cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18419/darus-4024
title: Source code and Numerical Examples for Decomposed Quasiconvex Optimization with Application to Generalized Cone Problems

description: This dataset contains source code and numerical examples for quasiconvex sum-of-squares problems via decomposed optimization.
description: &lt;h3&gt;Usage Notes&lt;/h3&gt;

&lt;p&gt;The files in the &lt;code&gt;examples&lt;/code&gt; folder correspond to the numerical examples provided in Section 6 of the accompanying publication.&lt;/p&gt;

&lt;p&gt;
Before running the examples,
&lt;ol&gt;
&lt;li&gt;Download required software from the links below; or unpack archives in the &lt;code&gt;library&lt;/code&gt; folder.
&lt;li&gt;Add file &lt;code&gt;gradsosopt.m&lt;/code&gt; as well as required software to MATLAB path.
&lt;/ol&gt;
&lt;/p&gt;
DOI: 10.18419/darus-3347/1
title: compute_diagonal_entries_eff.m

description: efficient algorithm for diagonal entries
DOI: 10.18419/darus-3347/2
title: generate_system_3d_frame.m

description: generate data for example 3
DOI: 10.18419/darus-3347/4
title: LICENSE.txt
DOI: 10.18419/darus-3347/5
title: compute_full_matrix_ref.m

description: reference method for full matrix
DOI: 10.18419/darus-3347/7
title: compute_full_matrix_eff.m

description: efficient algorithm for full matrix
DOI: 10.18419/darus-3347/8
title: generate_system_mero_roof.m

description: generate data for example 2
DOI: 10.15468/dl.xusu36
title: Occurrence Download

description: A dataset containing 81 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coelachne perpusilla (Nees ex Steud.) Thwaites"
 ]
}

 The dataset includes 81 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006816-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-3347/9
title: main.m
DOI: 10.18419/darus-3347/3
title: generate_system_truss_cylinder.m

description: generate data for example 1
DOI: 10.18419/darus-3763/1
title: EdgeStop_mes.tab

description: Contour that is executed with acceleration- and jerk-limited profile
DOI: 10.18419/darus-3372/1
title: yano.csv

description: A categorical dataset reconstructed from Yano et al. [KSDK11] describing property sales information from Singapor. It is manually reconstructed from the Parallel Sets visualization in Figure X of the publication by Yano et al. The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underlying data.

[KSDK11] L. C. Koh, A. Slingsby, J. Dykes and T. S. Kam, "Developing and Applying a User-Centered Model for the Design and Implementation of Information Visualization Tools," 2011 15th International Conference on Information Visualisation, 2011, pp. 90-95, doi: 10.1109/IV.2011.32
DOI: 10.18419/darus-3372/3
title: hassan.tab

description: A categorical dataset reconstructed from Hassan et al. [HP14] describing data storage security and cost data. It is manually reconstructed from the Parallel Sets visualization in Figure 2 of the publication. The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underlying data.

[HP14] Sabri Hassan and Günther Pernul. 2014. Efficiently Managing the Security and Costs of Big Data Storage using Visual Analytics. In Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services (iiWAS '14). Association for Computing Machinery, New York, NY, USA, 180–184. https://doi.org/10.1145/2684200.2684333
DOI: 10.18419/darus-3347
title: Matlab Implementation of Efficient Computation of Redundancy Matrices

description: This is a Demo for the manuscript 'Efficient Computation of Redundancy Matrices for Moderately Redundant Truss and Frames Structures' that demonstrates the speedup of the proposed algorithms. The computation is done in single-precision. Please open and run the main file. Further information is contained in this file.
DOI: 10.18419/darus-3347/6
title: compute_diagonal_entries_ref.m

description: reference method for diagonal entries
DOI: 10.18419/darus-3372/4
title: koh.tab

description: A categorical dataset reconstructed from Koh et al. [YKII15] describing software dependencies. It is manually reconstructed from the Parallel Sets visualization in Figure 4 of the publication by Yano et al. The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underlying data.

[YKII15] Y. Yano, R. G. Kula, T. Ishio and Katsuro Inoue. (2015). VerXCombo: An interactive data visualization of popular library version combinations. In Proceedings of the 2015 IEEE 23rd International Conference on Program Comprehension, pp. 291-294. doi: 10.1109/ICPC.2015.43
DOI: 10.18419/darus-3763/2
title: EdgeStop_sim.tab

description: Contour that is executed with acceleration- and jerk-limited profile
DOI: 10.18419/darus-3372/6
title: rogers-1.tab

description: The first categorical dataset reconstructed from Rogers et al. describing the results of a HCI study. It is manually reconstructed from the Parallel Sets visualization in Figure 1 (a) of the publication by Rogers et al. [RWH*16] The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underlying data.

[RWH*16] K. Rogers, J. Wiles, S. Heath, K. Hensby and J. Taufatofua, "Discovering patterns of touch: A case study for visualization-driven analysis in Human-Robot Interaction," 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2016, pp. 499-500, doi: 10.1109/HRI.2016.7451825.
DOI: 10.18419/darus-3372/7
title: categorical-data-map.zip

description: Code of the research prototype for "The Categorical Data Map"

All required source code and data are packaged in `categorical-data-map.zip` and ready for development and deployment.

Software Dependencies: The code in this component has been tested with *Docker version 24.0.5, build 24.0.5-0ubuntu1~22.04.1* and *docker-compose version 1.29.2*. All other dependencies are handled by *Docker*.

Development: To run execute `.deploy-dev`
and navigate to http://127.0.0.1:3000 in your browser.

Deployment: To deploy run `./deploy-prod` and navigate to http://127.0.0.1:3000 in your browser.
DOI: 10.18419/darus-3372/8
title: lincoff.tab

description: A mushroom dataset with 23 attributes and 8124 category combinations by G. Lincoff and N. A. Society. (1981). National Audubon Society field guide to North American mushrooms, ser. Audubon Society field guide series. Knopf: Distributed by Random House, New York.
DOI: 10.18419/darus-2483/9
title: AnalysingInitialRateDataUsingMichaelisMentenKineticsinterferENZY.ipynb

description: Used to generate an InterferEnzy file from an OMEX EnzymeML archive, which was the sent to the modeling platform to estimate the parameters.
DOI: 10.18419/darus-3372/2
title: dawson.tab

description: The well-known titanic dataset from Dawson R. J. M. (1995) [Daw95].

[Daw95] Dawson R. J. M.:The "unusual episode" data revisited, 1995. http://jse.amstat.org/v3n3/datasets.dawson.html, last accessed 2020-09-18.
DOI: 10.18419/darus-3372/9
title: Instructional_Video.mp4

description: An instructional video demonstrating the prototype.
DOI: 10.18419/darus-3763/5
title: ImpNew_mes.tab

description: Contour that is executed with the new impact control system
DOI: 10.18419/darus-3372
title: The Categorical Data Map - Replication Data

description: Source code and datasets used for our experiments are shared for replication purposes along our publication "The Categorical Data Map". We describe each of the six datasets individually on a per-file basis. All datasets are purely nominal datasets.
DOI: 10.18419/darus-3372/5
title: rogers-2.tab

description: The second categorical dataset reconstructed from Rogers et al. describing the results of a HCI study. It is manually reconstructed from the Parallel Sets visualization in Figure 1 (b) of the publication by Rogers et al. [RWH*16] The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underlying data.

[RWH*16] K. Rogers, J. Wiles, S. Heath, K. Hensby and J. Taufatofua, "Discovering patterns of touch: A case study for visualization-driven analysis in Human-Robot Interaction," 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2016, pp. 499-500, doi: 10.1109/HRI.2016.7451825.
DOI: 10.4121/fffc883e-6adb-4042-bc69-4e4c53dc4a24
title: Survey Data - Cluster Analysis of Pet Owners

description: The "Cluster Analysis of Pet Owners" dataset, consisting of 250 entries, provides a detailed view of various dimensions of pet ownership. It contains Likert scale items answered from 1 - Strongly Disagree to 4 - Strongly Agree. It includes personal assessments of the impact pets have on owners' well-being, with statements like "Owning a pet has helped my health" and "Owning a pet adds to my happiness." Additionally, it captures attachment levels and the emotional bonds owners feel toward their pets through statements such as "I am very attached to my pet" and "My pet and I have a close relationship." This dimension reflects how pet ownership affects emotional well-being and connection, critical for understanding the strength of these owner-pet relationships.Beyond emotional bonds, the dataset explores the interaction frequency and nature between owners and pets, such as through statements like "I play with my pet quite often" and "I often take my pet along when I visit friends." A separate set of variables examines companionship, with items like "My pet is like a friend that can keep me from being lonely," highlighting pets' social and emotional roles. Furthermore, the dataset includes Recency, Frequency, and Monetary (RFM) metrics, likely indicating recent engagement levels, frequency of interaction, and expenditures on pets. This mix of emotional, social, and financial metrics provides a rich basis for clustering pet owners based on their behaviors, attachment levels, and perceived benefits of pet ownership.
DOI: 10.18419/darus-3558/1
title: RSEG_V01.nc
DOI: 10.18419/darus-3763/6
title: ImpNew_sim.tab

description: Contour that is executed with the new impact control system
DOI: 10.4121/fffc883e-6adb-4042-bc69-4e4c53dc4a24.v1
title: Survey Data - Cluster Analysis of Pet Owners

description: The "Cluster Analysis of Pet Owners" dataset, consisting of 250 entries, provides a detailed view of various dimensions of pet ownership. It contains Likert scale items answered from 1 - Strongly Disagree to 4 - Strongly Agree. It includes personal assessments of the impact pets have on owners' well-being, with statements like "Owning a pet has helped my health" and "Owning a pet adds to my happiness." Additionally, it captures attachment levels and the emotional bonds owners feel toward their pets through statements such as "I am very attached to my pet" and "My pet and I have a close relationship." This dimension reflects how pet ownership affects emotional well-being and connection, critical for understanding the strength of these owner-pet relationships.Beyond emotional bonds, the dataset explores the interaction frequency and nature between owners and pets, such as through statements like "I play with my pet quite often" and "I often take my pet along when I visit friends." A separate set of variables examines companionship, with items like "My pet is like a friend that can keep me from being lonely," highlighting pets' social and emotional roles. Furthermore, the dataset includes Recency, Frequency, and Monetary (RFM) metrics, likely indicating recent engagement levels, frequency of interaction, and expenditures on pets. This mix of emotional, social, and financial metrics provides a rich basis for clustering pet owners based on their behaviors, attachment levels, and perceived benefits of pet ownership.
DOI: 10.18419/darus-2483/3
title: AnalysingInitialRateDataUsingMichaelisMentenKineticsJupyterNotebook.ipynb

description: Used to model the given OMEX EnzymeML archive using the Python package lmfit.
DOI: 10.18419/darus-3558/2
title: RSEG_V01.nc
DOI: 10.18419/darus-3558/3
title: RSEG_V01.nc
DOI: 10.18419/darus-3558/4
title: Maps_Of_Statistical_Indices.pdf
DOI: 10.18419/darus-3763/3
title: ImpNewWithoutReset_mes.tab

description: Contour that is executed with the new impact control system but without reset of velocity controller
DOI: 10.18419/darus-2483/6
title: Desulfuration_Cysteine_Pinto_input_interferENZY.txt

description: Text file that is used as input for InterferEnzy
DOI: 10.18419/darus-3558/5
title: RSEG_Summary_Table.pdf
DOI: 10.18419/darus-3558/6
title: RSEG_V01.nc
DOI: 10.18419/darus-2483/5
title: Desulfuration_Cysteine_Pinto.omex

description: OMEX EnzymeML archive that was generated from the data using the given spreadsheet.
DOI: 10.18419/darus-3558/7
title: QuantileLookUpFunctionRivDischWidthSimData.m
DOI: 10.18419/darus-3763/4
title: ImpNewWithoutReset_sim.tab

description: Contour that is executed with the new impact control system but without reset of velocity controller
DOI: 10.18419/darus-2483/2
title: model.svg
DOI: 10.18419/darus-3763/7
title: ImpRef_mes.tab

description: Contour that is executed with the previous impact control system
DOI: 10.18419/darus-3763
title: Replication Data for: Control Comparison of Impact Actuator
title: Simulative and experimental data for comparing the different strategies of control on the impact actuator

description: The Dataset contains the data, that was created simulating and measuring different control strategies for the impact actuator. Each table contains the time value, the set position and the actual position for the base axes as well as the actuators when executing a reference contour. &lt;br&gt;
There are two tables for each of the four control strategies, both simulative and experimental:
&lt;ol&gt;
&lt;li&gt;&lt;b&gt;EdgeStop:&lt;/b&gt; Reference Profile with acceleration an jerk limits &lt;/li&gt;
&lt;li&gt;&lt;b&gt;ImpRef:&lt;/b&gt; Previous Impact Control Strategie &lt;/li&gt;
&lt;li&gt;&lt;b&gt;ImpNewWithoutReset:&lt;/b&gt; New Strategie, but without controller reset &lt;/li&gt;
&lt;li&gt;&lt;b&gt;ImpNew:&lt;/b&gt; New Impact Conctrol strategie &lt;/li&gt;
&lt;/ol&gt;
DOI: 10.18419/darus-3763/8
title: ImpRef_sim.tab

description: Contour that is executed with the previous impact control system
DOI: 10.18419/darus-2483/4
title: Pinto_Measurements_(modelled_by_interferENZY)_Modelled.omex

description: Resulting OMEX EnzymeML archive from the given Jupyter Notebook
DOI: 10.18419/darus-3558
title: Remote Sensing-Based Extension of GRDC River Discharge Time Series

description: The quantification of river discharge is essential for understanding global freshwater dynamics. However, the Global Runoff Data Center (GRDC) dataset has faced a decline in the number of active gauges since the 1980s, leaving only 14% of gauges active as of 2020. We develop the Remote Sensing-based Extension for the GRDC (RSEG) dataset that can ingest legacy gauge discharge and remote sensing observations. We employ a stochastic nonparametric mapping algorithm to extend the monthly discharge time series for inactive GRDC stations, benefiting from satellite imagery- and altimetry-derived river width and water height observations. After a rigorous quality assessment of our estimated discharge, involving statistical validation, tests and visual inspection, results in the salvation of discharge records for 3377 out of 6015 GRDC stations with an average monthly discharge exceeding 10 m³/s. The RSEG dataset regains monitoring capability for 83% of global river discharge measured by GRDC stations, equivalent to 7895 km³/month, providing valuable insight into Earth's river systems with comprehensive and up-to-date information.
DOI: 10.18419/darus-3558/8
title: QuantileLookUpFunctionRivDischWidth.m
DOI: 10.18419/darus-2483/1
title: Pinto_Measurements_(modelled_by_Jupyter_Notebook).omex
DOI: 10.18419/darus-2483/7
title: Pinto_output_interferENZY.txt

description: Output that was given by InterferEnzy
DOI: 10.18419/darus-2483
title: Catalysis of L-cysteine by IscS in the presence of IscU

description: Measured was the reaction from cysteine to alanine by the Cysteine desulfurase, for six different initial concentrations of cysteine. The data consists of three data points over the course of 2.5 minutes for alanine. Additionally, the concentration of cysteine over time was calculated from the given values, for the later modelling.
description: PyEnzyme, 1.1.0
description: This dataset consists of two Jupyter notebooks that perform modelling based on InterferEnzy as well as a custom one written in Python that utilizes <b>lmfit</b> and <b>PyEnzyme</b>. In addition, the modeled EnzymeML files are included as well as the InterferEnzy output. The latter can be reproduced by using the web-based API and the generated input file "Desulfuration_Cysteine_Pinto_input_interferENZY.txt". In order to investigate the dataset, it is advised to use the PyEnzyme library.
DOI: 10.18419/darus-2483/8
title: time-course.svg
DOI: 10.18419/darus-3981/3
title: 02.05.2022_CircleGeo_3 Circles.3dm

description: Open source file format which is used for 3D graphics software
DOI: 10.18419/darus-3981/2
title: 3.pdf
DOI: 10.18419/darus-3981/4
title: 2.pdf
DOI: 10.18419/darus-3981/1
title: 4.pdf
DOI: 10.18419/darus-3981/5
title: GigamappingThePrototype.pdf
DOI: 10.18419/darus-3981/6
title: 1.pdf
DOI: 10.18419/darus-3981/7
title: 06.05.2022_Base_CNCMilling.3dm

description: Open source file format which is used for 3D graphics software
DOI: 10.18419/darus-3981/8
title: Pol-AI 10E v.8 3 circles.gh

description: Grasshopper project file. Grasshopper is part of Rhino3D.
DOI: 10.18419/darus-3981
title: COLife_00 - Gigamap and Fabrication Data

description: The dataset covers codesign gigamapping data and the 3d parametric models from Rhino 3d of a set of responsive wood insect hotels with pollinator's gardens from the year 2022/23. The first set of gigamaps are for the first intervention POL-AI1, the second for its iteration POL-AI2 within the 'Enacting Bateson Ecological Aesthetics in Architecture and Design' group. The first, POL-AI I, gigamapping was a sequence of codesign workshops achieved through a teaching block 'Synergetic Transformations' held with Leonie Fischer. This codesign information was turned into the fabrication drawings and installed in Seidenstrasse 36, Stuttgart in 2022. The iteration POL-AI II was installed at the Bauhaus University of Weimar at Geschwister-Scholl-Strasse 13 in 2023.
DOI: 10.18419/darus-3981/9
title: _Rhino6_06.05.2022_Base_CNCMilling.3dm

description: Open source file format which is used for 3D graphics software
DOI: 10.18419/darus-471
title: Benchmark C1.2 - Numerical results reference solution

description: Root water uptake simulation with a numerical scheme where the root-soil surface is explicitly resolved by the computational grid. The roots are a one-dimensional network grid embedded in a 3D soil domain. This dataset contains the grid files for root and soil. 8-day-old lupine, segmented from MRI measurements (courtesy of Magdalena Landl, FZ Jülich, Germany). Soil grid generated with CGAL from implicit description.
DOI: 10.18419/darus-471/1
title: DAP8_RootSys_Soil.msh

description: Soil grid lupine (Gmsh format)
DOI: 10.18419/darus-471/2
title: DAP8_RootSys.dgf

description: Root grid lupine (DGF, Dune grid format)
DOI: 10.18419/darus-471/3
title: results_variable_hydr_props.tar.gz

description: Results files in VTK format for the case with variable hydraulic root properties
DOI: 10.18419/darus-471/4
title: results_constant_hydr_props.tar.gz

description: Results files in VTK format for the case with constant hydraulic root properties
DOI: 10.18419/darus-471/5
title: c12_transpiration_plot.tar.gz

description: Archive with data and Python script to reproduce a plot of the transpiration rate over time
DOI: 10.18419/darus-471/6
title: c12_waterpot_plot.tar.gz

description: Archive with data and Python script to reproduce a plot of the water potential
DOI: 10.18419/darus-471/7
title: c12b_3d_plot.tar.gz

description: Archive with data to reproduce a 3d render of the water saturation results from the paper
DOI: 10.18419/darus-471/8
title: c12_psi_root_depth_plot.tar.gz

description: Archive with data and Python script to reproduce a plot of the root water potential over depth
DOI: 10.18419/darus-471/9
title: c12_psi_soil_depth_plot.tar.gz

description: Archive with data and Python script to reproduce a plot of the soil water potential over depth
DOI: 10.18419/darus-501/1
title: EngMetaRelevanz_Vis.png

description: Visualization of the relevance of different metadata categories
DOI: 10.18419/darus-501/2
title: EngMeta-Survey-CodebookEN.pdf

description: Codebook
DOI: 10.18419/darus-501/3
title: EngMeta-Survey-Questions.pdf

description: Questions
DOI: 10.18419/darus-501/4
title: EngMeta-DataWithAnalysis.xlsm

description: Excel-File including Data, Post-Processing and Analysis
DOI: 10.18419/darus-501/5
title: EvaluationEngMeta_Questionnaire_DE.pdf

description: Questionnaire in german
DOI: 10.18419/darus-501/6
title: EvaluationEngMeta_DescriptiveAnalysis.pdf

description: Report from EvaSys with descriptive analysis
DOI: 10.18419/darus-501/7
title: EngMeta-rawData.xlsx

description: RawData with CodeBook and Questions
DOI: 10.18419/darus-501/8
title: EngMeta-DataWithAnalysisWithoutMacros.xlsx

description: All data and analysis results without Code
DOI: 10.18419/darus-501/9
title: EngMeta-Survey-rawData_commas.tab

description: Raw data from EvaSys, comma separated
DOI: 10.18419/darus-813/1
title: ScatterPlotCADDataSet.html

description: Interactive plot of CAD feature data. Hovering over a datapoint gives the part picture, if the folder "01_PhysicalData.zip" is unzipped and saved in the same directory as the html file.
DOI: 10.18419/darus-813/2
title: 01_PhysicalData.zip

description: Part pictures for the interactive diagram ScatterPlotCADDataSet.html
DOI: 10.18419/darus-813/3
title: PointCloudData.h5

description: HDF5 table that contains the point cloud representation for all bodies which includes, besides the location, geometric characteristics for each point cloud point.
DOI: 10.18419/darus-813/4
title: BoxData.h5

description: HDF5 table that contains data of the minimal bounding box as well as some body properties for all bodies.
DOI: 10.18419/darus-813/5
title: PointCloudDistribution.h5

description: HDF5 table that contains the frequency of curvature categories in the point cloud representations of all bodies.
DOI: 10.18419/darus-813/6
title: SimilarityTable.h5

description: HDF5 table that contains relevant features for the similarity calculation of all bodies as well es some similarity calculation results.
DOI: 10.18419/darus-813/7
title: SurfacesDistribution_perArea.h5

description: HDF5 table that contains the frequency of face categories per area of all bodies.
DOI: 10.18419/darus-813/8
title: SurfacesDistribution_perNumber.h5

description: HDF5 table that contains the frequency of face categories per number of faces of all bodies.
DOI: 10.18419/darus-813
title: Results and raw data of an adjustable similarity calculation for computer aided design (CAD) data
title: Detected geometric features and point cloud representations of a public CAD data set and the evaluation of these

description: The here shown data results from a methodology for calculating the similarity of CAD parts. CAD parts are analyzed using various algorithms to find geometric features and discrete point cloud representations. 
The folder AnalyzedCADData contains a zip-file with the raw analyzed data of each part. All evaluated results can be found in folder EvaluationOfData in HDF5 formattet tables. An overview of the data set and the analyzed geometric features is given by an interactive HTML diagram in folder FeatureDiagram.
description: The ABC dataset was created using the CAD dataset from Onshape Incorporate.
The copyright of the CAD models is owned by their creators. For licensing details, see https://www.onshape.com/legal/terms-of-use#your_content.