Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
28
datasets available to search
ShareScore release 0.9.0
Dataset results
28 results for “algorithm generation”
Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms
<p>This repository contains a dataset for the research of domain generation algorithms (DGAs) and machine learning. More precisely, it targets dictionary-based DGAs.</p> <p><em>Constantinos Patsakis, Fran Casino: "Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms", Journal of Information Security and Applications, 2021.</em></p> <p>Features ordered as in the shared dataset:</p> <ul> <li>Family: DGA that the domain belongs to</li> <li>SLD: SLD of the Domain</li> <li>L-LEN: The length of Domain</li> <li>L-DIG: The number of digits in Domain</li> <li>L-CON-MAX: The maximum number of consecutive consonants Domain</li> <li>R-CON-VOW: Number of consonants divided by L-LEN </li> <li>L-SYM: The number of special characters</li> <li>R-SYM-LEN: L-SYM divided by L-LEN</li> <li>R-Dom-3G: Ratio of benign grams in Dom-3G</li> <li>R-Dom-4G: Ratio of benign grams in Dom-4G</li> <li>R-Dom-5G: Ratio of benign grams in Dom-5G</li> <li>L-W2: Number of words with more than 2 characters in Domain</li> <li>L-W3: Number of words with more than 3 characters in Domain</li> <li>R-WS-LEN: Dom-WS divided by L-LEN</li> <li>R-WDS-LEN: Dom-WDS divided by L-LEN</li> <li>R-W2-LEN: Dom-W2 divided by L-LEN</li> <li>R-W3-LEN: Dom-W3 divided by L-LEN</li> <li>M2-Dom-Ws: 2-Chain Markov English grams applied to Dom-WS</li> <li>M2-Dom-WDS: 2-Chain Markov English grams applied Dom-WDS</li> <li>E-Dom-WS: Entropy of Dom-WS </li> <li>E-Dom-WDS: Entropy of Dom-WDS</li> <li>E-Dom-W2: Entropy of Dom-W2</li> <li>E-Dom-W3: Entropy of Dom-W3</li> </ul>
Cloud_ICA: A deterministic cloud-overlap algorithm for generating a complete set of independent column atmospheres
<p>In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate‐relevant scales. We build benchmarking capability around a solar heating code (Solar‐J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice‐cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m−2 using a full climate re‐creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ∼2 W m−2 with greater latitudinal biases and are likely to affect a model's ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data
<p>In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 16. Failed Generations
<p>For some images there is a chance to get stuck where fitness function maxed, but we are not near to the original image like in figure 16, both row and column fitness matched. But image lost a key portion from original image, in these cases we should increase the weight of fitness function Fx(X), which will solve the issue.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 12. After few generation
<p>In figure 11 it is the initial population showed and figure 12 the population started to change and figure 13 we reached a convergence.</p>
Different orthology inference algorithms generate similar predicted orthogroups among Brassicaceae species
Open the record for dataset details and reuse information.
Cloud_ICA: A deterministic cloud-overlap algorithm for generating a complete set of independent column atmospheres
Open the record for dataset details and reuse information.
Synthetic realistic noise-corrupted PPG database and noise generator for the evaluation of PPG denoising and delineation algorithms
<p><strong>Overview </strong></p> <p>This database is meant to evaluate the performance of denoising and delineation algorithms for PPG signals affected by noise. The noise generator allows applying the algorithms under test to an artificially corrupted reference PPG signal and comparing its output to the output obtained with the original signal. Moreover, the noise generator can produce artifacts of variable intensities, permitting the evaluation of the algorithms' performance against different noise levels. The reference signal is a PPG sample of a healthy subject at rest during a relaxing session.</p> <p> </p> <p><strong>Database</strong></p> <p>The database includes 1 recording of 72 seconds of synchronous PPG and ECG signals sampled at 250 Hz using a Medicom device, ABP-10 module (Medicom MTD Ltd., Russia). It was collected from a healthy subject during an induced relaxation by guided autogenic relaxation. For more information about the data collection, please refer to the following publication: <a href="https://pubmed.ncbi.nlm.nih.gov/30094756/">https://pubmed.ncbi.nlm.nih.gov/30094756/</a></p> <p>In addition, PPG signals corrupted by the noise generator at different levels are also included in the database.</p> <p> </p> <p><strong>Realistic noise generator</strong></p> <p>Motion Artifacts in PPG signals generally appear in the form of sudden spikes (in correspondence to the subject's movement) and slowly varying offsets (baseline wander) due to the changes in distance between the skin and the sensor after every sudden movement. For this reason, conventional noise generators — using random noise drawn from different distributions such as Gaussian or Poissonian — do not allow to properly evaluate the algorithm's performance, as they can only provide unrealistic noises compared to the one commonly found in PPG signals. To overcome this issue, we designed a more realistic synthetic noise generator that can simulate those two behaviors, enabling us to corrupt a reference signal with different noise levels. The details about noise generation are available in the reference paper.</p> <p> </p> <p><strong>Data Files</strong></p> <p>The reference PPG signal can be found in <em>Datasets\GoodSignals\PPG</em> and the simultaneously acquired ECG in <em>Datasets\GoodSignals\ECG</em>. The folder <em>Datasets\NoisySignals</em> contains 340 noisy PPG signals affected by different levels of noise. The names describe the intensity of the noise (evaluated in terms of the standard deviation of the random noise used as input for the noise generator, see reference paper). Five noisy signals are produced for every noise level by running the noise generator with five random seeds each (for noise generation).</p> <p>Name convention: <em>ppg_stdx_y</em> denotes the y-th noisy PPG signal produced using a noise with a standard deviation of x.</p> <p><em>Datasets\BPMs</em> contains the ground truth for the heart-rate estimation computed in windows of 8s with an overlap of 2s.</p> <p><strong>Code</strong></p> <p>The folder <em>Code </em>contains the MATLAB scripts to generate the noisy files by generating the realistic noise with the function noiseGenerator.</p> <p><strong>When referencing this material, please cite:</strong></p> <p>Masinelli, G.; Dell'Agnola, F.; Valdés, A.A.; Atienza, D. SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 2725. <a href="https://doi.org/10.3390/s21082725">https://doi.org/10.3390/s21082725</a></p>
SEED-G: Simulated EEG Data Generator for testing connectivity algorithms
<p>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:</p> <ul> <li> <p><strong>main</strong>: 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.</p> </li> <li> <p><strong>dependencies</strong>: 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>].</p> </li> <li> <p><strong>real data</strong>: 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.</p> </li> <li> <p><strong>demo</strong>: 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’.</p> </li> <li> <p><strong>auxiliary functions</strong>: containing either original MATLAB functions or modified version of free available functions.</p> </li> </ul>
Generating a Labeled Dataset to Train Machine Learning Algorithms for Lithological Classification of Drill Cuttings
<p>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.</p>
Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples
<p>We present a <strong>D</strong>iagnostic dataset of <strong>IA</strong> <strong>Ref</strong>erences in a <strong>Pento</strong>mino domain (Pento-DIARef) that ties extensional and intensional definitions more closely together, insofar as the latter is the generative process creating the former.</p> <p>We create a novel synthetic dataset of examples that pairs visual scenes with generated referring expressions; examine two variants of the dataset, representing two different ways to exemplify the underlying task; and evaluate an LSTM-based baseline, a transformer and a modified version with region embeddings on them.</p> <p>See https://github.com/clp-research/pento-diaref for more information.</p>
Replication package for: "Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty"
<p>Package to the following paper:</p> <p>Leib, M; Köbis, N; Rilke, R M; Hagens, M; Irlenbusch, B (2023) Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty</p>
Hybrid ATPG (Automatic Test Pattern Generation) algorithm
Open the record for dataset details and reuse information.
Networkx graph generation algorithm sample binary graphs
<p>Example graph binary object from Networkx's random graph generation algorithms</p> <ul> <li>gnp: Erdős-Rényi graph</li> <li>gnx: Random graph</li> <li>regular: Random regular graph</li> <li>barabasi: Graph from Barabási-Albert preferential attachment model</li> </ul> <p>Parameters Used:</p> <ul> <li>P = 0.015</li> <li>N = 200</li> <li>DEGREE = 20</li> <li>SEED = 0</li> </ul>
Structural files of algorithmically generated molybdenum oxide clusters
<p>Structural files of algorithmically generated molybdenum oxide clusters. The new clusters were generated to fit experimental pair distribution functions of molybdenum oxide surface layers supported on Al<sub>2</sub>O<sub>3 </sub>and zeolites. Results are published in article XXX</p>
The 4D mesh generated by the 'satori' algorithm, and Voronoi regions of its nodes
<p>The data contained in the archive is the 4D mesh containing 5^4 translation units, that are 2^4 points of tesseract shifted with 'satori' algorithm (total 10 000 nodes), and Voronoi diagram calculated using the qhull package for these points.</p> <p>It is used to demonstrate the shape uniqueness of the 4096 diagram's inner regions. The tessellation of inner regions is a candidate to be a solution of Kelvin problem in 4D version, offering possible minimal interface hyper-area to hyper-volume ratio. The structure posesses PT-symmetry so it might have applications in particle physics. For instance, its 3D layers can be used as CPT-compatible cellular automata for 3D simulations in chiral space.</p> <p>Presumably, the structure can be self-assembling in conditions near to critical point of second order phase transitions.<br> </p>
Towards a Wearable Alcohol Biosensor: Examining the Accuracy of BAC Estimates From New-Generation Transdermal Technology Using Large-Scale Human Testing and Machine Learning Algorithms
ClinicalTrials.gov study NCT05692830. IPD Sharing: UNDECIDED. Countries: 1. Publications: 27.
Searching for evidence of algorithmic randomness and incomputability in the output of quantum random number generators
<p>This upload contains the data and code used in the following paper:</p> <p>J. T. Kavulich, B. P. Van Deren, and M. Schlosshauer, “Searching for evidence of algorithmic randomness and incomputability in the output of quantum random number generators,” <em>Phys. Lett. A</em> 388, 127032 (2021), <a href="https://doi.org/10.1016/j.physleta.2020.127032">doi.org/10.1016/j.physleta.2020.127032</a></p> <p>The contents of the data set are as follows:</p> <p>1) Random strings for two QRNGs and four PRNGs. For each RNG, a zip archive provides 100 strings containing 25 x 2<sup>26</sup> = 1,677,721,600 bits each.</p> <p>2) C++ code for the Chaitin–Schwartz–Solovay–Strassen (CSSS) and Borel-normality tests (code.zip, 13 KB).</p> <p>The bulk of this code is not ours, but was written and made publicly available at <a href="https://www.cs.auckland.ac.nz/research/groups/CDMTCS/export/80_random_seqs">this link</a> by the authors of the following paper:</p> <p>A. A. Abbott, C. S. Calude, M. J. Dinneen, and N. Huang, <em>Phys. Scri.</em> 94 (2019) 045103, <a href="https://doi.org/10.1088/1402-4896/aaf36a">doi:10.1088/1402-4896/aaf36a</a></p> <p>We have made just a few small modifications to their original code:</p> <ul> <li>For the CSSS tests, a text file containing the Carmichael numbers (for tests 1–3) and odd composites up to 100 (for test 4) is read in and used to perform the tests. (Note: The set of Carmichael numbers used in the tests was generously provided to us by R. G. E. Pinch. Reference: R. G. E. Pinch, The Carmichael numbers up to 10<sup>21</sup>, in: A.-M. Ernvall-Hytönen (Ed.), Proceedings of Conference on Algorithmic Number Theory, Vol. 46, Turku Centre for Computer Science, Turku, Finland, 2007, pp. 129–131.)</li> <li>We combined the first and second CSSS tests into a single program.</li> <li>We reformatted the display of the output, and included a VERBOSE flag for additional status output.</li> </ul> <p>3) Results from CSSS and Borel-normality tests in Python format (results.zip, 22 KB). This archive also contains a Python script (analyze.py) that reads the result files, carries out the statistical analysis, and displays the plots.</p>
Chord voicings for 370 chorales by J. S. Bach as generated by the 'voicer' algorithm
<p>This is a dataset of 370 chorales by J S. Bach as generated by the 'voicer' algorithm for automatic chord voicing. This dataset is intended to accompany the paper 'A Computational Model for the Analysis and Generation of Chord Voicings'.</p> <p>Note: there are 370 chorales, but the numbering goes from 1 to 371, with one chorale omitted, matching the original dataset from http://kern.humdrum.org.</p>
Spoofing Signal Generation Algorithm using the Processing Information of a Receiver for Authentic GPS Signals
<p>Experimental results analyzation files.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.