cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3806529
title: Supplementary Material for: Ethnic Inequalities in Overweight and Obesity Prevalence among Copenhagen Schoolchildren from 2002 to 2007

description: <i>Background:</i> The stabilization in levels of childhood overweight has masked increasing gaps among different ethnic and socioeconomic groups in several countries. <i>Objective: </i>To examine if levels and trends in childhood overweight and obesity differed by ethnicity and socioeconomic areas in Copenhagen schoolchildren.<i>Methods: </i>From measured heights and weights of 32,951 children 5-8 and 14-16 years of age, the prevalence of overweight (including obesity) and obesity were estimated using International Obesity Task Force criteria. Differences in prevalence levels and trends across six school years by ethnicity and socioeconomic areas were examined using logistic regression. <i>Results: </i>The prevalence of overweight significantly decreased from 2002 to 2007 among the youngest Western girls and boys, showed no significant changes among the oldest non-Western girls and increased among the oldest non-Western boys. In all years, the youngest non-Western children had significantly higher levels of overweight than Western children. Although the prevalence of overweight tended to be higher in low socioeconomic areas as compared with high socioeconomic areas, few differences were statistically significant. Consistent trends in overweight across the years by socioeconomic area were not observed. <i>Conclusion: </i>Ethnic and social inequalities exist in childhood overweight among Copenhagen schoolchildren; thus appropriate interventions targeting high-risk groups are needed
DOI: 10.6084/m9.figshare.3806529.v1
title: Supplementary Material for: Ethnic Inequalities in Overweight and Obesity Prevalence among Copenhagen Schoolchildren from 2002 to 2007

description: <i>Background:</i> The stabilization in levels of childhood overweight has masked increasing gaps among different ethnic and socioeconomic groups in several countries. <i>Objective: </i>To examine if levels and trends in childhood overweight and obesity differed by ethnicity and socioeconomic areas in Copenhagen schoolchildren.<i>Methods: </i>From measured heights and weights of 32,951 children 5-8 and 14-16 years of age, the prevalence of overweight (including obesity) and obesity were estimated using International Obesity Task Force criteria. Differences in prevalence levels and trends across six school years by ethnicity and socioeconomic areas were examined using logistic regression. <i>Results: </i>The prevalence of overweight significantly decreased from 2002 to 2007 among the youngest Western girls and boys, showed no significant changes among the oldest non-Western girls and increased among the oldest non-Western boys. In all years, the youngest non-Western children had significantly higher levels of overweight than Western children. Although the prevalence of overweight tended to be higher in low socioeconomic areas as compared with high socioeconomic areas, few differences were statistically significant. Consistent trends in overweight across the years by socioeconomic area were not observed. <i>Conclusion: </i>Ethnic and social inequalities exist in childhood overweight among Copenhagen schoolchildren; thus appropriate interventions targeting high-risk groups are needed
DOI: 10.6084/m9.figshare.3806505
title: six_city_FWW dataset .xlsx

description: Dataset for:Micro and macroscale drivers of nutrient concentrations in streams in South, Central and North America PONE-D-16-20557
DOI: 10.6084/m9.figshare.3806505.v1
title: Data.xlsx

description: Dataset for:Micro and macroscale drivers of nutrient concentrations in streams in South, Central and North America PONE-D-16-20557
DOI: 10.6084/m9.figshare.3806487
title: A few simple steps to improve the description of group results in neuroscience

description: Reproducibility package containing data, code and final plots of Figures 1 & 2 of this editorial in the European Journal of Neuroscience:<br>A few simple steps to improve the description of group results in neuroscienceGuillaume A. Rousselet, John J. Foxe and J. Paul Bolamin press<br>Figure 1 was made in R, using a dataset created for the editorial.<br>Figure 2 was made in Matlab, using a dataset and code that are part of a larger Matlab ERP tutorial available here:<br>Rousselet, G. (2016). Introduction to robust estimation of ERP data. figshare.https://dx.doi.org/10.6084/m9.figshare.3501728<br>See also related blog posts on data visualisation here:https://garstats.wordpress.com/<br>
DOI: 10.6084/m9.figshare.3806487.v1
title: A few simple steps to improve the description of group results in neuroscience

description: Reproducibility package containing data, code and final plots of Figures 1 & 2 of this editorial in the European Journal of Neuroscience:<br>A few simple steps to improve the description of group results in neuroscienceGuillaume A. Rousselet, John J. Foxe and J. Paul Bolamin preparation<br>Figure 1 was created in R, using a dataset created for the editorial.<br>Figure 2 was created in Matlab, using a dataset and code that are part of a larger Matlab ERP tutorial available here:<br>Rousselet, G. (2016). Introduction to robust estimation of ERP data. figshare.https://dx.doi.org/10.6084/m9.figshare.3501728<br>See also these related blog posts on data visualisation:https://garstats.wordpress.com/<br>
DOI: 10.26208/ntn7-wt94
title: Alleles in metabolic and oxygen-sensing genes are associated with antagonistic pleiotropic effects on life history traits and population fitness in an ecological 2014 - 2017

description: Genes with opposing effects on fitness at different life stages are the mechanistic basis for evolutionary theories of aging and life history. Examples come from studies of mutations in model organisms, but there is little knowledge of genetic bases of life history tradeoffs in natural populations. Here we test the hypothesis that alleles affecting oxygen sensing in Glanville fritillary butterflies have opposing effects on larval versus adult fitness-related traits. Intermediate-frequency alleles in Succinate dehydrogenase d, and to a lesser extent Hypoxia inducible factor 1?, are associated in larvae with variation in metabolic rate and activation of the hypoxia inducible factor (HIF) pathway, which affects tracheal development and delivery of oxygen to adult flight muscles. A dominant Sdhd allele is likely to cause antagonistic pleiotropy for fitness through its opposing effects on larval metabolic and growth rate versus adult flight and dispersal, and may have additional effects arising from sensitivity to low-iron host plants. Prior results in Glanville fritillaries indicate that fitness of alleles in Sdhd and another antagonistically pleiotropic metabolic gene, Phosphoglucose isomerase, depend strongly on the size and distribution of host plant patches. Hence, these intermediate-frequency alleles are involved in eco-evolutionary dynamics involving life history
DOI: 10.6084/m9.figshare.3806487.v2
title: A few simple steps to improve the description of group results in neuroscience

description: Reproducibility package containing data, code and final plots of Figures 1 & 2 of this editorial in the European Journal of Neuroscience:<br>A few simple steps to improve the description of group results in neuroscienceGuillaume A. Rousselet, John J. Foxe and J. Paul Bolamin preparation<br>Figure 1 was created in R, using a dataset created for the editorial.<br>Figure 2 was created in Matlab, using a dataset and code that are part of a larger Matlab ERP tutorial available here:<br>Rousselet, G. (2016). Introduction to robust estimation of ERP data. figshare.https://dx.doi.org/10.6084/m9.figshare.3501728<br>See also these related blog posts on data visualisation:https://garstats.wordpress.com/<br>
DOI: 10.6084/m9.figshare.3806487.v3
title: A few simple steps to improve the description of group results in neuroscience

description: Reproducibility package containing data, code and final plots of Figures 1 & 2 of this editorial in the European Journal of Neuroscience:<br>A few simple steps to improve the description of group results in neuroscienceGuillaume A. Rousselet, John J. Foxe and J. Paul Bolamin preparation<br>Figure 1 was created in R, using a dataset created for the editorial.<br>Figure 2 was created in Matlab, using a dataset and code that are part of a larger Matlab ERP tutorial available here:<br>Rousselet, G. (2016). Introduction to robust estimation of ERP data. figshare.https://dx.doi.org/10.6084/m9.figshare.3501728<br>See also these related blog posts on data visualisation:https://garstats.wordpress.com/<br>
DOI: 10.6084/m9.figshare.3806487.v4
title: A few simple steps to improve the description of group results in neuroscience

description: Reproducibility package containing data, code and final plots of Figures 1 & 2 of this editorial in the European Journal of Neuroscience:<br>A few simple steps to improve the description of group results in neuroscienceGuillaume A. Rousselet, John J. Foxe and J. Paul Bolamin preparation<br>Figure 1 was created in R, using a dataset created for the editorial.<br>Figure 2 was created in Matlab, using a dataset and code that are part of a larger Matlab ERP tutorial available here:<br>Rousselet, G. (2016). Introduction to robust estimation of ERP data. figshare.https://dx.doi.org/10.6084/m9.figshare.3501728<br>See also these related blog posts on data visualisation:https://garstats.wordpress.com/<br>
DOI: 10.6084/m9.figshare.3806487.v5
title: A few simple steps to improve the description of group results in neuroscience

description: Reproducibility package containing data, code and final plots of Figures 1 & 2 of this editorial in the European Journal of Neuroscience:<br>A few simple steps to improve the description of group results in neuroscienceGuillaume A. Rousselet, John J. Foxe and J. Paul Bolamin preparation<br>Figure 1 was created in R, using a dataset created for the editorial.<br>Figure 2 was created in Matlab, using a dataset and code that are part of a larger Matlab ERP tutorial available here:<br>Rousselet, G. (2016). Introduction to robust estimation of ERP data. figshare.https://dx.doi.org/10.6084/m9.figshare.3501728<br>See also these related blog posts on data visualisation:https://garstats.wordpress.com/<br>
DOI: 10.6084/m9.figshare.3806478
title: European NUTS2 population for five SSPs and six age groups

description: Please contact author emma.terama@environment.fi for updated article:Terama, E. <i>et al</i>. 2017. Modelling population structure in the context of urban land use change in Europe. <i>Regional Environmental Change</i> (submitted). And cite the accompanied data as: Terama, E. 2016. European regional - NUTS2-level - population projections with age structure across SSPs. Database. https://dx.doi.org/10.6084/m9.figshare.3806478.v1Age groups (6) AG1: ages 0...14 AG2: ages 15...29 AG3: ages 30...49 AG4: ages 50...64 AG5: ages 65...74 AG6: ages 75+ Population is given in <b>thousands</b>.Online first available: Terama, E., Clarke, E., Rounsevell, M., Fronzek, S., & Carter, T. R. (2017). Modelling population structure in the context of urban land use change in Europe. <em>Regional Environmental Change</em>. DOI: 0.1007/s10113-017-1194-5
DOI: 10.6084/m9.figshare.3806478.v1
title: European NUTS2 population for four SSPs and six age groups

description: PLEASE CITE as: Terama, E. <i>et al</i>. 2017. Modelling population structure within urban land use change in Europe. <i>Regional Environmental Change</i> (in prep). And the accompanied data as: Terama, E. 2016. European regional - NUTS2-level - population projections with age structure across SSPs. Database. DOI 10.6084/m9.figshare.3806478.Age groups (6) AG1: ages 0...14 AG2: ages 15...29 AG3: ages 30...49 AG4: ages 50...64 AG5: ages 65...74 AG6: ages 75+ Population is given in <b>thousands</b>.
DOI: 10.6084/m9.figshare.3806478.v2
title: European NUTS2 population for five SSPs and six age groups

description: PLEASE CITE as: Terama, E. <i>et al</i>. 2017. Modelling population structure within urban land use change in Europe. <i>Regional Environmental Change</i> (in prep). And the accompanied data as: Terama, E. 2016. European regional - NUTS2-level - population projections with age structure across SSPs. Database. https://dx.doi.org/10.6084/m9.figshare.3806478.v1Age groups (6) AG1: ages 0...14 AG2: ages 15...29 AG3: ages 30...49 AG4: ages 50...64 AG5: ages 65...74 AG6: ages 75+ Population is given in <b>thousands</b>.
DOI: 10.6084/m9.figshare.3806478.v4
title: European NUTS2 population for five SSPs and six age groups

description: Please contact author emma.terama@environment.fi for updated article:Terama, E. <i>et al</i>. 2017. Modelling population structure in the context of urban land use change in Europe. <i>Regional Environmental Change</i> (submitted). And cite the accompanied data as: Terama, E. 2016. European regional - NUTS2-level - population projections with age structure across SSPs. Database. https://dx.doi.org/10.6084/m9.figshare.3806478.v1Age groups (6) AG1: ages 0...14 AG2: ages 15...29 AG3: ages 30...49 AG4: ages 50...64 AG5: ages 65...74 AG6: ages 75+ Population is given in <b>thousands</b>.
DOI: 10.6084/m9.figshare.3806478.v3
title: European NUTS2 population for five SSPs and six age groups

description: PLEASE CITE as: Terama, E. <i>et al</i>. 2017. Modelling population structure in the context of urban land use change in Europe. <i>Regional Environmental Change</i> (submitted). And the accompanied data as: Terama, E. 2016. European regional - NUTS2-level - population projections with age structure across SSPs. Database. https://dx.doi.org/10.6084/m9.figshare.3806478.v1Age groups (6) AG1: ages 0...14 AG2: ages 15...29 AG3: ages 30...49 AG4: ages 50...64 AG5: ages 65...74 AG6: ages 75+ Population is given in <b>thousands</b>.
DOI: 10.6084/m9.figshare.3806469
title: High-order Eulerian incompressible SPH with transition to Lagrangian free-surface motion

description: Data associated with manuscript "High-Order Eulerian Incompressible Smoothed Particle Hydrodynamics with Transition to Lagrangian Free-Surface Motion" (Lind and Stansby, J. Comput. Phys., 2016)
DOI: 10.6084/m9.figshare.3806469.v1
title: High-order Eulerian incompressible SPH with transition to Lagrangian free-surface motion

description: Data associated with manuscript "High-Order Eulerian Incompressible Smoothed Particle Hydrodynamics with Transition to Lagrangian Free-Surface Motion" (Lind and Stansby, J. Comput. Phys., 2016)
DOI: 10.6084/m9.figshare.3806463
title: CT Measures.sav

description: Dataset for Conspiracy! An Examination of the Factorial and Convergent Validity of Four Measures of Conspiracist Ideation, with Recommendations for Researchers
DOI: 10.6084/m9.figshare.3806415
title: SPARQLの基礎

description: 2016-08-31 開催の第10回 AIツールセミナー入門にて利用した発表資料です。
DOI: 10.6084/m9.figshare.3806394
title: Project MILDRED Research Data Repository Survey, University of Helsinki

description: This dataset is part of Project MILDRED, Development Project of Research Data Infrastructure at University of Helsinki. The project started on April 29, 2016. Project aim is to provide University of Helsinki with state-of-the-art research data management service infrastructure. To gain knowledge about researchers' data storage and preservation practices in 2016, an e-survey was sent to the UH research staff about 1) what data repositories they use for depositing their research data; 2) what reasons they had for not depositing data and 3) what alternative storage devices and repository services they used for their data.The dataset consists of e-survey report master file and analysis of the original master file. The files have been anonymized. A readme.rtf file is included to provide full project and data level documentation.
DOI: 10.6084/m9.figshare.3806394.v1
title: Project MILDRED Research Data Survey

description: This dataset is part of Project MILDRED, Development Project of Research Data Infrastructure at University of Helsinki. The project started on April 29, 2016. Project aim is to provide University of Helsinki with state-of-the-art research data management service infrastructure. To gain knowledge about researchers' data storage and preservation practices in 2016, an e-survey was sent to the UH research staff about 1) what data repositories they use for depositing their research data; 2) what reasons they had for not depositing data and 3) what alternative storage devices and repository services they used for their data.The dataset consists of e-survey report master file and analysis of the original master file. The files have been anonymized. A readme.rtf file is included to provide full project and data level documentation.
DOI: 10.6084/m9.figshare.3806394.v2
title: Project MILDRED Research Data Depository Survey

description: This dataset is part of Project MILDRED, Development Project of Research Data Infrastructure at University of Helsinki. The project started on April 29, 2016. Project aim is to provide University of Helsinki with state-of-the-art research data management service infrastructure. To gain knowledge about researchers' data storage and preservation practices in 2016, an e-survey was sent to the UH research staff about 1) what data repositories they use for depositing their research data; 2) what reasons they had for not depositing data and 3) what alternative storage devices and repository services they used for their data.The dataset consists of e-survey report master file and analysis of the original master file. The files have been anonymized. A readme.rtf file is included to provide full project and data level documentation.
DOI: 10.6084/m9.figshare.3806394.v4
title: Project MILDRED Research Data Repository Survey, University of Helsinki

description: This dataset is part of Project MILDRED, Development Project of Research Data Infrastructure at University of Helsinki. The project started on April 29, 2016. Project aim is to provide University of Helsinki with state-of-the-art research data management service infrastructure. To gain knowledge about researchers' data storage and preservation practices in 2016, an e-survey was sent to the UH research staff about 1) what data repositories they use for depositing their research data; 2) what reasons they had for not depositing data and 3) what alternative storage devices and repository services they used for their data.The dataset consists of e-survey report master file and analysis of the original master file. The files have been anonymized. A readme.rtf file is included to provide full project and data level documentation.
DOI: 10.6084/m9.figshare.3806394.v3
title: Project MILDRED Research Data Repository Survey

description: This dataset is part of Project MILDRED, Development Project of Research Data Infrastructure at University of Helsinki. The project started on April 29, 2016. Project aim is to provide University of Helsinki with state-of-the-art research data management service infrastructure. To gain knowledge about researchers' data storage and preservation practices in 2016, an e-survey was sent to the UH research staff about 1) what data repositories they use for depositing their research data; 2) what reasons they had for not depositing data and 3) what alternative storage devices and repository services they used for their data.The dataset consists of e-survey report master file and analysis of the original master file. The files have been anonymized. A readme.rtf file is included to provide full project and data level documentation.
DOI: 10.6084/m9.figshare.3806355
title: Python script for pattern search in Sox2 binding sequences

description: Cock PA, Antao T, Chang JT, Bradman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B and de Hoon MJL (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25, 1422-1423<br>Chapman BA and Chang JT (2000). Biopython: Python tools for computational biology. ACM SIGBIO Newsletter, 20, 15-19<br>Shirley MD, Ma Z, Pedersen B, Wheelan S. Efficient "pythonic" access to FASTA files using pyfaidx. PeerJ PrePrints 3:e1196. 2015.<br>Dhanusha Yesudhas, Muhammad Ayaz Anwar, Suresh Panneerselvam, Kim Han Kyul & Sangdun Choi*An approach to evaluate binding affinities of Sox2 with distinct DNA binding sequences by Umbrella Sampling (Manuscript under preparation)* http://sangdunchoi.openwetware.org/<br>Boyer, L. A. et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122, 947-956, doi:10.1016/j.cell.2005.08.020 (2005).<br>
DOI: 10.6084/m9.figshare.3806355.v1
title: Python script for pattern search in Sox2 binding sequences

description: Cock PA, Antao T, Chang JT, Bradman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B and de Hoon MJL (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25, 1422-1423<br>Chapman BA and Chang JT (2000). Biopython: Python tools for computational biology. ACM SIGBIO Newsletter, 20, 15-19<br>Shirley MD, Ma Z, Pedersen B, Wheelan S. Efficient "pythonic" access to FASTA files using pyfaidx. PeerJ PrePrints 3:e1196. 2015.<br>
DOI: 10.6084/m9.figshare.3806355.v2
title: Python script for pattern search in Sox2 binding sequences

description: Cock PA, Antao T, Chang JT, Bradman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B and de Hoon MJL (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25, 1422-1423<br>Chapman BA and Chang JT (2000). Biopython: Python tools for computational biology. ACM SIGBIO Newsletter, 20, 15-19<br>Shirley MD, Ma Z, Pedersen B, Wheelan S. Efficient "pythonic" access to FASTA files using pyfaidx. PeerJ PrePrints 3:e1196. 2015.<br>
DOI: 10.6084/m9.figshare.3806307
title: Scopus IR aggregated data

description: Aggregated dataset of records from Institutional repositories in New Zealand that have citations in articles cited in SCOPUS. raw data plus pivot table results analysing the data.
DOI: 10.6084/m9.figshare.3806307.v2
title: Scopus IR aggregated data

description: Aggregated dataset of records from Institutional repositories in New Zealand that have citations in articles cited in SCOPUS. raw data plus pivot table results analysing the data.
DOI: 10.6084/m9.figshare.3806307.v1
title: Scopus IR aggregated data

description: Aggregated dataset of records from Institutional repositories in New Zealand that have citations in articles cited in SCOPUS. raw data plus pivot table results analysing the data.
DOI: 10.6084/m9.figshare.3806295.v1
title: Figure 8: MLTreeMap (Stark et al. 2010) analyses of contigs based on the 16S rRNA gene for maximum likelihood (ML) phylogeny using GEBA phylogeny ( Wu et al. 2009), from wet, cold and dry, warm metagenomic data (b). March

description: Figure 8: MLTreeMap (Stark et al. 2010) analyses of contigs based on the 16S rRNA gene for maximum likelihood (ML) phylogeny using GEBA phylogeny ( Wu et al. 2009), from wet, cold and dry, warm metagenomic data (b). March
DOI: 10.6084/m9.figshare.3806289
title: Rhino Tracks, Mud, Chester Zoo, UK

description: Photos + Models (generated with VisualSFM + MVS)<br>Taxa: Rhinoceros<br>Substrate: Mud<br>Location: Chester, UK<br>Upload date: 04/9/2016<br>Description: Individual tracks from a large Rhino, one in deep mud, the other shallower. Photos taken from a limited position - orange in the texture indicates areas obscured to the camera. Models are not scaled.<br>https://pfalkingham.wordpress.com/resources/neoichnology/<br>
DOI: 10.6084/m9.figshare.3806289.v1
title: Rhino Tracks, Mud, Chester Zoo, UK

description: Photos + Models (generated with VisualSFM + MVS)<br>Taxa: Rhinoceros<br>Substrate: Mud<br>Location: Chester, UK<br>Upload date: 04/9/2016<br>Description: Individual tracks from a large Rhino, one in deep mud, the other shallower. Photos taken from a limited position - orange in the texture indicates areas obscured to the camera. Models are not scaled.<br>https://pfalkingham.wordpress.com/resources/neoichnology/<br>