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Data from: Differential effects of fertilisers on pollination and parasitoid interaction networks
Grassland fertilisation drives non-random plant loss resulting in areas dominated by perennial grass species. How these changes cascade through linked trophic levels, however, is not well understood. We studied how grassland fertilisation propagates change through the plant assemblage into the plant-flower visitor, plant-leaf miner and leaf miner-parasitoid networks using a year's data collection from a long-term grassland fertiliser application experiment. Our experiment had three fertiliser treatments each applied to replicate plots 15 m2 in size: mineral fertiliser, farmyard manure, and mineral fertiliser and farmyard manure combined, along with a control of no fertiliser. The combined treatment had the most significant impact, and both plant species richness and floral abundance decreased with the addition of fertiliser. While insect species richness was unaffected by fertiliser treatment, fertilised plots had a significantly higher abundance of leaf miners and parasitoids and a significantly lower abundance of bumblebees. The plant-flower visitor and plant-herbivore networks showed higher values of vulnerability and lower modularity with fertiliser addition, while leaf miner-parasitoid networks showed a rise in generality. The different groups of insects were impacted by fertilisers to varying degrees: while the effect on abundance was the highest for leaf miners, the vulnerability and modularity of flower visitor networks was the most affected. The impact on the abundance of leaf miners was positive and three times higher than the impact on parasitoids, and the impact on bumblebee abundance was negative and double the magnitude of impact on flower abundance. Overall our results show that while insect species richness was unaffected by fertilisers, network structure changed significantly as the replacement of forbs by grasses resulted in changes in relative abundance across trophic levels, with the direction of change depending on the type of network. By studying multiple networks simultaneously, we were able to rank the relative impact of habitat change on the different groups of species within the community. This provided a more holistic picture of the impact of agricultural intensification and provides useful information when deciding on priorities for mitigation. 01-Oct-2020
Data for "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"
<p><strong>Data for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"</strong></p> <p>GitHub: <a href="https://github.com/gauchm/mts-lstm">https://github.com/gauchm/mts-lstm</a></p> <p>This dataset contains the hourly NLDAS forcings and USGS streamflow data.</p> <p>For training with our codebase, we recommend using the combined NetCDF file, but you can also use the csv files (but it will take much longer to load the data).</p> <p> </p> <p><em>Related Datasets: </em><a href="https://doi.org/10.5281/zenodo.4071885">https://doi.org/10.5281/zenodo.4071885</a> contains the models trained with the forcings and streamflow from this dataset.</p>
Syk signaling network comparison reveals the potential molecular determinants of its tumor promoter and suppressor functions
<p>Extra data for the comparison of Syk signaling networks including colored KEGG pathways and Cytoscape sessions.</p>
Models and Predictions for "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"
<p><strong>Models and Predictions for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"</strong></p> <p>GitHub: <a href="https://github.com/gauchm/mts-lstm">https://github.com/gauchm/mts-lstm</a></p> <p><strong>Results</strong></p> <p>The file `results.tar.gz` contains:</p> <ul> <li>ensembled predictions for all models (generated from the models in `models/` using the <a href="https://neuralhydrology.readthedocs.io/en/latest/api/neuralhydrology.utils.nh_results_ensemble.html">`nh-results-ensemble` command</a>). These predictions were used in the `results-analysis.ipynb` and `odelstm-analysis.ipynb` notebooks on the GitHub repository for the paper.</li> <li>the NWM predictions <ul> <li>`nwm_chrt_v2_1h.p` contains hourly NWM predictions for the CAMELS basins between 1993 and 2007. The file is derived from the reanalysis on <a href="https://docs.opendata.aws/nwm-archive/readme.html">aws</a>.</li> <li>`nwm_results.p` is derived from `nwm_chrt_v2_1h.p` and contains hourly and day-aggregated results and performance metrics for the test period of our paper.</li> </ul> </li> <li>a file `signatures.p` with hydrologic signatures that were calculated from the models' predictions. These signatures were used in the `results-analysis.ipynb` notebook on the GitHub repository for the paper.</li> </ul> <p><strong>Models</strong></p> <p>The tar.gz files prefixed with `models-` contain the trained MTS-LSTM, sMTS-LSTM, and ODE-LSTM models from our experiments. For each experiment, there exist 10 model setups (one for each random seed).<br> Besides the trained models, each model's tar.gz also contains the predictions on the test or validation perod and the configuration file used to train the model.</p> <p><em>MTS-LSTM</em></p> <ul> <li>`mtslstm_seed*` -- the MTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`mtslstm_multiforcing_seed*` -- the MTS-LSTM from the section on per-timescale input data, experiment "multi-forcing B" (using just NLDAS as hourly inputs)</li> <li>`mtslstm_multiforcing_dailyhourly_seed*` -- the MTS-LTSM from the section on per-timescale input data, experiment "multi-forcing A" (ingesting daily forcings into the hourly model)</li> <li>`mtsltsm_136H1D_seed*` -- the MTS-LTSM from the section on prediction at other timescales (1-, 3-, 6-hourly and daily predictions)</li> </ul> <p><em>sMTS-LSTM</em></p> <ul> <li>`smtslstm_seed*` -- the sMTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`smtslstm_noregularization_seed*` -- the sMTS-LSTM from the section on cross-timescale consistency (trained without regularization)</li> </ul> <p><em>Time-Continuous Experiments</em></p> <p>The file `models-timecontinuous.tar.gz` contains one sub-folder per basin on which we conducted our initial experiments.<br> Each basin directory contains:</p> <ul> <li>Experiment A (trained on daily and 12-hourly, evaluated on hourly): <ul> <li>`odelstm_a_seed*` -- the ODE-LSTM from experiment A</li> <li>`mtslstm_a_seed*` -- the MTS-LSTM from experiment A</li> </ul> </li> <li>Experiment B (trained on hourly and 3-hourly, evaluated on daily) <ul> <li>`odelstm_b_seed*` -- the ODE-LSTM from experiment B</li> <li>`mtslstm_b_seed*` -- the MTS-LSTM from experiment B</li> </ul> </li> </ul> <p><em>Related Datasets: </em><a href="https://doi.org/10.5281/zenodo.4072700">https://doi.org/10.5281/zenodo.4072700</a> contains the hourly NLDAS forcings and USGS streamflow required to use the models from this dataset.</p>
Joint Autoregressive and Graph Models for Software and Developer Social Networks
<p>This zip contains three CSV files and one folder. This dataset contains information for the recent ten distributions.</p> <ul> <li><strong>developer_attributes.csv</strong>: There are seven columns in this file. "distro" (str) represents distribution name. "source" (str) denotes source package name. "person_id" (str) indicates developer identity. "closes" (int), "high" (int), "medium" (int), "low" (int) are the features.</li> <li><strong>source_bugs.csv</strong>: In this file, three columns are present. "distro" (str) represents the distribution name. "source" (str) represents the source package name. "bug_count" (int) denotes the number of bugs that source package has at a particular distribution.</li> <li><strong>source_sizes.csv</strong>: In this file, three columns are present. "distro" (str) represents the distribution name. "source" (str) represents source package name. "size" (int) denotes the size of the package.</li> <li><strong>Dependency folder:</strong> Within this folder, ten dependency lists are present. Each file contains two columns i.e "start" (str) and "target" (str). Both of them represent source packages. So, we read as the "start" source package depends on "target" source package. </li> </ul> <p>Here is the arxiv version of our paper: <a href="https://arxiv.org/abs/2101.08729">https://arxiv.org/abs/2101.08729</a>. </p> <p>Here is the portal link: <a href="https://sites.google.com/view/rima-hazra/swnet">https://sites.google.com/view/rima-hazra/swnet</a></p>
Analysis and Figures from "Causal network inference from gene transcriptional time-series response to glucocorticoids"
<p>Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of 2,768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2,768 genes and 31,945 directed edges (FDR <= 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS</p> <p>This upload documents the analysis and figure files that support each numerical claim of the manuscript. Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript, assuming the home folder of port-from-della</p>
Experiment Dataset for L. Zhu, G. Casale, I. Perez, Fluid approximation of closed queueing networks with discriminatory processor sharing, Performance Evaluation (2020): 102094
<p>This dataset provides the results of the validation experiments for transient, steady-state and response time distribution<br> analysis published in L. Zhu, G. Casale, I. Perez, Fluid approximation of closed queueing networks with discriminatory processor sharing, Performance Evaluation (2020): 102094.</p>
Familiist (pro-natalist) communities in the social network VKontakte
<p>The database contains an upload of text comments in Russian from the social networkVkontakte in <strong>.csv format (UTF-8 encoding). </strong>Comments are collected from communities, which discuss pregnancy, childhood, motherhood, paternity, etc. The unloading contains comments under the posts with which the interaction took place. The absolute amount of likes was used as a criterion, (comments were collected where the number of likes is greater than or equal to 5). The text data was processed (stemmization and lemmatization). The data are suitable for thematic analysis (e.g. LDA - Latent Dirichlet Allocation), for modelling the graph structure of communities (the link_comment variable contains a unique identifier of the post, link_author contains a unique user identifier), for analysis of the tonalities of statements and forming a dictionary of demographic connotation. </p> <p>Sample Information:</p> <p>- Number of communities 38 </p> <p>- Content type of communities: communities in which users are mainly positive about the birth of children, motherhood, parenthood and own family are selected. But users (communities) with anti-familistic biases may be encountered. </p> <p>- Only comments with the number of likes >= 5 are collected </p> <p>- Comments are collected only from communities (the list of communities below) discussing issues related to childhood, motherhood, pregnancy, etc. </p> <p>- A sample of communities on average contains 309 thousand subscribers (maximum value - 1,482,303, minimum value - 72,570, total number of subscribers excluding intersections - 11,743 295) </p> <p>- The sample of comments contains 112,900 user comments </p> <p>Sample Structure: </p> <p>link_author - link to the author of the comment in the form of https://vk.com/*author identificator* </p> <p>gender of author (F - female, M - male, NaN - no data) </p> <p>link_comment - link to comment in the form of https://vk.com/* post identificatior on a *community wall*?reply=*comment id * </p> <p>date_time - date and time of publication (format “YYYY-MM-DD HH:MM:SS”) </p> <p>text - raw comment text </p> <p>likes - number of likes the comment has </p> <p>text_prep - processed text (punctuation marks removed, words brought down to lowercase) </p> <p>text_stem - processed text (based on the text_prep column stemmization using SnowBallstemmer (“Russian”) of the nltk library) is performed </p> <p>text_sw - processed text (based on the text_prep column stop words are deleted using word_tokenize (text) of the nltk library) </p> <p>text_lemm - processed text (lemmatization using mystem.lemmatize (text) of pymystem3 library is performed based on the text_prep column) </p> <p>List of communities (38 communities):</p> <p>https://vk.com/club52388302</p> <p>https://vk.com/club34677924</p> <p>https://vk.com/club99834596</p> <p>https://vk.com/club170234932</p> <p>https://vk.com/club20199180</p> <p>https://vk.com/club118030893</p> <p>https://vk.com/club14395935</p> <p>https://vk.com/club100104267</p> <p>https://vk.com/club181526404</p> <p>https://vk.com/club35095382</p> <p>https://vk.com/club58530763</p> <p>https://vk.com/club69716165</p> <p>https://vk.com/club29746763</p> <p>https://vk.com/club78865067</p> <p>https://vk.com/club20709572</p> <p>https://vk.com/club93466205</p> <p>https://vk.com/club61700163</p> <p>https://vk.com/club91423062</p> <p>https://vk.com/club69285929</p> <p>https://vk.com/club104012302</p> <p>https://vk.com/club20622108</p> <p>https://vk.com/club86333616</p> <p>https://vk.com/club24765</p> <p>https://vk.com/club87169444</p> <p>https://vk.com/club86688308</p> <p>https://vk.com/club93776129</p> <p>https://vk.com/club47207301</p> <p>https://vk.com/club39873171</p> <p>https://vk.com/club59224150</p> <p>https://vk.com/club7430494</p> <p>https://vk.com/club37739956</p> <p>https://vk.com/club59701255</p> <p>https://vk.com/club27427277</p> <p>https://vk.com/club126238531</p> <p>https://vk.com/club127678644</p> <p>https://vk.com/club57782234</p> <p>https://vk.com/club51314884</p> <p>https://vk.com/club134261249</p>
Data for: Infectious disease and sickness behaviour: tumour progression affects interaction patterns and social network structure in wild Tasmanian devils
<p>Infectious diseases, including transmissible cancers, can have a broad range of impacts on host behaviour, particularly in the latter stages of disease progression. However, the difficulty of early diagnoses makes the study of behavioural influences of disease in wild animals a challenging task. Tasmanian devils (<i>Sarcophilus harrisii</i>) are affected by a transmissible cancer, devil facial tumour disease (DFTD), in which tumours are externally visible as they progress. Using telemetry and mark-recapture data sets, we quantify the impacts of cancer progression on the behaviour of wild devils by assessing how interaction patterns within the social network of a population change with increasing tumour load. DFTD negatively influences devils' likelihood of interaction within their network, an effect which increases with increasing tumour load. Infected devils were more active within their network late in the mating season, a pattern with repercussions for DFTD transmission. Our study provides a rare opportunity to quantify and understand the behavioural feedbacks of disease in wildlife and how they may affect transmission and population dynamics in general.</p>
BDRC Educational Affiliation Network Data
<p>This set of files contains the edge list, the node list and the list of node attributes used for building the network of educational affiliation in the <em>Biographical Dictionary of Republican China</em> (BDRC). The network contains two categories of nodes: the individuals and the educational institutions they attended. The node attributes file contains two sheet: one for individuals, one for institutions.</p>
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment
<p>This repository contains the raw results (by word information-theoretic measures for the experimental stimuli) and the LSTM models analyzed in <a href="https://www.aclweb.org/anthology/2020.acl-main.179/">Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment</a>. The models from the synthetic experiments are given in the synthetic archive, as well as the training data generation script. There is a README included that gives more details for recreating/evaluating results from those experiments.</p> <p>The naming convention for each model in the models directory is:<br> [Language]_hidden[Hidden Units]_batch[Batch Size]_dropout[Dropout Rate]_lr[Learning Rate]_[Model Number].pt</p> <p>Language: en for English and es for Spanish<br> Hidden Units: All models had two layers with 650 hidden units per layer<br> Batch Size: The size of the batch (128 for English, 64 for Spanish)<br> Dropout Rate: All models used a dropout rate of 0.2<br> Learning Rate: All models has a learning rate of 20<br> Model Number: Identifier of the model (English model 0 is the best model from <a href="https://github.com/facebookresearch/colorlessgreenRNNs">Gulordava et al. (2018)</a>) </p> <p> </p> <p> </p>
Spatial variation in early-winter snow cover determines local dynamics in a network of alpine butterfly populations
<p>Snow cover is an extremely variable but critical component of alpine environments. We use long term population data on multiple small populations of the alpine butterfly <i>Parnassius smintheus</i>, combined with high-resolution satellite imagery of meadows, to show a strong link between fine-scale spatial and temporal variation in early-winter snow cover and annual change in butterfly population size, accounting for up to 80 percent of the variation in annual population change. Snow cover in early winter for each meadow is the best predictor of annual adult population change, despite being estimated for a relatively short time-window in late November. We identify a means by which subpopulation response to a local, short-term weather variable can be assessed over a large spatial extent, but also at a resolution relevant to the biology and local dynamics of this alpine species.</p>
Data/code for Interpretable, calibrated neural networks for analysis and understanding of neutron spectra
<p>Simulated neutron spectra from inelastic neutron scattering on the double perovskite PCSMO. The dataset is related to the publication <em>Interpretable, calibrated neural networks for analysis and understanding of neutron spectra</em>. The paper is yet to be submitted, but the references will be added in due course.</p> <p># Pre-trained model weights</p> <p>`model-weights.tgz`</p> <p>This file contains the weights for the models reported in the paper which can be downloaded and used to re-produce the results.</p> <p>Please download the `model-weights.tgz` file and extract it to the root of the repository `interpretable-ml-neutron-spectroscopy` from github.</p> <p># Data Generation</p> <p>`data_generation.tar.gz`</p> <p> Large data files for github repository associated with the publication _Interpretable, calibrated neural networks for analysis and understanding of neutron spectra_ These files relate to generation of the data in the paper.</p> <p>Please download the `data_generation.tar.gz` file and extract it to the root of the repository `interpretable-ml-neutron-spectroscopy` from github.</p> <p>#Training data</p> <p>`<a href="https://zenodo.org/api/files/47a40b89-ae6f-4655-aa4b-2d8fc11ec597/pcsmo-simulated-spectra.tgz?versionId=1ff107a9-85b9-4b46-9c77-84319ca9d59a">pcsmo-simulated-spectra.tgz</a>`</p> <p>Contains the data used to train the networks. Extract this file to a data directory and then point the `train.py` files for the various models to look for this `<datadir>` in the specified location in those files.</p>
Data for assessment of damage to residential dwellings using artificial neural networks
<p>The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture damage to residential home subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event. </p>
DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks
<p>Matlab output files and raw data used for the evaluation presented in "DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks" <a href="https://github.com/MetExplore/dexom">https://github.com/MetExplore/dexom</a></p>
Cluster configurations of the Hegselmann-Krause model on network ensembles
<p>This is the raw data underlying the results of the preprint [arxiv:2102.10910](https://arxiv.org/abs/2102.10910).</p> <p> </p> <p>## Data</p> <p>For each measured combination of the confidence and system size, there is one gzipped<br> file. For different ensembles, we collected data in different ranges and quality.<br> The paramters are:</p> <p>* Number of samples `m` per parameter combination<br> * Range `r` of confidences epsilon<br> * Distances `d` between values of epsilon (basically the resolution of the data)<br> * Largest size `N_max`</p> <p>The single files follow a naming scheme of `n{N}_e{epsilon}.cluster.dat.gz`, where<br> `{N}` signals the system size of the simulation and `{epsilon}` is the confidence<br> value of the simulation (without a decimal point, i.e., `0050` corresponds to `epsilon = 0.050`).<br> The sizes `N` are usually powers of two (or for the lattices, perfect squares close to powers of two).</p> <p>We present the data for each ensemble in one archive.</p> <p><br> * Fully connected `full.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 262144`<br> * Barabasi Albert with a mean degree of 4 `BA4.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 32768`<br> * Barabasi Albert with a mean degree of 10 `BA10.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with first nearest neighbors `lat1.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`<br> * Square lattice with second nearest neighbors `lat2.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`<br> * Square lattice with third nearest neighbors `lat3.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with fourth nearest neighbors `lat4.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with third nearest neighbors and 1% rewired edges `lat3_ws.tar`<br> * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.001`, `N_max = 16384`<br> * connected Erdos Renyi with mean degree of 10 `ER10.tar`<br> * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.002`, `N_max = 32768`</p> <p> </p> <p>## Data format</p> <p>Each final state is encoded as three lines:</p> <p>* The convergence time is a single integer with a line prefix '# sweeps: '<br> * The positions of all clusters in opinion space with a line prefix '# ' (unsorted)<br> * The number of agents in each of the clusters without a line prefix</p> <p> </p> <p>## Python example for reading the format</p> <p>An example script, which visualizes the S vs eps graph for the largest size of the fully connected<br> case, with a function to read this format is given in `example.py`.</p>
Supplementary material 11 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893
Identifying priority knowledge needs for implementing nature-based solutions-results from Malta and other Mediterranean islands
Supplementary material 9 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893
Management and restoration of Mediterranean wetlands to provide ecosystem services and other benefits
Supplementary material 12 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893
Non-material ecosystem benefits of protected areas as a bridge to future challenges in sustainable development
Agricultural intensification drives changes in hybrid network robustness by modifying network structure
<p>Within ecological communities, species engage in myriad interaction types, yet empirical examples of hybrid species interaction networks composed of multiple types of interactions are still scarce. A key knowledge gap is understanding how the structure and stability of such hybrid networks are affected by anthropogenic disturbance. Using 15,169 interaction observations, we constructed 16 hybrid herbivore-plant-pollinator networks along an agricultural intensification gradient to explore changes in network structure and robustness to local extinctions. We found that agricultural intensification led to declines in modularity but increases in nestedness and connectance. Notably, network connectance, a structural feature typically thought to increase robustness, caused declines in hybrid network robustness, but the directionality of changes in robustness along the gradient depended on the order of local species extinctions. Our results not only demonstrate the impacts of anthropogenic disturbance on hybrid network structure, but they also provide unexpected insights into the structure-stability relationship of hybrid networks.</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.