cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20321800.v1
title: Sites from Neo-taphonomic analysis of the misiam leopard lair from Olduvai gorge (Tanzania): understanding leopard–hyena interactions in open settings

description: MAU data in which sites are columns instead of rows.
DOI: 10.6084/m9.figshare.20321803.v1
title: MAU from Neo-taphonomic analysis of the misiam leopard lair from Olduvai gorge (Tanzania): understanding leopard–hyena interactions in open settings

description: MAU data for the hyena and felid assemblages and Misiam for GLRM analysis
DOI: 10.6084/m9.figshare.20321803
title: MAU from Neo-taphonomic analysis of the misiam leopard lair from Olduvai gorge (Tanzania): understanding leopard–hyena interactions in open settings

description: MAU data for the hyena and felid assemblages and Misiam for GLRM analysis
DOI: 10.6084/m9.figshare.20322628
title: Matlab code + Gnuplot scripts from Predicting ground reaction forces of human gait using a simple bipedal spring-mass model

description: Matlab code for the gait model used in this study, Gnuplot scripts and source data to generate figures
DOI: 10.6084/m9.figshare.20322628.v1
title: Matlab code + Gnuplot scripts from Predicting ground reaction forces of human gait using a simple bipedal spring-mass model

description: Matlab code for the gait model used in this study, Gnuplot scripts and source data to generate figures
DOI: 10.6084/m9.figshare.20322438
title: GAM percentage

description: Data matrix built from genetic discontinuities empirical data extracted from scientific papers published between 1987-2019 with sample sites within the Brazilian coast.
DOI: 10.6084/m9.figshare.20322438.v1
title: GAM percentage

description: Data matrix built from genetic discontinuities empirical data extracted from scientific papers published between 1987-2019 with sample sites within the Brazilian coast.
DOI: 10.25810/q1-ts57.22
title: Russian Newspaper Coverage of Climate Change or Global Warming, 2000-2022 - June 2022

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.25810/37f9-1j65.51
title: Spanish Newspaper Coverage of Climate Change or Global Warming, 2000-2021 - June 2022

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.6841249
title: Data used for Investigating the role of Amazonian mesoscale wind patterns and strength on the spatial distribution of Martian bedrock exposures

description: This upload contains four distinct zipped files with several different datasets contained within used for the analysis in publication "Investigating the role of Amazonian mesoscale wind patterns and strength on the spatial distribution of Martian bedrock exposures" by Gary-Bicas et al., 2022 Description for each dataset is below. - External data (Contains data used for thermophysical and morphological analysis). binary files NBmap2007.bin (Putzig and Mellon, 2007) and nmap2003.bin( Putzig et al., 2005) . These contain global Mars thermal inertia maps using the Thermal Emission Spectrometer (TES) onboard Mars Global Surveyor (MGS) Comma separated files with terminations "...USGS.csv" these are files extracting data for the studies' regions from the Mars global USGS geologic map #3292 (Tanaka et al., 2014) Shape file for bedrock designations (bedrock.[shp,shx,prj,dbf]) created by Cowart et al., 2019 where they mapped locations with bedrock exposures on Mars. We also include comma separated value files of the same maps for locations studied in this analysis (files with termination "...bedrock.csv") Shape file included has the locations of craters identified in all study regions for analysis (craters.point.[shp,shx,prj,dbf] and craters.polygon.[shp,shx,prj,dbf]) paired with the comma separated value intracrat.csv Shape file with study locations for analysis (windo_modeling_locations_revised3.[shp,shx,prj,dbf]) - MRAMS data files.zip Contains 11 simulated climate states for each of the ten study location in analysis as well as Jezero crater using the Mars Regional Atmospheric Modeling System (MRAMS, Rafkin and Michaels, 2019). For each simulated case there are 4 seasonal time steps equating to 44 simulated cases for each study region in total (484 files) see associated python software publication indicating ingestion and processing of MRAMS datasets - MRAMS output files.zip After ingesting the datasets in MARS data files.zip into a python algorithm (see associated software publication) values for Wind Erosion Potential were extracted from the datasets and weighted sums were conducted to get annual values (see manuscript publication and associated python software publication,"MRAMS Data Output.ipynb") data was output into comma separated values for ease of use -MRAMS elevation and slope files.zip MRAMS data from output files.zip was further ingested into other algorithms to extract elevation and terrain slope values (see associated python software publication, "MRAMS Data Output.ipynb") that were output into comma separated values for ease of use
DOI: 10.25810/qt7k-6r09.38
title: Swedish Newspaper Coverage of Climate Change or Global Warming, 2000-2021 - June 2022

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.6839080
title: Supplementary material of Kristallbrockensalt microstructures of the Zechstein-2 rock salt from the Northern Netherlands

description: This dataset represents supporting data for the paper entitled <strong>Grain size dependent large rheology contrasts of halite at low deviatoric stress: evidence from microstructural study of naturally deformed gneissic Zechstein-2 rock salt (Kristallbrockensalz) from the Northern Netherlands</strong> <strong>, </strong>which has been submitted to <strong>Solid Earth </strong>journal The so-called Kristallbrocken salt has a unique and quite spectacular grain size distribution and marks an important horizon throughout the entire Permian Basin. Based on microstructural observations we interpret that fine grained halite deformed much weaker (i.e. faster) and has characteristic pressure-solution structures, as compared to much less deformed Kristallbrocken megacrystals that developed subgrains during dislocation creep. This dataset contains (1) a collection of addiational micrographs from gamma irradiated halite thin sections, (2) high resolution sample overview scans in tramsmitted and reflected light microscopy and grain and subgrainsize measurements, (3) Supportind EBSD material, (4) BIB-SEM material and (5) XRD results of sedond phase inclusions. (1), (3) and (4) are compiled in PowerPoint (.pptx) format, (2) contains multiple files of microscopical images or panoramas overlain with interpreted digitized grain and subgrain boundaries compiled in Inkscape (.svg) files together with corresponding measurements in Excel (.xls) files and (5) a text document (.txt) of XRD second phase results.
DOI: 10.5281/zenodo.6839079
title: Supplementary material of Kristallbrockensalt microstructures of the Zechstein-2 rock salt from the Northern Netherlands

description: This dataset represents supporting data for the paper entitled <strong>Grain size dependent large rheology contrasts of halite at low deviatoric stress: evidence from microstructural study of naturally deformed gneissic Zechstein-2 rock salt (Kristallbrockensalz) from the Northern Netherlands</strong> <strong>, </strong>which has been submitted to <strong>Solid Earth </strong>journal The so-called Kristallbrocken salt has a unique and quite spectacular grain size distribution and marks an important horizon throughout the entire Permian Basin. Based on microstructural observations we interpret that fine grained halite deformed much weaker (i.e. faster) and has characteristic pressure-solution structures, as compared to much less deformed Kristallbrocken megacrystals that developed subgrains during dislocation creep. This dataset contains (1) a collection of addiational micrographs from gamma irradiated halite thin sections, (2) high resolution sample overview scans in tramsmitted and reflected light microscopy and grain and subgrainsize measurements, (3) Supportind EBSD material, (4) BIB-SEM material and (5) XRD results of sedond phase inclusions. (1), (3) and (4) are compiled in PowerPoint (.pptx) format, (2) contains multiple files of microscopical images or panoramas overlain with interpreted digitized grain and subgrain boundaries compiled in Inkscape (.svg) files together with corresponding measurements in Excel (.xls) files and (5) a text document (.txt) of XRD second phase results.
DOI: 10.18112/openneuro.ds004186.v2.0.0
title: HBN EO/EC task
DOI: 10.3886/e175341
title: Teachers’ Social Goal Orientations with the Job Demands-Resources Model: Data and Analysis Files

description: These files contain the anonymized data used to create the tables and figures found in "Exploring Teachers’ Social Goal Orientations with the Job Demands-Resources Model". <br><br>Please refer to the abstract for the paper below: <br>Positive teacher-student relationships are recognized as critical social resources for promoting dyadic well-being and adaptive instruction. However, there is scarce research on the relationship between teachers’ motivation for connecting with students and teachers’ psychological adjustment. To explore the relationship, this study applied the job demands-resources model to investigate how teachers’ social goal orientations corresponded with their well-being, job satisfaction, and school commitment, with burnout and work engagement as mediating variables. Whereas teachers’ social mastery-approach goals (attempting to improve student relationship-building competencies) were expected to serve as beneficial psychological resources, their social work-avoidance goals (putting minimum effort into student relationships) were hypothesized to function as would detrimental occupational demands. Findings from mediational structural equation modeling of questionnaire responses from Canadian K-12 teachers (N = 154) showed teachers with stronger social mastery-approach goals to be more engaged and, in turn, report greater well-being, job satisfaction, and school commitment. In contrast, teachers with stronger social work-avoidance goals reported less work engagement that was further associated with lower well-being and job satisfaction, as well as greater burnout and, in turn, poorer job satisfaction. Implications concerning teachers’ interpersonal appraisals and psychological health initiatives were discussed.
DOI: 10.3886/e175341v1
title: Teachers’ Social Goal Orientations with the Job Demands-Resources Model: Data and Analysis Files

description: These files contain the anonymized data used to create the tables and figures found in "Exploring Teachers’ Social Goal Orientations with the Job Demands-Resources Model". <br><br>Please refer to the abstract for the paper below: <br>Positive teacher-student relationships are recognized as critical social resources for promoting dyadic well-being and adaptive instruction. However, there is scarce research on the relationship between teachers’ motivation for connecting with students and teachers’ psychological adjustment. To explore the relationship, this study applied the job demands-resources model to investigate how teachers’ social goal orientations corresponded with their well-being, job satisfaction, and school commitment, with burnout and work engagement as mediating variables. Whereas teachers’ social mastery-approach goals (attempting to improve student relationship-building competencies) were expected to serve as beneficial psychological resources, their social work-avoidance goals (putting minimum effort into student relationships) were hypothesized to function as would detrimental occupational demands. Findings from mediational structural equation modeling of questionnaire responses from Canadian K-12 teachers (N = 154) showed teachers with stronger social mastery-approach goals to be more engaged and, in turn, report greater well-being, job satisfaction, and school commitment. In contrast, teachers with stronger social work-avoidance goals reported less work engagement that was further associated with lower well-being and job satisfaction, as well as greater burnout and, in turn, poorer job satisfaction. Implications concerning teachers’ interpersonal appraisals and psychological health initiatives were discussed.
DOI: 10.25810/jck1-hf50.51
title: United States Newspaper Coverage of Climate Change or Global Warming, 2000-2022 - June 2022

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.6614821
title: Ehrhart series coefficients for random lattice polytopes

description: <strong>Ehrhart series coefficients for random lattice polytopes</strong> A dataset of Ehrhart data for 2918 randomly generated lattice polytopes, in dimensions 2 to 8. The polytopes used to generate this data were produced by the following algorithm: Fix \(d\) a positive integer in \(\{2,\ldots,8\}\). Choose \(d + k\) lattice points \(\{v_1,\ldots,v_{d+k}\}\) uniformly at random in a box \([-5,5]^d\), where \(k\) is chosen uniformly at random in \(\{1,\ldots,5\}\). Set \(P := \mathrm{conv}\{v_1,\ldots,v_{d+k}\}\). If \(\mathrm{dim}(P)\neq d\) then return to step 2. The final dataset has duplicate records removed. The data is distributed by dimension \(d\) as follows: d 2 3 4 5 6 7 8 # 431 787 812 399 181 195 113 For details, see the paper: <em>Machine Learning the Dimension of a Polytope</em>, Tom Coates, Johannes Hofscheier, and Alexander M. Kasprzyk, 2022. If you make use of this data, please cite the above paper and the DOI for this data: doi:10.5281/zenodo.6614821 <strong>dimension.txt.gz</strong><br> The file "dimension.txt.gz" is a gzip-compressed plain text file containing key:value records with keys and values as described below, where each record is separated by a blank line. There are 2918 records in the file. <strong>Example record</strong><br> ULID: 1FTU9VGPXXU82CTDGD6WYMBF9<br> Dimension: 3<br> Volume: 342<br> EhrhartDelta: [1,70,223,48]<br> Ehrhart: [1,74,513,...]<br> LogEhrhart: [0.000000000000000000000000000000,4.30406509320416975378532779249,6.24027584517076953419476314266,...] (The values for Ehrhart and LogEhrhart in the example have been truncated.) For each polytope \(P\) of dimension \(d\) we record the following keys and values in the dataset: ULID: A randomly generated string identifier for this record.<br> Dimension: A positive integer. The dimension \(2 \leq d \leq 8\) of the polytope \(P\).<br> Volume: A positive integer. The lattice-normalised volume \(\mathrm{Vol}(P)\) of the polytope \(P\).<br> EhrhartDelta: A sequence \([1,a_1,a_2,\ldots,a_d]\) of integers of length \(d + 1\). This is the Ehrhart \(\delta\)-vector (or \(h^*\)-vector) of \(P\). The Ehrhart series \(\mathrm{Ehr}(P)\) of \(P\) is given by the power-series expansion of \((1 + a_1t + a_2t^2 + \ldots + a_dt^d) / (1 - t)^{d+1}\). In particular, \(\mathrm{Vol}(P) = 1 + a_1 + a_2 + \ldots + a_d\).<br> Ehrhart: A sequence \([1,c_1,c_2,\ldots,c_{1100}]\) of positive integers. The value \(c_i\) is equal to the number of lattice points in the \(i\)-th dilation of \(P\), that is, \(c_i = \#(iP \cap \mathbb{Z}^d)\). Equivalently, \(c_i\) is the coefficient of \(t^i\) in \(\mathrm{Ehr}(P) = 1 + c_1t + c_2t^2 + \ldots = (1 + a_1t + a_2t^2 + \ldots + a_dt^d) / (1 - t)^{d+1}\).<br> LogEhrhart: A sequence \([0,y_1,y_2,\ldots,y_{1100}]\) of non-negative floating point numbers. Here \(y_i := \log c_i\)
DOI: 10.5281/zenodo.6614820
title: Ehrhart series coefficients for random lattice polytopes

description: <strong>Ehrhart series coefficients for random lattice polytopes</strong> A dataset of Ehrhart data for 2918 randomly generated lattice polytopes, in dimensions 2 to 8. The polytopes used to generate this data were produced by the following algorithm: Fix \(d\) a positive integer in \(\{2,\ldots,8\}\). Choose \(d + k\) lattice points \(\{v_1,\ldots,v_{d+k}\}\) uniformly at random in a box \([-5,5]^d\), where \(k\) is chosen uniformly at random in \(\{1,\ldots,5\}\). Set \(P := \mathrm{conv}\{v_1,\ldots,v_{d+k}\}\). If \(\mathrm{dim}(P)\neq d\) then return to step 2. The final dataset has duplicate records removed. The data is distributed by dimension \(d\) as follows: d 2 3 4 5 6 7 8 # 431 787 812 399 181 195 113 For details, see the paper: <em>Machine Learning the Dimension of a Polytope</em>, Tom Coates, Johannes Hofscheier, and Alexander M. Kasprzyk, 2022. If you make use of this data, please cite the above paper and the DOI for this data: doi:10.5281/zenodo.6614821 <strong>dimension.txt.gz</strong><br> The file "dimension.txt.gz" is a gzip-compressed plain text file containing key:value records with keys and values as described below, where each record is separated by a blank line. There are 2918 records in the file. <strong>Example record</strong><br> ULID: 1FTU9VGPXXU82CTDGD6WYMBF9<br> Dimension: 3<br> Volume: 342<br> EhrhartDelta: [1,70,223,48]<br> Ehrhart: [1,74,513,...]<br> LogEhrhart: [0.000000000000000000000000000000,4.30406509320416975378532779249,6.24027584517076953419476314266,...] (The values for Ehrhart and LogEhrhart in the example have been truncated.) For each polytope \(P\) of dimension \(d\) we record the following keys and values in the dataset: ULID: A randomly generated string identifier for this record.<br> Dimension: A positive integer. The dimension \(2 \leq d \leq 8\) of the polytope \(P\).<br> Volume: A positive integer. The lattice-normalised volume \(\mathrm{Vol}(P)\) of the polytope \(P\).<br> EhrhartDelta: A sequence \([1,a_1,a_2,\ldots,a_d]\) of integers of length \(d + 1\). This is the Ehrhart \(\delta\)-vector (or \(h^*\)-vector) of \(P\). The Ehrhart series \(\mathrm{Ehr}(P)\) of \(P\) is given by the power-series expansion of \((1 + a_1t + a_2t^2 + \ldots + a_dt^d) / (1 - t)^{d+1}\). In particular, \(\mathrm{Vol}(P) = 1 + a_1 + a_2 + \ldots + a_d\).<br> Ehrhart: A sequence \([1,c_1,c_2,\ldots,c_{1100}]\) of positive integers. The value \(c_i\) is equal to the number of lattice points in the \(i\)-th dilation of \(P\), that is, \(c_i = \#(iP \cap \mathbb{Z}^d)\). Equivalently, \(c_i\) is the coefficient of \(t^i\) in \(\mathrm{Ehr}(P) = 1 + c_1t + c_2t^2 + \ldots = (1 + a_1t + a_2t^2 + \ldots + a_dt^d) / (1 - t)^{d+1}\).<br> LogEhrhart: A sequence \([0,y_1,y_2,\ldots,y_{1100}]\) of non-negative floating point numbers. Here \(y_i := \log c_i\)
DOI: 10.25810/c862-0e81.51
title: US Television Coverage of Climate Change or Global Warming, 2000-2022 - June 2022

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.25810/4c3b-b819.51
title: World Newspaper Coverage of Climate Change or Global Warming, 2004-2022 - June 2022

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.25810/k55w-7a04.51
title: World Radio Coverage of Climate Change or Global Warming, 2000-2022 - June 2022

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.6084/m9.figshare.19754662
title: HDX MS data

description: HDX data
DOI: 10.6084/m9.figshare.20323449
title: Cryopreservation LPS_Animals_Sandfoss et al 2022

description: Dataset for the Sandfoss et al. 2022 paper published in <em>Animals</em> special issue on Cryopreservation.
DOI: 10.6084/m9.figshare.20323449.v1
title: Cryopreservation LPS_Animals_Sandfoss et al 2022

description: Dataset for the Sandfoss et al. 2022 paper published in <em>Animals</em> special issue on Cryopreservation.
DOI: 10.5517/ccdc.csd.cc28nx3z
title: CCDC 2105461: Experimental Crystal Structure Determination

description: Related Article: Dan-Yang Zhang, Jing-Jing Zhou, Hui Yang, Min Wang, Ya-Nan Wang, Shuai Liu, Zi-Mo Zhang, Peng-Yu Zhuang, Xiao-Xia Wang, Hang Liu|2022|J.Org.Chem.|87|8623|doi:10.1021/acs.joc.2c00819
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.cc28nx40
title: CCDC 2105462: Experimental Crystal Structure Determination

description: Related Article: Dan-Yang Zhang, Jing-Jing Zhou, Hui Yang, Min Wang, Ya-Nan Wang, Shuai Liu, Zi-Mo Zhang, Peng-Yu Zhuang, Xiao-Xia Wang, Hang Liu|2022|J.Org.Chem.|87|8623|doi:10.1021/acs.joc.2c00819
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.cc28nx51
title: CCDC 2105463: Experimental Crystal Structure Determination

description: Related Article: Dan-Yang Zhang, Jing-Jing Zhou, Hui Yang, Min Wang, Ya-Nan Wang, Shuai Liu, Zi-Mo Zhang, Peng-Yu Zhuang, Xiao-Xia Wang, Hang Liu|2022|J.Org.Chem.|87|8623|doi:10.1021/acs.joc.2c00819
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.