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848 results for “representation”
Sharpening of Hierarchical Visual Feature Representations of Blurred Images
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Shared neural codes for visual and semantic information about familiar faces in a common representational space
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Dataset and program scripts for the reproducibility of the hierarchical data structure file. Related to the manuscript entitled: Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests
<p>We present an interoperable hierarchical data representation for battery tests, leading to improved scalability of data transmission and enhanced data accessibility and comprehensibility for both human interpretation and machine processing. The hierarchical data format includes the raw trace electrical measurement data, the metrological calibration and uncertainty data, the metadata such as experimental settings, instruments and software versions, as well as post-processed data such as electrochemical model fit parameters. This data representation allows repetition of the battery test under the exact same conditions such that identical results are achieved within defined error bounds. This is in line with the general F.A.I.R. data approach and provides repeatability and traceability in the battery value chain. As an application of the hierarchical data representation, we show the classification of cells as pass/fail being performed with quantitative confidence levels. We demonstrate the complete workflow of establishing the hierarchical data structure for electrochemical impedance spectroscopy (EIS), starting from metrological traceability of the calibration and uncertainty analysis towards the storage of the structured data as a single integrated file that preserves the hierarchical data format.</p>
Modeling the effect of explicit vs implicit representation of grazing on ecosystem carbon and nitrogen cycling in response to elevated carbon dioxide and warming in arctic tussock tundra, Alaska - Dataset A
We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change.
Modeling the effect of explicit vs implicit representation of grazing on ecosystem carbon and nitrogen cycling in response to elevated carbon dioxide and warming in arctic tussock tundra, Alaska - Dataset B
We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change.
columnar Finger representations
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Retrieval practice facilitates memory updating by enhancing and differentiating medial prefrontal cortex representations
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Model-based fMRI reveals co-existing specific and generalized concept representations
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Taste Quality Representation in the Human Brain
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Visual and auditory brain areas share a representational structure that supports emotion perception: fMRI data
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RDF Representation of RNA Metabolism Evolution data - version 3 (diagrammed in https://zenodo.org/deposit/47641/)
<p>Version 3 (replaces http://doi.org/10.5281/zenodo.50496)<br> <br> Protein complexes involved in RNA Metabolism; individual proteins and their orthologues through a wide range of fungal species spanning much of the kingdom (using yeast as the primary seed for orthology search, and using the EMBL-EBI orthologue database to identify orthologues). For each family of orthologues, the protein domain structure is determined, and then the presence/absence of that domain is evaluated in each of the species. The data is presented in RDF, and is visualized in the form of Heat Maps in http://doi.org/10.5281/zenodo.47641</p>
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>
Phase coding of spatial representations in the human entorhinal cortex
<p>Supporting Preprocessed Electrophysiology Data for the article titled "Phase coding of spatial representations in the human entorhinal cortex".</p> <p>Data were preprocessed and analyzed using Matlab.</p> <p>Data, after decompression, are organized hierarchically in folders and subfolders (two levels).</p> <p>Main folder names are composed as subjectID_date_EC_DATA_taskNo</p> <p>Sub-folders named as: CHn_single-unit ID </p> <p>[subjectID, date, task num] + [Electrode channel, and single-unit ID] </p> <p>subjectID: (subject1, subject2)</p> <p>date: mmm-dd</p> <p>task: (1,2,3,4) Virtual environments (1: backyard, 2: Louvre, 3: Luxor, 4: desert)</p> <p>electrode Channel: CH1,CH2, CH3, CH4, CH5</p> <p>single-unit ID: 0, 1, 2, 3, 4, 5, 6</p> <p>For each channel and single-unit, the following 9 datasets were computed and saved. For instance, for the first electrode and first single-unit class (CH=1, cell ID= 0):</p> <p>CH1_Clu0.mat -- Summary of firing features of this single-unit including firing rate, and grid score of the cell. (In Matlab mat format)<br> CH1_Clu0_MeanPhaseMap.csv -- Mean spike phase map relative to gamma-band LFP<br> CH1_Clu0_Phase.csv -- Spike phase CH1_Clu0_spikeData.csv<br> CH1_Clu0_spkT.csv -- Spike times in increental order<br> CH1_Clu0_VarPhaseMap.csv -- Map of the variance of spike times in increental order<br> CH1_Clu0_xval.mat -- Summary of firing features of 50% of single-unit spikes (every other spike) for cross-validation purposes. (In Matlab mat format)<br> CH1_Clu0_xyPos.csv -- X,Y coordinates of the avatar's position in 1 ms resolution. In other words, the path taken by the avatar sampled at 1 kHz.<br> CH1_Clu0_XYspkT.csv -- X,Y coordinates of the avatar at moments of spikes. In other words, the location in space where a spike was fired by the putative neuron.</p> <p> </p>
Test-Retest qt-dMRI datasets for "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time"
<p>We release these four diffusion MRI data sets as part of our recent journal publication; Fick, Rutger H.J., et al. "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time." <em>Medical Image Analysis</em> (2017). More detailed information about the use of these data sets can also be found in the publication.</p> <p>We acquired test-retest diffusion MRI spin echo sequences from two C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest acquisition were taken 48 hours from each other. The data consists of 80x160x5 voxels of size 110x110x500<span class="math-tex">\(\mu\)</span>m. Each data set consists of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells. The shells are spread over 7 gradient strength shells with a maximum gradient strength of 491 mT/m, 5 pulse separation shells between [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain mask and corrected the data from eddy currents and motion artifacts using FSL's eddy. We then drew a region of interest in the middle slice in the corpus callosum, where the tissue is reasonably coherent.</p> <p>- The diffusion MRI data are contained in the files with 'dwis' in the name.<br> <br> - The corpus callosum masks are contained in the files with 'mask' in the name.</p> <p>- The acquisition parameters are contained in the .txt files.</p>
Materials for 2d representation of the HathiTrust Library
<p>Materials to create the LargeVis visualization online at http://creatingdata.us/datasets/hathi-features/, and described in <em>Benjamin Schmidt, "Stable random projection: lightweight, general-purpose dimensionality reduction for digitized libraries," Journal of Cultural Analytics. October 3, 2018.</em></p> <p>Two items. First, `hathi_pca.bin`: a binary file with 100-dimensional representations of the complete Hathi Trust Extended Features set. These began as 1280-dimensional SRP features, and were reduced to 100 dimensions using a PCA transformation matrix derived using a random sample of the full 13 million book set. Vectors were reduced to unit length before PCA, but not afterwords; this means that in general, their length gives some sense of much information was lost in the PCA representation. This can be read using the code at https://github.com/bmschmidt/pySRP, or anything that reads word2vec formatted vectors. Includes HathiTrust identifiers.</p> <p>Second, `hathi.tsv.gz`: a row oriented set containing a variety of metadata fields for each set, including (as 'x' and 'y') the coordinates of a 2-d LargeVis visualization. This is the immediate input to the visualization at ttp://creatingdata.us/datasets/hathi-features/. Columns should be relatively straightforward; they are derived from the HathiTrust MARC records, which can be accessed through Hathi's public API. Classification codes ('lc1') are using the Library of Congress classification; they represent the subclass (generally two characters, though it can be one or three). The first character alone represents the LC class and can be useful for coloring high-level overviews.</p> <p>These two files can be merged through the Hathi Trust identifier present in both.</p> <p> </p>
Transfer of sensorimotor learning reveals phoneme representations in preliterate children - Dataset
<p>This file provides formants values in each speaker and for each trial of the experiment described in the article : Transfer of sensorimotor learning reveals phoneme representations in preliterate children.</p> <p> </p>
Excel template for the aggregate database on descriptive representation of the ActEU project
<p>This is the Excel template used to structure the databases that provide data at the legislature / party level for each of the six countries studied in Tasks 4.1 and 4.2 of the ActEU project.</p>
MCR LTER: Data from Duvall, Rosman and Hench, in review. Representation of coral reef roughness using obstacle and surface-based approaches, submitted to JGR: Oceans
This archive contains natural coral reef topography data from the northern coast of Mo’orea, French Polynesia. These data were used to compute reef roughness density using obstacle- and surface-based estimates and models, and to compare the two approaches for representing reef topography. Primary support for this product came from the National Science Foundation Physical Oceanography program (OCE-1435530 and OCE-1435133), and as well as Duke University and the University of North Carolina at Chapel Hill. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2019). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Neural Overlap in Item Representations Across Episodes Impairs Context Memory
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Briefly cueing memories leads to suppression of their neural representations
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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.