Find research datasets worth reusing

opencc-by-4.0Aug 2018View details →

Simulated Neutral Landscape Models and Scaling Results for Testing of Multi-Dimensional Grid-Point Scaling Algorithm

The data package contains (1) simulations of neutral landscape models of categorical data for benchmark testing of scaling algorithms, and (2) scaling results for testing consistency and sensitivity of the newly developed Multi-Dimensional Grid-Point (MDGP) scaling algorithm. Neutral landscapes were generated using the "nlmpy" python module.  The MDGP scaling algorithm and the test framework were implemented in R (https://github.com/gannd/landscapeScaling).

openother-openNov 2018View details →

Impact of algorithm choice in morphological phylogenetic analysis with inapplicable data

Files to accompany Brazeau et al. (2019), describing the impact of using our new algorithm for handling inapplicable data. Details of the datasets analysed are give at <a href="https://ms609.github.io/ExploreInapplicable/r_dataset_details.html">ms609.github.io/ExploreInapplicable/r_dataset_details.html</a>

opencc-by-4.0Jan 2019View details →

Data for: Two-Sided Matching for mentor-mentee allocations - Algorithms and manipulation strategies

These are the data files for the PLOS ONE journal article "Two-Sided Matching for mentor-mentee allocations - Algorithms and manipulation strategies". Three files are provided: - Data.xlsx: An overview of the original preferences of mentors and mentee, a data dictionary, and two summary tables used to create figures in the manuscript - MatchingTables.csv: The outcome matching tables for each simulated scenario and repetition - Preferences.csv: The (un)manipulated preferences that were used as input to calculate the solution for each simulated scenario and repetition.

opencc-by-4.0Feb 2019View details →

Data support for: "CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms"

Provided tomographic projection data supports the publication in SoftwareX journal "CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms" published in 2019. <ul> <li>TomoSim_data1550671417.h5 - is a simulated 3D tomographic projection data with noise and artifacts. The simulation is implemented using <a href="https://github.com/dkazanc/TomoPhantom">TomoPhantom</a> software.</li> <li>DendrData_3D.h5 - is a real dataset obtained at I13 branchline of Diamond Light Source. It features a selected time frame out of dynamically collected tomographic data. Data shows a <a href="https://www.sciencedirect.com/science/article/pii/S1359645418302994?via%3Dihub">dendritic grain growth in Mg alloys</a>.</li> </ul> The scripts to replicate the results shown in the paper are available at the Github page of the project: <a href="https://github.com/vais-ral/CCPi-Regularisation-Toolkit">CCPi-Regularisation-Toolkit</a>

openother-openApr 2019View details →

Test polynomials for approximate GCD algorithms

Dataset of test data (Tables 1–3) used in the paper: Akira Terui, GPGCD: An iterative method for calculating approximate GCD of univariate polynomials. Theoretical Computer Science, Volume 479 (Symbolic-Numerical Algorithms), April 2013, 127-149. <ul> <li><a href="https://doi.org/10.1016/j.tcs.2012.10.023">doi:10.1016/j.tcs.2012.10.023</a></li> <li><a href="https://arxiv.org/abs/1207.0630">arXiv:1207.0630</a></li> </ul>

opencc-by-nc-nd-3.0Dec 2004View details →

The DREAMS Databases and Assessment Algorithm

The DREAMS Databases and Assessment algorithm During the DREAMS project funded by Région Wallonne (Be), we collected a large amount of polysomnographic recordings (PSG) to tune, train and test our automatic detection algorithms. These recordings were annotated in microevents or in sleep stages by several experts. They were acquired in a sleep laboratory of a belgium hospital using a digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium). The standard European Data Format (EDF) was used for storing. In order to facilitate future research and performance comparision, we decided to publish these data on Internet. Therefore, eight DREAMS databases are available according to the annotation carried out (click on the link to open): •    The DREAMS Subjects Database: 20 whole-night PSG recordings coming from healthy subjects, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine; •    The DREAMS Patients Database: 27 whole-night PSG recordings coming from patients with various pathologies, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine; •    The DREAMS Artifacts Database: 20 excerpts of 15 minutes of PSG recordings annoted in artifacts (cardiac interference, slow ondulations, muscle artifacts, failing electrode, 50/60Hz main interference, saturations, abrupt transitions, EOG interferences and artifacts in EOG) by an expert; •    The DREAMS Sleep Spindles Database: 8 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in sleep spindles; PLEASE NOTICE THAT EXPERT 1's SCORED SPINDLE COUNTS WERE CUT OFF AFTER 1000 SECONDS. THIS MAKES IT DIFFICULT TO USE COUNTS FOR COMPARISON. •    The DREAMS K-complexes Database: 5 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in K-complexes; •    The DREAMS REMs Database: 9 excerpts of 30 minutes of PSG recordings in which rapid eye movements were annotated by an expert; •    The DREAMS PLMs Database: 10 whole-night PSG recordings coming from patients in which one of the two tibialis EMG was annoted in periodic limb movements by an expert; •    The DREAMS Apnea Database: 12 whole-night PSG recordings coming from patients annoted in respiratory events (central, obstructive and mixed apnea and hypopnea) by an expert. We also developped and tested several automatic procedures to detect micro-events such as sleep spindles, K-complexes, REMS, etc. and provide the source codes for them in the DREAMS Assessment Algorithm package. (MORE INFORMATION ON EACH DBA CAN BE FOUND in pdf file in this repository) All our publications on this subject can be found in : https://www.researchgate.net/scientific-contributions/35338616_S_Devuyst

Raw data for "Coupled ptychography and tomography algorithm improves reconstruction of experimental data"

Raw data used in "<a href="https://www.osapublishing.org/optica/abstract.cfm?uri=optica-6-10-1282">Coupled ptychography and tomography algorithm improves reconstruction of experimental data</a>" by M. Kahnt, J. Becher, D. Brückner, Y. Fam, T. Sheppard, T. Weissenberger, F. Wittwer, J.-D. Grunwaldt, W. Schwieger and C.G. Schroer

data set related to article Brainstem enlargement in preschool children with autism Results from an intermethod agreement study of segmentation algorithms

This record contains raw data related to article Brainstem enlargement in preschool children with autism Results from an intermethod agreement study of segmentation algorithms

The Locus Algorithm Exoplanet-Search Pointings Catalogue

Presented here is a Catalogue of Pointings for Optimal Differential Photometry for 61,662,376 stars presented in the form of a CSV file.  A total of 67,043,579 stars were analysed using the Locus Algorithm (Creaner et al, 2019), and the results of that analysis are presented here.  A paper detailing this work is currently in writing.  This catalogue is invisaged for use in the search for extrasolar planets by using the pointings presented here to allow for a maximum differential photometry precision and thus aid ground based searches for extrasolar planets. Instructions on how to use the data are contained in readme.md.  An SQL script to identify the reference stars is also presented. Also given here are source files containing the fits and csv files generated by the grid jobs in a .zip archive.

The Locus Algorithm Quasar Pointings Catalogue

Presented here is a Catalogue of Pointings for Optimal Differential Photometry for 23,779 Quasars presented in the form of a CSV file.  A total of 40,000 quasars were analysed using the Locus Algorithm (Creaner et al, 2019), and the results of that analysis are presented here.  A paper detailing this work has been submitted to MNRAS and is available in preprint on arxiv.org. Instructions on how to use the data are contained in readme.md.  An SQL script to identify the reference stars is also presented. Also given here are source files containing the fits and csv files generated by the grid jobs in a .zip archive.

An evaluation of compression algorithms applied to moving object trajectories

This file contains the dataset, source code and results presented in the paper entitled "An evaluation of compression algorithms applied to moving object trajectories" published in the International Journal of Geographical Information Science in 2019. Abstract: The amount of spatiotemporal data collected by gadgets is rapidly growing, resulting in increasing costs to transfer, process and store it. In an attempt to minimize these costs several algorithms were proposed to reduce the trajectory size. However, to choose the right algorithm depends on a careful analysis of the application scenario. Therefore, this paper evaluates seven general purpose lossy compression algorithms in terms of structural aspects and performance characteristics, regarding four transportation modes: Bike, Bus, Car and Walk. The lossy compression algorithms evaluated are: Douglas-Peucker (DP), Opening-Window (OW), Dead-Reckoning (DR), Top-Down Time-Ratio (TS), Opening-Window Time-Ratio (OS), STTrace (ST) and SQUISH (SQ). Pareto Efficiency analysis pointed out that there is no best algorithm for all assessed characteristics, but rather DP applied less error and kept length better-preserved, OW kept speed better-preserved, ST kept acceleration better-preserved and DR spent less execution time. Another important finding is that algorithms that use metrics that do not keep time information have performed quite well even with characteristics time-dependent like speed and acceleration. Finally, it is possible to see that DR had the most suitable performance in general, being among the three best algorithms in four of the five assessed performance characteristics.

opencc-byOct 2019View details →

Datasets to accompany "Evolutionary Dataset Optimisation: learning algorithm quality through evolution"

This archive contains the datasets generated to accompany the work entitled "Evolutionary Dataset Optimisation: learning algorithm quality through evolution". The source code used to generate these datasets is archived <a href="https://doi.org/10.5281/zenodo.3492236">here</a>. Details on how to use this archive are given in the README.

opencc-by-4.0Oct 2019View details →

Figures, plotting scripts, and data for "A fast, low-cost, and stable memory algorithm for implementing multicomponent transport in direct numerical simulations"

This dataset contains the figures, as well as the necessary plotting scripts and data to reproduce them, for the article "A fast, low-cost, and stable memory algorithm for implementing multicomponent transport in direct numerical simulations" by Aaron J. Fillo, Jason Schlup, Guillaume Beardsell, Guillaume Blanquart, and Kyle E. Niemeyer (2019). In addition, the code used to generate the eigenvalues in Table 1 is included. The scripts were run in Matlab 2019a, though none of the versions used should be version-dependent. Furthermore, non-standard functions are included with dependencies hard-coded. We used export_fig (https://github.com/altmany/export_fig) to generate high-quality figures, and redistribute the version used here for reproducibility (export_fig was developed by Oliver J. Woodford and Yair M. Altman, and made available openly under the BSD 3-Clause License). The code included in this dataset is released under the BSD 3-Clause License (see LICENSE.txt for details), other than the source of export_fig, as described. The figures are shared under the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).

opencc-by-4.0Aug 2024View details →

Data, scripts, and plots for the paper "A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies"

Data, scripts, and plots for the paper "A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies"

opencc-by-4.0Aug 2024View details →

Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms

Synthetic Lunar Terrain (SLT) is a dataset based on a reconstruction of a typical cratered lunar surface landscape at the <a href="https://set.adelaide.edu.au/atcsr/space-research/exterres-laboratory" target="_blank" rel="noopener">EXTERRES Laboratory</a> at University of Adelaide, Roseworthy Campus. On a surface area of 3.6m x 4.8m, multiple synthetic craters with different sizes and geometries were sculpted into lunar regolith simulant. A 9kW metal-halide lamp illuminated the scene, providing high contrast drop-shadows from the rims of craters and similar surface features that are characteristic for the Earth's moon. The purpose of this dataset is to provide multimodal recordings of visual information to develop and test algorithms on a hardware analogue of the moon rather than relying on computer simulations. In particular, comparisons between neuromorphic vision sensors like event-based cameras and imaging with conventional monocular cameras are at the core of this work. For this purpose, an event-based camera (Gen4 Prophesee with Prophesee-Sony IMX636 sensor) was mounted downward-pointing next to a optical camera (Basler a2A1920-160ucPRO with Sony IMX392 sensor) on an extendable rod which was moved above the surface in a slow and continuous sweep. In total, SLT consists of camera recordings from 21 different positions/settings, with clockwise and anti-clockwise motions under varying, extreme lighting conditions. The event-stream and grayscale image data can be further referenced via a detailed 3D point cloud obtained by a FARO Focus S70 3D Scanner. This 3D Scan was post-processed, realigned and resampled into a 3D point cloud of ~6.25M points, with a surface density of 1.862 p/mm², providing a ground-truth for the crater geometries. In detail, SLT contains the following: <ul> <li>eventbased.zip: <ul> <li>42 camera orbits in the binary EVT 3.0 format (<a href="https://docs.prophesee.ai/stable/data/encoding_formats/evt3.html" target="_blank" rel="noopener">Prophesee docs</a>) </li> <li>corresponding .mp4 event-frame video rendering for visualization purposes (33.333ms accumulation time at 30FPS)</li> <li>corresponding .bias file containing settings used during recording</li> </ul> </li> <li>code.zip: <ul> <li>Standalone C++ code of the metavision EVT3-to-RAW file decoder, allowing to convert the binary EVT 3.0 format into a plaintext .csv that includes <ul> <li>the coordinates of the event-pixel,</li> <li>the polarity change,</li> <li>and the time-stamp of the event.</li> </ul> </li> <li>This code is an unmodified redistribution from the <a href="https://www.prophesee.ai/metavision-intelligence/" target="_blank" rel="noopener">Metavision SDK</a>, version 4.6.0, released by Prophesee under Apache License 2.0.</li> </ul> </li> <li> optical.zip: <ul> <li>42 image sequences in .tif format (LZW, 1920x1200px, 8bit, grayscale) <ul> <li>Length of image sequences varies between about 300 to 700 images per sequence</li> </ul> </li> </ul> </li> <li>3d_scan.zip: <ul> <li>SLT3d_scan.ply: 3D point cloud of the scene Stanford Polygon File Format</li> <li>SLT3d_scan.xyz: 3D point cloud with plaintext x y z coordinates, white-space separated</li> </ul> </li> <li>cratermap.png: <ul> <li>Annotations of 130 different surface features that have been manually identified as crater-like with approximate x,y-coordinates.</li> </ul> </li> <li>positionmap.png: <ul> <li>Illustration of the different positions from which the rod was moved over the scene (not to scale).</li> </ul> </li> <li>sample.zip: <ul> <li>A sample containing 1 event-camera orbit with the corresponding image sequence (for convenience only, to test the dataset without the need to download it's entirety)</li> </ul> </li> </ul> The global coordinate frame of this dataset puts the origin at the centre of the scene. The shorter side of the terrain is roughly aligned with the x-axis, the longer side with the y-axis. The z-axis represents height/depth (compare with cratermap.png). The different conditions (compare with positionmap.png) from which the data was taken are encoded as follows: <ul> <li>A1, ..., A9 refer to the left side of the scene (negative x)</li> <li>B1, ..., B9 refer to the right side of the scene (positive x)</li> <li>S1, S2, S3 and S4 describe special lighting conditions and/or parameter settings</li> <li>CW refers to a "clockwise" sweeping of the camera-rod, relative to the position</li> <li>ACW refers to an "anti-clockwise" sweeping of the camera-rod, relative to the position</li> </ul> The light from the metal-halide lamp was directed through a small opening, shining along the positive y-axis. In some of the setups, an obstacle was placed between the surface and the opening, blocking out part of the light to create a light-dark separator on the surface, emulating the terminator on the Moon between it's day and night side, resulting in highly contrastive images. We encourage you to consult and cite our related publication, should you find SLT useful. <ul> <li>Märtens, M., Farries, K., Culton, J. and Chin, TJ. "Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms", Proceedings of  "International Symposium on Artificial Intelligence, Robotics and Automation in Space (I-SAIRAS), 2024", pp. 609-614</li> </ul>

Accompanying dataset for the paper "An implicit staggered algorithm for CPFEM-based analysis of aluminum"

<h2>Contributions</h2> <ul> <li>Pedro Areias did contribute to general programming and theory</li> <li>Charles dos Santos did contribute to investigation, specific programming and validation</li> <li>Rui Melicio did contribute to the text and typesetting</li> <li>Nuno Silvestre did contribute to the Scientific validation</li> </ul> <h2>Funding sources</h2> <ul> <li>FCT Fundação para a Ciência e a Tecnologia project LAETA Base Funding (DOI: 10.54499/UIDB/50022/2020)</li> </ul> <h2>Data structure and information</h2> <ul> <li>data - <code>dataset directory</code><ul> <li>convertFig2Eps - <code>script to convert xfig sources to EPS</code></li> <li>driftfigures - <code>data to assess drifting in \xi and effective strain</code> <ul> <li>cuboid.gid - <code>directory for a single cube analysis, to assess drifting</code></li> </ul> </li> <li>epcfigures - <code>these are the sources for the cylinder test with localization</code><ul> <li>cylindersinglecrystalcoarse.gid - <code>cylinder with ( $\theta=0.25\pi,\phi=0$)</code></li> <li>cylindersinglecrystalcoarseotherangles.gid - <code>cylinder with ($\theta=0.304\pi,\phi=0.25\pi$")</code></li> </ul> </li> <li>errorlogstrain - <code>contains the mathematica sheet for the plots in the logstrain error analysis</code></li> <li>originalfigures - <code>these are the original figures in the paper</code></li> <li>padeerrorgraf - <code>mathematica sheet for the analysis of padé approximation error</code></li> <li>reactions - <code>reactions sources for the localization problem</code></li> </ul> </li> <li>workflows - <code>reproducibility of some of the computational results</code></li> </ul> <h2>Dataset Description</h2> Contains: <ul> <li>Data sources from SimPlas (txt and order)</li> <li>Gnuplot files (gp)</li> <li>Tikz files (tikz)</li> <li>XFig files (fig)</li> <li>Mathematica scripts (nb)</li> <li>Script to convert Xfig in Eps: figtex2eps.sh</li> </ul> Original figures are also included. <h2>Paper Description</h2> In this paper, we propose an implicit staggered algorithm for crystal plasticity finite element method (CPFEM) which makes use of dynamic relaxation at the constitutive integration level. An uncoupled version of the constitutive system consists of a multi-surface flow law complemented by an evolution law for the hardening variables. Since a saturation law is adopted for hardening, a sequence of nonlinear iteration followed by a linear system is feasible. To tie the constitutive unknowns, the dynamic relaxation method is adopted. A Green-Nagdhi plasticity model is adopted based on the Hencky strain calculated using a [ 2/2 ] Padé approximation. For the incompressible case, the approximation error is calculated exactly. A enhanced-assumed strain (EAS) element technology is adopted, which was found to be especially suited to localization problems such as the ones resulting from crystal plasticity plane slipping. Analysis of the results shows significant reduction of drift and well defined localization without spurious modes or hourglassing.

Lightweight Dynamic Build Batching Algorithms for Continuous Integration

Replication package for Journal publication: "Lightweight Dynamic Build Batching Algorithms for Continuous Integration"

Prediction of Humpback Whale Sighting Zones based on Environmental Factors using Tree-based Algorithms

This is the datased used in the paper: Prediction of Humpback Whale Sighting Zones based on Environmental Factors using Tree-based Algorithms

opencc-by-4.0Aug 2024View details →

Visualization of the numerical pose optimization with the JAMDA scoring function using the BFGS and the LSL-BFGS algorithm

These videos demonstrate the behavior of two different optimization algorithms (BFGS and LSL-BFGS) during pose optimization using the JAMDA protein-ligand scoring function. Flachsenberg et al. (2020) (<a href="http://doi.org/10.1021/acs.jcim.0c01095" target="_blank" rel="noopener">10.1021/acs.jcim.0c01095</a>) describes the JAMDA protein-ligand scoring function and the LSL-BFGS algorithm. The data for these videos stems from Experiment 5 in Flachsenberg et al. (2020). In this experiment, the crystal structure of a ligand was numerically optimized in the binding site with respect to the JAMDA scoring function to create the JAMDA-minimized crystal structure. The JAMDA-minimized crystal structure was randomly deflected to generate various starting poses for the numerical optimization. These videos demonstrate the behavior of two optimization algorithms (BFGS and LSL-BFGS) when optimizing one of the generated starting poses. The chosen example for the videos is a structure of ribonuclease A with a 5'-deoxy-5'-N-piperidinouridine inhibitor (PDB code 3d6q, <a href="https://doi.org/10.2210/pdb3D6Q/pdb" target="_blank" rel="noopener">10.2210/pdb3D6Q/pdb</a>, <a href="https://doi.org/10.1021/jm800724t" target="_blank" rel="noopener">10.1021/jm800724t</a>). Each of the videos shows all the intermediate steps the optimization algorithm takes until convergence. The main observation (that is discussed in detail in Flachsenberg et al. (2020)) is that the BFGS algorithm tends to take inappropriately large steps when clashes are present in the structure, resulting in unwanted binding mode changes. This is not the case for the LSL-BFGS algorithm. <h3>Legend</h3> For each iteration, the JAMDA score value, the RMSD to the JAMDA-minimized crystal structure (yellow), and the RMSD to the optimization's starting point (blue) are given. Furthermore, the gradient's norm (representing the main convergence criterion) is shown. In addition to the optimized ligand, also the JAMDA-minimized crystal structure (yellow) and the optimization's starting structure (blue) are shown. Each optimization algorithm is shown in two videos: In one video, the optimized ligand is colored by elements. Here, atoms with clashes (positive JAMDA scores) are marked with orange balls. In the other video, the atoms and bonds of the optimized ligand are colored by their individual JAMDA score. <h3>Used Software</h3> The snapshots of the optimization algorithms were rendered using PyMOL 3.0 (<a href="https://pymol.org/" target="_blank" rel="noopener">https://pymol.org/</a>) and further processed using the Pillow 10.4 Python library (<a href="https://doi.org/10.5281/zenodo.12606429" target="_blank" rel="noopener">10.5281/zenodo.12606429</a>). Videos were created from the individual snapshots using FFmpeg 7.0 (<a href="https://www.ffmpeg.org/" target="_blank" rel="noopener">https://www.ffmpeg.org/</a>).

Assessment of Simulations in Faust and Tascar for the Development of Audio Algorithms in Acoustic Environments - Code and Data

Developing and testing audio algorithms with hard real-time constraints can be a complex task, requiring certain programming skills and/or specialized equipment. However, many things can be tested in simulations on an ordinary computer, using <a href="https://tascar.org/" target="_blank" rel="noopener">TASCAR</a> for acoustic scene creation and <a href="https://faust.grame.fr/" target="_blank" rel="noopener">FAUST</a> for signal processing. Their capability are evaluated and compared to measurements using an FxLMS algorithm for active noise control as example. This repository contains code and measured data of the publication “Assessment of simulations in FAUST and TASCAR for the development of audio algorithms in acoustic environments”, presented at the International Faust Conference 2024 in Turin, Italy.