cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5856888
title: pd_deformation_state_schema

description: The deformation state transforms bonds to their deformed images
DOI: 10.6084/m9.figshare.5856888.v1
title: pd_deformation_state_schema

description: The deformation state transforms bonds to their deformed images
DOI: 10.6084/m9.figshare.5856885
title: pd_interactions_schema

description: Material points nteractions within the PD framework.
DOI: 10.6084/m9.figshare.5856885.v1
title: pd_interactions_schema

description: Material points nteractions within the PD framework.
DOI: 10.6084/m9.figshare.5856882
title: dic_principle_schema

description: Principle of DIC using a correlation function
DOI: 10.6084/m9.figshare.5856882.v1
title: dic_principle_schema

description: Principle of DIC using a correlation function
DOI: 10.6084/m9.figshare.5856876
title: Dataset of CO2 concentrations observed in snowpacks of the Cape Breton Highlands

description: These three files complete a dataset corresponding to findings published in the article "Explaining CO<sub>2</sub> fluctuations observed in snowpacks" by Laura Graham and David Risk (St. Francis Xavier University, Antigonish, NS, Canada) in 2018 in <i>Biogeosciences</i>. <br>The 2014_NM1 and 2014_NM2 files are 2013-2014 winter datasets (CO<sub>2</sub>, wind, snow depth) for the two sites (NM1 and NM2). These two files are filtered so that CO<sub>2</sub> values given are for only during times of consistent snowpack, as defined in our paper. The 2015_NM2 file is the 2-week period of CO<sub>2</sub>, snow depth, and wind data analyzed for winter 2015.
DOI: 10.6084/m9.figshare.5856870
title: vfm_disc_schema

description: VFM applied to a disc in compression.
DOI: 10.6084/m9.figshare.5856870.v1
title: vfm_disc_schema

description: VFM applied to a disc in compression.
DOI: 10.6084/m9.figshare.5856861
title: pGWAS and functional filtering pipeline to identify candidate adaptive genetic traits in bacteria [accompanying data]

description: Accompanying data for:<br>https://github.com/sheppardlab/pGWAS/ <br>"pGWAS and functional filtering pipeline to identify candidate adaptive genetic traits in bacteria"<br>
DOI: 10.6084/m9.figshare.5856861.v1
title: example_files.zip

description: Accompanying data for:<br>https://github.com/sheppardlab/pGWAS/ <br>"pGWAS and functional filtering pipeline to identify candidate adaptive genetic traits in bacteria"<br>
DOI: 10.6084/m9.figshare.5856795
title: Modelo de Indicadores para a análise altmetrica das redes sociais

description: Nas últimas décadas as métricas para a avaliação e ranqueamento da produção científica tornaram-se assuntos que têm atraído a atenção de pesquisadores e agências de fomento em busca de indicadores que possibilitem a manutenção de um parâmetro para diferentes tipos de avaliações. Na tentativa de atender essa demanda surgiram as métricas alternativas. Um novo subcampo dos estudos métricos da informação cuja finalidade é medir a disseminação de documentos científicos nas mídias sociais. Os estudos de sua viabilidade ainda estão em fase de desenvolvimento, e na maioria dos casos são comparados com vários outros indicadores que surgiram ao longo dos anos cujo objetivo era tentar oferecer informações mais refinadas sobre a disseminação e/ou impacto das publicações de artigos científicos. Com base nestas questões sobre como medir o impacto da produção científica e utilizando do conhecimento e informações que podem ser extraídas das mídias sociais e também levando em consideração suas particularidades e peculiaridades, é que desenvolvemos essa pesquisa, cuja proposta é utilizar os dados gerados pela ferramenta Altmetric.com para a construção de indicadores que possa auxiliar nos estudos métricos da informação. <br>
DOI: 10.6084/m9.figshare.5856795.v2
title: Modelo de Indicadores para a análise altmetrica das redes sociais

description: Nas últimas décadas as métricas para a avaliação e ranqueamento da produção científica tornaram-se assuntos que têm atraído a atenção de pesquisadores e agências de fomento em busca de indicadores que possibilitem a manutenção de um parâmetro para diferentes tipos de avaliações. Na tentativa de atender essa demanda surgiram as métricas alternativas. Um novo subcampo dos estudos métricos da informação cuja finalidade é medir a disseminação de documentos científicos nas mídias sociais. Os estudos de sua viabilidade ainda estão em fase de desenvolvimento, e na maioria dos casos são comparados com vários outros indicadores que surgiram ao longo dos anos cujo objetivo era tentar oferecer informações mais refinadas sobre a disseminação e/ou impacto das publicações de artigos científicos. Com base nestas questões sobre como medir o impacto da produção científica e utilizando do conhecimento e informações que podem ser extraídas das mídias sociais e também levando em consideração suas particularidades e peculiaridades, é que desenvolvemos essa pesquisa, cuja proposta é utilizar os dados gerados pela ferramenta Altmetric.com para a construção de indicadores que possa auxiliar nos estudos métricos da informação. <br>
DOI: 10.6084/m9.figshare.5856795.v1
title: Modelo de Indicadores para a análise altmetrica das redes sociais

description: Nas últimas décadas as métricas para a avaliação e ranqueamento da produção científica tornaram-se assuntos que têm atraído a atenção de pesquisadores e agências de fomento em busca de indicadores que possibilitem a manutenção de um parâmetro para diferentes tipos de avaliações. Na tentativa de atender essa demanda surgiram as métricas alternativas. Um novo subcampo dos estudos métricos da informação cuja finalidade é medir a disseminação de documentos científicos nas mídias sociais. Os estudos de sua viabilidade ainda estão em fase de desenvolvimento, e na maioria dos casos são comparados com vários outros indicadores que surgiram ao longo dos anos cujo objetivo era tentar oferecer informações mais refinadas sobre a disseminação e/ou impacto das publicações de artigos científicos. Com base nestas questões sobre como medir o impacto da produção científica e utilizando do conhecimento e informações que podem ser extraídas das mídias sociais e também levando em consideração suas particularidades e peculiaridades, é que desenvolvemos essa pesquisa, cuja proposta é utilizar os dados gerados pela ferramenta Altmetric.com para a construção de indicadores que possa auxiliar nos estudos métricos da informação. <br>
DOI: 10.6084/m9.figshare.5856765
title: Genome skimming provides new insight into the relationships in Ludwigia section Macrocarpon (Onagraceae), a polyploid complex

description: Interpreting relationships within groups containing polyploids, which are frequent in angiosperms, can be greatly assisted by genomic techniques. In this study, we used a genome-skimming approach to investigate the evolutionary relationships and origins of polyploids in the monophyletic group, Ludwigia section Macrocarpon (Onagraceae), which includes diploid, tetraploid, and hexaploid taxa. Our results showed that the South American diploid taxa L. bonariensis and L. lagunae were closely related. We reported the first chromosome count (2n = 4x = 32) for L. neograndiflora, which is closely related to the two South American diploid taxa, although its exact origin remains unclear. The samples of the widespread, polyploid taxon L. octovalvis do not form a monophyletic group. Both tetraploid and hexaploid L. octovalvis lineages have originated more than once. At least one tetraploid L. octovalvis lineage may have been involved in the origins of hexaploids. One or more extinct/unsampled intermediate tetraploid L. octovalvis lineages were also likely involved the origins of hexaploids.
DOI: 10.6084/m9.figshare.5856765.v1
title: Genome skimming provides new insight into the relationships in Ludwigia section Macrocarpon (Onagraceae), a polyploid complex

description: Interpreting relationships within groups containing polyploids, which are frequent in angiosperms, can be greatly assisted by genomic techniques. In this study, we used a genome-skimming approach to investigate the evolutionary relationships and origins of polyploids in the monophyletic group, Ludwigia section Macrocarpon (Onagraceae), which includes diploid, tetraploid, and hexaploid taxa. Our results showed that the South American diploid taxa L. bonariensis and L. lagunae were closely related. We reported the first chromosome count (2n = 4x = 32) for L. neograndiflora, which is closely related to the two South American diploid taxa, although its exact origin remains unclear. The samples of the widespread, polyploid taxon L. octovalvis do not form a monophyletic group. Both tetraploid and hexaploid L. octovalvis lineages have originated more than once. At least one tetraploid L. octovalvis lineage may have been involved in the origins of hexaploids. One or more extinct/unsampled intermediate tetraploid L. octovalvis lineages were also likely involved the origins of hexaploids.
DOI: 10.6084/m9.figshare.5856717
title: 3pt_bending_schema

description: Geometry and loads of the three-point bending test.
DOI: 10.6084/m9.figshare.5856717.v2
title: 3pt_bending_schema

description: Geometry and loads of the three-point bending test.
DOI: 10.6084/m9.figshare.5856714
title: Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics

description: Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, <i>Bayesian hierarchical varying-sparsity regression</i> (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein–gene relationships as well as induces sparsity in both protein–gene and protein–survival relationships, to select genomically driven prognostic protein markers at the patient-level. Simulation studies demonstrate the superior performance of BEHAVIOR against competing method in terms of both protein marker selection and survival prediction. We apply BEHAVIOR to The Cancer Genome Atlas (TCGA) proteogenomic pan-cancer data and find several interesting prognostic proteins and pathways that are shared across multiple cancers and some that exclusively pertain to specific cancers. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available online.
DOI: 10.6084/m9.figshare.5856714.v1
title: Bayesian Hierarchical Varying-sparsity Regression Models with Application to Cancer Proteogenomics

description: Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, <i>Bayesian hierarchical varying-sparsity regression</i> (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein-gene relationships as well as induces sparsity in both protein-gene and protein-survival relationships, to select genomically driven prognostic protein markers at the patient-level. Simulation studies demonstrate the superior performance of BEHAVIOR against competing method in terms of both protein marker selection and survival prediction. We apply BEHAVIOR to The Cancer Genome Atlas (TCGA) proteogenomic pan-cancer data and find several interesting prognostic proteins and pathways that are shared across multiple cancers and some that exclusively pertain to specific cancers. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available online. These materials were reviewed for reproducibility
DOI: 10.6084/m9.figshare.5856714.v3
title: Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics

description: Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, <i>Bayesian hierarchical varying-sparsity regression</i> (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein–gene relationships as well as induces sparsity in both protein–gene and protein–survival relationships, to select genomically driven prognostic protein markers at the patient-level. Simulation studies demonstrate the superior performance of BEHAVIOR against competing method in terms of both protein marker selection and survival prediction. We apply BEHAVIOR to The Cancer Genome Atlas (TCGA) proteogenomic pan-cancer data and find several interesting prognostic proteins and pathways that are shared across multiple cancers and some that exclusively pertain to specific cancers. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available online.
DOI: 10.6084/m9.figshare.5856714.v2
title: Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics

description: Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, <i>Bayesian hierarchical varying-sparsity regression</i> (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein–gene relationships as well as induces sparsity in both protein–gene and protein–survival relationships, to select genomically driven prognostic protein markers at the patient-level. Simulation studies demonstrate the superior performance of BEHAVIOR against competing method in terms of both protein marker selection and survival prediction. We apply BEHAVIOR to The Cancer Genome Atlas (TCGA) proteogenomic pan-cancer data and find several interesting prognostic proteins and pathways that are shared across multiple cancers and some that exclusively pertain to specific cancers. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available online.
DOI: 10.6084/m9.figshare.5856651
title: Phylloicus hansoni larval population size structures in both predator-only and predator-third party stream settings

description: This data was collected in 2013 and 2011 in the southern slope of Trinidad’s Northern Range Mountains, within the Caroni River drainage. The dataset consists of larval caddisfly size distribution data across streams with different fish assemblages, with data on size, frequency and gut content data of predatory fish species.<br>The related study investigates the relationship between the size structures of the larval caddisfly (<i>Phylloicus hansoni</i> Denning) and different fish assemblages in Trinidadian streams in two settings:<b>KO:</b> Killifish-Only: upstream reaches where the predatory killifish, <i>Anablepsoides hartii</i>, is the only fish species<b>KG:</b> Killifish-Guppy: downstream reaches where killifish and the omnivorous guppy <i>Poecilia reticulata</i> coexist<br>The data files are four <b>.csv</b> text files, accessible via MS Excel, open office and openly accessible text edit applications.<br>___<br>The following data files are from 5 streams, each with reaches with differing fish assemblages in 2013.<br><br><b>PHYL-size_frequency_data.csv</b> - size frequency data consisting of body lengths and head capsule widths of caddisfly larvae:<b>- stream: </b>El Cedro = CED, Endler = END, Guard Dog Creek = GDC, Ramdeen = RDN, Trip Trace = TRT.<b>- reach:</b> stream setting - KG or KO<b>- instar.group: </b>HCW classification as Small = S (0.2 - 0.4 mm), Medium = M (0.5 - 0.8 mm), Large = L (0.9 - 1.1 mm)<b>- HCW:</b> head capsule width in mm<b>- count: </b>frequency<br><b>PHYL-site_survey_data.csv - </b>site survey data<b>(stream & reach </b>see above)<br><b>- transect</b> - transect id in the format <b>TTTRRX</b> (TTT = transect, RR = reach, X= sequential numerical id)<b>- wetted.width </b>in cm<br><b>- depth </b>in cm<br><b>- canopy.cover</b> in %<br><b>- CBOM:</b> coarse benthic organic matter gm<sup>-2</sup><b>- CPUE: </b>catch per unit effort<br><br><b>PHYL-fish_size_class_data.csv </b>- killifish size class data, with mean CPUE for each size class at each stream/reach:<b>(stream & reach </b>see above)<b><br></b><b>- fish.SC - </b>fish size classification: Juvenile = J (&lt; 15 mm), Small = S (15-30 mm), Medium = M (30-45 mm), Large = L (45-60 mm), and Extra-Large = XL (&gt; 60 mm)<b>- mean.CPUE</b>: mean CPUE (see above for definition) for specific stream and reach___<br>The following data was collected from killifish in 2011. Appropriate institutional permits were obtained.<br><b>PHYL-GCA_data.csv</b> - Gut content data (GCA) detailing <i>P. hansoni</i> in individual killifsh catches:<b>(stream, reach, fish.SC, HCW </b>see above)<br><b>- fish.size: </b>fish size in mm<b>- success: </b>1 = <i>P. hansoni</i> present, 0 = <i>P. hansoni</i> not present<b>- failure: </b>1 = <i>P. hansoni</i> not present, 0 = <i>P. hansoni</i> present<b>- instar: </b>instar size class - Juvenile = J, Small = S, Medium = M, Large = L, and Extra-Large = XL<br>___<br><b>Stoker-PHYL.R - </b>R code for analysis of data files listed above
DOI: 10.6084/m9.figshare.5856651.v1
title: Phylloicus hansoni larval population size structures in both predator-only and predator-third party stream settings

description: This data was collected in 2013 and 2011 in the southern slope of Trinidad’s Northern Range Mountains, within the Caroni River drainage. The dataset consists of larval caddisfly size distribution data across streams with different fish assemblages, with data on size, frequency and gut content data of predatory fish species.<br>The related study investigates the relationship between the size structures of the larval caddisfly (<i>Phylloicus hansoni</i> Denning) and different fish assemblages in Trinidadian streams in two settings:<b>KO:</b> Killifish-Only: upstream reaches where the predatory killifish, <i>Anablepsoides hartii</i>, is the only fish species<b>KG:</b> Killifish-Guppy: downstream reaches where killifish and the omnivorous guppy <i>Poecilia reticulata</i> coexist<br>The data files are four <b>.csv</b> text files, accessible via MS Excel, open office and openly accessible text edit applications.<br>___<br>The following data files are from 5 streams, each with reaches with differing fish assemblages in 2013.<br><br><b>PHYL-size_frequency_data.csv</b> - size frequency data consisting of body lengths and head capsule widths of caddisfly larvae:<b>- stream: </b>El Cedro = CED, Endler = END, Guard Dog Creek = GDC, Ramdeen = RDN, Trip Trace = TRT.<b>- reach:</b> stream setting - KG or KO<b>- instar.group: </b>HCW classification as Small = S (0.2 - 0.4 mm), Medium = M (0.5 - 0.8 mm), Large = L (0.9 - 1.1 mm)<b>- HCW:</b> head capsule width in mm<b>- count: </b>frequency<br><b>PHYL-site_survey_data.csv - </b>site survey data<b>(stream & reach </b>see above)<br><b>- transect</b> - transect id in the format <b>TTTRRX</b> (TTT = transect, RR = reach, X= sequential numerical id)<b>- wetted.width </b>in cm<br><b>- depth </b>in cm<br><b>- canopy.cover</b> in %<br><b>- CBOM:</b> coarse benthic organic matter gm<sup>-2</sup><b>- CPUE: </b>catch per unit effort<br><br><b>PHYL-fish_size_class_data.csv </b>- killifish size class data, with mean CPUE for each size class at each stream/reach:<b>(stream & reach </b>see above)<b><br></b><b>- fish.SC - </b>fish size classification: Juvenile = J (&lt; 15 mm), Small = S (15-30 mm), Medium = M (30-45 mm), Large = L (45-60 mm), and Extra-Large = XL (&gt; 60 mm)<b>- mean.CPUE</b>: mean CPUE (see above for definition) for specific stream and reach___<br>The following data was collected from killifish in 2011. Appropriate institutional permits were obtained.<br><b>PHYL-GCA_data.csv</b> - Gut content data (GCA) detailing <i>P. hansoni</i> in individual killifsh catches:<b>(stream, reach, fish.SC, HCW </b>see above)<br><b>- fish.size: </b>fish size in mm<b>- success: </b>1 = <i>P. hansoni</i> present, 0 = <i>P. hansoni</i> not present<b>- failure: </b>1 = <i>P. hansoni</i> not present, 0 = <i>P. hansoni</i> present<b>- instar: </b>instar size class - Juvenile = J, Small = S, Medium = M, Large = L, and Extra-Large = XL<br>___<br><b>Stoker-PHYL.R - </b>R code for analysis of data files listed above
DOI: 10.6084/m9.figshare.5856519
title: Lightning talk: A New database of Roman temples

description: Slides from lightning-round presentation on February 5, 2018, during NYCDH Week.
DOI: 10.6084/m9.figshare.5856519.v1
title: Lightning talk: A New database of Roman temples

description: Slides from lightning-round presentation on February 5, 2018, during NYCDH Week.
DOI: 10.6084/m9.figshare.5854692
title: Tempo and mode of genome evolution in the budding yeast subphylum

description: All budding yeast genome assemblies and annotations, phylogenetic data matrices, multiple sequence alignments, phylogenetic trees, the metabolic trait data matrix, trait ancestral character state reconstructions, additional figures and tables, R codes, and the custom Perl scripts used in this study are available on the Figshare data repository
DOI: 10.6084/m9.figshare.5854662
title: List of genes included in the Zymoseptoria tritici IPO323 ORFeome project

description: List of genes included in the Zymoseptoria tritici IPO323 ORFeome project
DOI: 10.6084/m9.figshare.5854662.v1
title: List of genes included in the Zymoseptoria tritici IPO323 ORFeome project

description: List of genes included in the Zymoseptoria tritici IPO323 ORFeome project
DOI: 10.6084/m9.figshare.5854659
title: Simulated data

description: This is a simulated dataset used for test.<br>
DOI: 10.6084/m9.figshare.5854644
title: CH3F5 Bioorganic Chemistry - Molecular Interactions Numerical Problems Class 2

description: CH3F5 Numerical problems class 2 - extends the approach taken by Abrahams and Hunter in using hydrogen bond scales to estimate strengths of interactions between molecules.
DOI: 10.6084/m9.figshare.5854644.v1
title: CH3F5 Bioorganic Chemistry - Molecular Interactions Numerical Problems Class 2

description: CH3F5 Numerical problems class 2 - extends the approach taken by Abrahams and Hunter in using hydrogen bond scales to estimate strengths of interactions between molecules.
DOI: 10.6084/m9.figshare.5854629
title: CH3F5 Bioorganic Chemistry - Molecular Interactions Numerical Problems Class 1

description: Problems class 1 that uses the approach developed by Michael Abrahams (UCL) and Christopher Hunter (Sheffield and Cambridge) to estimate strengths of interactions between molecules
DOI: 10.6084/m9.figshare.5854629.v3
title: CH3F5 Bioorganic Chemistry - Molecular Interactions Numerical Problems Class 1

description: Problems class 1 that uses the approach developed by Michael Abrahams (UCL) and Christopher Hunter (Sheffield and Cambridge) to estimate strengths of interactions between molecules
DOI: 10.6084/m9.figshare.5854629.v2
title: CH3F5 Bioorganic Chemistry - Molecular Interactions Numerical Problem Class 1

description: Problems class 1 that uses the approach developed by Michael Abrahams (UCL) and Christopher Hunter (Sheffield and Cambridge) to estimate strengths of interactions between molecules