cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4632968
title: Water sprinkling as a tool for heat abatement in farmed Iberian red deer: effects on calf growth and behaviour

description: Data sets and variable description used to produce "Water sprinkling as a tool for heat abatement in farmed Iberian red deer: effects on calf growth and behaviour" paper published in PlosOne 2021, by F. J. Pérez-Barbería, I. Arroyo-González, A. J. García, M. P. Serrano, L. Gallego, T. Landete-Castillejos
DOI: 10.5281/zenodo.4632967
title: Water sprinkling as a tool for heat abatement in farmed Iberian red deer: effects on calf growth and behaviour

description: Data sets and variable description used to produce "Water sprinkling as a tool for heat abatement in farmed Iberian red deer: effects on calf growth and behaviour" paper published in PlosOne 2021, by F. J. Pérez-Barbería, I. Arroyo-González, A. J. García, M. P. Serrano, L. Gallego, T. Landete-Castillejos
DOI: 10.3886/e120321v6-82121
title: Global Reactions to COVID-19 on Twitter: A Labelled Dataset with Latent Topic, Sentiment and Emotion Attributes

description: This project aims to present a large dataset for researchers to discover public conversation on Twitter surrounding the COVID-19 pandemic. As strong concerns and emotions are expressed in the publicly available tweets, we annotated seventeen latent semantic attributes for each public tweet using natural language processing techniques and machine-learning based algorithms. The latent semantic attributes include: 1) ten attributes indicating the tweet’s relevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness) and emotional intensities across four primary emotions of fear, anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
description: Twitter posts.
DOI: 10.5281/zenodo.4299148
title: Spine Generic Public Database (Single Subject)

description: <strong>About this dataset</strong> This dataset was acquired using the spine-generic protocol on a 38 y.o. male healthy subject, across multiple sites and multiple MRI vendors and models. The list of sites is available in participants.tsv. The contributors have the necessary ethics & permissions to share the data publicly. The dataset does not include any identifiable personal health information, including names, zip codes, dates of birth, facial features on structural scans. The dataset is about <strong>1 GB</strong> and it is structured according to the BIDS convention. <strong>Download</strong> We are using a tool to manage large datasets called git-annex. To download this dataset, you need to have `git` installed, and also install `git-annex` at version 8. Then run: <pre><code class="language-bash">git clone https://github.com/spine-generic/data-multi-subject && \ cd data-multi-subject && \ git annex init && \ git annex get</code></pre> You may substitute `git annex get` with more specific commands if you are only interested in certain subjects. For example: <pre><code class="language-bash">git annex get sub-nwu01/ sub-nwu03/ sub-nwu04/ sub-oxfordFmrib04/ sub-tokyoSkyra*/</code></pre> <br> <strong>Analysis</strong> The instructions to process this dataset are available in the spine-generic documentation.
DOI: 10.5281/zenodo.4632753
title: Lifetime hematopoiesis

description: Single-cell RNA-Seq analysis of lifetime human hematopoiesis.
description: "Human single-cell RNA-Seq data from fetal liver, pediatric and adult bone marrow were generated on the inDrops platform (Harvard Medical School, Boston, MA) and processed with the standard Dropseq pipeline (GRCh38/ENSEMBL85 annotation). The deposited matrix (in R's RDS format) contains count data for all genes and all cells used in all the main figures of the paper, referenced as Sample_cellbarcode."
DOI: 10.5281/zenodo.4632752
title: Lifetime hematopoiesis

description: Single-cell RNA-Seq analysis of lifetime human hematopoiesis.
description: "Human single-cell RNA-Seq data from fetal liver, pediatric and adult bone marrow were generated on the inDrops platform (Harvard Medical School, Boston, MA) and processed with the standard Dropseq pipeline (GRCh38/ENSEMBL85 annotation). The deposited matrix (in R's RDS format) contains count data for all genes and all cells used in all the main figures of the paper, referenced as Sample_cellbarcode."
DOI: 10.5281/zenodo.4420178
title: TweetsKB (Part 9, May 2020 - December 2020)

description: <strong>TweetsKB</strong><strong> </strong>is a public RDF corpus of anonymized data for a large collection of <strong>annotated </strong>tweets. The dataset currently contains data for more than <strong>2 billion tweets</strong>, spanning more than 7 years (February 2013 - December 2020). Metadata information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong> and <strong>URLs </strong>are exposed in RDF using established RDF/S vocabularies*. Example queries and more information are available through TweetsKB's home page: https://data.gesis.org/tweetskb/. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.5281/zenodo.4420177
title: TweetsKB (Part 9, May 2020 - December 2020)

description: <strong>TweetsKB</strong><strong> </strong>is a public RDF corpus of anonymized data for a large collection of <strong>annotated </strong>tweets. The dataset currently contains data for more than <strong>2 billion tweets</strong>, spanning more than 7 years (February 2013 - December 2020). Metadata information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong> and <strong>URLs </strong>are exposed in RDF using established RDF/S vocabularies*. Example queries and more information are available through TweetsKB's home page: https://data.gesis.org/tweetskb/. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.5281/zenodo.4593524
title: TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 3, June 2020 - December 2020)

description: <strong>TweetsCOV19</strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*. We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices: Tweet Id: Long. Username: String. Encrypted for privacy issues*. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ). #Followers: Integer. #Friends: Integer. #Retweets: Integer. #Favorites: Integer. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;". Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1"). Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;". Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;". URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;" To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.5281/zenodo.4593523
title: TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 3, June 2020 - December 2020)

description: <strong>TweetsCOV19</strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*. We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices: Tweet Id: Long. Username: String. Encrypted for privacy issues*. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ). #Followers: Integer. #Friends: Integer. #Retweets: Integer. #Favorites: Integer. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;". Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1"). Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;". Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;". URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;" To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.3886/e113923v1-82140
title: Replication data for: Correction and Update: The Economic Effects of Climate Change

description: Gremlins intervened in the preparation of my paper "The Economic Effects of Climate Change" published in the Spring 2009 issue of this journal. In Table 1 of that paper, titled "Estimates of the Welfare Impact of Climate Change," minus signs were dropped from the two impact estimates, one by Plambeck and Hope (1996) and one by Hope (2006). In Figure 1 of that paper, titled "Fourteen Estimates of the Global Economic Impact of Climate Change," and in the various analyses that support that figure, the minus sign was dropped from only one of the two estimates. The corresponding Table 1 and Figure 1 presented here correct these errors. Figure 2 titled,"Twenty-One Estimates of the Global Economic Impact of Climate Change" adds two overlooked estimates from before the time of the original 2009 paper and five more recent ones.
DOI: 10.5281/zenodo.4279890
title: Biomarker indices and concentrations and biomarker-based temperature estimates from Iberian Margin cores MD99-2331, MD95-2040 and MD95-2042 (160–45 ka BP)

description: Biomarker metadata and data for cores MD99-2331, MD95-2040, and MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT 2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. The 1σ analytical uncertainties are 0.008 (0.5 °C) for RI-OH, 0.007 (0.2 °C) for RI-OH′, 0.003 (0.1 °C) for TEX<sub>86</sub>, 0.263 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for U<sup>K′</sup><sub>37</sub>. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX<sub>86</sub><sup>H</sup>-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX<sub>86</sub><sup>H</sup> + 33.0 (Darfeuil et al., 2016). U<sup>K′</sup><sub>37</sub>-SST estimates are from the following global calibration: SST = 29.876 × U<sup>K′</sup><sub>37</sub> − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX<sub>86</sub>-SST and TEX<sub>86</sub>-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for U<sup>K′</sup><sub>37</sub>-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Core MD95-2040 U<sup>K′</sup><sub>37</sub> and summed di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentration data are from Pailler and Bard (2002). Core MD95-2042 U<sup>K′</sup><sub>37</sub> and summed di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentration data covering the 70–45 ka BP period are from Darfeuil et al. (2016). GDGT data for core MD95-2042 samples with TEX<sub>86</sub> values below 0.4 are deemed anomalous and are thus excluded from any description and analysis. Cmbsf, centimeters below sea floor; MIS, marine isotope stage; DO, Dansgaard–Oeschger event; GS, Greenland stadial; H, Heinrich event; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available.
DOI: 10.5281/zenodo.4279889
title: Biomarker indices and concentrations and biomarker-based temperature estimates from Iberian Margin cores MD99-2331, MD95-2040 and MD95-2042 (160–45 ka BP)

description: Biomarker metadata and data for cores MD99-2331, MD95-2040, and MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT 2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. The 1σ analytical uncertainties are 0.008 (0.5 °C) for RI-OH, 0.007 (0.2 °C) for RI-OH′, 0.003 (0.1 °C) for TEX<sub>86</sub>, 0.263 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for U<sup>K′</sup><sub>37</sub>. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX<sub>86</sub><sup>H</sup>-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX<sub>86</sub><sup>H</sup> + 33.0 (Darfeuil et al., 2016). U<sup>K′</sup><sub>37</sub>-SST estimates are from the following global calibration: SST = 29.876 × U<sup>K′</sup><sub>37</sub> − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX<sub>86</sub>-SST and TEX<sub>86</sub>-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for U<sup>K′</sup><sub>37</sub>-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Core MD95-2040 U<sup>K′</sup><sub>37</sub> and summed di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentration data are from Pailler and Bard (2002). Core MD95-2042 U<sup>K′</sup><sub>37</sub> and summed di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentration data covering the 70–45 ka BP period are from Darfeuil et al. (2016). GDGT data for core MD95-2042 samples with TEX<sub>86</sub> values below 0.4 are deemed anomalous and are thus excluded from any description and analysis. Cmbsf, centimeters below sea floor; MIS, marine isotope stage; DO, Dansgaard–Oeschger event; GS, Greenland stadial; H, Heinrich event; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available.
DOI: 10.5281/zenodo.4635344
title: Matlab data for simulation of past earthquakes in Gediz graben, turkey

description: The zip file contains FSDT code, excel input files, and databases for simulation of past earthquakes occurred in Manisa Fault Zone, Gediz graben, Turkey.
DOI: 10.5281/zenodo.4635343
title: Matlab data for simulation of past earthquakes in Gediz graben, turkey

description: The zip file contains FSDT code, excel input files, and databases for simulation of past earthquakes occurred in Manisa Fault Zone, Gediz graben, Turkey.
DOI: 10.34740/kaggle/dsv/2054752
title: Nearby Social Network - All Posts
DOI: 10.21227/zwgz-q495
title: January-27-Dong-Jiawei

description: January-27-Dong-Jiawei
DOI: 10.17632/ddvh8rdwxz.1
title: Data for: New approach for regulation of the internal recirculation flow rate by fuzzy logic in biological wastewater treatments

description: Proposed fuzzy controller for manipulation of the internal recirculation flow rate
DOI: 10.17632/yd3nn96zsm.1
title: Data for: Evolutionary Learning Based Simulation Optimization for Stochastic Job Shop Scheduling Problems

description: The Stochastic Job shop Scheduling Problem results Dateset proposed by Ghasemi et.al.
DOI: 10.17632/6j9gj2kfv8.1
title: Data for: Value and Momentum from Investors' Perspective: Evidence from Professionals' Risk-Ratings

description: Data of all experimental participants.
DOI: 10.17632/3vs8h448zk.1
title: Data for: Origins of homophiliy: Infants expect similar people to affiliate

description: Data for: Origins of homophiliy: Infants expect similar people to affiliate
DOI: 10.5281/zenodo.3370034
title: Bigwig files of ChIP and RNA sequencing experiments in Theileria annulata

description: This repository provides bigwig files for ChIP and RNA sequencing experiments in Theileria annulata. DeepTools generated Coverage files, ReadCount normalised files and SES normalised files are provided. Also provided are genome and annotation files for visualisation with IGV software.
DOI: 10.5281/zenodo.3370033
title: Bigwig files of ChIP and RNA sequencing experiments in Theileria annulata

description: This repository provides bigwig files for ChIP and RNA sequencing experiments in Theileria annulata. DeepTools generated Coverage files, ReadCount normalised files and SES normalised files are provided. Also provided are genome and annotation files for visualisation with IGV software.
DOI: 10.25919/82cy-5z78
title: Parkes observations for project P595 semester 2020OCTS_16

description: We propose to continue the PULSE@Parkes project in which high school students from Australia and around the World use the Parkes radio telescope in a remote observing model to observe and analyse pulsar data. The data from some PULSE@Parkes observations are used to support the FERMI Gamma ray mission, other observations supplement the P456 Pulsar Timing Array project and the remainder were chosen in order to make a detailed analysis of pulsar timing irregularities and intermittency.
DOI: 10.5281/zenodo.4635371
title: Data for the impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China

description: Data for the paper "The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China".
DOI: 10.5281/zenodo.4312245
title: Data for the impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China

description: Data for the paper "The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China".
DOI: 10.5517/ccdc.csd.cc234vbg
title: CCDC 1941075: Experimental Crystal Structure Determination

description: Related Article: Meng-Meng Su, Jia-Jia Kang, Shu-Qin Liu, Chang-Gong Meng, Yan-Qin Li, Jian-Jun Zhang, Jun Ni|2021|Inorg.Chem.|60|3773|doi:10.1021/acs.inorgchem.0c03529
DOI: 10.5517/ccdc.csd.cc234vdj
title: CCDC 1941077: Experimental Crystal Structure Determination

description: Related Article: Meng-Meng Su, Jia-Jia Kang, Shu-Qin Liu, Chang-Gong Meng, Yan-Qin Li, Jian-Jun Zhang, Jun Ni|2021|Inorg.Chem.|60|3773|doi:10.1021/acs.inorgchem.0c03529
DOI: 10.5517/ccdc.csd.cc25791s
title: CCDC 2003004: Experimental Crystal Structure Determination

description: Related Article: Roza Maouche, Sabrina Belaid, Belkacem Benmerad, Sofiane Bouacida, Carole Daiguebonne, Yan Suffren, Stéphane Freslon, Kevin Bernot, Olivier Guillou|2021|Inorg.Chem.|60|3707|doi:10.1021/acs.inorgchem.0c03417
DOI: 10.5517/ccdc.csd.cc269m5b
title: CCDC 2035031: Experimental Crystal Structure Determination

description: Related Article: Hiroaki Uchiyama, Dhanya Puthusseri, Jekabs Grins, Daniel Gribble, Gulaim A. Seisenbaeva, Vilas G. Pol, Vadim G. Kessler|2021|Inorg.Chem.|60|3593|doi:10.1021/acs.inorgchem.0c03087
DOI: 10.6084/m9.figshare.14261378
title: FOB microscopy - HeLa transfected polyglutamine

description: Fluorescence, optical diffraction tomography and Brillouin microscopy data of HeLa wildtype cells transiently transfected to form GFP-labeled polyglutamine aggregates.
DOI: 10.17870/bathspa.14174735
title: Coding the Contemplative Collective - Research Overview Document

description: Coding the Contemplative Collective investigates how aesthetic objectives related to contemplative practice might be embedded within the space of a mobile platform and how these objectives are shaped when authors from distinct domains (technology, business, and the arts) collaborate on a single artistic project. <br><br>The collection consists of the projects <i>$ echo </i>& <i>Infínity</i>, and the collaborative context for each project shaped the final artworks through managing the multiple perspectives on what constituted a suitable level and type of interactivity for a contemplative space.<br><br><b>This item contains a Research Overview document for the project.</b>
DOI: 10.17870/bathspa.14170703
title: Dramaturgies of Hauntology - Research Timeline & Research Questions

description: <i>Dramaturgies of Hauntology</i> encompasses two performances, <i>The Strange Geometry of Time</i> (2018) and <i>Dwelling</i> (2019), both of which aim to creatively activate theories of Hauntology and Spectrality, either through the haunted subject, placing the singularity of the self into doubt, or via explorations of ‘agency’ in places devoid of human existence, testing the implications of the uncanny in derelict locations. <br>This item contains the Research Timeline and Research Questions.
DOI: 10.25387/g3.14151611
title: Supplemental Material for Macias-Gonzalez et al., 2021

description: Raw data and supplementary figures for the manuscript number G3/2020/401943