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1,855 results for “Framework”
Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data: intermediate results
<p>This directory contains all intermediate results generated in the "Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data" for reproducibility purpose. Associated scripts are available at https://github.com/JiayuSuPKU/Smoother_paper. Raw and processed data can be downloaded at https://zenodo.org/records/10223862.</p><p>Note that some intermediate results and figures in the notebooks may not be exactly the same as those in the paper due to the randomness in some analysis steps, but should be very close.</p>
Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data: raw and processed data files
<p>This directory contains all spatial and single-cell omics datasets analyzed in the "Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data". Associated scripts are available at https://github.com/JiayuSuPKU/Smoother_paper. See https://github.com/JiayuSuPKU/Smoother_paper/blob/main/data/README.md for details on folder structure and data sources.</p>
Data from: "A framework for performing comparative LCA between repairing flooded houses and construction of dikes in non-stationary climate with changing risk of flooding"
<p>In the paper "A framework for performing comparative LCA between repairing flooded houses and construction of dikes in a non-stationary climate with changing risk of flooding", life cycle assessment is used to compare two ways to maintain the state of a coastal urban area in a changing climate with increasing flood risk. On one side, the construction of a dike, a hard and proactive scenario, is modeled using a bottom-up approach. On the other, the systematic repair of houses flooded by sea surges, a post-disaster measure, is assessed using a Monte Carlo simulation allowing for aleatory uncertainties in predicting future sea level rise and occurrences of extreme events. Two metrics are identified, normalized mean impacts and probability of dike being most efficient. The methodology is applied to three case studies in Denmark representing three contrasting areas, Copenhagen, Frederiksværk, and Esbjerg. For all case studies the distribution of the calculated impact of repairing houses is highly right skewed, which in some cases has implications for the comparative LCA. </p><p>This dataset contains the underlying data to support the findings of the paper. In particular, two sets of characterized environmental impacts are reported: (1) the impacts of flood-related repairs summed over a century, for each Monte Carlo simulation and (2) the impacts of building a dam. Both sets of results are reported for each of the three cities studied.</p>
The Blockchain Trilemma: an Evaluation Framework (Replication Package)
<p>This repository contains essential data files used to generate graphs and statistics related to various blockchain ecosystems. These files are the result of aggregation and cleaning procedures applied to raw data collected from reputable sources within each respective blockchain network.<br> </p> <h2><strong>Transaction Per Second</strong></h2> <ol> <li><strong>Cardano</strong>: <ol> <li>Theoretical: 5.35</li> <li>Maximum: 5.74</li> </ol> </li> <li><strong>Solana</strong>:<br> <ol> <li>Theoretical: 710,000</li> <li>Maximum: 1763</li> </ol> </li> <li><strong>Arbitrum</strong>: <ol> <li>Theoretical: 40000</li> <li>Maximum: 3.09</li> </ol> </li> <li><strong>zkSync</strong>: <ol> <li>Theoretical: 2000</li> <li>Maximum: 0.521</li> </ol> </li> <li><strong>Polygon:</strong> <ol> <li>Theoretical: 7200</li> <li>Maximum: 101.97 </li> </ol> </li> <li><strong>Bitcoin</strong>: <ol> <li>Theoretical: 27</li> <li>Maximum: 4.53</li> </ol> </li> <li><strong>Ethereum</strong>: <ol> <li>Theoretical: 30</li> <li>Maximum: 19.86</li> </ol> </li> </ol> <h2>Nakamoto Coefficients</h2> <ol> <li><strong>arbitrum_aggregators.json</strong> <ol> <li><strong>Description:</strong> presents information on Arbitrum network aggregators' transaction count</li> <li><strong>Data Source: </strong>https://etherscan.io/apis</li> </ol> </li> <li><strong>cardano_stake.json</strong> <ol> <li><strong>Description:</strong> details stake-related data pertinent to the Cardano blockchain. For each exchange address the amount of holded stake</li> <li><strong>Data Source: </strong>https://pooltool.io/</li> </ol> </li> <li><strong>polygon_validators.json</strong> <ol> <li><strong>Description:</strong> provides insights into validators within the Polygon network. For each validator's name, the amount of held stake.</li> <li><strong>Data Source: </strong>https://wallet.polygon.technology</li> </ol> </li> <li><strong>solana_validators.json</strong> <ol> <li><strong>Description: </strong>provides information on validators operating within the Solana network. For each validator, the amount of held stake and details about the validator node.</li> <li><strong>Data Source: </strong>https://www.validators.app/</li> </ol> </li> <li><strong>zksync_validators.json</strong> <ol> <li><strong>Description: </strong>Contains data on validators associated with the zkSync protocol. For each validator's address, the amount of transactions processed.</li> <li><strong>Data Source: </strong>https://etherscan.io/apis</li> </ol> </li> </ol> <h2>Security Quantitative Evaluation</h2> <ol> <li><strong>Cardano</strong><br> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 119</li> <li>33% Stake: 895,706,097.1264836 ADA</li> <li>ADA Value @ 13 Dec-2023 = 0.59</li> <li>Cost of Attack: 895,706,097.1264836 * 0.59 = 528,466,597.3046253 $</li> </ol> </li> <li><strong>Solana</strong> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 18</li> <li>33% Stake: 126,950,233.4364128 SOL</li> <li>SOL Value @ 13 Dec-2023 = 71.78</li> <li>Cost of Attack: 126,950,233.4364128 * 71.78 = 9,112,487,756.06571 $</li> </ol> </li> <li><strong>Polygon</strong><br> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 2</li> <li>33% Stake: 1,016,718,726.6246895 MATIC</li> <li>MATIC Value @ 13 Dec-2023 = 0.8317</li> <li>Cost of Attack: 1,016,718,726.6246895 * 71.78 = 845,604,964.9337542 $</li> </ol> </li> <li><strong>Ethereum</strong> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 1</li> <li>33% Stake: 28907872.7327942/3 = 9,635,957.577598067 ETH</li> <li>ETH Value @ 08 Jan-2024 = 2133,90</li> <li>Cost of Attack: 9,635,957.577598067 * 2133,90 = 20,562,169,874.8365 $</li> </ol> </li> <li><strong>Bitcoin: </strong>$7.9 billion <a href="https://www.investopedia.com/terms/1/51-attack.asp#:~:text=4-,What%20Is%20a%2051%25%20Attack%3F,total%20hashing%20or%20validating%20power.">source</a></li> </ol>
A Framework to Promote Diversity & Inclusion in Workforce Development in the Southeast States
<p>The lack of workforce diversity has made it challenging for transportation agencies to </p><p>incorporate resiliency into their long-term plans effectively. This project developed a </p><p>framework for creating an inclusive and functional workforce environment for transportation </p><p>agencies in the southeast region. The project identifies existing gaps, predicts future needs, and </p><p>captures the role transportation agencies, research centers, Departments of Transportations </p><p>(DOTs), and professional organizations can play to recruit, train, and maintain a diverse and </p><p>inclusive workforce. This research will ultimately contribute to creating a resilient </p><p>transportation workforce structure in the southeast region. The goal of this project is to present </p><p>an Equity, Diversity, and Inclusion (EDI) -focused framework for promoting diversity and </p><p>inclusion within the workforce. </p>
Data for "Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra"
<p>This record contains neuroevolution potential (NEP) and tensor neuroevolution potential (TNEP) models (nep*.txt) for molecular water species, liquid water as well as barium zirconate, along with training data<i> (*</i>.zip). The models were constructed using GPUMD 3.9 (https://gpumd.org/).</p>
Experimental data from the initial validation of spark-eclib, a new framework for distributed metaheuristics in Spark
<p>This repository contains the experimental data obtained from two series of experiments to validate and profile <a href="https://doi.org/10.5281/zenodo.8431048"><strong>spark-eclib</strong></a>, a framework written in Scala to support the development of distributed population-based metaheuristics and their application to the global optimization of large-scale problems in Spark clusters:</p> <ul> <li>Experiments to validate the proposal of a generic topology for distributed PSO algorithms.</li> <li>Experiments to profile the parallel implementations of a PSO template.</li> </ul>
[Data] Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
<p>The experimental setup utilized a co-axial color Charged Couple Device (CCD) camera, integrated into the laser deposition head. This camera operates at a frame rate of 30 frames per second and captures the morphology of the process area. The captured images consist of three RGB channels with a 640 × 480 pixels resolution. To enable the camera to capture the radiation from the process zone, a beam splitter is installed on Precitec's laser applicator head. An optical notch filter within the 650–675 nm range also blocks the laser wavelengths.</p> <p>The dataset consists of four categories that covers the process map of DED process [.rar file].<br>The dataset consist of around 48,000 images that are labelled into 4 categories [P1-P2-P3-P4]. The images correspond to DED process zone captured co-axially<br>The categories are function of linear laser energy deposited. The folder is already split into Train and Test.</p>
Efficient Drug-Target Interactions Prediction Framework via Transferable Knowledge Fusing
<p><span>Drug-target integrations (DTI) prediction is a niche in drug discovery, streamlining the search for potential drugs. Computer-aided drug discovery (CADD) has gained traction for its precise predictions, efficiency, and adaptability across various situations. Yet, the computational demands of current top CADD models hinder their practical use due to heavy resource needs.</span></p> <p><span>In this research, we introduce TransFusE DTI, an effective framework for predicting DTIs that leverages pre-trained knowledge to construct models that optimize predictive accuracy while minimizing computational demands. The encoder uses a pre-extracted embedding vector from ProtBERT to reduce computational load and adapts a smaller ProtBERT model. It also includes target-related functional text to boost predictive accuracy. We evaluate the performance of TransFusE DTI using three widely-recognized benchmark datasets: BIOSNAP, DAVIS, and BindingDB, and compare its results to prior studies.</span></p> <p><span>Our results demonstrate that TransFusE DTI exhibits superior predictive performance on the BIOSNAP and BindingDB datasets. Notably, the model's parameter count is only 60% of that of the previous top-performing model by </span><span><a href="https://www.mdpi.com/1999-4923/14/8/1710"><span>Kang et al. (2022)</span></a></span><span>, and it operates efficiently with a learning rate of 26%. Furthermore, the model's video memory requirement is 11.2 GB, rendering it suitable for use on general-purpose Graphics Processing Units (GPUs). </span></p>
A Refined Supply-demand Framework to Quantify variability in Ecosystem Services Related to Surface Water in support of Sustainable Development Goals
<p>The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang.</p>
Datasets of synthetic task flow graphs for evaluating a latency/energy optimization task allocation framework
<p>These datasets of synthetic task flow graphs were generated to evaluate the performance and scalability of an optimal task allocation approach for applications of various structures and sizes in an environment following the edge/hub/cloud paradigm. The system under study comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objective was the minimization of either overall latency or overall energy consumption, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device.<br> <br>We generated 18 task flow graphs of parallel, serial, and mixed (a combination of parallel and serial) structure with 10, 100, and 1000 nodes, and various in/out degrees, utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt and in [3].<br> <br>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10654551.<br> <br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.<br>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, "An optimization framework for task allocation in the edge/hub/cloud paradigm," Future Generation Computer Systems, vol. 155, pp. 354-366, Jun. 2024, doi: 10.1016/j.future.2024.02.005.</p>
Room-temperature quantum coherence of entangled multiexcitons in a metal-organic framework
<p><span>Singlet fission (SF) can generate an exchange-coupled quintet triplet pair state</span><span> </span><sup><span>5</span></sup><span>TT, which could lead to the realization of quantum computing and quantum sensing using entangled multiple qubits even at room temperature. However, the observation of the quantum coherence of <sup>5</sup>TT has been limited to cryogenic temperatures, and the fundamental question is what kind of material design will enable its room-temperature quantum coherence. Here we show that the quantum coherence of SF-derived <sup>5</sup>TT in a chromophore-integrated metal-organic framework (MOF) can be over hundred nanoseconds at room temperature. The subtle motion of the chromophores in ordered domains within the MOF leads to the enough fluctuation of the exchange interaction necessary for <sup>5</sup>TT generation, but at the same time does not cause severe <sup>5</sup>TT decoherence. Furthermore, the phase and amplitude of quantum beating can be controlled by molecular motion, opening the way to room-temperature molecular quantum computing based on multiple quantum gate control.</span></p>
Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks
<p>This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article <em>Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks</em> by Zijun Deng and Lev Sarkisov.</p>
Beyond a single temperature threshold: applying a cumulative thermal stress framework to plant heat tolerance
<p>Most plant thermal tolerance studies focus on single critical thresholds, which limit the capacity to generalise across studies and predict heat stress under natural conditions. In animals and microbes, thermal tolerance landscapes describe the more realistic, cumulative effects of temperature. We tested this in plants by measuring the decline in leaf photosynthetic efficiency (F<sub>V</sub>/F<sub>M</sub>) following a combination of temperatures and exposure times, then modelled these physiological indices alongside recorded environmental temperatures. We demonstrate that a general relationship between stressful temperatures and exposure durations can be effectively employed to quantify and compare heat tolerance within and across plant species and over time. Importantly, we show how F<sub>V</sub>/F<sub>M</sub> curves translate to plants under natural conditions, suggesting that environmental temperatures often impair photosynthetic function. Our findings provide more robust descriptors of heat tolerance in plants and suggest that heat tolerance in disparate groups of organisms can be studied with a single predictive framework.</p>
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework
<p>We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count. The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit. We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mangoes. Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net. </p>
Data For: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
<p>HDF5 data files for 1249 supraglacial lakes detected in ICESat-2 ATL03 data over Central West Greenland (melt seasons 2019 and 2020) and the Amery Ice Shelf Catchment (melt seasons 2018-19 and 2020-21). Each HDF5 data file is associated with a .jpg "quicklook" file of the same name, showing ATL03 photon elevations with the estimated along-track fits to the lake surface and lakebed and the resulting maximum lake depth, along with the corresponding ICESat-2 ground track over cloud-free concurrent satellite imagery.</p> <p>The data files are structured as following: </p> <div> <div> <div> <div> <div> <pre>group: depth_data/ - dataset: bathymetry_confidence - dataset: lakebed_fit_elevation_meters - dataset: lat - dataset: lon - dataset: surface_fit_elevation_meters - dataset: water_depth_meters - dataset: x_along_track_meters group: fluid_bathymetry_peaks/ - dataset: elevation_meters - dataset: peak_prominence - dataset: x_along_track_meters group: mframe_data/ - dataset: delta_time - dataset: density_ratio_1 - dataset: density_ratio_2 - dataset: density_ratio_3 - dataset: density_ratio_4 - dataset: major_frame_id - dataset: passes_bathymetry_check - dataset: passes_flatness_check - dataset: photon_density_peak_elevation - dataset: q_1_number_peaks - dataset: q_2_prominece - dataset: q_3_elev_spread - dataset: q_4_alignment - dataset: q_s - dataset: x_along_track_meters_end - dataset: x_along_track_meters_start group: photon_data/ - dataset: afterpulse_probability - dataset: fluid_signal_confidence - dataset: geoid_elevation_meters - dataset: lat - dataset: lon - dataset: photon_elevation_above_geoid_meters - dataset: pulse_saturation_level - dataset: x_along_track_meters group: properties/ - dataset: beam_number - dataset: beam_strength - dataset: cycle_number - dataset: granule_id - dataset: gtx - dataset: ice_sheet - dataset: lake_quality - dataset: lat - dataset: lon - dataset: melt_season - dataset: rgt - dataset: sc_orient - dataset: surface_elevation - dataset: time_utc </pre> </div> </div> </div> </div> </div>
Code, benchmarks and experiment data for the ICAPS 2024 paper "Merging or Computing Saturated Cost Partitionings? A Merge Strategy for the Merge-and-Shrink Framework"
<p>This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper. Finally, it contains an appendix with some more detailed results for the paper.</p> <p>appendix.pdf: document with more detailed results ommitted in the paper.</p> <p>sievers-et-al-icaps2024-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 7.1 for reproducing all experiments of the paper, under experiments/scp-ms. The scripts 2024-03-* contain configurations for running the experiments and the script paper-crc.py gathers the data and produces plots and tables. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.)</p> <p>sievers-et-al-icaps2024-ipc-benchmarks.zip contains the IPC benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2023 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks).</p> <p>sievers-et-al-icaps2024-lab.tar.gz contains a copy of Lab 7.1 (https://github.com/aibasel/lab).</p> <p>sievers-et-al-icaps2024-raw-data.zip and sievers-et-al-icaps2024-processed-data.zip contain the experimental data. Directories without the "-eval" ending (sievers-et-al-icaps2024-raw-data.zip) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" suffix (sievers-et-al-icaps2024-processed-data.zip) contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.</p>
GSA-GxE: A Framework of Global Sensitivity Analysis of Maize Coupled with Genetics by Environments (GxE) Model
<p>We present the coupled Global Sensitivity Analysis (GSA) and Genetics by Environment model (GxE) framework (Sarzaeim and Muñoz-Arriola, submitted). GSA-GxE uses the sensitivity analysis method PAWN (Pianosi and Wagener, 2015) coupled with the environmental covariance matrix used in GxE modeling (Jarquin et al., 2014). GSA-GxE estimates the relative sensitivity of maize yield predictability to hydroclimate variables that interact with maize genetics from the environmental covariances and genetic marker structures. We include hydroclimate variables like temperature (T), solar radiation (SR), rainfall (R), and relative humidity (RH). The data, codes, and scripts presented here were used to develop and test the GSA-GxE framework. They were built upon an enhanced version of the multi-dimensional Genomes to Fields (G2F) database consisting of maize genetic, phenotypic, environmental, and metadata in 84 field experiments in 2014-2017 across the U.S. and province of Ontario, Canada (Sarzaeim et al., 2020, 2022, 2023). This digital package contains a multi-dimensional Climate and Omics dataset, the GSA-GxE framework created in Python, and the GxE model developed in R.</p> <p><strong>Acknowledgement</strong></p> <p>This work was supported by the Agriculture and Food Research Initiative Grant number NEB-21-176 and NEB-21-166 from the USDA National Institute of Food and Agriculture, Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production. In addition, we thank the Genomes to Fields (G2F) Initiative for providing the database; and Quantifying Life Sciences Initiative at the University of Nebraska-Lincoln.</p>
Data from: indirect costs of reproduction and the tradeoff between offspring size and number: a framework illustrated by fitness costs and benefits of ovarian fluid
<p>Theory describing evolution of offspring size often assumes that the production cost per unit volume is the same for small and large offspring. However, this may not be true if indirect costs of reproduction (e.g., material and energetic costs of supporting offspring development) scale disproportionately with offspring size. Here we show how direct and indirect costs of reproduction can be explicitly modeled within the Smith-Fretwell framework and how observations of size-number relationships can thus be used to evaluate indirect costs. We applied this analysis to measures of egg volume and fecundity for over 300 individuals of a coastal fish species and found that the tradeoff was much stronger than the expected inverse (fecundity scaled with volume<sup>-1.843</sup>). Larger offspring were thus more expensive to produce. For our study species, an important indirect cost was that larger eggs were accompanied by disproportionately more ovarian fluid. Calorimetry and removal experiments were used to further measure both the energetic costs and fitness benefits of ovarian fluid. In addition, we show that indirect costs of reproduction can intensify size-number tradeoffs in a variety of fishes. Indirect costs of reproduction can be large and may therefore play an important role in the evolution of offspring size.</p>
Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"
<p>Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders:</p> <p>MethodA_proj/</p> <p>MethodB_proj/</p> <p>MethodC_proj/</p> <p>MethodD_proj/</p> <p>MethodE_proj/</p> <p> </p> <p>Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details):</p> <p>FUTUREverif/</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.