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Dataset results
281 results for “source code”
Data and code for zu Ermgassen et al. "Addressing indirect sourcing in zero deforestation commodity supply chains"
<p>Data and code required to reproduce figures and stats quoted in zu Ermgassen et al. "Addressing indirect sourcing in zero deforestation commodity supply chains"</p>
Source code and data used in "Reliable Editions from Unreliable Components"
<p>Source code and data used in Riddell, A. B. (2022). Reliable Editions from Unreliable Components: Estimating Ebooks from Print Editions Using Profile Hidden Markov Models.</p>
Source data and code for manuscript 'An executive network for the control of sequence-behavior in pigeons'
<p>The contents of this folder are part of the submission of the manuscript entitled 'An executive network for the control of sequence-behavior in pigeons', by Lukas Alexander Hahn & Jonas Rose</p> <p>Contact: lukas.hahn@ruhr-uni-bochum.de</p> <p>Data and code have been compressed into a .zip folder each. Unpack the contents of the folders to use the dataset. The dataset is split into two main folders and one Matlab file:</p> <p>'code'<br> contains all analysis code to produce all figures and reported statistics of the manuscript (refer to the<br> MATLAB live script 'manuscriptResultsLiveScript.mlx' to run the analysis, please adjust the path information of where the data is stored on your computer).</p> <p>'RESULTSSTATISTICS.mat'<br> contains all reported statistical values (generated by 'manuscriptResultsLiveScript.mlx')</p> <p>'sourceData'<br> Contains all required source data files (i.e. pre-processed data) required to run the analyses stored in 'code'.</p> <p>Data related to animal behavior was recorded using MATLAB (R2016b). Electrophysiological data was recorded by NeuroNexus microelectrodes and an INTAN RHD2000 headstage on an INTAN USB-Interface board, with a sampling rate of 30 kHz and was subsequently filtered for spike sorting at bandpass 0.5 - 7.5 kHz.</p> <p>Data format is the MATLAB '.mat' type (which can be loaded in by MATLAB, or alternatively by the freely available Octave Software (https://www.gnu.org/software/octave)).<br> Data is organized in MATLAB structures, one file per session for behavioral results, one file per neuron for different alignments and preprocessing conditions (refer to manuscriptResultsLiveScript).<br> Structures contain individual matrices (labelled by a descriptive name) that contain numerical values or character strings.<br> Matrices labelled by the keyword 'Info' contain character strings that give a brief description of the loaded data.<br> Source data contains two separate folders containing data of animal 1 ('P855'), and animal 2 ('T1003').</p> <p>Data was sorted into different subsets, for analysis of individual task phases. Subfolder 'NCL' refers to 'nidopallium caudolaterale', 'NIML' refers to 'nidopallium intermedium mediale pars laterale', the recorded brain regions.</p> <p> </p>
Source data and codes for the paper "Inviting atomic mechanics to macro-continua: A study on monocrystalline Si using a spatial multilevel coarsening model"
<p>Source data and codes for the paper "Inviting atomic mechanics to macro-continua: A study on monocrystalline Si using a spatial multilevel coarsening model"</p> <p>This file includes </p> <p>- Source data for Figs 1-5 and Supplementary Materials</p> <p>- LAMMPS codes and raw log files used to produce the results of this study</p> <p> </p>
Source data and code for "A diamond voltage imaging microscope"
<p>This data set contains both the source data and code used to generate figures and establish the conclusions of "A diamond voltage imaging microscope" (DOI: https://doi.org/10.1038/s41566-022-01064-1). It contains:</p> <p>- Raw source data (e.g., video data, fluorescence spectra).</p> <p>- Processed source data (e.g., calibration maps, calculated vales of contrast, sensitivity, etc).</p> <p>- Analysis code used to generate processed source data (this includes both MATLAB and Python scripts. MATLAB scripts require at least version R2021A).</p> <p>- Simulation code (Python) used to fit the equivalent RC circuit model described in the work to the experimental data.</p>
Build Prediction in Continuous Integration Using Textual Analysis of Source Code
<p>The data-set comprises of software metrics that characterize build outcomes in CI using traditional and token frequency metrics.</p>
VIC5 source code, parameter for the Colorado River Basin and USBR natural flow data records
<p>This parameter file is for VIC5 baseline simulation over the Colorado River basin.</p> <p>The spatial resolution is 1/16 degree.</p> <p>A few extra grid cells in the Mexico near the boarder is also unnecessarily included, which do not drainage to the CRB.</p> <p>Users can get rid of those pixels with a more precise domain mask.</p> <p>Also include VIC source code (see the readme.txt in the zipped file for details)</p> <p>The updates in Sep, 2022 includes the natural flow dataset used in the study for VIC streamflow evaluation</p>
BeEM source code and dataset
<ul> <li>BeEM-master.zip: C++ source code for BeEM.</li> <li>mmcif.zip: 2218 mmCIF format structures used to benchmark BeEM.</li> </ul>
Supporting data and source code for Hnilica et al. (submitted to HESS)
<p>Data and source codes to reproduce the results and plots presented in Technical note: Changes of cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers (submitted to Hydrology and Earth System Sciences)</p>
Source Code Classifications: Code Dataset of Programming Languages
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Source Data and Code - Calcaterra et al., 2024 (https://doi.org/10.1038/s41560-024-01606-7)
<p>Model results, code for processing it, and figure generation for Calcaterra et al., 2024, Nature Energy, "Reducing cost of capital to finance the energy transition in developing countries" (doi: <span>https://doi.org/10.1038/s41560-024-01606-7)</span></p> <div> <div> </div> </div>
Source Code Classification
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A Framework for Improving Social Inclusion using Network Analysis and IoT-based Contact Tracing, Dataset and Source Code
Open the record for dataset details and reuse information.
Code Smells and their Collocations : A Large-scale Experiment on Open-source Systems
<p>This dataset includes classes with code smells, acquired from Qualitas Corpus (QC).<br> Folder 'all' contains data coming from the QC rev.20130901 (92 systems).<br> Folder 'domains' contains data coming from QC rev.20111026 (76 systems updated to their most recent releases from rev.20130901). <br> Folder 'pca' includes results of the PCA analysis, generated with the R prcomp() function for regular PCA, and logisticPCA() function for the binary data.</p> <p>Filenames include information about the base release of the QC, and a number (25, 50 or 75) that specifies the minimum number of detectors that identified a specific smell instance (25%, 50%, and 75%, respectively). For example, if a given code smell in a class X has been identified by 1 out of 4 available detecting tools, then the smell for the class X will be reported in the respective file 25, but not in 50 or 75. Please note, that for smells detected with only one tool, the values would be equal in all datasets (in that case, the smell was detected by 0% or 100% of tools)</p> <p>In all files, "1" denotes that the smell was identified (subject to the limitations with the number of detectors, described above), and “0” that the smell was not found in a given class.</p> <p>The filename also includes the domain abbreviation (app, css, dev, dgdv) or a keyword ALL, which indicates that the dataset includes data from all domains.</p> <p>The smells have been detected by 11 tools. Most of the tools detect more than one smell. <br> Information about the tool used to detect a given smell is given in headers of each file. Additionally, in 'smell detectors.csv' file we present the information about smells detected by a specific tool.</p>
Source codes and trajectories for multiscale simulations of barnase/barstar complex association
<p>Source codes and trajectories for multiscale simulations of barnase/barstar complex association</p>
Supporting data and source codes for Hnilica et al. (submitted to HESS)
<p>Data and source codes to reproduce the results and plots presented in Technical note: Changes of cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers (submitted to Hydrology and Earth System Sciences)</p>
Non-coding regions are the main source of targetable tumor-specific antigens - DATASETS (k=24)
<p>Tumor-specific antigens (TSAs) represent ideal targets for cancer immunotherapy, but few have been identified thus far. We therefore developed a proteogenomic approach to enable the high-throughput discovery of TSAs coded by potentially all genomic regions. In two murine cancer cell lines and seven human primary tumors, we identified a total of 40 TSAs, about 90% of which derived from allegedly non-coding regions and would have been missed by standard exome-based approaches. Moreover, the majority of these TSAs derived from non-mutated yet aberrantly expressed transcripts (such as endogenous retroelements) that could be shared by multiple tumor types. In mice, the efficacy of TSA vaccination was influenced by two parameters that can be estimated in humans and could serve for TSA prioritization in clinical studies: TSA expression and the frequency of TSA-responsive T cells in the pre-immune repertoire. In conclusion, the strategy reported herein could considerably facilitate the identification and prioritization of actionable human TSAs.</p>
Non-coding regions are the main source of targetable tumor-specific antigens – DATASETS (k=33)
<p>Tumor-specific antigens (TSAs) represent ideal targets for cancer immunotherapy, but few have been identified thus far. We therefore developed a proteogenomic approach to enable the high-throughput discovery of TSAs coded by potentially all genomic regions. In two murine cancer cell lines and seven human primary tumors, we identified a total of 40 TSAs, about 90% of which derived from allegedly non-coding regions and would have been missed by standard exome-based approaches. Moreover, the majority of these TSAs derived from non-mutated yet aberrantly expressed transcripts (such as endogenous retroelements) that could be shared by multiple tumor types. In mice, the efficacy of TSA vaccination was influenced by two parameters that can be estimated in humans and could serve for TSA prioritization in clinical studies: TSA expression and the frequency of TSA-responsive T cells in the pre-immune repertoire. In conclusion, the strategy reported herein could considerably facilitate the identification and prioritization of actionable human TSAs.</p>
Research Artifact: 9.6 Million Links in Source Code Comments
<p>This is a research artifact for the ICSE'19 paper <strong>9.6 Million Links in Source Code Comments: Purpose, Evolution, and Decay</strong>. This artifact is a data repository including all 9,654,702 links associated with the information of languages and comment location (GitHub links including account names, repository names, commit hashes, file paths, and line numbers). The purpose of this artifact is enabling researchers to replicate our mixed-methods quantitative results of the paper, and to reuse our around 9.6 million links in source code comments for further software engineering research.</p>
Supplementary dataset and source code for the paper "Using Weaker Consistency Models with Monitoring and Recovery for Improving Performance of Key-Value Stores"
<p>Supplementary dataset and source code for the paper "Using Weaker Consistency Models with Monitoring and Recovery for Improving Performance of Key-Value Stores" submitted to Journal of the Brazilian Computer Society, LADC special issue.</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.