cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18419/darus-2890
title: Replication Data for: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation

description: Results of our proposed optical flow method on the Sintel and KITTI datasets. We provide the benchmark results before and after applying our refinement approach.
Additionally, we provide a supplementary material to our paper with more details on the minimisation, the numerical solution and additional results.
&lt;br&gt;
The data is structured as follows:
&lt;br&gt;
The file supplement.pdf contains the supplementary material of the paper.
Additionally, there are 10 data folders according to the test results in Table 3 of our paper. They are named using the scheme {input/output}_{datasetName}_{methodName}:
&lt;ul&gt;
&lt;li&gt;input/output: whether the files are the input for our method or the output of our method&lt;/li&gt;
&lt;li&gt;datasetName: one of kitti, sintelClean or sintelFinal relating to either the KITTI dataset [Menze, CVPR 2015] or the Sintel dataset [Butler, ECCV 2012]&lt;/li&gt;
&lt;li&gt;methodName: one of raft or raftWarm [Teed, ECCV 2020] or the refined variants from our method.&lt;/li&gt;
&lt;/ul&gt;
The data can be used to build on the output of our method or to compare a novel approach against our method by using the same input data.
&lt;br&gt;
The dataformat is pdf for the supplementary material and png and flo for the optical flow files as used in the respective benchmarks.
DOI: 10.18419/darus-2890/11
title: output_sintelClean_raftRefined.zip
DOI: 10.18419/darus-3438/1
title: ABxM.PlateStructures-1.1.0.zip
DOI: 10.18419/darus-3438/2
title: ABxM.PlateStructures.gha
DOI: 10.18419/darus-3438/3
title: ABxM.PlateStructuresCore.dll
DOI: 10.18419/darus-3438/4
title: ABM.PlateStructures-1.1.1-UML.pdf
DOI: 10.18419/darus-3438/5
title: ABM.PlateStructures-1.1.1.zip
DOI: 10.18419/darus-3438/6
title: ABxM.PlateStructures.gha
DOI: 10.18419/darus-3438/7
title: ABxM.PlateStructuresCore.dll
DOI: 10.18419/darus-3438/8
title: Release_notes.md
DOI: 10.18419/darus-3438/9
title: Release_notes.md
DOI: 10.18419/darus-3438/10
title: ABxM.PlateStructures-1.1.2.zip
DOI: 10.18419/darus-3438/11
title: ABxM.PlateStructuresCore.dll
DOI: 10.18419/darus-3438/12
title: ABxM.PlateStructures-1.1.2-UML.pdf
DOI: 10.18419/darus-3438
title: ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures

description: ABxM.PlateStructures is an add-on to ABxM.Core for agent-based design and development of plate structures, such as segmented timber shells. The add-on contains various agent system constructs and utilities for plate structure design and is intended to be used within Rhino/Grasshopper. This version contains the tools for designing plate structures such as the Landesgartenschau Exhibition Hall (2014), the BUGA Wood Pavilion (2019), and the LCRL Roof (2023). The package includes examples files that demonstrate how a model of an agent-based plate structure can be built.
description: Rhinoceros 3D, 6
description: Microsoft Visual Studio, 17
DOI: 10.18419/darus-3438/13
title: ABxM.PlateStructures.gha
DOI: 10.15468/dl.qtjetn
title: Occurrence Download

description: A dataset containing 87132 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Ficus carica L."
 ]
}

 The dataset includes 87132 records from 888 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006830-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-3442/1
title: avg_rank_risks.npy

description: Map (r: int-&gt; (t: int -&gt; R: float)) of average final risks "R" after training of QNNs with "t" entangled datasets of average Schmidt rank "r".
DOI: 10.18419/darus-3442/2
title: phases_in_orthogonal_training.zip

description: Contains training data (X), the target unitary (U) and the resulting unitary (V) as Numpy-Files for training using four orthogonal training samples for a high risk QNN and a low risk QNN (see Figure 1)
DOI: 10.18419/darus-3442/3
title: nlihx_exp_points.npy

description: Array containing the average risks after training with t = 1,2,4,8,16,32,64 entangled training samples that are not linearly independent in H_X.
DOI: 10.18419/darus-3442/4
title: avg_rank_losses.npy

description: Map (r: int-&gt; (t: int -&gt; l: float)) of average final losses "l" after training of QNNs with "t" entangled datasets of average Schmidt rank "r".
DOI: 10.15468/dl.nvctyc
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coelachne hackelii Merr."
 ]
}

 The dataset includes 13 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006832-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-3442/5
title: orthogonal_exp_points.npy

description: Array containing the average risks after training with t = 1,2,4,8,16,32,64 orthogonal entangled training samples.
DOI: 10.18419/darus-3442/6
title: orthogonal_results.zip

description: Raw data (losses and risks) for training QNNs using orthogonal entangled training data.
Directory t[a] contains results for QNNs trained with "a" training samples.
For analyzed results see orthogonal_exp_points.npy
DOI: 10.18419/darus-3442/7
title: not_linearly_independent_results.zip

description: Raw data (losses and risks) for training QNNs using entangled training data that is not linearly independent in H_X.
Directory t[a] contains results for QNNs trained with "a" training samples.
For analyzed results see nlihx_exp_points.npy.
DOI: 10.18419/darus-3442/8
title: average_rank_results.zip

description: Raw data (losses and risks) for training QNNs using entangled training data with varying degree of entanglement.
Directory t[a]r[b] contains results for QNNs trained with "a" training samples of average Schmidt rank "b".
For analyzed results see avg_rank_risks.npy.
DOI: 10.18419/darus-3442/9
title: orthogonal_experiments.pdf

description: The average risk after training a 6-qubit QNN for randomly generated target unitaries using orthogonal data. For each number of training pairs t, the Schmidt rank is chosen as such that r · t = d. The lower bound for the risk for this configuration is shown as a dashed line. The expected risk according to the proposed new lower bound is shown as a solid line.
DOI: 10.18419/darus-3442/10
title: nlihx_experiments.pdf

description: The average risk after training a 6-qubit QNN for randomly generated target unitaries using linearly dependent data according. For each number of training pairs t, the Schmidt rank is chosen as such that r · t = d. The lower bound for the risk for this configuration is shown as a dashed line, the expected risk according to the proposed new lower bound is shown as a solid line.
DOI: 10.18419/darus-3442/11
title: avg_rank_losses.pdf

description: The average losses at the end of training for QNNs using training data of varying Schmidt ranks with mean Schmidt ranks in {1,2,4,64}.
DOI: 10.18419/darus-3442/12
title: avg_rank_experiments.pdf

description: The average risk after training a 6-qubit QNN for randomly generated target unitaries. For this experiment, data of varying Schmidt rank with average ranks r ∈ {1,2,4,64} and different input sizes t ∈ {1,2,4,8,16,32,64} was used. The markers give the experimentally computed average risk for each pair of Schmidt rank and training set size. The lower bounds according according to the QNFL theorem are shown as solid lines.
DOI: 10.18419/darus-3442
title: Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs
title: Constraints on the Linear Structure of the Training Data

description: &lt;p&gt;
Simulation experiment data for training Quantum Neural Networks (QNNs) using entangled datasets.
&lt;/p&gt;

&lt;p&gt;
The experiments investigate the validity of the lower bounds for the expected risk after training QNNs given by the extensions to the Quantum No-Free-Lunch theorem presented in the related publication. The QNNs are trained with (i) samples of varying Schmidt rank, (ii) orthogonal samples of fixed Schmidt rank and (iii) linearly dependent samples of fixed Schmidt rank.
&lt;/p&gt;

&lt;p&gt;
The dataset contains raw experiment data (directory "raw_data"), analyzed mean risks and errors (directory "plot_data") and the resulting plots (directory "plots").
&lt;/p&gt;

&lt;p&gt;
&lt;h3&gt;Experiments:&lt;/h3&gt;
The experiments train QNNs using various compositions of training samples on a simulator and extract the risk after training to compute average risks.
&lt;ol type="i"&gt;
&lt;li&gt;Experiment 1: Trains QNNs using entangled training samples of varying Schmidt rank. The average Schmidt rank and the number of training samples are controlled. &lt;br /&gt;
Raw data: &lt;code&gt;average_rank_results.zip&lt;/code&gt;; Computed average risks: &lt;code&gt;avg_rank_risks.npy&lt;/code&gt;; Computed average losses: &lt;code&gt;avg_rank_losses.npy&lt;/code&gt;; Plotted average risks: &lt;code&gt;avg_rank_experiments.pdf&lt;/code&gt;; Plotted average losses: &lt;code&gt;avg_rank_losses.pdf&lt;/code&gt;.&lt;/li&gt;
&lt;li&gt;Experiment 2: Trains QNNs using entangled orthogonal training samples. The number of training samples is controlled and the Schmidt rank is fixed such that d=r*t for the dimension d of the Hilbert space.
&lt;br /&gt;
Raw data: &lt;code&gt;orthogonal_results.zip&lt;/code&gt;; Computed average risks: &lt;code&gt;orthogonal_exp_points.npy&lt;/code&gt;; Plotted average risks: &lt;code&gt;orthogonal_experiments.pdf&lt;/code&gt;.&lt;/li&gt;
&lt;li&gt;Experiment 3: Trains QNNs using entangled linearly dependent training samples. The number of training samples is controlled and the Schmidt rank is fixed such that d=r*t for the dimension d of the Hilbert space.
&lt;br /&gt;
Raw data: &lt;code&gt;not_linearly_independent_results.zip&lt;/code&gt;; Computed average risks: &lt;code&gt;nlihx_exp_points.npy&lt;/code&gt;; Plotted average risks: &lt;code&gt;nlihx_experiments.pdf&lt;/code&gt;&lt;/lI&gt;
&lt;/ol&gt;
Additionally, this repository contains the reproduction data for Figure 1 (&lt;code&gt;phases_in_orthogonal_training.zip&lt;/code&gt;). This file contains the training data, the target unitary and the resulting hypothesis unitary for orthogonal training samples of (i) high risk and (ii) low risk.
&lt;/p&gt;

&lt;p&gt;
For the code to reproduce and analyze the experiments see the &lt;a href="https://doi.org/10.18419/darus-3445"&gt;Code repository&lt;/a&gt;.
&lt;/p&gt;
DOI: 10.18419/darus-3442/13
title: figure_1.pdf

description: Figure 1: The eigenvalues introduced by U†VS after training with four orthogonal inputs for a for a low-risk hypothesis (left) and for a high-risk hypothesis (right).
DOI: 10.18419/darus-3454
title: Data for: NILK, entity linking dataset targeting NIL-linking cases

description: A dataset for the NIL-detection and NIL-disambiguation tasks. &lt;br/&gt;

The NILK dataset has two main features: 1) It marks NIL-mentions for NIL-detection by extracting mentions which belong to newly added entities in Wikipedia text. 2) It provides an entity label for NIL-disambiguation by marking NIL-mentions with WikiData IDs from the newer dump. &lt;br/&gt;

Dataset files contain JSON objects of the following structure: &lt;br/&gt; 
&lt;br/&gt;

{"mention":"Walter Damrosch", &lt;br/&gt;
"offset":348, &lt;br/&gt;
"length":15, &lt;br/&gt;
"context":"...the conductor Walter Damrosch. He scored the piece for the standard instruments of the symphony orchestra plus celesta, saxophone, and automobile horns...", &lt;br/&gt;
"wikipedia_page_id":"309", &lt;br/&gt;
"wikidata_id":"Q725579", &lt;br/&gt;
"nil":false} &lt;br/&gt;
&lt;br/&gt;

The dataset contains both linked and not linked mentions, one can distinguish between them by checking "nil" flag. To obtain NIL-mentions, we compared two WikiData dumps: from 2017 and 2021. NIL-mentions have WikiData ID from WikiData 2021, one can use it to check whether these mentions refer to the same entity.
DOI: 10.18419/darus-3689/1
title: dataset_2hps_1fixed_1000dp_2hp_gksi_1000dp.zip
DOI: 10.18419/darus-3689/2
title: 100dp_1000gksi_separate.zip
DOI: 10.18419/darus-3689/3
title: dataset_2hps_1fixed_100dp_2hp_gksi_100dp.zip
DOI: 10.18419/darus-3689/4
title: 1000dp_100gksi_separate.zip
DOI: 10.18419/darus-3689/5
title: 100dp_100gksi_separate.zip
DOI: 10.18419/darus-3689/6
title: 1000dp_1000gksi_merged.zip
DOI: 10.18419/darus-3689/7
title: BENCHMARK_DOMAIN2_2hp_gksi_100dp.zip
DOI: 10.18419/darus-3689/8
title: BENCHMARK_DOMAIN2_2hp_gksi_1000dp.zip
DOI: 10.15468/dl.begqcq
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coelachne hackelii Merr."
 ]
}

 The dataset includes 13 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006833-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-3689/9
title: 1000dp_1000gksi_separate.zip
DOI: 10.18419/darus-3689/10
title: dataset_2hps_1fixed_1000dp_2hp_gksi_100dp.zip
DOI: 10.18419/darus-3689/11
title: BENCHMARK_DOMAIN2_2hp_gksi_1000dp_merged.zip
DOI: 10.18419/darus-3689/12
title: dataset_2hps_1fixed_100dp_2hp_gksi_1000dp.zip
DOI: 10.18419/darus-3689
title: Models and Prepared Datasets for the Second Stage
title: based on the Two Heat Pumps Simulation Datasets

description: &lt;p&gt;Models trained with &lt;a href="https://github.com/JuliaPelzer/Heat-Plume-Prediction"&gt;Heat Plume Prediction&lt;/a&gt;
and datasets prepared with &lt;a href="https://github.com/JuliaPelzer/Heat-Plume-Prediction"&gt;Heat Plume Prediction&lt;/a&gt; into reasonable format + normalization etc, used for training these models. Last relevant git commit: 5d6c5eae5b00e438.&lt;/p&gt;


Based on raw data from &lt;a href="https://doi.org/10.18419/darus-3651"&gt;doi:darus-3651&lt;/a&gt; and &lt;a href="https://doi.org/10.18419/darus-3652"&gt;doi:darus-3652&lt;/a&gt;.
description: Please follow the instructions how to use the software documented in README.md.
DOI: 10.18419/darus-3689/13
title: dataset_2hps_1fixed_1000dp_2hp_gksi_1000dp_merged.zip
DOI: 10.18419/darus-4237/1
title: M4.txt
DOI: 10.18419/darus-4237/2
title: BP1.txt
DOI: 10.18419/darus-4237/3
title: hopper.png
DOI: 10.18419/darus-4237/4
title: BP2.txt
DOI: 10.18419/darus-4237/5
title: M1.txt
DOI: 10.18419/darus-4237/6
title: main.m
DOI: 10.18419/darus-4237/7
title: M5.txt
DOI: 10.18419/darus-4237/8
title: M0.txt
DOI: 10.18419/darus-4237/9
title: M3.txt
DOI: 10.18419/darus-4237/10
title: M6.txt
DOI: 10.18419/darus-4237/11
title: main_LiveScript.mlx
DOI: 10.18419/darus-4237/12
title: M2.txt
DOI: 10.18419/darus-4237
title: Periodic Trajectories of a Passive One-Legged Hopper

description: &lt;h3&gt;General&lt;/h3&gt;
The one-legged hopper depicted in &lt;em&gt;hooper.png&lt;/em&gt; is energetically conservative. The state of the system is given by the horizontal position (&lt;em&gt;x&lt;/em&gt;), the vertical position (&lt;em&gt;y&lt;/em&gt;), the leg rotation angle (&lt;em&gt;alpha&lt;/em&gt;), and their respective velocities. The data required to comprehensively describe a periodic motion (gait) consists of these 6 states, the period (&lt;em&gt;T&lt;/em&gt;), and the energy level (&lt;em&gt;E&lt;/em&gt;). Since the energy remains constant throughout any motion, &lt;em&gt;E&lt;/em&gt; is considered a (bifurcation) parameter.

&lt;h3&gt;File Setup&lt;/h3&gt;
The files are comma-separated text files encoding matrices of different sizes. The time-series data of a single periodic motion is represented as a 6-by-103 matrix, where the first column and the second column store the energy level (&lt;em&gt;E&lt;/em&gt;) and period (&lt;em&gt;T&lt;/em&gt;), respectively. The remaining columns (6-by-101) contain the time-series data of the 6 states at 101 equidistant time-steps ranging from time 0 to period time &lt;em&gt;T&lt;/em&gt;.
While the two bifurcation points stored in the files &lt;em&gt;BP1.txt&lt;/em&gt; and &lt;em&gt;BP2.txt&lt;/em&gt; include only a single periodic trajectory (a matrix of size 6-by-103), the families/branches of periodic motions (&lt;em&gt;M0.txt&lt;/em&gt; - &lt;em&gt;M6.txt&lt;/em&gt;) contain multiple trajectories. These families are represented by a matrix of size 6x&lt;em&gt;nPM&lt;/em&gt;-by-103, where &lt;em&gt;nPM&lt;/em&gt; is the number of periodic motions.
The Matlab script &lt;em&gt;main.m&lt;/em&gt; and Python script &lt;em&gt;main.py&lt;/em&gt; demonstrate how to import and modify the data from the text files and generates the bifurcation diagram shown in Fig. 3 of the corresponding journal paper.

&lt;h3&gt;Matlab Live Script&lt;/h3&gt;
In addition to &lt;em&gt;main.m&lt;/em&gt; and &lt;em&gt;main.py&lt;/em&gt;, the Matlab live script &lt;em&gt;main_LiveScript.mlx&lt;/em&gt; offers an interactive exploration of the data by allowing the user to select a periodic motion from the bifurcation diagram and display the corresponding time-series data.
description: MATLAB, 2023a
DOI: 10.18419/darus-4237/13
title: main.py
DOI: 10.18419/darus-4352/1
title: spec.json

description: Machine-readable description file
DOI: 10.18419/darus-4352/2
title: espargos-0001-diagonal1.tfrecords

description: Robot follows a trajectory of diagonal (southeast-northwest in antenna array nomenclature) meanders.
DOI: 10.18419/darus-4352/3
title: espargos-0001-diagonal2.tfrecords

description: Robot follows a trajectory of diagonal (southwest-northeast in antenna array nomenclature) meanders.
DOI: 10.18419/darus-4352/4
title: espargos-0001-meanders_ns1.tfrecords

description: Robot follows a trajectory of meanders (north-south in antenna array nomenclature), with interruption in the end.
DOI: 10.18419/darus-4352/5
title: espargos-0001-meanders_ns1_continued.tfrecords

description: Robot follows a trajectory of meanders (north-south in antenna array nomenclature), continuation of previous trajectory.
DOI: 10.18419/darus-4352/6
title: espargos-0001-meanders_we1.tfrecords

description: Robot follows a trajectory of meanders (west-east in antenna array nomenclature).
DOI: 10.18419/darus-4352/7
title: espargos-0001-randomwalk1.tfrecords

description: Robot follows a pseudo-random trajectory in the measurement area.
DOI: 10.18419/darus-4352/8
title: espargos-0001-randomwalk2.tfrecords

description: Robot follows a pseudo-random trajectory in the measurement area.
DOI: 10.18419/darus-4352/9
title: espargos-0001-spiral1.tfrecords

description: Robot follows a spiral-shaped trajectory in the measurement area.
DOI: 10.18419/darus-4352/10
title: espargos-0001-static_middle.tfrecords

description: Robot remains static in the middle of the measurement area.
DOI: 10.18419/darus-4352/11
title: espargos-0001-static_middle_with_person_walking_nearby.tfrecords

description: Robot remains static in the middle of the measurement area. A person is walking around in the room.
DOI: 10.18419/darus-4352/12
title: espargos-0001-circle.tfrecords

description: Robot drives around in a circle (several times).
DOI: 10.18419/darus-4352/13
title: espargos-0001-wobble_middle.tfrecords

description: Robot remains more or less in the middle of the measurement area, but moves around by a few centimeters.
DOI: 10.18419/darus-923/1
title: 00004132.JPG
DOI: 10.18419/darus-923/2
title: 00004129.JPG
DOI: 10.18419/darus-923/3
title: 00004142.JPG
DOI: 10.18419/darus-923/4
title: 00004138.JPG
DOI: 10.18419/darus-923/5
title: 00004141.JPG
DOI: 10.18419/darus-923/6
title: 00004134.JPG
DOI: 10.5281/zenodo.11146096
title: UBB_Quantitative data_Biocultural Tourism

description: Within WP2, “Case studies of transformative biodiversity practices”, UBB developed a questionnaire. The objective is to observe what variables influence the intention to have a biocultural touristic experience in a High Nature Value (HNV) area. The dependent variable is the “intention to have a biocultural touristic experience in an HNV area”. In developing the questionnaire, we started with the variables included in Ajzen’s Theory of Planned Behavior (attitude, subjective norm, perceived behavioral control, and intention). We extended the original model by adding the following variables: knowledge, awareness of HNV, values, trust in the authenticity of the biocultural experience, perception of the contribution of a touristic experience in HNV, and demographics. The questionnaire serves as a tool for the BIOTraCes community and beyond. It provides a data-driven foundation for understanding and influencing tourists’ intentions and promotes sustainable and transformative biodiversity practices in HNV areas.
DOI: 10.5281/zenodo.11146097
title: UBB_Quantitative data_Biocultural Tourism

description: Within WP2, “Case studies of transformative biodiversity practices”, UBB developed a questionnaire. The objective is to observe what variables influence the intention to have a biocultural touristic experience in a High Nature Value (HNV) area. The dependent variable is the “intention to have a biocultural touristic experience in an HNV area”. In developing the questionnaire, we started with the variables included in Ajzen’s Theory of Planned Behavior (attitude, subjective norm, perceived behavioral control, and intention). We extended the original model by adding the following variables: knowledge, awareness of HNV, values, trust in the authenticity of the biocultural experience, perception of the contribution of a touristic experience in HNV, and demographics. The questionnaire serves as a tool for the BIOTraCes community and beyond. It provides a data-driven foundation for understanding and influencing tourists’ intentions and promotes sustainable and transformative biodiversity practices in HNV areas.
DOI: 10.5281/zenodo.14065256
title: UBB_Quantitative data_Biocultural Tourism

description: Within WP2, “Case studies of transformative biodiversity practices”, UBB developed a questionnaire. The objective is to observe what variables influence the intention to have a biocultural touristic experience in a High Nature Value (HNV) area. The dependent variable is the “intention to have a biocultural touristic experience in an HNV area”. In developing the questionnaire, we started with the variables included in Ajzen’s Theory of Planned Behavior (attitude, subjective norm, perceived behavioral control, and intention). We extended the original model by adding the following variables: knowledge, awareness of HNV, values, trust in the authenticity of the biocultural experience, perception of the contribution of a touristic experience in HNV, and demographics. The questionnaire serves as a tool for the BIOTraCes community and beyond. It provides a data-driven foundation for understanding and influencing tourists’ intentions and promotes sustainable and transformative biodiversity practices in HNV areas.
DOI: 10.18419/darus-923/7
title: 00004139.JPG
DOI: 10.18419/darus-923/8
title: 00004130.JPG
DOI: 10.18419/darus-923/9
title: 00004133.JPG
DOI: 10.18419/darus-923/10
title: 00004131.JPG
DOI: 10.18419/darus-923/11
title: 00004136.JPG
DOI: 10.18419/darus-923/12
title: 00004135.JPG
DOI: 10.18419/darus-923/13
title: 00004140.JPG