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Dataset results
11 results for “Generative Encodings”
NEXT GENERATION OPTICAL ENCODER
<p>Linear encoders provide direct position feedback to various machine tool and automation systems. Working in a linear format allows extreme length position measurement and control. José Luis de la Peña from <a href="https://www.fagorautomation.com/en/">Fagor Automation</a> explains how <a href="https://www.laser4surf.eu">Laser4Surf</a> technology will make linear encoders even more precise with the help of lasered nano strucures on the tape.</p>
Supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding"
<h1>Info</h1> <p>This dataset contains the supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding". </p> <p>For <strong>source code </strong>and <strong>detailed instructions on usage, </strong>please refer to our <a href="https://github.com/meneshail/TopoDiff/tree/main" target="_blank" rel="noopener">github</a> .</p> <h1>Supplementary data</h1> <h2>weights.tar.gz</h2> <p>The trained model weights used in the paper.</p> <h2>dataset.zip</h2> <p>CATH-60 Dataset used in the paper. In the notebook directory of our <a href="https://github.com/meneshail/TopoDiff/tree/main" target="_blank" rel="noopener">github</a> , we provide an example on encoding and visualize it with our trained encoder.</p> <h2>design.zip</h2> <p>The 21 novel mainly-beta designs selected for experiment validation. Along with the generated backbone, we also provide the prediction results from AlphaFold and ESMFold.</p> <h2>benchmark_sample.zip</h2> <p>Sampled backbones used for all benchmark experiment (All methods and variants included).</p> <h2>evaluation.tar.gz</h2> <p>Precomputed CATH reference data for coverage metric computation. Need to be downloaded for using evaluation scripts. </p>
Fig. 4. Maximum Likelihood phylogenetic tree generated using N in The African buffalo parasite Theileria. sp. (buffalo) can infect and immortalize cattle leukocytes and encodes divergent orthologues of Theileria parva antigen genes
Fig. 4. Maximum Likelihood phylogenetic tree generated using N-terminal sequences of T. sp. (buffalo) and T. parva PIM antigen genes. Maximum composite likelihood trees were constructed using 1000 bootstrap replicates as implemented in MEGA5; the optimal nucleotide substitution model was identified using data monkey. The tree constructed with RAxML (Stamatakis et al., 2014) using a GTR/G/I model with 100 bootstrap iterations.
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>
Generative Fourier-based Auto-Encoders:Preliminary Results Dataset
<p><a href="https://urbansounddataset.weebly.com/urbansound8k.html">UrbanSound8K Dataset</a> snapshot used for the "Generative Fourier-based Auto-Encoders: Preliminary Results" paper. Only the sounds tagged "dog_bark" are present in this small dataset</p>
Data from: Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
In 1994 Karl Sims showed that computational evolution can produce interesting morphologies that resemble natural organisms. Despite nearly two decades of work since, evolved morphologies are not obviously more complex or natural, and the field seems to have hit a complexity ceiling. One hypothesis for the lack of increased complexity is that most work, including Sims', evolves morphologies composed of rigid elements, such as solid cubes and cylinders, limiting the design space. A second hypothesis is that the encodings of previous work have been overly regular, not allowing complex regularities with variation. Here we test both hypotheses by evolving soft robots with multiple materials and a powerful generative encoding called a compositional pattern-producing network (CPPN). Robots are selected for locomotion speed. We find that CPPNs evolve faster robots than a direct encoding and that the CPPN morphologies appear more natural. We also find that locomotion performance increases as more materials are added, that diversity of form and behavior can be increased with different cost functions without stifling performance, and that organisms can be evolved at different levels of resolution. These findings suggest the ability of generative soft-voxel systems to scale towards evolving a large diversity of complex, natural, multi-material creatures. Our results suggest that future work that combines the evolution of CPPN-encoded soft, multi-material robots with modern diversity-encouraging techniques could finally enable the creation of creatures far more complex and interesting than those produced by Sims nearly twenty years ago.
Data from: Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
Open the record for dataset details and reuse information.
Validation of THS-seq method, and comparison of published ENCODE DNase-seq data, self-generated ATAC-seq data and published ATAC-seq data, and THS-seq data for quantitation of chromatin accessibility.
GEO Series GSE72089. Homo sapiens. 11 samples. Type: Third-party reanalysis; Other; Genome binding/occupancy profiling by high throughput sequencing.
Next-generation sequencing of Drosophila melanogaster transcriptome upon removal of unique exons encoding Sgg-PA and Sgg-PB (major shaggy isoforms)
GEO Series GSE139040. Drosophila melanogaster. 14 samples. Type: Expression profiling by high throughput sequencing.
Next Generation Sequencing Facilitates Transcriptomes Quantitative Analysis of N2A cells transfected with the plasmids respectively encoding RfxCas13d/dRfxCas13d and crRNA.
GEO Series GSE222461. Mus musculus. 36 samples. Type: Expression profiling by high throughput sequencing.
Next Generation Sequencing Facilitates Transcriptomes Quantitative Analysis of HEK293T cells transfected with the plasmids respectively encoding RfxCas13d, crRNA and NeuN.
GEO Series GSE222451. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
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.