cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/4nf9-ag28
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. 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 these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. 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. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(200+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.Update: I have received a lot of requests, especially from Social Science researchers, to also make the geo-tagged tweets created between March 20, 2020, and April 28, 2020, available in this dataset. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I have started hydrating the IDs provided in the Coronavirus (COVID-19) Tweets Dataset, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task goes on. Bookmark this page for further updates.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.3886/e116334
title: Replication data for: Microcredit Impacts: Evidence from a Randomized Microcredit Program Placement Experiment by Compartamos Banco

description: We use a clustered randomized trial, and over 16,000 household surveys, to estimate impacts at the community level from a group lending expansion at 110 percent APR by the largest microlender in Mexico. We find no evidence of transformative impacts on 37 outcomes (although some estimates have large confidence intervals), measured at a mean of 27 months post-expansion, across 6 domains: microentrepreneurship, income, labor supply, expenditures, social status, and subjective well-being. We also examine distributional impacts using quantile regressions, given theory and evidence regarding negative impacts from borrowing at high interest rates, but do not find strong evidence for heterogeneity. (JEL C83, D14, G21, I31, J23, O12, O16)
DOI: 10.21227/c84w-nj38
title: network service traffic

description: The datasets includes 2 parts: private and public traffic.The private traffic is self-captured network traffic of serveral softwares, such as YouTube, Skype, streaming video, totally 16 categories.The public traffic is anopen VPN dataset, including numorous VPN or nonVPN network services, totally 24 categories.
DOI: 10.21227/sh9v-8d97
title: network service traffic

description: The datasets includes 2 parts: private and public traffic.The private traffic is self-captured network traffic of serveral softwares, such as YouTube, Skype, streaming video, totally 16 categories.The public traffic is anopen VPN dataset, including numorous VPN or nonVPN network services, totally 24 categories.
DOI: 10.5281/zenodo.3902063
title: Simulation data and scripts for CFD-DEM simulation of granular flows in ambient fluid

description: Data sets - contains the raw data required to replicate and validate all plots in the main article. Contains folders in containing measurements of basic flow properties, e.g. velocities, shear rates, etc., of monodisperse and bidisperse granular flows in different flow regimes. Each folder contains a ReadMe.txt briefly explaining the content and lay out of each data set. Sample case - a .zip file which includes codes which are needed to simulate a CFD-DEM case of a steady granular flow in water with cyclic boundaries in the stream wise direction. Also enclosed is a ReadMe.txt file detailing the implementation instructions for both Esys particle and OpenFOAM codes. Download links for Esys particle and OpenFOAM are also included. Geo file generator - a .zip file which includes Esys particle codes that can be used to generate a .geo file specifying the initial position of the particles used in the test simulations. A ReadMe.txt file is enclosed with more detailed implementation instructions.
DOI: 10.5281/zenodo.3902062
title: Simulation data and scripts for CFD-DEM simulation of granular flows in ambient fluid

description: Data sets - contains the raw data required to replicate and validate all plots in the main article. Contains folders in containing measurements of basic flow properties, e.g. velocities, shear rates, etc., of monodisperse and bidisperse granular flows in different flow regimes. Each folder contains a ReadMe.txt briefly explaining the content and lay out of each data set. Sample case - a .zip file which includes codes which are needed to simulate a CFD-DEM case of a steady granular flow in water with cyclic boundaries in the stream wise direction. Also enclosed is a ReadMe.txt file detailing the implementation instructions for both Esys particle and OpenFOAM codes. Download links for Esys particle and OpenFOAM are also included. Geo file generator - a .zip file which includes Esys particle codes that can be used to generate a .geo file specifying the initial position of the particles used in the test simulations. A ReadMe.txt file is enclosed with more detailed implementation instructions.
DOI: 10.5281/zenodo.3235254
title: Studi Fenomenologi Gay-Biseksual Menggunakan Media Sosial Hornet

description: Set data hasil penelitian menggunakan media sosial Hornet. Data berikut merupakan data mentah.
DOI: 10.5281/zenodo.3235253
title: Studi Fenomenologi Gay-Biseksual Menggunakan Media Sosial Hornet

description: Set data hasil penelitian menggunakan media sosial Hornet. Data berikut merupakan data mentah.
DOI: 10.21227/rsnz-ke52
title: network service traffic

description: The datasets includes 2 parts: private and public traffic.The private traffic is self-captured network traffic of serveral softwares, such as YouTube, Skype, streaming video, totally 16 categories.The public traffic is anopen VPN dataset, including numorous VPN or nonVPN network services, totally 24 categories.
DOI: 10.7910/dvn/vxmpif/6c5zir
title: covid19_replication.do

description: Stata do file for replicating results of "Electoral concerns reduce the stringency of restrictive measures during the COVID-19 pandemic", by M. Pulejo and P. Querubin.
DOI: 10.7910/dvn/vxmpif/9l43ic
title: readme.pdf

description: PDF guide for replicating results of "Electoral concerns reduce the stringency of restrictive measures during the COVID-19 pandemic", by M. Pulejo and P. Querubin.
DOI: 10.5281/zenodo.3593918
title: Tumor growth kinetics of human MDA-MB-231 cells transfected with dTomato lentivirus

description: Tumor growth data involve human MDA-MB-231 cells stably transfected with dTomato lentivirus. Animals were orthotopically implanted (80,000 cells at injection) into the mammary fat pads of 6-week-old female nude mice. Tumor size was monitored regularly with fluorescence imaging. The data comprised a total of 64 observations. In the file, the columns correspond to: ID: identifier of the animal Time: day of the tumor measurement after implantation Observation: tumor measurement (in phot./s) <strong>Please cite: </strong>Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. https://doi.org/10.1371/journal.pcbi.1007178
description: {"references": ["Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178.\u00a0https://doi.org/10.1371/journal.pcbi.1007178"]}
DOI: 10.15161/oar.it/30443
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.3902120
title: Flank vents locations on six shield volcanoes from the Solar System.

description: The dataset contains six .txt files with the location of flank vents from six shield volcanoes located on Venus, Earth, and the Moon. The shield volcanoes are the following:<br> -Mauna Kea, Earth, with a total of 204 vents (GCS_WGS_1984).<br> -Pinacate, Earth, with a total of 433 vents (GCS_WGS_1984).<br> -Marius Hills, Moon, with a total of 212 vents (GCS_Moon_2000).<br> -Mons Rümker, Moon, with a total of 24 vents (GCS_Moon_2000).<br> -Kunapipi Mons, Venus, with a total of 125 vents (GCS_Venus_1985).<br> -Var Mons, Venus, with a total of 88 vents (GCS_Venus_1985).<br> The files contain latitude and longitude coordinates.
DOI: 10.5281/zenodo.3902119
title: Flank vents locations on six shield volcanoes from the Solar System.

description: The dataset contains six .txt files with the location of flank vents from six shield volcanoes located on Venus, Earth, and the Moon. The shield volcanoes are the following:<br> -Mauna Kea, Earth, with a total of 204 vents (GCS_WGS_1984).<br> -Pinacate, Earth, with a total of 433 vents (GCS_WGS_1984).<br> -Marius Hills, Moon, with a total of 212 vents (GCS_Moon_2000).<br> -Mons Rümker, Moon, with a total of 24 vents (GCS_Moon_2000).<br> -Kunapipi Mons, Venus, with a total of 125 vents (GCS_Venus_1985).<br> -Var Mons, Venus, with a total of 88 vents (GCS_Venus_1985).<br> The files contain latitude and longitude coordinates.
DOI: 10.5281/zenodo.3841089
title: Plant_Innate_Immunity_ATAC-seq

description: <strong>Chromatin accessibility landscapes activated by cell surface and intracellular immune receptors</strong> Ding et al 2020 This repository stored all input dataset & output dataset except bam files. To make bam files, raw read data are stored in accession number PRJEB38923. ACR_annotation.zip, Correlation_replicates.zip, Integrate_ATAC-seq_and_RNA-seq.zip, Motif_binding_analysis.zip ... input/output files for each analysis. other zip files ... output files of MACS (call ACRs, call DARs) These outputs are generated by scripts stored in github. (https://github.com/slt666666/Plant_Innate_Immunity_ATAC-seq)
DOI: 10.5281/zenodo.3841088
title: Plant_Innate_Immunity_ATAC-seq

description: <strong>Chromatin accessibility landscapes activated by cell surface and intracellular immune receptors</strong> Ding et al 2020 This repository stored all input dataset & output dataset except bam files. To make bam files, raw read data are stored in accession number PRJEB38923. ACR_annotation.zip, Correlation_replicates.zip, Integrate_ATAC-seq_and_RNA-seq.zip, Motif_binding_analysis.zip ... input/output files for each analysis. other zip files ... output files of MACS (call ACRs, call DARs) These outputs are generated by scripts stored in github. (https://github.com/slt666666/Plant_Innate_Immunity_ATAC-seq)
DOI: 10.5519/qd.ap22x12c
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (158 records)
DOI: 10.21227/g1sk-ch58
title: Urban Semantic 3D Dataset

description: This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
DOI: 10.21227/garv-j446
title: Urban Semantic 3D Dataset

description: This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
DOI: 10.21227/mdy8-qp71
title: GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

description: Abstract:We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages.The dataset was collected using more than 800 multilingual keywords and hashtags. The complete list of keywords can be downloaded from here: https://crisisnlp.qcri.org/covid19 For more details, please refer to this paper: https://arxiv.org/abs/2005.11177Explore interesting trends in GeoCoV19 dataset using our new service: https://covid19-trends.qcri.org/
DOI: 10.5281/zenodo.3901659
title: Dataset of pictures of a single hand with different finger count

description: Dataset of pictures of a single hand with different finger-count from zero to 5. The dataset is already divided in 3 sets: train: Training set. test: Testing test. val: Validation test. Images are tagged on how how many numbers they represent in a range from 0 to 5. This dataset is intended to be use for a university assignment: https://github.com/cabellocarlos/keras-cnn
DOI: 10.21227/8d6c-3817
title: GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

description: Abstract:We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages.The dataset was collected using more than 800 multilingual keywords and hashtags. The complete list of keywords can be downloaded from here: https://crisisnlp.qcri.org/covid19 For more details, please refer to this paper: https://arxiv.org/abs/2005.11177Explore interesting trends in GeoCoV19 dataset using our new service: https://covid19-trends.qcri.org/
DOI: 10.21227/062f-xh71
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. 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 these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. 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. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(200+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.Update: I have received a lot of requests, especially from Social Science researchers, to also make the geo-tagged tweets created between March 20, 2020, and April 28, 2020, available in this dataset. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I have started hydrating the IDs provided in the Coronavirus (COVID-19) Tweets Dataset, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task goes on. Bookmark this page for further updates.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.17632/xrr3gxdkjy.1
title: Les étudiants de l'INSPE de Martinique et le confinement (COVID-19)

description: Questionnaire sur les conditions matérielles, sociales et socioéconomiques pour la formation à distance des étudiants de l'Institut national supérieur du professorat et de l'éducation (INSPE) pendant la période de confinement (mars-juin 2020). 22 questions. Questionnaire on material, social and socio-economic conditions for distance education for students of the Institut national supérieur du professorat et de l'éducation during the period of lockdown (March-June 2020). 22 questions.
DOI: 10.17632/x99dsf6yn5.1
title: Appendix of Dataset of Entrepreneurship Achievement Motivation: Factor Analysis

description: This data is the result of entrepreneurship achievement motivation analysis for students in Indonesia
DOI: 10.17632/x3tsxpsxkm.1
title: Thiosulfinate Tolerance Is a Virulence Strategy of an Atypical Bacterial Pathogen of Onion

description: This dataset includes deconvoluted graphs of figures 3D, 6D, and 7D from our linked publication. In addition the raw image files of bacterial strains plated on allicin amended LB plates are included with red lines indicating cropped regions selected for figures 6E and 7E.
DOI: 10.17632/hmpf545v3z.1
title: Slab roll-back triggered back-arc extension south of Paleo-Asian Ocean: insights from the Devonian MORB-like diabase dykes in Chinese Altai

description: Data for Slab roll-back triggered back-arc extension south of Paleo-Asian Ocean: insights from the Devonian MORB-like diabase dykes in Chinese Altai
DOI: 10.17632/x99dsf6yn5.2
title: Appendix of Dataset of Entrepreneurship Achievement Motivation: Factor Analysis

description: This data is the result of entrepreneurship achievement motivation analysis for students in Indonesia
DOI: 10.17632/3h8hbcww4f.1
title: Vitamin D among patients of pulmonary and extra pulmonary tuberculosis

description: This data evaluates the serum level of vitamin D among patients with pulmonary and extra pulmonary tuberculosis
DOI: 10.17632/3h8hbcww4f.2
title: Vitamin D among patients of pulmonary and extra pulmonary tuberculosis

description: This data evaluates the serum level of vitamin D among patients with pulmonary and extra pulmonary tuberculosis
DOI: 10.17632/tjk2hyg6zs.1
title: Land tenure and economic development: Evidence from Vietnam (Replication data and codes)

description: The relationship between private property rights and economic development has been investigated by numerous cross-country studies. Nevertheless, aggregate measures of private property rights have prevented cross-country studies in general from identifying the specific institutions governing private property rights that policy reforms should consider. The present paper investigates the impact of private property rights to land on economic development in a within-country setting, exploiting the 1993 nationwide land privatization in Vietnam. Using a random sample of more than 2000 rural communes across Vietnam, I find that the prevalence of private land tenure has a positive and significant impact on the level of economic development, as proxied by nighttime light intensity. The magnitude of the impact, however, is sensitive to both observed and unobserved confounding factors, and overall modest.
DOI: 10.17632/gzw84k3x4s.1
title: PyLOC - a Python application to localize cracks in beams with uncertain boundary conditions

description: The PyLOC application permits finding the crack depth and severity even in the case that the beam has non-ideal clamping. The application is free to be downloaded and used. In addition, we provide simulation data in the form of natural frequencies for healthy and damaged beams, which permit testing the application. The data is obtained both for a cantilevered beam, as well as for a beam clamped at both ends.
DOI: 10.17632/7j58kspmyd.1
title: Survey Data on National Election Participation in Nigeria

description: Election participation in Nigeria
DOI: 10.17632/nhc3xdsjy6.1
title: Data for Standard physical and chemical techniques for the characterization of river sediments: A case of study, the Moquegua River, Peru

description: XRD, FTIR, Mossbauer and DC-MPMS-SQUID raw data for Standard physical and chemical techniques for the characterization of river sediments: A case of study, the Moquegua River, Peru. The data can be easily plotted using the Origin program.