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166 results for “open-source”
RTPTorrent: An Open-source Dataset for Evaluating Regression Test Prioritization
<p>This dataset is designed to be used in evaluation studies of regression test prioritization techniques. It includes 20 open-source Java projects from GitHub and over 100,000 logs of real-world build logs from TravisCI. The projects span a wide range with regard to size, number of contributors, and maturity of open-source Java projects available on GitHub.</p> <p>Futher, the dataset includes the results of baseline approaches to ease the comparison of new techniques applied to the dataset.</p> <p>A readme file with a more detailed description of the structure of the dataset is included. For even more information see the corresponding MSR 2020 publication.</p> <p>Versions:</p> <ul> <li> 2020-09-23 (version 1.1) <ul> <li>Updated archived `deeplearning4j` repository with a fork that contains all of the original commits. Repository at the original GitHub location had been replaced. Defect identified by Daniel Elsner (Technische Universität München).</li> <li>Renamed root folder from MSR2 to rtp-torrent</li> </ul> </li> <li>2020-05-25 (version 1.0) <ul> <li>Initial release</li> </ul> </li> </ul> <p> </p>
Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors
<p>This repository contains data for the manuscript: "Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors."</p> <p> </p> <p>This includes:</p> <p>Raw data from the low-cost prototype EarthSense Zephyrs, as well as raw data from reference instrumentation.</p> <p>SC stands for "Summer Campaign" and WC stands for "Winter Campaign", denoting the two different campaigns assessed in this study.</p> <p> </p> <p><strong>Abstract</strong></p> <p>The last two decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensors using reference instrumentation with user-friendly guidelines, open access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO<sub>2</sub>) or ozone (O<sub>3</sub>). A full example of the application of this methodology to a case study in an urban environment using both Multiple Linear Regression (MLR) and the Random Forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOS). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit, transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensors, we suggest a number of best practices for future studies using low-cost sensors to ensure greater comparability of research.</p>
ThermoCyte: an inexpensive open-source temperature control system for in vitro live cell imaging
<p>Live-cell imaging is a common technique in microscopy to investigate dynamic cellular behaviour and permits the accurate and relevant analysis of a wide range of cellular and tissue parameters, such as motility, cell division, wound healing responses, and calcium (Ca2+) signalling in cell lines, primary cell cultures, and ex vivo preparations. Furthermore, this can take place under many experimental conditions, making live-cell imaging indispensable for biological research. Systems which maintain cells at physiological conditions outside of a CO<sub>2</sub> incubator are often bulky, expensive, and use proprietary components. Here we present an inexpensive, open-source temperature control system for in vitro live cell imaging. Our system 'ThermoCyte', which is constructed from standard electronic components, enables precise tuning, control, and logging of a temperature 'set point' for imaging cells at physiological temperature. We achieved stable thermal dynamics, with reliable temperature cycling and a standard deviation of 0.42°C over 1 hour. Furthermore, the device is modular in nature, and is adaptable to the researcher's specific needs. This represents simple, inexpensive, and reliable tool for laboratories to carry out custom live-cell imaging protocols, on a standard lab bench, at physiological temperature.</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>
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>
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 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).
Figure 10. Detection results for YOLOv5 with a in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 10. Detection results for YOLOv5 with a confidence interval of 0.15. False-positive detections of Mollugo verticillata and Abutilon theophrasti as Amoronthus polmeri are denoted by arrows pointing from "A" and "B," respectively.
Figure 9. YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 9. YOLOv5 detection results for Amoronthus polmeri and soybean using confidence thresholds of 0.15 (A) and 0.70 (B). The likelihood of false-negative (FN) detections increases as confidence thresholds increase, as can be seen in B. Objects assigned a confidence interval of less than 0.70 are not detected in B. FN A. palmeri and soybean detections in B are indicated by the orange and white arrows, respectively.
Figure 7 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 7. Image annotation of soybean at the R2 growth stage. As soybean populations were much higher than Amoronthus polmeri populations, there was a high level of soybean overlap. Therefore, it was necessary to include multiple soybean plants in each image. However, A. polmeri plants typically did not have as much overlap, and in most cases, it was much easier to identify and label individual plants.
Figure 3 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 3. Intersection over union (IoU) equation, defined as the overlap between the ground truth annotation and the computer prediction bounding box, divided by the total area of the two bounding boxes.IoU overlaps greater than 0.5 were considered true-positive predictions,whereas overlaps less than 0.5 were considered false-positive predictions.
Figure 2 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 2. Illustration of the annotation process. Amoronthus polmeri and soybean plants are labeled in this figure with orange and white boxes, respectively. Bounding boxes overlap with neighboring bounding boxes when plant features are irregular. In cases where a single bounding box could not encompass a plant without including a plant of another species, multiple irregular bounding boxes were drawn on a single specimen.
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.