cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc241qzx
title: CCDC 1967879: Experimental Crystal Structure Determination

description: Related Article: Aman K.K. Bhasin, Pooja Chauhan, Savita Chaudhary|2021|Sens.Actuators,B|330|129328|doi:10.1016/j.snb.2020.129328
DOI: 10.5281/zenodo.4024177
title: COVID-19 Tweets : A dataset contaning more than 600k tweets on the novel CoronaVirus

description: This dataset contains 653 996 tweets related to the Coronavirus topic and highlighted by hashtags such as: #COVID-19, #COVID19, #COVID, #Coronavirus, #NCoV and #Corona. The tweets' crawling period started on the 27<sup>th</sup> of February and ended on the 25<sup>th</sup> of March 2020, which is spread over four weeks. The tweets were generated by 390 458 users from 133 different countries and were written in 61 languages. English being the most used language with almost 400k tweets, followed by Spanish with around 80k tweets. The data is stored in as a CSV file, where each line represents a tweet. The CSV file provides information on the following fields: Author: the user who posted the tweet Recipient: contains the name of the user in case of a reply, otherwise it would have the same value as the previous field Tweet: the full content of the tweet Hashtags: the list of hashtags present in the tweet Language: the language of the tweet Relationship: gives information on the type of the tweet, whether it is a retweet, a reply, a tweet with a mention, etc. Location: the country of the author of the tweet, which is unfortunately not always available Date: the publication date of the tweet Source: the device or platform used to send the tweet The dataset can as well be used to construct a social graph since it includes the relations "Replies to", "Retweet", "MentionsInRetweet" and "Mentions".
DOI: 10.5281/zenodo.4024176
title: COVID-19 Tweets : A dataset contaning more than 600k tweets on the novel CoronaVirus

description: This dataset contains 653 996 tweets related to the Coronavirus topic and highlighted by hashtags such as: #COVID-19, #COVID19, #COVID, #Coronavirus, #NCoV and #Corona. The tweets' crawling period started on the 27<sup>th</sup> of February and ended on the 25<sup>th</sup> of March 2020, which is spread over four weeks. The tweets were generated by 390 458 users from 133 different countries and were written in 61 languages. English being the most used language with almost 400k tweets, followed by Spanish with around 80k tweets. The data is stored in as a CSV file, where each line represents a tweet. The CSV file provides information on the following fields: Author: the user who posted the tweet Recipient: contains the name of the user in case of a reply, otherwise it would have the same value as the previous field Tweet: the full content of the tweet Hashtags: the list of hashtags present in the tweet Language: the language of the tweet Relationship: gives information on the type of the tweet, whether it is a retweet, a reply, a tweet with a mention, etc. Location: the country of the author of the tweet, which is unfortunately not always available Date: the publication date of the tweet Source: the device or platform used to send the tweet The dataset can as well be used to construct a social graph since it includes the relations "Replies to", "Retweet", "MentionsInRetweet" and "Mentions".
DOI: 10.5281/zenodo.3463379
title: Multilingual Medical Corpora

description: The amount of digital data derived from healthcare processes have increased tremendously in the last years. This applies especially to unstructured data, which are often hard to analyze due to the lack of available tools to process and extract information. Natural language processing is often used in medicine, but the majority of tools used by researchers are developed primarily for the English language. For developing and testing natural language processing methods, it is important to have a suitable corpus, specific to the medical domain that covers the intended target language. To improve the potential of natural language processing research, we developed tools to derive language specific medical corpora from publicly available text sources. n order to extract medicine-specific unstructured text data, openly available pub-lications from biomedical journals were used in a four-step process:(1) medical journal databases were scraped to download the articles,(2) the articles were parsed and consolidated into a single repository,(3) the content of the repository was de-scribed, and (4) the text data and the codes were released. In total, 93 969 articles were retrieved, with a word count of 83 868 501 in three different languages (German, English, and Spanish) from two medical journal databases Our results show that unstructured text data extraction from openly available medical journal databases for the construction of unified corpora of medical text data can be achieved through web scraping techniques.
DOI: 10.5281/zenodo.3463378
title: Multilingual Medical Corpora

description: The amount of digital data derived from healthcare processes have increased tremendously in the last years. This applies especially to unstructured data, which are often hard to analyze due to the lack of available tools to process and extract information. Natural language processing is often used in medicine, but the majority of tools used by researchers are developed primarily for the English language. For developing and testing natural language processing methods, it is important to have a suitable corpus, specific to the medical domain that covers the intended target language. To improve the potential of natural language processing research, we developed tools to derive language specific medical corpora from publicly available text sources. n order to extract medicine-specific unstructured text data, openly available pub-lications from biomedical journals were used in a four-step process:(1) medical journal databases were scraped to download the articles,(2) the articles were parsed and consolidated into a single repository,(3) the content of the repository was de-scribed, and (4) the text data and the codes were released. In total, 93 969 articles were retrieved, with a word count of 83 868 501 in three different languages (German, English, and Spanish) from two medical journal databases Our results show that unstructured text data extraction from openly available medical journal databases for the construction of unified corpora of medical text data can be achieved through web scraping techniques.
DOI: 10.5281/zenodo.4459510
title: Supplementary material - Posch KW_2021 - Appendix 1-5

description: Supplementary material on the thesis "The Kleinwalsertal. A Mesolithic Landscape with far reaching contacts. <em>Archaeological studies on a cultural and natural landscape between the Boreal and Older Atlantic" </em> by Caroline Posch, <em>University of Innsbruck, 2021</em> <em>Appendix 1: Radiocarbon dates - Eastern Alps (Austria and adjacent border areas)</em> <em>Appendix 2: Data sheets attribute analysis</em> <em>Appendix 3: Synthax SPSS</em> <em>Appendix 4: Results factor analysis and cluster</em> <em>Appendix 5: Survey Kleinwalsertal – topographic data sheets</em>
DOI: 10.5281/zenodo.4459509
title: Supplementary material - Posch KW_2021 - Appendix 1-5

description: Supplementary material on the thesis "The Kleinwalsertal. A Mesolithic Landscape with far reaching contacts. <em>Archaeological studies on a cultural and natural landscape between the Boreal and Older Atlantic" </em> by Caroline Posch, <em>University of Innsbruck, 2021</em> <em>Appendix 1: Radiocarbon dates - Eastern Alps (Austria and adjacent border areas)</em> <em>Appendix 2: Data sheets attribute analysis</em> <em>Appendix 3: Synthax SPSS</em> <em>Appendix 4: Results factor analysis and cluster</em> <em>Appendix 5: Survey Kleinwalsertal – topographic data sheets</em>
DOI: 10.5281/zenodo.3605654
title: Meta-analysis of multiple ion uptake kinetics in crop roots

description: This dataset collates measures of Michaelis-Menten root uptake parameters for several nutrients based on reported literature. The analysis appears in a review article by Marcus Griffiths (mdgriffiths at noble.org) and Larry York (lmyork at noble.org). Please cite this repository and the review article if the data is reused. <strong>Citation:</strong> Griffiths, M. G., York, L. M. (2020). Targeting root ion uptake kinetics to increase plant productivity and nutrient use efficiency. <em>Plant Physiology</em> <strong>182</strong>, 2854 - 1868. https://doi.org/10.1104/pp.19.01496 <strong>File descriptions and use</strong> The R script assumes that all data files are in the working directory and that the "tidyverse" meta-package is installed. <strong>Kinetics_metaanalysis_all_files_v2.zip</strong> - Contains all the files.<br> <br> <strong>Kinetics_metaanalysis_column_definitions.txt </strong>- Definitions of column names used in data files (not used by R script)<br> <br> <strong>Kinetics_metaanalysis_csv.csv </strong>- Complete dataset in CSV format<strong> </strong>(used by R script)<br> <br> <strong>Kinetics_metaanalysis_excel.xlsx</strong> - Complete dataset in Excel format (not used by R script)<br> <br> <strong>Kinetics_metaanalysis_figure_subset.csv </strong>- Subset of total data used for figure generation (used by R script)<br> <br> <strong>Kinetics_metaanalysis_figures_code.R </strong>- Required R script for statistical analysis Version 2 of this repository includes minor updates to include new papers and data.
DOI: 10.25625/ffe7lj/mt0ehf
title: Dataset Excel Tables I, II, III, IV.tab

description: :unav
DOI: 10.25625/ffe7lj/hjayet
title: Online_Video_II_Reentry_figof8_stable.mov

description: :unav
DOI: 10.25625/ffe7lj/otxplw
title: Online_Video_I_Reentry_multifoci_nonstable.mov

description: :unav
DOI: 10.25625/ffe7lj/cbafyt
title: 316547R3_Online Supplement-final.pdf

description: :unav
DOI: 10.25625/ffe7lj/mps686
title: 316547R3_MajorResourcesTable.pdf

description: :unav
DOI: 10.25625/ffe7lj
title: Online Supplemental Files for "Caveolin3 Stabilizes McT1-mediated Lactate/Proton Transport in Cardiomyocytes"

description: Caveolin3 Stabilizes McT1-mediated Lactate/Proton Transport in Cardiomyocytes - Supplemental Files
DOI: 10.34740/kaggle/dsv/1872502
title: US counties COVID 19 dataset
DOI: 10.6084/m9.figshare.10061948
title: Distribution of Ipomoea violacea (Convolvulaceae): patterns, gaps and reports for its occurrence in Brazil and West Tropical Africa - Supplementary Data

description: These are 4 supplementary data files for the paper "Distribution of <i>Ipomoea violacea </i>(Convolvulaceae): patterns, gaps, and reports for its occurrence in Brazil and West Tropical Africa" published in the journal <i>Rodriguésia</i>. <br>The CSV files include geographical coordinates for the taxonomically verified specimens of <i>Ipomoea violacea</i> L. that form the foundation of the study.There are 4 separate files: <br>African & Indian Ocean records [29 records]; <br>Americas (North, Middle, South, Caribbean) records [146 records]; <br>Asian & Pacific records [226 records]; <br>and a combined file with all the records merged [401 records]. <br>These geographical data form the basis for the global distribution reported for <i>Ipomoea violacea</i> and are the basis for our discussion of its biology and dispersal. <br>Specimen records were compiled in BRAHMS software between 2002-2020 and have been exported as Excel tables in CSV format with selected fields only, including: <br>scientific namecollector name(s)collector numberduplicates (acronyms follow <i>Index Herbariorum</i>)collection date <br>countrymajor areaminor areagazetteerLatitude (NS)Longitude (EW) <br>unitscoordinatesLatitude decimalLongitude decimal<br>
DOI: 10.6084/m9.figshare.10061948.v2
title: Distribution of Ipomoea violacea (Convolvulaceae): patterns, gaps and reports for its occurrence in Brazil and West Tropical Africa - Supplementary Data

description: These are 4 supplementary data files for the paper "Distribution of <i>Ipomoea violacea </i>(Convolvulaceae): patterns, gaps, and reports for its occurrence in Brazil and West Tropical Africa" published in the journal <i>Rodriguésia</i>. <br>The CSV files include geographical coordinates for the taxonomically verified specimens of <i>Ipomoea violacea</i> L. that form the foundation of the study.There are 4 separate files: <br>African & Indian Ocean records [29 records]; <br>Americas (North, Middle, South, Caribbean) records [146 records]; <br>Asian & Pacific records [226 records]; <br>and a combined file with all the records merged [401 records]. <br>These geographical data form the basis for the global distribution reported for <i>Ipomoea violacea</i> and are the basis for our discussion of its biology and dispersal. <br>Specimen records were compiled in BRAHMS software between 2002-2020 and have been exported as Excel tables in CSV format with selected fields only, including: <br>scientific namecollector name(s)collector numberduplicates (acronyms follow <i>Index Herbariorum</i>)collection date <br>countrymajor areaminor areagazetteerLatitude (NS)Longitude (EW) <br>unitscoordinatesLatitude decimalLongitude decimal<br>
DOI: 10.6084/m9.figshare.10061948.v1
title: Distribution of Ipomoea violacea (Convolvulaceae): patterns, gaps and reports for its occurrence in Brazil and West Tropical Africa - Supplementary Data

description: These are 4 supplementary data files for the paper "Distribution of <i>Ipomoea violacea </i>(Convolvulaceae): patterns, gaps, and reports for its occurrence in Brazil and West Tropical Africa" published in the journal <i>Rodriguésia</i>. <br>The CSV files include geographical coordinates for the taxonomically verified specimens of <i>Ipomoea violacea</i> L. that form the foundation of the study.There are 4 separate files: <br>African & Indian Ocean records [29 records]; <br>Americas (North, Middle, South, Caribbean) records [146 records]; <br>Asian & Pacific records [226 records]; <br>and a combined file with all the records merged [401 records]. <br>These geographical data form the basis for the global distribution reported for <i>Ipomoea violacea</i> and are the basis for our discussion of its biology and dispersal. <br>Specimen records were compiled in BRAHMS software between 2002-2020 and have been exported as Excel tables in CSV format with selected fields only, including: <br>scientific namecollector name(s)collector numberduplicates (acronyms follow <i>Index Herbariorum</i>)collection date <br>countrymajor areaminor areagazetteerLatitude (NS)Longitude (EW) <br>unitscoordinatesLatitude decimalLongitude decimal<br>
DOI: 10.21227/twgk-dp05
title: DREAM: Data Rang or EArth Monitoring

description: The DREAM (Data Rang or EArth Monitoring): a multimode database including optics, radar, DEM and OSM labels for deep machine learning purposes.DREAM, is a multimodal remote sensing database, developed from open-source data.The database has been created using the Google Earth Engine platform, the GDAL python library; the “pyosm” python package developed by Alexandre Mayerowitz (Airbus, France) It includes two subsets:France on a 10mx10 m UTM Grid:Radar Sentinel-1 data, with and without temporal speckle filtering, optical Sentinel-2 data, rasterized Open Street Map datasets OSM (© OpenStreetMap contributors), and DEM data (SRTM)USA on a 5 mx5 m WGS84 GridRGB visible airborne images (NAIP), UAVSAR L-band radar, Sentinel-2 data, Sentinel1-data with temporal speckle filtering, and DEM data (SRTM)Warning : - Some tiles in the France database may be smaller than 1024x1024 pixels. - Some OSM raster data does not exist or contain only null labels (Coast Areas for example) https://www.openstreetmap.org/copyright/en
DOI: 10.6084/m9.figshare.13622666
title: Toxicity of sodium nitrite-based vertebrate pesticides for European starlings (Sturnus vulgaris).

description: Data related to "Toxicity of sodium nitrite-based vertebrate pesticides for European starlings (Sturnus vulgaris)."<br><br>
DOI: 10.5281/zenodo.3735528
title: Input data for manuscript "An open-source low-cost sensor for SNR-based GNSS reflectometry: Design and long-term validation towards sea level altimetry"

description: Input data for Fagundes et al. (2021), "An open-source low-cost sensor for SNR-based GNSS reflectometry: Design and long-term validation towards sea level altimetry", GPS Solutions (in press). preprint
DOI: 10.5281/zenodo.3735527
title: Input data for manuscript "An open-source low-cost sensor for SNR-based GNSS reflectometry: Design and long-term validation towards sea level altimetry"

description: Input data for Fagundes et al. (2021), "An open-source low-cost sensor for SNR-based GNSS reflectometry: Design and long-term validation towards sea level altimetry", GPS Solutions (in press). preprint
DOI: 10.17632/y2kpy4jz5d.1
title: Data for: Geochronology and petrogenesis of intrusive rocks in the Coastal Cordillera of northern Chile (26-28°S): insights from zircon U-Pb dating and trace element geochemistry

description: U-Pb geochronology and trace element analysis of zircon grains of the study
DOI: 10.6084/m9.figshare.13634147
title: Data of Laville et al., 2021

description: This dataset contains : <br>- PDB file of the 155 mutant structures : mutant_pdbFiles.tgz<br>- PDB file of pocket extracted from each mutant structures: pocket_pdbFiles.tgz- list of atoms involved in the interface of each structures: listAtm_interface.res- correspondance to the structure numerotation and mutant: correspond_mutation_numMuntant.txt<br><br>
DOI: 10.3886/e129181v1-73700
title: Axios-Ipsos Coronavirus Index

description: Since the COVID-19 pandemic began, Ipsos has partnered with Axios to release a weekly Coronavirus Index survey. The survey examines Americans' behaviors, perceptions of risk, and attitudes around COVID-19. All data is publicly available here: https://www.ipsos.com/en-us/news-polls/axios-ipsos-coronavirus-index
DOI: 10.6084/m9.figshare.13634039
title: negative-words

description: Contiene la lista de palabras negativas proveniente de traducidas al español a partir de la fuente: Palomino Gayete Arturo. (2017). <b>"¿Cómo reformular la estrategia de comercio electrónico de una organización mediante el análisis de datos?" </b>. Editorial Oberta UOC. 94P. España.<br>
DOI: 10.6084/m9.figshare.13634039.v1
title: negative-words

description: Contiene la lista de palabras negativas proveniente de traducidas al español a partir de la fuente: Palomino Gayete Arturo. (2017). <b>"¿Cómo reformular la estrategia de comercio electrónico de una organización mediante el análisis de datos?" </b>. Editorial Oberta UOC. 94P. España.<br>
DOI: 10.6084/m9.figshare.13634165
title: positive-words

description: Contiene la lista de palabras positivas provenientes de traducidas al español a partir de la fuente: Palomino Gayete Arturo. (2017). <b>"¿Cómo reformular la estrategia de comercio electrónico de una organización mediante el análisis de datos?" </b>. Editorial Oberta UOC. 94P. España.
DOI: 10.6084/m9.figshare.13634165.v1
title: positive-words

description: Contiene la lista de palabras positivas provenientes de traducidas al español a partir de la fuente: Palomino Gayete Arturo. (2017). <b>"¿Cómo reformular la estrategia de comercio electrónico de una organización mediante el análisis de datos?" </b>. Editorial Oberta UOC. 94P. España.
DOI: 10.6084/m9.figshare.13634180
title: Extracción de tendencias de Google Trends

description: Frecuencia de búsqueda para las categorías de investigación del sistema financiero colombiano desde el 1 de enero de 2019 hasta el 31 de diciembre de 2020. Fuente: Google Trends https://trends.google.es/trends/?geo=ES<br><br>
DOI: 10.6084/m9.figshare.13634180.v1
title: Extracción de tendencias de Google Trends

description: Frecuencia de búsqueda para las categorías de investigación del sistema financiero colombiano desde el 1 de enero de 2019 hasta el 31 de diciembre de 2020. Fuente: Google Trends https://trends.google.es/trends/?geo=ES<br><br>
DOI: 10.6084/m9.figshare.13621169.v4
title: Speedtest Open Data - Four International cities - MEL, BKK, SHG, LAX

description: This dataset compares four cities broadband internet speeds:- Melbourne, AU- Bangkok, TH- Shanghai, CN- Los Angeles, US<br>*lines of data for each geojson file; a line equates to a 600m^2 location, inc total tests, devices used, and average upload and download speed- MEL 16181 lines- SHG 31745 lines- BKK 29296 lines<br>Geojsons of these 2* by 2* extracts for MEL, BKK, SHG now added, and LAX will be added shortly.<br>This dataset unpacks, geospatially, data summaries provided in Speedtest Global Index (linked below). See Jupyter Notebook (*.ipynb) to interrogate geo data. See link to install Jupyter.<br>** To DoWill add Google Map versions so everyone can see without installing Jupyter.- Link to Google Map (BKK) added below. Key:Green &gt; 100Mbps(Superfast). Black &gt; 500Mbps (Ultrafast). CSV provided. Code in Speedtestv1.1.ipynb Jupyter Notebook.<br>** Pythonmelb = au_tiles.cx[144:146 , -39:-37] #Lat/Lon extract<br>shg = tiles.cx[120:122 , 30:32] #Lat/Lon extract<br>bkk = tiles.cx[100:102 , 13:15] #Lat/Lon extract<br>- LAX yet to add<br>Histograms, and data visualisations will be provided.Data Sourced from - This is an extract of Speedtest Open data available at Amazon WS (link below - opendata.aws).<br>**VERSIONSv4 Add BKK Google map(link below), and BKK csv mapping files.v3 replaced MEL map with big key version. Prev key was very tiny in top right corner.v2 Uploaded MEL, SHG, BKK data and Jupyter Notebookv1 Metadata record<br>** LICENCEAWS data licence on Speedtest data is "CC BY-NC-SA 4.0", so use of this data must be:- non-commercial (NC)- reuse must be share-alike (SA)(add same licence).This restricts the standard CC-BY Figshare licence.<br>
DOI: 10.5517/ccdc.csd.cc25qv92
title: CCDC 2017954: Experimental Crystal Structure Determination

description: Related Article: Kenta Sato, Masashi Hasegawa, Yuki Nojima, Nobuyuki Hara, Tomohiko Nishiuchi, Yoshitane Imai, Yasuhiro Mazaki|2020|Chem.-Eur.J.|27|1323|doi:10.1002/chem.202004283
DOI: 10.5517/ccdc.csd.cc25qv81
title: CCDC 2017953: Experimental Crystal Structure Determination

description: Related Article: Kenta Sato, Masashi Hasegawa, Yuki Nojima, Nobuyuki Hara, Tomohiko Nishiuchi, Yoshitane Imai, Yasuhiro Mazaki|2020|Chem.-Eur.J.|27|1323|doi:10.1002/chem.202004283
DOI: 10.6084/m9.figshare.13634198.v1
title: Ribosomal protein database of Clostridium botulinum AM1195

description: This work presents the ribosomal protein database of <i>Clostridium botulinum </i>AM1195<i>. </i>Original data for the work came from the annotated proteome data of the bacterium downloaded from UniProt. Using an in-house MATLAB ribosomal protein database analysis software, the original proteome data file was parsed to extract protein name and amino acid sequence of all ribosomal proteins in the species. The database also includes calculated variables such as number of residues, molecular weight, and nucleotide sequence. Overall, the presented database could serve as a ribosomal protein mass fingerprint for use in microbial identification, or it could be used in fundamental studies seeking to uncover new insights into ribosomal protein biology.
DOI: 10.6084/m9.figshare.13634198
title: Ribosomal protein database of Clostridium botulinum AM1195

description: This work presents the ribosomal protein database of <i>Clostridium botulinum </i>AM1195<i>. </i>Original data for the work came from the annotated proteome data of the bacterium downloaded from UniProt. Using an in-house MATLAB ribosomal protein database analysis software, the original proteome data file was parsed to extract protein name and amino acid sequence of all ribosomal proteins in the species. The database also includes calculated variables such as number of residues, molecular weight, and nucleotide sequence. Overall, the presented database could serve as a ribosomal protein mass fingerprint for use in microbial identification, or it could be used in fundamental studies seeking to uncover new insights into ribosomal protein biology.