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Dataset results
99 results for “processing code”
Dataset and codes of the article "Neural correlates of hierarchical predictive processes in autistic adults"
<p>Data and code related to the article "Neural correlates of hierarchical predictive processes in autistic adults" by Laurie-Anne Sapey-Triomphe, Lauren Pattyn, Veith Weilnhammer, Philipp Sterzer and Johan Wagemans (Nature Communications):</p> <p>- Behavioral dataset of the 26 neurotypical participants (NT_behavioral_data.zip) and of the 26 autistic participants (ASD_behavioral_data.zip)</p> <p>- Source data of the graphics appearing in the article (Source data.xls)</p> <p>- Matlab codes used to run the experiment (Codes_to_run_experiment.zip)</p> <p>- Matlab codes to perform the main behavioral analyses (Codes_behavioral_analyses.zip) and to analyze the behavioral data with the HGF models (Codes_comput_model_analyses.zip)</p> <p>- Matlab codes to preprocess (Codes_fMRI_preprocessing.zip) and run the main fMRI analyses (Codes_fMRI_analyses.zip)</p>
Raw data and analysis code for "Higher-order Process Matrix Tomography of a passively-stable Quantum SWITCH"
<p>This folder contains the raw data and analysis coded need to reproduce all of the major results in the manuscript "Higher-order Process Matrix Tomography of a passively-stable Quantum SWITCH". </p>
Raw P-SHG data and processing codes for Raoux et al, Light Science and Applications 2023
<p><strong>Raw P-SHG data and processing codes</strong> for Raoux et al, Light Science and Applications 2023</p> <p>Article DOI: 10.1038/s41377-023-01224-0</p> <p>Raw P-SHG data: 10 human corneas</p> <p>Important: crop 20 pixels on left and right sides of all images to remove scanning artefacts and obtain 250 x 250 images</p> <p>voxel size: 1 µm in all directions<br> Channel 0: trans-2PEF<br> Channel 1: epi-2PEF<br> Channel 2: epi-SHG<br> Channel 3: trans-SHG</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part I: Pre-processing)
<p>This is Part I of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files to generate the simulation grids.</p>
Models and post-processing codes for paper "Quantitative stratigraphic analysis in a source-to-sink numerical framework"
<p>This package contains all the files required to reproduce the experiments in the manuscript: <strong>Quantitative stratigraphic analysis in a source-to-sink numerical framework</strong>.</p>
Code Blue Outcomes & Process Improvement Through Leadership Optimization Using Teleintensivists-Simulation
ClinicalTrials.gov study NCT03000829. IPD Sharing: YES. Countries: 1. Publications: 5.
Code for processing Beebox development ata
Open the record for dataset details and reuse information.
Wayqecha Amazon cloud curtain ecosystem experiment: Climate data and R processing code
Open the record for dataset details and reuse information.
Data and code from "A dimmer shade of pale: revealing the faint signature of local assembly processes on the structure of strongly filtered plant communities"
<p>Trait-based ecology suggests that abiotic filtering is the main mechanism structuring the regional species pool in different subsets of habitat-specific species. At more local spatial scales, other ecological processes may add on giving rise to complex patterns of functional diversity (FD). Understanding how assembly processes operating on the habitat-specific species pools produce the locally observed plant assemblages is an ongoing challenge. Here, we evaluated the importance of different processes to community assembly in an alpine fellfield, assessing its effects on local plant trait FD. Using classical randomization tests and linear mixed models, we compared the observed FD with expectations from three null models that hierarchically incorporate additional assembly constraints: stochastic null models (random assembly), independence null models (each species responding individual and independently to abiotic environment), and co-occurrence null models (species responding to environmental variation and to the presence of other species). We sampled species composition in 115 quadrats across 24 locations in the central Pyrenees (Spain) that differed in soil conditions, solar radiation and elevation. Overall, the classical randomization tests were unable to find differences between the observed and expected functional patterns, suggesting that the strong abiotic filters that sort out the flora of extreme regional environments blur any signal of other local processes. However, our approach based on linear mixed models revealed the signature of different ecological processes. In the case of seed mass and leaf thickness, observed FD significantly deviated from the expectations of the stochastic model, suggesting that fine-scale abiotic filtering and facilitation can be behind these patterns. Our study highlights how the hierarchical incorporation of ecological additional constraints may shed light on the dim signal left by local assembly processes in alpine environments.</p>
The data and code for "Original Speech and Its Echo are Segregated and Separately Processed in the Human Brain"
<p>This dataset is associated with the manuscript "Original Speech and Its Echo are Segregated and Seperately Processed in the Human Brain", and provides the preprocessed MEG response (resampling to 100 Hz), auditory stimulus, individual quantitative observations underlying the data summarized in figures, and the analysis codes.</p>
Code and datasets for "Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes"
<p>Code and datasets for:</p> <p>Delaney I., M. A. Werder, D. Felix, I. Albayrak, R. M. Boes, D. Farinotti, 2024, Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes. Water Resources Research. </p> <p>For more information, contact Ian Delaney (ianarburua.delaney@unil.ch).</p>
The processing results, dataset and original codes of Coral reef PtCloud segmentation model (based on proposed underwater camera systems)
<p><strong># Data Introduction</strong><br>The proposed camera system includes three operation modes: Surface Mode, Horizontal Mode, and Curved Mode.</p> <ul> <li><strong>Surface Mode</strong>: Provides 3D reconstructions (point cloud), DEM, and orthophoto maps of the seabed (covering a line of 70m in length).</li> <li><strong>Horizontal Mode</strong>: Provides 3D reconstructions (point cloud and mesh) and orthophoto maps of a single transect line.</li> <li><strong>Curved Mode</strong>: Offers 3D reconstructions (mesh) of various targets, including artificial coral reefs, coral reefs with snails, and coral reefs with starfish.</li> </ul> <p>All mesh results are saved in <code>.FBX</code> format, and point cloud results are saved in <code>.TXT</code> format.</p> <p><strong># Code Introduction</strong><br>The backbone of our point cloud segmentation model is the KPConv model.<br>If you encounter any issues during code deployment, please refer to the original KPConv repository (<a href="https://github.com/HuguesTHOMAS/KPConv" target="_new" rel="noopener">Original Code: https://github.com/HuguesTHOMAS/KPConv</a>).<br>We provide our modified code (customized for our task) along with the complete point cloud dataset.</p>
Processed datasets and codes for differential expression analysis on polulation-level RNA-seq data
<p>This version includes codes and data necessary to reproduce all results in our response to the correspondences ("Response to 'Neglecting normalization impact in semi‑synthetic RNA‑seq data simulation generates artificial false positives' and 'Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples'") (<a href="https://doi.org/10.1186/s13059-024-03232-8">https://doi.org/10.1186/s13059-024-03232-8</a>).</p> <p>It also includes a README file to guide the reproduction of the results in our original publication and resources for the goodness of fit test in the original publication, "Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples" (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4</a>).</p>
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
<p>Diet analysis integrates a wide variety of visual, chemical and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, e.g., highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package <i>mvtweedie</i> that extends capabilities of widely used packages in R (<i>mgcv</i> and <i>ggplot2</i>) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted puffins (<i>Fratercula cirrhata</i>) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980-2000) and then Pacific herring and capelin (2000-2020), while wolves (<i>Canis lupus ligoni</i>) in Southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines. </p>
Adiabatic and diabatic signatures of ocean temperature variability - ACCESS-CM2 processing/plotting code and processed data
<p>Contains processed data and processing/plotting code for the figures in the article:</p> <p>Holmes, R.M., Sohail, T. and Zika, J.D. (2022): Adiabatic and diabatic signatures of ocean temperature variability, Journal of Climate, <a href="https://doi.org/10.1175/JCLI-D-21-0695.1">https://doi.org/10.1175/JCLI-D-21-0695.1</a></p> <p>For more information please see the published article, as well as the github repository where the code is described in more detail: <a href="https://github.com/rmholmes/CM2_HCvar/tree/JCLI-D-21-0695">https://github.com/rmholmes/CM2_HCvar/tree/JCLI-D-21-0695</a></p>
Genome graphs detect human polymorphisms in active epigenomic states during influenza infection: code and processed data
<p>Manuscript, figure, and analysis code and processed data for the Groza et al (2022) preprint.</p>
Datasets and Code for "Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes"
<p>This is the set of data and computer code used for reproducing the results in Jin, Tuo, Tiwari, Bukkapatnam, Aracne-Ruddle, Lighty, Hamza, and Ding, 2022, “Hypothesis tests with functional data for surface quality change detection in surface finishing processes,” <em>IISE Transactions</em>, in press.</p>
Datasets and code for "Mapping the plague through natural language processing"
<p>This project investigates the performance of various NLP libraries and geocoding services for the semi-automated generation of quantitative datasets from narrative texts. We provide the original files, several intermediate data products as well as the final plague datasets. Please note that some of the steps in this process were done manually, thus some of the scripts cannot be run completely. </p> <p>This work is based on two plague treatises:</p> <p>- Sticker, G. 1908 <em>Abhandlungen aus der Seuchengeschichte und Seuchenlehre. Band 1: Die Pest</em>. Giessen, A. Töpelmann.</p> <p>- Biraben, J.-N. 1975 <em>Les hommes et la peste en France et dans les pays européens et méditerranéens</em>. Paris, Mouton.</p> <p>The final geocoded, plague datasets are:</p> <p><strong>- plague_sticker_v1.csv</strong></p> <p><strong>- plague_biraben_v1.csv</strong></p> <p>A data dictionary is available as</p> <p><strong>plague_datadict.xlsx</strong></p> <p>Other files:</p> <table> <tbody> <tr> <td>file name</td> <td>content</td> </tr> <tr> <td>sticker_OCR_orig.txt</td> <td>Original OCR text</td> </tr> <tr> <td>sticker_OCR.txt</td> <td>Original OCR text without parenthesis (author names)</td> </tr> <tr> <td>sticker_textprep.rds</td> <td>Original OCR text with further preparations</td> </tr> <tr> <td>sticker_goldstandard_annotated_1.tsv</td> <td>manual annotations file 1</td> </tr> <tr> <td>sticker_goldstandard_annotated_2.tsv</td> <td>manual annotations file 2</td> </tr> <tr> <td>sticker_goldstandard_annotated_consensus.tsv</td> <td>consenus annotation file</td> </tr> <tr> <td>sticker_standard_toponyms.csv</td> <td>Gold standard for toponym recognition. Contains the tokenization, the start/end character respective to the OCR text (orig and without parenthesis) and whether a token is a location or other</td> </tr> <tr> <td>sticker_comparison_NER.rds</td> <td>Comparison of NER performance</td> </tr> <tr> <td>sticker_comparison_geocoding.rds</td> <td>Comparison of Geocoding performance</td> </tr> </tbody> </table> <p> </p>
Datasets and Code for "A Gaussian process model-guided surface polishing process in additive manufacturing"
<p>These are the datasets and computer code for reproducing the results in Jin, Iquebal, Bukkapatnam, Gaynor, and Ding, 2020, “A Gaussian process model-guided surface polishing process in additive manufacturing.” <em>ASME Transactions, Journal of Manufacturing Science and Engineering</em>, Vol. 142(1), pp. 011003.1–011003.12.</p>
Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES)
<p>Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES). Detailed description of the files in README.txt.</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.