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281 results for “source code”
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 7. The HTML source (partial) code, integrating the X3D model
<p>To integrate the model into a web page, a model conversion to X3D format and an X3DOM output under the form of an HTML5 encoded webpage (Figure 7) were needed. Instant Reality distribution provides a command line transcoding tool, named Avalon Optimizer (aopt), that was used to convert a VRML format (wrl extension) of the model to X3D. </p>
Improving source code change set analysis by using a visual tool. Controlled experiment dataset.
<p>Modern software development is performed by developing features in isolated branches by each member of a software development team. When these branches need to be integrated, they have to be manually reviewed by an integrator. Source code reviewing can be a tedious and time consuming task, which is normally performed by hand reviewing a textual diff of the change set.</p> <p>The difficulty of reviewing source code changes can have a negative impact on the accep- tance of these changes. It has been shown that this can imply the rejection of important bug fixes for a software project.</p> <p>In order to facilitate the task of reviewing source code change sets, we designed and implemented a visual tool. Our visual tool helps in assessing source code change sets by providing different views of the change set: an overall overview of the change set with metrics, and a visualization representing the structural changes in the source code. We evaluated our visual tool by performing a controlled experiment with software practitioners.</p> <p>Our experimental results show no significant differences between using our visual tool and a textual diff tool in the following terms: time of analysis, precision of the analysis, and inference of intention of changes. However, we did find a positive user perception, and a reduced mental load when using our visual tool.</p> <p>Anonymized datasets with the answers to the controlled experiments for evaluating the Git Thermite visual tool for assessing source code change sets. Copies of these datasets are provided in both, CSV format, and OpenDocument format.</p> <p>Exact copies of the learning materials that was provided to the participants during the execution of the controlled experiments is also included along the dataset.</p>
Source Code Embeddings
<p>A set of six pretrained fastText models for semantic representations of source code. </p> <p>Each of the models has been trained on high-quality GitHub repositories where the primary language is one of Java, Python, C++, C#, C, PHP. For collecting training data 13.144 repositories were cloned, 2.402.790.348 lines of code were read out of 944,467,560 files and preprocessed, to finally produce a total of 944.467.560 tokens of clean training data. </p> <p>For further details refer to the following paper: </p> <p>Efstathiou, V., Spinellis, D., 2019. "Semantic Source Code Models Using Identifier Embeddings". In <em>16th International Conference on Mining Software Repositories: Data Showcase Track. MSR'19. </em></p>
Subject source code for Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams
<p>The set of open source Java projects packaged as Eclipse projects used for assessing our refactoring. Please refer to the included README.md file for building instructions and the LICENSE.md file for licensing information.<br> </p>
main source codes and files of "Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks"
<p>These source codes and their related files are associated the study. "Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks"</p> <p>This study is in process of publication.</p>
Codes and source data for "Common occurrences of subsurface heatwaves and cold-spells in ocean eddies"
<p>This repository contains the MATLAB (R2022b) codes (*.m files) and the figure source data (.mat files) for the paper "Common occurrences of subsurface heatwaves and cold-spells in ocean eddies" (He et al., 2024). </p> <p>For installation of MatLab, please refer to: https://au.mathworks.com/products/matlab.html</p> <p>For queries about this repository and its contents, please contact Dr. Qingyou He (qyhe@scsio.ac.cn).</p> <p>%% Fig1.m: For plotting main Fig.1.<br>%% Fig2.m: For plotting main Fig.2.<br>%% Fig3.m: For plotting main Fig.3.<br>%% Fig4.m: For plotting main Fig.4.<br>%% Fig5.m: For plotting main Fig.5.<br>%% Fig6.m: For plotting main Fig.6.</p> <p>%% Data Fig1.mat: For main Fig.1.<br>%% Data Fig2.mat: For main Fig.2.<br>%% Data Fig3.mat: For main Fig.3.<br>%% Data Fig4.mat: For main Fig.4.<br>%% Data Fig5.mat: For main Fig.5.<br>%% Data Fig6.mat: For main Fig.6.</p> <p><br>References:</p> <p><span>He, Q., W. Zhan, M. Feng, Y. Gong, S. Cai, and H. Zhan (2024), Common occurrences of subsurface heatwaves and cold spells in ocean eddies, <em>Nature</em>, <em>634</em>, 1111–1117, doi:10.1038/s41586-024-08051-2.</span></p>
Source code and experimental data of human brain tissue (visual cortex, corona radiata) for poro-viscoelastic parameter identification
<p>Computer code and experimental data that we used for our inverse parameter identification of poro-viscoelastic material parameters for two different brain regions: visual cortex (gray matter) and corona radiata (white matter). The experimental data comprises large-strain cyclic loading and compression/tension relaxation. For details see the corresponding publication: "Model-driven exploration of poro-viscoelasticity in human brain tissue: Be careful with the parameters!".</p> <p>Further explanation regarding the specimen preparation, experimental setup, as well as the assignment of regions and governing regions can be found in Hinrichsen, J., Reiter, N., Bräuer, L. et al. Inverse identification of region-specific hyperelastic material parameters for human brain tissue. Biomech Model Mechanobiol (2023). <a href="https://doi.org/10.1007/s10237-023-01739-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10237-023-01739-w</a>.</p> <p>The file "<span>nonlinear-poro-viscoelasticity.cc</span>" contains our C++ Finite Element code based on the open source library deal.II. It is accompanied by an exemplary parameter file.</p> <p><strong>Funding:</strong> The support from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the grants BU 3728/1-1, BU 3728/3-1, STE 544/70-1 as well as through project number 460333672 CRC1540 Exploring Brain Mechanics is gratefully acknowledged.</p>
Source code for R tutorials and dataset for empirical case study on Malurus elegans (red-winged fairy wren)
<p>Biological processes exhibit complex temporal dependencies due to the sequential nature of allocation decisions in organisms' life-cycles, feedback loops, and two-way causality. Consequently, longitudinal data often contain cross-lags: the predictor variable depends on the response variable of the previous time-step. Although statisticians have warned that regression models that ignore such covariate endogeneity in time series are likely to be inappropriate, this has received relatively little attention in biology. Furthermore, the resulting degree of estimation bias remains largely unexplored.</p> <p>We use a graphical model and numerical simulations to understand why and how regression models that ignore cross-lags can be biased, and how this bias depends on the length and number of time series. Ecological and evolutionary examples are provided to illustrate that cross-lags may be more common than is typically appreciated and that they occur in functionally different ways.</p> <p>We show that routinely used regression models that ignore cross-lags are asymptotically unbiased. However, this offers little relief, as for most realistically feasible lengths of time series conventional methods are biased. Furthermore, collecting time series on multiple subjects–such as populations, groups or individuals—does not help to overcome this bias when the analysis focusses on within-subject patterns (often the pattern of interest). Simulations (R tutorial 1 & 2), a literature search and a real-world empirical example on fairy wrens (data archived here with analyses presented in R-tutorial 3) together suggest that approaches that ignore cross-lags are likely biased in the direction opposite to the sign of the cross-lag (e.g. towards detecting density-dependence of vital rates and against detecting life history trade-offs and benefits of group living). Next, we show that multivariate (e.g. structural equation) models can dynamically account for cross-lags, and simultaneously address additional bias induced by measurement error, but only if the analysis considers multiple time series.</p> <p>We provide guidance on how to identify a cross-lag and subsequently specify it in a multivariate model, which can be far from trivial. Our tutorials with data and R code of the worked examples provide step‐by‐step instructions on how to perform such analyses.</p> <p>Our study offers insights into situations in which cross-lags can bias analysis of ecological and evolutionary time series and suggests that adopting dynamical models can be important, as this directly affects our understanding of population regulation, the evolution of life histories and cooperation, and possibly many other topics. Determining how strong estimation bias due to ignoring covariate endogeneity has been in the ecological literature requires further study, also because it may interact with other sources of bias.</p>
Identity Based Proxy Re-encryption Source Code for Security and Privacy in Connected Vehicle
<p>This is a source code for identity based proxy re-encryption using special string attribute for connected vehicle and Privacy, designed in python using Charm Cryptographic library using Pairing Group ss512 and 1024 bits.</p>
Code & Data from: Development of a low cost open-source ultrasonic device for plant height measurements
<p>We here provide code and data for the study "Development of a low cost open-source ultrasonic device for plant height measurements"</p> <p>Code:<br> - Arduino code (management of the electronic circuit): "Arduino_ultrasonic_sensor.ino"<br> - OpenSCAD code (3D-printing): "3DShells_ultrasonic_sensor.scad"<br> - R code (statistical analysis of field test): "Statistical_analysis.R"</p> <p>Data:<br> - "manual_vs_sensor_controlled.csv": this file contains the comparison between the ultrasonic device and the ruler in standardized laboratory conditions. It has three columns: "manual_value", the height value measured manually; "sensor_value", the height value obtained from the ultrasonic device; "height_range", the interval to which the height value belongs (we worked with 25 cm intervals).<br> - "manual_vs_ruler_field.csv": this file contains the comparison between the ultrasonic device and the ruler in field conditions. Plant height measurements were performed on 26 sorghum genotypes. The file has four columns: "Genotype", the id of the measured genotype; "rep" the replicate (3 plants were measured for each genotype); "manual_value", the height value measured manually; "sensor_value", the height value obtained from the ultrasonic device. When using the ruler, the operator spent 15 min and 23 s to complete all measurements in the field, and 3 min and 27 s to enter all data manually in a digital file. When using the sensor, the operator spent 10 min and 52 s to complete all measurements in the field, and manual transcription was not needed since all measurements are instantaneously saved on an SD card.</p> <p>More details on the experimental data can be found in the article "Development of a low cost open-source ultrasonic device for plant height measurements".</p> <p>We also provide a tutorial to explain how to build the ultrasonic-sensor ("tutorial.docx")</p>
Research Compendium for Harrington et al. (2021): "An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code: I. Design, Tests, and Application to Exoplanet HD 189733 b"
<p>This archive is the Reproducible Research Compendium for<br> <br> An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code: I. Design, Tests, and Application to Exoplanet HD 189733 b<br> <br> by Harrington et al. (2021), published in The Planetary Science Journal.<br> <br> BART is an atmospheric parameter retrieval code. It infers the properties of planetary atmospheres from spectroscopic observations. The compendium includes all the software, documentation, configuration files, plots, and data published in the paper. The compendium is under the Reproducible Research Software License; see LICENSE file. The README provides additional information and describes the contents of each compressed .tar.gz file.</p>
CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models
<p>The raw datasets and tasks of the paper "CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models". Source code is available at https://anonymous.4open.science/r/CrossCodeBench-C538/.</p>
Data and Source codes: Light alters activity but do not disturb tandem coordination of termite mating pairs
<p>This repository provides access to the tracking data and analysis code used for the manuscript</p> <p>Light alters activity but does not disturb tandem coordination of termite mating pairs</p> <p>by Nobuaki Mizumoto and Thomas Bourguignon</p> <p>Okinawa Institute of Science & Technology Graduate University, Onna-son, Okinawa, Japan</p> <p>published in the Ecological Entomology.<br> </p>
Crosswalk between Source Classification Codes (SCCs) and 14 sectors of economy
<p>This dataset is a crosswalk between US EPA Source Classification Codes (SCCs) and sectors of economy. The 14 sectors include all anthropogenic emission sources, and exclude biogenic, wildfire, and international sources. The 14 sectors are: 1) agriculture (“Ag”); 2) coal electricity utility (“Coal Elec.”); 3) noncoal electricity utility (“Non-coal Elec.”); 4) commercial cooking (“Cooking”); 5) construction (“Const.”); 6) diesel heavy-duty vehicle (“Diesel HD Veh.”); 7) gasoline light-duty vehicle (“Gas LD Veh.”); 8) industrial; 9) road dust (“Road Dst”); 10) residential gas combustion (“Res. Gas”); 11) residential wood combustion (“Res. Wood”); 12) residential others (“Res. Other”); 13) off-highway vehicle and equipment (“Offroad”); and 14) miscellaneous (“Misc.”). </p> <p>The SCC list are downloaded from <a href="https://ofmpub.epa.gov/sccwebservices/sccsearch/">https://ofmpub.epa.gov/sccwebservices/sccsearch/</a> [Accessed Sep 22, 2022]. </p> <p>In the data table, the first column is the SCC; the second column is the classification of the 14 sectors; and the reset of the columns includes detailed description of each SCC from EPA. </p> <p> </p>
Datasets and R source code of manuscript "No evidence for an effect of chronic boat noise on the fitness of reared water fleas"
<p>Datasets and R source code of manuscript "No evidence for an effect of chronic boat noise on the fitness of reared water fleas"</p> <p>Experiments : exposition of Daphnia magna to boatnoise or silence along all their life. Measure of survival and clonal reproduction.</p>
Data and source code for "Spherical Air Mass Factors in One and Two Dimensions with SASKTRAN 1.6.0"
<p>Supplementary data for "Spherical Air Mass Factors in One and Two Dimensions with SASKTRAN 1.6.0":</p> <ul> <li>source: source code and data required to reproduce all figures</li> <li>amf-tables: data in NETCDF4 format for the air mass factor tables discussed in Section 5, which were used in the following publications by Griffin et al.: <ul> <li>"High-Resolution Mapping of Nitrogen Dioxide With TROPOMI: First Results and Validation Over the Canadian Oil Sands" (2018, <a href="https://doi.org/10.1029/2018GL081095">https://doi.org/10.1029/2018GL081095</a>)</li> <li>"Biomass burning nitrogen dioxide emissions derived from space with TROPOMI: methodology and validation" (2021, <a href="https://doi.org/10.5194/amt-2021-223">https://doi.org/10.5194/amt-2021-223</a>)</li> </ul> </li> </ul>
Large-eddy simulation source code and data for (LS)2D reference publication in JAMES.
<p>This archive contains the MicroHH large-eddy simulation source code, the (LS)2D source code, and all simulation input and statistics, used for the publication:</p> <p><em>"The Benefits and Challenges of Downscaling a Global Reanalysis with Doubly-Periodic Large-Eddy Simulations" </em>by B.J.H. van Stratum et al.</p>
Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant
<p>The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA – Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers, or benchmarking mutation testing frameworks, and more applications are yet to be discovered. This dataset is a supplementary material for a paper entitled <a href="https://doi.org/10.3390/data8060109"><strong>Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant</strong></a> submitted to the <strong>MDPI Data</strong> journal.</p>
Detect Related Bugs from Lucene Source Code Using Bug Information
<p>This dataset uses web metrics.</p> <p>More information at http://openscience.us/repo/defect/ck/lucene.html</p>
Datasets and R source code of manuscript "From behaviour to complex communities: Resilience to anthropogenic noise in a fish-induced trophic cascade" by Emilie Rojas et al.
<p>Datasets and R source code of manuscript "From behaviour to complex communities: Resilience to anthropogenic noise in a fish-induced trophic cascade" by Emilie Rojas et al.</p>
ScienceDex guides
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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.