Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data

This page contains the code and datasets used in "quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data". File test_datasets.zip containes three datasets: <ul> <li>D1.RData: scRNA-seq omics data derived from Salcher et. al (2022)</li> <li>D2.RData: scRNA-seq omics data derived from Pineda et al. (2024)</li> <li>D3.RData: in silico WGS SNP data.</li> </ul> File test_scripts.zip containes the code to reproduce the results.

opencc-zeroJun 2024View details →

Data from: A cerebellar granule cell–climbing fiber computation to learn to track long time intervals

In classical cerebellar learning, Purkinje cells (PkCs) associate climbing fiber (CF) error signals with predictive granule cells (GrCs) active just prior (~150ms). Cerebellum also contributes to behaviors characterized by longer timescales. To investigate how GrC-CF-PkC circuits might learn seconds-long predictions, we imaged simultaneous GrC-CF activity over days of forelimb operant conditioning for delayed water reward. As mice learned reward timing, numerous GrCs developed anticipatory activity ramping at different rates until reward delivery, followed by widespread time-locked CF spiking. Relearning longer delays further lengthened GrC activations. We computed CF-dependent GrC→PkC plasticity rules, demonstrating that reward-evoked CF spikes sufficed to grade many GrC synapses by anticipatory timing. We predicted and confirmed that PkCs could thereby continuously ramp across seconds-long intervals from movement to reward. Learning thus leads to new GrC temporal bases linking predictors to remote CF reward signals—a strategy well-suited to learn to track long intervals common in cognitive domains.

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 2. bid value for time factor

Each consumer is looking for utilizing its requested service with minimum price before its deadline. To utilize a service all required resources should be allocated before deadline and otherwise service failed to utilize and consumer must pay penalty to providers for all other resources which is allocated to it. So Consumer should adjust its bid price rapidly to the acceptable price of the market. Since consumers are generally sensitive to deadline in acquiring requested service, it is intuitive to consider deadline time when formulating the bid price. Consumer agent time dependent bid price formula is determined in.

Figure 5. Sensory score and period of storage for processed cheese-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

R2 was found to be 96.5 percent of the total variation as explained by sensory scores. Period of storage (days) for which the processed cheese has been in the shelf can be determined based on sensory score (Fig. 5).

Figure 4. Comparison of ASS and PSS for multilayer model R-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

TDNN models with single and multi layers were developed taking soluble nitrogen, pH, standard plate count, yeast & mould count, spore count as input parameters, and sensory score as output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were used in order to compare the prediction ability of the developed TDNN models. Regression equations were developed for predicting the shelf life of processed cheese, which came out as 28.25 days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence from the study it can be concluded that TDNN artificial neural network models are quite efficient in predicting shelf life of processed cheese.

Figure 2. Training pattern of TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

The Neural Network Toolbox under MATLAB software was used for developing the TDNN models. Training pattern of TDNN models is presented in Fig.2.

Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate count, yeast & mould count, and spore count were taken as input parameters, and sensory score as output parameter for developing TDNN single and multilayer models (Fig.1).

Figure 3. Comparison of ASS and PSS single layer model-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

opencc-by-4.0Feb 2012View details →

GTSRB - German Traffic Sign Recognition Benchmark by Real-Time Computer Vision at Ruhr-Universität Bochum

<div> <div>The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011.  Our benchmark has the following properties: - Single-image, multi-class classification problem - More than 40 classes - More than 50,000 images in total - Large, lifelike database Acknowledgements: [INI Benchmark Website][1] [1]: http://benchmark.ini.rub.de/</div> </div>

opencc-by-4.0Aug 2022View details →

Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"

Software repository for the reproduction of the test cases from Thomas Richter, Rolf Ulrich, Markus Janczyk: Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods" This software is used in particular for the reproducibility of the results. However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact thomas.richter@ovgu.de. Parts of this repository General setup Python collects all Python script. Here, Python/PythonTools are several internal functions, e.g. the realizations of KFE and random walks. Python/results and Python/pics are the directories where the results (figures and text-files) are put. C++ collects the C++ scripts. Case I Reproduces Case I of the paper (time-independent) <ul> <li>Python/TestCase1.py</li> </ul> runs the test-case with random walks, integral equation method and with KFE. It produces output in Python/pics and Python/results. These results will be used in C++/testcase1.cc (as reference solution) and by Python/TestCase1-Plot.py <ul> <li>C++/testcase1.cc</li> </ul> runs the stochastic Euler simulation. Script is started by C++/run-testcase1.sh. It reads in the reference solution generated by Python/TestCase1.py for computing errors. <ul> <li>Python/TestCase1-Plot.py</li> </ul> produces Fig. 6 of the paper. It requires the outputs of Python/TestCase1.py and C++/testcase1.cc Case II Reproduces Case II of the paper (time-dependent thresholds and drift) <ul> <li>Python/TestCase2.py</li> </ul> runs the test-case with random walks, integral equation method and with KFE. It produces output in Python/pics and Python/results. These results will be used in C++/testcase2.cc (as reference solution) and by Python/TestCase2-Plot.py <ul> <li>C++/testcase2.cc</li> </ul> runs the stochastic Euler simulation. Script is started by C++/run-testcase2.sh. It reads in the reference solution generated by Python/TestCase2.py for computing errors. <ul> <li>Python/TestCase2-Plot.py</li> </ul> produces Fig. 7 of the paper. It requires the outputs of Python/TestCase2.py and C++/testcase2.cc <ul> <li>Python/TestCase2-AdjustRandomWalks.py</li> </ul> runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations. Case III Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift) <ul> <li>Python/TestCase3.py</li> </ul> runs the test-case with random walks, integral equation method and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8. <ul> <li>C++/testcase3.cc</li> </ul> runs the stochastic Euler simulation. Script is started by C++/run-testcase3.sh. It reads in the reference solution generated by Python/TestCase3.py for computing errors. <ul> <li>Python/TestCase3-Plot.py</li> </ul> produces second part of Fig. 8. Depends on the output of Python/TestCase3.py Case IV Reproduces Case IV of the paper (accuracy and efficiency for Dirac initial data) <ul> <li>Python/TestCase4.py</li> </ul> runs the test-case with random walks, integral equation and with KFE for a refined discretizations. <ul> <li>Python/TestCase4-Plot.py</li> </ul> produces Fig. 9. Depends on the output of Python/TestCase4.py <ul> <li>Python/TestCase4-showsolution.py</li> </ul> Solves with the KFE and plots the solution as surface plot over time and space variable. This skript is used to create Fig. 10 of the paper. Problem parameters and discretization can be adjusted at the top of the script. To test the different stabilization strategies, one can either adjust the value of theta, or one activates Rannacher time-marching by commenting in the marked lines in the skript PythonTools/kfe.py, here in kfe_ale(..) Data Fitting Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.Cognitive Psychology, 78 , 148–174 <ul> <li>Python/DataFitting-Simon.py</li> </ul> runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1. <ul> <li>Python/Eriksen-Fletcher.py</li> </ul> runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2. Installation & running the examples Python The python skripts can just be started. Just note that they depend on each other, i.e.: Python/TestCase1.py produces a reference solution that is required by C++/testcase1.cc and the results of both are needed in Python/TestCase1-Plot.py The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda. C++ The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials N_tr and a given time step dt and this simulation is repeated 64 times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading. Configuration The C++ test cases must be compiled. The test cases are set up to use cmake. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts): <ol> <li>Create a directory for compilation, e.g. C++/bin now called the bin-dir</li> <li>In the bin-dir calls cmake by cmake .. (adjust the path, if the bin-dir is not a subdirectory of the C++-dir.</li> <li>Several options can be adjusted. In C++/bin call ccmake . to make all necessary changes.</li> </ol> If you change the location of the bin-dir you will have to modify the run-scripts run-testcase[123].sh. Compilation Initially and whenever you change the code, the programs must be re-compiled <ol> <li>In C++/bin just call make</li> </ol> Running the examples The programs are started in C++. For each of the test-case there is a skript to start the program. <ol> <li>In C++ call sh ./run-testcase1.sh (or sh ./run-testcase2.sh, etc.)</li> </ol> Each script will start the programs several times. For Case I, Case II and Case IV the simulation is started on a sequence of finer and finer discretizations, for Case III the value of tau will be changed. The scripts store the output in C++/results. Old outputs will be overwritten! Further, the scripts read information about the reference solution from Python/resuts. The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling export OMP_NUM_THREADS=8 before calling the run-scripts. License Information Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022, 2023 (thomas.richter@ovgu.de) You are free to use the scripts under the Creative Commons Attribution 4.0 License.

opencc-zeroAug 2023View details →

Data from: Using adversarial networks to extend brain computer interface decoding accuracy over time

Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the "decoder" at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder's mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called "Adversarial Domain Adaptation Network" (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by factor analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time.

publicJun 2024View details →

Data from: A cerebellar granule cell–climbing fiber computation to learn to track long time intervals

Open the record for dataset details and reuse information.

publicSep 2023View details →

Data from: Using adversarial networks to extend brain computer interface decoding accuracy over time

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2020View details →

Real-time benchmark dynamics of the Ohmic Spin-Boson Model computed with Time-Dependent Variational Matrix Product States. (TDVMPS) coupling strength and temperature parameter space

Data describing the complete propagators (maps) for the evolution of the Ohmic Spin-Boson Model are made available, here. Using a time-dependent variotnal matrix product states (TDVMPS) respresentation of the complete spin-environment wave function, non -perturbative results are presented over a wide range of coupling strengths, temperatures and initial conditions. The results in this repository are associated with the article:  https://www.preprints.org/manuscript/202012.0016/v1   A mathematica notebook that allows the data to be visualised and manipulated is also provided.

opencc-by-4.0Dec 2021View details →

Computation Time, Searched Nodes and Path Length for Navigation Using Improved A-Star, Directional ORCA and FLC-ORCA

Naivation simulation using Improved A*, Directional ORCA and Novel FLC-ORCA method. The computational times, searched nodes and path length are recorded for comparison. A simulation study for Robotic Navigation Aid (RNA)

dryad36/100

Data from: Correcting a bias in the computation of behavioral time budgets that are based on supervised learning

Supervised learning of behavioral modes from body-acceleration data has become a widely used research tool in Behavioral Ecology over the past decade. One of the primary usages of this tool is to estimate behavioral time budgets from the distribution of behaviors as predicted by the model. These serve as the key parameters to test predictions about the variation in animal behavior. In this paper we show that the widespread computation of behavioral time budgets is biased, due to ignoring the classification model confusion probabilities. Next, we introduce the confusion matrix correction for time budgets -- a simple correction method for adjusting the computed time budgets based on the model's confusion matrix. Finally, we show that the proposed correction is able to eliminate the bias, both theoretically and empirically in a series of data simulations on body acceleration data of a fossorial rodent species (Damaraland mole-rat, Fukomys damarensis). Our paper provides a simple implementation of the confusion matrix correction for time budgets, and we encourage researchers to use it to improve accuracy of behavioral time budget calculations.

opencc-zeroMar 2022View details →

opencc-by-4.0Oct 2017View details →

Discontinuous Galerkin time-domain method for the computation of electromagnetic resonant modes

Data generated for the paper "The application of a high-order discontinuous Galerkin time-domain method for the computation of electromagnetic resonant modes" published in Applied Mathematical Modelling. It includes: <ul> <li>Meshes</li> <li>Time domain signals</li> <li>Spectra</li> <li>Figures</li> </ul>

opencc-by-4.0Sep 2023View details →

Data supporting "A real-time, scalable, fast and resource-efficient decoder for a quantum computer"

Data includes the circuits (stim_circuits.zip) used to create samples to benchmark CC decoder across different noise rates and code sizes. The resulting accuracy and cycle data is in fpga_accuracy_data.csv. The memory footprint (in KB) of the algorithm for different code sizes is in fpga_memory_data.csv. Weights of syndromes for different noise rates for both phenomenological and circuit-level noise at distance d=23 and d=21 are in noise_rate_sampling_full_d23.csv and noise_rate_sampling_full_d21.csv respectively.