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Dataset results
99 results for “processing code”
Codes and data for Software lock-in processing
<p>These codes and data sets are going to be published in:</p> <p>Oppermann, F., Günther, T.: A remote-control datalogger for large-scale resistivity surveys and robust processing of its signals using a software lock-in approach; Geosci. Instrum. Method. Data Syst.</p>
Code and data for publication "pyGRETA, pyCLARA, pyPRIMA: A pre-processing suite to generate flexible model regions for energy system models"
<p>This dataset contains the code of the three pre-processing tools <a href="https://github.com/tum-ens/pyGRETA">pyGRETA</a>, <a href="https://github.com/tum-ens/pyPRIMA">pyPRIMA</a> and <a href="https://github.com/tum-ens/pyCLARA">pyCLARA</a> and an examplary database for the scope of Austria.</p> <p>To run the code with full functionality additional data is needed. Check the documentation of the tools for further information.</p> <p> </p> <p>Sources for data can be found here: </p> <p>pyGRETA: https://pygreta.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyPRIMA: https://pyprima.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyCLARA: https://pyclara.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p>
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
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Data and R code from: Downscaling species to individual-level networks reveals the importance of population-level processes in mediating generalized community-wide interaction patterns
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Data and code from: Pest host expansion as a scale-free stepwise process across the host phylogeny
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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"
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Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects"
<p>Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects" with README included</p>
JTeC: A Large Collection of Java Test Classes forTest Code Analysis and Processing
<p>The recent push towards test automation and test-driven development continues to scale up the dimensions of test code that needs to be maintained, analysed, and processed side-by-side with production code. As a consequence, on the one side regression testing techniques, e.g., for test suite prioritization or test case selection, capable to handle such large-scale test suites become indispensable; on the other side, as test code exposes own characteristics, specific techniques for its analysis and refactoring are actively sought. We present JTeC, a large-scale dataset of test cases that researchers can use for benchmarking the above techniques or any other type of tool expressly targeting test code. JTeC collects more than 2.5M+ test classes belonging to 31K+ GitHub projects and summing up to more than 430 Million LOCs of ready-to-use real-world test code.</p>
Data and Code Accompanying the Study "Developing an annotation framework for word formation processes in comparative linguistics"
<p>Cite the source of the dataset as:</p> <blockquote> <p>Schweikhard, N. E. and List, J.-M. (2020): Developing an annotation framework for word formation processes in comparative linguistics. SKASE Journal of Theoretical Linguistics. 17.1: 2-26.</p> </blockquote>
Effect of desk materials on affective states and cognitive performance: Datasets and processing code
<p>In this data repository, data related to the study <em>Effect of desk materials on affective states and cognitive performance </em>is deposited, and includes datasets and processing code.</p>
Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America
<p>The presence of many pathogens varies in a predictable manner with latitude, with infections decreasing from the equator towards the poles. We investigated the geographic trends of pathogens infecting a widely distributed carnivore: the gray wolf (<i>Canis lupus</i>). We compiled a large serological dataset of nearly 2000 wolves from 17 study areas, spanning 80º longitude and 50º latitude. Generalized linear mixed models were constructed to predict the probability of seropositivity of four important viruses: canine adenovirus, herpesvirus, parvovirus, and distemper virus – and two parasites: <i>Neospora caninum </i>and <i>Toxoplasma gondii</i>.</p> <p>Canine adenovirus and herpesvirus were the most widely distributed pathogens, whereas <i>N. caninum</i> was relatively uncommon. Canine parvovirus and distemper had high annual variation, with western populations experiencing more frequent outbreaks than eastern populations. Seroprevalence of all infections increased as wolves aged, and denser wolf populations had a greater risk of exposure. Probability of exposure was positively correlated with human density, suggesting that dogs and synanthropic animals may be important pathogen reservoirs. Pathogen exposure did not appear to follow a latitudinal gradient, with the exception of <i>N. caninum</i>. Instead, clustered study areas were more similar: wolves from the Great Lakes region had lower odds of exposure to the viruses, but higher odds of exposure to <i>N. caninum</i> and <i>T. gondii</i>; the opposite was true for wolves from the central Rocky Mountains. Overall, mechanistic predictors were more informative of seroprevalence trends than latitude and longitude. Individual host characteristics as well as inherent features of ecosystems determined pathogen exposure risk on a large scale.</p> <p>Here we provide the serological dataset and the R code used in Brandell et al. 2021. See the README file for a description of the dataset and generalized linear mixed models (GLMM); see Brandell et al. 2021 main text and Supplementary Information for additional information about data collection and cleaning, research permits, and variable descriptions and rationales.</p>
Code and processed data - Control points for design of taxonomic composition in synthetic human gut consortia
<p>Code, optical density data, and sequencing tally files (relative abundance from 16S sequencing of defined communities) for <strong>Control points for design of taxonomic composition in synthetic human gut consortia. </strong>Files are organized by experiment with a readme file contained within the .zip file. "Setup" folders have information about expeirmental design, "rawData" folders have optical density and tallyfiles, "analysis" folders have scripts used to analyze data and generate models. </p>
Model code and output for "Transformation Processes in the Oder Lagoon as seen from a Model Perspective"
<p>This dataset contains the numerical model output files that were used in the analysis, the numerical model input files that were used to produce the model simulation, and the specific source code for the model components presented in "Transformation Processes in the Oder Lagoon as seen from a Model Perspective", submitted to Biogeoscience.</p> <p>data1.tar is the direct diagnostic model output. data2.tar are condensed data which are used for the analysis.</p> <p>forcing.tar contains data to run the model. Most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time (1995) is provided for the first year of the simulation. This dataset does not include the coastdat atmospheric data, which can be downloaded directly from https://www.wdc-climate.de/ui/entry?acronym=coastDat-2_COSMO-CLM.</p> <p>src.tar contains the model source code.</p>
The HEC method processing code and output for constrained precipitation extremes reveal unequal future socioeconomic exposure
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Code and Data for manuscript "Floral phenotypic divergence and genomic insights in an Ophrys orchid: Unraveling early speciation processes"
<p>Floral phenotypic divergence and genomic insights in an Ophrys orchid: Unraveling early speciation</p> <p>--------</p> <p>This repository contains all the R code used in the manuscript:</p> <p>* Title: "Floral phenotypic divergence and genomic insights in an Ophrys orchid: Unraveling early speciation"</p> <p>* Authors: Anais Gibert, Schatz Bertrand, Buscail Roselyn, Dominique Nguyen, Baguette Michel, Bartes Nicolas and Joris Bertrand</p> <p>* Year of publication: 2024</p> <p>* doi: https://doi.org/10.1101/2024.03.21.586062</p> <p> </p> <p>Synopsis of the study</p> <p>--------</p> <ul> <li> <p>Adaptive radiation in <em>Ophrys</em> orchids leads to complex floral phenotypes that vary in scent, color and shape.</p> </li> <li> <p>Using a novel pipeline to quantify these phenotypes, we investigated trait divergence at early stages of speciation in six populations of <em>Ophrys aveyronensis</em> experiencing recent allopatry. By integrating different genetic/genomic techniques, we investigated: (i) variation and integration of floral components (scent, color and shape), (ii) phenotypes and genomic regions under divergent selection, and (iii) the genomic bases of trait variation.</p> </li> <li> <p>We identified a large genomic island of divergence, associated with phenotypic variation in particular in floral odor. We detected potential divergent selection on macular color, while convergent selection was suspected on floral morphology and for several volatile olfactive compounds. We also identify candidate genes involved in anthocyanin and in steroid biosynthesis pathways associated with standing genetic variation in color and odor.</p> </li> <li> <p>This study sheds light on early differentiation in <em>Ophrys</em>, revealing patterns that often become invisible over time, i.e., the geographic mosaic of traits under selection and the early appearance of strong genomic divergence. It also supports a crucial genomic region for future investigation and highlights the value of a multifaceted approach in unraveling speciation within taxa with large genomes.</p> </li> </ul> <p> </p> <p>Running the code</p> <p>--------</p> <p>Here we present the data and code for carrying out the analyses, as well as the figures and tables from the article and the supplementary material. Once you have installed the necessary packages, run the commands in 'analysis_share.R'. This script uses several functions available in the '/R' directory.</p> <p>Figures and tables are produced in a 'manuscript/figures' and 'manuscript/tables' directory. <br>The `/data' directory contains the data used in the analyses (data/input or data/output), but also the resulting datasets produced by the code (data/RData/).</p> <p> </p>
Processed Data and codes for visualization
<p>Processed Data and codes for visualization</p>
Code and data for Oxygen dimerization as a defect-driven process in bulk LiNiO2
<p>Data and code required to generate figures in the article "Oxygen dimerization as a defect-driven process in bulk LiNiO2".<br><br>Available as a preprint at 10.26434/chemrxiv-2024-lcmk</p>
Dataset and Code for "Mining Micro-Patterns of Issue Resolution Processes for Open Source Software Projects"
<p>Dataset and Code for "Mining Micro-Patterns of Issue Resolution Processes for Open Source Software Projects" with README included</p>
The source code for a new capillary and adsorption‒force model predicting hydraulic conductivity of soil during freeze‒thaw processes
<p>The source code is related to "A New Capillary and Adsorption‒Force Model Predicting Hydraulic Conductivity of Soil during Freeze‒thaw Processes" (Shufeng Qiao, Rui Ma, Yunquan Wang, Ziyong Sun, Helen Kristine French, Yanxin Wang)</p>
MATLAB Code for "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"
<p>This MATLAB code is part of the study titled <em>"Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"</em>, which has been accepted for publication in the <em>Journal of Imaging (MDPI)</em>. The code supports image processing, feature extraction, and deep learning model training (including LSTM and RexNet) to classify pediatric patients as anemic or non-anemic based on palm, conjunctival, and fingernail images. Full study details are available in this paper:</p> <p>Berghout T. Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients. <em>Journal of Imaging</em>. 2024; 10(10):245. <a href="https://doi.org/10.3390/jimaging10100245">https://doi.org/10.3390/jimaging10100245 </a></p> <p>The datsets use in this work are:</p> <p>Asare, J. W., Appiahene, P. & Donkoh, E. (2022). Anemia Detection using Palpable Palm Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/ccr8cm22vz.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2023). CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana. Mendeley Data. https://doi.org/10.17632/m53vz6b7fx.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2020). Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/2xx4j3kjg2.1</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.