cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5598143
title: German CBOW FastText embeddings with min count 250

description: FastText embeddings built from Common Crawl german dataset Parameters Parameters Value(s) Dimensions 256 and 384 Context window 5 Negative sampled 10 Epochs 1 Number of buckets 131072 or 262144 Min n 3 Max n 6
description: {"references": ["@inproceedings{grave2018learning, title={Learning Word Vectors for 157 Languages}, author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas}, booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)}, year={2018} }"]}
DOI: 10.15468/a3a828
title: Fauna marina de la costa vasca - Sistema de Información de la Naturaleza de Euskadi

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.5281/zenodo.5598215
title: NASDUCK floquet main

description: In this folder are individual bounds corresponding to the first arXiv version of the manuscript "NASDUCK: New Constraints on Axion-like Dark Matter from Floquet Quantum Detector". The bound was divided into several pickle folders so that it can be uploaded to a GitHub repository, but to get the full bound, one needs to use the data of all files. In each file, one can find a span of 95% CL bounds on a span of masses, specified (in Hz), by the file name. Each file is a single dictionary, saved using the python3 pickle library, and should be loaded with pickle as well. The keys of each dictionary are (i)'mass' for the vector of masses in this file (in Hz units, to change to eV, divide by h), (ii) 'bounds', for the bounds on epsilon_P gaPP+epslion_N gaNN in units of GeV^{-1}, and (iii), 'logll' for -2*log(LL(gaPP=gaNN=0)/LL(best fit)). Note that due to the numerical method of computing the 'logll', it is entirely possible for the best fit point to be 0, and receive 'logll' which is e.g. 10^(-5). If the 'logll' is small enough, it usually implies that the null hypothesis is preferred. To compute bounds on neutrons, one must divide the bounds by 0.63, and to compute those on proton, one must divide by the highly theoretically uncertain proton-fraction. To compute bounds on Quadratic type model, one must take QuadBounds={},QuadBounds['mass']=ALPBounds['mass']/2, QuadBounds['bounds']=ALPBounds['bounds']QuadBounds['mass']/sqrt[2rhoDM],QuadBounds['logll']=ALPBounds['logll']. And to get the correct GeV^{-2} units, one should take sqrt(2rhoDM)=599.5=sqrt(20.4GeVhbar^3c^3/cm^3)/(GeVh*Hz). After doing this, one should divide by the proper particle factor (again, 0.63 for neutrons, and model-dependent for protons).<br>
DOI: 10.5281/zenodo.5598214
title: NASDUCK floquet main

description: In this folder are individual bounds corresponding to the first arXiv version of the manuscript "NASDUCK: New Constraints on Axion-like Dark Matter from Floquet Quantum Detector". The bound was divided into several pickle folders so that it can be uploaded to a GitHub repository, but to get the full bound, one needs to use the data of all files. In each file, one can find a span of 95% CL bounds on a span of masses, specified (in Hz), by the file name. Each file is a single dictionary, saved using the python3 pickle library, and should be loaded with pickle as well. The keys of each dictionary are (i)'mass' for the vector of masses in this file (in Hz units, to change to eV, divide by h), (ii) 'bounds', for the bounds on epsilon_P gaPP+epslion_N gaNN in units of GeV^{-1}, and (iii), 'logll' for -2*log(LL(gaPP=gaNN=0)/LL(best fit)). Note that due to the numerical method of computing the 'logll', it is entirely possible for the best fit point to be 0, and receive 'logll' which is e.g. 10^(-5). If the 'logll' is small enough, it usually implies that the null hypothesis is preferred. To compute bounds on neutrons, one must divide the bounds by 0.63, and to compute those on proton, one must divide by the highly theoretically uncertain proton-fraction. To compute bounds on Quadratic type model, one must take QuadBounds={},QuadBounds['mass']=ALPBounds['mass']/2, QuadBounds['bounds']=ALPBounds['bounds']QuadBounds['mass']/sqrt[2rhoDM],QuadBounds['logll']=ALPBounds['logll']. And to get the correct GeV^{-2} units, one should take sqrt(2rhoDM)=599.5=sqrt(20.4GeVhbar^3c^3/cm^3)/(GeVh*Hz). After doing this, one should divide by the proper particle factor (again, 0.63 for neutrons, and model-dependent for protons).<br>
DOI: 10.5281/zenodo.5596979
title: Purity, Politics, and Polarization: Political Ideology Moderates Shifts in Moral Purity Beliefs to Economic Threat

description: Study 1 Variables: Threat: Var00001 (1=threat, 2=control) Anxiety manipulation check: Anxiety Moral Foundations Endorsement: MFQ_HARM_AVG, MFQ_FAIRNESS_AVG, MFQ_INGROUP_AVG, MFQ_AUTHORITY_AVG, MFQ_PURITY_AVG Political Ideology (higher scores mean the participant is more conservative): PolitID Pro-life New Zealand charity: Purity_donation_true Study 2 Variables: Threat: Condition (1=threat, 0=control) Anxiety manipulation check: AffectMC11 Purity Endorsement: MFQ_HARM_AVG, MFQ_FAIRNESS_AVG, MFQ_INGROUP_AVG, MFQ_AUTHORITY_AVG, MFQ_PURITY_AVG Political Ideology (higher scores mean the participant is more conservative): POLiD Casual sex disapproval: xCASUAL_SEX
DOI: 10.5281/zenodo.5597881
title: LOLA. Flamenco tagged audios Dataset

description: This dataset holds ~1500 audio samples of 3 different flamenco styles. Those styles are: Bulerías Alegrías Sevillanas Each audio is in a folder with the same name as the style to which it belongs. Each audio has a duration between 10 and 15 seconds and is saved in mp3 format.
DOI: 10.5281/zenodo.5597880
title: LOLA. Flamenco tagged audios Dataset

description: This dataset holds ~1500 audio samples of 3 different flamenco styles. Those styles are: Bulerías Alegrías Sevillanas Each audio is in a folder with the same name as the style to which it belongs. Each audio has a duration between 10 and 15 seconds and is saved in mp3 format.
DOI: 10.6084/m9.figshare.16640293
title: Source data for Aphelenchus avenae genome

description: These datasets provide source data for the paper entitled "<b>The <i>Aphelenchus avenae</i> Genome Highlights Evolutionary Adaptation to Desiccation</b>"
DOI: 10.5281/zenodo.5598219
title: Lorius garrulus decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5598218
title: Lorius garrulus decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5598255
title: Purity, Politics, and Polarization: Political Ideology Moderates Shifts in Moral Purity Beliefs to Economic Threat

description: Study 1 Variables: Threat: Var00001 (1=threat, 2=control) Anxiety manipulation check: Anxiety Moral Foundations Endorsement: MFQ_HARM_AVG, MFQ_FAIRNESS_AVG, MFQ_INGROUP_AVG, MFQ_AUTHORITY_AVG, MFQ_PURITY_AVG Political Ideology (higher scores mean the participant is more conservative): PolitID Pro-life New Zealand charity: Purity_donation_true Study 2 Variables: Threat: Condition (1=threat, 0=control) Anxiety manipulation check: Anxiety Purity Endorsement: MFQ_HARM_AVG, MFQ_FAIRNESS_AVG, MFQ_INGROUP_AVG, MFQ_AUTHORITY_AVG, MFQ_PURITY_AVG Political Ideology (higher scores mean the participant is more conservative): POLiD Casual sex disapproval: xCASUAL_SEX
DOI: 10.5281/zenodo.5596978
title: Purity, Politics, and Polarization: Political Ideology Moderates Shifts in Moral Purity Beliefs to Economic Threat

description: Study 1 Variables: Threat: Var00001 (1=threat, 2=control) Anxiety manipulation check: Anxiety Moral Foundations Endorsement: MFQ_HARM_AVG, MFQ_FAIRNESS_AVG, MFQ_INGROUP_AVG, MFQ_AUTHORITY_AVG, MFQ_PURITY_AVG Political Ideology (higher scores mean the participant is more conservative): PolitID Pro-life New Zealand charity: Purity_donation_true Study 2 Variables: Threat: Condition (1=threat, 0=control) Anxiety manipulation check: Anxiety Purity Endorsement: MFQ_HARM_AVG, MFQ_FAIRNESS_AVG, MFQ_INGROUP_AVG, MFQ_AUTHORITY_AVG, MFQ_PURITY_AVG Political Ideology (higher scores mean the participant is more conservative): POLiD Casual sex disapproval: xCASUAL_SEX
DOI: 10.6084/m9.figshare.16847377.v1
title: September 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets 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.16847377
title: September 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets 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.16847797
title: August 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in August, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/).<br>Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview<br>Note: 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/).<br><br>
DOI: 10.6084/m9.figshare.16847797.v1
title: August 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in August, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/).<br>Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview<br>Note: 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/).<br><br>
DOI: 10.6084/m9.figshare.16850170
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.16850170.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.16850212
title: June 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>June, 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.16850212.v1
title: June 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>June, 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: 10.5519/qd.wxg9icow
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.5598266
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Stylianos Chatzimanolis, https://orcid.org/0000-0001-9008-0611. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.6084/m9.figshare.13366730
title: Morphometric Study of Craters on Rhea

description: Figures and accompanying data for :Aponte-Hernandez et al. (2021) PSJ, DOI: 10.6084/m9.figshare.13366730.<br>All data is provided as a .txt file in csv and figures in .pdf. <br>%%%% Data %%%%<br> Simple_RheaData.txt contains data only from craters classified as simple and Complex_RheaData.txt contains data from craters classified as complex.Simple_RheaData.txt and Complex_RheaData.txt each consists of eight columns. From left to right the values correspond to the following variables: Diameter (km), Diameter error, depth (km), depth error, d/D, d/D error, Crater wall slope and Crater wall slope error.<br>The file transition.txt has two columns. The first column contains the surface gravity in cm/s^2 and the second column has reported transition diameters for icy and rocky bodies in km.
DOI: 10.6084/m9.figshare.14775204.v2
title: AJP _ Supplement Material.pdf

description: Our data describes the link between VEGFB and cardiac LPL.
DOI: 10.17632/r6t9tmzzmz.3
title: KAMC_COVID-19

description: We retrospectively collected data of admitted confirmed COVID-19 patients (positive RT-PCR test) to King Abdulaziz Medical City in Riyadh. Starting from the first case, which was on April 2, 2020, till June 18, 2020. We retrieved 5,739 patients records from the EHR system. However,  1513 COVID-19 patients passed the criteria set to be included for mortality endpoint, and 1508 patients passed the inclusion criteria for the ventilatory support requirement endpoint. For ventilatory support requirement, any patient that is lacking one of the following are excluded: 1) confirmed RT-PCR for COVID-19, 2) age >18, 3) CXR, 4) CBC test results, 5) availability of medical history, and 6) Full code status at the time of COVID-19 diagnosis. For mortality inclusion criteria is same as ventilatory support endpoint except that COVID-19 patients who are assigned no-code before the SARS-CoV-2 test are also included. The data include the 36 variables and we below describe how it is obtained and:
1) Gender
2) Age at diagnosis
3) Ventilation support status
4) Vital status
5) Haematocrit
6) Hemoglobin
7) MCHC
8) MCH
9) MCV
10) MPV
11) RBC
12) Platelet_count
13) RDW
14) WBC: 
15) CXR zone 1
16) CXR zone 2
17) CXR zone 3
18) CXR zone 4
19) CXR zone 5
20) CXR zone 6
21) CXR zone 7
22) CXR zone 8
23) CXR zone 9
24) CXR zone 10
25) CXR zone 11
26) CXR zone 12
27) cancer_ICD-10_C00-C97
28) chronic_ischaemic_heart_disease_coronary_artery_disease_ICD-10_I25_I250-I259
29) hypertension_ICD-10_I10
30) asthma_ICD-10_J45_J450-J459
31) chronic_obstructive_pulmonary_disease_ICD-10_J44_J440-J449
32) type_2_diabetes_mellitus_ICD-10_E11
33) liver_cirrhosis_ICD-10_K74_K740-K746
34) chronic_viral_hepatitis_B_ICD-10_B181
35) chronic_viral_hepatitis_C_ICD-10_B182
36) chronic_kidney_disease_all_stages_ICD-10N18_N181-N189

Variable 1 is obtained from EHR. Variable 2 obtained from the EHR system as date of birth and converted to age using Covid-19 test date. Variable 3 is filled manually by a physician from the Infectious Disease Department at KAMC, the value MV assigned for mechanical ventilation, NIV for noninvasive ventilation. MV label is assigned if the patient received MV within 30 days of diagnosis. Variable 4:  Filled manually by looking at EHR system discharge charts. If the patient is not discharge after 30 days of follow-up and the patient is still alive. The vital status is then reported as Alive. Variables 5-14 are obtained from CBC test results  (within 3 days of DX). Variables 15-26, are annotated for CXR severity by four radiologists at KAMC.  The images are baseline (within 3 days of DX). The radiologist are blinded to the outcome (mortality, or ventilator support requirement). A value of 0 is assigned for no presence of ground glass opacity (GGO) , a value of 1 for moderate GGO, and a value of 2 for severe manifestation of GGO (see included CXR figure for CXR zones). Variables 27-36 are obtained from the EHR system a value of 0 for disease absence and 1 for disease presence.
DOI: 10.17632/v2jrv3s58c.1
title: Dataset for SEM and ANN analysis in cloud computing acceptance

description: This article includes analytical results an raw data for our research. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), this study seeks the predictors of individual-level adoption of cloud computing (CC) by combining structural equation modeling (SEM) and an artificial neural network (ANN) to analyze data collected by an online survey in South Korea. The dataset consists of four hundred valid responses of general public in South Korea using cloud computing. The dataset has seven variables such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit and behavioral intention.
DOI: 10.17632/bwdj9tvssg.1
title: RNF168 E3 ligase participates in ubiquitin signaling and recruitment of SLX4 during DNA crosslink repair. Katsuki et al

description: SLX4/FANCP is a key Fanconi anemia (FA) protein and a DNA repair scaffold for incision around a DNA interstrand crosslink (ICL) by its partner XPF nuclease. The tandem UBZ4 ubiquitin-binding domains of SLX4 are critical for the recruitment of SLX4 to damage sites, likely via binding to K63-linked polyubiquitin chains. However, the identity of the ubiquitin E3 ligase that mediates SLX4 recruitment remains unknown. Using siRNA screening with a GFP-tagged N-terminal half of SLX4 (termed SLX4-N), we identified RNF168 E3 ligase as a critical factor for mitomycin C (MMC)-induced SLX4 foci formation. RNF168 and GFP-SLX4-N colocalized in MMC-induced ubiquitin foci. Accumulation of SLX4-N at psoralen-laser ICL tracks or of endogenous SLX4 at Digoxigenin-psoralen/UVA ICL was dependent on RNF168. Finally, we found that RNF168 was epistatic with SLX4 in promoting MMC tolerance. We conclude that RNF168 is a critical component of the signal transduction that recruits SLX4 to ICL damage.