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Structure of Bg10: an alcohol-tolerant and glucose-stimulated beta-glucosidase
<p>Dataset collected at Diamond beamline I04 on 13-04-2019.</p> <p>Wavelength: 0.9795 A</p> <p>Detector: DECTRIS EIGER2 XE 16M</p>
Sign-specific stimulation "hot" and "cold" spots in Parkinson's disease validated with machine learning
<p><strong>Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation.</strong></p> <p> </p> <p><strong>We studied 275 PD patients who underwent STN-DBS between 2003 and 2018. We selected pre-DBS and best available post-DBS scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to DBS. Volumes of tissue activated (VTAs) were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n=51 STN-DBS patients from another institution).</strong></p> <p><strong> </strong></p> <p><strong>Tremor and rigidity hot spots were largely located outside and dorsolateral to STN whereas hot spots for bradykinesia and axial signs had larger overlap with STN. Using VTA overlap with sign-specific hot and cold spots, support vector machine (SVM) classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering VTA overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using VTA overlap with the sign-specific hot and cold spots.</strong></p> <p><br> <strong>In two independent datasets, distinct brain areas could predict sign-specific clinical changes in PD patients with STN-DBS. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement. </strong></p> <p><strong>Hot and cold spots for each sign are publicly available as binary labels in NIfTI format. </strong></p>
Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation
<p><strong>Supporting materials for the GX Dataset.</strong> </p> <p> The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.</p> <p><strong>Publication</strong></p> <p>A full data descriptor is published in <a href="https://doi.org/10.1038/s41597-021-01046-y">Nature Scientific Data</a>. <strong>Please cite this work as</strong>:</p> <blockquote> <p>Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y</p> </blockquote> <p> </p> <p><strong>Descriptions</strong></p> <p>A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES; including tDCS and tACS). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG or EKG, EOG), and continuous behavioral vigilance/alertness metrics (CTT task).</p> <p><strong>Acknowledgments</strong></p> <p>Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499.</p> <p>We would like to thank Yuxin Xu and Michaela Chum for all their technical assistance.</p> <p> </p> <p><strong>Extras</strong></p> <p>For downsampled data (1 kHz ) please see (in .mat format):</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3840614">Experiment 1</a> : https://doi.org/10.5281/zenodo.3840614</li> <li><a href="https://doi.org/10.5281/zenodo.3840616">Experiment 2</a> : https://doi.org/10.5281/zenodo.3840616</li> </ul> <p> </p> <p>Code used to import, process, and plot this dataset can be found here:</p> <ul> <li><a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">GitHub</a> : <a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior</a></li> </ul> <p> </p> <p>Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:</p> <ul> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_PSD/14810517">PSD</a>: <a href="https://doi.org/10.6084/m9.figshare.14810517.v1">https://doi.org/10.6084/m9.figshare.14810517.v1</a></li> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_Topoplots/14810478">Topoplots During Stimulation:</a> <a href="https://doi.org/10.6084/m9.figshare.14810478">https://doi.org/10.6084/m9.figshare.14810478</a></li> <li><a href="https://doi.org/10.6084/m9.figshare.14810442.v1">Voltage timeseries, spectrogram and behavior:</a> <a href="https://doi.org/10.6084/m9.figshare.14810442">https://doi.org/10.6084/m9.figshare.14810442.v1</a></li> </ul> <p> </p> <p>The full dataset is also provided in <a href="https://bids.neuroimaging.io/">BIDS</a> format here:</p> <ul> <li><a href="https://doi.org/10.18112/openneuro.ds003670.v1.1.0">Data in BIDS format:</a> https://doi.org/10.18112/openneuro.ds003670.v1.1.0</li> </ul> <p><strong>Data License </strong><br><a href="https://creativecommons.org/licenses/by/4.0/">Creative Common 4.0 with attribution (CC BY 4.0)</a></p> <p> </p> <p><strong>NOTE</strong></p> <p><strong>Please email ngebodh01@citymail.cuny.edu with any questions.</strong></p> <p> </p>
Differential gene expression in iPSC-derived macrophages after IFNg stimulation and Salmonella infection
<p>We used likelihood ratio test implemented in DESeq2 v1.10.0 (test = “LRT”) to test if a model that allowed different mean expression in each condition explained the data better than a null model assuming the same mean expression across conditions. See the manuscript for more details: http://www.biorxiv.org/content/early/2017/05/18/102392 .</p> <p>We used the following commands in DESeq2:<br> #Run DESeq2<br> dds = DESeq2::DESeqDataSetFromMatrix(combined_expression_data_filtered$counts, design, ~condition_name) <br> dds = DESeq2::DESeq(dds, test = "LRT", reduced = ~ 1)</p> <p>#Extract differentially expressed genes in each condition<br> ifng_genes = results(dds, contrast=c("condition_name","IFNg","naive")) <br> sl1344_genes = results(dds, contrast=c("condition_name","SL1344","naive")) <br> ifng_sl1344_genes = results(dds, contrast=c("condition_name","IFNg_SL1344","naive"))</p>
Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation-Exp1-Data Downsampled
<p><strong>GX Dataset downsampled - Experiment 1</strong></p> <p>The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.<br>Here the GX Dataset for <strong>Experiment 1</strong> is downsampled to 1 kHz and saved in .MAT format which can be used in both MATLAB and Python.</p> <p><strong>Publication</strong></p> <p>A full data descriptor is published in <a href="https://doi.org/10.1038/s41597-021-01046-y">Nature Scientific Data</a>. <strong>Please cite this work as</strong>:</p> <blockquote> <p>Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y</p> </blockquote> <p><strong>Descriptions</strong></p> <p>A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG, EOG), and continuous behavioral vigilance/alertness metrics.</p> <p><strong>Acknowledgments</strong></p> <p>Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499.</p> <p><strong>Extras</strong></p> <p>Back to <a href="https://doi.org/10.5281/zenodo.4456079">Full GX Dataset</a> : https://doi.org/10.5281/zenodo.4456079</p> <p> </p> <p>For downsampled data (1 kHz ) please see (in .mat format):</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3840614">Experiment 1</a> : https://doi.org/10.5281/zenodo.3840614</li> <li><a href="https://doi.org/10.5281/zenodo.3840616">Experiment 2</a> : https://doi.org/10.5281/zenodo.3840616</li> </ul> <p> </p> <p>Code used to import, process, and plot this dataset can be found here:</p> <ul> <li><a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">GitHub</a> : <a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior</a></li> </ul> <p> </p> <p>Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:</p> <ul> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_PSD/14810517">PSD</a>: <a href="https://doi.org/10.6084/m9.figshare.14810517.v1">https://doi.org/10.6084/m9.figshare.14810517.v1</a></li> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_Topoplots/14810478">Topoplots During Stimulation:</a> <a href="https://doi.org/10.6084/m9.figshare.14810478">https://doi.org/10.6084/m9.figshare.14810478</a></li> <li><a href="https://doi.org/10.6084/m9.figshare.14810442.v1">Voltage timeseries, spectrogram and behavior:</a> <a href="https://doi.org/10.6084/m9.figshare.14810442">https://doi.org/10.6084/m9.figshare.14810442.v1</a></li> </ul> <p> </p> <p>The full dataset is also provided in <a href="https://bids.neuroimaging.io/">BIDS</a> format here:</p> <ul> <li><a href="https://doi.org/10.18112/openneuro.ds003670.v1.1.0">Data in BIDS format:</a> https://doi.org/10.18112/openneuro.ds003670.v1.1.0</li> </ul> <p><strong>Data License </strong><br><a href="https://creativecommons.org/licenses/by/4.0/">Creative Common 4.0 with attribution (CC BY 4.0)</a></p> <p> </p> <p><strong>NOTE</strong></p> <p><strong>Please email ngebodh01@citymail.cuny.edu with any questions.</strong></p> <p> </p> <p><br><strong>Updates</strong></p> <ul> <li><strong>Version 2.1.0</strong> <ul> <li>Behavioral data (ptracker) adjusted for all files. Previous version had one participant's data overwriting all behavioral data when downsampled. Mat format now explicitly >v7.3 </li> </ul> </li> <li><strong>Version 2</strong> <ul> <li>Stimulation trigger labels now adjusted. Previous labels were missmatched for Experiment 1's data. </li> </ul> </li> </ul> <p> </p>
Magnetoencephalography recordings on newborns with auditory stimulations
<p>This dataset is organized in 3 main folders:<br>1. The folder 'data' contains the magnetoencephalography (MEG) recording data, organized in a BIDS-like structure: first by subjects and then by modalities. The optically pumped magnetometers (OPM) data folders contains the raw data and the preprocessing output files. The cryogenic MEG (SQUID) data folders contain the data after signal space separation (using MaxFilter) and the preprocessing output files.<br>The MEG files contain a time variable (time), a MEG data variable (data_meg), a sampling frequency variable (sfreq) and a variable containing the time stamps of the stimulations (trials). The OPM data in addition has an OPM position on the layout variable (positions) and a OPM labels variable (labels).<br>The preprocessing output files contain a variable with the artifact time stamps (bad_times) and a variable containing the heart and artifact component of an independent component analysis (ICA). If a second ICA was performed, another variable is present (IC). When computed, the frequency ranges of those components is also included (freq).<br>2. The folder 'layouts' contains the layouts files needed for the analysis.<br>3. The folder 'derivatives' contains the derivative data obtained by the analysis scripts which are the data used for generating the plots in the article.<br>All the files are in the Matlab matrix file format ('.mat').</p>
Figure 4 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 4. Scanning Electron Microscopy (SEM) images of the upper-surface of leaves sprayed with water (control), Silical®, Postar®, and Ultrafit® to visualize the diversity of trichome types (glandular: GT and non-glandular: NGT) and densities for tomato cultivars K-186 F1 and 023 F1.
Figure 2 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 2. Population of movable stages of Tetranychus urticae on tomato leaves over old 3–13 weeks after transplanting for two cultivars treated with commercial stimulants during the 2017 and 2018 summer seasons. Ultrafat * applied by adding to soil below the plants. Columns with the same letter represent means that are not significantly different according to Tukey's multiple range test (p <0.05). Vertical bars represent ± standard error of the mean (n = 36).
Figure 1 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 1. Population of movable stages of Tetranychus urticae on tomato leaves of several plant ages for cultivars K186 F1 and 023 F1 treated with some commercial stimulants during the 2017 (A) and 2018 (B) summer seasons. Population at three weeks after transplanting was immediately before treatment. Columns with the same letter represent means that are not significantly different according to Tukey's multiple range test (p <0.05). Vertical bars represent ± standard error of the mean (n = 21).
Figure 3 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 3. Scanning Electron Microscopy (SEM) images of the lower-surface of leaves sprayed with water (control), Silical®, Postar®, and Ultrafit® to visualize the diversity of trichome types (glandular: GT and non-glandular: NGT) and densities for tomato cultivars K-186 F1 and 023 F1.
Soil organic carbon loss decreases biodiversity but stimulates multitrophic interactions that promote belowground metabolism
<p>Soil organic carbon (SOC) plays an essential role in mediating community structure and metabolic activities of belowground biota. Unraveling the evolution of belowground communities and their feedback mechanisms on SOC dynamics helps embed the ecology of soil microbiome into carbon cycling, which serves to improve biodiversity conservation and carbon management strategy under global change. Here, croplands with a SOC gradient were used to understand how belowground metabolisms and SOC decomposition were linked to the diversity, composition, and co-occurrence networks of belowground communities encompassing archaea, bacteria, fungi, protists, and invertebrates. As SOC decreased, the diversity of prokaryotes and eukaryotes also decreased, but their network complexity showed contrasting patterns: prokaryotes increased due to intensified niche overlap, while that of eukaryotes decreased possibly because of greater dispersal limitation owing to the breakdown of macro aggregates. Despite the decrease in biodiversity and SOC stocks, the belowground metabolic capacity was enhanced as indicated by increased enzyme activity and decreased enzymatic stoichiometric imbalance. This could, in turn, expedite carbon loss through respiration, particularly in the slow-cycling pool. The enhanced belowground metabolic capacity was dominantly driven by greater multitrophic network complexity and particularly negative (competitive and predator-prey) associations, which fostered the stability of the belowground metacommunity. Interestingly, soil abiotic conditions including pH, aeration, and nutrient stocks, exhibited a less significant role. Overall, this study reveals a greater need for soil C resources across multitrophic levels to maintain metabolic functionality as declining SOC results in biodiversity loss. Our researchers highlight the importance of integrating belowground biological processes into models of SOC turnover, to improve agroecosystem functioning and carbon management in the face of intensifying anthropogenic land-use and climate change.</p>
Investigations on single and multi-grain optically stimulated luminescence (OSL) sensitivity and electron spin resonance (ESR) signals in quartz derived from sandstones: Insights on provenance of quartz in ancient depositional systems
<p><span>Trapped charge techniques of luminescence and electron spin resonance (ESR) are classic tools for dating Quaternary deposits. Over the past decade, these techniques have been routinely applied to investigate provenance and /or the sedimentary history of grains based on the different luminescence and ESR characteristics of quartz. Of these, optically stimulated luminescence (OSL) sensitivity is one of the most widely investigated parameter for luminescence-based provenance approach. A majority of studies on this parameter are based on evaluation of multi-grain OSL sensitivity of the samples. This is particularly concerning because single-grain quartz luminescence studies have shown that the luminescence signal of a multi-grain aliquot is contributed by less than ~1-10% of the total grains. Since the sole criteria for discrimination of sources based on luminescence sensitivity relies on its intensity, therefore the results based on multi-grain analysis will most likely be skewed depending on the proportion and ‘brightness’ of a few grains. This demands a need to evaluate the potential of single-grain quartz OSL sensitivity in provenance studies. In this study, we investigate single and multi-grain quartz OSL sensitivities from compositionally different sandstones with well-characterised sources based on U-Pb zircon ages. We further complement this analysis with characterisation of ESR centres commonly used in quartz provenance, namely E’<sub>1</sub> and [AlO<sub>4</sub>]<sup>0</sup> centres. Our study shows that single-grain quartz OSL sensitivity can help distinguish between sediments that have a predominant input from a single source as compared to those with contribution from multiple sources, which otherwise cannot be inferred from multi-grain studies. Moreover, our results on characterisation of quartz-based ESR intensity of E’<sub>1</sub> and saturated [AlO<sub>4</sub>]<sup>0</sup> centres successfully differentiates between sandstones and further complements the luminescence-based characterisation. </span></p>
Data Sets "Proteinoids--Polyaniline Interaction with Stimulated Neurons on Living and Plastic Surfaces"
<p>Data sets for the paper Proteinoids--Polyaniline Interaction with Stimulated Neurons on Living and Plastic Surfaces.</p>
First-in-human study of epidural spinal cord stimulation in individuals with spinal muscular atrophy
<p>Data from the paper: First-in-human study of epidural spinal cord stimulation in individuals with spinal muscular atrophy, Nature Medicine 2024</p>
Bulk RNA Seq of Electrically Stimulated Retinal Organoids
<p>Considering the significant role played by both intrinsic and extrinsic electric fields in the growth and maturation of the central nervous system, the impact of short exposure to external electric fields on the development and differentiation of retinal organoids was investigated.Organoids derived from human embryonic stem cells at day 80, a critical time point in their differentiation and maturation, were used. A single 60-minute exposure to a distinct biphasic electrical field influenced neuronal development and increased the population of photoreceptors. Initial immunohistochemistry and qPCR studies confirmed the influence of electrical stimulation on the development and differentiation of various RO cell types. RNA sequencing data revealed elevated expression of rod photoreceptors, Müller cells, horizontal cells, and amacrine cells, alongside the downregulation of retinal pigment epithelium and retinal ganglion cell genes.Furthermore, our study demonstrated varying degrees of organoid development and maturation depending on the specific electrical field applied. These findings highlight the significant impact of extrinsic electrical fields on early retinal development and suggest that optimizing electrical field parameters could effectively address certain limitations in retinal organoid technology, potentially reducing the reliance on chemicals and small molecules.</p>
A personalized value-based justification in food swaps to stimulate healthy online food choices
<p>This study examined the effect of a personalized value-based justification in explaining the rationale behind healthy food swaps. Additionally, consumers' willingness to share their personal information with retailers to personalize swap recommendations is explored.</p>
Dataset Friebus-Kardash et al, A chemerin peptide analog stimulates tumor growth in two xenograft mouse models of human colorectal carcinoma
<p>Dataset Friebus-Kardash et al, A chemerin peptide analog stimulates tumor growth in two xenograft mouse models of human colorectal carcinoma</p> <p> </p>
Dataset related to article"Globus Pallidus Internus Deep Brain Stimulation Using Frame-Based vs. Frameless Stereotaxy in Dystonia: A Single-Center Experience"
<p>Demographic, clinical (BFMDRS at baseline and in follow up), implantation (frame and frameless) data for the sample considered in the study.</p>
raw data for Optogenetic Stimulation of Prelimbic Pyramidal Neurons Maintains Fear Memories and Modulates Amygdala Pyramidal Neuron Transcriptome
<p>Figure Legend </p> <p>Modulation of cellular excitability of Prelimbic (PrL) pyramidal neurons by optogenetic stimulation. (<strong>A</strong>) Representative traces in current-clamp configuration reporting evoked firing activity triggered by a series of depolarizing current steps (0 to 400 pA) applied to PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups. The cumulative plot shows the changes in firing activity. (<strong>B</strong>) Representative traces of PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups showing the firing activity triggered by linear depolarization from 0 to 800 pA. Graph (on the right) reports the effects of optogenetic stimulation on rheobase value. Namely, PrL pyramidal neurons of OPTO FEAR and No-EX groups recorded after optogenetic stimulation showed a clear reduction in the rheobase value in comparison to neurons of SHAM FEAR group (* at least <em>p</em>= 0.01). (<strong>C</strong>) Representative traces of Excitatory Post-Synaptic Currents (EPSC) of PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups. Graph plot (in the middle) and cumulative curve (on the right) depict the clear increase in firing frequency in PrL pyramidal neurons of OPTO FEAR and No-EX groups (* at least <em>p</em> = 0.01). (<strong>D</strong>) Graphs and cumulative curves report no significant differences in cellular excitability in PrL pyramidal neurons of SHAM NOT FEAR (black) and OPTO NOT FEAR (blue) groups. Data are reported as median with interquartile range.</p>
Sublethal exposure to deltamethrin stimulates reproduction and has limited effects on post-hatching maternal care in the European earwig
<p>Data set and associated R script used to obtain the statistical results presented in the corresponding study.</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.