Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Packing-Inspired Algorithms for Periodic Scheduling Problems with Harmonic Periods - instances

Instances for periodic scheduling problem used in conference paper  <a title="Paper Details, Citation and Download" href="https://www.scitepress.org/PublicationsDetail.aspx?ID=nP/EuJcR7dI=&t=1">Packing-Inspired Algorithms for Periodic Scheduling Problems with Harmonic Periods</a>    <a href="https://doi.org/10.5220/0012325800003639" target="_blank" rel="noopener">10.5220/0012325800003639</a>

opencc-by-4.0Jul 2021View details →

Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data

Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data

opencc-by-4.0Jul 2021View details →

Reproduction Package for A Partial Reproduction of A Guided Genetic Algorithm for Crash Reproduction

A reproduction package for "A Partial Reproduction of A Guided Genetic Algorithm for Crash Reproduction" Includes datasets and source code for reproducing our results

opencc-by-4.0Aug 2021View details →

Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"

DATA belonging to the paper "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction" Katharina Löffler, Tim Scherr, Ralf Mikut doi: https://doi.org/10.1101/2021.03.16.435631 ----------------------------- To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data: - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed") - percentages: 1,2,5,10,20 of errorneous masks per dataset - runs: 5 randomly initialized runs per combination - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking ---------------------------- RESULTS We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA. This repository contains all metrics as xls files and all tracking results as image sequences. PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions. xls files ----------- compare_all_trackers_on_synt_bm.csv Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets). Reported scores are DET, SEG and TRA from the Cell Tracking Challenge (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data) compare_postprocessing_on_synth_bm.csv Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets). Reported scores are DET, SEG and TRA from the Cell Tracking Challenge (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)   folders (decompressed approximately 90GB of data!) ----------- tracking_data     compare_all_synth_bm     Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()     compare_all_synth_bm_no_error     Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data     compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]     Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm     the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key     Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)     all ("untangle + masks" in the paper)     nd ("no untangle + masks")     nd_ns-l ("no untangle + no masks")     ns-l ("untangle + no masks")

SEED-G: Simulated EEG Data Generator for testing connectivity algorithms

SEED-G toolbox was developed in MATLAB environment (tested on version R2017a and R2020b) and released on the GitHub page <a href="https://github.com/aanzolin/SEED-G-toolbox">https://github.com/aanzolin/SEED-G-toolbox</a> (accessed date 12 April 2021). It is organized in the following subfolders: <ul> <li> main: it is the core of the toolbox and contains all the functions for the generation of EEG data according to a predefined ground-truth network. </li> <li> dependencies: containing parts of other toolboxes required to successfully run SEED-G functions. The links to the full packages can be found in the documentation on the GitHub page. The additional packages are Brain Connectivity Toolbox (BCT) [<a href="https://www.mdpi.com/1424-8220/21/11/3632/htm#B40-sensors-21-03632">40</a>], FieldTrip [<a href="https://www.mdpi.com/1424-8220/21/11/3632/htm#B41-sensors-21-03632">41</a>], Multivariate Granger Causality Toolbox (MVGC) [<a href="https://www.mdpi.com/1424-8220/21/11/3632/htm#B24-sensors-21-03632">24</a>], and AsympPDC Package (PDC_AsympSt) [<a href="https://www.mdpi.com/1424-8220/21/11/3632/htm#B42-sensors-21-03632">42</a>,<a href="https://www.mdpi.com/1424-8220/21/11/3632/htm#B43-sensors-21-03632">43</a>]. Additionally, the implemented forward model is solved according to the New York Head (NYH) model, whose parameters are contained in the structure available on the ICBM-NY platform [<a href="https://www.mdpi.com/1424-8220/21/11/3632/htm#B28-sensors-21-03632">28</a>]. </li> <li> real data: containing real EEG data acquired from one healthy subject during resting state at scalp level (‘EEG_real_sources.mat’) and its reconstructed version in source domain (‘sLOR_cortical_sources.mat’). These signals can be employed to extract the AR components to be included in the model to generate data with the same spectral properties of the real ones. </li> <li> demo: containing examples of MATLAB scripts to be used to learn the different functionalities of the toolbox. For example, the code ‘run_generation.m’ allows to specify the directory containing the real sources and each specific input of the function ‘simulatedData_generation.m’. </li> <li> auxiliary functions: containing either original MATLAB functions or modified version of free available functions. </li> </ul>

Randomized Cooperative Overtake Maneuvers for Use in Machine Learning Algorithms

Randomized cooperative overtake maneuvers involving one host vehicle and up to three remote vehicles. Maneuver containers are defined as specified in Häfner et al., (2020) "CVIP: A Protocol for Complex Interactions Among Connected Vehicles." If for a maneuver container "rel-target-lane" is given, then the maneuver type is a lane change left/right. If, instead, "rel-target-speed" is given, then the maneuver type is "change speed". Within the raw data, the "validate_stats" files contain validation data as specified in the accompanying conference paper.

Landslide mapping using satellite imagery and machine learning algorithms

Cyclone Idai made landfall on 15th March near Beira, Mozambique, and caused heavy rainfall across Mozambique, Malawi, Madagascar, and eastern Zimbabwe. Chimanimani District of Zimbabwe received 200 to 400 mm rainfall between 15th and 19th March, which caused widespread flooding and triggered thousands of landslides. This study aims to map the landslides in Chimanimani District and differentiate concurrent flooding from the landslides using high resolution PlanetScope imagery and DEM. Three machine learning algorithms namely, Random Forest, Artificial Neural Network, and Support Vector Machine have been deployed for the supervised landslide classification.

Generating a Labeled Dataset to Train Machine Learning Algorithms for Lithological Classification of Drill Cuttings

This dataset contains 16,700 fully labeled SEM images of rock chips isolated from 14 thin sections of drill cutting samples. These samples come from a low-permeability reservoir in western Canada.

opencc-by-4.0Oct 2021View details →

Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders: Synthetic dataset for validation of trace gas retrieval algorithms

This data set is described in detail in a paper submitted to AMTD: Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders - Part I: Synthetic dataset for validation of trace gas retrieval algorithms by Claudia Emde, Huan Yu, Arve Kylling, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and Bernhard Mayer   The subdirectory boxcloud includes synthetic reflectances for clearsky, 1D cloud and box cloud. The subdirectory les_cloud includes synthetic reflectances for the LES cloud scenario for low earth orbit (leo) and geostationary orbit (geo). All data are provided in netcdf format.

opencc-by-4.0Jun 2022View details →

Efficient Approximation Algorithms for the Diameter-Bounded Max-Coverage Group Steiner Tree Problem

It contains all the data used in our experiments, including 5 real graphs (<code>MONDIAL</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, and <code>DBpedia</code>) and 3 synthetic graphs (<code>LUBM-50K</code>, <code>LUBM-500K</code>, and <code>LUBM-5M</code>). Each real KG directory contains 8 files, including: <ul> <li><code>graph.txt</code>: The first value is the number of vertices. Then each line 'u v' means there is an undirected edge between 'u' and 'v'.</li> <li><code>Weightgraph.txt</code>: The first value is the number of vertices. Then each line 'u v w' means there is an undirected edge between 'u' and 'v' weighted by 'w' which is computed by the Informativeness-based Weighting (IW) scheme.</li> <li><code>nodeName.txt</code>: Mapping from vertex ID to vertex name (i.e., entity URI).</li> <li><code>query.txt</code>: Each line is a keyword query containing a set of keyword names.</li> <li><code>kwName.txt</code>: Mapping from keyword ID to keyword name.</li> <li><code>kwMap.txt</code>: Mapping from keyword ID to vertex IDs. The first value of each line is keyword ID, and the rest are vertex IDs.</li> <li><code>UWHBLL.txt</code>: The HBLL index file which was built based on the Unit Weighting.</li> <li><code>IWHBLL.txt</code>: The HBLL index file which was built based on the Informativeness-based Weighting.</li> </ul> Each synthetic directory contains 6 files, including: <ul> <li><code>graph.txt</code>: same as above.</li> <li><code>Weightgraph.txt</code>: same as above.</li> <li><code>nodeName.txt</code>: same as above.</li> <li><code>queryList.txt</code>: Each line contains a set (separated by ',') of sets of vertex IDs.</li> <li><code>UWHBLL.txt</code>: same as above.</li> <li><code>IWHBLL.txt</code>: same as above.</li> </ul> Apart from that, <code>Dbpedia</code> and <code>LUBM-5M</code> also contain a <code>PLLlabel.txt</code> file which was the supplementary file for the HBLL index.

Diagnostic algorithm for surgical management of limbal stem cell deficiency

Supplementary material (tables) for the article entitled "Diagnostic algorithm for surgical management of limbal stem cell deficiency".

Data for MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications

This repository contains the results of simulations exposed in the article MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications. Notebooks for easy plotting of the results are contained in this repository. The README.txt contains light explainations regarding how to use those notebooks. This article is yet to be published at the time of writing.

Dataset and code for the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm

Dataset and code for the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm

opencc-zeroNov 2022View details →

dryad36/100

Data from: Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents

Uncertainty in reconstruction accuracy is a critical problem faced in the current traffic accident reconstruction process. The purpose of this study is to explore the use of an improved optimization algorithm combined with MAthematical DYnamic MOdels (MADYMO) multibody simulations and crash data to conduct accurate reconstructions of vehicle–pedestrian accidents. The performance of three commonly employed multiobjective optimization algorithms, including nondominated sorting genetic algorithm-II (NSGA-II), neighbourhood cultivation genetic algorithm (NCGA) and multiobjective particle swarm optimization (MOPSO) were compared and evaluated. The effects of the number of objective functions, the selection of different objective functions and the optimal number of iterations are also investigated. The present study indicated that NSGA-II had better convergence and generated more noninferior solutions and better final solutions than NCGA and MOPSO. And multibody simulations coupled with optimization algorithms can be used to accurately reconstruct vehicle-pedestrian collisions.

single-cell RNAseq data (data set 20) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset20) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from breast cancer samples downloaded from the GEO website (GSE180286). see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: https://github.com/sysbiolux/scFASTCORMICS For more information, version updates of the scFASTCORMICS.

single-cell RNAseq data (data set 18) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset18) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from Liver cancer set 1 samples downloaded from the GEO website (GSE125449).  see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: https://github.com/sysbiolux/scFASTCORMICS For more information, version updates of the scFASTCORMICS.

single-cell RNAseq data (data set 17) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset17 was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from PBMC metastatic MCC samples downloaded from the GEO website (GSE117988).  see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: https://github.com/sysbiolux/scFASTCORMICS For more information, version updates of the scFASTCORMICS.

single-cell RNAseq data (data set 12) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset12) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor10 downloaded from the GEO website  (GSE114297).    see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: https://github.com/sysbiolux/scFASTCORMICS For more information, version updates of the scFASTCORMICS.

single-cell RNAseq data (data set 16) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset16) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CD4 T-cells in PACA samples downloaded from the GEO website (GSE156728).  see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: https://github.com/sysbiolux/scFASTCORMICS For more information, version updates of the scFASTCORMICS.

single-cell RNAseq data (data set 11) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset11) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor9 downloaded from the GEO website  (GSE114297).    see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: https://github.com/sysbiolux/scFASTCORMICS For more information, version updates of the scFASTCORMICS.