cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/xjxq-tv61
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(190+ 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 make available the geo-tagged tweets created between March 20, 2020, and April 28, 2020. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I myself 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 gets complete. I'll be adding new CSV files and the naming convention for these newly added files will be day-wise (instead of period-wise). Bookmark this page for further updates.The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.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: 10.5255/ukda-sn-854340
title: British higher education staff, survey data: career trajectories, satisfaction and aspirations 2019
DOI: 10.21227/9sk6-e061
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(190+ 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 make available the geo-tagged tweets created between March 20, 2020, and April 28, 2020. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I myself 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. I'll be adding new CSV files and the naming convention for these newly added files will be day-wise (instead of period-wise). Bookmark this page for further updates.The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.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: 10.17632/3gndhj3t6h.1
title: Data for: "Self-correction of science: a comparative study of negative citations and post-publication peer review"

description: In order to evaluate negative citations in articles and comments on PubPeer as mechanisms for correcting science, we have built up a corpus of articles associated with retraction or correction notices. As we are interested in citations, we focus here on articles with at least 1 citation according to Scite database, that is 45,811 articles.
DOI: 10.21227/a1x1-ht28
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(190+ 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. I'll be adding new CSV files, and the naming convention for these newly added files will be day-wise (instead of period-wise). Bookmark this page for further updates.The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.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: 10.5281/zenodo.3900133
title: S2 | STOFFIDENT | HSWT/LfU STOFF-IDENT Database of Water-Relevant Substances

description: This is the collection associated with list S2 STOFFIDENT<strong> HSWT/LfU STOFF-IDENT Database of Water-Relevant Substances</strong> on the NORMAN Suspect List Exchange https://www.norman-network.com/nds/SLE/ The database enables the search for exact masses from target or unknown lists and the automatic use of a Retention Time Index. See: https://www.lfu.bayern.de/stoffident/#!home (single search for free; batch search after free registration). Nov 17 2019 update: added CSV for PubChem upload.<br> Jun 18, 2020 update: fixed synonym for CAS=1245526-82-2 after feedback from PubChem (Jeff).
description: Please see original website for more information: https://www.lfu.bayern.de/stoffident/#!home
DOI: 10.5066/f7p848xh
title: Official Recognized Boundary of Southeast Aquatic Resource Partnership, 2020
DOI: 10.5066/f7fn146x
title: National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data for Hawaii Linked to NFHAP Catchments (developed using the 1:24,000 scale National Hydrography Dataset)
DOI: 10.5287/bodleian:zbwngqee1
title: Male reproductive ageing arises via multifaceted mating-dependent sperm and seminal proteome declines, but is postponable in Drosophila
DOI: 10.21227/rg3s-6z53
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: 10.25592/uhhfdm.1049
title: 3D Model of a Acer pseudoplatanus L.

description: Acer pseudoplatanus_Berg-Ahorn<br> scientific name: Acer pseudoplatanus L.<br> vernacular name: Berg-Ahorn object: Holzbibliothek in Buchform gefertigt aus Holz von Berg-Ahorn, Buchrücken mit Rinde, Deckel mit Habitusaufnahme, innen mit belaubten Zweigen, Blüten und Früchten sowie für den Berg-Ahorn charakteristische Pilze
DOI: 10.21227/dch5-pm30
title: Novel Method for the Prediction of Drug-Drug Interactions Based on Gene Expression Profiles

description: The accurate prediction of new interactions between drugs is important for avoiding unknown(mild or severe) adverse reactions to drug combinations. The development of effective in silico methodsfor evaluating drug interactions based on gene expression data requires an understanding of how variousdrugs alter gene expression. Current computational methods for the prediction of drug-drug interactions(DDIs) utilize data for known DDIs to predict unknown interactions. However, these methods are limited inthe absence of known predictive DDIs. To improve DDIs’ interpretation, a recent study has demonstratedstrong non-linear (i.e., dose-dependent) effects of DDIs. In this study, we present a new unsupervisedlearning approach involving tensor decomposition (TD)-based unsupervised feature extraction (FE) in 3D.We utilize our approach to reanalyze available gene expression profiles for Saccharomyces cerevisiae. Wefound that non-linearity is possible, even for single drugs. Thus, non-linear dose-dependence cannot alwaysbe attributed to DDIs. Our analysis provides a basis for the design of effective methods for evaluating DDIs.
DOI: 10.21227/ppy8-6c31
title: Novel Method for the Prediction of Drug-Drug Interactions Based on Gene Expression Profiles

description: The accurate prediction of new interactions between drugs is important for avoiding unknown(mild or severe) adverse reactions to drug combinations. The development of effective in silico methodsfor evaluating drug interactions based on gene expression data requires an understanding of how variousdrugs alter gene expression. Current computational methods for the prediction of drug-drug interactions(DDIs) utilize data for known DDIs to predict unknown interactions. However, these methods are limited inthe absence of known predictive DDIs. To improve DDIs’ interpretation, a recent study has demonstratedstrong non-linear (i.e., dose-dependent) effects of DDIs. In this study, we present a new unsupervisedlearning approach involving tensor decomposition (TD)-based unsupervised feature extraction (FE) in 3D.We utilize our approach to reanalyze available gene expression profiles for Saccharomyces cerevisiae. Wefound that non-linearity is possible, even for single drugs. Thus, non-linear dose-dependence cannot alwaysbe attributed to DDIs. Our analysis provides a basis for the design of effective methods for evaluating DDIs.
DOI: 10.5284/1047566
title: 1 Park View, Woodchester, Gloucestershire. Archaeological Watching Brief (OASIS ID: urbanarc1-291078)

description: In May 2017 Urban Archaeology undertook an archaeological watching brief during groundworks associated with the construction of an extension at 1 Park View, Woodchester, Gloucestershire. The watching brief comprised the monitoring of the reduction of the footprint of the extension, the excavation of foundation trenches to the north and east, and the excavation of underpinning pits around three sides of the existing extension. The excavation of a new soakaway and drainage run was also monitored. Natural clay was sealed by a colluvial subsoil which was observed across the site; cut into the natural clay was a pit containing a single fragment of abraded Roman CBM and which appeared to be sealed by the colluvium. Fragments of abraded Roman CBM and a fragment of clay tobacco pipe stem were recovered from the colluvial layer. No significant archaeological remains were encountered on the site and it is unlikely that the construction of the existing extension or associated landscaping has removed or impacted upon any significant archaeological deposits.
DOI: 10.5287/bodleian:qr6nzeqo1
title: Metabolomics of 2-HG treated LN18 wild-type cells
DOI: 10.5281/zenodo.3900172
title: Database_of_negative_coordinators

description: A database of negative coordinators in a sample of 250 languages.
DOI: 10.5281/zenodo.3900176
title: Database_of_negative_coordinators

description: A database of negative coordinators in a sample of 250 languages.
DOI: 10.25592/uhhfdm.1051
title: 3D Model of Zamiaceae - Encephalartos spec.

description: Zamiaceae_Brotpalmfarn<br> scientific name: Encephalartos spec.<br> vernacular name: Brotpalmfarn<br> object: weiblicher Zapfen eines Brotpalmfarns, Anschauungsobjekt für die Blütenverhältnisse bei den Zamiaceae (Brotpalmfarngewächse)
DOI: 10.17909/t9-47y9-v476
title: Data from "The Dark World: A Tale of WASP-43b in Reflected Light withHSTWFC3/UVIS"

description: Optical, reflected light eclipse observations provide a direct probe of the exoplanet scattering properties, such as from aerosols. We present here the photometric, reflected light observations of WASP-43b using theHSTinstrument with the F350LP filter (346-822nm) encompassing the entire optical band. This is the first reflected light, photometric eclipse usingHSTWFC3/UVIS in scanning mode; as such we further detail our scanning extraction and analysis pipelineArctor. Our HST WFC3/UVIS eclipse light curve for WASP-43 b resulted in 172 ppm residual scatter. Our observations derived a 3-σupper limit of 67 ppm on the eclipse depth, which implies that WASP-43b has a very dark dayside atmosphere. With our atmospheric modeling campaign, we compared our reflected light constraints with predictions from global circulation and cloud models, benchmarked with HST and Spitzer observations of WASP-43b. We infer that we do not detect clouds on the dayside within the pressure levels probed by HSTWFC3/UVIS with the F350LP filter (P&gt;1 bar). This is consistent with the GCM predictions based on previous WASP-43b observations. Dayside emission spectroscopy results from WASP-43b with HST and Spitzer observations are likely to not be significantly affected by contributions from cloud particles
DOI: 10.5281/zenodo.3899914
title: Data for manuscript "The proportion of non-depressed subjects in a study sample strongly affects the results of psychometric analyses of depression symptoms"

description: Data sets underlying the manuscript "The proportion of non-depressed subjects in a study sample strongly affects the results of psychometric analyses of depression symptoms" (doi: 10.1371/journal.pone.0235272). The file "data_table1.rds" contains the data underlying table 1 of the manuscript, whereas the file "data_figure1.rds" contains the data underlying figure 1. Both files are stored in RDS-format, which is a data format used in the programming language R. RDS-files can be loaded using R with the function readRDS(). Note that "data_figure1.rds" is a list of 22 elements. Elements 1, 21, and 22 are original data sets (depressed subjects, general population, and nondepressed subjects, respectively), while the remaining elements are lists each containing 500 newly generated data sets as described in the method section of the manuscript. For more details about the data please refer to the manuscript.
DOI: 10.5281/zenodo.3899913
title: Data for manuscript "The proportion of non-depressed subjects in a study sample strongly affects the results of psychometric analyses of depression symptoms"

description: Data sets underlying the manuscript "The proportion of non-depressed subjects in a study sample strongly affects the results of psychometric analyses of depression symptoms" (doi: 10.1371/journal.pone.0235272). The file "data_table1.rds" contains the data underlying table 1 of the manuscript, whereas the file "data_figure1.rds" contains the data underlying figure 1. Both files are stored in RDS-format, which is a data format used in the programming language R. RDS-files can be loaded using R with the function readRDS(). Note that "data_figure1.rds" is a list of 22 elements. Elements 1, 21, and 22 are original data sets (depressed subjects, general population, and nondepressed subjects, respectively), while the remaining elements are lists each containing 500 newly generated data sets as described in the method section of the manuscript. For more details about the data please refer to the manuscript.
DOI: 10.21227/447d-hv04
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(190+ 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. I'll be adding new CSV files, and the naming convention for these newly added files will be day-wise (instead of period-wise). Bookmark this page for further updates.The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.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: 10.5281/zenodo.3785083
title: Dataset for article titled "The influence of packing structure and interparticle forces on ultrasound transmission in granular media"

description: Dataset containing supporting materials for article titled "The influence of packing structure and interparticle forces on ultrasound transmission in granular media". An enclosed PDF file describes the data.
DOI: 10.5281/zenodo.3785082
title: Dataset for article titled "The influence of packing structure and interparticle forces on ultrasound transmission in granular media"

description: Dataset containing supporting materials for article titled "The influence of packing structure and interparticle forces on ultrasound transmission in granular media". An enclosed PDF file describes the data.
DOI: 10.17614/q4bn9x89n
title: 2,3-dichlorobutadiene
DOI: 10.13145/bacdive9957.20170829.2
title: Myxococcus virescens Thaxter 1892 emend. Lang et al. 2008

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.14457/cmu.the.2006.427
title: การใช้กิจกรรมกลุ่มในชั่วโมงโฮมรูม เพื่อเสริมสร้างความฉลาดทางจริยธรรมของนักเรียนชั้นมัธยมศึกษาปที่ 5 /