cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19795435
title: Construction of an evenly-distributed genetic map using contig-tag-SNPs for quantitative trait loci (QTL) analysis of fiber-related traits in kenaf (<i>Hibiscus cannabinus</i> L.)

description: Kenaf is one of the most important natural fiber crops worldwide, which aims at harvesting bast fiber. Mining QTL loci of fiber yield and quality traits will facilitate fiber improvement and molecular marker-assisted breeding in kenaf. In this study, Fuhong 952 and Zanyin No. 1 were used as parents to construct two mapping populations, F<sub>2</sub> and F<sub>2:3</sub>, and an evenly distributed genetic linkage map was constructed by re-sequencing. The map contains 2512 contig-tag-SNP markers, and 18 linkage groups with a total length of 1287.63 cM and an average distance of 0.51 cM. Totally, 32 and 28 QTLs were detected in the F<sub>2</sub> and F<sub>2:3</sub> populations, respectively. Through Blast searching against the reference genome using the sequences of flanking molecular markers linked to QTLs, 374 candidate genes related to cell wall formation and photoperiod regulating flowering were found in these loci, including cellulose synthase-like genes, MYB genes, and Agamous-like genes. These findings could lay a foundation for the improvement of fiber-related traits and gene cloning in kenaf.
DOI: 10.6084/m9.figshare.19795435.v1
title: Construction of an evenly-distributed genetic map using contig-tag-SNPs for quantitative trait loci (QTL) analysis of fiber-related traits in kenaf (<i>Hibiscus cannabinus</i> L.)

description: Kenaf is one of the most important natural fiber crops worldwide, which aims at harvesting bast fiber. Mining QTL loci of fiber yield and quality traits will facilitate fiber improvement and molecular marker-assisted breeding in kenaf. In this study, Fuhong 952 and Zanyin No. 1 were used as parents to construct two mapping populations, F<sub>2</sub> and F<sub>2:3</sub>, and an evenly distributed genetic linkage map was constructed by re-sequencing. The map contains 2512 contig-tag-SNP markers, and 18 linkage groups with a total length of 1287.63 cM and an average distance of 0.51 cM. Totally, 32 and 28 QTLs were detected in the F<sub>2</sub> and F<sub>2:3</sub> populations, respectively. Through Blast searching against the reference genome using the sequences of flanking molecular markers linked to QTLs, 374 candidate genes related to cell wall formation and photoperiod regulating flowering were found in these loci, including cellulose synthase-like genes, MYB genes, and Agamous-like genes. These findings could lay a foundation for the improvement of fiber-related traits and gene cloning in kenaf.
DOI: 10.18419/darus-3973/10
title: backend_webservices.zip

description: :unav
DOI: 10.13140/rg.2.2.29999.92322/1
title: Khānsār-Bāzārbālā, Version des 17. Februar 2024
DOI: 10.6084/m9.figshare.20319822
title: GCN Data_AMF.zip

description: A multi-layered financial network (MRFN) dataset, where each layer represents a thematic interaction between the firms. The MRFN datasets can act as benchmark network datasets that could be used to extend the research on graph learning in predicting the financial risks of the firms.
DOI: 10.5281/zenodo.5764917
title: Data from: Global patterns and controls of nutrient immobilization on decomposing cellulose in riverine ecosystems

description: Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low-nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low-nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature-dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature. Collectively, we demonstrated that exogenous nutrient supply and immobilization are critical control points for decomposition of organic matter.
DOI: 10.5281/zenodo.10688938
title: Dataf from: Global patterns and controls of nutrient immobilization on decomposing cellulose in riverine ecosystems

description: Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low-nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low-nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature-dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature. Collectively, we demonstrated that exogenous nutrient supply and immobilization are critical control points for decomposition of organic matter.
DOI: 10.5281/zenodo.5764916
title: Dataf from: Global patterns and controls of nutrient immobilization on decomposing cellulose in riverine ecosystems

description: Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low-nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low-nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature-dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature. Collectively, we demonstrated that exogenous nutrient supply and immobilization are critical control points for decomposition of organic matter.
DOI: 10.18419/darus-3973/9
title: sparqlprov.sh

description: :unav
DOI: 10.18419/darus-3973/8
title: stardog_single_multiple_optional.xlsx

description: :unav
DOI: 10.6084/m9.figshare.24514291
title: Dimension Reduction for Fréchet Regression

description: With the rapid development of data collection techniques, complex data objects that are not in the Euclidean space are frequently encountered in new statistical applications. Fréchet regression model (Petersen and Müller) provides a promising framework for regression analysis with metric space-valued responses. In this article, we introduce a flexible sufficient dimension reduction (SDR) method for Fréchet regression to achieve two purposes: to mitigate the curse of dimensionality caused by high-dimensional predictors, and to provide a visual inspection tool for Fréchet regression. Our approach is flexible enough to turn any existing SDR method for Euclidean (<i>X</i>, <i>Y</i>) into one for Euclidean <i>X</i> and metric space-valued <i>Y</i>. The basic idea is to first map the metric space-valued random object <i>Y</i> to a real-valued random variable <i>f</i>(<i>Y</i>) using a class of functions, and then perform classical SDR to the transformed data. If the class of functions is sufficiently rich, then we are guaranteed to uncover the Fréchet SDR space. We showed that such a class, which we call an ensemble, can be generated by a universal kernel (cc-universal kernel). We established the consistency and asymptotic convergence rate of the proposed methods. The finite-sample performance of the proposed methods is illustrated through simulation studies for several commonly encountered metric spaces that include Wasserstein space, the space of symmetric positive definite matrices, and the sphere. We illustrated the data visualization aspect of our method by the human mortality distribution data from the United Nations Databases. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24514291.v2
title: Dimension Reduction for Fréchet Regression

description: With the rapid development of data collection techniques, complex data objects that are not in the Euclidean space are frequently encountered in new statistical applications. Fréchet regression model (Petersen and Müller) provides a promising framework for regression analysis with metric space-valued responses. In this article, we introduce a flexible sufficient dimension reduction (SDR) method for Fréchet regression to achieve two purposes: to mitigate the curse of dimensionality caused by high-dimensional predictors, and to provide a visual inspection tool for Fréchet regression. Our approach is flexible enough to turn any existing SDR method for Euclidean (<i>X</i>, <i>Y</i>) into one for Euclidean <i>X</i> and metric space-valued <i>Y</i>. The basic idea is to first map the metric space-valued random object <i>Y</i> to a real-valued random variable <i>f</i>(<i>Y</i>) using a class of functions, and then perform classical SDR to the transformed data. If the class of functions is sufficiently rich, then we are guaranteed to uncover the Fréchet SDR space. We showed that such a class, which we call an ensemble, can be generated by a universal kernel (cc-universal kernel). We established the consistency and asymptotic convergence rate of the proposed methods. The finite-sample performance of the proposed methods is illustrated through simulation studies for several commonly encountered metric spaces that include Wasserstein space, the space of symmetric positive definite matrices, and the sphere. We illustrated the data visualization aspect of our method by the human mortality distribution data from the United Nations Databases. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24514291.v1
title: Dimension Reduction for Fréchet Regression †

description: With the rapid development of data collection techniques, complex data objects that are not in the Euclidean space are frequently encountered in new statistical applications. Fréchet regression model (Peterson & Müller 2019) provides a promising framework for regression analysis with metric space-valued responses. In this paper, we introduce a flexible sufficient dimension reduction (SDR) method for Fréchet regression to achieve two purposes: to mitigate the curse of dimensionality caused by high-dimensional predictors, and to provide a visual inspection tool for Fréchet regression. Our approach is flexible enough to turn any existing SDR method for Euclidean (X,Y) into one for Euclidean X and metric space-valued Y. The basic idea is to first map the metric space-valued random object Y to a real-valued random variable f(Y) using a class of functions, and then perform classical SDR to the transformed data. If the class of functions is sufficiently rich, then we are guaranteed to uncover the Fréchet SDR space. We showed that such a class, which we call an ensemble, can be generated by a universal kernel (cc-universal kernel). We established the consistency and asymptotic convergence rate of the proposed methods. The finite-sample performance of the proposed methods is illustrated through simulation studies for several commonly encountered metric spaces that include Wasserstein space, the space of symmetric positive definite matrices, and the sphere. We illustrated the data visualization aspect of our method by the human mortality distribution data from the United Nations Databases.
DOI: 10.18419/darus-3973/7
title: finalresults.xlsx

description: :unav
DOI: 10.18419/darus-3973/6
title: stardog_optional.xlsx

description: :unav
DOI: 10.6084/m9.figshare.25257343.v1
title: Long Intergenic Non-Coding RNAs of human chromosome 18

description: Predicted secondary structures of long intergenic non-coding RNAs of human chromosome 18. The archive contains files in json format. One of the ways to read the files is to use the appropriate applications, for example, on the web server: http://rna.tbi.univie.ac.at/forna/Kerpedjiev P, Hammer S, Hofacker IL (2015). Forna (force-directed RNA): Simple and effective online RNA secondary structure diagrams. Bioinformatics 31(20):3377-9.
DOI: 10.15468/kyxjgk
title: 2019 Poursuite d'exploitation de la carrière et de l'usine de carbonate de Maxey-sur-Vaise (55)-Inventaire des insectes par ENCEM sur la carrière de Maxey-sur-Vaise en 2019

description: Inventaire des insectes par ENCEM sur la carrière de Maxey-sur-Vaise en 2019. Observations notées entre mars et juillet 2019.
DOI: 10.18419/darus-3973/4
title: watdivgraphds.ipynb

description: :unav
DOI: 10.15468/5s3hs5
title: Projet éolien du ruisseau de Margot-Inventaire des insectes

description: Le jeu de données regroupe les données relatives aux lépidoptères, aux orthoptères et autres insectes
DOI: 10.15468/kj7dhc
title: Projet de centrale photovoltaïque au sol de Vouneuil-sous-Biard-Inventaire de l'entomofaune

description: Inventaire réalisé dans le cadre du projet sur un cycle biologique pour l'entomofaune
DOI: 10.5281/zenodo.8190848
title: Elasticity tensors of 10276 crystals from DFT computations

description: Paper introducing this dataset

Wen, M., Horton, M., Munro, J., Huck, P., & Persson, K. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. Digital Discovery. DOI:https://doi.org/10.1039/D3DD00233K

 

This dataset consists of three data files in the json format. Each file is explained below.

crystal_elasticity_tensor.json

DFT computed elastic tensors of 10276 crystals used for developing the MatTen model.

structure: crystal structure of the materialformula_pretty: chemical formulacrystal_system: crystal systemelastic_tensor: full fourth-rank elastic tensorelastic_tensor_voigt: 6x6 Voigt matrix of the elastic tensorsplit: split of the data into train, validation, and test subsets for model development

max_directional_E.json

New crystals with large maximum directional Young's modulus.

material_id: Materials Project identifierformula_pretty: chemical formula

structure_original: crystal structure from the Materials Project databaseelastic_tensor_matten_original: MatTen predicted elastic tensor using `structure_original`max_directional_E_matten_original: MatTen predicted maximum directional Young's modulus using `structure_original`

structure: further DFT optimized structure with a tigher criterionelastic_tensor: DFT elastic tensor corresponding to `structure`max_directional_E: DFT maximum directional Young's modulus using `structure`elastic_tensor_matten: MatTen predicted elastic tensor using `structure`max_directional_E_matten: MatTen predicted maximum directional Young's modulus using `structure`

elemental_cubic_metal_max_E_along_100_direction.json

New crystals with its maximum directional Young's modulus along the [100] direction.

material_id: Materials Project identifierformula_pretty: chemical formula

structure_original: crystal structure from the Materials Project databaseelastic_tensor_matten: MatTen predicted elastic tensor using `structure_original`Delta_S_matten: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor_matten`

structure: further DFT optimized structure with a tigher criterionelastic_tensor: DFT elastic tensor corresponding to `structure`Delta_S: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor`
DOI: 10.5281/zenodo.8190849
title: Elasticity tensors of 10276 crystals from DFT computations

description: Paper introducing this dataset

Wen, M., Horton, M., Munro, J., Huck, P., & Persson, K. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. Digital Discovery. DOI:https://doi.org/10.1039/D3DD00233K

 

This dataset consists of three data files in the json format. Each file is explained below.

crystal_elasticity_tensor.json

DFT computed elastic tensors of 10276 crystals used for developing the MatTen model.

structure: crystal structure of the materialformula_pretty: chemical formulacrystal_system: crystal systemelastic_tensor: full fourth-rank elastic tensorelastic_tensor_voigt: 6x6 Voigt matrix of the elastic tensorsplit: split of the data into train, validation, and test subsets for model development

max_directional_E.json

New crystals with large maximum directional Young's modulus.

material_id: Materials Project identifierformula_pretty: chemical formula

structure_original: crystal structure from the Materials Project databaseelastic_tensor_matten_original: MatTen predicted elastic tensor using `structure_original`max_directional_E_matten_original: MatTen predicted maximum directional Young's modulus using `structure_original`

structure: further DFT optimized structure with a tigher criterionelastic_tensor: DFT elastic tensor corresponding to `structure`max_directional_E: DFT maximum directional Young's modulus using `structure`elastic_tensor_matten: MatTen predicted elastic tensor using `structure`max_directional_E_matten: MatTen predicted maximum directional Young's modulus using `structure`

elemental_cubic_metal_max_E_along_100_direction.json

New crystals with its maximum directional Young's modulus along the [100] direction.

material_id: Materials Project identifierformula_pretty: chemical formula

structure_original: crystal structure from the Materials Project databaseelastic_tensor_matten: MatTen predicted elastic tensor using `structure_original`Delta_S_matten: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor_matten`

structure: further DFT optimized structure with a tigher criterionelastic_tensor: DFT elastic tensor corresponding to `structure`Delta_S: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor`
DOI: 10.5519/qd.q9w05mjr
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (54 records)
DOI: 10.18419/darus-3973/3
title: npcs.sh

description: :unav
DOI: 10.5519/qd.gqqykzdp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (54 records)
DOI: 10.18419/darus-3973/2
title: queries.zip

description: :unav
DOI: 10.18419/darus-3973/1
title: backend_webservices.zip

description: :unav
DOI: 10.18419/darus-3973/5
title: stardog_multiple.xlsx

description: :unav
DOI: 10.5281/zenodo.10681925
title: MD simulation trajectory data for "An Evaluation of Force Field Accuracy for the Mini-Protein Chignolin using Markov State Models"
title: MD simulation trajectories of chignolin folding

description: Molecular simulation trajectory data of the mini-protein chignolin, as described in the manuscript:



An Evaluation of Force Field Accuracy for the Mini-Protein Chignolin using Markov State Models. Tim Marshall, Robert M. Raddi, and Vincent A. Voelz
DOI: 10.5281/zenodo.10681926
title: MD simulation trajectory data for "An Evaluation of Force Field Accuracy for the Mini-Protein Chignolin using Markov State Models"
title: MD simulation trajectories of chignolin folding

description: Molecular simulation trajectory data of the mini-protein chignolin, as described in the manuscript:



An Evaluation of Force Field Accuracy for the Mini-Protein Chignolin using Markov State Models. Tim Marshall, Robert M. Raddi, and Vincent A. Voelz
DOI: 10.21227/jpr2-4689
title: APPENDIXS - An Occupational Safety Risk Management System for Coastal Construction Projects

description: Inadequate management of safety risks causes fatalities, injuries, delays, and cost overruns in construction projects. Unpredictable and hostile conditions in coastal construction exacerbate these safety risks. Successful risk management requires effective management and communication of risk information as well as rapid access to relevant knowledge. For this purpose, safety risk management systems were developed using digital technologies, however, none focus on coastal construction, where the activities, risk factors, and mitigation methods are significantly different. This study aims to develop an occupational safety risk management system tailored to the specific needs of coastal construction projects that (1) collects risk data from the site in a structured and continuous manner and (2) facilitates knowledge-based decision-making to support effective risk management utilizing digital technologies. A prototype is developed and use cases are presented to illustrate the main objectives of the prototype and the proposed vision for its integration into building information modeling (BIM). The deployment in two real-world projects demonstrates that the developed prototype is applicable, practical, usable and that real-time risk data can be collected and analyzed with the proposed system. Using the risk information collected by the prototype, which supports proactive and dynamic risk management, managers can make critical decisions about the current project, undertake long-term safety planning, and utilize an organizational learning approach for risk avoidance in future projects.
DOI: 10.6084/m9.figshare.12980277
title: Additional file 2 of A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

description: Additional file 2: Table S1. Differentially expressed mRNAs and lncRNAs.
DOI: 10.6084/m9.figshare.12980277.v1
title: Additional file 2 of A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

description: Additional file 2: Table S1. Differentially expressed mRNAs and lncRNAs.
DOI: 10.6084/m9.figshare.24559096.v1
title: Data base-Authorized and unauthorized consumption of SVOD content.sav

description: These files contain all variables and cases analyzed in the study entitled, “Authorized and unauthorized consumption of SVOD content: Modeling determinants of demand and measuring effects of enforcing access control." <br>