cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3999420
title: NUclear Receptor Activity (NURA) dataset

description: <strong>Dataset information</strong> NURA (NUclear Receptor Activity) dataset collects curated information on small molecules that modulate nuclear receptors (NRs), to be intended for both pharmacological and toxicological applications. NURA contains bioactivity annotations for 15,247 molecules and 11 selected NRs, and it was obtained by integrating and curating data from toxicological and pharmacological databases (<em>i.e.,</em> Tox21, ChEMBL, NR-DBIND and BindingDB). NURA dataset is a useful tool to bridge the gap between toxicology- and medicinal-chemistry-related databases, as it is enriched in terms of number of molecules, structural diversity and covered atomic scaffolds compared to the single sources. To the best of our knowledge, NURA dataset is the most exhaustive collection of small molecules annotated for their modulation of the chosen nuclear receptors. NURA dataset is intended to support decision-making in pharmacology and toxicology, as well as to contribute to data-driven applications, such as machine learning. <strong>Content</strong> Three files are provided: "Nura_v1.0.0.csv" [datafile] dataset containing activity labels for each molecule (rows, identified by a unique ID and the canonical SMILES string) and each nuclear receptor endpoint (columns). "Nura_v1.0.0_details" [datafile], containing information on the individual records used to generate the dataset. "curation_pipeline.zip" [software], containing the data curation pipeline in KNIME ("NURA_Dataset.knwf") as well as a help file ('help.pdf'). Additional details on the content and curation pipeline can be found in the uploaded, non peer-reviewed, preprint ("NURApreprint.pdf"). <strong>Version information</strong> v1.0.1: data curation pipeline uploaded. v.1.0.0: initial upload. <strong>Contact</strong> If you have any question, contact us! (Francesca Grisoni, francesca.grisoni@pharma.ethz.ch; Davide Ballabio, davide.ballabio@unimib.it)
description: {"references": ["Valsecchi, Cecile, Francesca Grisoni, Stefano Motta, Laura Bonati, and Davide Ballabio. \"NURA: A curated dataset of nuclear receptor modulators.\" Toxicology and Applied Pharmacology (2020): 115244."]}
DOI: 10.5281/zenodo.3991561
title: NUclear Receptor Activity (NURA) dataset

description: <strong>Dataset information</strong> NURA (NUclear Receptor Activity) dataset collects curated information on small molecules that modulate nuclear receptors (NRs), to be intended for both pharmacological and toxicological applications. NURA contains bioactivity annotations for 15,247 molecules and 11 selected NRs, and it was obtained by integrating and curating data from toxicological and pharmacological databases (<em>i.e.,</em> Tox21, ChEMBL, NR-DBIND and BindingDB). NURA dataset is a useful tool to bridge the gap between toxicology- and medicinal-chemistry-related databases, as it is enriched in terms of number of molecules, structural diversity and covered atomic scaffolds compared to the single sources. To the best of our knowledge, NURA dataset is the most exhaustive collection of small molecules annotated for their modulation of the chosen nuclear receptors. NURA dataset is intended to support decision-making in pharmacology and toxicology, as well as to contribute to data-driven applications, such as machine learning. <strong>Content</strong> Three files are provided: "Nura_v1.0.0.csv" [datafile] dataset containing activity labels for each molecule (rows, identified by a unique ID and the canonical SMILES string) and each nuclear receptor endpoint (columns). "Nura_v1.0.0_details" [datafile], containing information on the individual records used to generate the dataset. "curation_pipeline.zip" [software], containing the data curation pipeline in KNIME ("NURA_Dataset.knwf") as well as a help file ('help.pdf'). Additional details on the content and curation pipeline can be found in the uploaded, non peer-reviewed, preprint ("NURApreprint.pdf"). <strong>Version information</strong> v1.0.1: data curation pipeline uploaded. v.1.0.0: initial upload. <strong>Contact</strong> If you have any question, contact us! (Francesca Grisoni, francesca.grisoni@pharma.ethz.ch; Davide Ballabio, davide.ballabio@unimib.it)
description: {"references": ["Valsecchi, Cecile, Francesca Grisoni, Stefano Motta, Laura Bonati, and Davide Ballabio. \"NURA: A curated dataset of nuclear receptor modulators.\" Toxicology and Applied Pharmacology (2020): 115244."]}
DOI: 10.21227/t263-8x74
title: Coronavirus (COVID-19) Global Sentiment Trend

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple positive peaks and negative peaks that need to be further studied. The n-grams on those peaking and dropping periods can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format.
DOI: 10.21227/3er1-wg08
title: Coronavirus (COVID-19) Global Sentiment Trend

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple positive peaks and negative peaks that need to be further studied. The n-grams on those peaking and dropping periods can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format.
DOI: 10.6084/m9.figshare.12482813
title: Data files and code on the comparison of SARS-CoV-2 with non-segmented RNA viruses

description: This fileset contains 15 data files and 1 ReadMe file.The data files are as follows:<br>Five Results files in <b>.fasta</b> file format. These are: Result_MacroDomain_.fasta, Result_Spike1_.fasta, Result_Spike2_.fasta, Result_Spike2protein_.fasta and Result_Viroporin_.fasta.<br>Two power point presentations (.<b>pptx</b> file format). These are: Analysis by MegaX_.pptx and Open Reading Frames_Conserved Domain Found in ORF and CDD_Children_.pptx.<br>Three data files in .<b>nwk</b> file format. These are: NeurotropicRNA.noSegmentR1_original tree.nwk, MacroDomainGen_BootstrapTree.nwk and ViroporinGen_BootstrapTree.nwk.<br>One code file in .<b>R</b> file format. This is: Protein Alignment RStudio_msa package_.R.<br>One file in .<b>tex</b> file format. This is: Covid19_.tex.<br>Two files in .<b>txt </b>file format. These are: Covid19_.txt and texshade.sty package_.txt.<br>One file in .<b>sty </b>file format. This is: texshade_.sty.<br>The 5 fasta files contain the results of the multiple protein sequence alignment.The power point presentations Open Reading Frames_Conserved Domain Found in ORF and CDD_Children.pptx contains the search results (snapshot figures) obtained by the Open Reading Frame (ORF) finder and Conserved Domains Database (CDD) database (NCBI). This file provides evidence to show how Figure2 and 3 were made.The power point presentation Analysis by MegaX_.pptx contains the evidence (parameters) to show how the sequences were aligned and how the tree files were made in Figure 1 by MegaX software.The three .nwk files (in Newick tree format) were produced using the MEGAX software. These files contain the data used to construct the phylogenetic trees shown in figures 1, 2B and 2C of the article.The R file contains all the codes required to produce figures 2 and 3 in the article.The Covid19.tex file works together with R Studio, containing the msa package (an R package for Multiple Sequence Alignment) to make Figures 2 and 3 in the article. The sty file is a system file for LaTex and contains codes. <br><br><b>Study aims and methodology</b>: The primary objective of the current study was to determine the possible evolutionary and molecular relationships between SARS-CoV-2 and non-segmented RNA viruses, especially the viruses that can infect the nervous system in infants and children.The whole-genome sequences of 35 non-segmented RNA viruses including 13 CoVs were retrieved from the National Center for Biotechnology Information (NCBI), for the purpose of phylogenetic analysis, which was conducted with MEGAX (Penn State University, PA, USA). All genomic sequences were aligned with the ClustalW algorithm and phylogenetic prediction inferred by the maximum likelihood method and Tamura-Nei model. RStudio (RStudio, Inc., Boston, MA, USA) with msa package was used for multiple protein sequence alignment. For more details on the methodology, please read the related article.<br>
DOI: 10.6084/m9.figshare.12482813.v1
title: Data files and code on the comparison of SARS-CoV-2 with non-segmented RNA viruses

description: This fileset contains 15 data files and 1 ReadMe file.The data files are as follows:<br>Five Results files in <b>.fasta</b> file format. These are: Result_MacroDomain_.fasta, Result_Spike1_.fasta, Result_Spike2_.fasta, Result_Spike2protein_.fasta and Result_Viroporin_.fasta.<br>Two power point presentations (.<b>pptx</b> file format). These are: Analysis by MegaX_.pptx and Open Reading Frames_Conserved Domain Found in ORF and CDD_Children_.pptx.<br>Three data files in .<b>nwk</b> file format. These are: NeurotropicRNA.noSegmentR1_original tree.nwk, MacroDomainGen_BootstrapTree.nwk and ViroporinGen_BootstrapTree.nwk.<br>One code file in .<b>R</b> file format. This is: Protein Alignment RStudio_msa package_.R.<br>One file in .<b>tex</b> file format. This is: Covid19_.tex.<br>Two files in .<b>txt </b>file format. These are: Covid19_.txt and texshade.sty package_.txt.<br>One file in .<b>sty </b>file format. This is: texshade_.sty.<br>The 5 fasta files contain the results of the multiple protein sequence alignment.The power point presentations Open Reading Frames_Conserved Domain Found in ORF and CDD_Children.pptx contains the search results (snapshot figures) obtained by the Open Reading Frame (ORF) finder and Conserved Domains Database (CDD) database (NCBI). This file provides evidence to show how Figure2 and 3 were made.The power point presentation Analysis by MegaX_.pptx contains the evidence (parameters) to show how the sequences were aligned and how the tree files were made in Figure 1 by MegaX software.The three .nwk files (in Newick tree format) were produced using the MEGAX software. These files contain the data used to construct the phylogenetic trees shown in figures 1, 2B and 2C of the article.The R file contains all the codes required to produce figures 2 and 3 in the article.The Covid19.tex file works together with R Studio, containing the msa package (an R package for Multiple Sequence Alignment) to make Figures 2 and 3 in the article. The sty file is a system file for LaTex and contains codes. <br><br><b>Study aims and methodology</b>: The primary objective of the current study was to determine the possible evolutionary and molecular relationships between SARS-CoV-2 and non-segmented RNA viruses, especially the viruses that can infect the nervous system in infants and children.The whole-genome sequences of 35 non-segmented RNA viruses including 13 CoVs were retrieved from the National Center for Biotechnology Information (NCBI), for the purpose of phylogenetic analysis, which was conducted with MEGAX (Penn State University, PA, USA). All genomic sequences were aligned with the ClustalW algorithm and phylogenetic prediction inferred by the maximum likelihood method and Tamura-Nei model. RStudio (RStudio, Inc., Boston, MA, USA) with msa package was used for multiple protein sequence alignment. For more details on the methodology, please read the related article.<br>
DOI: 10.5517/ccdc.csd.cc1p4g2c
title: CCDC 1553412: Experimental Crystal Structure Determination

description: Related Article: Philipp Langer, Lixu Yang, Constance R. Pfeiffer, William Lewis, Neil R. Champness|2019|Dalton Trans.|48|58|doi:10.1039/C8DT04096F
DOI: 10.5517/ccdc.csd.cc1p4g3d
title: CCDC 1553413: Experimental Crystal Structure Determination

description: Related Article: Philipp Langer, Lixu Yang, Constance R. Pfeiffer, William Lewis, Neil R. Champness|2019|Dalton Trans.|48|58|doi:10.1039/C8DT04096F
DOI: 10.5517/ccdc.csd.cc1p4g4f
title: CCDC 1553414: Experimental Crystal Structure Determination

description: Related Article: Philipp Langer, Lixu Yang, Constance R. Pfeiffer, William Lewis, Neil R. Champness|2019|Dalton Trans.|48|58|doi:10.1039/C8DT04096F
DOI: 10.5517/ccdc.csd.cc1p5hv6
title: CCDC 1554428: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p5hw7
title: CCDC 1554429: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p5hx8
title: CCDC 1554430: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p5hzb
title: CCDC 1554432: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p5j3h
title: CCDC 1554436: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p5j4j
title: CCDC 1554437: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p5j5k
title: CCDC 1554438: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p5j6l
title: CCDC 1554439: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p5j7m
title: CCDC 1554440: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p5j8n
title: CCDC 1554441: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p5j9p
title: CCDC 1554442: Experimental Crystal Structure Determination

description: Related Article: Sunil Kumar Nechipadappu, Indukuru Ramesh Reddy, Kartick Tarafder, Darshak R. Trivedi|2019|Cryst.Growth Des.|19|347|doi:10.1021/acs.cgd.8b01451
DOI: 10.5517/ccdc.csd.cc1p6r1p
title: CCDC 1555612: Experimental Crystal Structure Determination

description: Related Article: Maciej Ptak, Bartosz Zarychta, Dagmara Stefańska, Aneta Ciupa, Waldeci Paraguassu|2019|Dalton Trans.|48|242|doi:10.1039/C8DT04246B
DOI: 10.5517/ccdc.csd.cc1p6r2q
title: CCDC 1555613: Experimental Crystal Structure Determination

description: Related Article: Maciej Ptak, Bartosz Zarychta, Dagmara Stefańska, Aneta Ciupa, Waldeci Paraguassu|2019|Dalton Trans.|48|242|doi:10.1039/C8DT04246B
DOI: 10.5517/ccdc.csd.cc1p93dh
title: CCDC 1557886: Experimental Crystal Structure Determination

description: Related Article: Ting Zhang, Guang-Lu Zhang, Qian-Qian Yan, Li-Peng Zhou, Li-Xuan Cai, Xiao-Qing Guo, Qing-Fu Sun|2018|Inorg.Chem.|57|3596|doi:10.1021/acs.inorgchem.7b02502
DOI: 10.5517/ccdc.csd.cc1pbktd
title: CCDC 1559294: Experimental Crystal Structure Determination

description: Related Article: Nuray Esra Aksakal, Yurii Chumakov, Fatma Yuksel|2019|J.Chem.Cryst.|49|72|doi:10.1007/s10870-018-0735-1
DOI: 10.5517/ccdc.csd.cc1pbkvf
title: CCDC 1559295: Experimental Crystal Structure Determination

description: Related Article: Nuray Esra Aksakal, Yurii Chumakov, Fatma Yuksel|2019|J.Chem.Cryst.|49|72|doi:10.1007/s10870-018-0735-1
DOI: 10.5517/ccdc.csd.cc1pghrd
title: CCDC 1563074: Experimental Crystal Structure Determination

description: Related Article: Fangyuan Wang, Lei Mei, Weiqun Shi, Taiwei Chu|2018|J.Coord.Chem.|71|2415|doi:10.1080/00958972.2018.1479034
DOI: 10.5517/ccdc.csd.cc1pghsf
title: CCDC 1563075: Experimental Crystal Structure Determination

description: Related Article: Fangyuan Wang, Lei Mei, Weiqun Shi, Taiwei Chu|2018|J.Coord.Chem.|71|2415|doi:10.1080/00958972.2018.1479034
DOI: 10.6084/m9.figshare.13042664
title: Data_Sx_Impact.xlsx

description: Impact of COVID-19 on surgical care delivery across Indian Hospital. Period of study April 2019 with "control" period as April 2020.
DOI: 10.6084/m9.figshare.13042664.v1
title: Data_Sx_Impact.xlsx

description: Impact of COVID-19 on surgical care delivery across Indian Hospital. Period of study April 2019 with "control" period as April 2020.
DOI: 10.21227/5tbn-en04
title: Coronavirus (COVID-19) Global Sentiment Trend

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple positive peaks and negative peaks that need to be further studied. The n-grams on those peaking and dropping periods can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format.
DOI: 10.6084/m9.figshare.13043078
title: Patterns of ice cover for 31 lakes in the Northern Hemisphere

description: The dataset includes ice-free cover for 31 lakes that extend between 41 and 1146 years. The data was obtained from the National Snow and Ice Data Center. Connected with the ice-free cover data is the physical characteristics of lake obtained from HydroLAKES and mean winter air temperature obtained from CRU Climate Data Center.
DOI: 10.6084/m9.figshare.13043045
title: Meta-control & Categorization

description: Performances of individual pigeons (excel File)<br>
DOI: 10.6084/m9.figshare.13043045.v1
title: Meta-control & Categorization

description: Performances of individual pigeons (excel File)<br>
DOI: 10.21227/ksea-ys17
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.-------------------------------------Recently added dataset: Coronavirus (COVID-19) Global Sentiment Trend-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 235,960 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time of each CSV file present in this dataset.
DOI: 10.21227/z9g5-xy18
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.-------------------------------------Recently added dataset: Coronavirus (COVID-19) Global Sentiment Trend-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 235,960 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time of each CSV file present in this dataset.