cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20294337.v1
title: Data supporting: Morphological traits and species diversity determine vegetation patterns in Alxa plateau in NW China

description: A intergret dataset of vegetation investigation in Alxa plateau in NW China to explore the interralationship of abiotic and biotic factors that affect the patch size distribution. And another dataset of the relative abundence of shrub species inhabited in the area to show the convergence and dissimilarity of morphorlogical traits under climate and soil gredient. Further information including detailed methods is including in the manuscript and its supporting information.
DOI: 10.6084/m9.figshare.20296689
title: e.m_dp2.miss0.5maf0.01.vcf

description: Sequence data file of the screened 37,195 SNPs of Eremias multiocellata.
DOI: 10.6084/m9.figshare.20296689.v1
title: e.m_dp2.miss0.5maf0.01.vcf

description: Sequence data file of the screened 37,195 SNPs of Eremias multiocellata.
DOI: 10.5281/zenodo.6821915
title: UCF-CAP dataset

description: A video captioning dataset, extracted from the UCF-Crime dataset videos and described at the ICIP 2022 paper "UCF-CAP, video captioning in the wild"
description: Two zips are included: a) YouTubeClips.zip contains all the videos of the dataset b) captions.zip contains four files youtube_mapping.txt includes the mapping of the file name to the video id sents_*_lc_nopunc.txt (*-&gt;train,test,val) includes the caption of each video id
DOI: 10.5281/zenodo.6821914
title: UCF-CAP dataset

description: A video captioning dataset, extracted from the UCF-Crime dataset videos and described at the ICIP 2022 paper "UCF-CAP, video captioning in the wild"
description: Two zips are included: a) YouTubeClips.zip contains all the videos of the dataset b) captions.zip contains four files youtube_mapping.txt includes the mapping of the file name to the video id sents_*_lc_nopunc.txt (*-&gt;train,test,val) includes the caption of each video id
DOI: 10.15151/esrf-es-774880443
title: Cryo-electron microscopy EPN BAG

description: At the EPN campus level structural biology work is carried out in all the 4 institutes (IBS, ESRF, ILL and EMBL). Today all structural biology groups are turning to cryo-EM. The EM demand is therefore extremely high and present in the 4 Institutes. In a nutshell, there are 42 PIs that could benefit from Krios time on the EPN campus (more than 90 different samples). Around 20 % of the sample are ready for high resolution data collection with already prepared grids that have been pre-screened on Glacios microscopes. Whereas some work could be carried out on Glacios microscopes, for difficult samples the use of a krios is indispensable (heterogeneous sample or limited concentration requiring a large number of images; small protein etc). Only these samples will be selected for Krios time at the BAG level.
DOI: 10.26182/cje3-nk80
title: MFRC Histomorphometry for Age-At-Death Estimation
DOI: 10.5517/ccdc.csd.cc2bymzx
title: CCDC 2173440: Experimental Crystal Structure Determination

description: Related Article: Jia-bing Wu, Rui-Kang Huang, Kiyonori Takahashi, Takayoshi Nakamura|2022|Dalton Trans.|51|10595|doi:10.1039/D2DT01615J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2byn0z
title: CCDC 2173441: Experimental Crystal Structure Determination

description: Related Article: Jia-bing Wu, Rui-Kang Huang, Kiyonori Takahashi, Takayoshi Nakamura|2022|Dalton Trans.|51|10595|doi:10.1039/D2DT01615J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.34740/kaggle/dsv/3937162
title: AutoXGB
DOI: 10.34740/kaggle/dsv/3937154
title: Health Workforce Shortage Areas
DOI: 10.6084/m9.figshare.20296773
title: Neritidae_supplementary_data.zip

description: To verify the reliability of obtaining mitochondrial genes from transcriptome and the data obtained, the transcriptomic data of <em>Nerita albicilla</em> (SRR1924221), were downloaded, assembled, annotated and aligned with published mitochondrial genome (MK516738.1).
DOI: 10.6084/m9.figshare.20296773.v1
title: Neritidae_supplementary_data.zip

description: To verify the reliability of obtaining mitochondrial genes from transcriptome and the data obtained, the transcriptomic data of <em>Nerita albicilla</em> (SRR1924221), were downloaded, assembled, annotated and aligned with published mitochondrial genome (MK516738.1).
DOI: 10.5281/zenodo.6823059
title: Datasets for the CUT&RUN data analysis

description: The datasets are used for the Galaxy CUT&RUN training material. CUT&RUN data was generated by Zhu et al. 2019 and down sampled to speed up the training. ChIP-seq data comes from an experiment for GATA1 by Canver et al. 2017.
DOI: 10.5281/zenodo.6823058
title: Datasets for the CUT&RUN data analysis

description: The datasets are used for the Galaxy CUT&RUN training material. CUT&RUN data was generated by Zhu et al. 2019 and down sampled to speed up the training. ChIP-seq data comes from an experiment for GATA1 by Canver et al. 2017.
DOI: 10.24400/802406/ehsc-njkg
title: CASSINI RPWS L2 Dynamic Spectra (Power on 1 Antenna)

description: This data set contains 24h dynamic spectra plots of spectral power measured by the RPWS/HFR instrument on one antenna with arbitrary units. Two spectra are available : raw data (top) and background subtracted data (bottom). The subtracted background level is computed for each day at each observation frequency. Plots are in PDF format.
DOI: 10.24400/802406/7epq-ygjb
title: CASSINI RPWS L3 Dynamic Spectra (Flux Density and Circular Polarization degree)

description: This data set contains 24h dynamic spectra plots of spectral flux densities (top panel) and circular polarization degree (bottom panel) measured by the RPWS/HFR instrument after a goniopolarimetric inversion. Plots are in PDF format.
DOI: 10.24400/802406/xjmj-re5z
title: CASSINI RPWS L3 Electron Density and Temperature from Quasi-Thermal Noise Analysis

description: This data set contains 24h plots of electron density and temperature from quasi-thermal noise analysis on RPWS/HFR spectra. Plots are in PDF format.
DOI: 10.34740/kaggle/dsv/3937228
title: WallStreetBets 2022
DOI: 10.5281/zenodo.6826153
title: Smart Energy Europe scenarios

description: Scenarios for Smart Energy Europe, PRIMES 1.5 TECH and 2050 Baseline. Includes scenario files and distribution time series. Requires EnergyPLAN to run the models.
DOI: 10.5281/zenodo.6826152
title: Smart Energy Europe scenarios

description: Scenarios for Smart Energy Europe, PRIMES 1.5 TECH and 2050 Baseline. Includes scenario files and distribution time series. Requires EnergyPLAN to run the models.
DOI: 10.5281/zenodo.6826071
title: Lagrangian trajectories representing surface drift from the Cape Verde islands

description: <strong>Example trajectories from a biophysical Lagrangian simulation</strong> This is 25x 10.000 example trajectories from biophysical experiments performed with Parcels. The trajectories are a tiny subset of a much bigger collection of trajectories that have been simulated with the aim of learning about the fate of particles drifting away from the Cape Verde islands. <em>Note, that these trajectories should not be used for biological or physical science but merely serve as study objects for developing, testing, or benchmarking (stasticical) methods and algorithms.</em> <strong>Details of the experiments</strong> The trajectories are taken from 25 sets of biophysical simulations which differ in the year they represent. Particles are seeded between mid of August and start of December of the years 1993 to 2017. They are subject to Ocean surface currents simulated by a high-resolution ocean model and subject to Stokes Drift estimated by a wave simulation data provided by the Copernicus Marine Service (https://marine.copernicus.eu/). <strong>Data</strong> The full data come as a Zarr store inside of a ZIP file <code>cape_verde_drift_trajectories_1993-2017.zarr.zip</code> which you need to download and unzip to be able to read it, e.g., with Xarray's <code>open_zarr</code> method. There are subsets of 10000 trajectories provided as compressed csv files split into years called <code>cape_verde_drift_trajectories_1-10000_1993.csv.gz</code> ... <code>cape_verde_drift_trajectories_1-10000_2017.csv.gz</code>. And there are subsets of 100 trajectories provided as un-compressed csv files split into years <code>called cape_verde_drift_trajectories_1-100_1993.csv</code> ... <code>cape_verde_drift_trajectories_1-100_2017.csv</code>. <strong>Variables and their meaning</strong> <code>"obs"</code> contains the time step since the larva started to exist. Each trajectory covers up to 881 daily positions. <code>"traj"</code> indicates the trajectory ID. <code>"lat"</code> and <code>"lon"</code> contain the horizontal positions in degrees Latitude and Longitude. <code>"temp"</code> contains the ambient temperature in degrees Celsius the simulated larva would have felt. <code>"time"</code> contains time stamps for each position. <code>"z"</code> contains the vertical positions of the simulated larva in meters counted downwards. As <code>"z</code>" does not vary (all particles are at the surface), it is omitted from the CSV files. <strong>Details on the subsetting</strong> See cape_verde_turtle_subsetting.ipynb. <strong>Using the data</strong> This data set is licensed under a <em>Creative Commons Attribution 4.0 International License</em>. If you use the data, we'd love to get a notice to wrath@geomar.de. This is, however, not required.
DOI: 10.5281/zenodo.6587207
title: Lagrangian trajectories representing surface drift from the Cape Verde islands

description: <strong>Example trajectories from a biophysical Lagrangian simulation</strong> This is 25x 10.000 example trajectories from biophysical experiments performed with Parcels. The trajectories are a tiny subset of a much bigger collection of trajectories that have been simulated with the aim of learning about the fate of particles drifting away from the Cape Verde islands. <em>Note, that these trajectories should not be used for biological or physical science but merely serve as study objects for developing, testing, or benchmarking (stasticical) methods and algorithms.</em> <strong>Details of the experiments</strong> The trajectories are taken from 25 sets of biophysical simulations which differ in the year they represent. Particles are seeded between mid of August and start of December of the years 1993 to 2017. They are subject to Ocean surface currents simulated by a high-resolution ocean model and subject to Stokes Drift estimated by a wave simulation data provided by the Copernicus Marine Service (https://marine.copernicus.eu/). <strong>Data</strong> The full data come as a Zarr store inside of a ZIP file <code>cape_verde_drift_trajectories_1993-2017.zarr.zip</code> which you need to download and unzip to be able to read it, e.g., with Xarray's <code>open_zarr</code> method. There are subsets of 10000 trajectories provided as compressed csv files split into years called <code>cape_verde_drift_trajectories_1-10000_1993.csv.gz</code> ... <code>cape_verde_drift_trajectories_1-10000_2017.csv.gz</code>. And there are subsets of 100 trajectories provided as un-compressed csv files split into years <code>called cape_verde_drift_trajectories_1-100_1993.csv</code> ... <code>cape_verde_drift_trajectories_1-100_2017.csv</code>. <strong>Variables and their meaning</strong> <code>"obs"</code> contains the time step since the larva started to exist. Each trajectory covers up to 881 daily positions. <code>"traj"</code> indicates the trajectory ID. <code>"lat"</code> and <code>"lon"</code> contain the horizontal positions in degrees Latitude and Longitude. <code>"temp"</code> contains the ambient temperature in degrees Celsius the simulated larva would have felt. <code>"time"</code> contains time stamps for each position. <code>"z"</code> contains the vertical positions of the simulated larva in meters counted downwards. As <code>"z</code>" does not vary (all particles are at the surface), it is omitted from the CSV files. <strong>Details on the subsetting</strong> See cape_verde_turtle_subsetting.ipynb. <strong>Using the data</strong> This data set is licensed under a <em>Creative Commons Attribution 4.0 International License</em>. If you use the data, we'd love to get a notice to wrath@geomar.de. This is, however, not required.
DOI: 10.24400/802406/11h3-gydd
title: HFR Native L1 Data

description: This data set contains the CASSINI RPWS HFR raw data in telemetry values. It is composed of hourly binary files.
DOI: 10.24400/802406/2qr0-wg3p
title: HFR Native L2 Data

description: This data set contains the CASSINI RPWS HFR calibrated measurements in physical values (V^2/Hz.) It is composed of hourly binary files.
DOI: 10.24400/802406/0kcy-04es
title: CASSINI RPWS L2 Data

description: This dataset includes CASSINI RPWS HFR calibrated data , with binary formated metadata description. This dataset is composed of hourly binary files.
DOI: 10.24400/802406/vqt7-1k9p
title: CASSINI RPWS L3 Electron Density and Temperature from Quasi-Thermal Noise Analysis on RPWS/HFR Spectra

description: This dataset contains electron density and temperature from quasi-thermal noise analysis on RPWS/HFR spectra.
DOI: 10.24400/802406/9m6y-d41d
title: HFR Status

description: This data set contains operating mode information for the CASSINI RPWS HFR instrument.
DOI: 10.5281/zenodo.6451758
title: LivingNER terminology: NCBI Taxonomy translated to Spanish

description: <pre>Official NCBI Taxonomy FTP dump (https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/) with the terms translated to Spanish by a Neural Machine Translator fine-tuned for the biomedical domain.</pre> We have added the following terms: <pre><code>2560602 Mumps orthorubulavirus Mumps orthorubulavirus scientific name Paperas ortorubulavirus 2560526 Human orthorubulavirus 4 Human orthorubulavirus 4 scientific name orthorubulavirus humano 4 2847144 hepatitis C virus genotype 1a hepatitis C virus genotype 1a scientific name virus de la hepatitis C genotipo 1a 2560525 Human orthorubulavirus 2 Human orthorubulavirus 2 scientific name orthorubulavirus humano 2 _NOCODE_ out of NCBI Taxonomy scope out of NCBI Taxonomy scope NA mención no codificable a NCBI Taxonomy</code></pre> The first 4 were added because they appear in the LivingNER corpus, and are present in the browser version of NCBI Taxonomy. The last one (_NOCODE_) is added to identify terms in LivingNER corpus not present in the NCBI Taxonomy. <strong>Format:</strong> Tab-separated file with the following columns: tax_id: the id of node associated with this name name_txt: name itself unique name: the unique variant of this name if name not unique name class: (synonym, common name, scientific name, ...) Spanish name: name in Spanish <strong>Please cite if you use this dataset:</strong> A. Miranda-Escalada, E. Farré-Maduell, S. Lima-López, D. Estrada, L. Gascó, M. Krallinger, Mention detection, normalization & classification of species, pathogens, humans and food in clinical documents: Overview of livingner shared task and resources, <em>Procesamiento del Lenguaje Natural</em> (2022) <pre><code>@article{amiranda2022nlp, title={Mention detection, normalization \& classification of species, pathogens, humans and food in clinical documents: Overview of LivingNER shared task and resources}, author={Miranda-Escalada, Antonio and Farr{\'e}-Maduell, Eul{`a}lia and Lima-L{\'o}pez, Salvador and Estrada, Darryl and Gasc{\'o}, Luis and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, year={2022} }</code></pre> <strong>Resources</strong> <strong>Web</strong> <strong>Training and validation set</strong> <strong>Annotation Guidelines</strong> <strong>Evaluation library</strong> For more information visit https://temu.bsc.es/livingner/ or email us at encargo-pln-life@bsc.es Check out the translator demo: https://textmining.bsc.es/translator
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.5281/zenodo.6390506
title: LivingNER terminology: NCBI Taxonomy translated to Spanish

description: <pre>Official NCBI Taxonomy FTP dump (https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/) with the terms translated to Spanish by a Neural Machine Translator fine-tuned for the biomedical domain.</pre> We have added the following terms: <pre><code>2560602 Mumps orthorubulavirus Mumps orthorubulavirus scientific name Paperas ortorubulavirus 2560526 Human orthorubulavirus 4 Human orthorubulavirus 4 scientific name orthorubulavirus humano 4 2847144 hepatitis C virus genotype 1a hepatitis C virus genotype 1a scientific name virus de la hepatitis C genotipo 1a 2560525 Human orthorubulavirus 2 Human orthorubulavirus 2 scientific name orthorubulavirus humano 2 _NOCODE_ out of NCBI Taxonomy scope out of NCBI Taxonomy scope NA mención no codificable a NCBI Taxonomy</code></pre> The first 4 were added because they appear in the LivingNER corpus, and are present in the browser version of NCBI Taxonomy. The last one (_NOCODE_) is added to identify terms in LivingNER corpus not present in the NCBI Taxonomy. <strong>Format:</strong> Tab-separated file with the following columns: tax_id: the id of node associated with this name name_txt: name itself unique name: the unique variant of this name if name not unique name class: (synonym, common name, scientific name, ...) Spanish name: name in Spanish <strong>Please cite if you use this dataset:</strong> A. Miranda-Escalada, E. Farré-Maduell, S. Lima-López, D. Estrada, L. Gascó, M. Krallinger, Mention detection, normalization & classification of species, pathogens, humans and food in clinical documents: Overview of livingner shared task and resources, <em>Procesamiento del Lenguaje Natural</em> (2022) <pre><code>@article{amiranda2022nlp, title={Mention detection, normalization \& classification of species, pathogens, humans and food in clinical documents: Overview of LivingNER shared task and resources}, author={Miranda-Escalada, Antonio and Farr{\'e}-Maduell, Eul{`a}lia and Lima-L{\'o}pez, Salvador and Estrada, Darryl and Gasc{\'o}, Luis and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, year={2022} }</code></pre> <strong>Resources</strong> <strong>Web</strong> <strong>Training and validation set</strong> <strong>Annotation Guidelines</strong> <strong>Evaluation library</strong> For more information visit https://temu.bsc.es/livingner/ or email us at encargo-pln-life@bsc.es Check out the translator demo: https://textmining.bsc.es/translator
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.6084/m9.figshare.20079026
title: TelomereLengthHeritabilityMetaAnalysis

description: Datasets and R code used to conduct a meta-analysis on the heritability of telomere length in vertebrate species. The dataset was collected from a systematic literature search on the Web of Science, and screened based on relevance, study species, accessibility, etc. The screened dataset was then used to investigate mean telomere length heritability, and sources of heterogeneity in telomere length heritability estimates.
DOI: 10.1285/i22390359v47
title: The Languages and Anti-Languages of Health Communication in the Age of Conspiracy Theories, Mis/Disinformation and Hate Speech
DOI: 10.1285/i22390359v47p1
title: Title Page
DOI: 10.1285/i22390359v47p3
title: Table of Contents
DOI: 10.5281/zenodo.6826009
title: Urban Tunnel Cameras (UTCam)

description: This dataset contains recordings from french road tunnels and their associated flow rate (Q), occupancy (T) and velocity (V) values. Recordings were collected over two days at various hours on five different cameras. An approximate 10,000 datatpoints are available for a total of 57 hours of recordings. Each datapoint is composed of a RGB recording, its associated motion vector frames (as generated by the MPEG4 part2 encoding) and the associated flow rate (Q), occupancy (T) and velocity (V) values.
DOI: 10.5281/zenodo.5547937
title: Urban Tunnel Cameras (UTCam)

description: This dataset contains recordings from french road tunnels and their associated flow rate (Q), occupancy (T) and velocity (V) values. Recordings were collected over two days at various hours on five different cameras. An approximate 10,000 datatpoints are available for a total of 57 hours of recordings. Each datapoint is composed of a RGB recording, its associated motion vector frames (as generated by the MPEG4 part2 encoding) and the associated flow rate (Q), occupancy (T) and velocity (V) values.
DOI: 10.1285/i22390359v47p13
title: Dramatising Crisis. Rhetorical Responses to the COVID-19 Pandemic by Right-Wing Populist Leaders in the USA and UK

description: Building on the theoretical lens of Critical Discourse Analysis and Conceptual Metaphor Theory, this paper examines the rhetorical responses to the COVID-19 pandemic of two right-wing populist leaders whose management of the emergency has been viewed as controversial, namely former American President Donald J. Trump and UK Prime Minister Boris Johnson. Through the critical examination of a dataset of speeches, press conferences and social media posts, and focusing on the discursive strategies employed in framing the pandemic, attributing responsibility, people-building, and policy-making, our study reveals that through different trajectories, the two leaders attempted to exploit the emergency to perform a "crisis within the crisis" in the typical populist style to serve their political interests, based on Moffitt's (2015) framework. In his trademark style, Trump used the pandemic as a stage to call out and blame multiple enemies both at home (the Congress, the media) and abroad (China). On the other hand, Johnson, who, unlike Trump, did not lend an ear to conspiratorial thinking but still initially minimised the extent of the danger, framed the pandemic as the fight of a nation "walking alone" in a nationalist sense.