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1,025 results for “Encoding”
Automatic TEI encoding of manuscripts catalogues with GROBID-Dictionaries
<p>Manuscript Sales Catalogues (MSC) are highly important for authenticating documents and studying the reception of authors. Their regular publication throughout Europe since the beginning of the 19th c. has consequently raised the interest around scaling up the means for automatically structuring their contents. </p> <p>Following successful first encoding tests with <em>GROBID-Dictionaries</em> on a single MSC collection, we aim in this paper to present the results of more advanced tests of the system’s capacity to handle a larger corpus with MSC of different dealers, and therefore multiple layouts. Four different types of catalogues published between the middle of the 19th c. and the beginning of the 20th c. have been tested.</p>
Selective excitation enables encoding and measurement of multiple diffusion parameters in a single experiment
<p>NMR spectra data for diffusion experiments.</p> <p>NMR pulse sequence compatible with Bruker spectrometers for the selective diffusion measurement.</p>
The Vibrio Type III Secretion System 2 is not restricted to the Vibrionaceae and encodes differentially distributed repertoires of effector proteins
<p>Supplementary Dataset for the work entitled "The Vibrio Type III Secretion System 2 is not restricted to the Vibrionaceae and encodes differentially distributed repertoires of effector proteins".</p> <p>This dataset includes files for the T3SS2 reconstructed phylogenetic tree (Newick tree and fasta file), hierarchical clustering data analysis file from MORPHEUS, Table S1 with genome accession numbers, and all the data of the absence/presence of T3SS2-related components, Table S2 with the prediction of novel effector proteins.</p>
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>
Analysis accompanying "Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the Drosophila larval brain"
<p>This repository documents the raw data processing and figure generation for the article “Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the <em>Drosophila </em>larval brain”, doi: 10.1261/rna.079552.122.</p>
Agreeableness personality trait and social information encoding
Open the record for dataset details and reuse information.
The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI: Supporting Data
<p>This data was created to perform the first experiments with the SILVER method of optimizing radial MRI acquisition. The files are mainly .mat files containing the numerical data used to assess the SILVER method using MATLAB (Version R2018b). Instructions on how to use the data and how to reproduce the experiments are available on https://github.com/SophieSchau/SILVER</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>
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>
Data set for "Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior"
<p>Data set for: Oryshchuk A, Sourmpis C, Weverbergh J, Asri R, Esmaeili V, Modirshanechi A, Gerstner W, Petersen CCH, Crochet S (2024) Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior. Cell Reports 43: 113618. https://doi.org/10.1016/j.celrep.2023.113618</p> <p> </p> <p>There are 2 files in this upload:</p> <p> </p> <p>1. The file named "2024_Oryshchuk_CellReports.pdf" is the Open Access pdf of the online publication in Cell Reports.</p> <p> </p> <p>2. The file named " Oryshchuk _data_code.zip" (~1.8 GB) is a zipped version of a folder "Oryshchuk _data_code" (~2.3 GB), which contains the preprocessed data analyzed in the study along with the Matlab and Python codes used to generate the published figures. To access the data and codes, first unzip the file.</p> <p>· The subfolder “Atlas” contains templates from the Allen Mouse Brain Reference Altas of anatomical brain sections used to map the location of the silicon probes (Supplementary Figure S1).</p> <p>· The subfolder “Clustering-master” contains the Matlab codes used for the clustering on neuronal activity (Figure 1). The output is the data structure ‘Data_Clustering.mat’ file already provided in the folder ‘Data’.</p> <p>· The subfolder “Code” contains the main Matlab codes used to analyze the data and plot the figures. The ouput from the clustering and decoding analyses are provided in the ‘Data’ folder, thus the Matlab codes can be run independently, without running the ‘clustering’ or ‘decoding’ codes first.</p> <p>· The subfolder “Data” contains the Matlab data structures containing the electrophysiological and behavioral data from whisker rewarded (‘DataWR.mat’) and non-rewarded (‘DataWnonR.mat’) mice, the behavioral data for optogenetic inactivation in rewarded mice, the clustering results (‘Data_Clustering.mat’) and a subfolder containing the results from the decoding analyses (“Decoding”).</p> <p>· The subfolder “decoding” contains the Python codes used for the decoding analyses. The required configuration can be found in the file ‘requirements.txt’. To run the codes, follow instructions from the ‘README.md’ file.</p> <p>· The subfolder “Figures” will be populated with figures saved in .png and .eps formats as well as a ‘Methods.txt’ files when running the main Matlab codes.</p> <p>· The subfolder “Functions” contains subfunctions used by the main Matlab codes to analyze the data and plot the figures.</p> <p>· The subfolder “Results” will be populated with Matlab data structures as well as a ‘.xlsx’ files when running the main Matlab codes.</p> <p>When running the code, you need to set the Matlab file path to be "Oryshchuk _data_code". In addition, you should add the folder "Oryshchuk_data_code" with subfolders to the Matlab path. Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication. Please note that some of the code can take several hours to execute.</p>
refering rawdata and code of "Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE)"
<p>Corresponding raw data and codes that produce all relative video and imaging results for real-time monitoring the physicochemical phenomena of microfluidics, microorganism's group, and chemical reactions, supporting and verifying the research article "Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE)".</p>
Galaxy Training Data for "Designing plasmids encoding predicted pathways by using the BASIC assembly method"
<p>This dataset provides the data needed for the Galaxy BASIC assembly workflow training tutorial (<a href="https://galaxy-synbiocad.org">https://galaxy-synbiocad.org</a>). This workflow provides a pathway to design plasmids encoding predicted metabolic pathways using the BASIC assembly method (<a href="https://doi.org/10.1021/sb500356d">https://doi.org/10.1021/sb500356d</a>). It generates scripts allowing the automatic construction of these plasmids using an Opentrons liquid handling robot. After downloading these scripts on a computer connected to an Opentrons (<a href="https://opentrons.com">https://opentrons.com</a>), the user can perform the automatic construction of the plasmids on the bench.</p> <p>The content of the dataset is as follows:</p> <ul> <li> <p>an SBML file modeling a heterologous pathway producing lycopene such as those produced by the Pathway Analysis Workflow (<a href="https://galaxy-synbiocad.org">https://galaxy-synbiocad.org</a>).</p> </li> <li> <p>a CSV file listing the parts to be used (linkers, backbone and promoters) in the constructions.</p> </li> <li> <p>two YAML files providing two examples of settings, i.e. providing the identifiers of the laboratory equipment and the parameters of the DNA robot.</p> </li> </ul>
Phylogenetic analysis of policistronic amino-acid sequences encoded by 116 flavivirus genomes
<p><span>Recently, Genome Biology and Evolution (11:3341-3352) published three statistical tests for testing whether alignments of sequence data violate the phylogenetic assumption of evolution under homogeneous conditions. The tests extend the matched-pairs tests of symmetry, marginal symmetry, and internal symmetry for pairs of aligned homologous sequences to the case where a whole alignment is considered. Here we reveal that the new tests are misleading. We explain why this is so, reveal how the tests of whole alignments may be done, and release new bioinformatics tools that implement statistically sound methods of dealing with multiple comparisons (i.e., by controlling the family-wise error rate or the false discovery rate). Using the new software to analyse an alignment of amino acids encoded by 116 flavivirus genomes, we reveal, for the first time, that these genomes are unlikely to have evolved under stationary, reversible, and homogeneous Markovian conditions.</span></p>
Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography
<p>This repository contains the raw K-space data and all MATLAB (The MathWorks, Natick, MA) code used for reconstruction, simulations, data analysis, and figure generation used in the article titled "Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography".</p> <p>## Referencing</p> <p>If you use any of the data or the code provided here, please cite:</p> <p>1. Woods JG, Schauman SS, Chiew M, Chappell MA, Okell TW. Time-encoded pseudo-continuous arterial spin labeling: Increasing SNR in ASL dynamic angiography. Magnetic Resonance in Medicine. 2023; doi:10.1002/mrm.29491<br> 2. This Zenodo repository: Joseph G. Woods, S. Sophie Schauman, Mark Chiew, Michael A. Chappell & Thomas W. Okell. (2022). Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6791097</p> <p> </p> <p>If you use the reconstruction code provided here, please also cite:</p> <p>1. Fessler JA, Sutton BP. Nonuniform fast fourier transforms using min-max interpolation. IEEE Transactions on Signal Processing. 2003;51(2):560-574. doi:10.1109/TSP.2002.807005<br> 2. Fessler JA. Michigan Image Reconstruction Toolbox. https://web.eecs.umich.edu/~fessler/code/. Accessed February 26, 2018.<br> 3. Schauman SS. Accelerated_TEASL. https://github.com/SophieSchau/Accelerated_TEASL. Accessed October 19, 2020.<br> 4. Chiew M. MR Linear Encoding Operators. https://users.fmrib.ox.ac.uk/~mchiew/Tools.html. Accessed March 30, 2020.</p> <p>## Notes for use:</p> <p>All data processing is performed in MATLAB (tested with 2021a and 2021b on macOS 10.14 (Mojave) and 12 (Monterey))</p> <p>The "code/" and "data/" folders should be placed within the same folder. The necessary file paths within each script in "code/" will be set up automatically.</p> <p>**Note**: the file paths use "/", so would need to be manually changed for use on Windows, which uses "\" in file paths.</p> <p>- For performing full reconstruction of in vivo K-space data, **run reconalldata.m**.<br> - All code provided<br> - Uses:<br> 1. Jeff Fessler's MIRT: http://web.eecs.umich.edu/~fessler/irt/fessler.tgz<br> 2. Mark Chiew's: xfm_NUFFT.m, etc: https://users.fmrib.ox.ac.uk/~mchiew/Tools.html<br> 3. Sophie Schauman's Accelerated_TEASL: https://github.com/SophieSchau/Accelerated_TEASL</p> <p>- For plotting in-vivo data, **run figures_invivo.m**.<br> - FSL's read_avw to read in NIFTIs (but can replace this with MATLAB's in-built niftiread instead).<br> - (Install FSL from here: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation)<br> - All other code provided</p> <p>- For performing simulation and plotting results, **run figures_simulation.m**.<br> - All code provided</p> <p>- Location of specific Figure generation code:<br> - Figure 1: no code - manually made.<br> - Figure 2: figures_invivo.m<br> - Figure 3: figures_simulation.m<br> - Figure 4: figures_invivo.m<br> - Figure 5: figures_invivo.m<br> - Figure 6: figures_invivo.m<br> - Figure 7: figures_invivo.m<br> - Figure 8: figures_invivo.m<br> - Figure 9: figures_simulation.m<br> - Figure 10: figures_invivo.m<br> - Supporting Information Figure S1: figures_simulation.m<br> - Supporting Information Figure S2: figures_invivo.m<br> - Supporting Information Figure S3: figures_invivo.m<br> - Supporting Information Figure S4: figures_invivo.m<br> - Supporting Information Figure S5: figures_invivo.m<br> - Supporting Information Figure S6: figures_invivo.m<br> - Supporting Information Figure S7: figures_invivo.m<br> - Supporting Information Figure S8: figures_invivo.m<br> - Supporting Information Figure S9: figures_invivo.m<br> - Supporting Information Figure S10: figures_simulation.m</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>
Operator Inference for Quadratic Manifold Encodings
<p>This is the code and data for the numerical results in our preprint</p> <p>> Benner, Goyal, Heiland, Pontes: *A quadratic decoder approach to nonintrusive reduced-order modeling of nonlinear dynamical systems*</p> <p>To reproduce the results and plots, unzip this repo, `cd` into it, and run</p> <p>```sh<br> python runme.py<br> ```</p> <p>in an environment that comes with</p> <p> * `python` (tested for version `3.8.10`)<br> * `numpy` (tested for version `1.19.5`, `1.23.3`)<br> * `scipy` (tested for version `1.5.2`, `1.9.1`)<br> * `matplotlib` (tested for version `3.3.2`, `3.6.0`)</p> <p> </p>
Figure 2. Example AEP-Selecting Operations for Assembler Encoding
<p>Figure 2. Example AEP and its NDM (AEP presented on the right includes four operations and four memory<br> cells. Operation 0 updates a single entry in NDM. To this end, it uses three consecutive memory cells. The first<br> two cells store an address to the element in NDM being updated. To determine the final address of the element<br> mentioned, values of registers are also used. The third memory cell used by Operation 0 stores a new value for<br> the element. The value is scaled before NDM is updated. A pointer to the memory part of AEP where three cells<br> used by Operation 0 are located is included in Operation itself.)</p>
Figure 1. Diagram of AE-Selecting Operations for Assembler Encoding
<p>Using evolutionary techniques to generate Artificial Neural Networks (ANNs) is<br> usually connected with encoding the latter. There are many ANN encoding methods, e.g.<br> connectivity matrix proposed by Miller, Todd and Hedge [9], Kitano’s matrix rewriting<br> encoding scheme [7], Gruau’s cellular encoding [5], edge encoding proposed by Luke and<br> Spector [8], Symbiotic Adaptive NeuroEvolution devised by Moriarty and Miikkulainen [10],<br> the schemes proposed by Nolfi and Parisi [11] and Cangelosi, Parisi and Nolfi [2] or<br> Cooperative Synapse Neuroevolution proposed by Gomez et al. [4]. Assembler Encoding<br> (AE) [15-18] is one of such methods. It originates from the cellular and edge encoding but it<br> also has features common with Linear Genetic Programming presented, among other things,<br> in [12]. AE represents ANN in the form of Assembler Encoding Program (AEP) whose<br> structure is similar to the structure of a simple assembler program. The task of AEP is to<br> create Network Definition Matrix (NDM) containing all the information necessary to produce<br> ANN. The process of ANN construction consists of three stages. First, Genetic Algorithm<br> (GA) is used to produce AEPs. Next, each AEP creates and fills up NDM. Finally, NDM is<br> transformed into ANN (Fig. 1).</p>
Figure 7. Using ADDN and DELN by AEP-Selecting Operations for Assembler Encoding
<p>In addition to the operations whose task is to modify a content of NDM, AE also uses<br> a jump operation denoted as JMP. The jump makes it possible to repeatedly use the same code<br> of AEP in different places of NDM. It is possible thanks to changing values of the registers<br> once the jump is run. An example use of the jump is demonstrated in Fig 6. The program<br> presented in the figure proceeds as follows. First, both registers are initiated to 0. Then, the<br> first two operations are carried out, the result of which is visible in the top left corner of<br> NDM. In the next step, the jump, denoted in the figure as JMP(0,2,0,*), is run. It first<br> updates values of the registers and then control goes back to the first operation of AEP. R1 is<br> set to 0 (Memory cell 0) whereas R2 to 2 (Memory cell 1). At this point, the two operations<br> preceding the jump are carried out once again. This time, however, both operations update a<br> different fragment of NDM. Since the jump is run twice, each time with different values of<br> the registers, the first two operations of AEP are executed in three different areas of NDM.</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.