest, align well with BOB and OVA respectively. In contrast, Naïve Bayes and Logistic Regression stand out for speed, particularly with OVA. Along with the Support Vector Machine, these classifiers demonstrate versatility across all strategies, balancing accuracy and training time. Additionally, OVO and BOB prove to be advantageous for handling unbalanced data, by focusing on individual class pairings. OVA emerges as the fastest strategy, while ECOC's performance is classifier-dependent. Our analysis underscores the importance of selecting the appropriate classifier and strategy pairing in MCC tasks, particularly in imbalanced datasets. Importantly, this study underlines the environmental impact of computational choices, advocating for efficient, accurate predictions to minimize energy consumption and optimize machine learning applications' ecological footprint.
DOI: 10.15468/dl.hkz3xc
title: Occurrence Download

description: A dataset containing 946 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Megalurothrips usitatus"
 ]
}

 The dataset includes 946 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039089-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z6hms2
title: Occurrence Download

description: A dataset containing 129 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acacia pinguiculosa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 129 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039088-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21950/6mnu17
title: Mapping of digital pedagogies in higher education dataset

description: The database contains the results obtained in the research, which aims to analyse the evolution and trends in research on digital pedagogy in higher education through the application of bibliometric analysis and systematic review of scientific output. The analysis focuses on studies on digitisation, university education and educational quality, three categories that are grouped around digital pedagogies and methodologies.
description: The dataset consists of an Excel file containing scientific publications downloaded from the Web of Sciences database. The sample contains 242 publications from the Web of Science (WoS), from 2005 to 2022, in the area of digital pedagogies in higher education, in English and Spanish, from the category Education Educational Research, regardless of the database of origin.

The variables extracted were:
Title, Authors, Book Editors, Source Title, Publication Date, Publication Year, Volume, Issue, Special Issue, Beginning Page, Ending Page, Article Number, DOI, Conference, Title, Conference, Date, Total Citations, Average per Year, and finally, the frequency is indicated by year (from 1900 to 2023).

The search chain was:

 TOPIC “Digital technology*” OR “Digitization*” OR “Digitisation*” OR “Digitali*” AND TOPIC “Higher education*” OR “Higher education* instituti*” OR “University” OR “High education*” OR “high education* superior*” OR “Higher education* superior*” OR “College*” OR “Institution* of higher learning*” AND TOPIC “Educational quality” OR “Educational improvement” OR TOPIC “digital methodolog*” OR “virtual methodolog*” OR “digital educational sciences*” OR “digital Pedagog*”
DOI: 10.1285/i20705948v18n2p264
title: Imputation of Missing Values with Adaptive Elastic Net for Gene Selection in High-dimensional Data

description: Missing data is a problem that often arises in a variety of real-world systems. The performance of classification algorithms operating on these systems would suffer as a result. Effective imputation approaches abound to tackle this issue in case of missing data with low dimensions. In addition, one of the most common methods for concurrently doing variable selection and coefficient estimation in high-dimensional data is the penalized regression technique. However, one of the most significant problems associated with high-dimensional data is that it often includes an enormous quantity of missing data, which means that conventional imputation methods may not adequately address it. This paper proposes the imputation of missing values with the adaptive elastic net as an extension of penalized techniques to enhance gene selection and impute missing values in high-dimensional data. The effectiveness of the proposed method is evaluated by applying it to high-dimensional datasets that are taken from real-world situations with varying numbers of features, sample sizes, and percentages of missing datasets. A comparison is made between the proposed approach and various imputation-penalized methods that are currently in use for high-dimensional data. The findings of the comparison experiments reveal that the proposed technique is superior to its rivals since it achieves a better value for classification accuracy, sensitivity, and specificity than its competitors.
DOI: 10.15468/dl.tbd8hy
title: Occurrence Download

description: A dataset containing 2640436 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rhodamnia argentea, Saururus chinensis, Rauvolfia tetraphylla, Avellara fistulosa, Eucalyptus caleyi, Prosopis pallida, Abelmoschus esculentus, Carex rostrata, Cyperus iria, Myristica yunnanensis, Oryza latifolia, Thalia dealbata, Myriophyllum alterniflorum, Carex depauperata, Cryptocoryne crispatula, Phyllospadix torreyi, Castanea dentata, Thymus mandschuricus, Ficus carica, Salix caprea, Gardenia jasminoides, Posidonia oceanica, Cyperus rotundus, Elaeagnus moorcroftii, Melilotus officinalis, Pyrus sinkiangensis, Malus domestica, Rhododendron micranthum, Vitis wilsoniae, Aristolochia contorta, Prunus mongolica, Euryodendron excelsum, Primula vulgaris, Gleditsia sinensis, Lathyrus oleraceus, Populus ilicifolia, Scolopia chinensis, Melaleuca alternifolia, Avena nuda, Eucalyptus sideroxylon, Carex laevigata, Comarum palustre, Rhododendron championiae, Polygonum aviculare, Salix variegata, Salix exigua, Dillenia turbinata, Dimocarpus longan)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2640436 records from 5036 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039063-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9wten8
title: Occurrence Download

description: A dataset containing 3786 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anthemis cotula",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3786 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037722-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ardnyv
title: Occurrence Download

description: A dataset containing 152 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tribulus pentandrus pterophorus"
 ]
}

 The dataset includes 152 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037724-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c9xv99
title: Occurrence Download

description: A dataset containing 214 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ophiopogon stenophyllus"
 ]
}

 The dataset includes 214 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037726-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cfgwry
title: Occurrence Download

description: A dataset containing 2415 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Taraxacum serotinum"
 ]
}

 The dataset includes 2415 records from 134 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037725-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/3j1dw-rgr23
title: Xanthomonas vs Caulobac (-T1SS)

description: Xac WT pBRaφ pBRR MCS5 GFP (code 9) vs Caulobacter (delta-cdzAB) competition. Caulobacter strain is a T1SS deletion strain.
description: <strong>Tilt Series Date:</strong> 2017-10-27</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Xanthomonas citri</p>
<strong>Strain:</strong> pv citri 306</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 120.0 eV/Å²,  defocus: -7.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> reupload_downloaded</p>
<strong>Processing Software Used:</strong> TOMO3D</p>
<strong>Collaborators and Roles:</strong> Chuck Farah lab made the Xac strain, Caulobacter strain is from Laub lab. Data collected and processed by Mohammed Kaplan and Debnath Ghosal</p>
<strong>Purification / Growth Conditions / Treatment:</strong> 4 µL mixture of Xac and Caulobacter cells were pipetted on top of Au-Finder grids. These grids were glow discharged in advance and kept on top of Agar pads. Carbon side up. These were then kept at the BSL-II hood and left for about 10 min before freezing.</p>
<strong>Sample Preparation:</strong> Manual blotting, humidity 100%</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-10-27-6/rawdata/</p>Xan_CaulDelT1_20172710c0004.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-10-27-6/rawdata/Xan_CaulDelT1_20172710c0004.mrc"
        > <i class="download icon"></i> Download </a></p>

        Xan_CaulDelT1_20172710c0004_SIRT_1k.rec,  Reconstruction (Pixel Size 0.0 nm), 1.1 GB  
        
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        Xan_CaulDelT1_20172710c0004.ali,  434.8 MB  
        
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        Xan_CaulDelT1_20172710c0004.tlt,  488.0 B  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-10-27-6/file_25873/Xan_CaulDelT1_20172710c0004.tlt"
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        Xan_CaulDelT1_20172710c0004_fid.tlt,  488.0 B  
        
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DOI: 10.22002/sz10r-kd941
title: Xanthomonas (-B7) vs Caulobacter

description: Xanthomonas ΔVirB7 Vs Caulobacter competition
description: <strong>Tilt Series Date:</strong> 2017-03-17</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Xanthomonas citri</p>
<strong>Strain:</strong> pv citri 306</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-50.0°, 50.0°), step: 2°, constant angular increment, dosage: 160.0 eV/Å²,  defocus: -8.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> reupload_downloaded</p>
<strong>Processing Software Used:</strong> TOMO3D</p>
<strong>Collaborators and Roles:</strong> Chuck Farah lab made the strains, data collected by Debnath Ghosal.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> 4 µL mixture of Xac (-VirB7) and Caulobacter cells were pipetted on top of Au-Finder grids. These grids were glow discharged in advance and kept on top of Agar pads. Carbon side up. These were then kept at the BSL-II hood and left for about 10 min before freezing.</p>
<strong>Sample Preparation:</strong> Freezing conditions: Blot force 6, wait time=0, drain time =1 sec, blot time= 9 sec</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-03-17-15/rawdata/</p>DeltaVirB7_Cc_20170316na20020.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.5 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-03-17-15/rawdata/DeltaVirB7_Cc_20170316na20020.mrc"
        > <i class="download icon"></i> Download </a></p>

        DeltaVirB7_Cc_20170316na20020_SIRT_1k.rec,  Reconstruction (Pixel Size 0.0 nm), 1.1 GB  
        
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        DeltaVirB7_Cc_20170316na20020.ali,  256.6 MB  
        
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        DeltaVirB7_Cc_20170316na20020.tlt,  288.0 B  
        
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        DeltaVirB7_Cc_20170316na20020_fid.tlt,  288.0 B  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-03-17-15/file_25916/DeltaVirB7_Cc_20170316na20020_fid.tlt"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/sck3b-6mv35
title: Xanthomonas vs Caulobac (-T1SS)

description: Xac WT pBRaφ pBRR MCS5 GFP (code 9) vs Caulobacter (delta-cdzAB) competition. Caulobacter strain is a T1SS deletion strain.
description: <strong>Tilt Series Date:</strong> 2017-10-27</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Xanthomonas citri</p>
<strong>Strain:</strong> pv citri 306</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 120.0 eV/Å²,  defocus: -7.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> reupload_downloaded</p>
<strong>Processing Software Used:</strong> TOMO3D</p>
<strong>Collaborators and Roles:</strong> Chuck Farah lab made the Xac strain, Caulobacter strain is from Laub lab. Data collected and processed by Mohammed Kaplan and Debnath Ghosal</p>
<strong>Purification / Growth Conditions / Treatment:</strong> 4 µL mixture of Xac and Caulobacter cells were pipetted on top of Au-Finder grids. These grids were glow discharged in advance and kept on top of Agar pads. Carbon side up. These were then kept at the BSL-II hood and left for about 10 min before freezing.</p>
<strong>Sample Preparation:</strong> Manual blotting, humidity 100%</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-10-27-11/rawdata/</p>Xan_CaulDelT1_20172710e0002.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.7 GB  
        
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        Xan_CaulDelT1_20172710e0002_SIRT_1k.rec,  Reconstruction (Pixel Size 0.0 nm), 1.1 GB  
        
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        Xan_CaulDelT1_20172710e0002.ali,  313.6 MB  
        
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        Xan_CaulDelT1_20172710e0002.tlt,  352.0 B  
        
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        Xan_CaulDelT1_20172710e0002_fid.tlt,  352.0 B  
        
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DOI: 10.15468/dl.6hyzm6
title: Occurrence Download

description: A dataset containing 1735 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Taraxacum scanicum"
 ]
}

 The dataset includes 1735 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037727-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hyd435
title: Occurrence Download

description: A dataset containing 171487 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Betula nana"
 ]
}

 The dataset includes 171487 records from 418 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037585-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.