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.
575
datasets available to search
ShareScore release 0.9.0
Dataset results
575 results for “Modeling Codes”
Parameter uncertainty quantification of wake models to analyze effects of wake superposition: data and code
<p>Codebase for wake deficit, wake superposition, and wake-added turbulence modeling within Markov-chain Monte Carlo framework. Data for results and figures in associated paper is also included.</p>
Populations of local direction-selective cells encode global motion patterns generated by self-motion. Data, Code and Model.
<p>Directional tuning of the population of local motion detectors T4/T5 in the visual system of the fruit fly <em>Drosophila melanogaster</em>. Direction tuning and receptive field location was measured by recording responses to visual stimuli containing dark or bright edges/stripes moving into 8 directions. All provided MATLAB scripts were used to analyze and illustrate data show in the manuscript 'Populations of local direction-selective cells encode global motion patterns generated by self-motion.'</p> <p>All data were obtained using <em>in vivo </em>two photon microscopy. Image time series were preprocessed using SIMA python software for motion alignment and further processed using custom written matlab or python code.</p> <p>Please find all relevant information to use the code in the README file.</p>
Data, code and supplementary material for "A data integration framework for spatial interpolation of temperature observations using climate model data"
<p>Each zipped file contains code and data to reproduce the results in the paper and supplementary material. The Cyprus folder contains also the files to run the model, as well as the associated results. The Morocco folder only contains the results and the code used to manipulate it. </p>
CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models
<p>The raw datasets and tasks of the paper "CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models". Source code is available at https://anonymous.4open.science/r/CrossCodeBench-C538/.</p>
Holocene Reconstruction: Model and proxy data for running code
<p>This is the model output and proxy data for running the Holocene data assimilation.</p> <p>The data assimilation code can be found by searching for "Holocene reconstruction code". Newer versions of the code may be found at https://github.com/Holocene-Reconstruction/Holocene-code. To read more about the Holocene data assimilation, see the paper Erb et al., 2022: "Reconstructing Holocene temperatures in time and space using paleoclimate data assimilation".</p> <p>Once downloaded, unzip this holocene_da_data.zip file. It contains a variety of directories. Some are empty but are present to accommodate output generated by the data assimilation code mentioned above. Directories with data are:</p> <ul> <li>models/original_model_data/TraCE_21ka/: TraCE-21ka temperature output from: https://www.earthsystemgrid.org/project/trace.html</li> <li>models/original_model_data/HadCM3_transient21k/: HadCM3 temperature output (the spatially smoothed version, signified by "_s" at the end of the filename)</li> <li>proxies/temp12k/ - Temperature 12k proxy records from: https://lipdverse.org/</li> </ul>
Codes in R for spatial statistics analysis, ecological response models and spatial distribution models
<p>In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg <em>et al.</em>, 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).</p> <p>It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter <em>et al.</em>, 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (<em>e.g.</em>, Naoki <em>et al.</em>, 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:</p> <p>In the first instance, we generated the background points and extracted the values of the variables (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code2_Extract_values_DWp_SC.R?versionId=c1ea0c61-53fe-4f95-ab88-0c1cb28399cb">Code2_Extract_values_DWp_SC.R</a>). Barbet-Massin <em>et al. </em>(2012) recommend the use of 10,000 background points when using regression methods (<em>e.g.</em>, Generalized Linear Model) or distance-based models (<em>e.g.</em>, DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (<em>e.g.</em>, bioclimatic, topographic, demographic, habitat) in function of presence and background points (<em>e.g.</em>, Hijmans and Elith, 2017).</p> <p>Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).</p> <p>After that, we ran the code for the GBM method (Gradient Boost Machine; <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code3_GBM_Relative_contribution.R?versionId=1656bbae-66aa-409e-bb91-d8007dee8f95">Code3_GBM_Relative_contribution.R</a> and <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code4_Relative_contribution.R?versionId=0e1d9352-e6b2-43da-984b-d6853a914258">Code4_Relative_contribution.R</a>), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (<em>e.g.</em>, Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.</p> <p>Subsequently, the correlation of the variables is run by Pearson's method (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code5_Pearson_Correlation.R?versionId=275f8dd4-b056-44d2-bfe5-f6264bc3298b">Code5_Pearson_Correlation.R</a>) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (<em>e.g.</em>, Awan <em>et al.</em>, 2021).</p> <p>Once the above codes were run, we uploaded the same subgroups (<em>i.e.</em>, presence and background groups with 75% training and 25% testing) (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code6_Presence&backgrounds.R?versionId=d797b528-782f-4a19-bd61-cfb197f38513">Code6_Presence&backgrounds.R</a>) for the GLM method code (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code7_GLM_model.R?versionId=e4aca276-d601-49ec-a62c-a9223b05a7ed">Code7_GLM_model.R</a>). Here, we first ran the GLM models per variable to obtain the <em>p</em>-significance value of each variable (alpha ≤ 0.05); we selected the value one (<em>i.e.</em>, presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (<em>e.g.</em>, Fielding and Bell, 1997; Allouche <em>et al.</em>, 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.</p> <p>On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).</p> <p>Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).</p> <table align="center"> <tbody> <tr> <td> <p> </p> </td> <td> <p>Validation set</p> </td> </tr> <tr> <td> <p>Model</p> </td> <td> <p>True</p> </td> <td> <p>False</p> </td> </tr> <tr> <td> <p>Presence</p> </td> <td> <p>A</p> </td> <td> <p>B</p> </td> </tr> <tr> <td> <p>Background</p> </td> <td> <p>C</p> </td> <td> <p>D</p> </td> </tr> </tbody> </table> <p>We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche <em>et al.</em>, 2006).</p> <p>The last code (<em>i.e.</em>, <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code8_DOMAIN_SuitHab_model.R?versionId=d951a8f2-d3a4-4804-b862-1b2762061876">Code8_DOMAIN_SuitHab_model.R</a>) is for species distribution modelling using the DOMAIN algorithm (Carpenter <em>et al.</em>, 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.</p> <p>Regarding the model evaluation and estimation, we selected the following estimators:</p> <p>1) partial ROC, which evaluates the approach between the curves of positive (<em>i.e.</em>, correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).</p> <p>2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long <em>et al.</em>, 1988).</p>
Model setup code and input for internal tide-eddy simulation
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>The dataset includes the setup code and input files for simulations of internal tide-eddy interactions using MITgcm. Due to the large size of the model output data, it is not included but can be made available upon request at <a target="_new">yangwangow@gmail.com</a>. If you have any questions about using or testing these files, please feel free to reach out. Cheers!</p> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Grounded Copilot: How Programmers Interact with Code-Generating Models
<p>Powered by recent advances in code-generating models, AI assistants like Github Copilot promise to change the face of programming forever. But what is this new face of programming? We present the first grounded theory analysis of how programmers interact with Copilot, based on observing 20 participants---with a range of prior experience using the assistant---as they solve diverse programming tasks across four languages. Our main finding is that interactions with programming assistants are bimodal: in acceleration mode, the programmer knows what to do next and uses Copilot to get there faster; in exploration mode, the programmer is unsure how to proceed and uses Copilot to explore their options. Based on our theory, we provide recommendations for improving the usability of future AI programming assistants.</p> <p> </p> <p>This artifact contains:<br> - The scripts to generate our plots</p> <p>- Detailed study information to re-run our user study</p> <p>- Livestreams that we observed and included in our dataset</p> <p>- Our codebook</p>
Data and code supporting Thomas & Yelland 2023 "Terrestrial Effects of Nearby Supernovae: Updated Modeling"
<p>Data and code supporting publication Thomas & Yelland 2023 "Terrestrial Effects of Nearby Supernovae: Updated Modeling"</p> <p>Paper citation: Brian C. Thomas and Alexander M. Yelland 2023 ApJ 950 41<br> DOI: 10.3847/1538-4357/accf8a</p> <p>Contents of this repository:</p> <p>* Data files (in netCDF format) generated by GSFC 2D atmospheric chemistry-dynamics model, for cases described in referenced paper.</p> <p>* Code "SNCR_Flux_and_Ionization.m" used to calculate supernova cosmic ray proton flux and resulting terrestrial atmospheric ionization rates, for cases described in the referenced paper. Also includes necessary input data files.</p> <p>* Data files (SNCR_flux.zip and SNCR_ionization.zip) generated by "SNCR_Flux_and_Ionization.m" code, as presented, discussed, and used in referenced paper.</p> <p>* Code "SNCR_muon-flux.m" used to calculate terrestrial surface-level cosmic ray secondary muon flux in the cases described in referenced paper, as well as biological damage values due to that flux. Also includes necessary input data files.</p> <p>Code files were created and run in Octave, and should run on Matlab as well.</p> <p> </p>
Code and data for: Modeling the interaction between salmon management and consumption by coastal brown bears
<p>Harvest management policy for species with strong trophic connections can reverberate through food webs and cause unintended consequences such as altering the abundance of a harvested species' predators or prey. Pacific salmon (<em>Oncorhynchus</em> spp.), a key food for many predators and an economically valuable harvested species, is generally managed for maximum sustained harvests without explicit consideration for the freshwater and terrestrial food webs which they support. The density of brown bear (<em>Ursus</em> <em>arctos</em>) populations in Alaska, USA is correlated with the amount of salmon they can access and consume, so it seems likely their populations are inadvertently affected by salmon management. We simulated the effect of salmon management policy on brown bears by customizing a general bear-salmon model using empirical data from three watersheds in southwest Kodiak, Alaska. Our goal was to quantify the effect of current salmon management policy (i.e., escapement goals and early/late run allocations) on salmon consumption by brown bears. Bears in the individually based model evaluated the value of each foraging site based on salmon abundance, salmon vulnerability, and competition with other bears and made movement decisions (among salmon spawning sites) accordingly. </p> <p>The two code files provided here contain the brown bear salmon simulation, the structure for setting and adjusting the parameters of the model, and two empirical datasets needed to run simulations.</p>
Training patches and prediction codes of deep learning (LANA) model for Landsat 8/9 cloud/shadow mask
<p>This dataset includes (i) the image patches dataset and (ii) application/prediction (not training) codes for Landsat 8 cloud and cloud shadow masking used in a paper in review and uploaded here: </p> <p>Hankui Zhang, Dong Luo, David Roy, A learning attention network algorithm (LANA) for accurate Landsat-8 cloud and shadow masking, <em>Remote Sensing of Environment</em> </p> <p>The documentation is in <a href="https://zenodo.org/api/files/5462baa5-2bba-4b0f-92aa-c17681b6464b/l8_training_data_readme_new.pdf?versionId=95253efb-6447-46d8-ae4b-ac0c93b43532">l8_training_data_readme_new.pdf</a>. </p>
Design of Blockchain-based Applications using Model-Driven Engineering and Low-Code / No-Code Platforms - SLR Dataset
<p>Dataset of the publications collected at various stages of the Structured Literature Review titled "<a href="https://link.springer.com/article/10.1007/s10270-023-01109-1">Design of Blockchain-based Applications using Model-Driven Engineering and Low-Code / No-Code Platforms</a>" published in Software and Systems Modeling:</p> <p>The creation of blockchain-based software applications requires today considerable technical knowledge, particularly in software design and programming. This is regarded as a major barrier in adopting this technology in business and making it accessible to a wider audience. As a solution, low-code and no-code approaches have been proposed that require only little or no programming knowledge for creating full-fledged software applications. In this paper we extend a review of academic approaches from the discipline of model-driven engineering as well as industrial low-code and no-code development platforms for blockchains. This includes a content-based, computational analysis of relevant academic papers and the derivation of major topics. In addition, the topics were manually evaluated and refined. Based on these analyses we discuss the spectrum of approaches in this field and derive opportunities for further research.</p>
Code and initial metapopulation data for model construction and simulation analyses for: Genetic rescue from protected areas is modulated by migration, hunting rate and timing of harvest
<p>Migrants from protected areas may buffer the risk of harvest-induced evolutionary changes in exploited populations that face strong selective harvest pressures in both terrestrial and marine ecosystems. Understanding the mechanisms favouring genetic rescue through migration could help ensure sustainable harvest outside protected areas and conserve genetic diversity inside those areas. We developed a stochastic individual-based metapopulation model to evaluate the potential for migration from protected areas to mitigate the evolutionary consequences of selective harvest. We parameterized the model with detailed data from individual monitoring of two populations of bighorn sheep subjected to trophy hunting. We tracked horn length through time in a metapopulation including large protected and trophy-hunted populations connected through male breeding migrations. We quantified and compared declines in horn length and rescue potential under various combinations of migration rate, hunting rate in hunted areas and temporal overlap in timing of harvest and migrations, which affects the migrants' survival and chances to breed within exploited areas. Our simulations suggest that the effects of size-selective harvest on male horn length in hunted populations can be dampened or avoided if harvest pressure is low, migration rate is substantial, and migrants have a low risk of being shot. Intense size-selective harvest impacts the phenotypic and genetic diversity in horn length, and population structure through changes in proportions of large-horned males, sex ratio and age structure. When hunting pressure is high and overlaps with male migrations, effects of selective removal also emerge in the protected population, so that instead of a genetic rescue of hunted populations, our model predicts undesirable effects inside protected areas. Our results stress the importance of a metapopulational approach to management, to promote genetic rescue from protected areas and limit ecological and evolutionary impacts of harvest on both harvested and protected populations.</p>
Image dataset for cow identification, including code to train deep learning model, as well as analysis of results (SmARtview, 51088)
<p>This dataset and code was a result of the UKRI project "SmARtview: An AI-powered Augmented Reality Tool for Animal Health and Productivity", linked here: <a href="https://gtr.ukri.org/projects?ref=51088">https://gtr.ukri.org/projects?ref=51088</a></p> <p>These files are intended to be used for an accompanying publication in an academic journal.</p> <p>Anyone is free to use the contents for research and teaching purposes.</p>
Data and Code for Blaszczak et al. 2023, Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience
<p>Data and code for analyses in Blaszczak et al. 2023, Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience.</p> <p>See publication and ReadMe file for analysis description and further details. </p> <p>bioRxiv pre-print: Blaszczak, J.R., Yackulic, C., Shriver, R., & R.O. Hall, Jr. 2023. Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience. https://doi.org/10.1101/2023.04.11.535773</p>
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance
<p>Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (<em>Prionace</em> <em>glauca</em>), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases. </p>
An Empirical Comparison of Pre-Trained Models of Source Code
<p>The replication package of the paper "An Empirical Comparison of Pre-Trained Models of Source Code". For the source code, please refer to <a href="https://github.com/NougatCA/FineTuner">https://github.com/NougatCA/FineTuner</a>.</p>
Inferring the evolutionary model of community-structuring traits with convolutional kitchen sinks: Code and data
<p>When communities are assembled through processes such as filtering or limiting similarity acting on phylogenetically conserved traits, the evolutionary signature of those traits may be reflected in patterns of community membership. We show how the model of trait evolution underlying community-structuring traits can be inferred from community membership data using both a variation of a traditional eco-phylogenetic metric--the mean pairwise distance (MPD) between taxa--and a recent machine learning tool, Convolutional Kitchen Sinks (CKS). Both methods perform well across a range of phylogenetically informative evolutionary models, but CKS outperforms MPD as tree size increases. We demonstrate CKS by inferring the evolutionary history of freeze tolerance in angiosperms. Our analysis is consistent with a late burst model of freeze tolerance, suggesting it evolved recently. We suggest that data ordered on phylogenies such as trait values, species interactions, or community presence/absence are good candidates for CKS modeling because the generative models produce structured differences between neighboring points that CKS is well-suited for. We introduce the R package <em>kitchen</em> to perform CKS for generic application of the technique.</p>
CPT-1 pre-computed whole-proteome variant effect predictions and model source code
<p><strong>Cross-protein transfer learning for variant effect prediction</strong></p><p>This repository contains the variant effect predictions of CPT-1 for 18,602 human proteins, initially released with the manuscript "Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects". The proteins are split into three files.</p><p><i>CPT1_score_EVE_set.zip</i>: Proteins in the EVE set (<a href="https://www.nature.com/articles/s41586-021-04043-8">Frazer et al., 2021</a>)</p><p><i>CPT1_score_no_EVE_set_1.zip</i> & <i>CPT1_score_no_EVE_set_2.zip</i>: Proteins not in the EVE set. Predictions for these proteins use imputed values for features depending on the EVE MSA.</p><p>The protein names are UniProt gene names.</p><p>We also provide source code to train CPT-1 model and reproduce results in the manuscript :</p><p><i>source_code.zip </i>(corresponds to GitHub repository songlab-cal/CPT version as of Jul 12, 2023)</p><p> </p><p><strong>Citation</strong></p><p>Jagota, M.*, Ye, C.*, Albors, C., Rastogi, R., Koehl, A., Ioannidis, N., and Song, Y.S.†<br>"Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects", bioRxiv (2022)</p><p>*These authors contributed equally to this work.<br>†To whom correspondence should be addressed: <a href="mailto:yss@berkeley.edu">yss@berkeley.edu</a></p><p>DOI: <a href="https://doi.org/10.1101/2022.11.15.516532">https://doi.org/10.1101/2022.11.15.516532</a></p><p> </p>
Data and code for: A quantitative model for spatio-temporal dynamics of root gravitropism
<p>This repository contains the experimental data presented in "A quantitative model for spatio-temporal dynamics of root gravitropism" and Python scripts for the presented root model.</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.