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12
datasets available to search
ShareScore release 0.9.0
Dataset results
12 results for “neural context”
Neural responses to naturalistic clips of behaving animals in two different task contexts
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Neural Overlap in Item Representations Across Episodes Impairs Context Memory
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Stimulating Wnt signaling reveals context-dependent genetic effects on gene regulation in primary human neural progenitors
<p>Summary statistics for chromatin accessibility and gene expression quantitative trait loci (ca/eQTLs) from Matoba, N., Le, B.D., Valone, J.M. <em>et al.</em> Stimulating Wnt signaling reveals context-dependent genetic effects on gene regulation in primary human neural progenitors. <em>Nat Neurosci</em> (2024). https://doi.org/10.1038/s41593-024-01773-6</p>
Data for: The hippocampal representation of context is preserved despite neural drift
<p>The hippocampus is thought to mediate episodic memory through the instantiation and reinstatement of context-specific cognitive maps. However, recent longitudinal experiments have challenged this view, reporting that most hippocampal cells change their tuning properties over days even in the same environment. Often referred to as<em> neural</em> or <em>representational drift</em>, these dynamics raise questions about the capacity and content of the hippocampal code. One such question is whether and how these long-term dynamics impact the hippocampal code for context. To address this, we imaged large CA1 populations over more than a month of daily experience as freely behaving mice participated in an extended geometric morph paradigm. We find that long-timescale changes in population activity occurred orthogonally to the representation of context in network space, allowing for consistent readout of contextual information across weeks. This population-level structure was supported by heterogeneous patterns of activity at the level of individual cells, where we observed evidence of a positive relationship between interpretable contextual coding and long-term stability. Together, these results demonstrate that long-timescale changes to the CA1 spatial code preserve the relative structure of contextual representation.</p>
Data for: The hippocampal representation of context is preserved despite neural drift
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Train and Test Dataset for a Neural Network for the task of RSA Segmentation in the context of a Master thesis 2024
<p>The data corresponds to the work of the master thesis "3D Segmentation of Plant Roots from MRI Images for Enhanced Automated Root Tracing using Deep Neural Networks" by Daniel Weißen from the 24/07/2024</p> <p>train_data.tar<br>- contains the training and validation data used for the DNN<br> - data_gmm contains the soil noise data based on a Gaussian Mixture Model<br> - data_FS contains the soil noise data based on the Fourier Synthesis approach described in the thesis</p> <p>test_data.tar<br>- contains the test data which is manually labeled</p> <p>segmentations.tar<br>- contains the segmentations of the trained SwinUNETR-V2+ on the real test MRIs<br> - fs_model_output: contains the Fourier Synthesis trained NNs segmentation<br> - fmm_model_output: contains the GMM trained NNs segmentation<br> - threshold_output: contains the Dice optimized threshold labeling</p> <p>FS_checkpoint.ckpt, GMM_checkpoint.ckpt<br>- contains the checkpoint for the trained SwinUNETR-V2+ on the Fourier synthesis, and GMM soil noise respectively</p>
Dentate gyrus ensembles gate context-dependent neural states and memory retrieval
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Single-cell reconstruction with spatial context of migrating neural crest cells and their microenvironments during vertebrate head and neck formation
GEO Series GSE146809. Gallus gallus. 8 samples. Type: Expression profiling by high throughput sequencing.
Cross-species Analyses Unravel the Complexity of H3K27me3 and H4K20me3 in the Context of Neural Stem Progenitor Cells
GEO Series GSE81353. Papio anubis. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 2
<p>Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 2.</p> <p>The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with:</p> <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> <p>To get original file back, run</p> <pre><code>cat detection_data.tar.gz.* > detection_data.tar.gz</code></pre> <p>GitHub link to the project: <a href="https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection">https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection</a></p>
Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 1
<p>Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 1. Part 2 accessible here: <a href="https://doi.org/10.5281/zenodo.3719225">https://doi.org/10.5281/zenodo.3719225</a></p> <p>The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with:</p> <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> <p>To get original file back, run</p> <pre><code>cat detection_data.tar.gz.* > detection_data.tar.gz</code></pre> <p>GitHub link to the project: <a href="https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection">https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection</a></p>
Replication data for Convolutional neural networks with hierarchical context transfer for high-resolution spatiotemporal predictions
<p>Data represents number of posts from Instagram in area for six large cities (New York, London, Moscow, Vienna, Tokyo, and Saint Petersburg) covering 2017 and 2018. We aggregated data by hours and split each city using hierarchical area split by 10x10 grid with three levels. The size of the cell on the micro-level equals to 50 meters. Thus, we get 8760 examples for each year. Cells with zero number of posts in an hour are not listed.</p> <p>Data stored in JSON files with the following format:<br> {"2018-12-13 04:00:00":<br> {"(4, 4)":<br> {"sum":1,<br> "data":<br> {"(9, 6)":<br> {"sum":1,<br> "data":{"(2, 4)":1}}}}}}</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.