cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5256852
title: Pang_movie

description: Pang_movie dataset is composed of more than 10K user movies reviews. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
DOI: 10.5281/zenodo.5256853
title: Pang_movie

description: Pang_movie dataset is composed of more than 10K user movies reviews. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
DOI: 10.5281/zenodo.7555283
title: Pang_movie

description: Pang_movie dataset is composed of more than 10K user movies reviews. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
DOI: 10.5281/zenodo.5256860
title: Reuters90

description: Reuters90 (REUT)} is a classical text dataset, composed by news articles collected and annotated by Carnegie Group, Inc. and Reuters, Ltd. We consider here a set of 13, 327 articles, classified into 90 categories. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
description: {"references": ["https://doi.org/10.1016/j.ipm.2020.102263", "https://doi.org/10.1016/j.ipm.2020.102481"]}
DOI: 10.5281/zenodo.5256861
title: Reuters90

description: Reuters90 (REUT)} is a classical text dataset, composed by news articles collected and annotated by Carnegie Group, Inc. and Reuters, Ltd. We consider here a set of 13, 327 articles, classified into 90 categories. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
description: {"references": ["https://doi.org/10.1016/j.ipm.2020.102263", "https://doi.org/10.1016/j.ipm.2020.102481"]}
DOI: 10.5281/zenodo.7555298
title: Reuters90

description: Reuters90 (REUT)} is a classical text dataset, composed by news articles collected and annotated by Carnegie Group, Inc. and Reuters, Ltd. We consider here a set of 13, 327 articles, classified into 90 categories. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
description: {"references": ["https://doi.org/10.1016/j.ipm.2020.102263", "https://doi.org/10.1016/j.ipm.2020.102481"]}
DOI: 10.5281/zenodo.5256929
title: Binary Stanford Sentiment Treebank 2 (SST-2)

description: Binary Stanford Sentiment Treebank (SST2) is a binary version of SST and Movie Review dataset (the neutral class was removed), that is, the data was classified only into positive and negative classes. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5256930
title: Binary Stanford Sentiment Treebank 2 (SST-2)

description: Binary Stanford Sentiment Treebank (SST2) is a binary version of SST and Movie Review dataset (the neutral class was removed), that is, the data was classified only into positive and negative classes. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.7555310
title: Binary Stanford Sentiment Treebank 2 (SST-2)

description: Binary Stanford Sentiment Treebank (SST2) is a binary version of SST and Movie Review dataset (the neutral class was removed), that is, the data was classified only into positive and negative classes. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5256914
title: Stanford Sentiment Treebank (SST)

description: Stanford Sentiment Treebank (SST) is an extension of Movie Review dataset with fine-grained labels ranging between very positive and very negative. The authors extended the MR by adding a more curated human annotation into 5 classes.<br> The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5256915
title: Stanford Sentiment Treebank (SST)

description: Stanford Sentiment Treebank (SST) is an extension of Movie Review dataset with fine-grained labels ranging between very positive and very negative. The authors extended the MR by adding a more curated human annotation into 5 classes.<br> The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.7555319
title: Stanford Sentiment Treebank (SST)

description: Stanford Sentiment Treebank (SST) is an extension of Movie Review dataset with fine-grained labels ranging between very positive and very negative. The authors extended the MR by adding a more curated human annotation into 5 classes.<br> The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5256963
title: Subjectivity (Subj)

description: Subjectivity dataset (Subj) with 10,000 documents, is a binary classification dataset in which the task is classify a document (i.e. sentence) as subjective or objective classes. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5256964
title: Subjectivity (Subj)

description: Subjectivity dataset (Subj) with 10,000 documents, is a binary classification dataset in which the task is classify a document (i.e. sentence) as subjective or objective classes. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.7555339
title: Subjectivity (Subj)

description: Subjectivity dataset (Subj) with 10,000 documents, is a binary classification dataset in which the task is classify a document (i.e. sentence) as subjective or objective classes. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5256999
title: TREC

description: TREC with 5,952 documents (i.e. questions), is a question classification dataset in which the task is classify a question into 6 main subject categories: such as human, location, entity, abbreviation, description and numeric value. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5257000
title: TREC

description: TREC with 5,952 documents (i.e. questions), is a question classification dataset in which the task is classify a question into 6 main subject categories: such as human, location, entity, abbreviation, description and numeric value. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.7555342
title: TREC

description: TREC with 5,952 documents (i.e. questions), is a question classification dataset in which the task is classify a question into 6 main subject categories: such as human, location, entity, abbreviation, description and numeric value. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5257037
title: vader_movie

description: vader_movie dataset is composed of more than 10K user movies reviews. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5257038
title: vader_movie

description: vader_movie dataset is composed of more than 10K user movies reviews. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.7555354
title: vader_movie

description: vader_movie dataset is composed of more than 10K user movies reviews. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5257069
title: vader_nyt

description: vader_nyt dataset is composed by comments from the New York Times website content. Some comments are directly related to the news they were inserted to. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5257070
title: vader_nyt

description: vader_nyt dataset is composed by comments from the New York Times website content. Some comments are directly related to the news they were inserted to. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.7555361
title: vader_nyt

description: vader_nyt dataset is composed by comments from the New York Times website content. Some comments are directly related to the news they were inserted to. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition
DOI: 10.5281/zenodo.5257152
title: Web Of Science (WOS-5736)

description: Web Of Science (WOS-5736) is a collection of academic articles/journals. 5,736 documents classified in 11 classes. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
DOI: 10.5281/zenodo.5257153
title: Web Of Science (WOS-5736)

description: Web Of Science (WOS-5736) is a collection of academic articles/journals. 5,736 documents classified in 11 classes. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
DOI: 10.5281/zenodo.7555379
title: Web Of Science (WOS-5736)

description: Web Of Science (WOS-5736) is a collection of academic articles/journals. 5,736 documents classified in 11 classes. The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
DOI: 10.5281/zenodo.7501988
title: Ambulance decision game dataset

description: A dataset that consists of the payoff matrices A, B, R, of the game theoretic interface between two Emergency Departments and the Emergency Medical Services.
DOI: 10.5281/zenodo.7555152
title: Ambulance decision game dataset

description: A dataset that consists of the payoff matrices A, B, R, of the game theoretic interface between two Emergency Departments and the Emergency Medical Services.
DOI: 10.5281/zenodo.7555182
title: Ambulance decision game dataset

description: A dataset that consists of the payoff matrices A, B, R, of the game theoretic interface between two Emergency Departments and the Emergency Medical Services.
DOI: 10.5281/zenodo.7555236
title: Ambulance decision game dataset

description: A dataset that consists of the payoff matrices A, B, R, of the game theoretic interface between two Emergency Departments and the Emergency Medical Services.
DOI: 10.5281/zenodo.5257194
title: Web Of Science (WOS-11967)

description: Web Of Science (WOS-11967) is a collection of academic articles/journals. WOS-11967, with 11,967 documents partitioned across 33 categories; The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
DOI: 10.5281/zenodo.5257195
title: Web Of Science (WOS-11967)

description: Web Of Science (WOS-11967) is a collection of academic articles/journals. WOS-11967, with 11,967 documents partitioned across 33 categories; The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
DOI: 10.5281/zenodo.7555385
title: Web Of Science (WOS-11967)

description: Web Of Science (WOS-11967) is a collection of academic articles/journals. WOS-11967, with 11,967 documents partitioned across 33 categories; The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition.
DOI: 10.5281/zenodo.5257236
title: yelp_reviews

description: yelp_reviews dataset is focused on product reviews, opinions and comments of business.
description: {"references": ["https://www.yelp.com/dataset/challenge"]}
DOI: 10.5281/zenodo.5257237
title: yelp_reviews

description: yelp_reviews dataset is focused on product reviews, opinions and comments of business.
description: {"references": ["https://www.yelp.com/dataset/challenge"]}
DOI: 10.5281/zenodo.7555396
title: yelp_reviews

description: yelp_reviews dataset is focused on product reviews, opinions and comments of business.
description: {"references": ["https://www.yelp.com/dataset/challenge"]}
DOI: 10.5281/zenodo.7555401
title: neosilurus_mollespiculum_ragtag_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7555400
title: neosilurus_mollespiculum_ragtag_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7554706
title: Twitter

description: The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition. The .zip contains all aforementioned files + the tfidf representation in the CSR matrix format.
DOI: 10.5281/zenodo.7555421
title: zanclus_cornutus_ragtag_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7555420
title: zanclus_cornutus_ragtag_1.fasta.gz

description: .
DOI: 10.5281/zenodo.6650871
title: The emergence of high-fitness variants accelerates the slowdown of genome heterogeneity in the coronavirus

description: Supplement of the paper “The emergence of high-fitness variants accelerates the slowdown of genome heterogeneity in the coronavirus” Since the outbreak of the COVID-19 pandemic, the SARS-CoV-2 coronavirus accumulated an important amount of genome variability through mutation and recombination. To test evolutionary trends that could inform us on the adaptive process of the virus to its human host, we compute a genome-wide measure of Sequence Compositional Complexity (SCC) in high-quality coronavirus genomes from across the globe, covering the full span of the pandemic. In early samples, we find no statistical support for any trend in SCC values over time, although the virus genome appears to evolve faster than Brownian Motion expectation. However, in samples taken after the emergence of Variants of Concern with higher transmissibility, and controlling for phylogenetic and sampling effects, we detect a declining trend for SCC and an increasing one for its absolute evolutionary rate. This means that the decline in SCC itself accelerated over time, and that increasing fitness of variant genomes lead to a reduction of their genome sequence heterogeneity. Supplementary files * SupplementaryTables S1-S18.xlsx. The strain name, the collection date, and the SCC values for each analyzed genome.<br> * SupplementaryTableS19.pdf. A complete list acknowledging all originating and submitting laboratories for the sequence data in GISAID EpiCoV on which these analyses are based.<br> * SupplementaryTable S20.pdf. A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample.<br> * PhylogeneticTimetrees_NexusFormat.zip. Phylogenetic timetrees (Nexus format).<br> * PhylogeneticTimetrees_NewickFormat.zip. Phylogenetic timetrees (Newick format).<br> * SCCdata.zip. SCC data.
description: This project was funded by grants from the Spanish Minister of Science, Innovation and Universities (former Spanish Minister of Economy and Competitiveness) to J.L.O. (Project AGL2017-88702-C2-2-R) and A.M. (Project PID2019-105969GB-I00), a grant from Generalitat Valenciana to A.M. (Project Prometeo/2018/A/133) and co-financed by the European Regional Development Fund (ERDF). The most time-demanding computations were done on the servers of the Laboratory of Bioinformatics, Dept. of Genetics & Institute of Biotechnology, Center of Biomedical Research, 18100, Granada, Spain.
DOI: 10.5281/zenodo.6655691
title: The emergence of high-fitness variants accelerates the slowdown of genome heterogeneity in the coronavirus

description: Supplement of the paper “The emergence of high-fitness variants accelerates the slowdown of genome heterogeneity in the coronavirus” Since the outbreak of the COVID-19 pandemic, the SARS-CoV-2 coronavirus accumulated an important amount of genome variability through mutation and recombination. To test evolutionary trends that could inform us on the adaptive process of the virus to its human host, we compute a genome-wide measure of Sequence Compositional Complexity (<em>SCC</em>) in high-quality coronavirus genomes from across the globe, covering the full span of the pandemic. In early samples, we find no statistical support for any trend in <em>SCC</em> values over time, although the virus genome appears to evolve faster than Brownian Motion expectation. However, in samples taken after the emergence of Variants of Concern with higher transmissibility, and controlling for phylogenetic and sampling effects, we detect a declining trend for <em>SCC</em> and an increasing one for its absolute evolutionary rate. This means that the decline in <em>SCC</em> itself accelerated over time, and that increasing fitness of variant genomes lead to a reduction of their genome sequence heterogeneity. Supplementary files <strong>File</strong> <strong>Description</strong> SupplementaryTables S1-S18.xlsx The strain name, the collection date, and the SCC values for each analyzed genome. SupplementaryTableS19.pdf A complete list acknowledging all originating and submitting laboratories for the sequence data in GISAID EpiCoV on which these analyses are based. SupplementaryTable S20.pdf A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample. PhylogeneticTimetrees_NexusFormat.zip Phylogenetic timetrees (Nexus format). PhylogeneticTimetrees_NewickFormat.zip Phylogenetic timetrees (Newick format). SCCdata.zip SCC data.
description: This project was funded by grants from the Spanish Minister of Science, Innovation and Universities (former Spanish Minister of Economy and Competitiveness) to J.L.O. (Project AGL2017-88702-C2-2-R) and A.M. (Project PID2019-105969GB-I00), a grant from Generalitat Valenciana to A.M. (Project Prometeo/2018/A/133) and co-financed by the European Regional Development Fund (ERDF). The most time-demanding computations were done on the servers of the Laboratory of Bioinformatics, Dept. of Genetics & Institute of Biotechnology, Center of Biomedical Research, 18100, Granada, Spain.
DOI: 10.5281/zenodo.6655724
title: The emergence of high-fitness variants accelerates the slowdown of genome heterogeneity in the coronavirus

description: Supplement of the paper “The emergence of high-fitness variants accelerates the slowdown of genome heterogeneity in the coronavirus” Since the outbreak of the COVID-19 pandemic, the SARS-CoV-2 coronavirus accumulated an important amount of genome variability through mutation and recombination. To test evolutionary trends that could inform us on the adaptive process of the virus to its human host, we compute a genome-wide measure of Sequence Compositional Complexity (<em>SCC</em>) in high-quality coronavirus genomes from across the globe, covering the full span of the pandemic. In early samples, we find no statistical support for any trend in <em>SCC</em> values over time, although the virus genome appears to evolve faster than Brownian Motion expectation. However, in samples taken after the emergence of Variants of Concern with higher transmissibility, and controlling for phylogenetic and sampling effects, we detect a declining trend for <em>SCC</em> and an increasing one for its absolute evolutionary rate. This means that the decline in <em>SCC</em> itself accelerated over time, and that increasing fitness of variant genomes lead to a reduction of their genome sequence heterogeneity. Supplementary files <strong>File</strong> <strong>Description</strong> SupplementaryTables S1-S18.xlsx The strain name, the collection date, and the SCC values for each analyzed genome. SupplementaryTableS19.pdf A complete list acknowledging all originating and submitting laboratories for the sequence data in GISAID EpiCoV on which these analyses are based. SupplementaryTable S20.pdf A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample. PhylogeneticTimetrees_NexusFormat.zip Phylogenetic timetrees (Nexus format). PhylogeneticTimetrees_NewickFormat.zip Phylogenetic timetrees (Newick format). SCCdata.zip SCC data.
description: This project was funded by grants from the Spanish Minister of Science, Innovation and Universities (former Spanish Minister of Economy and Competitiveness) to J.L.O. (Project AGL2017-88702-C2-2-R) and A.M. (Project PID2019-105969GB-I00), a grant from Generalitat Valenciana to A.M. (Project Prometeo/2018/A/133) and co-financed by the European Regional Development Fund (ERDF). The most time-demanding computations were done on the servers of the Laboratory of Bioinformatics, Dept. of Genetics & Institute of Biotechnology, Center of Biomedical Research, 18100, Granada, Spain.