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Dataset results
27 results for “Encoding models”
Processed data for "Model identification of neural encoding (MINE)" publication
<p>This dataset contains mouse and zebrafish data processed by MINE. These datafiles were used to generate the publication figures for the mouse cortical dataset [m<em>usall.hdf5</em>] (Figure 5) and the zebrafish whole-brain [<em>main_analysis.hdf5</em>] (Figures 6 and 7) and reticulospinal datasets [r<em>s_analysis.hdf5</em>] (Figure 6).</p> <p> </p> <p><em>Musall.hdf5 </em>contains reordered data from "Musall, S., Kaufman, M.T., Juavinett, A.L. <em>et al.</em> Single-trial neural dynamics are dominated by richly varied movements. <em>Nat Neurosci</em> <strong>22</strong>, 1677–1686 (2019)."</p> <p>The contents of each dataset are described in <em>DataContent_xxx.pdf</em></p>
Convolutional Neural Net (CNN) models for ENCODE-Roadmap DNase-seq peaks and Transcription Factor ChIP-seq peaks - Basset architecture
<p>Deep learning models trained on epigenomic landscapes from ENCODE and Roadmap Epigenomics. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4059038. The file `nn.encode-roadmap.models.basset.clf.tar.gz` contains 10 cross-validated models in Tensorflow framework files as well as details on the architecture, cross-validation scheme, and training of these models. The file `nn.encode-roadmap.models.basset.clf.np_weights.tar.gz` contains the 10 cross-validated models' weights extracted to numpy array files (.npz).</p>
Data from: Learning of probabilistic punishment as a model of anxiety produces changes in action but not punisher encoding in the dmPFC and VTA
<p>Previously, we developed a novel model for anxiety during motivated behavior by training rats to perform a task where actions executed to obtain a reward were probabilistically punished and observed that after learning, neuronal activity in the ventral tegmental area (VTA) and dorsomedial prefrontal cortex (dmPFC) represent the relationship between action and punishment risk (Park & Moghaddam, 2017). Here we used male and female rats to expand on the previous work by focusing on neural changes in the dmPFC and VTA that were associated with the learning of probabilistic punishment, and anxiolytic treatment with diazepam after learning. We find that adaptive neural responses of dmPFC and VTA during the learning of anxiogenic contingencies are independent from the punisher experience and occur primarily during the peri-action and reward period. Our results also identify peri-action ramping of VTA neural calcium activity, and VTA-dmPFC correlated activity, as potential markers for the anxiolytic properties of diazepam.</p>
Populations of local direction-selective cells encode global motion patterns generated by self-motion. Data, Code and Model.
<p>Directional tuning of the population of local motion detectors T4/T5 in the visual system of the fruit fly <em>Drosophila melanogaster</em>. Direction tuning and receptive field location was measured by recording responses to visual stimuli containing dark or bright edges/stripes moving into 8 directions. All provided MATLAB scripts were used to analyze and illustrate data show in the manuscript 'Populations of local direction-selective cells encode global motion patterns generated by self-motion.'</p> <p>All data were obtained using <em>in vivo </em>two photon microscopy. Image time series were preprocessed using SIMA python software for motion alignment and further processed using custom written matlab or python code.</p> <p>Please find all relevant information to use the code in the README file.</p>
CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 2
<p>This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset and the Musall et al. mouse dataset processed by MINE. This set contains the last fish and the mouse MINE model weights. The other 24 fish are contained in Set 1.</p>
CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 1
<p>This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset processed by MINE. This set contains 24/25 fish. The last fish and mouse MINE model weights are contained in Set 2.</p>
Data from: Learning of probabilistic punishment as a model of anxiety produces changes in action but not punisher encoding in the dmPFC and VTA
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Data from: Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning
<p>How the brain representation of conceptual knowledge vary as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. Here we asked how the brain representation of semantic categories is shaped by the depth of processing during mental simulation. Participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models - associated with the image referents of the words - and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.</p>
Data from "Testing models of peripheral encoding using metamerism in an oddity paradigm"
<p>Raw data and stimuli from the experiments reported in Wallis, Bethge & Wichmann (under review). "Testing models of peripheral encoding using metamerism in an oddity paradigm". Journal of Vision.</p> <p>For the code, see http://doi.org/10.5281/zenodo.34218.</p> <p>Please consult the README file in the archive for detailed information on reproducing the results of the paper.</p> <p>Stimuli are modified from the "Judd" dataset (https://people.csail.mit.edu/tjudd/WherePeopleLook/index.html). The citation is </p> <p>Judd, T., Ehinger, K., Durand, F., & Torralba, A. (2009). Learning to predict where humans look. In <em>Computer Vision, 2009 IEEE 12th international conference on</em> (pp. 2106–2113). IEEE.</p> <p>We have been granted permission to reshare these images by Tilke Judd (thanks!).</p>
ENCODE LR-RNA-seq models and expression values
<p>In this object are the following files:</p> <p> </p> <ul> <li>filt_ab_tpm_mouse.tsv / filt_ab_tpm_human.tsv: Expression levels in TPM for each transcript in human and mouse</li> <li>lr_mouse_library_data_summary.tsv / lr_human_library_data_summary.tsv: Metadata for each dataset in human and mouse</li> <li>cerberus.gtf / mouse_cerberus.gtf: Transcript models in GTF format</li> <li>human_ucsc_transcripts.gtf / mouse_ucsc_transcripts.gtf: Transcript models for transcripts that passed expression filtering (>= 1 TPM in at least one library; transcripts from known </li> <li>human_protein_summary.tsv / mouse_protein_summary.tsv: Summary of protein coding predictions, including ORF locations and NMD status</li> </ul>
Data from: Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning
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An actor-model framework for visual sensory encoding
<pre>This folder contains the dataset used in the article 'An actor-model framework for visual sensory encoding'. </pre> <pre>There are three main folders 'models' contains the trained neural network for the different experiments.<br>The code to load the model and generate the figures in the article is available<br>in https://github.com/lne-lab/actor-retina<br>or at https://doi.org/10.5281/zenodo.10519578 'spikes_data' contains the data used to train the neural network.<br>This can be used to train a network from scratch. 'Van_Hateren_image' contains the image dataset used in the experiments.<br><br>UPDATE (2025-08-22)<br>'stimulus' contains the stimulus used as input to train the forward and actor models.</pre>
CNF Encoded Isomorphic and Optimized Miters from Hardware Model Checking Competition 2012 Models
<p>These miter benchmarks are used in a paper on congruence closure in 2024.</p> <p>The miter AIGs in the [`aig`](aig) directory are the same as those used to<br>generate CNFs of miter benchmarks submitted to the SAT Challenge in 2012.<br>Some of them have regularly been used in the SAT Competitions since then.</p> <p>The AIGs are separated into two sets:</p> <p>- [`aig/iso`](aig/iso) miters of isomorphic circuits<br>- [`aig/opt`](aig/opt) miters of optimized versus original circuit</p> <p>The first set checks equivalence of one circuit with itself in the<br>[`aig/iso`](aig/iso) (isomorphic) sub-directory and second set contains<br>equivalence checking problems (aka miters) comparing the original circuit<br>with an optimized version in the [`aig/opt`](aig/opt) (optimized)<br>sub-directory. The optimized versions of the circuits were obtained in 2012<br>by the `dc2` script for [ABC](https://people.eecs.berkeley.edu/~alanmi/abc).<br>We did not re-run ABC with a newer version in order to make sure we have as<br>base-line in our experiments the same CNFs, as they are well-known, i.e., <br>they have been used in the SAT competitions for several years.</p> <p>Note that the original AIGER models have state elements (called "latches"<br>in AIGER terminology), but both the optimization with ABC as well as our<br>equivalence checking is purely combinational, by treating latches as<br>additional pseudo inputs, and next-state functions as output-functions. The<br>miters were accordingly generated (by the 2011 version) of `aigmiter` using<br>the `-c` option. For the same reason as for ABC explained above, we did not<br>rerun `aigmiter` either.</p> <p>We then encoded the two sets of AIGs into CNF in two different ways.<br>The first encoding just uses a simple Tseitin encoding, without<br>Plaisted-Greenbaum optimization, i.e., using the `--no-pg` option, as in 2012.<br>Even though we use a new version of `aigtocnf` in `src/aiger/aigtocnf.c` we<br>made sure that identical CNFs are generated for this first encoding variant<br>by checking that the MD5 sum of the (actually compressed) CNF files match.<br>This required to disable a newly introduced cone-of-influence (COI)<br>optimization with `--no-coi` which skips unreachable gates even when the<br>Plaisted-Greenbaum optimization is disabled.</p> <p>The second variant of the encoding detects XOR and ITE gates by pattern<br>matching and produces more concise CNFs by skipping internal AND gates<br>of detected XOR and ITE gates producing a direct encoding instead.</p> <p>This gives the following sets of CNFs;</p> <p>- [`cnf/ands/iso`](cnf/ands/iso) isomorphic circuits with ANDs only<br>- [`cnf/ands/opt`](cnf/ands/opt) optimized versus original with ANDs only<br>- [`cnf/xits/iso`](cnf/xits/isor) isomorphic circuits with ANDs, XORs, ITEs<br>- [`cnf/xits/opt`](cnf/xits/opt) optimized versus original with ANDs, XORs, ITEs</p> <p>All the CNFs in these directories have the same original file name of the<br>AIGER model to simplify run-time comparison. In `cnf/all` we further<br>provide links with qualified names to distinguish them.</p>
CNF Encoded Isomorphic and Optimized Miters from Hardware Model Checking Competition 2020 Models
<p>From the Hardware Model Checking Competition 2020 we have collected 324<br>sequential model checking problems and for each generated an isomorphic and<br>an optimized miter. The isomorphic miters just compares two identical<br>copies while for the optimized miter one copy went through optimization<br>with ABC using the `dc2` command.</p> <p>The CNFs are generated with a new version of `aigtocnf` which detects<br>XOR and ITE gates in the AIGER circuit and if detected uses a more compact<br>encoding (4 clauses clauses instead of 9) for each detected gate.</p>
Graph Positional and Structural Encodings (model weights and precomputed encodings)
<p>Model weights for pre-trained GPSE model and selected pre-computed GPSE encodings using the model pre-trained on MolPCBA</p> <p> </p>
Intratumoral administration of lipid nanoparticles carrying mRNA encoding for IL-21, IL-7, and 4-1BBL induces anti-tumor immunity in preclinical tumor models.
GEO Series GSE249674. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
RNA sequencing of T cells in the genetically encoded mouse model of T cell anergy (W131AOTII)
GEO Series GSE167169. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
Vaccination with an mRNA-encoded membrane-bound HIV envelope trimer induces neutralizing antibodies in animal models
GEO Series GSE298795. Macaca mulatta. 36 samples. Type: Expression profiling by high throughput sequencing.
Supplements for "The Proteomic Code: Novel Amino Acid Residue Pairing Models "Encode" Protein Folding and Protein-Protein Interactions"
<p><strong>Supplements for "The Proteomic Code: Novel Amino Acid Residue Pairing Models “Encode” Protein Folding and Protein-Protein Interactions"</strong></p> <p>This supplement includes the following files:</p> <ul> <li>Main_v02.R --- Script in R language to process protein structures and produce the datasets.</li> <li>datasets_v02.zip --- final filtered version of datasets produced in R language (by "Main_v02.R"). </li> <li>Dataset key v02.txt --- key and descriptions to column names in ("datasets.zip"). </li> </ul> <p>The article featuring these datasets is submitted to:</p> <p>Journal: Computers in Biology and Medicine (Reference: CBM_110033)<br>Title: "The Proteomic Code: Novel Amino Acid Residue Pairing Models "Encode" Protein Folding and Protein-Protein Interactions"<br>Authors: Hameduh, Tareq; Miller, Andrew D. ; Heger, Zbynek; Haddad, Yazan</p> <p> </p> <p> </p>
Design of news recommendation model based on sub-attention news encoder
<p>data set used for the project Design of news recommendation model based on sub-attention news encoder</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.