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.
42
datasets available to search
ShareScore release 0.9.0
Dataset results
42 results for “efficient coding”
Code from: Efficiency traps beyond the climate crisis: Exploration-exploitation tradeoffs and rebound effects
<p>These data are part of a data portal that accompanies the special issue 'Climate change adaptation needs a science of culture,' published in Philosophical Transactions of the Royal Society B in 2023. To access the data portal, please visit:<strong> <a href="https://doi.org/10.5061/dryad.bnzs7h4h4">https://doi.org/10.5061/dryad.bnzs7h4h4</a>.</strong></p> <p>This code represents a computational model investigating the dynamics of coupled and decoupled resource use and efficiency gains. It can be used to simulate the effects of exploration-exploitation strategies on efficiency, consumption and sustainability, considering different levels of direct and indirect rebound effects. </p> <p>The model simulates a population of agents who make decisions on whether to explore or exploit a natural resource. These agents become more efficient over time based on their chosen strategy, affecting resource consumption. Different scenarios are considered, including various rebound effects, which influence how efficiency gains impact resource use.</p> <p>The key elements of the model include agents' uncertainty about the efficiency of their actions, the operationalization of efficiency as a reward, and the calculation of resource consumption based on efficiency gains and rebound effects. The model provides insights into how agents' decisions and resource use evolve over time under different conditions.</p> <p>This computational framework offers a valuable tool for exploring the complex dynamics of resource consumption and management in the face of environmental challenges. It can be applied to gain a deeper understanding of the Jevons Paradox and its implications for sustainable resource use.</p>
Linguistic law-like compression strategies emerge to maximize coding efficiency in marmoset vocal communication
<p>Human language follows statistical regularities or linguistic laws. For instance, Zipf's law of brevity states that the more frequently a word is used, the shorter it tends to be. All human languages adhere to this word structure. However, it is unclear whether Zipf's law emerged de novo in humans or whether it also exists in the non-linguistic vocal systems of our primate ancestors. Using a vocal conditioning paradigm, we examined the capacity of marmoset monkeys to efficiently encode vocalizations. We observed that marmosets adopted vocal compression strategies at three levels: (i) increasing call rate, (ii) decreasing call duration, and (iii) increasing the proportion of short calls. Our results demonstrate that marmosets, when able to freely choose what to vocalize, exhibit vocal statistical regularities consistent with Zipf's law of brevity that go beyond their context-specific natural vocal behavior. This suggests that linguistic laws emerged in non-linguistic vocal systems in the primate lineage.</p>
Data and code for "Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification"
<p>It contains the code and the data to reproduce the figures in the paper "Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification" published in arXiv: https://arxiv.org/abs/2309.09976</p>
Code and scripts used to generate results for: Designing transmissible viral vaccines for evolutionary robustness and maximum efficiency
Open the record for dataset details and reuse information.
Linguistic law-like compression strategies emerge to maximize coding efficiency in marmoset vocal communication
Open the record for dataset details and reuse information.
Code from: Efficiency traps beyond the climate crisis: Exploration-exploitation tradeoffs and rebound effects
Open the record for dataset details and reuse information.
Energy CSV files for analysing the energy efficiency of Code LLaMA generated code solutions
<p>This dataset contains CSV files for the different runs of the code solutions for three problems (Closest Numbers, Two Sum, String Replacement) in three programming languages (C++, JavaScript, Python) either generated by Code LLaMA upon request or implemented by a human. </p>
Dataset related to "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes"
<p>This is an accompanying dataset to the article with the title "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes" (DOI: <a href="https://doi.org/10.1101/2024.03.15.585196">10.1101/2024.03.15.585196</a>). It contains structural and activity data related to the morphologically detailed model of the rat somatosensory cortex (<a href="https://doi.org/10.1016/j.cell.2015.09.029" target="_blank" rel="noopener">Markram et al., 2015</a>), refered to as "BBP" data in the article. Specificaly, following data items are included:</p> <p><strong>Simulation data:</strong> <code>simulation.xz</code></p> <p><em>"Reliability" protocol:</em></p> <p>Separate folders <code>BlobStimReliability_O1v5-SONATA_<circuit-name></code> with simulation data using the baseline and all manipulated connectomes respectively (see Technical info below), each of which containing:</p> <ul> <li><code>working_dir/connectome.h5</code>: Connectivity matrix in <code>ConnectivityMatrix</code> format, which can be loaded using <a href="https://github.com/BlueBrain/ConnectomeUtilities" target="_blank" rel="noopener">ConnectomeUtilities</a>.</li> <li><code>working_dir/raw_spikes_exc_<sim>.npy</code>: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column). One file for each of the 10 simulations with different simulator seeds, i.e., <sim> is 0..9.</li> <li><code>working_dir/stim_stream.npy</code>: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.</li> <li><code>working_dir/time_windows.npy</code>: Time windows in ms in numpy .npy format, corresponding to the stimulus train.</li> <li><code>working_dir/processed_data_store.h5</code>: Data store in HDF5 format with preprocessed spike signals (e.g., required for <em>Gaussian kernel reliability</em> computations), which contains... <ul> <li><code>spike_signals_exc</code>: Group of simulations datasets "sim_0" to "sim_9", each of which is an array of size <#gids x #t_bins> and contains binned spike signals filtered with a Gaussian kernel.</li> <li><code>sigma</code>: Sigma in ms of Gaussian kernel used for smoothing.</li> <li><code>gids</code>: List of excitatory neuron GIDs.</li> <li><code>t_bins</code>: List of time bins in ms.</li> <li><code>firing_rates</code>: Average firing rates per simulation (0..9; rows) and (excitatory) neuron GID (columns); average firing rates were computed as the inverse of the mean inter-spike interval per neuron.</li> </ul> </li> </ul> <p><em>"Classification" protocol:</em></p> <p>Single folder <code>Toposample_O1v5-SONATA</code> with simulation data using the baseline connectome, stored in a format compatible with the <a href="https://github.com/JasonPSmith/TriDy" target="_blank" rel="noopener">TriDy</a> (<a href="https://doi.org/10.1162/netn_a_00228">Conceição et al., 2022</a>) and <a href="https://github.com/BlueBrain/topological_sampling/tree/merge_samples" target="_blank" rel="noopener">TopoSampling</a> (<a href="https://doi.org/10.1371/journal.pone.0261702">Reimann et al., 2022</a>) pipelines, containing:</p> <ul> <li><code>toposample_input/connectivity.npz</code>: Sparse connectivity matrix in Compressed Sparse Column format , which can be loaded using <code>scipy.sparse.load_npz</code>.</li> <li><code>toposample_input/neuron_info.pickle</code>: Pandas dataframe in pickle format, which can be loaded using <code>pandas.read_pickle</code>, containing additional information about each neuron.</li> <li><code>toposample_input/raw_spikes_exc.npy</code>: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column).</li> <li><code>toposample_input/stim_stream.npy</code>: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.</li> <li><code>toposample_input/time_windows.npy</code>: Time windows in ms in numpy .npy format, corresponding to the stimulus train.</li> </ul> <p> </p> <p><strong>Classification data:</strong> <code>classification.xz</code></p> <p>Selected neighborhoods and classification results for the "PCA" method (<a href="https://github.com/BlueBrain/topological_sampling/tree/merge_samples" target="_blank" rel="noopener">TopoSampling</a> pipeline) as well as the "network_based" method using active subnetworks (<a href="https://github.com/JasonPSmith/TriDy" target="_blank" rel="noopener">TriDy</a> pipeline, using <a href="https://github.com/jlazovskis/TriDy-tools" target="_blank" rel="noopener">TriDy-tools</a> wrapper), stored as:</p> <p><em>"PCA" method:</em></p> <ul> <li><code>PCA/community_database_PCA.pkl</code>: Pandas dataframe in pickle format, containing the binary selection of 50 neighborhood centers (neuron GIDs; rows) for the different selection parameters (columns).</li> <li><code>PCA/classification_results_PCA.pkl</code>: Pandas dataframe in pickle format, containing the classification accuracies for all selection parameters (rows) and 6 cross-validation folds plus mean (columns).</li> </ul> <p><em>"network_based" method:</em></p> <ul> <li><code>network_based/selections_reliability.pkl</code>: Pandas dataframe in pickle format, containing different combinations of first/second selection parameters for the <em>double selection procedure</em> (rows) together with neuron indices (w.r.t. the EXC subcircuit) of the corresponding 50 neighborhood centers (chief0..49; columns).</li> <li><code>network_based/partition_reliability.npy</code>: Partition indices in numpy .npy format required to launch the pipeline using <a href="https://github.com/jlazovskis/TriDy-tools" target="_blank" rel="noopener">TriDy-tools</a>, which is an array of 50 neuron indices (w.r.t. the full circuit!) of the neighborhood centers (columns) for each combination of selections as in <code>selections_reliability.pkl</code> (rows).</li> <li><code>network_based/results/...</code>: Subfolder containing a list of pickle files with the classification results using different featurization parameters, as indicated by the filename. Each file contains a Pandas dataframe with the classification accuracies and numbers of (non-zero) features (columns) for each combination of selections as in <code>selections_reliability.pkl</code> (rows).</li> </ul> <p> </p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Simulation codes and data from: Efficient Flocking: metric versus topological interactions
<p>Flocking is a fascinating phenomenon observed across a wide range of living organisms. We investigate, based on a simple theoretical particle model, how the emergence of ordered motion in a collectively moving group is influenced by the local rules of interactions among the individuals, namely, metric versus topological interactions as debated over in the current literature.</p>
Code and Data for "Efficiency and Resilience: Key Drivers of Distribution Network Growth"
<p>This repository contains the code and data used in the paper titled "Efficiency and Resilience: Key Drivers of Distribution Network Growth".</p> <p>The data includes adjacency matrices for the 22 distribution networks analyzed in the study.</p> <p>The code implements the network growth model presented in the paper.</p>
Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency: raw data and code
<p>Raw datasets and media accompanying the manuscript: <strong>Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency</strong>: data, published in <em>Applied Spectroscopy </em>in 2021</p>
Data and code for "Efficient calculation of the lattice thermal conductivity by atomistic simulations with ab-initio accuracy"
<p>This data set contains data and code related to the publication "Efficient calculation of the lattice thermal conductivity by atomistic simulations with ab-initio accuracy".</p>
No More In-Context Learning? Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
<p>Data and models part of the replication package of the ICSE 24 submission entitled "<em>No More In-Context Learning? Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models</em>".</p>
Simulation codes and data from: Efficient Flocking: metric versus topological interactions
Open the record for dataset details and reuse information.
The Hidden Cost of Code Completion: Understanding the Impact of the Recommendation-list Length on its Efficiency
<p>This contains the data set and the code we take advantage of to process data and make visualization. The data set needs to be unzipped before the program could run on it. Besides, we choose another visualization tool to make the results more explicit.</p>
MATLAB code and interfaces, quantum efficiency data for Hyper-sampling imaging
<p>Supplementary Materials for "Hyper-sampling imaging by measurement of intra-pixel quantum efficiency using steady wave field"</p>
Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation: Model Data and Analysis Code
<p>This repository contains model data and analysis codes related to the manuscript entitled "Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation", as follows:</p> <ol> <li>Model data are maximum water surface elevations of ensemble 48-hr forecast ADCIRC model simulations for three historical US landfalling hurricanes: 2017 Irma, 2018 Florence, and 2020 Laura. These are located in the "NameYYYY_Results.tar" archive files as "maxele.63.nc" files. Also included in the tar files are the hurricane forecast track files in Automated Tropical Cyclone Forecasting (ATCF) system format (*.22) and the error variable parameters (*.json) for each forecast. </li> <li>Model data of best-track runs for the 2017 Irma, 2018 Florence, and 2020 Laura hurricanes, and astronomical tide-only runs for the corresponding time periods are located in the "NameYYYY_besttrack+tides.tar" archive files. Both the maximum water surface elevations "maxele.63.nc" and the time series of water surface elevations "fort.63.nc" are included. </li> <li>ADCIRC input mesh (*.14) and mesh property files (*.13) are included in "ADCIRC_mesh_files.zip".</li> <li>Joint Karhunen-Loeve Polynomial Chaos (KL-PC) analysis python scripts with and without considering inundation are located in "klpc_analysis_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> <li>Python scripts for analyzing and plotting the KL-PC results (Figures 6-14 and Table 1 in the manuscript) are located in "results_plotting_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> </ol>
Main code and data for manuscript - Global Coal Power Plants Efficiency Analysis
<p>This repository contains the datasets and MATLAB code utilized in the study Revisiting Global Coal Power Plants Efficiency Gains in Delivering Climate Goals. The research integrates artificial neural networks (ANN), optimization models, and Monte Carlo-based sensitivity analyses to assess efficiency improvements in global coal-fired power plants and their potential contributions to climate objectives.</p>
Online repository for Paper "GTE: A Framework for Learning Code AST Representation Efficiently and Effectively"
<p>The online repository for the under review IJCAI2024 paper "<strong>GTE: A Framework for Learning Code AST Representation Efficiently and Effectively</strong>"</p><p><strong>GTE-main.zip</strong> contains the source code of GTE, please see <strong>README.md</strong> in GTE-main.zip<strong> </strong>for more guidance.</p><p><strong>Appendix.pdf </strong>contains more<strong> </strong>details about the dataset and probing task design.</p>
Code and data for "An allosteric switch ensures efficient unidirectional information transmission by the histidine kinase DesK from Bacillus subtilis"
<p>Code and data used in the paper "An allosteric switch ensures efficient unidirectional information transmission in the histidine kinase DesK from <em>Bacillus subtilis"</em></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.