cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e105583v5-82040
title: Apparent Per Capita Alcohol Consumption: National, State, and Regional Trends 1977-2018

description: <b>Version 5 release notes:</b>Changes release notes description, does not change data.<b>Version 4 release notes: <b><br></b></b>Adds 2018 data.<b></b><b></b>Please note that this file has some revisions on the pre-2018 data because the report changed those values, not due to any changes in my code. <b><b><br></b></b><b>Version 3 release notes: <b><br></b></b><b></b>Adds 2017 data<b><b>.<br></b></b><b>Version 2 release notes: <br></b>Makes columns showing the number of beers, glasses of wine, shots of liquor, and total drinks consumed based on the amount of ethanol consumed for each category that was already included.<b><br></b><br>This data set contains the per capita (persons aged 14+) consumption of ethanol (in gallons) for each state, Washington D.C., and totals for census regions and the United States as a whole, for the years 1977-2018. This includes total ethanol consumed as well as consumption by three categories: beer, wine, and shots of liquor ("spirits"). The PDF includes a method to convert the ethanol variables into total drinks of each type. I used this method to create columns for how many beers, glasses of wine, shots of liquor, and total drinks were consumed.<br><br>The PDF doesn't say how many ounces of fluid is in each drink type (except for the number_of_drinks_total variable) so I used the information provided by the National Institute on Alcohol Abuse and Alcoholism here - https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/what-standard-drink. Please note that the number_of_drinks_total variable is based on the conversion formula provided, not by adding the individual drink categories together and therefore will be slightly different than that way of measuring it. <br><br><b>This data comes from a report by Dr. Megan E. Slater and </b><b>Dr. Hillel R. Alpert at the National Institute on Alcohol Abuse and Alcoholism (downloaded here </b><b>https://pubs.niaaa.nih.gov/publications/surveillance115/CONS18.htm</b><b>)</b><b>. That report is one of the files available to download and is included as it explains the methodology the two authors used for the data. I am not affiliated with the original report at all. If you do use this data please also cite the original report.</b><br><br>When using this data consider that it is rate per capita (persons aged 14+) based on the population in that state so states that experience lots of visitors (e.g. Nevada and Washington D.C.) may have incorrect numbers. <br><br>
description: Persons aged 14 and over living in the United States<br>. Smallest Geographic Unit: State, Region, Country
DOI: 10.6084/m9.figshare.14141474
title: Metadata record for the manuscript: SOX4 and SMARCA4 cooperatively regulate PI3K signaling through transcriptional activation of TGFBR2

description: <b>Summary</b><br> This metadata record provides details of the data supporting the claims of the related manuscript: “SOX4 and SMARCA4 cooperatively regulate PI3K signaling through transcriptional activation of TGFBR2”. The related study aimed to demonstrate that the transcription factor SOX4 is a key regulator of PI3K signalling in triple-negative breast cancers (TNBCs). Type of data: RNA sequencing data (n=1,031) from human tumors; Illumina HT-29 v3 expression data for the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) project (n=1,992) Subject of data: human breast tumors and breast cancer cell lines <b>Data access</b> RNAseq data have been deposited in the Gene Expression Omnibus (GEO) under accession number https://identifiers.org/geo:GSE158295. Proteomic data from MIB/MS (https://identifiers.org/pride.project:PXD022596) and IP-MS (https://identifiers.org/pride.project:PXD022811) analyses have been made available through the PRIDE database. SMARCA4 (https://identifiers.org/geo:GSE72141), H3K27ac and H3K4me3 (https://identifiers.org/geo:GSE85158) ChIP-Seq raw data from MDA-MB-231 cells were acquired from Gene Expression Omnibus (GEO). The data for Figures 1, 2, and 5 are presented in supplemental Tables S1, S2, S3, S4, and S5, which are available in Excel format as part of this metadata record, as well as in PDF format via the supplementary materials of the related article. <b>Corresponding author(s) for this study</b> Michael L. Gatza, Ph.D., Rutgers Cancer Institute of New Jersey, 195 Little Albany Street CINJ 4558, New Brunswick NJ 08903, Ph: 732-235-8751, Michael.gatza@cinj.rutgers.edu
DOI: 10.6084/m9.figshare.14141474.v1
title: Metadata record for the manuscript: SOX4 and SMARCA4 cooperatively regulate PI3K signaling through transcriptional activation of TGFBR2

description: <b>Summary</b><br> This metadata record provides details of the data supporting the claims of the related manuscript: “SOX4 and SMARCA4 cooperatively regulate PI3K signaling through transcriptional activation of TGFBR2”. The related study aimed to demonstrate that the transcription factor SOX4 is a key regulator of PI3K signalling in triple-negative breast cancers (TNBCs). Type of data: RNA sequencing data (n=1,031) from human tumors; Illumina HT-29 v3 expression data for the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) project (n=1,992) Subject of data: human breast tumors and breast cancer cell lines <b>Data access</b> RNAseq data have been deposited in the Gene Expression Omnibus (GEO) under accession number https://identifiers.org/geo:GSE158295. Proteomic data from MIB/MS (https://identifiers.org/pride.project:PXD022596) and IP-MS (https://identifiers.org/pride.project:PXD022811) analyses have been made available through the PRIDE database. SMARCA4 (https://identifiers.org/geo:GSE72141), H3K27ac and H3K4me3 (https://identifiers.org/geo:GSE85158) ChIP-Seq raw data from MDA-MB-231 cells were acquired from Gene Expression Omnibus (GEO). The data for Figures 1, 2, and 5 are presented in supplemental Tables S1, S2, S3, S4, and S5, which are available in Excel format as part of this metadata record, as well as in PDF format via the supplementary materials of the related article. <b>Corresponding author(s) for this study</b> Michael L. Gatza, Ph.D., Rutgers Cancer Institute of New Jersey, 195 Little Albany Street CINJ 4558, New Brunswick NJ 08903, Ph: 732-235-8751, Michael.gatza@cinj.rutgers.edu
DOI: 10.5281/zenodo.3894543
title: Verification datasets for publication "Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP over the United Arab Emirates" - UAE weather station data

description: These are excel files containing measurement data for 2015 in the UAE, used for the submission to GMD journal, "Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP over the United Arab Emirates". All data is courtesy of NCM, UAE. Also included is a station_ids.csv file.
DOI: 10.5281/zenodo.4629926
title: Mesh files for idealized and image-based periarterial and perivenous spaces

description: Data set for the idealized periarterial (artery.geo), idelized perivenous (vein.geo), image-based periarterial (artery2.geo) and image-based perivenous space. Geo-files were used to create meshes in the study <em>Brain solute transport is more rapid in periarterial than perivenous spaces.</em>
DOI: 10.5519/qd.c7s9644r
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (17 records)
DOI: 10.5281/zenodo.4287381
title: Raw datasets for publication: The new Mountain Observatory of the Project "Optimizing Cloud Seeding by Advanced Remote Sensing and Land Cover Modification (OCAL)" in the United Arab Emirates: First results on Convection Initiation" - Case studies only (5 and 6 Sept 2018)

description: Here are zip files containing the raw data sets collected from the Halo Doppler lidar and the Mira Doppler cloud radar from the OCAL Observatory, UAE, on the 5th and 6th September 2018. All RHI and PPI scans at all angles and all time steps from these days are included.
DOI: 10.5517/ccdc.csd.cc25w1ch
title: CCDC 2021986: Experimental Crystal Structure Determination

description: Related Article: Ying-Ying Zhang, Feng-Yi Qiu, Hua-Tian Shi, Weibin Yu|2021|Chem.Commun.|57|3010|doi:10.1039/D0CC08052G
DOI: 10.5281/zenodo.4629921
title: Data for YAMP v0.9.5+ (https://github.com/alesssia/YAMP)

description: This dataset includes two files used by YAMP v0.9.5+ (https://github.com/alesssia/YAMP): - a FASTA file for removing human contaminant provided by Brian Bushnell (see here: http://seqanswers.com/forums/showthread.php?t=42552). Filename: hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz - the MetaPhlAn databases (version: mpa_v30_CHOCOPhlAn_201901) to be used for taxonomic profiling. Filename: metaphlan_databases.tar.gz
DOI: 10.5281/zenodo.4629920
title: Data for YAMP v0.9.5+ (https://github.com/alesssia/YAMP)

description: This dataset includes two files used by YAMP v0.9.5+ (https://github.com/alesssia/YAMP): - a FASTA file for removing human contaminant provided by Brian Bushnell (see here: http://seqanswers.com/forums/showthread.php?t=42552). Filename: hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz - the MetaPhlAn databases (version: mpa_v30_CHOCOPhlAn_201901) to be used for taxonomic profiling. Filename: metaphlan_databases.tar.gz
DOI: 10.5281/zenodo.4629544
title: Data from: A Hierarchical 3D-motion Learning Framework for Animal Spontaneous Behavior Mapping

description: These datasets are associated with Shank3B<sup>-/-</sup> mice spontaneous behavior test from A Hierarchical 3D-motion Learning Framework for Animal Spontaneous Behavior Mapping
description: To use these data, please cite the DOI (https://doi.org/10.5281/zenodo.4626951) for the repository provided by Zenodo, and a citation of the preprint: Kang Huang, Yaning Han, Ke Chen, Hongli Pan, Gaoyang Zhao, Wenling Yi, Xiaoxi Li, Siyuan Liu, Pengfei Wei, Liping Wang. "A Hierarchical 3D-motion Learning Framework for Animal Spontaneous Behavior Mapping." bioRxiv (2021): 2020-09.
DOI: 10.5281/zenodo.4629543
title: Data from: A Hierarchical 3D-motion Learning Framework for Animal Spontaneous Behavior Mapping

description: These datasets are associated with Shank3B<sup>-/-</sup> mice spontaneous behavior test from A Hierarchical 3D-motion Learning Framework for Animal Spontaneous Behavior Mapping
description: To use these data, please cite the DOI (https://doi.org/10.5281/zenodo.4626951) for the repository provided by Zenodo, and a citation of the preprint: Kang Huang, Yaning Han, Ke Chen, Hongli Pan, Gaoyang Zhao, Wenling Yi, Xiaoxi Li, Siyuan Liu, Pengfei Wei, Liping Wang. "A Hierarchical 3D-motion Learning Framework for Animal Spontaneous Behavior Mapping." bioRxiv (2021): 2020-09.
DOI: 10.6084/m9.figshare.13489323
title: Genome-wide screening of SARS-CoV-2 infection-related genes based on the blood leucocyte sequencing dataset of patients with COVID-19

description: The coronavirus disease 2019 (COVID-19) is a global epidemic disease caused by a novel virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing serious adverse effects on human health. In this study, we collected the blood leucocyte sequencing dataset of COVID-19 patients from the GEO database and obtained differentially expressed genes (DEGs). We further analysed these DEGs by protein–protein interaction analysis and Gene Ontology enrichment analysis and obtained the DEGs closely related to SARS-CoV-2 infection. Then, we constructed a 6-gene model (comprising <i>IFIT3</i>, <i>OASL</i>, <i>USP18</i>, <i>XAF1</i>, <i>IFI27</i> and <i>EPSTI1</i>) by logistic regression analysis and calculated the area under the ROC curve (AUC) for the diagnosis of COVID-19. The AUC values of the training group, testing group and entire group were 0.930, 0.914 and 0.921, respectively. The six genes were highly expressed in patients with COVID-19 and positively correlated with the expression of SARS-CoV-2 invasion-related genes (<i>ACE2</i>, <i>TMPRSS2</i>, <i>CTSB</i> and <i>CTSL</i>). The risk score calculated by this model was also positively correlated with the expression of <i>TMPRSS2</i>, <i>CTSB</i> and <i>CTSL</i>, indicating that the six genes were closely related to SARS-CoV-2 infection. In conclusion, we comprehensively analysed the functions of DEGs in the blood leucocytes of patients with COVID-19 and constructed a 6-gene model that may contribute to the diagnosis of COVID-19, which contributes to the development of new diagnostic and therapeutic ideas for COVID-19. Moreover, these six genes may be the therapeutic targets for COVID-19.<br>
DOI: 10.6084/m9.figshare.13489323.v3
title: Genome-wide screening of SARS-CoV-2 infection-related genes based on the blood leucocyte sequencing dataset of patients with COVID-19

description: The coronavirus disease 2019 (COVID-19) is a global epidemic disease caused by a novel virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing serious adverse effects on human health. In this study, we collected the blood leucocyte sequencing dataset of COVID-19 patients from the GEO database and obtained differentially expressed genes (DEGs). We further analysed these DEGs by protein–protein interaction analysis and Gene Ontology enrichment analysis and obtained the DEGs closely related to SARS-CoV-2 infection. Then, we constructed a 6-gene model (comprising <i>IFIT3</i>, <i>OASL</i>, <i>USP18</i>, <i>XAF1</i>, <i>IFI27</i> and <i>EPSTI1</i>) by logistic regression analysis and calculated the area under the ROC curve (AUC) for the diagnosis of COVID-19. The AUC values of the training group, testing group and entire group were 0.930, 0.914 and 0.921, respectively. The six genes were highly expressed in patients with COVID-19 and positively correlated with the expression of SARS-CoV-2 invasion-related genes (<i>ACE2</i>, <i>TMPRSS2</i>, <i>CTSB</i> and <i>CTSL</i>). The risk score calculated by this model was also positively correlated with the expression of <i>TMPRSS2</i>, <i>CTSB</i> and <i>CTSL</i>, indicating that the six genes were closely related to SARS-CoV-2 infection. In conclusion, we comprehensively analysed the functions of DEGs in the blood leucocytes of patients with COVID-19 and constructed a 6-gene model that may contribute to the diagnosis of COVID-19, which contributes to the development of new diagnostic and therapeutic ideas for COVID-19. Moreover, these six genes may be the therapeutic targets for COVID-19.<br>
DOI: 10.6084/m9.figshare.13489323.v2
title: Identification of SARS-CoV-2 infection related genes based on the blood leukocytes sequencing dataset of patients with COVID-19

description: The coronavirus disease 2019 (COVID-19) is a global epidemic disease caused by a novel virus, SARS-CoV-2, causing serious adverse effects on human health. The development of more effective methods for the diagnosis and treatment of COVID-19 is of great significance to control the spread of the epidemic. In this study, we collected the blood leukocyte sequencing dataset of COVID-19 patients from the GEO database and obtained differentially expressed genes (DEGs). We further analyzed these DEGs by protein-protein interaction (PPI) analysis and gene ontology (GO) enrichment analysis and obtained the DEGs closely related to SARS-CoV-2 infection. Then, we constructed a 6-gene model, composed of 6 genes (IFIT3, OASL, USP18, XAF1, IFI27, and EPSTI1) by logistic regression analysis, and the area under the ROC curve (AUC) was calculated. The AUC of the training group, testing group, and entire group was 0.930, 0.914, and 0.921, respectively, and this model combined with ferritin and fibrinogen in the diagnosis of COVID-19 can achieve better diagnostic efficiency (AUC=0.976). The results of expression analysis showed that these six genes were highly expressed in patients with COVID-19 and positively correlated with the expression of SARS-CoV-2 invasion related genes (ACE2, TMPRSS2, CTSB, and CTSL). The risk score calculated by this model was also positively correlated with the expression of TMPRSS2, CTSB, and CTSL, indicating that the six genes were closely related to SARS-CoV-2 infection. In conclusion, our study comprehensively analyzed the functions of DEGs in blood leukocytes of COVID-19 patients and constructed a 6-gene model that may contribute to the diagnosis of COVID-19, which contributes to the development of new diagnostic and therapeutic ideas for COVID-19. Also, these six genes may be therapeutic targets for COVID-19.
DOI: 10.15129/a9ef64ab-3c90-4bb9-aed5-ac47d5f74ed7
title: Data for: "Microstructure Formation in Radially Counterstreaming Electron Flows"

description: Dataset to support the letter "Microstructure Formation in Radially Counterstreaming Electron Flows". The dataset contains EPOCH particle-in-cell input decks as well as the data used to obtain figure 1,2 and 6
DOI: 10.15468/n9bzbt
title: Type Specimens Of Birds In The American Museum Of Natural History Part 12. Passeriformes: Ploceidae, Sturnidae, Buphagidae, Oriolidae, Dicruridae, Callaeidae, Grallinidae, Corcoracidae, Artamidae, Cracticidae, Ptilonorhynchidae, Cnemophilidae, Paradisaeidae, And Corvidae

description: This dataset contains the digitized treatments in Plazi based on the original journal article Lecroy, Mary (2014): Type Specimens Of Birds In The American Museum Of Natural History Part 12. Passeriformes: Ploceidae, Sturnidae, Buphagidae, Oriolidae, Dicruridae, Callaeidae, Grallinidae, Corcoracidae, Artamidae, Cracticidae, Ptilonorhynchidae, Cnemophilidae, Paradisaeidae, And Corvidae. Bulletin of the American Museum of Natural History 2014 (393): 1-165, DOI: https://doi.org/10.1206/885.1
DOI: 10.21227/jw80-qb27
title: Day-Ahead Forecasting using LSTM with rolling windows

description: The project involves day-ahead forecasting which takes a model fit into historical data and predicts future demand of electricity. Time series forecasting methods are used as the dataset contains an explicit order dependence between observations, i.e. a time dimension which makes the problem difficult to handle but makes the prediction more accurate. Various machine learning and deep learning algorithms were used to accomplish this work. Results obtained by autoregressive integrated moving average(ARIMA), autoregressive moving average with explanatory variable(ARIMAX), seasonal autoregressive moving average(SARIMA), seasonal autoregressive moving average(SARIMAX) and long short term memory(LSTM) recurrent neural networks with rolling windows were compared and the one with least error, i.e. LSTM with rolling windows was used for time series prediction.
DOI: 10.6084/m9.figshare.12912257
title: GazeBase Data Repository

description: GazeBase is a large-scale longitudinal dataset consisting of 12,334 monocular (left) eye-movement recordings captured from 322 college-aged subjects. Subjects completed a battery of seven tasks in two adjacent sessions during each round of recording, including a - 1) fixation task, 2) horizontal saccade task, 3) random oblique saccade task, 4) reading task, 5/6) free viewing of cinematic video task, and 7) gaze-driven gaming task. A total of nine rounds of recording were conducted over a 37 month period, with participants in each subsequent round recruited exclusively from prior rounds. All data was collected using an EyeLink 1000 eye tracker at a 1,000 Hz sampling rate, with a calibration and validation protocol performed before each task to ensure data quality. Due to its large number of participants and longitudinal nature, GazeBase is well suited for exploring research hypotheses in eye movement biometrics, along with other emerging applications applying machine learning techniques to eye movement signal analysis. Eye movement classification labels produced by the instrument's real-time parser are provided for a relevant subset of GazeBase, along with pupil area. Full details regarding the experimental protocol are available in the corresponding data descriptor manuscript.
DOI: 10.6084/m9.figshare.14269022
title: Supplementary Material for: Appraisal of the Current Guidelines for Management of Malignant Left-Sided Colonic Obstruction Using the Appraisal of Guidelines Research and Evaluation II Instrument

description: <b><i>Introduction:</i></b> Colorectal cancer (CRC) is the fourth leading cause of death with 1.4 million new cases occurring annually worldwide. High-quality clinical practice guidelines are needed to tailor high-quality individualized treatment. The aim of the present study was to evaluate the methodological quality of the current guidelines for the management of acute malignant left-sided colonic bowel obstruction. <b><i>Methods:</i></b> A systematic search of the literature was carried out using electronic databases. The Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument was used to assess the quality of each guideline. <b><i>Results:</i></b> Search results returned a total of 14 guidelines appropriate for assessment. Both domain I (scope and purpose) and domain VI (editorial independence) were assessed with the same median score of 83%. The lowest scoring domain was domain V (applicability), scoring only 43%. The 2 guidelines that had the highest score were the National Institute for Health and Care Excellence (NICE) and Scottish Intercollegiate Guidelines Network (SIGN), each scoring 100%. However, there were significant variations in terms of quality. The NICE and New Zealand guidelines were voted unanimously for use unchanged, whilst 8 other guidelines were voted for use with modifications. <b><i>Conclusion:</i></b> Variation in guideline quality in CRC is a concern despite some clearly excellent published guidelines. All guidelines score poorly when it comes to describing how the guidelines could be applied. Lack of patient participation in guideline development is also a shortcoming that requires urgent redress.
DOI: 10.6084/m9.figshare.14269022.v1
title: Supplementary Material for: Appraisal of the Current Guidelines for Management of Malignant Left-Sided Colonic Obstruction Using the Appraisal of Guidelines Research and Evaluation II Instrument

description: <b><i>Introduction:</i></b> Colorectal cancer (CRC) is the fourth leading cause of death with 1.4 million new cases occurring annually worldwide. High-quality clinical practice guidelines are needed to tailor high-quality individualized treatment. The aim of the present study was to evaluate the methodological quality of the current guidelines for the management of acute malignant left-sided colonic bowel obstruction. <b><i>Methods:</i></b> A systematic search of the literature was carried out using electronic databases. The Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument was used to assess the quality of each guideline. <b><i>Results:</i></b> Search results returned a total of 14 guidelines appropriate for assessment. Both domain I (scope and purpose) and domain VI (editorial independence) were assessed with the same median score of 83%. The lowest scoring domain was domain V (applicability), scoring only 43%. The 2 guidelines that had the highest score were the National Institute for Health and Care Excellence (NICE) and Scottish Intercollegiate Guidelines Network (SIGN), each scoring 100%. However, there were significant variations in terms of quality. The NICE and New Zealand guidelines were voted unanimously for use unchanged, whilst 8 other guidelines were voted for use with modifications. <b><i>Conclusion:</i></b> Variation in guideline quality in CRC is a concern despite some clearly excellent published guidelines. All guidelines score poorly when it comes to describing how the guidelines could be applied. Lack of patient participation in guideline development is also a shortcoming that requires urgent redress.