cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc23ct5h
title: CCDC 1947766: Experimental Crystal Structure Determination

description: Related Article: Jonathan M. Goldberg, Louise M. Guard, Gene W. Wong, Daniel F. Brayton, Werner Kaminsky, Karen I. Goldberg, D. Michael Heinekey|2020|Organometallics|39|3323|doi:10.1021/acs.organomet.0c00403
DOI: 10.5517/ccdc.csd.cc23ct6j
title: CCDC 1947767: Experimental Crystal Structure Determination

description: Related Article: Jonathan M. Goldberg, Louise M. Guard, Gene W. Wong, Daniel F. Brayton, Werner Kaminsky, Karen I. Goldberg, D. Michael Heinekey|2020|Organometallics|39|3323|doi:10.1021/acs.organomet.0c00403
DOI: 10.5517/ccdc.csd.cc25cj36
title: CCDC 2007067: Experimental Crystal Structure Determination

description: Related Article: Fei Yu, Bao-Qian Ji, Marko Jagodič, Yan-Min Su, Shan-Shan Zhang, Lei Feng, Mohamedally Kurmoo, Zvonko Jagličić, Di Sun|2020|Inorg.Chem.|59|13524|doi:10.1021/acs.inorgchem.0c01915
DOI: 10.5281/zenodo.4059718
title: Raw data and results for the paper "Conditional non-parametric bootstrap for non-linear mixed effect models"

description: *Data* (comets_condBoot_data.zip) Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse. *Results:* (comets_scenarioXXX_results.zip, XXX=1,.. 6) 6 simulation scenarios were assessed in the paper. Each file corresponds to 1 of 6 folders, one for each scenario:<br> - scenario 1 - pdemax.rich/results<br> - scenario 2 - pdemax.sparse/results<br> - scenario 3 - pdhillhigh.rich/results<br> - scenario 4 - pdhillhigh.sparse/results<br> - scenario 5 - pdhillhigh.n20rich/results<br> - scenario 6 - pdhillhigh.n20sparse/results In each "results" subfolder, the results for each bootstrap method and each dataset are written to a separate file, eg for simulation 1 in the first scenario:<br> - case bootstrap: scenarioHill1_bootstrapCase_sim1.res <br> - non-parametric bootstrap: scenarioHill1_bootstrapNP_sim1.res<br> - conditional non-parametric bootstrap: scenarioHill1_bootstrapNPc_sim1.res<br> - parametric bootstrap: scenarioHill1_bootstrapPar_sim1.res<br> The folder also contains:<br> - the saemix estimates for the 200 simulations: scenarioHill1_fitOrig.res<br> - tables with the bias and SE for the different bootstraps over the set of simulations, used to evaluate the methods: rbiasSEboot200.res, rbiasSEboot.res, rbiasWRsampleEstimates.res
DOI: 10.5281/zenodo.4059717
title: Raw data and results for the paper "Conditional non-parametric bootstrap for non-linear mixed effect models"

description: *Data* (comets_condBoot_data.zip) Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse. *Results:* (comets_scenarioXXX_results.zip, XXX=1,.. 6) 6 simulation scenarios were assessed in the paper. Each file corresponds to 1 of 6 folders, one for each scenario:<br> - scenario 1 - pdemax.rich/results<br> - scenario 2 - pdemax.sparse/results<br> - scenario 3 - pdhillhigh.rich/results<br> - scenario 4 - pdhillhigh.sparse/results<br> - scenario 5 - pdhillhigh.n20rich/results<br> - scenario 6 - pdhillhigh.n20sparse/results In each "results" subfolder, the results for each bootstrap method and each dataset are written to a separate file, eg for simulation 1 in the first scenario:<br> - case bootstrap: scenarioHill1_bootstrapCase_sim1.res <br> - non-parametric bootstrap: scenarioHill1_bootstrapNP_sim1.res<br> - conditional non-parametric bootstrap: scenarioHill1_bootstrapNPc_sim1.res<br> - parametric bootstrap: scenarioHill1_bootstrapPar_sim1.res<br> The folder also contains:<br> - the saemix estimates for the 200 simulations: scenarioHill1_fitOrig.res<br> - tables with the bias and SE for the different bootstraps over the set of simulations, used to evaluate the methods: rbiasSEboot200.res, rbiasSEboot.res, rbiasWRsampleEstimates.res
DOI: 10.5281/zenodo.4061060
title: SELENE_COU_test

description: Données structurées des glissements sémantiques de COU dans les langues sénégambiennes
DOI: 10.14457/tu.the.2019.485
title: A study of the factors that influence customer decision regarding the purchase of clothing from online channels

description: As the economic is growing, there are various online channels for customers to purchase products online. Each online platform has different advantages and disadvantages. However, it is challenging for clothing businesses to strategically choose which channels they should pay attention to since there is still lack of insightful understanding of which channel can be used as the most practical tool to sell their products to the best advantage. For this reason, in-depth interviews has been conducted in order to gain general insight and crucial factors that customers perceive from each online channel and to understand the customer journey better. After the insight is collected enough, questionnaires were created and distributed to 200 people in order to discuss and analyze data in a manner of quantitative research. The results and findings are presented in figures, graphs, and statistics as well as a narrative discussion.The objective of this research is to help e-commerce and other online clothing businesses identify the components of each online channel such as delivery lead time, return policy, review system, and other factors that affect the buying decision for customers from different segmentation. Then, the components are further discussed and addressed the association with customers. In other words, the research determines the important of factors that influence decision on which online channel to buy clothes based on customer segment.
DOI: 10.6084/m9.figshare.11336165.v1
title: Data for 'Evaluating the performance of forest herbs in hedgerows across temperate Europe'

description: Datasets:Hedgerows trait dataset: functional trait values for Anemone nemorosa, Ficaria verna, Poa nemoralis and Geum urbanum<br>Hedgerows environment dataset: abiotic and biotic environmental condtions for forest and hedgerow habitats<br>
DOI: 10.6084/m9.figshare.11336165
title: Data for 'Evaluating the performance of forest herbs in hedgerows across temperate Europe'

description: Datasets:Hedgerows trait dataset: functional trait values for Anemone nemorosa, Ficaria verna, Poa nemoralis and Geum urbanum<br>Hedgerows environment dataset: abiotic and biotic environmental condtions for forest and hedgerow habitats<br>
DOI: 10.6084/m9.figshare.13031831
title: Supplementary Material for: Can a Lifestyle Genomics Intervention Motivate Patients to Engage in Greater Physical Activity than a Population-Based Intervention? Results from the NOW Randomized Controlled Trial

description: <b><i>Background:</i></b> Lifestyle genomics (LGx) is a science that explores interactions between genetic variation, lifestyle components such as physical activity (PA), and subsequent health- and performance-related outcomes. The objective of this study was to determine whether an LGx intervention could motivate enhanced engagement in PA to a greater extent than a population-based intervention. <b><i>Methods:</i></b> In this pragmatic randomized controlled trial, participants received either the standard, population-based Group Lifestyle Balance<sup>TM</sup> (GLB) program intervention or the GLB program in addition to the provision of LGx information and advice (GLB + LGx). Participants (<i>n</i> = 140) completed a 7-day PA recall at baseline, 3, 6, and 12 months. Data from the PA recalls were used to calculate metabolic equivalents (METs), a measure of energy expenditure. Statistical analyses included split plot analyses of covariance and binary logistic regression (generalized linear models). Differences in leisure time PA weekly METs, weekly minutes of moderate + high-intensity PA, and adherence to PA guidelines were compared between groups (GLB and GLB + LGx) across the 4 time points. <b><i>Results:</i></b> Weekly METs were significantly higher in the GLB + LGx group (1,114.7 ± 141.9; 95% CI 831.5–1,397.8) compared to the standard GLB group (621.6 ± 141.9 MET/week; 95% CI 338.4–904.8) at the 6-month follow-up (<i>p</i> = 0.01). All other results were non-significant. <b><i>Conclusions:</i></b> The provision of an LGx intervention resulted in a greater weekly leisure time PA energy expenditure after the 6-month follow-up. Future research should determine how this could be sustained over the long-term. <b><i>Clinical Trial Registration:</i></b> NCT03015012.
DOI: 10.6084/m9.figshare.13031831.v1
title: Supplementary Material for: Can a Lifestyle Genomics Intervention Motivate Patients to Engage in Greater Physical Activity than a Population-Based Intervention? Results from the NOW Randomized Controlled Trial

description: <b><i>Background:</i></b> Lifestyle genomics (LGx) is a science that explores interactions between genetic variation, lifestyle components such as physical activity (PA), and subsequent health- and performance-related outcomes. The objective of this study was to determine whether an LGx intervention could motivate enhanced engagement in PA to a greater extent than a population-based intervention. <b><i>Methods:</i></b> In this pragmatic randomized controlled trial, participants received either the standard, population-based Group Lifestyle Balance<sup>TM</sup> (GLB) program intervention or the GLB program in addition to the provision of LGx information and advice (GLB + LGx). Participants (<i>n</i> = 140) completed a 7-day PA recall at baseline, 3, 6, and 12 months. Data from the PA recalls were used to calculate metabolic equivalents (METs), a measure of energy expenditure. Statistical analyses included split plot analyses of covariance and binary logistic regression (generalized linear models). Differences in leisure time PA weekly METs, weekly minutes of moderate + high-intensity PA, and adherence to PA guidelines were compared between groups (GLB and GLB + LGx) across the 4 time points. <b><i>Results:</i></b> Weekly METs were significantly higher in the GLB + LGx group (1,114.7 ± 141.9; 95% CI 831.5–1,397.8) compared to the standard GLB group (621.6 ± 141.9 MET/week; 95% CI 338.4–904.8) at the 6-month follow-up (<i>p</i> = 0.01). All other results were non-significant. <b><i>Conclusions:</i></b> The provision of an LGx intervention resulted in a greater weekly leisure time PA energy expenditure after the 6-month follow-up. Future research should determine how this could be sustained over the long-term. <b><i>Clinical Trial Registration:</i></b> NCT03015012.
DOI: 10.5281/zenodo.3839756
title: Analysis of the water-power nexus in the North, Eastern and Central African Power Pools

description: This dataset underpins the report provided to the project "Analysis of the water-power nexus in the North, Eastern and Central African Power Pools" The report provides insights into the balance between energy supply and demand, power generation, total system costs, water consumption and withdrawal as well as carbon dioxide emissions for the North, Eastern and Central African power pools.
DOI: 10.5281/zenodo.3839755
title: Analysis of the water-power nexus in the North, Eastern and Central African Power Pools

description: This dataset underpins the report provided to the project "Analysis of the water-power nexus in the North, Eastern and Central African Power Pools" The report provides insights into the balance between energy supply and demand, power generation, total system costs, water consumption and withdrawal as well as carbon dioxide emissions for the North, Eastern and Central African power pools.
DOI: 10.6084/m9.figshare.12594446
title: Temperature effects on forest understorey plants in hedgerows: a combined warming and transplant experiment

description: <b>(1) lascar_combo2.csv</b><i>Hourly temperature data from open-top chambers and control plots inside hedgerows and forest patches, recorded with Lascar EL-USB-1 data loggers.</i><b>(2) traits.csv</b><i>Functional life-history traits measured on adult individuals of Anemone nemorosa and Geum urbanum, measured in open-top chambers and control plots of hedgerows and forest patches.</i><i>plant_height - vertical plant height (cm)<br></i><i>seed_height - seed releasing height (cm)<br></i><i>flower_nr - total number of flowers<br></i><i>fruit_nr - total number of fruits<br></i><i>rep_nr = flower_nr + fruit_nr<br></i><i>SLA - specific leaf area (mm²/mg)<br></i><i>biomass - total aboveground biomass (mg)<br></i><i>seed_nr - number of seeds for one flowering head<br></i><i>seed_nr_tot = rep_nr x seed_nr<br></i><i>seed_mass - average mass of one seed (mg)<br></i><i>RIR = seed_nr_tot x seed_mass</i><b>(3) canopy.csv</b><i>Canopy cover data (percentage cover of all individual shrub and tree species) in hedgerow and forest sites.</i><br>
DOI: 10.6084/m9.figshare.12594446.v2
title: Temperature effects on forest understorey plants in hedgerows: a combined warming and transplant experiment

description: <b>(1) lascar_combo2.csv</b><i>Hourly temperature data from open-top chambers and control plots inside hedgerows and forest patches, recorded with Lascar EL-USB-1 data loggers.</i><b>(2) traits.csv</b><i>Functional life-history traits measured on adult individuals of Anemone nemorosa and Geum urbanum, measured in open-top chambers and control plots of hedgerows and forest patches.</i><i>plant_height - vertical plant height (cm)<br></i><i>seed_height - seed releasing height (cm)<br></i><i>flower_nr - total number of flowers<br></i><i>fruit_nr - total number of fruits<br></i><i>rep_nr = flower_nr + fruit_nr<br></i><i>SLA - specific leaf area (mm²/mg)<br></i><i>biomass - total aboveground biomass (mg)<br></i><i>seed_nr - number of seeds for one flowering head<br></i><i>seed_nr_tot = rep_nr x seed_nr<br></i><i>seed_mass - average mass of one seed (mg)<br></i><i>RIR = seed_nr_tot x seed_mass</i><b>(3) canopy.csv</b><i>Canopy cover data (percentage cover of all individual shrub and tree species) in hedgerow and forest sites.</i><br>
DOI: 10.6084/m9.figshare.12594446.v1
title: Temperature effects on forest understorey plants in hedgerows: a combined warming and transplant experiment

description: <b>(1) lascar_combo2.csv</b><i>Hourly temperature data from open-top chambers and control plots inside hedgerows and forest patches, recorded with Lascar EL-USB-1 data loggers.</i><i>plant_height - vertical plant height (cm)<br></i><i>seed_height - seed releasing height (cm)<br></i><i>flower_nr - total number of flowers<br></i><i>fruit_nr - total number of fruits<br></i><i>rep_nr = flower_nr + fruit_nr<br></i><i>SLA - specific leaf area (mm²/mg)<br></i><i>biomass - total aboveground biomass (mg)<br></i><i>seed_nr - number of seeds for one flowering head<br></i><i>seed_nr_tot = rep_nr x seed_nr<br></i><i>seed_mass - average mass of one seed (mg)<br></i><i>RIR = seed_nr_tot x seed_mass</i><br><b>(2) traits.csv</b><i>Functional life-history traits measured on adult individuals of Anemone nemorosa and Geum urbanum, measured in open-top chambers and control plots of hedgerows and forest patches.</i><br><b>(3) canopy.csv</b><i>Canopy cover data (percentage cover of all individual shrub and tree species) in hedgerow and forest sites.</i><br>
DOI: 10.6084/m9.figshare.12951041.v1
title: Comparative transcriptomics and host-specific parasite gene expression profiles inform on drivers of proliferative kidney disease - Supplementary Spreadsheets

description: This entry contains the supplementary material spreadsheets for the publication: "Comparative transcriptomics and host-specific parasite gene expression profiles inform on drivers of proliferative kidney disease". <br>For full details, please refer to the publication. DOI to be added shortly once published. <br>File S1: Trinotate output of all detected contigs in the T. bryosalmonae intersect transcriptome. File S2: Gene Ontology analysis (WEGO levels 2, 3 and 4) of the T. bryosalmonae intersect transcriptome. File S3: MEROPS prediction of T. bryosalmonae proteases within the intersect transcriptomeFile S4: Manually annotated T. bryosalmonae lipases within the intersect transcriptomeFile S5: Gene names and primer sequences for T. bryosalmonae transcripts analysed by RTqPCR.
DOI: 10.5281/zenodo.4061174
title: SELENE_COU_test

description: Données structurées des glissements sémantiques de COU dans les langues sénégambiennes
DOI: 10.6084/m9.figshare.12195003
title: Coreinae_uces_50p_total_evidence

description: Individual locus alignments for 50p total evidence dataset
DOI: 10.6084/m9.figshare.12195027
title: Coreinae_uces_70p_total_evidence

description: Individual locus alignments for 70p total evidence dataset
DOI: 10.6084/m9.figshare.12195063
title: Maximum likelihood optimal gene trees

description: Maximum likelihood optimal gene trees for 50p total evidence, 50p 25% most informative, 70p total evidence, and 70p 25% most informative datasets.
DOI: 10.6084/m9.figshare.12195309
title: Coreinae_uces_70p_total_evidence_bootstrap_genetrees

description: Bootstrap gene trees for 70p total evidence dataset
DOI: 10.6084/m9.figshare.12195042
title: concatenated_phylip_alignments

description: Concatenated phylip alignments for 50p total evidence, 50p 25% most informative, 70p total evidence, and 70p 25% most informative datasets
DOI: 10.6084/m9.figshare.12195306
title: Coreinae_uces_50p_total_evidence_bootstrap_genetrees

description: Bootstrap gene trees for 50p total evidence dataset
DOI: 10.6084/m9.figshare.12195303
title: Coreinae_uces_70p_25%_most_informative_bootstrap_genetrees

description: Bootstrap gene trees for the 70p 25% most informative dataset.
DOI: 10.6084/m9.figshare.12410390
title: Two Vertical Seismic Profiles from RESPONDER acquisitions on Store Glacier, Greenland, July 2019

description: Two VSP datasets, acquired in July 2019 using a Silixa <i>iDAS</i><sup>TM</sup> distributed acoustic sensing system. Fibre-optic cable is installed in a vertical borehole, 1043 m deep, on Store Glacier, a fast-flowing outlet of the Greenland Ice Sheet. The survey site (<i>L028</i>) is the drained bed of a supraglacial lake, located at [70.56793°N, 50.08697°W].<br>The seismic source for both shots is a 7 kg sledgehammer, striking a small polyethylene impact plate. For the zero-offset shot ('<i>RESPONDER_Jul2019_L028_ZO.sgy</i>'), the source is located directly at the borehole top. For the offset-shot ('<i>RESPONDER_Jul2019_L028_300mSouth.sgy</i>'), the source is located 306.4 m south of the borehole top.<br>Seismic VSP data are sampled every 1 m along the length of the cable. The gauge length of the <i>iDAS</i><sup>TM</sup> interrogator is 10 m. The time-sampling frequency of the VSP is 4000 Hz. The first trace in the VSP is located at 0 m depth. The VSPs are stacks of ~25 individual shots, and a 20-40-250-500 Hz Ormsby bandpass filter is applied. <br>The analysis of these data is documented by the authors in the Geophysical Research Letters article "<i>Distributed Acoustic Sensing (DAS) of Seismic Properties in A Borehole drilled on a Fast-Flowing Greenlandic Outlet Glacier</i>".
DOI: 10.6084/m9.figshare.12194985
title: Coreinae_uces_50p_25%_most_informative

description: Individual locus alignments for 50p 25% most informative dataset
DOI: 10.6084/m9.figshare.12195300
title: Coreinae_uces_50p_25%_most_informative_bootstrap_genetrees

description: Bootstrap gene trees for the 50p 25% most informative dataset
DOI: 10.6084/m9.figshare.12195303.v1
title: Coreinae_uces_70p_25%_most_informative_bootstrap_genetrees

description: Bootstrap gene trees for the 70p 25% most informative dataset.
DOI: 10.6084/m9.figshare.12195306.v1
title: Coreinae_uces_50p_total_evidence_bootstrap_genetrees

description: Bootstrap gene trees for 50p total evidence dataset
DOI: 10.6084/m9.figshare.12194985.v1
title: Coreinae_uces_50p_25%_most_informative

description: Individual locus alignments for 50p 25% most informative dataset
DOI: 10.6084/m9.figshare.12195042.v1
title: concatenated_phylip_alignments

description: Concatenated phylip alignments for 50p total evidence, 50p 25% most informative, 70p total evidence, and 70p 25% most informative datasets
DOI: 10.6084/m9.figshare.12410390.v1
title: Two Vertical Seismic Profiles from RESPONDER acquisitions on Store Glacier, Greenland, July 2019

description: Two VSP datasets, acquired in July 2019 using a Silixa <i>iDAS</i><sup>TM</sup> distributed acoustic sensing system. Fibre-optic cable is installed in a vertical borehole, 1043 m deep, on Store Glacier, a fast-flowing outlet of the Greenland Ice Sheet. The survey site (<i>L028</i>) is the drained bed of a supraglacial lake, located at [70.56793°N, 50.08697°W].<br>The seismic source for both shots is a 7 kg sledgehammer, striking a small polyethylene impact plate. For the zero-offset shot ('<i>RESPONDER_Jul2019_L028_ZO.sgy</i>'), the source is located directly at the borehole top. For the offset-shot ('<i>RESPONDER_Jul2019_L028_300mSouth.sgy</i>'), the source is located 306.4 m south of the borehole top.<br>Seismic VSP data are sampled every 1 m along the length of the cable. The gauge length of the <i>iDAS</i><sup>TM</sup> interrogator is 10 m. The time-sampling frequency of the VSP is 4000 Hz. The first trace in the VSP is located at 0 m depth. The VSPs are stacks of ~25 individual shots, and a 20-40-250-500 Hz Ormsby bandpass filter is applied. <br>The analysis of these data is documented by the authors in the Geophysical Research Letters article "<i>Distributed Acoustic Sensing (DAS) of Seismic Properties in A Borehole drilled on a Fast-Flowing Greenlandic Outlet Glacier</i>".
DOI: 10.6084/m9.figshare.12195063.v1
title: Maximum likelihood optimal gene trees

description: Maximum likelihood optimal gene trees for 50p total evidence, 50p 25% most informative, 70p total evidence, and 70p 25% most informative datasets.
DOI: 10.6084/m9.figshare.12195300.v1
title: Coreinae_uces_50p_25%_most_informative_bootstrap_genetrees

description: Bootstrap gene trees for the 50p 25% most informative dataset
DOI: 10.6084/m9.figshare.12195309.v1
title: Coreinae_uces_70p_total_evidence_bootstrap_genetrees

description: Bootstrap gene trees for 70p total evidence dataset
DOI: 10.6084/m9.figshare.12195027.v1
title: Coreinae_uces_70p_total_evidence

description: Individual locus alignments for 70p total evidence dataset
DOI: 10.6084/m9.figshare.12452555
title: temperature data interval 20

description: This is soil temperature data under controlled condition.
DOI: 10.6084/m9.figshare.12818633
title: Fatal Falls Data 2020 Plos One

description: This data set contains 511 fall related death cases in year 2014 which were within the jurisdiction of the Harris Institute of Forensic Sciences.
DOI: 10.6084/m9.figshare.11687055
title: Raw data - 16S

description: Illumina MiSeq sequencing of the V1-V3 regions of the 16S ribosomal RNA gene
DOI: 10.6084/m9.figshare.12818633.v1
title: Fatal Falls Data 2020 Plos One

description: This data set contains 511 fall related death cases in year 2014 which were within the jurisdiction of the Harris Institute of Forensic Sciences.