cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4625071
title: Dataset of Seismic Velocity Model of the Northern Hikurangi Margin, New Zealand, using Land and Ocean Bottom Seismometers

description: The dataset provided in this repository contains: File Yarce_Hikurangi_HOBITSS3D.ascii: 3D distribution of Vp and Vp/Vs ratios for the northern Hikurangi margin of New Zealand. The model was constructed using data from ocean bottom seismometer and the permanent seismometer network of New Zealand (HOBITSS experiment). We used the finite-difference tomographic inversion method (TomoFD) developed by Roecker et al. (2006, 2017).<br> File Hobitss_arrival4tomo.dat: Contains body wave arrival data for individual events use to construct the 3D seismic tomography model. Consists of an event location header followed by a list of phase arrival observations terminated by a blank line.<br> The fields in the location header are (year, Julian day, hour, min, second) of origin time followed by latitude, longitude, and depth in km of the event location. The last field in the event identification number (evid)<br> The fields in the phase arrival time lines are station Code, year, Julian day, hour, min, second of arrival time, phase identifier (nominally P or S) and the uncertainty in the observation in seconds.<br> File Hobitss_relocated3D_EQs.dat: Each line contains information for individual events. The fields are evid, year, Julian day, hour, minute, and second for time of origin. Latitude, longitude, and depth for the hypocenter location. Number of P arrivals, number of S arrivals, and number of P and S arrivals (P+S)
DOI: 10.5281/zenodo.4625070
title: Dataset of Seismic Velocity Model of the Northern Hikurangi Margin, New Zealand, using Land and Ocean Bottom Seismometers

description: The dataset provided in this repository contains: File Yarce_Hikurangi_HOBITSS3D.ascii: 3D distribution of Vp and Vp/Vs ratios for the northern Hikurangi margin of New Zealand. The model was constructed using data from ocean bottom seismometer and the permanent seismometer network of New Zealand (HOBITSS experiment). We used the finite-difference tomographic inversion method (TomoFD) developed by Roecker et al. (2006, 2017).<br> File Hobitss_arrival4tomo.dat: Contains body wave arrival data for individual events use to construct the 3D seismic tomography model. Consists of an event location header followed by a list of phase arrival observations terminated by a blank line.<br> The fields in the location header are (year, Julian day, hour, min, second) of origin time followed by latitude, longitude, and depth in km of the event location. The last field in the event identification number (evid)<br> The fields in the phase arrival time lines are station Code, year, Julian day, hour, min, second of arrival time, phase identifier (nominally P or S) and the uncertainty in the observation in seconds.<br> File Hobitss_relocated3D_EQs.dat: Each line contains information for individual events. The fields are evid, year, Julian day, hour, minute, and second for time of origin. Latitude, longitude, and depth for the hypocenter location. Number of P arrivals, number of S arrivals, and number of P and S arrivals (P+S)
DOI: 10.6084/m9.figshare.14252948
title: Cryptic eco-evolutionary feedback in the city

description: The ZIP file contains data & R-scripts used for the statistical analyses reported in the manuscript "Cryptic eco-evolutionary feedback in the city: urban evolution of prey buffers the effect of urban evolution of the predator".Readme files are provided for column name (i.e. variable) descriptions.
DOI: 10.6084/m9.figshare.14252948.v1
title: Cryptic eco-evolutionary feedback in the city

description: The ZIP file contains data & R-scripts used for the statistical analyses reported in the manuscript "Cryptic eco-evolutionary feedback in the city: urban evolution of prey buffers the effect of urban evolution of the predator".Readme files are provided for column name (i.e. variable) descriptions.
DOI: 10.25721/ej33-sb11
title: Apririlean gaua da labur
DOI: 10.25721/k2qe-d812
title: Jeiki Jeiki
DOI: 10.25721/w4nf-5q95
title: Oilanda gazte
DOI: 10.25721/2jsx-kg95
title: Maritxu nora zoaz
DOI: 10.25721/24n7-bq77
title: Neure semearen seme
DOI: 10.25721/pawq-zm38
title: Haurtxoa seaskan
DOI: 10.25721/vk7q-bw86
title: Ume eder bat
DOI: 10.25721/m36p-ke05
title: Adios izar ederra
DOI: 10.25721/qnex-yv36
title: Jose Manuel
DOI: 10.25721/xsnk-8990
title: Ama (Ai, zer plazerra!)
DOI: 10.25721/cc9c-pa40
title: Goizean goizik, avec introduction et final sur le thème "Bilin Bolon"
DOI: 10.25721/y656-0v66
title: Buba niñaño (formulette pour endormir les enfants)
DOI: 10.25721/m0nt-sy82
title: Ama
DOI: 10.25721/j3h4-cs68
title: Ama
DOI: 10.25721/shcs-a818
title: Uso xuria
DOI: 10.25721/vy77-4a05
title: Kanpañatarrak gira gu (fin)
DOI: 10.25721/eps8-m927
title: Kanpañatarrak gira gu
DOI: 10.25721/sax0-7t11
title: ** Iruten ari nuzu
DOI: 10.25721/kyb7-bw29
title: * Improvisation : salutation
DOI: 10.25721/859n-hj13
title: Maitea nun zira
DOI: 10.25721/ygr1-ca39
title: Iruten ari nuzu
DOI: 10.25721/kttc-d672
title: *** Neure Andrea
DOI: 10.25721/pzpq-qh16
title: Nere etxea (Ikusten duzu goizean)
DOI: 10.6084/m9.figshare.14252944
title: PAN_vf_test

description: Open source data for test set of PAN task
DOI: 10.6084/m9.figshare.14252944.v1
title: PAN_vf_test

description: Open source data for test set of PAN task
DOI: 10.25721/ghq1-8x16
title: Nere etxea (Ikusten duzu goizean)
DOI: 10.25721/t698-za16
title: Adora dezagun (Jesus amodio)
DOI: 10.25721/70fn-3t92
title: Comptine
DOI: 10.25721/d8mf-2b09
title: Bèth cèu de Pau
DOI: 10.5281/zenodo.4625151
title: Towards A Reliable Ground-Truth For Biased Language Detection

description: Reference texts such as encyclopedias and news articles can manifest biased language when objective reporting is substituted by subjective writing. Existing methods to detect linguistic cues of bias mostly rely on annotated data to train machine learning models. However, low annotator agreement and comparability is a substantial drawback in available media bias corpora. To improve available datasets, we collect and compare labels obtained from two popular crowdsourcing platforms. Our results demonstrate the existing crowdsourcing approaches' lack of data quality, underlining the need for a trained expert framework to gather a more reliable dataset. Improving the agreement from Krippendorff's \(\alpha\) = 0.144 (crowdsourcing labels) to \(\alpha\) = 0.419 (expert labels), we assume that trained annotators' linguistic knowledge increases data quality improving the performance of existing bias detection systems. The expert annotations are meant to be used to enrich the dataset <em>MBIC </em>– <em>A Media Bias Annotation Dataset Including Annotator Characteristics</em> available at https://zenodo.org/record/4474336#.YBHO6xYxmK8.
DOI: 10.5281/zenodo.4625150
title: Towards A Reliable Ground-Truth For Biased Language Detection

description: Reference texts such as encyclopedias and news articles can manifest biased language when objective reporting is substituted by subjective writing. Existing methods to detect linguistic cues of bias mostly rely on annotated data to train machine learning models. However, low annotator agreement and comparability is a substantial drawback in available media bias corpora. To improve available datasets, we collect and compare labels obtained from two popular crowdsourcing platforms. Our results demonstrate the existing crowdsourcing approaches' lack of data quality, underlining the need for a trained expert framework to gather a more reliable dataset. Improving the agreement from Krippendorff's \(\alpha\) = 0.144 (crowdsourcing labels) to \(\alpha\) = 0.419 (expert labels), we assume that trained annotators' linguistic knowledge increases data quality improving the performance of existing bias detection systems. The expert annotations are meant to be used to enrich the dataset <em>MBIC </em>– <em>A Media Bias Annotation Dataset Including Annotator Characteristics</em> available at https://zenodo.org/record/4474336#.YBHO6xYxmK8.
DOI: 10.6084/m9.figshare.11829306
title: Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks

description: <b>Reference</b>: Mingxiao Li, Song Gao, Feng Lu, Kang Liu, Hengcai Zhang, Wei Tu. (2021) Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks. <i>International Journal of Geographical Information Science</i>. X(X), XX-XX.<b>Abstract</b>: Dynamic human activity intensity information is of great importance in many location-based applications. However, two limitations remain in the prediction of human activity intensity. First, it is hard to learn the spatial interaction patterns across scales for predicting human activities. Second, social interaction can help model the activity intensity variation but is rarely considered in the existing literature. To mitigate these limitations, we proposed a novel dynamic activity intensity prediction method with deep learning on graphs using the interactions in both physical and social spaces. In this method, the physical interactions and social interactions between spatial units were integrated into a fused graph convolutional network to model multi-type spatial interaction patterns. The future activity intensity variation was predicted by combining the spatial interaction pattern and the temporal pattern of activity intensity series. The method was verified with a country-scale anonymized mobile phone dataset. The results demonstrated that our proposed deep learning method with combining graph convolutional networks and recurrent neural networks outperformed other baseline approaches. This method enables dynamic human activity intensity prediction from a more spatially and socially integrated perspective, which helps improve the performance of modeling human dynamics.
DOI: 10.21227/47v6-9588
title: Forecast for January 23

description: Forecast for January 23
DOI: 10.6084/m9.figshare.3761562.v1811
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.4625172
title: Ooh Na Na Properties

description: A combination of all properties indexed by PyOBO. Original content licensed as CC0, derived content licensed under the respective sources' licenses.
DOI: 10.5281/zenodo.4625167
title: Ooh Na Na Relations

description: Relations extracted from ontologies in OBO Foundry, OLS, and other sources. Original content licensed under CC0, derivative content licensed same as the original sources.
DOI: 10.21227/h607-4223
title: January-23-Tang-Wenyuan.csv

description: January-23-Tang-Wenyuan.csv
DOI: 10.5517/ccdc.csd.cc25vy99
title: CCDC 2021891: Experimental Crystal Structure Determination

description: Related Article: Florence Charnay-Pouget, Matthieu Le Liepvre, Hendrik Eijsberg, Régis Guillot, Jean Ollivier, Francesco Secci, Angelo Frongia, David J. Aitken|2021|Tetrahedron Lett.|68|152912|doi:10.1016/j.tetlet.2021.152912
DOI: 10.5517/ccdc.csd.cc25vybb
title: CCDC 2021892: Experimental Crystal Structure Determination

description: Related Article: Florence Charnay-Pouget, Matthieu Le Liepvre, Hendrik Eijsberg, Régis Guillot, Jean Ollivier, Francesco Secci, Angelo Frongia, David J. Aitken|2021|Tetrahedron Lett.|68|152912|doi:10.1016/j.tetlet.2021.152912
DOI: 10.5517/ccdc.csd.cc26j4yv
title: CCDC 2041317: Experimental Crystal Structure Determination

description: Related Article: Tereza Cristina Santos Evangelista, Maicon Delarmelina, Dinesh Addla, Rafael A. Allão, Carlos Roland Kaiser, José Walkimar de M. Carneiro, Floriano Paes Silva-Jr, Sabrina Baptista Ferreira|2021|Tetrahedron Lett.|68|152937|doi:10.1016/j.tetlet.2021.152937
DOI: 10.5517/ccdc.csd.cc26xjsg
title: CCDC 2053216: Experimental Crystal Structure Determination

description: Related Article: Katherine L. Meehan, Devon F.A. Fontaine, Alistair D. Richardson, Samantha M. Fowles, Benjamin Mukda, Jeffrey C. Monroe, Christopher P. Landee, Diane A. Dickie, Mark M. Turnbull, Shu Jiang, Fan Xiao|2021|Polyhedron|200|115094|doi:10.1016/j.poly.2021.115094
DOI: 10.5517/ccdc.csd.cc26xjwk
title: CCDC 2053219: Experimental Crystal Structure Determination

description: Related Article: Katherine L. Meehan, Devon F.A. Fontaine, Alistair D. Richardson, Samantha M. Fowles, Benjamin Mukda, Jeffrey C. Monroe, Christopher P. Landee, Diane A. Dickie, Mark M. Turnbull, Shu Jiang, Fan Xiao|2021|Polyhedron|200|115094|doi:10.1016/j.poly.2021.115094
DOI: 10.5517/ccdc.csd.cc26xjxl
title: CCDC 2053220: Experimental Crystal Structure Determination

description: Related Article: Katherine L. Meehan, Devon F.A. Fontaine, Alistair D. Richardson, Samantha M. Fowles, Benjamin Mukda, Jeffrey C. Monroe, Christopher P. Landee, Diane A. Dickie, Mark M. Turnbull, Shu Jiang, Fan Xiao|2021|Polyhedron|200|115094|doi:10.1016/j.poly.2021.115094
DOI: 10.5517/ccdc.csd.cc272yy7
title: CCDC 2058429: Experimental Crystal Structure Determination

description: Related Article: De-Yin Chen, Shuai Song, Ling-Yan Chen, Xinfeng Ren, Ya Li|2021|Tetrahedron Lett.|68|152919|doi:10.1016/j.tetlet.2021.152919
DOI: 10.3886/e135421
title: Replication Files for: "Skilled Immigrants and Technology Adoption: Evidence from the German Settlements in the Russian Empire"

description: This package contains all the data sets and code that are necessary for replicating the results in "Skilled Immigrants and Technology Adoption: Evidence from the German Settlements in the Russian Empire" to be published in Explorations in Economic History.
DOI: 10.3886/e135421v1
title: Replication Files for: "Skilled Immigrants and Technology Adoption: Evidence from the German Settlements in the Russian Empire"

description: This package contains all the data sets and code that are necessary for replicating the results in "Skilled Immigrants and Technology Adoption: Evidence from the German Settlements in the Russian Empire" to be published in Explorations in Economic History.
DOI: 10.18112/openneuro.ds003459.v1.0.1
title: Component processes of word reading in adults and children
DOI: 10.18112/openneuro.ds003076.v1.0.1
title: Brain Correlates of Deductive Reasoning in Adults
DOI: 10.25919/aeqy-gj33
title: Parkes observations for project P1040 semester 2020OCTS_09

description: We have re-processed all the observations in the Parkes pulsar data archive obtained during the first four years of the Parkes multibeam receiver and have discovered two new fast radio bursts (FRBs) and five single pulse candidates (SPCs) from unknown sources, but with Galactic dispersion measures. These signals were only detected in a single beam of the receiver suggesting that they are unlikely to be terrestrial. There are no known pulsars in the pointing direction of the five SPCs and therefore are likely giant pulses from currently unknown pulsars. Using the Ultra Wideband Low (UWL) receiver we have opportunity to detect repeating signals from the two FRBs and to confirm the five SPCs. Our previous observations have obtained new detections for one of these targets and therefore confirming that source. Therefore we propose for more observation in an attempt to confirm the remainder and to study the newly discovered source.
DOI: 10.5281/zenodo.4625193
title: CoVaxxy Tweet IDs data set

description: A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see https://arxiv.org/abs/2101.07694 for more information.
DOI: 10.5063/3n21tv
title: Salinity and inundation effects on growth of brackish tidal marsh plants from the San Francisco Bay-Delta estuary, 2016

description: In this data release we provide above- and below-ground plant biomass, shoot count, shoot height, and fecundity data for field and greenhouse experiments conducted in northern California in 2016 to assess tidal marsh plant responses to gradients of inundation and salinity. This dataset accompanies the publication Janousek et al. 2020, “Salinity and inundation effects on productivity of brackish tidal marsh plants in the San Francisco Bay-Delta Estuary” in the journal Hydrobiologia.
DOI: 10.6084/m9.figshare.14253215.v1
title: Ribosomal protein database of Shewanella putrefaciens ATCC BAA-453

description: This work presents the ribosomal protein database of <i>Shewanella putrefaciens </i>ATCC BAA-453. Original data for the work came from the annotated proteome data of the bacterium downloaded from UniProt. Using an in-house MATLAB ribosomal protein database analysis software, the original proteome data file was parsed to extract protein name and amino acid sequence of all ribosomal proteins in the species. The database also includes calculated variables such as number of residues, molecular weight, and nucleotide sequence. Overall, the presented database could serve as a ribosomal protein mass fingerprint for use in microbial identification, or it could be used in fundamental studies seeking to uncover new insights into ribosomal protein biology.
DOI: 10.6084/m9.figshare.14253215
title: Ribosomal protein database of Shewanella putrefaciens ATCC BAA-453

description: This work presents the ribosomal protein database of <i>Shewanella putrefaciens </i>ATCC BAA-453. Original data for the work came from the annotated proteome data of the bacterium downloaded from UniProt. Using an in-house MATLAB ribosomal protein database analysis software, the original proteome data file was parsed to extract protein name and amino acid sequence of all ribosomal proteins in the species. The database also includes calculated variables such as number of residues, molecular weight, and nucleotide sequence. Overall, the presented database could serve as a ribosomal protein mass fingerprint for use in microbial identification, or it could be used in fundamental studies seeking to uncover new insights into ribosomal protein biology.
DOI: 10.6084/m9.figshare.12085428
title: Data availability for global burned area trends study

description: <b>Data availability for the paper:</b>Wu, C., Venevsky, S., Sitch, S., Mercado, L.M., Huntingford, C., and Staver, A.C. (2021) Historical and future global burned area with changing climate and human demography. All raw data used to generate the figures and tables in the main text and Supplemental Information. If you have further questions, please contact Chao Wu (chaowu.thu@gmail.com)<br><b>Summary of this dataset</b>DATA: All raw data used to generate the figures 1-5 in the main text and figures 2-5, 7-12, and 14 in the Supplemental Information.This DATA includes the results for 34 ESMs, 1 offline, 6 factorial analyses, 1 demographic scenario, the result for the Amazon region, and other files that help to generate the figures.Each ESM includes 4 scenarios (RCP2.6, 4.5, 6, and 8.5).Factorial analysis to investigate the dominant limiting factors based on 3 ESMs are shown in “test_driver_*”, each of that includes 6 scenarios: fixed temperature (“rcp6_tem”), precipitation (“rcp6_pre”), wind speed (“rcp6_wind”), population density (“rcp6_pop”), ratio of rural population to total population (“rcp6_rur”), and distance to cities (“rcp6_dis”).Factorial analysis to investigate the dominant drivers based on 3 ESMs are shown in “fac_*”, each of that includes 7 scenarios: varying temperature only (“rcp6_tem”), varying precipitation only (“rcp6_pre”), varying wind speed only (“rcp6_wind”), varying population density only (“rcp6_pop”), varying ratio of rural population to total population only (“rcp6_rur”), varying distance to cities only (“rcp6_dis”), and control (“rcp6”).“ssps_MIROC_ESM” includes all scenarios to generate Figure 3C.“fac_GISS_E2_R_CC” includes all scenarios to generate Figure 5.“test_driver_GISS_E2_R_CC” includes all scenarios to generate Figure S12. “amazon_MIROC_ESM” includes all scenarios to generate Figure S11.For each scenario, there is one global result "SEVERGlobe.out" and one grid-based result for annual burned area ("aburntarea.dat").Each column in "SEVERGlobe.out" represents “year”, and the global annual “burned area”, “precipitation over land”.GFED4s product was accessed from http://www.globalfiredata.org/index.html; FireCCI51 product was accessed from https://geogra.uah.es/fire_cci/firecci51.php.The python code to interpret data and prepare the figures are available on request from the corresponding author.<br>
DOI: 10.6084/m9.figshare.12085428.v2
title: Data availability for global burned area trends study

description: <b>Data availability for the paper:</b>Wu, C., Venevsky, S., Sitch, S., Mercado, L.M., Huntingford, C., and Staver, A.C. (2021) Historical and future global burned area with changing climate and human demography. All raw data used to generate the figures and tables in the main text and Supplemental Information. If you have further questions, please contact Chao Wu (chaowu.thu@gmail.com)<br><b>Summary of this dataset</b>DATA: All raw data used to generate the figures 1-5 in the main text and figures 2-5, 7-12, and 14 in the Supplemental Information.This DATA includes the results for 34 ESMs, 1 offline, 6 factorial analyses, 1 demographic scenario, the result for the Amazon region, and other files that help to generate the figures.Each ESM includes 4 scenarios (RCP2.6, 4.5, 6, and 8.5).Factorial analysis to investigate the dominant limiting factors based on 3 ESMs are shown in “test_driver_*”, each of that includes 6 scenarios: fixed temperature (“rcp6_tem”), precipitation (“rcp6_pre”), wind speed (“rcp6_wind”), population density (“rcp6_pop”), ratio of rural population to total population (“rcp6_rur”), and distance to cities (“rcp6_dis”).Factorial analysis to investigate the dominant drivers based on 3 ESMs are shown in “fac_*”, each of that includes 7 scenarios: varying temperature only (“rcp6_tem”), varying precipitation only (“rcp6_pre”), varying wind speed only (“rcp6_wind”), varying population density only (“rcp6_pop”), varying ratio of rural population to total population only (“rcp6_rur”), varying distance to cities only (“rcp6_dis”), and control (“rcp6”).“ssps_MIROC_ESM” includes all scenarios to generate Figure 3C.“fac_GISS_E2_R_CC” includes all scenarios to generate Figure 5.“test_driver_GISS_E2_R_CC” includes all scenarios to generate Figure S12. “amazon_MIROC_ESM” includes all scenarios to generate Figure S11.For each scenario, there is one global result "SEVERGlobe.out" and one grid-based result for annual burned area ("aburntarea.dat").Each column in "SEVERGlobe.out" represents “year”, and the global annual “burned area”, “precipitation over land”.GFED4s product was accessed from http://www.globalfiredata.org/index.html; FireCCI51 product was accessed from https://geogra.uah.es/fire_cci/firecci51.php.The python code to interpret data and prepare the figures are available on request from the corresponding author.<br>