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8,537 results for “Openness”
Waveguide-PAINT: An open platform for large field-of-view super-resolution imaging
<p>This data corresponds to the publication "An open platform for large field-of-view waveguide-PAINT".</p>
Published needs from Open Nature Innovation Arena (Open311 standard)
<p>This dataset has been generated from the Open Nature Innovation Arena according to the Open311 standard. It contains all the problems published from the users into the Open Nature Innovation Arena.</p>
Suspended and bedload transport in an open channel laboratory flume
<p>Notwithstanding the large number of studies on bedforms such as dunes and antidunes, performing quantitative predictions of bedform type and geometry remains an open problem. Here we present the results of laboratory experiments specifically designed to study how sediment supply and caliber may impact equilibrium bedform type and geometry in the upper regime. Experiments were performed in a sediment feed flume with flow rates varying between 8 l/s and 15 l/s, sand supply rates varying between 0.6 kg/min and 16 kg/min, uniform and non-uniform sediment grain sizes with geometric mean diameter varying between 0.22 mm and 0.87 mm. The experimental data and the comparison with datasets available in the literature revealed that the ratio of the volume transport of sediment to the volume transport of water <em>Q<sub>s</sub></em>/<em>Q<sub>w</sub></em> plays a prime control in the equilibrium bed configuration. The equilibrium bed configuration transitions from washed-out dunes (lower regime), to downstream migrating antidunes (upper regime) for <em>Q<sub>s</sub></em>/<em>Q<sub>w</sub></em> between 0.0003 and 0.0007. For values of <em>Q<sub>s</sub></em>/<em>Q<sub>w</sub></em> greater than those typical of downstream migrating antidunes, the bedform wavelength increases with <em>Q<sub>s</sub></em>/<em>Q<sub>w</sub></em>. At these high values of <em>Q<sub>s</sub></em>/<em>Q<sub>w</sub></em> equilibrium bed configurations with fine sand are characterized by upstream migrating antidunes or cyclic steps, and significant suspended load. In experiments with coarse sand, equilibrium is characterized by plane bed with bedload transport in sheet flow mode. Standing waves form at the transition between downstream migrating antidunes and bed configurations with upstream migrating bedforms. </p>
How do CONTRIBUTING files support newcomers in Open Source projects?
<p> </p> <p>Welcome to the replication package for the paper entitled "Do CONTRIBUTING files provide information about OSS newcomers' onboarding barriers?"</p> <p><strong>Repository Structure </strong><br> Our package is divided in folders and it is organized as follows:</p> <ul> <li><strong>app</strong>: This folder contains the implementation of our streamlit application, used to demonstrate the capabilities of our classification model. </li> <li><strong>data</strong>: If you are looking for the data we have used for classification, this is the folder where it is located. This folder also contains the raw contributing files of each project analyzed, and the spreadsheets qualitatively analyzed by the authors. </li> <li><strong>results</strong>: This folder contains all the files related to our results section. From the analysis of our classification model to the results of our online questionnaire.</li> <li><strong>scripts</strong>: If you want to see the code we wrote during the whole classification process, including but not limited to the classification process, this is the folder you are looking for.</li> <li><strong>survey</strong>: instrument used in the survey, raw answers and a spreadsheet with analyzed data.</li> <li><strong>misc</strong>: This folder contains miscellaneous files that were not used in this paper, but support the statements of it (e.g. a screenshot of the top ten languages used on GitHub from the Octoverse website).</li> </ul> <p><em>Each folder has its specific README.md with more specific details about their content.</em></p> <p><strong>I want to use your model!</strong><br> We are glad you are interested in our classification model. The final model is available as a `classification_model.sav` file inside the `app` folder that you can load using Pickle. If you are not familiar with Pickle or don't know how to load a model, we recommend you to take a look at our code implementation inside the `app` folder (`classify_content.py` is a good starting point).<br> </p>
Whole database with all 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> <p> </p>
DQA: An Open-Domain Dataset of Deep Questions Answering
<p>DQA, the first open-domain question answering dataset consisting of comprehensive questions with verbose answers. The dataset comprises of 4 question types which are classified as Level 1 questions according to Bloom’s Taxonomy i.e understanding type Q/A. </p>
A verified open-access AI-based chemical microparticle image database for in-situ particle visualization and quantification in multi-phase flow
<p>This report provided a new method and idea for the detection, segmentation, classification, and quantitative analysis of four dispersed phase particles - "DPPs" (agglomeration, bubble, crystal, and droplet) in chemical multi-phase flow processes.</p>
MULTIPLIERS_WP2_Dataset on needs analysis in open-schooling_UCY_20230517_v1
<p>The contents of this dataset can be accessed by referring to the dataset <strong>"MULTIPLIERS_WP2_Needs analysis in open schooling and interview guidelines_UCY_20231006_v2"</strong> with <strong>doi https://doi.org/10.5281/zenodo.13986314. </strong></p> <p>Indeed, as the contents of the dataset "MULTIPLIERS_WP2_Needs analysis in open schooling and interview guidelines_UCY_20231006_v1" with doi https://doi.org/10.5281/zenodo.8413666 and the present dataset "MULTIPLIERS_WP2_Dataset on needs analysis in open-schooling_UCY_20230517_v1" with doi https://doi.org/10.5281/zenodo.7944791 were useful for generating the same output, they were merged to generate the dataset <strong>"MULTIPLIERS_WP2_Needs analysis in open schooling and interview guidelines_UCY_20231006_v2" </strong>with doi <strong>https://doi.org/10.5281/zenodo.13986314</strong>. </p> <p>The present dataset was edited in restricted mode to avoid duplication of data. </p>
Full Data Set for the Paper: An Evaluation of Open Source Trajectory Planners for Robotic Manipulators with Focus on Human-Robot Collaboration
<p>This data sets contain the full evaluation data for the paper "An Evaluation of Open Source Trajectory Planners for Robotic Manipulators with Focus on Human-Robot Collaboration".</p>
Wu et al. Polysulfamide paper data - open access
<p>Data used in the main manuscript of "Investigating hydrogen bond-induced self-assembly of polysulfamides using molecular simulations and experiments" by Wu et al., published on Macromolecules in 2023</p>
Open_Source_Aircraft
<p>This data set contains the results which where created in the FutPrInt50 project for the ATR 42-500 lookalike, the conventional reference aircraft (CRA) and the hybrid-electric aircraft (HEA). The data contained is explained in further detail in the Deliverable 4.4 of the FutPrInt50 project</p>
Open Addresses Nationally Representative Subset (1m)
<p>Dataset used to validate POINT geocoder. Derived from Open Addresses by randomly sampling 1 million addresses weighted by state population.</p>
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
Research data for "Stability and decomposition pathways of the NiOOH OER active phase of NiOx electrocatalysts at open circuit potential traced by ex-situ and in-situ spectroscopies"
<p>This dataset supports the paper " Stability and decomposition pathways of the NiOOH OER active phase of NiOx electrocatalysts at open circuit potential traced by ex-situ and in-situ spectroscopies" in Catalysis Science and Technology. The paper is online here: <a href="https://doi.org/10.1039/D3CY00674C">https://doi.org/10.1039/D3CY00674C</a></p>
An Empirical Comparison of Ethnic and Gender Diversity of DevOps and non-DevOps Contributions to Open-Source Projects
<p>This is the replication package for the paper titled "An Empirical Comparison of Ethnic and Gender Diversity of DevOps and non-DevOps Contributions to Open-Source Projects." </p> <p>The preview is our online appendix. We encourage you to read the README.md file for more information about the replication package and the folder structure. </p> <p> </p>
WNVDB: an open access dataset of reported West Nile outbreaks in Italy
<p>In Italy, the epidemiological surveillance of West Nile virus (WNV) infections in humans is regulated by the <a href="https://www.statoregioni.it/media/2371/p-1-csr-rep-n-1-15gen2020.pdf">“National prevention, surveillance and response plan for arboviral diseases (PNA) 2020-2025“</a>. The document integrates veterinary (animal and entomological) surveillance of West Nile virus (WNV) - essential for risk estimation - and human cases into a single plan.</p> <p>Surveillance of imported and local human infections is carried out all year round throughout the country, and between early May and late November in Regions where an endemic area has been identified. In these areas the surveillance of human cases must be strengthened and special attention must be paid to the diagnosis of WNV disease in the population. The reporting system also collects positivities for WNV detected in donors of blood, blood derivatives and organs, tissues and cells, with a view to the application of specific preventive measures and any febrile clinical forms found in the country.</p> <p>Human surveillance is coordinated at national level by the Istituto Superiore di Sanità (ISS) and the Ministry of Health, which transmits the data to the European Commission and the ECDC. The Regions, in full autonomy, define the regulatory-programmatic documents for epidemiological and laboratory surveillance on their territory and transmit the data to the ISS and the Ministry. The Department of Infectious Diseases of the Istituto Superiore di Sanità, with the coordination of Office V of the Directorate General for Prevention of the Ministry of Health and in collaboration with the Centre for the Study of Exotic Diseases (Cesme) of the Experimental Zooprophylactic Institute of Abruzzo and Molise 'Giuseppe Caporale' (IZS Teramo), publishes the data of the surveillance system in a periodical bulletin.</p> <p>In order to inform citizens and make the collected data available, which is only useful for communication and information purposes, the following information is made available under the CC-BY-4.0 licence</p> <ul> <li>National evolution data</li> <li>Regional data</li> <li>Provincial data</li> <li>Summary bulletins</li> </ul> <p> </p> <p>The complete list of bulletins is available at the following link: <a href="https://www.epicentro.iss.it/westnile/bollettino">https://www.epicentro.iss.it/westnile/bollettino</a></p>
Open source code for Brain-inspired bodily self-perception model for robot rubber hand illusion
<p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi">RHI</a></p> <p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi_matlab">RHI_Matlab</a></p> <p>This file is the open-source code for 'Brain-inspired bodily self-perception model for robot rubber hand illusion', mainly used in simulation environments, and can reproduce various rubber hand illusion experiments.</p> <p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi_braincog">RHI_BrainCog</a></p> <p>We are building an open source spiking neural network based brain-inspired cognitive intelligence engine for Brain-inspired Artificial Intelligence and brain simulation. Therefore, we also implemented the core Proprioceptive drift experiment of the rubber hand illusion experiment using Braincog.</p> <p><a href="https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/Embodied_Cognition/RHI">The open source code built by BrainCog</a></p> <p>BrainCog provides essential and fundamental components to model biological and artificial intelligence. The current version of BrainCog contains at least 18 functional spiking neural network algorithms (including but not limited to perception and learning, decision making, knowledge representation and reasoning, motor control, social cognition, etc.) built based on BrainCog infrastructures, and BrainCog also provide brain simulations to drosophila, rodent, monkey, and human brains at multiple scales based on spiking neural networks at multiple scales.</p>
Supplementary material for the article entitled "Socioeconomic Analysis of the Proposed Opening of the Brazilian energy Market Through the Combination of Forecast Method with the Optimized Tariff Model"
<p>This supplementary material includes the input data used for the simulations conducted in the article.</p>
Turban (Open)
A turban is a type of headwear based on cloth winding. Featuring many variations, it is worn as customary headwear by people of various cultures. Communities with prominent turban-wearing traditions can be found in the Indian subcontinent, Southeast Asia, the Arabian Peninsula, the Middle East, Central Asia, North Africa, West Africa, and East Africa. Wearing turbans is common among Sikhs, including women. The headgear also serves as a religious observance, including among Shia Muslims, who regard turban-wearing as Sunnah fucadahass (confirmed tradition). The turban is also the traditional headdress of Sufi scholars. Additionally, turbans have often been worn by nobility, regardless of religious background. They are also sometimes donned to protect hair or as a headwrap for women following cancer treatments. *1 model (medium poly) with materials. Source: Objaverse 1.0 / Sketchfab
Shinique Smith - Open Word Lattice
Ink, acrylic, fabric and paper collage, and found objects on cut wood panel 2014, this 3D scan 2014 Scanned at: Museum of Fine Arts, Boston Source: Objaverse 1.0 / Sketchfab
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.