cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18419/darus-3154
title: Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5

description: Python code to generate the meshes and FEM solutions to Section 5 of the paper &lt;i&gt;Uncertainty visualization: Fundamentals and recent developments&lt;/i&gt;.&lt;br&gt;
Comments are in the code to explain it.&lt;br&gt;
Paraview is used for the visualization.
DOI: 10.18419/darus-3154/1
title: main_section_5.py
DOI: 10.18419/darus-3553
title: Visual Analysis System to Explore the Visual Quality of Multidimensional Time Series Projections

description: Source code of our visual analysis system for the exploration of the visual quality of multidimensional time series projections.&lt;br&gt;
This project contains source code for preprocessing data and the visual analysis system. Additionally, we added precomputed data for immediate use in the visual analysis system.&lt;br&gt;
&lt;br&gt;
Our project contains the following directories/files of interest:
&lt;ul&gt;
 &lt;li&gt;&lt;i&gt;datasets:&lt;/i&gt; Data sets for the use with our visual analysis system. The data can also be generated with the data preparation scripts.&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;static, templates, and dimRed:&lt;/i&gt; Java script / Python code of our visualization approach.&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;run_windows:&lt;/i&gt; Scripts to run our system on windows.&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;run_linux:&lt;/i&gt; Scripts to run our system on linux.&lt;/li&gt;
 &lt;li&gt;&lt;i&gt;datasets.txt:&lt;/i&gt; List of directories used in preprocessing and for the visualization.&lt;/li&gt;
&lt;/ul&gt;
Please have a look at the &lt;a href="https://github.com/MunzT/visualQuality/blob/master/README.md"&gt;README&lt;/a&gt; file for more details.
description: You may find the most recent version of the source code on GitHub: https://github.com/MunzT/visualQuality
DOI: 10.18419/darus-3553/1
title: visualQuality_v1.0.zip

description: Source code of our visual analysis system to explore multidimensional time series projections.
DOI: 10.18419/darus-3561
title: Replication data of Kästner group for: "Tethering chiral Rh diene complexes inside mesoporous solids: experimental and theoretical study of substituent, pore and linker effects on asymmetric catalysis"

description: In this dataset, all simulation data are listed. That includes all geometry optimizations and single-point calculations and files from the conformational sampling. Furthermore, the spreadsheet with the collected data and the figures with the visualizations of the structures are listed. All structures are named "geom_opt.xyz", all input-files "*.chm" and all output-files "*.out" The folders in the file are named "entry_x" according to the table S10 in the SI of the publication. One folder includes all calculations belonging to the same ligand.
DOI: 10.15468/dl.z5258v
title: Occurrence Download

description: A dataset containing 8858 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aliger gigas (Linnaeus, 1758)"
 ]
}

 The dataset includes 8858 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003327-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-3573
title: Data for: Tracking the activity and position of mitochondrial beta-barrel proteins

description: Total interference reflection fluorescence microscopy (TIRF) of lipid bilayers is an effective technique for studying the lateral motion and ion channel activity of individual integral membrane proteins by optical means. This dataset contains a demo video for tracking the activity and position of individual ion channel proteins in supported lipid bilayers to determine possible relationships between lateral membrane motion and channel activity.
description: Dataset of raw video images in TIFF format
DOI: 10.18419/darus-3573/1
title: Wang_et_al_Demo_2023.tif

description: Demo stack VideoS1
DOI: 10.15468/dl.fqmegh
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Country derived from coordinates, Continent derived from coordinates)",
 "TaxonKey is Tor khudree (Sykes, 1839)"
 ]
}

 The dataset includes 30 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003329-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-3589
title: Visual Analysis System for Scene-Graph-Based Visual Question Answering
title: GraphVQA Explorer

description: Source code of our visual analysis system to explore scene-graph-based visual question answering.
This approach is built on top of the state-of-the-art 
&lt;a href="https://github.com/codexxxl/GraphVQA"&gt;GraphVQA framework&lt;/a&gt;
which was trained on the 
&lt;a href="https://cs.stanford.edu/people/dorarad/gqa/"&gt;GQA dataset&lt;/a&gt;.
&lt;br&gt;
Instructions on how to use our system can be found in the &lt;a href="https://github.com/Noeliel/GraphVQA-Explorer/blob/main/README.md"&gt;README&lt;/a&gt;.
description: You may find the most recent version of the source code on GitHub: https://github.com/Noeliel/GraphVQA-Explorer
<br>
DOI: 10.18419/darus-3589/1
title: GraphVQA-Explorer_v1.0.zip

description: Source code of our visual analytics system
DOI: 10.18419/darus-3648/1
title: livMats_Fabrication_DataSet.json
DOI: 10.15468/dl.a926b4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Haematopus ostralegus Linnaeus, 1758",
 "Year is 18",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003330-241107131044228/datasets/export for details.
DOI: 10.18419/darus-3656
title: Replication data of Kästner group for: "Neutral and Cationic Molybdenum Imido Alkylidene Cyclic Alkyl Amino Carbene (CAAC) Complexes for Olefin Metathesis"

description: In this dataset, all simulation data are listed. That includes all geometry optimizations and single-point calculations. Furthermore, the spreadsheet with the collected data is listed. The folders are named according to the nomenclature in the publication. All calculations were performed in Gaussian and the collected data is processed in the spreadsheet. The optimized geometries are named geom_opt.xyz and the partial charges of each atom for each method can be found in the output-files geom_opt.log
DOI: 10.18419/darus-3656/1
title: calculations.tgz

description: In this dataset, all simulation data are listed. That includes all geometry optimizations and single-point calculations. Furthermore, the spreadsheet with the collected data is listed. The folders are named according to the nomenclature in the publication. Each folder contains all calculations belonging to the structure it is named after. The spreadsheet contains all processed results.
DOI: 10.18419/darus-3100
title: Supplemental Materials for: Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations

description: The supplemental materials of the paper titled Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations, which was accepted for presentation at IEEE VIS 2022 conference. The structure of the folder is as follows: 
&lt;pre&gt;
&lt;code&gt;
.
└── code
 |── NetworkGeneration # the R code to generate the network data
 |── NetworkVis # the code that used to generate the visual stimuli
 |── BPStudy # the code to re-run the entire study
 |── BPStudy\stimuli # the visual stimuli used throughout the study
 └── StatisticalAnalysis # the code used to perform the statistical analysis 
└── data
 |── network_data # the network data files in json format
 └── stats_data # the raw data used for statistical analysis
└── extra
 |── force-layout # The code and data for the force-layout experiment 
 └── screen_shots.pdf # screenshots of study
└── README.md # this file
&lt;/code&gt;
&lt;/pre&gt;

Please check the README.md file within each directory for further information on how to use/run the code.
DOI: 10.18419/darus-3100/1
title: Comparative_Evaluation_of_Bipartite_Supplemental.zip
DOI: 10.18419/darus-3107
title: Constrained Dynamic Mode Decomposition - Supplemental Material

description: In this supplemental material, we provide additional application results of constrained Dynamic Mode Decomposition and a parameter study for the delay parameter d that complement the evaluation in the main document: The paper on Constrained Dynamic Mode Decomposition published with IEEE Transactions on Visualization and Computer Graphics. In this context, a univariate time series is considered, where no prior knowledge about seasonal patterns or time-scales is assumed. The dataset describes the sales of new one-family houses sold in the USA (1963-1990).
DOI: 10.18419/darus-3107/1
title: constrainedDMD_supplemental.pdf
DOI: 10.18419/darus-3112
title: Densities of simulated aqueous methanol mixtures

description: In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. 
&lt;br&gt;&lt;/br&gt;
The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. &lt;a href="https://github.com/FAIRChemistry/pyThermoML"&gt;PyThermoML&lt;/a&gt; enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.
&lt;br&gt;&lt;/br&gt;
The usage of pyThermoML is demonstrated in the following &lt;a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb"&gt;example workflow&lt;/a&gt; and can be utilized to read the given ThermoML file.
DOI: 10.18419/darus-3112/1
title: sim_dens_meth.xml
DOI: 10.18419/darus-3113
title: Densities of simulated aqueous glycerol mixtures

description: In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. 
&lt;br&gt;&lt;/br&gt;
The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. &lt;a href="https://github.com/FAIRChemistry/pyThermoML"&gt;PyThermoML&lt;/a&gt; enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.
&lt;br&gt;&lt;/br&gt;
The usage of pyThermoML is demonstrated in the following &lt;a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb"&gt;example workflow&lt;/a&gt; and can be utilized to read the given ThermoML file.
DOI: 10.18419/darus-3113/1
title: sim_dens_glyc.xml
DOI: 10.18419/darus-3114
title: Self-diffusion coefficients of simulated aqueous methanol mixtures

description: In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. 
&lt;br&gt;&lt;/br&gt;
The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. &lt;a href="https://github.com/FAIRChemistry/pyThermoML"&gt;PyThermoML&lt;/a&gt; enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.
&lt;br&gt;&lt;/br&gt;
The usage of pyThermoML is demonstrated in the following &lt;a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb"&gt;example workflow&lt;/a&gt; and can be utilized to read the given ThermoML file.
DOI: 10.18419/darus-3114/1
title: sim_transp_meth.xml
DOI: 10.15468/dl.hjbdry
title: Occurrence Download

description: A dataset containing 193 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bassaricyon neblina Helgen, Pinto, Kays, Helgen, Tsuchiya, Quinn, Wilson & Maldonado, 2013",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 193 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003332-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-3115
title: Self-diffusion coefficients of simulated aqueous glycerol mixtures

description: In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. 
&lt;br&gt;&lt;/br&gt;
The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. &lt;a href="https://github.com/FAIRChemistry/pyThermoML"&gt;PyThermoML&lt;/a&gt; enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.
&lt;br&gt;&lt;/br&gt;
The usage of pyThermoML is demonstrated in the following &lt;a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb"&gt;example workflow&lt;/a&gt; and can be utilized to read the given ThermoML file.
DOI: 10.18419/darus-3115/1
title: sim_transp_glyc.xml
DOI: 10.18419/darus-3116
title: Densities of experimental aqueous methanol mixtures

description: In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. 
&lt;br&gt;&lt;/br&gt;
The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. &lt;a href="https://github.com/FAIRChemistry/pyThermoML"&gt;PyThermoML&lt;/a&gt; enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.
&lt;br&gt;&lt;/br&gt;
The usage of pyThermoML is demonstrated in the following &lt;a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb"&gt;example workflow&lt;/a&gt; and can be utilized to read the given ThermoML file.
DOI: 10.18419/darus-3116/1
title: exp_dens_meth.xml
DOI: 10.15468/dl.wamdnz
title: Occurrence Download

description: A dataset containing 369 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Registro de Fauna en Áreas Protegidas de la jurisdicción de CORPOGUAVIO para la formulación y actualización de Planes de Manejo"
 ]
}

 The dataset includes 369 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003333-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-3117
title: Densities of experimental aqueous glycerol mixtures

description: In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. 
&lt;br&gt;&lt;/br&gt;
The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. &lt;a href="https://github.com/FAIRChemistry/pyThermoML"&gt;PyThermoML&lt;/a&gt; enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.
&lt;br&gt;&lt;/br&gt;
The usage of pyThermoML is demonstrated in the following &lt;a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb"&gt;example workflow&lt;/a&gt; and can be utilized to read the given ThermoML file.
DOI: 10.18419/darus-3117/1
title: exp_dens_glyc.xml
DOI: 10.5284/1122936
title: Images from The Mary Rose (Unpath'd Waters), 2007-2011

description: This collection comprises photographs taken between 2007 and 2011 from the artefacts dataset of material recovered from the Mary Rose wreck site.