cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3723273
title: Thermal model of the Los Humeros super-hot geothermal system, Mexico

description: The dataset contains 3D thermal model (format - .vtk and .h5) of Los Humeros geothermal system at a local scale (extent defined in Calcagno et al., 2018). The boundary conditions used for this thermal model are obtained from Scenario 3b of regional model discussed in<strong> </strong>EU Deliverable D6.3<strong> </strong>(10.5281/zenodo.3723039) and D6.6 (10.5281/zenodo.3723224). Before using the results of the model, the user is advised to carefully read the model parameters, assumptions and uncertainties associated with the model as reported in Deliverable D6.3 and Deliverable D6.6. The .h5 file contains data and attributes (quantity, unit) The .vtk files contains the following information x, y, z UTM coordinates (m) temp Temperature (°C) head Hydraulic head (m) pres Pressure (MPa) por Porosity (-) q Heat flow (W m<sup>-2</sup>) kx, ky, kz Permeability (m<sup>2</sup>) vx, vy, vz Specific discharge or Darcy velocity (m s<sup>-1</sup>) lx, ly, lz Thermal conductivity (W m<sup>-1</sup> K<sup>-1</sup>) Additional information regarding the model is presented in the PDF document.
description: This version contains coordinate error in the .vtk file. Please refer to 10.5281/zenodo.3724084 for the corrected dataset.
description: {"references": ["Deb, P., Knapp, D, Marquart, G., & Montegrossi, G., 2019. Thermal model of the Los Humeros super-hot geothermal system, Mexico (Version D6.6_Report_Local). Zenodo. https://doi.org/10.5281/zenodo.3723224", "Deb, P., Knapp, D., Marquart, G., & Clauser, C. ,2019. Thermal model of the Los Humeros super-hot geothermal system, Mexico (Version D6.3_Report_Regional). Zenodo, doi: 10.5281/zenodo.3719193", "Deb et al, 2019, Modeling natural steady-state of super hot geothermal rerservoir at Los Humeros, Mexico, European Geothermal Congress, 2019, The Hague, The Netherlands, doi: 10.5281/zenodo.3719198", "Calcagno, P., Evanno, G., Trumpy, E., Guti\u00e9rrez-Negr\u00edn, L. C., Mac\u00edas, J. L., Carrasco-N\u00fa\u00f1ez, G., Liotta, D., 2018. Preliminary 3-D geological models of Los Humeros and Acoculco geothermal fields (Mexico) \u2013 H2020GEMex Project., Advances in Geosciences, 45, 321 \u2013 333.", "Calcagno, P., Trumpy, E., Guti\u00e9rrez-Negr\u00edn, L. C., Norini, G., Mac\u00edas, J. L., Carrasco-N\u00fa\u00f1ez, G., Liotta, D., Gardu\u00f1o-Monroy, V. H., Hersir, G. P., Vaessen, L., Evanno, G., Arango Galv\u00e1n, C.: Updating the 3D Geomodels of Los Humeros and Acoculco Geothermal Systems (Mexico) \u2013 H2020 GEMex Project, World Geothermal Congress proceedings, 2020 (submitted)", "B\u00e4r, K., Weydt, L., Deb, P., Kummerow, J.,Ferrero, A., Vagnon, F., Colombero, C., Chicco, J., Vinciguerra, S., Siegfried, R., Comina, C., Mandrone, G., Laciska, A., Kemp, S., Kilpatrick, A., Rochelle, C., Milsch, H., Vacha, D., Rushton, J., 2019. Comprehensive report on the rock and fluid samples and their physical properties in the Acoculco and Los Humeros regions. GEMex \u2013 H2020 project report D6.1, https://data.d4science.net/zVLc"]}
DOI: 10.5281/zenodo.3723289
title: The prognostic value of the detection of microbial translocation in the blood of colorectal cancer patients

description: Results of a retrospective analysis aiming to investigate whether microbial translocation occurs in colorectal cancer patients and to evaluate the usefulness of PCR for diagnosis of such translocation in the blood of these patients before treatment initiation. The secondary objective of the study was the correlation of the presence of previously investigated by our group toll-like and vitamin D receptor gene polymorphisms with the detection of microbial DNA fragments in the blood of colorectal cancer patients. The detection of microbial DNA fragments in CRC patients highlights the role of these microbes in cancer development, progression and patients’ survival. Clearly, this increased risk in colorectal patients should be taken into consideration by the clinicians when planning any surgical intervations or during chemotherapy
DOI: 10.5281/zenodo.3723290
title: The prognostic value of the detection of microbial translocation in the blood of colorectal cancer patients

description: Results of a retrospective analysis aiming to investigate whether microbial translocation occurs in colorectal cancer patients and to evaluate the usefulness of PCR for diagnosis of such translocation in the blood of these patients before treatment initiation. The secondary objective of the study was the correlation of the presence of previously investigated by our group toll-like and vitamin D receptor gene polymorphisms with the detection of microbial DNA fragments in the blood of colorectal cancer patients. The detection of microbial DNA fragments in CRC patients highlights the role of these microbes in cancer development, progression and patients’ survival. Clearly, this increased risk in colorectal patients should be taken into consideration by the clinicians when planning any surgical intervations or during chemotherapy
DOI: 10.5281/zenodo.3723298
title: The eclipsing binary systems with the delta Scuti component. KIC10661783

description: A set of inlists for MESA 12115, used to compute a grid of binary evolutionary tracks used in paper "The eclipsing binary systems with the delta Scuti component. KIC10661783".
DOI: 10.5281/zenodo.3723291
title: Predicting the Long-Term Stability of Compact Multi-Planet Systems

description: REBOUND N-body simulation archives used to train and test the machine learning models in Tamayo et al. (2020), 'Predicting the Long-Term Stability of Compact Multi-Planet Systems'. See https://github.com/dtamayo/spock for scripts to process the data, train and test the SPOCK XGBoost model.
DOI: 10.5281/zenodo.3723292
title: Predicting the Long-Term Stability of Compact Multi-Planet Systems

description: REBOUND N-body simulation archives used to train and test the machine learning models in Tamayo et al. (2020), 'Predicting the Long-Term Stability of Compact Multi-Planet Systems'. See https://github.com/dtamayo/spock for scripts to process the data, train and test the SPOCK XGBoost model.
DOI: 10.5281/zenodo.3723299
title: LUNA16 Part 2/2

description: This is part 2/2 of the record LUNA16 Part 1/2.
DOI: 10.5281/zenodo.3723319
title: Number of cases of coronavirus disease (COVID-19) in Ireland

description: Datasets in this publication report the number of diagnoses with coronavirus disease (COVID-19) as reported by the Department of Health in Ireland. This includes new cases diagnosed per day and cumulative cases, as well as cases across age groups. The latter also include population estimates by age group for 2019 from Ireland's Central Statistics Office, in order to express cases per million population. For the files <em>YYYYMMDD_covid_ie_age_groups.csv</em>, variable descriptions are as follows: age_group: Age groups, in years cases: Total cases of COVID-19 diagnosed in Ireland by age group, as per the Department of Health pop_estimate: National population estimates by age group for 2019 in Ireland, as per the Central Statistics Office (Table 7 https://www.cso.ie/en/releasesandpublications/er/pme/populationandmigrationestimatesapril2019/), expressed in thousands. cases_per_million: Cases of COVID-19 diagnosed in Ireland by age group, expressed per 1 million individuals For the files <em>YYYYMMDD_covid_ie_daily_cases</em>, variable descriptions are as follows: date: Date, in DD-MM-YYYY format daily_cases: New cases of COVID-19 diagnosed per day in Ireland, as per the Department of Health (https://www.gov.ie/en/news/7e0924-latest-updates-on-covid-19-coronavirus/) cumulative_cases: Cumulative number of COVID-19 cases in Ireland percent_daily_increase: New cases of COVID-19 diagnosed per day in Ireland as a percentage of cumulative number of cases up to that date.
DOI: 10.5281/zenodo.3722580
title: Fastq data for Vero E6 cell infeceted with SARS CoV 2

description: Vero cells infected with SARS-CoV 2 and the transcriptome sequenced by dRNAseq on nanopore.
description: linked to a proteome produced at the same time
DOI: 10.5281/zenodo.3722590
title: Vero cells infected with SARS CoV 2 no quantitation slices 1-10 of 20

description: Slices 1 -10 of 20 slices from a gel. Vero cells infected with SARS CoV2 at Bristol University march 2020
DOI: 10.5281/zenodo.3722591
title: Vero cells infected with SARS CoV 2 no quantitation slices 1-10 of 20

description: Slices 1 -10 of 20 slices from a gel. Vero cells infected with SARS CoV2 at Bristol University march 2020
DOI: 10.5281/zenodo.3722596
title: vero cells infected with SARS CoV2 slices 11-20 of 20 slices

description: Vero cells infected with SARS COV 2 at Bristol University march 2020 slices 11-20 form a 20 slice gel with no quant. matched to a dRNAseq dataset done at the same time.
DOI: 10.5281/zenodo.3722597
title: vero cells infected with SARS CoV2 slices 11-20 of 20 slices

description: Vero cells infected with SARS COV 2 at Bristol University march 2020 slices 11-20 form a 20 slice gel with no quant. matched to a dRNAseq dataset done at the same time.
DOI: 10.5281/zenodo.3722579
title: Fastq data for Vero E6 cell infeceted with SARS CoV 2

description: Vero cells infected with SARS-CoV 2 and the transcriptome sequenced by dRNAseq on nanopore.
description: linked to a proteome produced at the same time
DOI: 10.21227/4pvg-s072
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 2,475,980This dataset includes CSV files which contain the tweet IDs. The tweets have been collected by the LSTM model deployed here at sentiment.live. The model monitors the real-time Twitter feed for corona virus-related tweets. As per the Twitter Developer Policy, it is not possible for me to provide information other than the Tweet IDs (this dataset has been completely re-designed on March 20, 2020, to comply with data sharing policies set by Twitter). Note: This dataset should be solely used for non-commercial research purpose.Schema of the CSV files: First column: tweet ID, Second column: Sentiment score for the particular tweet.Files details:corona_tweets_01.csv: 831,327 tweets (March 20, 2020 1:37:15.711 AM GMT+05:45 to March 20, 2020 10:28:19.305 AM GMT+05:45)corona_tweets_02.csv: 870,924 tweets (March 20, 2020 10:31:07.044 AM GMT+05:45 to March 20, 2020 9:43:47.869 PM GMT+05:45)corona_tweets_03.csv: 773,729 tweets (March 20, 2020 9:49:26.736 PM GMT+05:45 to March 21, 2020 9:25:05.488 AM GMT+05:45)To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the second column. New databases will be added to this dataset every day. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.5281/zenodo.3723109
title: mantle structure of Fujian and Taiwan Strait

description: 3-D crustal and upper mantle shear-wave velocity structure of Fujian and Taiwan Strait: DSurfTomo.inMeasure.dat; Rayleigh wave phase velocity dispersion curves from ambient noise cross-correlation and teleseismic surface-wave two-station method: mergedata.zip
DOI: 10.5281/zenodo.3723110
title: mantle structure of Fujian and Taiwan Strait

description: 3-D crustal and upper mantle shear-wave velocity structure of Fujian and Taiwan Strait: DSurfTomo.inMeasure.dat; Rayleigh wave phase velocity dispersion curves from ambient noise cross-correlation and teleseismic surface-wave two-station method: mergedata.zip
DOI: 10.21227/6aqb-vm60
title: OSCD - Onera Satellite Change Detection

description: The DatasetThe Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.It comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018. Locations are picked all over the world, in Brazil, USA, Europe, Middle-East and Asia. For each location, registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites are provided. Images vary in spatial resolution between 10m, 20m and 60m.Pixel-level change ground truth is provided for 14 of the image pairs. The annotated changes focus on urban changes, such as new buildings or new roads. These data can be used for training and setting parameters of change detection algorithms. The BenchmarkThe algorithms can be tested in a benchmark for change detection.The ground truth for the 10 remaining images remain undisclosed. Change prediction maps can be uploaded for evaluation on the IEEE GRSS DASE website. Various metrics such as per-class accuracy and confusion matrices are automatically computed on the website, and are available for participants. Comparison to the best performing methods is provided in the leaderboard associated with this benchmark.ReferencesIf you use this work for your projects, please take the time to cite our paper:Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks, R. Caye Daudt, B. Le Saux, A. Boulch, Y. Gousseau. IEEE International Geoscience and Remote Sensing Symposium (IGARSS’2018). Valencia, Spain. July 2018.[PDF] [BibTeX] The TeamRodrigo Caye Daudt, rodrigo.daudt@onera.frBertrand Le Saux, bertrand.le_saux@onera.frAlexandre Boulch, alexandre.boulch@onera.frYann Gousseau, yann.gousseau@telecom-paristech.fr
DOI: 10.21227/fh04-m903
title: OSCD - Onera Satellite Change Detection

description: The DatasetThe Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.It comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018. Locations are picked all over the world, in Brazil, USA, Europe, Middle-East and Asia. For each location, registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites are provided. Images vary in spatial resolution between 10m, 20m and 60m.Pixel-level change ground truth is provided for 14 of the image pairs. The annotated changes focus on urban changes, such as new buildings or new roads. These data can be used for training and setting parameters of change detection algorithms. The BenchmarkThe algorithms can be tested in a benchmark for change detection.The ground truth for the 10 remaining images remain undisclosed. Change prediction maps can be uploaded for evaluation on the IEEE GRSS DASE website. Various metrics such as per-class accuracy and confusion matrices are automatically computed on the website, and are available for participants. Comparison to the best performing methods is provided in the leaderboard associated with this benchmark.ReferencesIf you use this work for your projects, please take the time to cite our paper:Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks, R. Caye Daudt, B. Le Saux, A. Boulch, Y. Gousseau. IEEE International Geoscience and Remote Sensing Symposium (IGARSS’2018). Valencia, Spain. July 2018.[PDF] [BibTeX] The TeamRodrigo Caye Daudt, rodrigo.daudt@onera.frBertrand Le Saux, bls@ieee.orgAlexandre Boulch, alexandre.boulch@onera.frYann Gousseau, yann.gousseau@telecom-paristech.fr
DOI: 10.5281/zenodo.3723156
title: Number of diagnoses with coronavirus disease (COVID-19) in The Netherlands

description: Datasets in this publication report the number of diagnoses with coronavirus disease (COVID-19) based on RIVM reports in The Netherlands. Since 3 March, RIVM reports the number of diagnoses with the coronavirus and their municipality of residence on a daily base. The data contains the total number of positively tested patients. It is not a dataset with the current number of sick people in the Netherlands. The RIVM does not currently provide data on people who have been cured. RIVM provides daily updates of the data. The data is not stored in a persistent way and is updated on the fly. RIVM removes data from previous days from their website. Therefore, it is not possible to monitor the spread of the coronavirus disease in the Netherlands on this data standalone. The data in this publication is composed of hourly downloads of the data of the website of RIVM. All code to compose the data was found on https://github.com/J535D165/CoronaWatchNL as well as graphs based on the data.
DOI: 10.3886/e117921v1-23221
title: National Neighborhood Data Archive (NaNDA): Parks by Census Tract, United States, 2018

description: This dataset describes the number and area of parks in each census tract in the United States. Measures include the total number of parks, park area, and proportion of park area within each census tract.
description: Census tract in the United States, including Alaska and Hawaii,Smallest Geographic Unit: census tract
DOI: 10.5281/zenodo.3723382
title: IO Islamic 926. The Revenues of the Dakhan, Full Statistical Tables of the Assessment of All the Various Provinces

description: IO Islamic 926. The Revenues of the Dakhan, Full Statistical Tables of the Assessment of All the Various Provinces
DOI: 10.5281/zenodo.3723383
title: IO Islamic 926. The Revenues of the Dakhan, Full Statistical Tables of the Assessment of All the Various Provinces

description: IO Islamic 926. The Revenues of the Dakhan, Full Statistical Tables of the Assessment of All the Various Provinces
DOI: 10.6084/m9.figshare.3761562.v1428
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.25345/c5tx21
title: MassIVE MSV000085132 - Low input proteomics for sorted mammary cells
title: Method development for low input proteomics for sorted mammary cells
DOI: 10.6084/m9.figshare.12000708.v1
title: IO_Multivariate_Reconstructed_SL_F1900T2013.mat

description: Sea-Level data has been reconstructed for the Indian Ocean using a new multivariate CSEOF reconstruction technique for the period 1900-2013. This dataset is created using sea surface temperature and sea level pressure in addition to sea level data (to help overcome sparse spatio-temporal tide gauge sampling) and consists of improved 20<sup>th</sup> century reconstructed sea level estimates for the Indian Ocean. <br>This is a .mat structure file (Rcon) created using Matlab. Under the Rcon structure, there are four variables:sla ==&gt; Reconstructed sea level anomaly. [Units: Meters].lat ==&gt; Latitude. [Resolution: 0.5 degrees].lon ==&gt; Longitude.[Resolution: 0.5 degrees].t ==&gt; Time. [Resolution: Monthly].*** NOTE:Due to the 12 month fitting window of the CSEOF basis functions, data (sla) for the first 6 months and last 6 months are absent.<br><br>
DOI: 10.6073/pasta/54ae21ba0fef2a54a6f9781dd4939244
title: SBC LTER: Reef: Long-term experiment: Taxon-specific seasonal net primary production (NPP) for macroalgae

description: This dataset provides estimates of seasonal net primary production (NPP) for all taxa of macroalgae sampled in fixed plots at SBC LTER's long-term kelp removal experiment sites. NPP of understory taxa are calculated using field measurements of irradiance and biomass (derived from abundance) and laboratory estimates of taxon-specific photosynthetic parameters. NPP for the giant kelp, Macrocystis pyrifera, is calculated using linear relationships between frond density in a given season and average NPP for that season. Importantly, estimates for understory species include production lost as dissolved exudates while estimates of NPP for giant kelp do not.
DOI: 10.21227/sd7z-xz59
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 2,475,980This dataset includes CSV files which contain the tweet IDs. The tweets have been collected by the LSTM model deployed here at sentiment.live. The model monitors the real-time Twitter feed for corona virus-related tweets. As per the Twitter Developer Policy, it is not possible for me to provide information other than the Tweet IDs (this dataset has been completely re-designed on March 20, 2020, to comply with data sharing policies set by Twitter). Note: This dataset should be solely used for non-commercial research purpose.Schema of the CSV files: First column: tweet ID, Second column: Sentiment score for the particular tweet.Files details (time: GMT+5:45):corona_tweets_01.csv: 831,327 tweets (March 20, 2020 1:37 AM - March 20, 2020 10:28 AM)corona_tweets_02.csv: 870,924 tweets (March 20, 2020 10:31 AM - March 20, 2020 9:43 PM)corona_tweets_03.csv: 773,729 tweets (March 20, 2020 9:49 PM - March 21, 2020 9:25 AM)corona_tweets_04.csv: 1,233,340 tweets (March 21, 2020 9:27 AM - March 22, 2020 7:46 AM)To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the second column. New databases will be added to this dataset every day. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.21227/mfbg-mr32
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 3,709,320This dataset includes CSV files which contain the tweet IDs. The tweets have been collected by the LSTM model deployed here at sentiment.live. The model monitors the real-time Twitter feed for corona virus-related tweets. As per the Twitter Developer Policy, it is not possible for me to provide information other than the Tweet IDs (this dataset has been completely re-designed on March 20, 2020, to comply with data sharing policies set by Twitter). Note: This dataset should be solely used for non-commercial research purpose.Schema of the CSV files: First column: tweet ID, Second column: Sentiment score for the particular tweet.Files details (time: GMT+5:45):corona_tweets_01.csv: 831,327 tweets (March 20, 2020 1:37 AM - March 20, 2020 10:28 AM)corona_tweets_02.csv: 870,924 tweets (March 20, 2020 10:31 AM - March 20, 2020 9:43 PM)corona_tweets_03.csv: 773,729 tweets (March 20, 2020 9:49 PM - March 21, 2020 9:25 AM)corona_tweets_04.csv: 1,233,340 tweets (March 21, 2020 9:27 AM - March 22, 2020 7:46 AM)To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the second column. New databases will be added to this dataset every day. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.5281/zenodo.3723469
title: S3 | NORMANCT15 | NORMAN Collaborative Trial Targets and Suspects

description: This is the collection associated with list S3 NORMANCT15 on the NORMAN Suspect List Exchange. https://www.norman-network.com/nds/SLE/ S3 | NORMANCT15 | <strong>NORMAN Collaborative Trial Targets and Suspects</strong> LC-MS: CSV, XLSX (3/10/2017) GC-MS: CSV, XLSX (3/10/2017) CompTox NORMANCT15 List LC-MS InChIKeys (31/10/2016) GC-MS InChIKeys (31/10/2016) Schymanski <em>et al</em>. 2015.<br> DOI: 10.1007/s00216-015-8681-7 Upload 22/3/2020: added merged InChIKey file for PubChem data extraction.
DOI: 10.5281/zenodo.3723478
title: S21 | UATHTARGETS | University of Athens Target List

description: This is the collection associated with list S21 UATHTARGETS on the NORMAN Suspect List Exchange. https://www.norman-network.com/nds/SLE/ S21 | UATHTARGETS | <strong>University of Athens Target List </strong> Target List CSV, XLSX (8/3/2018) CompTox UATHTARGETS List UoA Targets InChIKeys (8/03/2018) List of target substances from University of Athens, provided by Nikiforos Alygizakis. For more see Alygizakis <em>et al</em>. 2019, DOI: 10.1016/j.envint.2019.03.060 Update 22/3/2020: added InChIKey file.
description: For more author details see referenced manuscript