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311 results for “Open source”
Figure 4 in Reconstruction of a passive tracer boundary source in an open water area
Figure 4. Convergence on the boundary.
Why is my community reacting like this? Understanding reactions in open-source communities
<p>In 2016, GitHub introduced the Reactions feature to facilitate the expression of sentiments and reduce noise in communications on its platform. Recent studies indicated that developers has been adopting the feature and was observed a reduction of noise on conversations inside the platform. However, the patterns of usage and profiles of users expressing these reactions in inside their communities remain underexplored. Identifying these patterns may help maintainers to better understand members' behaviors in their communities, and researchers to build supporting tools focused on users' reactions. This paper presents an initial study to (i) understand these interactions on open-source software communities, (ii) identify types of resources that receive the most reactions, (iii) analyzing seasonal factors influencing usage, and (iv) correlating the provided reactions with the roles of developers within the community. Preliminary results indicate that users primarily react to comments in Issues, with notable periods of heightened activity. Additionally, significant differences were observed between the reactions of maintainers and other members of the community.</p>
An Economical Open-Source Lagrangian Drifter Design to Measure Deep Currents in Lakes
<p>An economical, open-source Lagrangian drifter designed to collect current data on lakes<200km2 was evaluated against existing designs. The new design was tested in deep inland lakes in the Finger Lakes region of New York, USA and is effective at tracking deep currents. The ease and low-cost of fabrication and launch/recovery should facilitate use of this design by less-advantaged communities & researchers.</p> <p>This project includes data and code for preparation of graphs and charts to illustrate Lagrangian drifter experiments in Seneca Lake and Keuka Lake, New York, USA.</p>
Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem - the dataset
<pre><em>Replication pack, FSE2018 submission #164: </em><em>------------------------------------------ </em></pre> <pre><strong>**</strong>Working title:<strong>** </strong>Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem <strong>**</strong>Note:<strong>** </strong>link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. <em>Content description </em><em>=================== </em> <strong>- **</strong>ghd-0.1.0.zip<strong>** </strong>- the code archive. This code produces the dataset files described below <strong>- **</strong>settings.py<strong>** </strong>- settings template for the code archive. <strong>- **</strong>dataset_minimal_Jan_2018.zip<strong>** </strong>- the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) <strong>- **</strong>dataset_full_Jan_2018.tgz<strong>** </strong>- full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. <strong>- **</strong>build_model.r, helpers.r<strong>** </strong>- R files to process the survival data (`survival_data.csv` in <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>**</strong>, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in <strong>**</strong>dataset_full_Jan_2018.tgz<strong>**</strong>) <strong>- **</strong>Interview protocol.pdf<strong>** </strong>- approximate protocol used for semistructured interviews. <strong>- </strong>LICENSE - text of GPL v3, under which this dataset is published <strong>- </strong>INSTALL.md - replication guide (~2 pages)</pre> <pre><em>Replication guide </em><em>================= </em> <em>Step 0 - prerequisites </em><em>---------------------- </em> <strong>- </strong>Unix-compatible OS (Linux or OS X) <strong>- </strong>Python interpreter (2.7 was used; Python 3 compatibility is highly likely) <strong>- </strong>R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): <strong>- </strong>up to 2Tb of disk space (see Step 2 detalization levels) <strong>- </strong>at least 16Gb of RAM (64 preferable) <strong>- </strong>few hours to few month of processing time <em>Step 1 - software </em><em>---------------- </em> <strong>- </strong>unpack <strong>**</strong>ghd-0.1.0.zip<strong>**</strong>, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. <strong>- </strong>copy `settings.py` into the extracted folder. Edit the file: <strong> * </strong>set `DATASET_PATH` to some newly created folder path <strong> * </strong>add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` <strong>- </strong>install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` <strong>- </strong>install libarchive and headers: `sudo apt-get install libarchive-dev` <strong>- </strong>(optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. <strong>- </strong>install Python libraries: `pip install --user -r requirements.txt` . <strong>- </strong>disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. <em>Step 2 - obtaining the dataset </em><em>----------------------------- </em> The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: <em>####Option 2.a, difficulty level: easiest </em> Just use the precomputed data. Step 1 is not necessary under this scenario. <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>** </strong><strong>- </strong>get `survival_data.csv`, go to the next step <em>####Option 2.b, difficulty level: easy </em> Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. <strong>- </strong>create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018<strong>** </strong>to the newly created folder <strong>- </strong>rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. <em>####Option 2.c, difficulty level: medium </em> Use predownloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. <strong>- </strong>Download <strong>**</strong>dataset_full_Jan_2018.tgz<strong>** </strong>from http://k.soberi.us/dataset_full_Jan_2018.tgz . This file is not included in this archive because of its size (5.4Gb compressed, 34Gb unpacked). <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` <strong>- </strong>extract the content of this archive into `<DATASET_PATH>`. <strong>- </strong>clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. <em>####Option 2.d, difficulty level: hard </em> Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). <strong>- </strong>ensure the `<DATASET_PATH>` is empty <strong>- </strong>add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. <em>Step 3 - run the regression </em><em>--------------------------- </em> install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>
Replication Package for "Ensuring Open Source Integrity: The Intersection of Copy-Based Reuse and License Compliance"
<p>Replication Package for "Ensuring Open Source Integrity: The Intersection of Copy-Based Reuse and License Compliance"<br><br>Includes datasets, R and bash code.</p>
Underrepresented Groups in Open Source Software Development
<p>This repository contains the artifacts generated when researching minority groups in open source software development, which aimed to analyze knowledge about minority groups in OSS projects. This set of artifacts consists of two main components:</p> <p><strong>Research Protocol (PDF):</strong></p> <p>The protocol in PDF format provides a detailed overview of the research design, methodology, and objectives of the study. It describes the scope of the research, research questions, and data collection approach. This protocol is a fundamental reference for researchers interested in understanding how the study was designed.</p> <p><strong>Search Data (CSV):</strong></p> <p>The CSV file contains the raw data collected during the research. The data includes results returned by each database, articles considered in each phase of the study, and the set of seed articles. The XLSX spreadsheet is a source for analyzing the data used in the research.</p> <p>This repository aims to promote a more transparent understanding of the process that guided this research. Researchers and the open source developer community can utilize this dataset for academic studies, replications, and informed decision-making to promote equity and representation in open source projects.</p>
Data from: An inexpensive and open-source method to study large terrestrial animal diet and behavior using time-lapse video and GPS
1. The behavior of free-ranging animals is difficult to study, especially on the large spatial and temporal scales relevant to long-lived large species. Animal-borne video and environmental data collection systems (AVEDs) record behavior and other data in real time as animals conduct daily activities. However, few studies have combined systematically collected, long term AVED foraging data with environmental and movement data to test hypotheses on animal foraging. Additionally, AVEDs are often either prohibitively expensive, or require extensive fabrication and programming knowledge. 2. The video and coordinate animal-mounted system (VACAMS) is an animal-mounted data collection system based on a modified GoPro® action camera platform that records short, first "person" perspective videos of animal behavior on an automated time-lapse schedule. As most videos are georeferenced, researchers can return to the locations of specific behaviors and collect accurate, fine-grained data on non-woody vegetation and other habitat characteristics that may influence animal behavior. Moreover, VACAMS are inexpensive and easy to use. 3. This study describes VACAMS preliminary data on cattle foraging and a hypothesis exploring free-ranging cattle browsing habits throughout the rainy season in the tropical dry forest of Sonora, Mexico. I generated a database of vegetation types consumed by cows each month (Annual, Woody, and Leaf litter) and compared actual vegetation type frequencies to a priori assumptions based on seasonal patterns of forage availability. During the monsoons, when palatable vegetation was abundant, frequencies of annual and woody perennial vegetation in cattle diets did not differ from month to month. When the rains ceased and palatable vegetation became scarce, cows switched to leaf litter, dead annual vegetation, twigs, and dried leguminous fruits. 4. Open source software and commercially available hardware make VACAMS financially attainable for many researchers, land managers, students, and other user groups. VACAMS could be used on a range of domestic and semi-domestic free-ranging animals, particularly in dense forests where conventional observations are impossible. With improvements to GPS battery life and durability, the weakest points of the system, VACAMS could also potentially apply to studies of other large terrestrial animals.
Open-Source Software Product Line Extraction Processes: the ArgoUML-SPL and Phaser Cases
<p>Collection of datasets and analysis scripts supporting the information provided in the text.</p> <p>There are two compressed files, one for the ArgoUML data and one for the Phaser data. Each compressed file contains a README describing important information.</p>
A Systematic Mapping of the Classification of Open Educational Resources for Computer Science Education in Digital Sources (Data)
<p>Data from a Systematic Mapping of the classification of Open Educational Resources for Computer Science Education.</p> <p>Content:</p> <ul> <li>Studies selected</li> <li>Digital sources used to classify Open Educational Resources for Computer Science Education</li> <li>Computer Science domains explored by Open Educational Resources</li> <li>Approaches for the classification of Open Educational Resources for Computer Science Education</li> </ul>
DeepPlastic: An Open Source Image Dataset for Epipelagic Marine Plastic Detection
<p>Deep Plastic</p> <ul> <li>Enhanced Object Detection for Epipelagic Plastic.</li> <li>This repository contains source code for the method developed in <a href="https://arxiv.org/pdf/2105.01882.pdf">DeepPlastic: Identifying Marine Plastic In The Epipelagic Zone using Computer Vision and Deep Learning</a></li> <li> <p>Information:</p> </li> <li>Paper: [Coming Soon]</li> <li>YouTube video of Results: <a href="https://youtu.be/8zBdFxaK4Os">https://youtu.be/8zBdFxaK4Os</a></li> <li> <p>Object Detection Model</p> </li> <li>Four models: YOLOv4, YOLOv5, MobileSSD, Faster RCNN Inception V2</li> <li>Small efficient and high precision models can be used for real-time object detection.</li> <li>Model architecture and implementation details: <a href="https://arxiv.org/">https://arxiv.org/</a></li> <li>Weights for YOLOv4 and YOLOv5 are provided in the model/ <ul> <li>YOLOv4: best. weights; use <a href="https://drive.google.com/file/d/1YOTtZ2cHbqgxHukzLp01OVsUoa2CwwXs/view?usp=sharing">best.weights</a></li> <li>YOLOv5: best.pt; use <a href="https://drive.google.com/file/d/14mBOhtLrE2d3hudqjwBZmawKAvTF4zxS/view?usp=sharing">best.pt</a></li> </ul> </li> <li> <p>Google Colab Links</p> <p>Note: Click on File and Save Copy in Drive. If you try to edit my file it'll ask you for permission and send me an email. Please make your own copy.</p> </li> <li>YOLOv5: <a href="https://colab.research.google.com/drive/1_qzbpBWkNfxQ0ny-DvsKicCeM0aFU4eW?usp=sharing">https://colab.research.google.com/drive/1_qzbpBWkNfxQ0ny-DvsKicCeM0aFU4eW?usp=sharing</a></li> <li> <p>DeepTrash DataSet</p> </li> <li>1900 training images, 637 test images, 637 validation images (60, 20, 20 split)</li> <li>Field images taken from Lake Tahoe, San Francisco Bay and Bodega Bay in CA.</li> <li>Deep Sea images are from JAMSTEK JEDI dataset: <a href="http://www.godac.jamstec.go.jp/">http://www.godac.jamstec.go.jp/</a></li> </ul>
OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images
<p>This is a dataset of OCTA images used in the development of the manuscript <em>OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images</em></p>
Dependency Management Bots in Open-Source Systems - Prevalence and Adoption
<p>This is a re-analysis package for the paper: Dependency Management Bots in Open-Source Systems - Prevalence and Adoption. The package includes scripts and processed data collected from GitHub about five investigated dependency management bots: Dependabot, Depfu, Greenkeeper, Pyup and Renovate.</p>
Literature review of Design in Open Source Agriculture - Images
<p>Literature review of Design in Open Source Agriculture - Images</p> <ol> <li>Fig. 1. Publications by subject areas</li> <li>Fig. 2. Publications by country</li> <li>Fig. 3. Yearly output of publications</li> <li>Fig. 4. Network of co-authorship (generated with VOSviewer) <em>(Extra image not included in the article)</em></li> <li>Fig. 5. Network of co-citation (generated with VOSviewer) <em>(Extra image not included in the article)</em></li> <li>Fig. 6. Network of bibliographic coupling(generated with VOSviewer) <em>(Extra image not included in the article)</em></li> <li>Fig. 7. Co-word analysis: network of terms from title and abstract (generated with VOSviewer) <em>(Extra image not included in the article)</em></li> <li>Fig. 8. Co-word analysis: network of keywords (generated with VOSviewer) <em>(Extra image not included in the article)</em></li> <li>Fig. 9. Thematic Map based on Authors’ keywords (generated with bibliometrix) <em>(Extra image not included in the article)</em></li> <li>Fig. 10. Thematic Map based on Titles (just single words - unigrams) (generated with bibliometrix) <em>(Extra image not included in the article)</em></li> <li>Fig. 11. Thematic Map based on Abstracts (just single words - unigrams) (generated with bibliometrix) <em>(Extra image not included in the article)</em></li> <li>Fig. 12. Trend Topics (generated with bibliometrix) <em>(Extra image not included in the article)</em></li> </ol>
A New Open-source Geomagnetosphere Propagation Tool (OTSO) and its Applications - Data
<p>Data files for the computations done with OTSO for the asymptotic cones and effective cut-off rigidities for several neutron monitor stations during three ground-level enhancement events (GLE 66, 70, 71). The computations for GLE 66 and 71 were done using three external geomagnetic field models (TSY89, TSY96, TSY01). Data for the global map of effective cut-off rigidities during GLE70 is also included. Data is in CSV format.</p>
Open-source Software Governance Documentation Dataset on GitHub
<p>This dataset contains 710 GitHub-hosted OSS projects, which contain a governance file in the root directory of the project. It also contains commits, issues, and comments on each project.</p>
Proof of Concept database with inputs and outputs of the Master thesis: Analyzing Software Delivery Performance behavior in popular Open Source Software Projects on a Release timeline basis through delivery metrics
<p>The software has become one of the main assets to deliver services today. Thus, software delivery has been dealing with a competitive and dynamic environment where the demand for faster and more assertive deliverables, called here Releases, only increases. Agile development methods emerged helping to accelerate software delivery, embracing industry and open source community. Since then, the software delivery frequency has expanded and improved bringing more adopters of rapid release cycles to reduce their time-to-market. However, using only rapid releases can not be enough as measuring software delivery can answer essential questions, like how software delivery is happening and how it should be. Some approaches for measuring software delivery appeared such as Software Delivery Performance (SDP) where software delivery is measured as a consequence of capabilities evolution. Popularity in Open Source Software Projects (OSSP) means that a project is mature enough in the community to fit the software demand and, therefore, is likely to be ready to be measured through a software delivery approach like SDP. In light of it, this work offers means to analyze SDP behavior in popular Open Source Software Projects on a Release timeline basis through delivery metrics. The results demonstrated that popularity is efficient filtering, as it improves the OSSP delivery, supporting the work's reliability and accuracy. The source code and methodology are published as a replication package to encourage reproducibility and future research.</p>
MALDI-MS dataset for use with open-source untargeted metabolomic workflow for complex biological samples
<p class="MsoNormal">Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free to-use data processing and analysis tools exist for various untargeted MS approaches, but choosing the 'correct' pipeline isn't straight-forward. This data set can be used in conjunction with a user-friendly online guide which presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. The workflow provides practical advice concerning experimental design, organisation of data and downstream analysis, and offers details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/ changing methodologies and increased clarity and detail as user participation becomes more common allowing contributions and improvements to the workflow via the online repository. </p>
Supporting material for PyESDv1.0.1 An open-source Python framework for empirical-statistical downscaling of climate information
<p>The nature and severity of climate change impacts varies significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the coupling of Empirical Statistical Downscaling (ESD) models to General Circulation Model (GCM) simulations of future climate. Here, we present a new open-source Python package (<em>pyESD; </em>github.com/Dan-Boat/PyESD) that implements several Perfect Prognosis ESD (PP-ESD) methods and the whole downscaling cycle. The latter includes routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established Machine Learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, hyperparameter optimization, etc., in relatively few lines of code. The package is highly modular and flexible and allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even glacial retreat. The dataset presented here serves as supporting material for the package description and evaluation manuscript</p>
CCG: Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System)
<p>Seven clicSAND scenario files for <strong>Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System).</strong> </p> <p><strong>How to Visualise Results Online and Offline</strong> outline the steps required to re-run the scenarios on OSeMOSYS Cloud</p> <p><strong>Scenario Short Note</strong> outlines the steps to replicate the analysis and rebuild the scenarios</p> <p><strong>Annex - Input Data and Assumptions</strong> listing the data sources and assumptions in the scenarios</p>
Over and Under Sampled Data-sets of Code Issues in Java Open-Source Projects
<p>The dataset comprises code changes made to 15 Java Open-Source projects, classified with sentiment values (0 for negative and 1 for positive) based on developer reviews during various revision submissions. The dataset is available in 8 versions, each containing a sampled dataset using an over or under-sampling technique.</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.