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1,045 results for “Generated Data”
Raw Picarro L2140i data - Support to "A versatile water vapor generation module for vapor isotope calibration and liquid isotope measurements"
<p>Support data to reproduce Figure 3 - Figure 10 from the article:</p> <p><em>A versatile water vapor generation module for vapor isotope calibration and liquid isotope measurements</em></p> <p>by Hans Christian Steen-Larsen and Daniele Zannoni</p> <p>Accepted in Atmospheric Measurement Techniques on 25/05/2024 (Preprint available at <a href="https://doi.org/10.5194/amt-2023-160" rel="nofollow">https://doi.org/10.5194/amt-2023-160</a>)</p> <p>Figure numbers refer to the final (peer-reviewed and accepted) version of the manuscript.</p> <p>To reproduce the figures, the data can be used in conjunction with the code available at <a href="https://github.com/danielez83/AMT-2023-160" target="_blank" rel="noopener">https://github.com/danielez83/AMT-2023-160 </a>(https://zenodo.org/doi/10.5281/zenodo.12741980)</p> <p><strong>File description</strong></p> <p>Figure numbers are referring to the final (peer-reviewd and accepted) version of the manuscript.</p> <ul> <li>ADEV_BER17k_withmemory_CALIBRATED_R1.csv <ul> <li>Allan Deviation data without removing memory effect, used in FIgure 3</li> </ul> </li> <li>Cal_Pulses_MultiOven_new20230609.csv <ul> <li>Data obtained with the multioven configuration, used in Figure 9</li> </ul> </li> <li>Cal_Pulses_Selector.csv<br> <ul> <li>Data obtained with the VICI selector configuration (only one oven working), used in Figure 9</li> </ul> </li> <li>HKDS2092.zip<br> <ul> <li>Compressed archive of the Picarro L2140i (HKDS2092) raw data.</li> </ul> </li> <li>HKDS2156.zip<br> <ul> <li>Compressed archive of the Picarro L2140i (HKDS2156) raw data.</li> </ul> </li> <li>HKDS2156_IsoWater_20221116_165037.csv<br> <ul> <li>Results of liquid injections with Picarro vaporizer, used in Figure 4 and Figure 10</li> </ul> </li> <li>SP_BER_inj_time.csv <ul> <li>Date and times of injections, used in Figure 4 and Figure 10</li> </ul> </li> <li>Timings_Picarro.xlsx <ul> <li>Excel spreadsheet with time and dates of experiment. It is used as a lookup table to retrieve the raw data correctly</li> </ul> </li> </ul>
Search-Based Test Data Generation for SQL Queries: Appendix
<p>The appendix of our ICSE 2018 paper "Search-Based Test Data Generation for SQL Queries: Appendix".</p> <p>The appendix contains:</p> <ul> <li>The queries from the three open source systems we used in the evaluation of our tool (the industry software system is not part of this appendix, due to privacy reasons)</li> <li>The results of our evaluation.</li> <li>The source code of the tool. Most recent version can be found at https://github.com/SERG-Delft/evosql.</li> <li>The results of the tuning procedure we conducted before running the final evaluation.</li> </ul>
Input files and data for path generation of alanine dipeptide isomerization in virtual reality
<p>The input files and resulting data for the accelerated sampling of the isomerization of alanine dipeptide used in the thesis:</p> <p>"Accelerated Sampling Methods for High Dimensional Molecular Systems", Mike O'Connor, University of Bristol. </p> <p> </p> <p> </p>
Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"
<p>Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"</p>
VERTEX simulator snapshot with pre-generated data and analysis scripts for electric field stimulation simulations
<p>This dataset contains a snapshot of the VERTEX Matlab toolbox for simulating spiking neural networks, along with Matlab files for pre-generated results from simulations run using VERTEX. The scripts and results files here are to accompany the manuscript 'Predicting the impact of electric field stimulation in a detailed computational model of cortical tissue' and pertain to simulations of electric field stimulation on a cortical tissue model. Analysis scripts for the simulation results along with scripts for setting up and running simulations are included.</p>
Data and scripts used in: "Exploring Biological Neuronal Correlations with Quantum Generative Models"
<div>Data and script for the manuscript "Exploring Biological Neuronal Correlations with Quantum Generative Models", by Vinicius Hernandes and Eliska Greplova.</div> <h3>main scripts</h3> <div> <p><strong><em>generate_activity_dataset.py</em></strong></p> <p>reshape data in <em>neuronData.npy</em> to 50k samples of (neurons, timesteps) shape, saved in <em>activity_data.npy</em></p> <p><strong><em>create_target_distributions.py</em></strong></p> <p>based on the dataset, makes dicionary with the the target distribution for each (neurons, timesteps) pair, saved in <em>distribution_target_dictionary.pkl</em></p> <p><strong><em>create_hyperparameters_file.py</em></strong></p> <p>generates <em>hyperparameters.csv</em>, containing:</p> </div> <ul> <li>number of neurons</li> <li>number of timesteps</li> <li>number of auxiliary_qubits</li> <li>batch_size</li> <li>learning rate of generator</li> <li>learning rate of critic</li> <li>number parametrized layers</li> <li>number of training iterations</li> <li>loss type</li> </ul> <p>for each run</p> <p><strong><em>train_qgan.py</em></strong></p> <div> <p>trains models defined <em>models.py</em> using <em>activity_data.npy</em> dataset, and for the hyperparameters defined in <em>hyperparameters.csv</em></p> </div> <div>saves loss functions, and the trained models for each 10 iterations, in specific folders indexed by the run specified in the hyperparameters file</div> <div> </div> <div><strong><em>generate_fake_activity.py</em></strong></div> <div> </div> <div>uses trained models saved in <em>output/models/run{run}/i{training_step}.pth</em> for a specific <em>training_step</em> and <em>run</em> to generate fake data, and save them in <em>output/generated_data/run{run}/i{training_step}.npy</em> files</div> <div> </div> <div><strong><em>analyze_error.py</em></strong></div> <div> </div> <div>uses generated data saved in <em>output/generated_data/run{run}/i{training_step}.npy</em> to generate two statistical quantities (k-probs and firing rate), using the function in <em>metrics.py</em>, and compare the errors in those quantities between the models using k-loss and standard-loss</div> <div> </div> <div><strong><em>analyze_stats.py</em></strong></div> <div> </div> <div>uses generated data saved in <em>output/generated_data/run{run}/i{training_step}.npy</em> to generate:</div> <ul> <li>js diverge for each training step, and final distribution of generated states, stored in <em>distribution_target_dictionary.pkl</em></li> <li>other statistical quantities, using the function in <em>metrics.py</em> file</li> </ul> <h3>auxiliary scripts</h3> <div><strong><em>metrics.py</em></strong></div> <div> </div> <div>functions to calculate neuronal statistics</div> <div> </div> <div><strong><em>aux.py</em></strong></div> <div> </div> <div>auxiliary functions:</div> <ul> <li>to generate states distribution given a dataset</li> <li>custom js divergence</li> </ul> <h3>Data</h3> <p><strong><em>neuronData.npy</em></strong></p> <p>neuronal data from Marre et al., Multi-electrode array recording from salamander retinal ganglion cells (2017)</p> <p><strong><em>activity_data.npy</em></strong></p> <p>dataset obtained from <em>neuronData.npy</em>, taking 50 thousand samples of shape (neurons, timesteps)</p> <p><strong><em>output</em></strong></p> <p>results obtained from <em>train_qgan.py</em> and <em>generate_fake_activity.py</em> </p> <p>contains:</p> <ul> <li><strong><em>losses</em></strong></li> </ul> <p>generator and critic loss for all training runs and steps</p> <ul> <li><strong><em>models</em></strong></li> </ul> <p>saved torch models every 10 training steps, for all training runs</p> <ul> <li><strong><em>generated_data</em></strong></li> </ul> <p>generated data for all models saved in <em>models</em></p>
Data for: In situ generation of (sub) nanometer pores in MoS2 membranes for ion-selective transport
<p>Source data for Fig 1-3 includes data for ion transport measurements and pore size distributions.</p> <p>Source data for Fig. 4 includes trajectory data for molecular dynamics simulations, exclusively for ions within MoS2 nanopores, as well as full trajectory data for the calculation of water-diffusion in 1 nm, 2 nm pores and the bulk reservoir (without an applied electric field). </p>
Probabilistic Data Generating Process-based Crop Type Map for the EU 2010-2020
<h3>General Description</h3> <p>This dataset consists of probabilistic crop type maps for the EU-28 for the years 2010-2020 that distinguish 28 crop types at 1km resolution (EPSG:3035). The maps were generated using the Data Generating Process-based procedure developed by Baumert, Heckelei and Storm (2024) [<em><span><a href="https://doi.org/10.1016/j.ecoinf.2024.102836">https://doi.org/10.1016/j.ecoinf.2024.102836</a></span></em>]. We refer to this paper for details on the generation and validation of the maps. The code used to create the maps including a detailed list of the input data can be found here: <a href="https://github.com/JoBaumert/Probabilistic_Crop_Mapping_EU">GitHub - JoBaumert/Probabilistic_Crop_Mapping_EU</a> . </p> <h3>Downloadable Data</h3> <p>The file “EU_expected_crop_shares.zip” consists of 11 raster files, one for each year from 2010 – 2020. The raster files indicate the expected shares for each of the 28 distinguished crop types in a grid cell for the entire EU-28 (see readme.txt contained in the zipped folder). Note that this raster file does not contain uncertainty information.</p> <p>The other 28 zip files contain the entire crop map ensemble (i.e., including uncertainty information), each for one of the EU countries and the United Kingdom. Each of those zip files contain 11 raster files, one for each year from 2010 – 2020. Each raster file has 2830 bands: the first two bands indicate the weight of the cell (proportional to the utilized agricultural area in a cell) and the estimated number of agricultural fields in a cell, respectively. The next 28 bands indicate the expected shares for each of the 28 crops in the respective cell. The remaining 2800 bands compose the crop type map ensemble, i.e., 100 simulated crop shares for each of the 28 crops. The zipped country folder also includes a csv file named “bands” that describes which band refers to which crop. Note that all crop shares were multiplied by 1000 when writing them to the raster files (saving them as integers requires less storage capacity), i.e., if a crop share is 0.325 or 32.5% it will appear as 325 in the raster files. </p> <p>The distinguished crops are (with abbreviation used in "bands.csv"):</p> <ul> <li>Apples and other fruits, nuts and berries (APPL+OFRU)</li> <li>Barley (BARL)</li> <li>Citrus fruits (CITR)</li> <li>Durum wheat (DWHE)</li> <li>Flowers and ornamental plants (FLOW)</li> <li>Grassland (GRAS)</li> <li>Maize (both green maize as well as grain maize, LMAIZ)</li> <li>Rape and turnip (LRAPE)</li> <li>Nurseries (NURS)</li> <li>Oats (OATS)</li> <li>Other cereals (OCER)</li> <li>Other permanent crops (OCRO)</li> <li>Other forage plants (OFAR)</li> <li>Other industrial plants (OIND)</li> <li>Olives (OLIVGR)</li> <li>Rice (PARI)</li> <li>Potatoes (POTA)</li> <li>Pulses (PULS)</li> <li>Fodder roots and brassicas (ROOF)</li> <li>Rye (RYEM)</li> <li>Soybeans (SOYA)</li> <li>Sugar beets (SUGB)</li> <li>Sunflowers (SUNF)</li> <li>Soft/common wheat (SWHE)</li> <li>Other oilseeds and fibre crops (TEXT)</li> <li>Tobacco (TOBA)</li> <li>Fresh vegetables, melons, strawberries (TOMA+OVEG)</li> <li>Vineyards (VINY)</li> </ul> <p> </p> <p> </p>
Coarse-Grained and Multi-Dimensional Data-Driven Molecular Generation: A Structure-Based Framework for Selective Inhibitor Design and Optimization
<p><span>Many approaches not only fail to consider the intricate binding pocket interactions, leading to molecules with suboptimal properties and stability, but also struggle with designing selective inhibitors. To address this challenge, we have developed an innovative structure-based three-dimensional molecular generation framework named </span><span>Coarse-grained and Multi-dimensional Data-driven molecular generation (CMD-GEN). This framework bridges three-dimensional ligand-protein complex data with two-dimensional drug-like molecule data by utilizing coarse-grained pharmacophore points sampled from diffusion models, thereby enriching the training data for generative models.</span> <span>Through a hierarchical architecture, it decomposes the generation of three-dimensional molecules within the pocket into sampling of coarse-grained pharmacophore points, generating of chemical structures, and alignment of conformations, avoiding the instability issues associated with inherent in deep generative model-based generation of molecular conformations.<br><br>This project provide the source dataset used to train and evaluate the overall model.<br></span></p>
Data and model output for "Evidence of subsurface control on the coevolution of hillslope morphology and runoff generation"
<p>Data, model output, and scripts supporting the manuscript:</p> <p>Litwin, D. G., & Harman, C. J. (2024) Evidence of subsurface control on the coevolution of hillslope morphology and runoff generation. <em>Water Resources Research</em>, 60, e2024WR037301. https://doi.org/10.1029/2024WR037301</p>
Supplemental Dataset Excel files and Source Data Excel file for "START domains generate paralog-specific regulons from a single network architecture"
<p>Supplemental Dataset Excel files and Source Data Excel file for "START domains generate paralog-specific regulons from a single network architecture" in Nat Comms</p>
Experimental data generated on the thermal behaviour during the intrusion–extrusion of ZIF-8 with different salt solutions
<p>/* **********<br>/* This work is licensed under a Creative Commons Attribution 4.0 International License.<br>/* **********</p> <p>Open access to experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union) along with the research on the thermal behaviour of hydrophobic porous materials to be used in intrusion-extrusion applications. Research pertaining to Task 3.1 (WP3).<br>Underlying data for the publication Bartolomé, L. et al. Tuning Wetting-Dewetting Thermomechanical Energy for Hydrophobic Nanopores via Preferential Intrusion. The Journal of Physical Chemistry Letters 2023, 15, 880-887. https://doi.org/10.1021/acs.jpclett.3c03330. Data related to Figures 1, 3 and 4 in the article.</p> <p>Dataset Identifier: 10.5281/zenodo.13927359 </p> <p>Contact person: Luis Bartolomé (CIC energiGUNE). ORCID: https://orcid.org/0000-0001-9649-1470</p> <p><br>The archive 'JPCL_Tuning.zip' contains 2 folders with 64 files in total.</p>
Generative AI for designing and validating easily synthesizable and structurally novel antibiotics: Data and Models
<p>This repository contains data and models used in the following paper.</p> <p>Swanson, K., Liu, G., Catacutan, D., Zou, J. & Stokes, J. <a href="https://www.nature.com/articles/s42256-024-00809-7">Generative AI for designing and validating easily synthesizable and structurally novel antibiotics</a>. <em>Nature Machine Intelligence, </em>2024.</p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/SyntheMol">SyntheMol</a> code. More details about how to use the data and models with the code are available <a href="https://github.com/swansonk14/SyntheMol/tree/main/docs">here</a>.</p> <p>The Data.zip file has the following structure. Note that the numbers for the Data subdirectories correspond to the supplementary data numbers in the paper (e.g., 1_training_data corresponds to Supplementary Data 1).</p> <p>Data</p> <p> 1_training_data: The <em>Acinetobacter baumannii</em> inhibition data used to train antibiotic property prediction models.</p> <p> 2_chembl: Known antibiotic and antibacterial molecules from <a href="https://www.ebi.ac.uk/chembl/">ChEMBL</a>, which are used to compute the novelty of generated antibiotic candidates.</p> <p> 4_real_space: Data files and statistics for the <a href="https://enamine.net/compound-collections/real-compounds/real-space-navigator">Enamine REAL Space</a>. The molecular building blocks file is version 2021 q3-4 while all other REAL Space details are computed from the full enumerated REAL space version 2022 q1-2 (downloaded on August 30, 2022).</p> <p> 5_generations_clogp: Compounds generated by SyntheMol using Chemprop models trained to predict cLogP.</p> <p> 6_generations_chemprop: Compounds generated by SyntheMol using Chemprop models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 7_generations_chemprop_rdkit: Compounds generated by SyntheMol using Chemprop-RDKit models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 8_generations_random_forest: Compounds generated by SyntheMol using random forest models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 9_synthesized: Information on the 58 SyntheMol-generated compounds that were successfully synthesized by Enamine.</p> <p>The Models.zip file contains one folder for each model used in the paper. Note that each model is technically an ensemble of ten individual models, so each directory contains ten model files.</p>
Experimental and theoretical data generated on optimization of the wetting-drying characteristics of hydrophobic metal organic frameworks via crystallite size
<p>Open access to experimental data generated by the project <a href="https://www.electro-intrusion.eu/en">Electro-Intrusion</a> (101017858, Horizon 2020, European Union) along with the research on the mechanical behaviour of hydrophobic porous materials to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2).</p> <p>Underlying data for the publication Johnson, L. J. W. et al. Optimization of the wetting-drying characteristics of hydrophobic metal organic frameworks via crystallite size: The role of hydrogen bonding between intruded and bulk liquid. Journal of Colloids and Interface Science 2023, 645, 775-783. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jcis.2023.04.059" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.jcis.2023.04.059</span></span></a>. Data related to Figures 2, 4, 5 and S1 in the article.</p>
Experimental and theoretical data generated to study effect of crystallite size on the flexibility and negative compressibility of ZIF-8
<p><span>Open access to experimental data generated by the project </span><a href="https://www.electro-intrusion.eu/en"><span>Electro-Intrusion</span></a><span> (101017858, Horizon 2020, European Union) along with the research on the thermal behaviour of hydrophobic porous materials to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2).</span></p> <p><span>Underlying data for the publication Johnson, L. J. W. et al. Effect of Crystallite Size on the Flexibility and Negative Compressibility of Hydrophobic Metal–Organic Frameworks. Nano Letters 2023, 23, 10682-10686. <a title="DOI URL" href="https://doi.org/10.1021/acs.nanolett.3c02431">https://doi.org/10.1021/acs.nanolett.3c02431</a> Data related to Figures 1, 2, and 3 in the article.</span></p>
Experimental data generated on the study of partial intrusion and extrusion of C8-CH3 silica
<p>Open access to experimental data generated by the project <a href="https://www.electro-intrusion.eu/en">Electro-Intrusion</a> (101017858, Horizon 2020, European Union) along with the research on the stability of hydrophobic porous materials to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2).</p> <p>Underlying data for the publication Paulo, G. et al. Partial Water Intrusion and Extrusion in Hydrophobic Nanopores for Thermomechanical Energy Dissipation. The Journal of Physical Chemistry C 2024, 128(29), 12036-12045. <a title="DOI URL" href="https://doi.org/10.1021/acs.jpcc.4c02900">https://doi.org/10.1021/acs.jpcc.4c02900</a>. Data related to Figures 2, 4 and 5 in the article.</p>
Benchmark datasets to study fairness in synthetic data generation
<p>The traveltime dataset is based on the Folktables project covering US census data. The target is a binary variable encoding whether or not the individual needs to travel more than 20 minutes for work; here, having a shorter travel time is the desirable outcome. We use a subset of data from the states of California, Florida, Maine, New York, Utah, and Wyoming states in 2018. Although the folktables dataset does not have any missing values, there are some values recorded as NaN due to the Bureau's data collection methodology. We remove the "esp" column, which encodes the employment status of parents, and has 99.55% missing values. We encode the missing values in the povpip, income to poverty ratio (0.85%), to -1 in accordance to the methodology in Ding et al.. See https://arxiv.org/pdf/2108.04884 for metadata.</p> <p>The cardio (a) dataset contains patient data recorded during medical examination, including 3 binary features supplied by the patient. The target class denotes the presence of cardiovascular disease. This dataset represents predictive tasks that allocate access to priority medical care for patients, and has been used for fairness evaluations in the domain.</p> <p>The credit dataset contains historical financial data of borrowers, including past non-serious delinquencies. Here, a serious delinquency is considered to be 90 days past due, and this is the target variable.</p> <p>The German Credit dataset (https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data) contains financial and personal information regarding loan-seeking applicants.</p>
Data from: Environmental heterogeneity and not vicariant biogeographic barriers generate community wide population structure in desert adapted snakes
Genetic structure can be influenced by local adaptation to environmental heterogeneity and biogeographic barriers, resulting in discrete population clusters. Geographic distance among populations, however, can result in continuous clines of genetic divergence that appear as structured populations. Here we evaluate the relevant importance of these three factors over a landscape characterized by environmental heterogeneity and the presence of a hypothesized biogeographic barrier in producing population genetic structure within 13 codistributed snake species using a genomic dataset. We demonstrate that geographic distance and environmental heterogeneity across western North America contribute to population genomic divergence. Surprisingly, landscape features long thought to contribute to biogeographic barriers play little role in divergence community wide. Our results suggest that isolation by environment is the most important contributor to genomic divergence. Furthermore, we show that models of population clustering that incorporate spatial information consistently outperform nonspatial models, demonstrating the importance of considering geographic distances in population clustering. We argue that environmental and geographic distances as drivers of community-wide divergence should be explored before assuming the role of biogeographic barriers.
Supplementary data for "Enhancing Resource-based Test Case Generation For RESTful APIs with SQL Handling"
<p>Supplement to: <em>Enhancing Resource-based Test Case Generation For RESTful APIs with SQL Handling</em></p> <p>In this repository, we provide <em>tests</em> and their <em>coverage reports</em> (conducted by Intellij coverage reports) that are used in the Discussion section of the paper.</p>
Code and data for publication "pyGRETA, pyCLARA, pyPRIMA: A pre-processing suite to generate flexible model regions for energy system models"
<p>This dataset contains the code of the three pre-processing tools <a href="https://github.com/tum-ens/pyGRETA">pyGRETA</a>, <a href="https://github.com/tum-ens/pyPRIMA">pyPRIMA</a> and <a href="https://github.com/tum-ens/pyCLARA">pyCLARA</a> and an examplary database for the scope of Austria.</p> <p>To run the code with full functionality additional data is needed. Check the documentation of the tools for further information.</p> <p> </p> <p>Sources for data can be found here: </p> <p>pyGRETA: https://pygreta.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyPRIMA: https://pyprima.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyCLARA: https://pyclara.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</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.