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7,507 results for “generate”
Computational generation of long-range axonal morphologies
<p>This repository contains both the data and scripts to reproduce all figures of the related article.</p> <p>Usually, one should download all the files in a directory and read the README.md file.</p>
LLM Generated Synthetic Dataset of DoS Exposed Solidity Contracts
<p>This dataset provides the replication package for the paper 'Large Language Models for Synthetic Dataset<br>Generation: A Case Study on Ethereum Smart Contract DoS Vulnerabilities' accepted for publication at the 8th International Workshop on Blockchain Oriented Software Engineering. The provided sources encompass:<br>1) The synthetic contracts (Vulnerable, Exploit, and Patched contract for each use case) generated by Claude and GPT4.<br>2) The configuration files of the hardhat-based testing environment.<br>3) The test suite that showcases the vulnerabilities of the generated contracts (including mock contracts) (hardhat is required to run and test contracts).<br><br></p> <div> <p> </p> </div>
A Subset of HTP-MD dataset used for training different generative models
Open the record for dataset details and reuse information.
PrimeKGQA, the dataset from paper: Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset
<p>Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total,<br>we create 83999 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset – PrimeKGQA – serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA system.</p>
Time series generated by nonlinear Langevin equation
<p>Datasets used in papers:</p> <p>Telesca L. and Z. Czechowski, Fisher–Shannon Investigation of the Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series, Entropy 2023, 25, 1650.</p> <p>Czechowski Z. and L. Telesca, Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series, Chaos, Solitons and Fractals 183 (2024), 114927</p>
Monthly Hydropower Generation Dataset for Western Canada
<p>The presented dataset contains the following simulation-based monthly hydropower generation data for 110 facilities in British Columbia and Alberta, to support Western-US interconnect grid system studies:<br>1) Monthly hydropower generation estimates<br>2) Monthly hydropower flexibility metrics (minimum and maximum hourly generation and daily fluctuations)</p> <p>The hydropower generation estimates are provided with reference to the facility list that contains the corresponding metadata for each facility.</p> <p>For more details, please refer to Son, Y., Bracken, C., Broman, D. et al. Monthly hydropower generation data for Western Canada to support Western-US interconnect power system studies. <em>Sci Data</em> <strong>12</strong>, 874 (2025). <a href="https://doi.org/10.1038/s41597-025-05098-2" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-05098-2</a>.</p> <p>Corresponding author(s): Youngjun Son (youngjun.son@pnnl.gov) and Nathalie Voisin (nathalie.voisin@pnnl.gov)</p> <p>For data reproduction, please see the GitHub repository at <a title="tgw-hydro-canada" href="https://github.com/GODEEEP/tgw-hydro-canada" target="_blank" rel="noopener">https://github.com/GODEEEP/tgw-hydro-canada</a>.</p> <h1>Hydropower Facility List</h1> <p>The file, <code><strong>CAN_hydropower_facilities&scaling.csv</strong></code>, provides essential information on 146 hydropower facilities in British Columbia and Alberta, derived from <a title="Renewable Energy Power Plants, 1 MW or more, by Energy Source" href="https://www.eia.gov/trilateral/#!/maps" target="_blank" rel="noopener">Renewable Energy Power Plants, 1 MW or more, by Energy Source</a> by North American Cooperation on Energy Information (NACEI). Additionally, the facility information has been updated with corresponding <a title="National Hydrographic Network (NHN) Work Units" href="https://open.canada.ca/data/en/dataset/a4b190fe-e090-4e6d-881e-b87956c07977">National Hydrographic Network (NHN) Work Units</a>, global reservoir and lake database (<a title="GRanD: Global Reservoirs and Dams Database" href="https://www.globaldamwatch.org/grand" target="_blank" rel="noopener">GRanD: Global Reservoirs and Dams Database</a> and <a title="HydroLAKES" href="https://www.hydrosheds.org/products/hydrolakes" target="_blank" rel="noopener">HydroLAKES</a>), diversion intake flow rates based on water license information (hydropower), and so on. Below are the descriptions for each column in the facility metadata:</p> <ul> <li><em>fid</em>: Facility id according to NACEI data. New four-digit id starting with '9' are assigned for facilities with no fid in NACEI data</li> <li><em>Facility</em>: Name of the facility</li> <li><em>X</em>: Longitude of the facility's powerhouse</li> <li><em>Y</em>: Latitude of the facility's powerhouse</li> <li><em>Province</em>: Province where the facility is located</li> <li><em>Hydro_MW</em>: Nameplate capacity of the facility</li> <li><em>NHN_Work_U</em>: Associated NHN Work Units</li> <li><em>GRanD_ID</em>: Associated reservoir id from the GRanD dataset</li> <li><em>HydroLAKES_ID</em>: Associated lake id from the HydroLAKES dataset</li> <li><em>GINDEX</em>: Grid id from the mosartwmpy Canada model</li> <li><em>GINDEX_CONUS</em>: Grid id from the mosartwmpy CONUS model, used for facilities in the Columbia River Basin</li> <li><em>Basin_Note</em>: Indicator for facilities located in the Columbia River Basin or outside of the meteorological forcing domain of the perturbed thermodynamics simulations</li> <li><em>WECC_ADS_2032</em>: Indicator for facilities without the WECC ADS 2032 reference hydropower generation data</li> <li><em>Intake_Flow_Rate</em>: Diversion intake flow rates based on hydropower water license information</li> <li><em>Type</em>: Type of facility</li> <li><em>Water_License</em>: Link to the source of water license information</li> <li><em>Scaling</em>: Annual total scaling factor (total hydropower generation / total streamflow volume for 2008)</li> <li><em>Scaling_IntakeCap</em>: Annual total scaling factor, constrained by intake flow rates from hydropower water license (total hydropower generation / total streamflow volume not exceeding intake flow rate constraint for 2008)</li> </ul> <p>Among the 146 hydropower facilities listed, only 110 facilities, which are within the applied meteorological forcings domain and have reference hydropower generation data, are considered for monthly hydropower generation estimates.</p> <h1>Monthly Hydropower Generation Estimates and Flexibility Metrics</h1> <p>Each file contains a monthly timeseries dataset (rows: monthly timestamps) from 1981 to 2019 for 110 facilities (columns: <em>Facility</em> listed in <strong><code>CAN_hydropower_facilities&scaling.csv</code>).</strong></p> <ol> <li><code><strong>CAN_hydropower_monthly_generation_MWh.csv</strong></code>: monthly total hydropower generation in MWh</li> <li><code><strong>CAN_hydropower_monthly_p_min_MW.csv</strong></code>: monthly flexibility metric of minimum generation capacity in MW</li> <li><code><strong>CAN_hydropower_monthly_p_max_MW.csv</strong></code>: monthly flexibility metric of maximum generation capacity in MW</li> <li><code><strong>CAN_hydropower_monthly_p_ador_MW.csv</strong></code>: monthly flexibility metric of the daily operation range in MW</li> </ol> <h1>Update Log</h1> <p><strong>- V</strong><strong>ersion 1.1.0</strong>: "Scaling" and "Scaling_IntakeCap" colums have been added to <strong>Hydropower Facility List</strong>, and the file for hydropower facilities has been renamed from <code><strong>CAN_hydropower_facilities.csv</strong></code> to <code><strong>CAN_hydropower_facilities&scaling.csv</strong></code>.</p> <h1>Funding Acknowledgements</h1> <p>This work was supported under the Laboratory Directed Research and Development (LDRD) Program (Project # 79583) at the Pacific Northwest National Laboratory (PNNL).</p> <p>The PNNL is a multi-program national laboratory operated by Battelle Memorial Institute for the U.S. Department of Energy (DOE) under Contract No. DE-AC05-76RL01830.</p> <h1>Disclaimer</h1> <p>The presented dataset aims to support robust, long-term power system planning under diverse water conditions. However, it should not be used to assess hydropower generation during extreme flood events when facilities may need to be disconnected from power grids due to dam safety and potential loss of control that could propagate into grid instability. Similarly, the dataset should not be utilized for unprecedented drought conditions where reservoir levels may fall below critical power pool levels. Furthermore, evolving water policies, including the Columbia River Treaty, can alter seasonal and monthly hydrological patterns. It is important to note that our hydropower generation dataset, which is derived based on Year 2008, does not account for any historical and future changes in environmental regulations, water management, or water policies.</p> <p>The dataset was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor the U.S. Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof, or Battelle Memorial Institute.</p>
Data and Code: Familial transmission of neural representations for mental arithmetic across two generations
<p>Here we provide anonymized behavioral data, individual beta maps and analyses codes used in "Familial transmission of neural representations for mental arithmetic across two generations".</p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu/">GDPR</a>), we cannot provide raw MRI data. <br>Therefore, the fMRI data consists of individual beta maps from the first-level analysis, which correspond to the brain activity associated with increases in problem size for each operation (addition and subtraction). Maps are normalized into the MNI template. See paper for details about the preprocessing and first-level analysis.</p> <p>The dataset consists of mother-child dyads. Mothers are assigned codes of 200 or higher. Children are assigned codes below 200. Each child's code is exactly 200 less than their mother's code.</p> <p>The analyses codes require Python version 3.8.8 and Nilearn version 0.8.1.</p> <p>If you have any questions, please send an email to charlotte.constant@inserm.fr. </p> <p> </p>
Touché25-Image-Retrieval-and-Generation-for-Arguments
<p>Data for the <a href="https://touche.webis.de/clef25/touche25-web/image-retrieval-for-arguments.html">Image Retrieval/Generation for Arguments</a> task at Touché 2025.</p> <p> </p> <p>Only the main.zip and nodes.zip are uploaded here due to space restrictions. Find the web page screenshots and web archives here: <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-25/version-2025-04-02/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-25/version-2025-04-02/</a></p>
Metadata of "Polydopamine/Ethylenediamine Nanoparticles Embedding a Photosynthetic Bacterial Reaction Center for Efficient Photocurrent Generation"
<p>Metadata of "Polydopamine/Ethylenediamine Nanoparticles Embedding a Photosynthetic Bacterial Reaction Center for Efficient Photocurrent Generation"</p>
Data and source code for Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
<p>We present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.</p>
Dataset of "Critical role of H-aggregation for high-efficiency photoinduced charge generation in pristine pentamethine cyanine salts"
<p>Dataset underpinning the published article:</p> <p>Critical role of H-aggregation for high-efficiency photoinduced charge generation in pristine pentamethine cyanine salts Phys. Chem. Chem. Phys. 2021, 23, 23886-23895. DOI: 10.1039/D1CP03251H</p>
Phase response analyses support a relaxation oscillator model of locomotor rhythm generation in Caenorhabditis elegans
<p>This dataset contains all data and codes that are used in the manuscript entitled "Phase response analyses support a relaxation oscillator model of locomotor rhythm generation in <em>Caenorhabditis elegans</em>".</p> <p>The data include raw videos and intermediate data for optogenetic experiments of all strains, experimental conditions (illumination duration, illuminated region, fluid viscosity and date). Within the parent folder 'Videos', each subfolder represents data of a group of experiments using the same strain under the same condition, as indicated explicitly by the subfolder name. Within each subfolder, there are raw videos of freely moving worms perturbed by transient optogenetic perturbations and intermediate data which include locomotory information and the corresponding figure plots (kymographs) that were generated by analysing the raw videos with the image analysis software (also in the dataset)</p> <p>The codes include scripts for image data analysis and model simulations. The image data analysis codes include scripts specifically for generating phase portrait graphs, phase response curves, head oscillation stability plots, phase isochron map and vector field. The model simulation codes include scripts for model oscillators implementation, paramter estimation/optimization and simulations of optogenetic inhibition.</p>
Code to generate figures 3 and 4 of: "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."
<p>Code to generate figures 3 and 4 of the manuscript titled "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."</p> <p> </p>
Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa's rangelands and fill Protected Area funding gaps
<p>Many savanna-dependent species in Africa including large herbivores and apex predators are at increasing risk of extinction. Achieving effective management of protected areas (PAs) in Africa where lions live will cost an estimated USD >$1-2 B/year in new funding. We explored the potential for fire management-based carbon-financing programs to fill this funding gap and benefit degrading savanna ecosystems. We demonstrated how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods ranging from USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M–$1.7 B/year (at USD $13/ton). We highlighted variable but significant PCR for savanna PAs from USD $1.5–$44.4 M/year per PA. We suggest investing in fire management programs to jump-start the United Nations Decade of Ecological Restoration to help restore degraded African savannas and conserve imperiled keystone herbivores and apex predators. <br> <br> Open Access article: <a href="https://doi.org/10.1016/j.oneear.2021.11.013">https://doi.org/10.1016/j.oneear.2021.11.013</a></p>
Audio samples from generative models trained on the TIMIT speech data.
<p>This is a posting of audio snippets to accompany the paper "Benchmarking Generative Latent Variable Models for Speech".</p> <p>The snippets include samples and reconstructions. All samples are completely unconditional and utilise only the prior internal representations learned by the model. Reconstructions are computed from a given test audio snippet by first encoding it to a learned representation and then decoding that to a reconstruction of the audio.</p> <p>All models are trained on the TIMIT speech dataset (<a href="https://catalog.ldc.upenn.edu/LDC93s1">https://catalog.ldc.upenn.edu/LDC93s1</a>). Some snippets are from models trained at different temporal resolutions denoted by `s1` and `s64`. We refer to the paper for details.</p> <p>The files include:</p> <ul> <li>`clockwork-vae-s64-reconstruction-*` <ul> <li>Four reconstructions using a two-layered Clockwork VAE trained with temporal resolution s=64.</li> </ul> </li> <li>`clockwork-vae-s64-sample-*` <ul> <li>Four samples from the prior of a Clockwork VAE trained with temporal resolution s=64.</li> </ul> </li> <li>`original-*` <ul> <li>Four original samples from TIMIT corresponding in pairs to the reconstructions.</li> </ul> </li> <li>`vrnn-s64-sample-*` <ul> <li>Two samples from the prior of a VRNN trained with temporal resolution s=64.</li> </ul> </li> <li>`vrnn-s1-sample-*` <ul> <li>Two samples from the prior of a VRNN trained with temporal resolution s=1.</li> </ul> </li> <li>`srnn-s64-sample-*` <ul> <li>Two samples from the prior of a SRNN trained with temporal resolution s=64.</li> </ul> </li> <li>`srnn-s1-sample-*` <ul> <li>Two samples from the prior of a SRNN trained with temporal resolution s=1.</li> </ul> </li> <li>`wavenet-s64-sample-*` <ul> <li>Four samples from a WaveNet trained with temporal resolution s=1.</li> </ul> </li> <li>`wavenet-s1-sample-*` <ul> <li>Two samples from a WaveNet trained with temporal resolution s=64.</li> </ul> </li> </ul>
Original data and RML mapping used to generate RDF results for K-CAP 2021
<p>These materials include the following:</p> <ul> <li>The csvs used to track the accepted papers, authors and resources in K-CAP 2021</li> <li>The RML mappings generated to transform them to RDF (paths to data will have to be adjusted)</li> <li>The resultant RDF file generated when applying the mapping (out.ttl)</li> </ul>
Podcast annotation dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts "
<p>Dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts". Please refer to the paper for details. Compared to the dataset used in the paper, 20 out of the 417 episodes have been removed due to copyright issues. </p> <p>The data file contains the following fields:</p> <p>- "episode_intro_start": the time stamp for episode introduction start (in milliseconds)</p> <p>- "episode_intro_end": the time stamp for episode introduction end (in milliseconds)</p> <p>- "program_intro_start": the time stamp for program introduction start (in milliseconds)</p> <p>- "program_intro_end": the time stamp for program introduction end (in milliseconds)</p> <p>- "program_name": name of the podcast program</p> <p>- "episode_name": name of the podcast episode</p> <p>- "transcription": JSON string containing the transcription, including the timestamps.</p> <p>- "annotator": anonymized annotator ID.</p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, main part
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <p><strong>Datasets:</strong></p> <ul> <li> <strong>models.zip </strong>- Datasets parameterizing kinetic nonlinear models of a wild-type <em>E. coli </em>strain used for training generative adversarial networks <ul> <li>subfolder 1: kinetic - contains the kinetic model (kin_varma_curated.yml)</li> <li>subfolder 2: thermo - contains the thermodynamic model for all the four physiologies (varma_fdp1, varma_fdp2, varma_fdp3, varma_fdp4)</li> <li>subfolder 3: steady_state_samples: contains the TFA steady state profiles for all four physiologies (samples_fdp1, sample_fdp2, samples_fdp3, samples_fdp4)</li> <li>subfolder 4: parameters - contains the kinetic parameter training dataset for each physiology (.hdf5 files), maximal eigenvalues (training labels) (maximal_eigenvalues.csv) and the minimum eigenvalues (minimal_eigenvalues.csv)</li> </ul> </li> <li><strong>vanilla_learning_training.zip:</strong> contains 4 folders for each of the 4 physiologies. <ul> <li>each of these folders contains 6 subsubfolders in the format N-<em>{n} </em>( N-10, N-50, N-100, N-500, N-1000, N-72000), where <em>{n} </em>represents the number of used training data samples.</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy (Note: eigenvalues were not calculated for N=10, 50, 100 as traning failed)/</li> </ul> </li> </ul> </li> <li><strong>transfer_learning_training.zip</strong> - contains 12 subfolders "tl_fdpi_fdpj" where i,j ={1,2,3,4} for each of the 12 transfer learning case <ul> <li>each of these folders contains 5 subsubfolders N-10, N-50, N-100, N-500, N-1000</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy </li> </ul> </li> </ul> </li> </ul> <ul> <li><strong>best_generators.zip</strong> <ul> <li>The best generators (with the highest incidence of relevant models) for each physiology (generator1- 4.h5)</li> <li>The normalizing scaling parameters for each generator (d_scaling.pkl).</li> <li> </li> </ul> </li> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>vanilla_ODE_sample_parameters.zip</strong> - contains (i) 1000 REKINDLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total) (ii) 1000 ORACLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total). These parameter sets parameterize the ODEs which are integrated.</li> <li><strong>ode_solutions_physiology1.zip (available at </strong><a href="https://zenodo.org/record/5818192">https://zenodo.org/record/5818192</a><strong>) - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li><strong>ode_solutions_physiology1_ORACLE.zip (available at </strong><a href="https://zenodo.org/record/5819669">https://zenodo.org/record/5819669</a><strong>) - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li><strong>ode_solutions_physiologies2-4.zip -</strong> contains 6 subfolders (physiology_2-4, physiology_2-4_ORACLE), with each subfolder containing 10 sub subfolders. Each sub subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE / ORACLE generated parameter sets for physiology 2-4, each of the 1000 models having a random perturbation.</li> <li><strong>transfer_learning_ODE_solutions.zip - </strong>contains two subfolders N_10, N_50, each subfolder contains 12 subsubfolders titled i_j (where i = {1,2,3,4} and j = {1,2,3,4} where 1_2 represent the transfer learning case from physiology 2 to physiology 1 and when using <em>{n}</em> samples from physiology 2 and so on (where <em>{n}</em>=10 and 50 respectively). Each subsubfolders contain <ul> <li>i_j.hdf5: contains 300 kinetic parameter sets generated using (i) REKINDLE for this transfer learning case</li> <li>i_j.csv: the maximal eigenvalues of the parameter sets</li> <li>solutions.csv: ODE integrated time series data for the relevant kinetic parameters out of the 300 generated.</li> </ul> </li> </ul> </li> </ul> <p> </p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 2
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <ul> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>ode_solutions_physiology1_ORACLE.zip</strong> - contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> </ul> </li> </ul> <p>The detailed instructions and the main body of the dataset is available here: <a href="https://zenodo.org/record/5803120">https://zenodo.org/record/5803120</a></p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 1
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <ul> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>ode_solutions_physiology1.zip - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> </ul> </li> </ul> <p>The detailed instructions and the main body of the dataset is available here: <a href="https://zenodo.org/record/5803120">https://zenodo.org/record/5803120</a></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.