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311 results for “Open source”
A Dataset of Atoms of Confusion in the Android Open Source Project
<p>This is a dataset cataloging the presence of Atoms of Confusion in the Android Open Source Project, alongside metrics<br>of object-oriented software development.</p> <p> </p> <h3>Schema</h3> <h4>ac_reports</h4> <p><strong>id </strong>INTEGER The primary key <br>project_name VARCHAR Name of the project/repository in the AOSP<br>line INTEGER NOT NULL Line where the AC was found<br>snippet VARCHAR NOT NULL The excerpt of code that represents the Atom of Confusion<br>class_name VARCHAR Name of the class where the AC was found<br>ac VARCHAR NOT NULL Type of Atom of Confusion<br>path VARCHAR Path to the file in the AOSP superproject<br>"commit" VARCHAR SHA of the commit of the file in the repository<br>loc VARCHAR Number of lines of code in the file</p> <h4>files</h4> <p>path VARCHAR The path of the file</p> <p>loc INTEGER Lines of code</p> <p>project_name VARCHAR Name of the project/repository in the AOSP</p> <h4>projects</h4> <p>name VARCHAR The name of the project/repository in the AOSP</p> <p> </p> <h4>classes</h4> <p>id INTEGER The primary key </p> <p>file_path VARCHAR Path to the file in the AOSP superproject</p> <p>name VARCHAR Name of the class</p> <p>type VARCHAR The type of class (e.g. interface, anonymous...)</p> <p>cbo INTEGER From CK: Coupling between objects, number of dependencies a class has.</p> <p>"cboModified" INTEGER From CK: Coupling between objects, number of dependencies a class has. (References from other classes to this class + references from this class to other classes)</p> <p>fanin INTEGER From CK: number of classes that reference this particular class.</p> <p>fanout INTEGER From CK: the number of classes referenced by this class</p> <p>wmc INTEGER From CK: Weight Method Class | McCabe's complexity | Cyclomatic Complexity</p> <p>dit INTEGER From CK: Depth Inheritance Tree</p> <p>noc INTEGER From CK: Number of Children</p> <p>rfc INTEGER From CK: Response for Class</p> <p>lcom INTEGER From CK: Lack of Cohesion of Methods</p> <p>lcom_normalized DOUBLE From CK: Refers to the LCOM* metric that computes Lack of Cohesion of Methods between 0 and 1.</p> <p>tcc DOUBLE From CK: Tight Class Cohesion</p> <p>lcc DOUBLE From CK: Loose Class Cohesion</p> <p>"totalMethodsQty" INTEGER From CK: Number of methods (constructors are included)</p> <p>"staticMethodsQty" INTEGER From CK: Number of static methods</p> <p>"publicMethodsQty" INTEGER From CK: Number of public methods</p> <p>"privateMethodsQty" INTEGER From CK: Number of private methods</p> <p>"protectedMethodsQty" INTEGER From CK: Number of protected methods</p> <p>"defaultMethodsQty" INTEGER From CK: Number of default methods</p> <p>"visibleMethodsQty" INTEGER From CK: Number of visible methods</p> <p>"abstractMethodsQty" INTEGER From CK: Number of abstract methods</p> <p>"finalMethodsQty" INTEGER From CK: Number of final methods</p> <p>"synchronizedMethodsQty" INTEGER From CK: Number of synchronized methods</p> <p>"totalFieldsQty" INTEGER From CK: Number of fields</p> <p>"staticFieldsQty" INTEGER From CK: Number of static fields</p> <p>"publicFieldsQty" INTEGER From CK: Number of public fields</p> <p>"privateFieldsQty" INTEGER From CK: Number of private fields</p> <p>"protectedFieldsQty" INTEGER From CK: Number of protected fields</p> <p>"defaultFieldsQty" INTEGER From CK: Number of default fields</p> <p>"finalFieldsQty" INTEGER From CK: Number of final fields</p> <p>"synchronizedFieldsQty" INTEGER From CK: Number of synchronized fields</p> <p>nosi INTEGER From CK: Number of static invocations</p> <p>loc INTEGER From CK: Lines of code</p> <p>"returnQty" INTEGER From CK: Number of return statements</p> <p>"loopQty" INTEGER From CK: Number of repetition structures (while, for)</p> <p>"comparisonsQty" INTEGER From CK: Number of comparisons, e.g. ==, !=</p> <p>"tryCatchQty" INTEGER From CK: Number of try...catch statements</p> <p>"parenthesizedExpsQty" INTEGER From CK: Number of expressions inside parenthesis.</p> <p>"stringLiteralsQty" INTEGER From CK: Number of string literals. Repeated strings are counted as well.</p> <p>"numbersQty" INTEGER From CK: Number of numbers, e.g. ints, floats, doubles</p> <p>"assignmentsQty" INTEGER From CK: Number of assignments, usage of the = operator.</p> <p>"mathOperationsQty" INTEGER From CK: Number of math operations.</p> <p>"variablesQty" INTEGER From CK: Number of variables</p> <p>"maxNestedBlocksQty" INTEGER From CK: Number of the highest nested block in the class.</p> <p>"anonymousClassesQty" INTEGER From CK: Number of anonymous classes</p> <p>"innerClassesQty" INTEGER From CK: Number of inner classes</p> <p>"lambdasQty" INTEGER From CK: Number of lambdas</p> <p>"uniqueWordsQty" INTEGER From CK: Number of unique words in the source file</p> <p>modifiers INTEGER From CK: Number of modifiers in class/methods (usage of "public", "static", "abstract"...)</p> <p>"logStatementsQty" INTEGER, From CK: Number of log statements.</p> <p> </p> <h4>methods</h4> <p>id_ INTEGER NOT NULL The primary key</p> <p>file_path VARCHAR NOT NULL Path to the file in the AOSP superproject</p> <p>class_id INTEGER NOT NULL ID of the class where this method is</p> <p>name VARCHAR NOT NULL The name of the method</p> <p>constructor BOOLEAN NOT NULL From CK: True if method is a constructor</p> <p>line INTEGER From CK: Line where this method is located</p> <p>cbo INTEGER From CK: Coupling between objects, number of dependencies a class has.</p> <p>"cboModified" INTEGER From CK: Coupling between objects, number of dependencies a class has. (References from other classes to this class + references from this class to other classes)</p> <p>fanin INTEGER From CK: number of classes that reference this particular class.</p> <p>fanout INTEGER From CK: the number of classes referenced by this class</p> <p>wmc INTEGER From CK: Weight Method Class | McCabe's complexity | Cyclomatic Complexity</p> <p>rfc INTEGER From CK: Response for Class</p> <p>loc INTEGER From CK: Lines of code</p> <p>"returnQty" INTEGER From CK: Number of return statements</p> <p>"variablesQty" INTEGER From CK: Number of variables</p> <p>"parametersQty" INTEGER From CK: Number of parameters</p> <p>"methodsInvokedQty" INTEGER From CK: all methods invoked</p> <p>"methodsInvokedLocalQty" INTEGER From CK: methods invoked locally</p> <p>"methodsInvokedIndirectLocalQty" INTEGER From CK: number of indirect local invocations</p> <p>"loopQty" INTEGER From CK: Number of repetition structures (while, for)</p> <p>"comparisonsQty" INTEGER From CK: Number of comparisons, e.g. ==, !=</p> <p>"tryCatchQty" INTEGER From CK: Number of try...catch statements</p> <p>"parenthesizedExpsQty" INTEGER From CK: Number of expressions inside parenthesis.</p> <p>"stringLiteralsQty" INTEGER From CK: Number of string literals. Repeated strings are counted as well.</p> <p>"numbersQty" INTEGER From CK: Number of numbers, e.g. ints, floats, doubles</p> <p>"assignmentsQty" INTEGER From CK: Number of assignments, usage of the = operator.</p> <p>"mathOperationsQty" INTEGER From CK: Number of math operations.</p> <p>"maxNestedBlocksQty" INTEGER From CK: Number of the highest nested block in the class.</p> <p>"anonymousClassesQty" INTEGER From CK: Number of anonymous classes</p> <p>"innerClassesQty" INTEGER From CK: Number of inner classes</p> <p>"lambdasQty" INTEGER From CK: Number of lambdas</p> <p>"uniqueWordsQty" INTEGER From CK: Number of unique words in the source file</p> <p>modifiers INTEGER From CK: Number of modifiers in class/methods (usage of "public", "static", "abstract"...)</p> <p>"logStatementsQty" INTEGER, From CK: Number of log statements.</p> <p>"hasJavaDoc" BOOLEAN From CK: True if method has a JavaDoc</p>
Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes input data to run the OnStove Nepal model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All result files and figures can be downloaded from the permanent repository <a href="https://doi.org/10.5281/zenodo.10643983">https://doi.org/10.5281/zenodo.10643983</a>.</p> <p>The "<strong>GIS_input_data/</strong>" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "<strong>1. Data</strong>"<strong> </strong>folder in your project. </p> <p>The "<strong>Scenario_inputs/</strong>" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the <strong>supplementary material</strong> of the related publication in the link <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>. To run the model extract the scenario data inside your "<strong>2. Scenario inputs</strong>"<strong> </strong>folder in your project. </p>
Dataset - FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control
<p>This dataset contains the data and model used in the paper</p> <blockquote> <p>Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, and Meritxell Bach Cuadra. "FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control." <a href="https://arxiv.org/abs/2311.04780"><em>arXiv preprint arXiv:2311.04780</em></a> (2023).</p> </blockquote> <p>If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This dataset contains manual quality annotations and image quality metrics (IQMs) obtained from 1647 stacks of T2-weighted (T2w) slices of fetal brain magnetic resonance (MR) images collected from 233 subjects at four different institutions Lausanne University Hospital (CHUV) in Switzerland, BCNatal at Hospital Sant Joan de Déu in Barcelona (Spain), University Children's Hospital Zürich (KISPI) in Switzerland and La Timone University Hospital in Marseille, France. The data were acquired on scanners from different vendors (Siemens at CHUV, BCNatal and Marseille, General Electrics at KISPI), MR sequences (Half Fourier Single-shot Turbo spin-Echo –HASTE– for Siemens scanners and Single-Short Fast Spin Echo –SS-FSE– for GE scanners), magnetic field strengths (1.5 T and 3 T), image resolutions, fields of view, repetition times and echo times, with both neurotypical and pathological cases.</p> <p>These data and the derived IQMs were used to train and evaluate models for quality assessment and quality control of fetal brain MR images. The code to reproduce the experiments is available on <a href="https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc">GitHub.</a></p> <p>Each entry describe the information for a single stack of T2w slices. It contains information regarding which subject it belongs to, its manual quality rating, scanner-related information and 332 IQMs, starting at the `centroid` column in the file. Further description of the data is available in the materials and methods section of the <a href="https://arxiv.org/abs/2311.04780">paper</a>.</p> <p>The model is a 2D nnUNet [1] segmentation network trained on the super-resolution reconstructed data and manual segmentations available as part of the<a href="https://www.synapse.org/#!Synapse:syn25649159/wiki/610007"> Fetal Tissue Annotation Challenge</a> (FeTA).</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p> <p>[1] Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." <em>Nature methods</em> 18.2 (2021): 203-211.</p>
Survey on Developer and Researcher Views on the Ethics of Experiments on Open-Source Projects
<p>Results of a survey of 180 GitHub developers and 44 authors of research papers concerning the ethics of performing experiments on open source projects.</p>
Open-Source Terraform Repositories - SAST (tfsec, terrascan, checkov) vulnerability snapshot
<p>Vulnerability findings of open-source Terraform repositories in GitHub, collected with 3 static-code analysis tools (tfsec, terrascan, checkov).</p>
Dvoice : An open source dataset for Automatic Speech Recognition on Moroccan dialectal Arabic
<p>Dialectal Voice is a community project initiated by AIOX Labs to facilitate voice recognition by Intelligent Systems. Today, the need for AI systems capable of recognizing the human voice is increasingly expressed within communities. However, we note that for some languages such as Darija, there are not enough voice technology solutions. To meet this need, we then proposed to establish this program of iterative and interactive construction of a dialectal database open to all in order to help improve models of voice recognition and generation.</p>
Evaluating the Usability of Open Source Frameworks in Energy System Modelling (Supplementary Material)
<p>Dataset and source code for analysis of the Energy System Modelling Usability Testing (ESMUT) procedure applied in the open_MODEX project.</p> <p>This is supplementary material for the publication:</p> <pre>Berendes et al. (2022). Evaluating the Usability of Open Source Frameworks in Energy System Modelling. <em>Renewable and Sustainable Energy Reviews. DOI: </em><a href="https://doi.org/10.1016/j.rser.2022.112174">https://doi.org/10.1016/j.rser.2022.112174</a></pre> <p> </p> <p> </p>
Random Sample of Open Source Ventilators
<p>This is a random sample of references to open source ventilators based on the data published by the PubInv project (https://github.com/PubInv/covid19-vent-list CC0-1.0). It was used to verify technology and documentation readiness scales introduced in a CIRP design conference paper. This upload shall make the data set used for the conference paper publicly accessible.</p>
Dataset of the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"
<p>This archive contains the artifacts (datasets) for the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"</p>
Dataset of the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"
<p><br> #Dataset Package for the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"</p> <p><br> Description of the content:</p> <p><br> 1) "1_RQ-CPS-bugs-Taxonomy" folder contains all the main experimental data concerning the issues sampled and analyzed from all the Projects considered in the study,<br> including row-data on the taxonomy validtion steps.<br> <br> - Under "the sub-folder "1_Taxonomy-Raw-data" are reported the row-data concerning the taxonomy validtion steps </p> <p><br> 2) "2_Scripts" contains all scripts used to generate the issue data and sampled issue raw-data in the previous folders: </p> <p><br> - "setup.md" file in the folder describes how to set=up and run the script used for collecting and sampling the issues for the validation steps:<br> <br> - runJSONtoCSV.sh<br> - JSONtoCSV.py<br> - generateListOfAllSamples.py<br> - generateAllSamples.r<br> <br> Under "the sub-folder "1_Scripts/1_Data_Collection":<br> <br> <br> 3) "3_Final Taxonomy" folder contains the final Table representation (also reported in the previous folder) and main figures of the CPSs Bugs Taxonomy.</p>
AID: Open-Source Anechoic Interferer Dataset
<p>A dataset of anechoic recordings of various sound sources encountered in domestic environments is provided, which is intended to be a resource of non-stationary, environmental noise signals that, when convolved with acoustic impulse responses, can be used to simulate complex acoustic scenes.</p> <p>The dataset consists of anechoic recordings of 43 different types of sound sources encountered in domestic environments, with the number of individual recordings per sound source varying between two and eleven. The sound sources, which are mostly household devices and utilities, include door keys, plastic bags, clothing, a drilling machine, an electric blender, glass jars and metal boxes but also a few human-made sounds, such as clapping, breathing, snapping or whistling. The recordings cover a wide range of timbres. Multiple sounds from every individual source were recorded by different ways of excitation, such as hitting and shaking, or switching on and off the electric devices. Three different microphones were used to record the various sound sources.</p> <p>In addition, a <em>Python</em> library is provided that can be used to randomly arrange multiple anechoic noise recordings into a single channel interference signal. The number of individual recordings concurrently playing at any point in time in an interference signal can be specified by the user, providing control over the temporal density. The signal generator implementation is hosted on <a href="https://github.com/audiolabs/anechoic-noise">GitHub</a>.</p>
Image data for bioRxiv article named: mtFociCounter - Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci
<p>Raw imaging data to reproduce and test the findings of the bioRxiv article: <strong>mtFociCounter </strong>- Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci. It contains data from three imaging days and 2 or three technical replicates on each day.</p> <p> </p>
Bee Tracker – an open-source machine-learning based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees
<p>The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radio-frequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honey bees, behavioral studies on solitary bees still largely depend on direct observations, which is very time-consuming.</p> <p>Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity-nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by a machine learning based software. This <i>Bee Tracker</i> software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees' thorax as well as the IDs of their nests according to their positions in the nesting unit.</p> <p>The software is able to identify each nest of each individual nesting bee, which permits to measure individual-based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters.</p> <p>The software could be adapted to various experimental setups by training it to an according set of videos. The presented method allows to efficiently collect large amounts of data on cavity-nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi-field and field conditions.</p>
Data for FEgrow: An Open-Source Molecular Builder and Free Energy Preparation Workflow
<p>Data illustrating the use of de novo design in building and scoring protein-ligand complexes.</p> <p>This is relationship to the FEgrow publication with the intiial preprint here: <br> https://chemrxiv.org/engage/chemrxiv/article-details/6287bb98a42e9c78d34769f6<br> </p> <p>The FEgrow software snapshot used can be found here: https://zenodo.org/record/7105647#.YzFwINLMIUE</p>
Open source measurement data of the ground water physio-chemical parameters in the Peshawar District, Pakistan
<p>This excel file provides the measurement data of the physio-chemical parameters in the Peshawar district, Pakistan. The physio-chemical parameters include pH, electrical conductivity, total dissolved solids, Ca hardness, Mg hardness, Total hardness, Turbidity, Nitrate and Chloride. The data also include the latitude and longitude and location and depth to groundwater level (water table). The data was collected and sample were analyzed in June-August 2012.</p> <p>The full data collection and case studies are described in Adnan and Iqbal 2014, Adnan et al. 2018 and Adnan et al. 2019.</p>
Free Open Source Communities Sustainability: Does It Make a Difference in Software Quality?
<p><strong>Context:</strong> Free and Open Source Software (FOSS) communities' ability to stay viable and productive over time is pivotal for society as they maintain the building blocks that digital infrastructure, products, and services depend on. Sustainability may, however, be characterized from multiple aspects, and less is known how these aspects interplay and impact community outputs, and software quality specifically.</p> <p><strong>Objective: </strong>This study, therefore, aims to empirically explore how the different aspects of FOSS sustainability impact software quality.</p> <p><strong>Method: </strong>16 sustainability metrics across four categories were sampled and applied to a set of 217 OSS projects sourced from the Apache Software Foundation Incubator program. The impact of a decline in the sustainability metrics was analyzed against eight software quality metrics using Bayesian data analysis, which incorporates probability distributions to represent the regression coefficients and intercepts.</p> <p><strong>Results: </strong>Findings suggest that selected sustainability metrics do not significantly affect defect density or code coverage. However, a positive impact of community age was observed on specific code quality metrics, such as risk complexity, number of very large files, and code duplication percentage. Interestingly, findings show that even when communities are experiencing sustainability, certain code quality metrics are negatively impacted.</p> <p><strong>Conclusion:</strong> Findings imply that code quality practices are not consistently linked to sustainability, and defect management and prevention may be prioritized over the former. Results suggest that growth, resulting in a more complex and large codebase, combined with a probable lack of understanding of code quality standards, may explain the degradation in certain aspects of code quality.</p>
Open-source quality control routine and multi-year power generation data of 175 PV systems
<p><strong>Description</strong></p> <p>The repository contains an extensive dataset of PV power measurements and a python package (qcpv) for quality controlling PV power measurements. The dataset features four years (2014-2017) of power measurements of 175 rooftop mounted residential PV systems located in Utrecht, the Netherlands. The power measurements have a 1-min resolution.</p> <p><strong>PV power measurements</strong></p> <p>Three different versions of the power measurements are included in three data-subsets in the repository. Unfiltered power measurements are enclosed in <em>unfiltered_pv_power_measurements.csv</em>. Filtered power measurements are included as <em>filtered_pv_power_measurements_sc.csv </em>and<em> filtered_pv_power_measurements_ac.csv</em>. The former dataset contains the quality controlled power measurements after running single system filters only, the latter dataset considers the output after running both single and across system filters. The metadata of the PV systems is added in<em> metadata.csv</em>. This file holds for each PV system a unique ID, start and end time of registered power measurements, estimated DC and AC capacity, tilt and azimuth angle, annual yield and mapped grids of the system location (north, south, west and east boundary).</p> <p><strong>Quality control routine</strong></p> <p>An open-source quality control routine that can be applied to filter erroneous PV power measurements is added to the repository in the form of the Python package qcpv (<em>qcpv.py</em>). Sample code to call and run the functions in the qcpv package is available as <em>example.py.</em></p> <p><strong>Objective</strong></p> <p>By publishing the dataset we provide access to high quality PV power measurements that can be used for research experiments on several topics related to PV power and the integration of PV in the electricity grid.</p> <p>By publishing the qcpv package we strive to set a next step into developing a standardized routine for quality control of PV power measurements. We hope to stimulate others to adopt and improve the routine of quality control and work towards a widely adopted standardized routine. </p> <p><strong>Data usage</strong></p> <p>If you use the data and/or python package in a published work please cite: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Units</strong></p> <p>Timestamps are in UTC (YYYY-MM-DD HH:MM:SS+00:00).</p> <p>Power measurements are in Watt.</p> <p>Installed capacities (DC and AC) are in Watt-peak.</p> <p><em><strong>Additional information</strong></em></p> <p>A detailed discussion of the data and qcpv package is presented in: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Corrections are discussed in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2024. </em><em>Erratum: Open-source quality control routine and multiyear power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Acknowledgements </strong></p> <p>This work is part of the Energy Intranets (NEAT: ESI-BiDa 647.003.002) project, which is funded by the Dutch Research Council NWO in the framework of the Energy Systems Integration & Big Data programme. The authors would especially like to thank the PV owners who volunteered to take part in the measurement campaign. </p>
Supporting material for: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data
<p>Supplementary material for the paper called: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data. The data without "indexes" in the name are map-projected M3 cubes, they can be used in the python library <i><strong>MoonIndex </strong></i>to obtain the spectral indexes stored in the files with "indexes" in the name.</p><p>This research was done on the framework of the EXPLORE project, that has received funding from the European Union's 2020 research and innovation program under grant agreement No 101004214. </p>
Figure 8. F1 scores for YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 8. F1 scores for YOLOv5 indicating the harmonic mean between precision and recall scores. Data indicated that detection results for both species would be best at a confidence threshold of 0.298.
Figure 11. YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 11. YOLOv5 precision (A), recall (B), and F1 score (C) changes as a function of Amoronthus polmeri density (plants m−2).
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.