cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7294/16655860.v2
title: Experimental data to accompany the paper ‘The structure of a highly-decelerated axisymmetric turbulent boundary layer’ Journal of Fluid Mechanics, In press, 2021.

description: This dataset accompanies the paper ‘The structure of a highly-decelerated axisymmetric turbulent boundary layer’, Balantrapu N. A., Hickling. C., Alexander W. N., Devenport W. J., Journal of Fluid Mechanics, in press, 2021. The turbulent boundary layer over an axisymmetric body, relevant to vehicular configurations, is documented and studies to discover the underlying physics in addition supplying information towards advancing turbulence modelling and aeroacoustics predictions. Measurements of the mean flow, turbulence and correlation structure were documented using hotwire anemometry and Particle Image Velocimetry and are discussed in the stated publication. Here, the relevant data are presented.
DOI: 10.5517/ccdc.csd.cc11xd3g
title: CCDC 980595: Experimental Crystal Structure Determination

description: Related Article: Azees Khan Haleel, Ummer Muhammed Rafi, Mugamathu Ali Jayathuna, Aziz Kalilur Rahiman|2022|J.Mol.Struct.|1250|131706|doi:10.1016/j.molstruc.2021.131706
DOI: 10.5517/ccdc.csd.cc11xlm5
title: CCDC 980797: Experimental Crystal Structure Determination

description: Related Article: Azees Khan Haleel, Ummer Muhammed Rafi, Mugamathu Ali Jayathuna, Aziz Kalilur Rahiman|2022|J.Mol.Struct.|1250|131706|doi:10.1016/j.molstruc.2021.131706
DOI: 10.5517/ccdc.csd.cc11xln6
title: CCDC 980798: Experimental Crystal Structure Determination

description: Related Article: Azees Khan Haleel, Ummer Muhammed Rafi, Mugamathu Ali Jayathuna, Aziz Kalilur Rahiman|2022|J.Mol.Struct.|1250|131706|doi:10.1016/j.molstruc.2021.131706
DOI: 10.5517/ccdc.csd.cc24nbdk
title: CCDC 1985748: Experimental Crystal Structure Determination

description: Related Article: Zhihua Wang, Li Wei, Zhendong Cheng, Jianhui Xia, Zhiyuan Chen|2021|Chin.Chem.Lett.|32|2756|doi:10.1016/j.cclet.2021.03.030
DOI: 10.5517/ccdc.csd.cc26lm3k
title: CCDC 2043678: Experimental Crystal Structure Determination

description: Related Article: Wei Huang, Ming-Liang Han, Yu-Wen Liu, Hui Xu, Hui-Xiong Dai|2021|Chin.Chem.Lett.|32|2765|doi:10.1016/j.cclet.2021.02.048
DOI: 10.5517/ccdc.csd.cc27tcq5
title: CSD 2079967: Experimental Crystal Structure Determination

description: Related Article: Yang Yun|2021|CSD Communication|||
description: Related Article: Jiongquan Chen, Zhihua Yang, Kui Wu, Yun Yang, Shilie Pan|2022|J.Mol.Struct.|1247|131382|doi:10.1016/j.molstruc.2021.131382
DOI: 10.5281/zenodo.5607764
title: Dataset: Magic in transition metal dichalcogenide bilayers

description: Dataset for the paper "Magic in transition metal dichalcogenide bilayers" (arXiv:2106.11954).
description: {"references": ["arXiv:2106.11954"]}
DOI: 10.5281/zenodo.5607763
title: Dataset: Magic in transition metal dichalcogenide bilayers

description: Dataset for the paper "Magic in transition metal dichalcogenide bilayers" (arXiv:2106.11954).
description: {"references": ["arXiv:2106.11954"]}
DOI: 10.18154/rwth-2019-06868
title: R Scripts for Survey Analysis

description: Survey on attitudes towards e-assessment and Bring Your Own Device (BYOD) among students and teachers. The archive contains both the collected data and R scripts for their evaluation and processing.
DOI: 10.6084/m9.figshare.16896877.v1
title: Metagenome assembled genomes

description: 114 metagenome assembled genomes recovered from metagenomic sequencing reads of 5 pig faecal samples. A Each MAG represents a representative genome from the complete catalogue dereplicated at 99% ANI. <br>Details of all MAGs can be found in table S2 of Additional File.
DOI: 10.5281/zenodo.5608465
title: Figure 5A spectra

description: PA spectrum of GNRs in the agar drop.
DOI: 10.5281/zenodo.5608464
title: Figure 5A spectra

description: PA spectrum of GNRs in the agar drop.
DOI: 10.18154/rwth-2019-06840
title: e-Assessment: Examiner Survey 2019

description: Raw data of the e-assessment survey among teachers. The archive contains an XML description of the survey itself and the raw data collected.
DOI: 10.18154/rwth-2021-09666
title: Dataset to: "In-line Monitoring of Microgel Synthesis: Flow versus Batch Reactor"

description: This data set contains in-line Raman spectroscopy measurements inside a customized measurement cell for monitoring in a tubular flow reactor. The setup aims at monitoring the microgel synthesis in a flow reactor while aiming at a high measurement precision. The measurements include a systematic accuracy analysis, where different aspects of the flowing analyte are considered: solvent flow, flowing monomer solution, and flowing microgel solution. In addition, measurements for different calibration strategies are included. Lastly, this data set contains measurements of the microgel synthesis at varying residence times inside the tubular flow reactor.
DOI: 10.6084/m9.figshare.16897006
title: September 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>September, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897006.v1
title: September 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>September, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897015
title: February 2021 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>February, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897015.v1
title: February 2021 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>February, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.25423/cmcc/sst_med_phy_subskin_l4_nrt_010_036
title: Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis
title: SST_MED_PHY_SUBSKIN_L4_NRT_010_036

description: Within the Copernicus Marine Environment Monitoring Service (CMEMS), an operational Mediterranean diurnal Sea Surface Temperature (SST) product has been developed. This product provides gap-free (Level-4, L4) hourly-mean maps of sub-skin SST at 1/16° horizontal resolution over the Mediterranean Sea from 2019 to near real time, by combining infrared satellite and model analyses through optimal interpolation. This approach takes advantage of the availability of hourly SST data from the geostationary SEVIRI sensor and the CMEMS Mediterannean model analyses, the former used as the input signal source and the latter as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day. The product is updated daily and provides 24 hourly-mean data of the previous day, centered at 00:00, 01:00, 02:00,…, 23:00 UTC. The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions.
DOI: 10.6084/m9.figshare.16897003
title: September 2021 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>September, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16896997
title: February 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>February, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.18154/rwth-2019-04775
title: e-Assessment: Student Survey 2018

description: Raw data of the e-assessment survey among students. The archive contains an XML description of the survey itself and the raw data collected.
DOI: 10.6084/m9.figshare.16897018
title: April 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>April, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.5255/ukda-sn-855096
title: Simultaneously Presented Facial and Contextual Information Influences Observers' Facial Expressions and Self-reports of Emotion, 2018-2019

description: We receive emotional signals from different sources, including the face and its surrounding context. Previous research has shown the effect that facial expressions and contextual affective information have on people’s brain responses. This study measured physiological responses and ratings of affect to face-context compounds of varied emotional content. Forty-two participants freely viewed face-context and context-only natural threat, mutilation, happy, erotic and neutral scenes whilst corrugator, zygomatic and startle eyeblink responses were recorded. Concerning the emotional content presented, participants’ corrugator, zygomatic, startle eyeblink responses and their valence and arousal ratings varied with the stimuli valence and arousal matched the stimuli valence. Face-context threat and mutilation scenes elicited more negative emotional experiences and larger corrugator responses than context-only scenes. In contrast, happy face-context scenes elicited more positive emotional experiences and a decreased corrugator response. The zygomatic showed an enhanced response to face-context scenes, regardless of the valence of the scenes. Our results show that the simultaneous perception of emotional signals from faces and contextual information induce enhanced facial reactions and affective responses.
DOI: 10.5281/zenodo.5604954
title: peihunglin/Xplacer-dataset: initial release

description: This release contains the original raw dataset in CSV and arff format from Xplacer.
DOI: 10.5281/zenodo.5604953
title: peihunglin/Xplacer-dataset: initial release

description: This release contains the original raw dataset in CSV and arff format from Xplacer.
DOI: 10.6084/m9.figshare.16897027
title: July 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>July, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897027.v1
title: July 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>July, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897033
title: August 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>August, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897033.v1
title: August 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>August, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897039
title: May 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>May, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897039.v1
title: May 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>May, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.5255/ukda-sn-855242
title: Assessing the Factors That Influence Next-of-kin Donation in Opt-out Systems for Organ Donation, 2019

description: This study was conducted with the support of a British Academy/ Leverhulme Small Research Grant (SRG1819\191190). Participants (N = 848) were recruited for this online study through Qualtrics (www.qualtrics.com). To be eligible, participants needed to be 18 years or older and living in Wales. We asked people not to take part if they were likely to feel distressed discussing organ donation. After giving consent, participants were asked to provide some information about themselves (e.g., age, sex, blood donor status, organ donor status). We then provided participants with some information about organ donation in Wales (see Questionnaire and data key document). Participants were then asked to imagine a loved one had died. We randomly allocated participants to imagine the deceased had either a) registered as an organ donor, b) registered as a non-donor, or c) not registered a decision and in Wales this means they are assumed to support organ donation. Following this, participants rated their uncertainty about the deceased’s wishes, anticipated regret for not donating their loved one’s organs, and intention to approve of donating different organs and tissue. Participants then completed measures assessing affective attitudes towards organ donation (O’Carroll et al., 2011) and risk aversion (Bibby & Ferguson, 2011). Finally, participants were debriefed.