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81 results for “Long-term memory”
Long-term Memory (LTM) for famous Faces, Places, and common Objects
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Data associated with "A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations"
<p><strong>Datasets used in <em>A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations.</em></strong></p> <p><strong>Datatypes:</strong></p> <ol> <li>Multi-index pandas dataframe (.pkl)</li> <li>Numpy array (.npy)</li> <li>Collection of numpy arrays (.npz)</li> <li>Python dictionary objects (.pkl)</li> </ol> <p><strong>Datasets:</strong></p> <p><strong>alignments.pkl: A dataframe containing numpy arrays of image displacements for each mouse in each memory context.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are ['T', 'F1', 'N1', 'F2', 'N2'] for the training, recall 1-hour, neutral, recall 1-day, neutral day 2 memory contexts respectively. Each element of this dataframe is a numpy array of shape images x 2 that hold x and y image displacements respectively. These alignments are computed after the inscopix software motion correction and are used in Supplemental Figure 2 of the paper.</p> <p><strong>behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id.The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context (*) in ('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2').</p> <p><strong>correlated_pairs_df.pkl: A dataframe containing arrays of neuron indices that have a correlation in activity pattern > 0.3.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a numpy array with three columns. The first two columns are the neuron indices that are correlated and the last column is the strength of the correlation.</p> <p><strong>dredd_freezes_df.pkl: A dataframe containing freezing percentages for SOM-Cre and RTT-SOM-Cre mice treated with DREADDS.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment (mcherry, hm3d, hm4d). The columns contain one of ['Neutral', 'Fear', 'Fear_2']. Each element of the dataframe is a freezing percentage for a single mouse. This dataframe is built from reading the dredd_behavior.xlsx excel file. This is used to generate figure 5E of the paper.</p> <p><strong>high_degree_df.pkl: A dataframe containing list of high degree neuron indices.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'=not applicable since no DREADD used). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a list of neuron indices that are high-degree cells.</p> <p><strong>N006_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N006 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Supplemental Figure 2 of the paper.</p> <p><strong>N006_wt_cxtbasis.pkl: A dictionary containing arrays for basis images and singular values for each context.</strong></p> <p>This dictionary has keys, ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing the memory contexts. Each value is a 2 element list containing the U-basis images as column vectors and singular values, one per basis image in U. The shape of the basis images is the same shape stored in N006_wt_basis.pkl. This dataset is used in Supplementary Figure 2 to track cells across contexts of the CFC task (see also N006_wt_cxtsources.pkl)</p> <p><strong>N006_wt_cxtsources.pkl: A dictionary containing the independent component source images computed from the basis images for automatically identifying regions of interest (ROIs). </strong></p> <p>The dictionary is keyed on ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] contexts. Each value in the dictionary at a given key is a 3-D numpy array of shape sources x height x width. These data were used to construct the source images and max intensity projection image of the sources in Supplemental Figure 2F-J of the paper.</p> <p><strong>N006_wt_rois.pkl: A dictionary containing the boundaries and annuli coordinates of all rois for mouse N006 of genotype wild-type.</strong></p> <p>This dictionary is keyed on ['boundaries', 'annuli'] contexts and each value is a 179 element list of arrays of boundary line coordinates or annulus point coordinates one per ROI detected for this mouse.</p> <p><strong>N006_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N006 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=205 source images, height=517 pixels and width=704 pixels. This data was used to construct Supplemental Figure 3F.</p> <p><strong>N019_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N019 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 1C of the paper.</p> <p><strong>N019_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=204 source images, height=516 pixels and width=698 pixels. This data was used to construct Figure 1C of the paper.</p> <p><strong>P80_animals.pkl: A pandas multi-index object containing the genotype, mouse_id and treatment of the top 80% behavioral performance animals.</strong></p> <p>In this study, we drop the lowest 20% performing WT and RTT animals based on freezing percentage during the recall contexts. This multi-index is used to filter the data before each computation or plot in this study. So for example Figure 1B contains only the top 80% performing WT and RTT mice.</p> <p><strong>pc_sipscs_amps.pkl: A dictionary containing the amplitudes of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC amplitudes, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>pc_sipscs_freqs.pkl: A dictionary containing the frequencies of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC frequencies, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>rois_df.pkl: A multi-index dataframe containing all ROI information for each non-DREADD treated cell in this study (Figures 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0. The columns are ['centroid', 'cell_boundary', 'annulus_boundary']. The centroid for each cell is a 2-tuple of row, column pixel centroid coordinates. The cell_boundary is a two-column array of row, col boundary points for each ROI. The annulus_boundary is a two-column array of row, column interior points in the annulus. The annulus region excludes points of overlap with nearby cell bodies (See STAR methods of the paper).</p> <p><strong>signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD experiments used in this study (Figs 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 5771 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This dataframe is used in the construction of Figures 1-3 in the paper.</p> <p><strong>som_behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context in *=('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'). This dataframe was not used in the paper but may still be useful for further analysis.</p> <p><strong>som_sepsc_amplitudes:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC amplitudes. This data was used in Figure 4E-G.</p> <p><strong>som_sepsc_freqs:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC frequencies. This data was used in Figure 4E-G.</p> <p><strong>som_signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD SOM cell recordings used in this study (Figs 5).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 710 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This data was used to construct Figure 5B-C.</p> <p><strong>ssn33_sstcre_basis.npz: a dict containing three numpy arrays representing the basis images for mouse ssn33 of genotype sst-cre.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 100 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 5A of the paper.</p> <p><strong>ssn33_sstcre_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=86 source images, height=516 pixels and width=654 pixels. This data was used to construct Figure 5A of the paper.</p>
Differentiation of functional networks during long-term memory retrieval in children and adolescents
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Long-term spatial memory, across large spatial scales, in Heliconius butterflies
<p>Data accompanying "Long-term spatial memory, across large spatial scales, in <em>Heliconius </em>butterflies", <em>Current Biology </em>2023:</p> <p> </p> <p>exp1.csv. Behavioural data from experiment 1.</p> <p>exp2.csv. Behavioural data from experiment 2.</p> <p>exp3.csv. Behavioural data from experiment 3.</p> <p>Exp1&2.csv. Behavioural data comparing experiment 1 and 2.</p> <p>Exp1byDay.csv. Behavioural data for experiment 1 split by day.</p> <p>Exp2byDay.csv. Behavioural data for experiment 2 split by day.</p> <p>Exp3byDay.csv. Behavioural data for experiment 3 split by day.</p> <p>exp1.R. R code for experiment 1 analysis.</p> <p>exp2.R. R code for experiment 2 analysis.</p> <p>exp3.R. R code for experiment 3 analysis.</p> <p>exp1vsExp2.R. R code for comparing experiment 1 and 2.</p>
Data from: Long-term anti-predator learning and memory N. canaliculata
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Lateralisation of short- and long-term visual memories in an insect
The formation of memories within the vertebrate brain is lateralised between hemispheres across multiple modalities, however, in invertebrates evidence for lateralisation is restricted to olfactory memories, primarily from social bees. Here we use a classical conditioning paradigm with a visual conditioned stimulus to show that visual memories are lateralised in the wood ant, Formica rufa. We show that a brief contact between a sugar reward and either the right or left antenna (reinforcement) is sufficient to produce a lateralised memory, even though the visual cue is visible to both eyes throughout training and testing. Reinforcement given to the right antenna induced short-term memories whereas reinforcement given to the left antenna induced long-term memories. Thus, short- and long-term visual memories are lateralised in wood ants. This extends the modalities across which visual memories are lateralised in insects and suggests that such memory lateralisation may have evolved multiple times, possibly linked to the evolution of eusociality in the Hymenoptera.
Conditioning, short-term and long-term memory tests of Drosophila melanogaster grown with and without predators
<p>Conditioning (i.e., associative learning) and memory tests were performed following the methods of previous studies. We used samples of 10 adult flies (males only), raised in standard conditions and aged 6-7 days. The conditioning procedure consisted of 5 training sessions separated by 20-minute intervals (i.e., spaced protocol). During associative conditioning, flies were first exposed for 30 seconds (s) to one odorant simultaneously with a mechanical shock of 2000 rpm vibration pulses of 1 s duration, delivered every 5 s by a test tube shaker (Heidolph Instruments, Schwabach, Germany). This period was followed by a 60 s rest period (no odor and no shock). Then, for 30 s, another odorant was delivered without shock. The training session ended with a second rest period of 60 s. 3-octanol and 4-methylcyclohexanol (both 0.6 mL/L of paraffin) were used as odorants. Each fly group was chosen to be conditioned randomly to either 3-octanol or 4-methylcyclohexanol. The results of the final trial (the fifth out of 5 trials, referred to as the “conditioning” group hereafter) of associative conditioning were used to characterize the learning of fruit flies. </p> <p>We tested 1 h (short-term memory, STM) and 24 h memory (LTM) retention after associative conditioning. During the memory retention assay, the flies walked to the choice point of a T-maze, in which they were exposed to two converging currents of air, one carrying 3-octanol and the other 4-methylcyclohexanol, and then allowed to choose between the two odors for 60 s. The memory score was calculated as the difference in the proportion of individuals choosing 3-octanol between flies conditioned to avoid 4-methylcyclohexanol and those conditioned to avoid 3-octanol.</p>
Data for Huang, Luo, et al., Dopamine-mediated interactions between short- and long-term memory dynamics, Nature, 2024
<p>Zip files contain voltage imaging traces and on-ball behavioral data from the five main figures in Huang, Luo, et al., Dopamine-mediated interactions between short- and long-term memory dynamics, Nature, 2024</p>
Dataset for "A Double Dissociation between Savings and Long-Term Memory in Motor Learning"
<p>Data for "A Double Dissociation between Savings and Long-Term Memory in Motor Learning"<br> <br> For analysis code and any updates, please visit https://github.com/AlkisMH/Savings_vs_Long_Term_Memory<br> <br> For any questions, please contact Alkis Hadjiosif (alkis [at] seas [dot] harvard [dot] edu; ahadjiosif [at] gmail [dot] com)</p> <p>These .mat files contain data collected for Experiments 1-4 and S1, used to reproduce Figures 1-6 and S1 in the paper. Data for each experiment are in the form of a Matlab structure, with each field consisting of a [Num_trials x Num_participants] matrix.</p> <p>The fields are:</p> <p><strong>TN:</strong> Trial Number</p> <p><strong>VF:</strong> Whether visual feedback was given during the trial (1: online visual feedback; 2: no visual feedback)</p> <p><strong>Rotation:</strong> The visuomotor rotation imposed on each trial (in degrees). CCW is positive, CW is negative.</p> <p><strong>Wait:</strong> Whether, right before the trial, a wait time was imposed (1) or not (0). Trials immediately following breaks are indicated by (2).</p> <p><strong>Instruction:</strong> Whether an instruction was given for the trial (1) or not (0). Only present in Experiment 3. Note that Experiment 3 includes:</p> <p>-> Two specific instruction trials during learning and and two during relearning, before and after the 1-minute wait ("Move your hand to the center of the target","Move your hand to the far end of the target")</p> <p>-> Six random instruction trials before initial learning and six before relearning ("Move your hand to the left/right/near/far end of the target"), to familiarize participants with the instruction process</p> <p><strong>ITI:</strong> Time (in seconds) since the last trial (regardless of direction).</p> <p><strong>theta_target:</strong> Target direction (in degrees) relative to the 12 o'clock position. Only included for Experiment 4: target is always at 12 o'clock for Experiments 1-3.</p> <p><strong>theta:</strong> = reaching direction relative to theta_target, measured 150ms into the movement. Note that this is flipped based on the rotation sign so that adaptation towards the imposed visuomotor rotation is always positive.</p> <p><strong>theta_end:</strong> = reaching direction relative to theta_target, measured at the end of movement. Only included for Experiment 4: it is to be used for the no visual feedback blocks in Experiment 4.</p> <p>Note for <strong>Experiment 4</strong>: the last two blocks (last 114 trials) were done on day 2.</p>
Data & R Code for "Enhanced long-term memory and increased mushroom body plasticity in Heliconius butterflies"
<p>This ZIP file contains the data and R code used to analyse it for the paper "Enhanced long-term memory and increased mushroom body plasticity in Heliconius butterflies". Behavioural and neuroanatomical data are in separate folders.</p>
Wayfinding Intervention in High-Fidelity Long-Term Memory
ClinicalTrials.gov study NCT04253587. IPD Sharing: NO. Countries: 1. Publications: 1.
Dataset for the transcriptome analysis of hippocampal subfields identifies gene expression profiles associated with long-term active place avoidance memory
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Data from: Dual transcranial electromagnetic stimulation of the precuneus boosts human long-term memory
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Data from: Long-term social memory of mate copying in Drosophila melanogaster is localized in the mushroom bodies
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Lateralisation of short- and long-term visual memories in an insect
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Processing in working memory boosts long-term memory representations and their retrieval
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Learning strategies and long-term memory in Asian short-clawed otters (Aonyx cinereus) data
<p>Data submitted here, are those used in the writing of our manuscript entitled "Learning strategies and long-term memory in Asian short-clawed otters (<i>Aonyx cinereus</i>)" which has been submitted to Royal Society Open Science for publication. Abstract for that manuscript is below</p> <p>Social learning, namely learning from information acquired from others or their products, is widespread throughout the animal kingdom. There is growing evidence that animals selectively employ 'social learning strategies', which for example, determine when<i> </i>they should copy others instead of learning asocially, and whom they should copy. Furthermore, once animals have acquired new information, it is beneficial for them to commit it to long-term memory, especially when it concerns the discovery of profitable resources. Research into social learning strategies and long-term memory has covered a wide range of taxa. However, otters (subfamily Lutrinae), popular in zoos due to their sociability and playfulness, remained neglected until a recent study provided evidence of social learning in captive smooth-coated otters (<i>Lutrogale perspicillata</i>), but not in Asian short-clawed otters (<i>Aonyx cinereus</i>). We investigated Asian short-clawed otters' learning strategies and long-term memory performance in a foraging context. We presented novel extractive foraging tasks twice to captive family groups and used network-based diffusion analysis to provide evidence of social learning and long-term memory in this species. A major cause of wild Asian short-clawed otter declines is prey scarcity. Furthering our understanding of how they learn about and remember novel food sources could inform key conservation strategies.</p>
Long-term memory T cells as preventive anticancer immunity elicited by TuA-derived heteroclitic peptides
<p>The host’s immune system may be primed against antigens during the lifetime (e.g. microorganisms antigens—MoAs), and swiftly<br> recalled upon growth of a tumor expressing antigens similar in sequence and structure. C57BL/6 mice were immunized in a preventive<br> setting with tumor antigens (TuAs) or corresponding heteroclitic peptides specific for TC-1 and B16 cell lines. AQ1 Immediately or 2-<br> months after the end of the vaccination protocol, animals were implanted with cell lines. The specific anti-vaccine immune response as<br> well as tumor growth were regularly evaluated for 2 months post-implantation. The preventive vaccination with TuA or their<br> heteroclitic peptides (hPep) was able to delay (B16) or completely suppress (TC-1) tumor growth when cancer cells were implanted<br> immediately after the end of the vaccination. More importantly, TC-1 tumor growth was significantly delayed, and suppressed in 6/8<br> animals, also when cells were implanted 2-months after the end of the vaccination. The vaccine-specific T cell response provided a<br> strong immune correlate to the pattern of tumor growth. A preventive immunization with heteroclitic peptides resembling a TuA is able<br> to strongly delay or even suppress tumor growth in a mouse model. More importantly, the same effect is observed also when tumor<br> cells are implanted 2 months after the end of vaccination, which corresponds to 8 – 10 years in human life. The observed potent tumor<br> control indicates that a memory T cell immunity elicited during the lifetime by a antigens similar to a TuA, i.e. viral antigens, may<br> ultimately represent a great advantage for cancer patients and may lead to a novel preventive anti-cancer vaccine strategy.</p>
Experimental data for: Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels
<p>Experimental data set for: Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels. Provides all raw data needed to reproduce experimental graphs presented within the paper.</p>
Fig. 1 in Long-term memory in the parasitoid Trichogramma telengai Sorokina, 1987 (Hymenoptera: Trichogrammatidae)
Fig. 1. The performance of T. telengai in each of three tests: a — the percentage (%) of time spent by T. telengai in the target sector in each of the three tests (M ± SE), t-test: *p <0.05. b — the learning index in each of the three tests (M ± SE), t-test: *p <0.05. The column headers indicate the sample sizes. Рис. 1. ПокаЗатели перемеЩениЯ T. telengai в каждом иЗ трех тестов: a — процент (%) времени, проведенного T. telengai в целевом секторе в каждом иЗ трех тестов (M ± SE), t-test: *p <0.05. b — индекс обучениЯ в каждом иЗ трех тестов (M ± SE), t-test: *p <0.05. В основании столбцов укаЗаны объемы выборок.
ScienceDex guides
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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.