cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14907879
title: Summary of Results Quality Indicators Rev 1.xlsx

description: Focus group summary of quality indicators
DOI: 10.6084/m9.figshare.14907879.v1
title: Summary of Results Quality Indicators Rev 1.xlsx

description: Focus group summary of quality indicators
DOI: 10.5281/zenodo.5068311
title: humanities_keywords Dataset

description: The WE1S humanities_keywords dataset contains word-frequency and other non-consumptive-use data about 474,930 unique documents (no duplicate or close variants) mentioning the word "humanities" in English-language news sources. and other keywords related to the humanities in English-language news sources. Other keywords include "liberal arts," "the arts," "literature," "history," and "philosophy." The documents came from 850 U.S. and 437 international news sources with their associated blogs (including student newspapers) published mostly during 1989-2019. <em>(See WE1S Research Materials Overview for the relation between the project's "datasets" and "collections.")</em>
description: WE1S makes available word frequency data only "non-consumptive use". This dataset cannot be used to access, read, or reconstruct the original texts.The data has been archived in jsonl format (each json document is delimited by a line break).
DOI: 10.5281/zenodo.5068310
title: humanities_keywords Dataset

description: The WE1S humanities_keywords dataset contains word-frequency and other non-consumptive-use data about 474,930 unique documents (no duplicate or close variants) mentioning the word "humanities" in English-language news sources. and other keywords related to the humanities in English-language news sources. Other keywords include "liberal arts," "the arts," "literature," "history," and "philosophy." The documents came from 850 U.S. and 437 international news sources with their associated blogs (including student newspapers) published mostly during 1989-2019. <em>(See WE1S Research Materials Overview for the relation between the project's "datasets" and "collections.")</em>
description: WE1S makes available word frequency data only "non-consumptive use". This dataset cannot be used to access, read, or reconstruct the original texts.The data has been archived in jsonl format (each json document is delimited by a line break).
DOI: 10.5281/zenodo.5068756
title: comparison_sciences Dataset

description: The <code>comparison_sciences</code> dataset contains word-frequency and other non-consumptive-use data about 553,699 unique English-language news documents (no duplicate or close-variant documents) that contain the words "science" or "sciences." The documents came from U.S. mainstream and student news sources published during 1977-2019 (though mostly from 1985-2019). WE1S researchers use this data to understand how public discourse about the humanities compares to public discourse about science. We gathered this data using keyword searches for "science," which found articles containing either (or both) the words "science" and "sciences." We took data from the top 10 circulating newspapers in the U.S. and from University Wire sources (student newspapers). Documents in this dataset may also contain the word "humanities," just as documents in the <code>humanities_keyword</code> dataset may contain the words "science" or "sciences." <em>(See WE1S Research Materials Overview for the relation between the project's "datasets" and "collections.")</em>
description: WE1S makes available word frequency data only "non-consumptive use". This dataset cannot be used to access, read, or reconstruct the original texts.The data has been archived in jsonl format (each json document is delimited by a line break).
DOI: 10.5281/zenodo.5068755
title: comparison_sciences Dataset

description: The <code>comparison_sciences</code> dataset contains word-frequency and other non-consumptive-use data about 553,699 unique English-language news documents (no duplicate or close-variant documents) that contain the words "science" or "sciences." The documents came from U.S. mainstream and student news sources published during 1977-2019 (though mostly from 1985-2019). WE1S researchers use this data to understand how public discourse about the humanities compares to public discourse about science. We gathered this data using keyword searches for "science," which found articles containing either (or both) the words "science" and "sciences." We took data from the top 10 circulating newspapers in the U.S. and from University Wire sources (student newspapers). Documents in this dataset may also contain the word "humanities," just as documents in the <code>humanities_keyword</code> dataset may contain the words "science" or "sciences." <em>(See WE1S Research Materials Overview for the relation between the project's "datasets" and "collections.")</em>
description: WE1S makes available word frequency data only "non-consumptive use". This dataset cannot be used to access, read, or reconstruct the original texts.The data has been archived in jsonl format (each json document is delimited by a line break).
DOI: 10.5281/zenodo.5068699
title: comparison_not_humanities Dataset

description: The <code>comparison_not_humanities</code> dataset contains word-frequency and other non-consumptive-use data about 1,380,456 unique English-language news documents (no duplicate or close-variant documents) that do not contain the word "humanities." The documents came from mainstream U.S. news sources published during 2000-2019. WE1S gathered this data using keyword searches of 3 of the most common words in the English language (based on a well-known analysis of the Oxford English Corpus) that LexisNexis indexes and thus makes available for search: "person," "say," and "good". We took data from the top 15 circulating newspapers in the U.S. from 2000-2019, randomly selecting 1 month per year for each keyword in order to limit results to more manageable numbers (each year searched therefore includes data from 3 months of that year). We also took data from every other LexisNexis source from which we had gathered data for our <code>humanities_keywords</code> dataset. (We were not able fully to replicate previous searches, however, so some sources do not have comparison data.) For this purpose, we focused on the years 2013-2019 and randomly selected 1 month per year for each keyword in order to limit results. To exclude articles containing the word "humanities" from the results, we searched within each of our selected sources for articles containing "person <strong>AND NOT</strong> humanities," "say <strong>AND NOT</strong> humanities," and "good <strong>AND NOT</strong> humanities." This search included the plural forms of each of these words, so documents in this dataset may contain the words "persons," "people," "says," and "goods." <em>(See WE1S Research Materials Overview for the relation between the project's "datasets" and "collections.")</em>
description: WE1S makes available word frequency data only "non-consumptive use". This dataset cannot be used to access, read, or reconstruct the original texts.The data has been archived in jsonl format (each json document is delimited by a line break). Because this is a large dataset, the jsonl files are broken into 139 separate files, each containing 10,000 documents. These files are zipped for easy downloading.
DOI: 10.5281/zenodo.5068698
title: comparison_not_humanities Dataset

description: The <code>comparison_not_humanities</code> dataset contains word-frequency and other non-consumptive-use data about 1,380,456 unique English-language news documents (no duplicate or close-variant documents) that do not contain the word "humanities." The documents came from mainstream U.S. news sources published during 2000-2019. WE1S gathered this data using keyword searches of 3 of the most common words in the English language (based on a well-known analysis of the Oxford English Corpus) that LexisNexis indexes and thus makes available for search: "person," "say," and "good". We took data from the top 15 circulating newspapers in the U.S. from 2000-2019, randomly selecting 1 month per year for each keyword in order to limit results to more manageable numbers (each year searched therefore includes data from 3 months of that year). We also took data from every other LexisNexis source from which we had gathered data for our <code>humanities_keywords</code> dataset. (We were not able fully to replicate previous searches, however, so some sources do not have comparison data.) For this purpose, we focused on the years 2013-2019 and randomly selected 1 month per year for each keyword in order to limit results. To exclude articles containing the word "humanities" from the results, we searched within each of our selected sources for articles containing "person <strong>AND NOT</strong> humanities," "say <strong>AND NOT</strong> humanities," and "good <strong>AND NOT</strong> humanities." This search included the plural forms of each of these words, so documents in this dataset may contain the words "persons," "people," "says," and "goods." <em>(See WE1S Research Materials Overview for the relation between the project's "datasets" and "collections.")</em>
description: WE1S makes available word frequency data only "non-consumptive use". This dataset cannot be used to access, read, or reconstruct the original texts.The data has been archived in jsonl format (each json document is delimited by a line break). Because this is a large dataset, the jsonl files are broken into 139 separate files, each containing 10,000 documents. These files are zipped for easy downloading.
DOI: 10.5281/zenodo.5068267
title: reddit Dataset

description: The WE1S <code>reddit</code> dataset contains 1,034,174 Reddit comments containing the terms "humanities", "liberal arts", or "the arts", downloaded by Raymond Steding using pushshift.io. Initially, comments posted between 2006 and 2018 were collected. Comments from 2019 were later added. This data has been processed using the WhatEvery1Says preprocessor, and, in addition to metadata downloaded from Reddit, sentiment scores generated with Textblob have been recorded. A description of the process at an early stage in the production of this dataset can be found in Steding's blog post "A Digital Humanities Study of Reddit Student Discourse about the Humanities". <em>(See WE1S Research Materials Overview for the relation between the project's "datasets" and "collections.")</em>
description: The data has been archived in jsonl format (each json document is delimited by a line break).
DOI: 10.5281/zenodo.5068266
title: reddit Dataset

description: The WE1S <code>reddit</code> dataset contains 1,034,174 Reddit comments containing the terms "humanities", "liberal arts", or "the arts", downloaded by Raymond Steding using pushshift.io. Initially, comments posted between 2006 and 2018 were collected. Comments from 2019 were later added. This data has been processed using the WhatEvery1Says preprocessor, and, in addition to metadata downloaded from Reddit, sentiment scores generated with Textblob have been recorded. A description of the process at an early stage in the production of this dataset can be found in Steding's blog post "A Digital Humanities Study of Reddit Student Discourse about the Humanities". <em>(See WE1S Research Materials Overview for the relation between the project's "datasets" and "collections.")</em>
description: The data has been archived in jsonl format (each json document is delimited by a line break).
DOI: 10.5517/ccdc.csd.cc260gnc
title: CCDC 2026242: Experimental Crystal Structure Determination

description: Related Article: Pawan Kumar, Sourav Biswas, Abinash Swain, Joydev Acharya, Vierandra Kumar, Pankaj Kalita, Jessica Flores Gonzalez, Olivier Cador, Fabrice Pointillart, Gopalan Rajaraman, Vadapalli Chandrasekhar|2021|Inorg.Chem.|60|8530|doi:10.1021/acs.inorgchem.1c00249
DOI: 10.5517/ccdc.csd.cc260h80
title: CCDC 2026261: Experimental Crystal Structure Determination

description: Related Article: Pawan Kumar, Sourav Biswas, Abinash Swain, Joydev Acharya, Vierandra Kumar, Pankaj Kalita, Jessica Flores Gonzalez, Olivier Cador, Fabrice Pointillart, Gopalan Rajaraman, Vadapalli Chandrasekhar|2021|Inorg.Chem.|60|8530|doi:10.1021/acs.inorgchem.1c00249
DOI: 10.5517/ccdc.csd.cc260h91
title: CCDC 2026262: Experimental Crystal Structure Determination

description: Related Article: Pawan Kumar, Sourav Biswas, Abinash Swain, Joydev Acharya, Vierandra Kumar, Pankaj Kalita, Jessica Flores Gonzalez, Olivier Cador, Fabrice Pointillart, Gopalan Rajaraman, Vadapalli Chandrasekhar|2021|Inorg.Chem.|60|8530|doi:10.1021/acs.inorgchem.1c00249
DOI: 10.5517/ccdc.csd.cc272lrp
title: CCDC 2058082: Experimental Crystal Structure Determination

description: Related Article: Pawan Kumar, Sourav Biswas, Abinash Swain, Joydev Acharya, Vierandra Kumar, Pankaj Kalita, Jessica Flores Gonzalez, Olivier Cador, Fabrice Pointillart, Gopalan Rajaraman, Vadapalli Chandrasekhar|2021|Inorg.Chem.|60|8530|doi:10.1021/acs.inorgchem.1c00249
DOI: 10.5517/ccdc.csd.cc278p7g
title: CCDC 2063925: Experimental Crystal Structure Determination

description: Related Article: Sosthene Nyomba Kamanda, Ayesha Jacobs|2021|J.Mol.Struct.|1244|130830|doi:10.1016/j.molstruc.2021.130830
DOI: 10.5517/ccdc.csd.cc278p9j
title: CCDC 2063927: Experimental Crystal Structure Determination

description: Related Article: Sosthene Nyomba Kamanda, Ayesha Jacobs|2021|J.Mol.Struct.|1244|130830|doi:10.1016/j.molstruc.2021.130830
DOI: 10.5517/ccdc.csd.cc278pbk
title: CCDC 2063928: Experimental Crystal Structure Determination

description: Related Article: Sosthene Nyomba Kamanda, Ayesha Jacobs|2021|J.Mol.Struct.|1244|130830|doi:10.1016/j.molstruc.2021.130830
DOI: 10.5517/ccdc.csd.cc27drr5
title: CCDC 2067847: Experimental Crystal Structure Determination

description: Related Article: Cristiana Cesari, Beatrice Berti, Marco Bortoluzzi, Cristina Femoni, Maria Carmela Iapalucci, Stefano Zacchini|2021|Inorg.Chem.|60|8811|doi:10.1021/acs.inorgchem.1c00752
DOI: 10.5517/ccdc.csd.cc27drs6
title: CCDC 2067848: Experimental Crystal Structure Determination

description: Related Article: Cristiana Cesari, Beatrice Berti, Marco Bortoluzzi, Cristina Femoni, Maria Carmela Iapalucci, Stefano Zacchini|2021|Inorg.Chem.|60|8811|doi:10.1021/acs.inorgchem.1c00752
DOI: 10.5517/ccdc.csd.cc27drt7
title: CCDC 2067849: Experimental Crystal Structure Determination

description: Related Article: Cristiana Cesari, Beatrice Berti, Marco Bortoluzzi, Cristina Femoni, Maria Carmela Iapalucci, Stefano Zacchini|2021|Inorg.Chem.|60|8811|doi:10.1021/acs.inorgchem.1c00752
DOI: 10.5517/ccdc.csd.cc27drv8
title: CCDC 2067850: Experimental Crystal Structure Determination

description: Related Article: Cristiana Cesari, Beatrice Berti, Marco Bortoluzzi, Cristina Femoni, Maria Carmela Iapalucci, Stefano Zacchini|2021|Inorg.Chem.|60|8811|doi:10.1021/acs.inorgchem.1c00752
DOI: 10.5517/ccdc.csd.cc27drw9
title: CCDC 2067851: Experimental Crystal Structure Determination

description: Related Article: Cristiana Cesari, Beatrice Berti, Marco Bortoluzzi, Cristina Femoni, Maria Carmela Iapalucci, Stefano Zacchini|2021|Inorg.Chem.|60|8811|doi:10.1021/acs.inorgchem.1c00752
DOI: 10.5517/ccdc.csd.cc27drxb
title: CCDC 2067852: Experimental Crystal Structure Determination

description: Related Article: Cristiana Cesari, Beatrice Berti, Marco Bortoluzzi, Cristina Femoni, Maria Carmela Iapalucci, Stefano Zacchini|2021|Inorg.Chem.|60|8811|doi:10.1021/acs.inorgchem.1c00752
DOI: 10.5517/ccdc.csd.cc27dryc
title: CCDC 2067853: Experimental Crystal Structure Determination

description: Related Article: Cristiana Cesari, Beatrice Berti, Marco Bortoluzzi, Cristina Femoni, Maria Carmela Iapalucci, Stefano Zacchini|2021|Inorg.Chem.|60|8811|doi:10.1021/acs.inorgchem.1c00752
DOI: 10.5517/ccdc.csd.cc27ds1h
title: CCDC 2067856: Experimental Crystal Structure Determination

description: Related Article: Cristiana Cesari, Beatrice Berti, Marco Bortoluzzi, Cristina Femoni, Maria Carmela Iapalucci, Stefano Zacchini|2021|Inorg.Chem.|60|8811|doi:10.1021/acs.inorgchem.1c00752
DOI: 10.34740/kaggle/dsv/2395331
title: US counties COVID 19 dataset
DOI: 10.6084/m9.figshare.14907906
title: PTB data for machine learning modeling

description: Data used to compute the machine learning models used in the manuscript: <b>Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach</b>
DOI: 10.6084/m9.figshare.14907906.v1
title: PTB data for machine learning modeling

description: Data used to compute the machine learning models used in the manuscript: <b>Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach</b>
DOI: 10.6084/m9.figshare.14907909
title: Missing value analysis - PTB Brazil

description: missing value analysis of the PTB dataset
DOI: 10.5519/qd.vkaea9fu
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (419 records)
DOI: 10.5281/zenodo.4619862
title: data set

description: Data file for the article <strong>Compensating across the Offline and Online Service Mediums</strong>
DOI: 10.5281/zenodo.4619861
title: data set

description: Data file for the article <strong>Compensating across the Offline and Online Service Mediums</strong>
DOI: 10.5517/ccdc.csd.cc27k6tv
title: CCDC 2072127: Experimental Crystal Structure Determination

description: Related Article: Zheng Zeng, Wei Wang, Xiaohong Xiong, Nengxiu Zhu, Yangyang Xiong, Zhangwen Wei, Ji-Jun Jiang|2021|Inorg.Chem.|60|8456|doi:10.1021/acs.inorgchem.1c01045
DOI: 10.5517/ccdc.csd.cc27k6yz
title: CCDC 2072131: Experimental Crystal Structure Determination

description: Related Article: Zheng Zeng, Wei Wang, Xiaohong Xiong, Nengxiu Zhu, Yangyang Xiong, Zhangwen Wei, Ji-Jun Jiang|2021|Inorg.Chem.|60|8456|doi:10.1021/acs.inorgchem.1c01045
DOI: 10.5517/ccdc.csd.cc27nhp3
title: CCDC 2075285: Experimental Crystal Structure Determination

description: Related Article: Huiyeong Ju, Aya Taniguchi, Kaoru Kikukawa, Hiroki Horita, Mari Ikeda, Shunsuke Kuwahara, Yoichi Habata|2021|Inorg.Chem.|60|9141|doi:10.1021/acs.inorgchem.1c01161
DOI: 10.5517/ccdc.csd.cc27nhq4
title: CCDC 2075286: Experimental Crystal Structure Determination

description: Related Article: Huiyeong Ju, Aya Taniguchi, Kaoru Kikukawa, Hiroki Horita, Mari Ikeda, Shunsuke Kuwahara, Yoichi Habata|2021|Inorg.Chem.|60|9141|doi:10.1021/acs.inorgchem.1c01161
DOI: 10.5517/ccdc.csd.cc27nht7
title: CCDC 2075289: Experimental Crystal Structure Determination

description: Related Article: Huiyeong Ju, Aya Taniguchi, Kaoru Kikukawa, Hiroki Horita, Mari Ikeda, Shunsuke Kuwahara, Yoichi Habata|2021|Inorg.Chem.|60|9141|doi:10.1021/acs.inorgchem.1c01161
DOI: 10.5517/ccdc.csd.cc27drzd
title: CCDC 2067854: Experimental Crystal Structure Determination

description: Related Article: Cristiana Cesari, Beatrice Berti, Marco Bortoluzzi, Cristina Femoni, Maria Carmela Iapalucci, Stefano Zacchini|2021|Inorg.Chem.|60|8811|doi:10.1021/acs.inorgchem.1c00752
DOI: 10.5517/ccdc.csd.cc27ds0g
title: CCDC 2067855: Experimental Crystal Structure Determination

description: Related Article: Cristiana Cesari, Beatrice Berti, Marco Bortoluzzi, Cristina Femoni, Maria Carmela Iapalucci, Stefano Zacchini|2021|Inorg.Chem.|60|8811|doi:10.1021/acs.inorgchem.1c00752
DOI: 10.5517/ccdc.csd.cc278p8h
title: CCDC 2063926: Experimental Crystal Structure Determination

description: Related Article: Sosthene Nyomba Kamanda, Ayesha Jacobs|2021|J.Mol.Struct.|1244|130830|doi:10.1016/j.molstruc.2021.130830
DOI: 10.5281/zenodo.5069008
title: CoVaxxy Tweet IDs data set

description: A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see https://arxiv.org/abs/2101.07694 for more information.
DOI: 10.5281/zenodo.5068196
title: tvarchive Dataset

description: The <code>tvarchive</code> dataset contains word-frequency and other non-consumptive-use data about 1,205,844 English-language transcriptions of U.S. television news broadcasts. The documents were scraped from the Internet Archive's TV News Archive, which includes automatic captions of select U.S. news broadcasts since 2009. While the complete TV News Archive contains over 2.2 million transcripts, WE1S researchers were only able to collect about 1.2 million documents containing complete transcripts. The full TV News Archive includes transcripts from 33 networks and hundreds of shows. Unlike other WE1S datasets, the <code>tvarchive</code> dataset was not collected using keyword searches for specific terms (i.e., documents containing the word "humanities"). <em>(See WE1S Research Materials Overview for the relation between the project's "datasets" and "collections.")</em>
description: WE1S makes available word frequency data only "non-consumptive use". This dataset cannot be used to access, read, or reconstruct the original texts.The data has been archived in jsonl format (each json document is delimited by a line break).
DOI: 10.5281/zenodo.5068195
title: tvarchive Dataset

description: The <code>tvarchive</code> dataset contains word-frequency and other non-consumptive-use data about 1,205,844 English-language transcriptions of U.S. television news broadcasts. The documents were scraped from the Internet Archive's TV News Archive, which includes automatic captions of select U.S. news broadcasts since 2009. While the complete TV News Archive contains over 2.2 million transcripts, WE1S researchers were only able to collect about 1.2 million documents containing complete transcripts. The full TV News Archive includes transcripts from 33 networks and hundreds of shows. Unlike other WE1S datasets, the <code>tvarchive</code> dataset was not collected using keyword searches for specific terms (i.e., documents containing the word "humanities"). <em>(See WE1S Research Materials Overview for the relation between the project's "datasets" and "collections.")</em>
description: WE1S makes available word frequency data only "non-consumptive use". This dataset cannot be used to access, read, or reconstruct the original texts.The data has been archived in jsonl format (each json document is delimited by a line break).
DOI: 10.5519/qd.9sw8nh0c
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (131 records)
DOI: 10.5281/zenodo.5023747
title: IoT bugs based on Issues

description: I collected bugs related to IoT systems from GitHub Issues.
DOI: 10.6084/m9.figshare.14906799
title: anterior PFC seed connectivity maps

description: Single-subject anterior PFC connectivity maps. See accompanying metadata<br>
DOI: 10.6084/m9.figshare.14906799.v2
title: anterior PFC seed connectivity maps

description: Single-subject anterior PFC connectivity maps. See accompanying metadata<br>
DOI: 10.6084/m9.figshare.14906799.v1
title: seedconmap_ant_pfc.nii.zip

description: Single-subject anterior PFC connectivity maps. See accompanying metadata<br>
DOI: 10.5281/zenodo.5069029
title: Mr.

description: Primary data for the <sup>1</sup>H NMR, <sup>13</sup>C NMR and <sup>31</sup>P NMR spectra are uploaded at the web site:https://zenodo.org/. The files for each compound are contained in separate folders with sub- folders coded as follows: <sup>1</sup>H NMR, <sup>13</sup>C NMR and <sup>31</sup>P NMR. The files can be processed using the MestReC programs.
DOI: 10.5281/zenodo.5069028
title: Mr.

description: Primary data for the <sup>1</sup>H NMR, <sup>13</sup>C NMR and <sup>31</sup>P NMR spectra are uploaded at the web site:https://zenodo.org/. The files for each compound are contained in separate folders with sub- folders coded as follows: <sup>1</sup>H NMR, <sup>13</sup>C NMR and <sup>31</sup>P NMR. The files can be processed using the MestReC programs.
DOI: 10.6084/m9.figshare.14908002
title: Tribolium castaneum attraction to microfungi

description: Data collected from field and laboratory experiments. Tribolium castaneum adults were exposed to a range of microfungal species to determine if they were attractive to the insects.