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3,688 results for “Computer”
Reconstructing illusory camouflage patterns on moth wings using computer vision - Datas and codes
<p>This repository contains the data, codes and pre-trained weights for the experiments in our paper "Reconstructing illusory camouflage patterns on moth wings using computer vision", accepted for publication in the Journal of The Royal Society Interface.</p> <p>The images, in photos.zip, are available under CC-BY-SA 4.0 International license.</p> <p>The c++ codes, available in codes_closed_forms.zip, are available under a GPL 3.0 license.</p> <p>The monocular depth reconstruction toolbox we used to test different deep learning models for monocular reconstruction is available under the Apache 2.0 software license.</p>
Testing nectar price effect on bumblebee feeding by automatized computer-controlled laboratory platform
Open the record for dataset details and reuse information.
SAUUHUPP Based Innovations in Network Computing and Cybersecurity
<p>Dear Zenodo Visitor,</p> <p> </p> <p>Welcome to the SAUUHUPP Innovations in Network Computing and Cybersecurity repository. This repository is dedicated to pioneering technologies grounded in the SAUUHUPP framework, a transformative approach for enhancing adaptability, security, and efficiency in network computing and cybersecurity.</p> <p> </p> <p>The innovations presented here explore how SAUUHUPP principles—focusing on scalable adaptability, universal harmony, and purposeful patterning—can be applied to the design and optimization of advanced networking and cybersecurity solutions. These solutions include dynamic, decentralized protocols for robust network connectivity, story encryption as an enhanced data protection technique, and VPNs that utilize the SAUUHUPP model to provide secure, context-aware communication channels.</p> <p> </p> <p>Within this repository, you’ll find technical papers, prototype designs, and implementation guides for:</p> <p> </p> <p>• Adaptive Wireless Mesh Protocol (AWMP), a scalable, energy-efficient alternative to Ethernet that combines Software-Defined Radio (SDR) and decentralized mesh networking.</p> <p>• Story Encryption methods, which transform data into narrative-driven encryption models, leveraging SAUUHUPP to integrate story logic, making unauthorized decryption significantly more complex and intuitive to monitor.</p> <p>• SAUUHUPP-based VPNs that adapt to network conditions and user contexts to provide seamless, resilient, and secure access across diverse environments, improving upon traditional VPN structures.</p> <p> </p> <p>We hope these resources inspire new directions in network computing and security. Whether you’re a developer, researcher, or enthusiast, we invite you to explore, contribute, and collaborate with us as we continue advancing these technologies.</p> <p> </p> <p>Thank you for your interest and support in pushing the boundaries of network computing with SAUUHUPP. Together, we aim to create adaptable, secure, and universally harmonious systems for the next era of technology.</p> <p> </p> <p>Warm regards,</p> <p>Prudencio L. Mendez</p> <p> SAUUHUPP Innovations Team</p>
Efficient Implementation of a Novel Decomposition Approach for the Hazmat Network Design Problem with Capacity Constraints in Java Including Computational Results
<p>Supplementary material for the Publication "Solving Multi-Follower Mixed-Integer Bilevel Problems with Binary Linking Variables"</p>
Chinese Reference Population: open-source age-dependent computational phantoms of reference Chinese population
<p>The<strong> Chinese Reference Population (CRP)</strong> phantoms dataset encompass <strong>30 phantoms</strong> available in both voxel and NURBS formats, with age in 0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 18 years and adult male and female, as well as 4 pregnant women and fetus in early pregnancy, first trimester, second trimester and third trimester.</p> <ul> <li><strong>Voxelized phantoms</strong> are accessible in NII format :<strong> <em>"XXX.nii", which could be opened in AMIDE software.</em></strong></li> <li>Excel file<strong> </strong>containing<strong> organ masses and other descriptive information </strong>:<strong> <em>"CRP_descriptive_Info.xlsx"</em></strong></li> <li>In the application of F18−FDG dose calculation, <strong>organ absorbed doses per unit activity administered </strong>is provided in an Excel file :<strong> <em>"Application_F18-FDG.xlsx"</em></strong></li> </ul> <p>All data are stored on Zenodo and can be publicly accessed.</p>
FractiScope: Unlocking Hidden Patterns in a Networked Fractal Computing AI Universe
<p>This Zenodo record serves as a central repository for all research, tools, and insights related to FractiScope, a groundbreaking AI-driven fractal intelligence scope powered by ChatGPT-4o and Novelty 1.0, and rooted in the SAUUHUPP framework. FractiScope is designed to uncover hidden fractal patterns and interconnected dynamics across science, technology, art, and creativity, framing these discoveries within the Networked Fractal Computing AI Universe.</p> <p> </p> <p>The record includes:</p> <p>• Foundational papers on SAUUHUPP, Novelty 1.0, and Unipixels.</p> <p>• Applications of FractiScope across disciplines such as chemistry, physics, genetics, language, and cosmology.</p> <p>• Case studies, white papers, and user guides detailing the use of FractiScope in research and innovation.</p> <p>• Resources for researchers, creatives, and visionaries to explore how fractal intelligence can revolutionize their work.</p> <p> </p> <p>This collection aims to foster collaboration, inspire new ideas, and advance the understanding of fractal intelligence and its implications for society.</p>
Data for the paper "Computing MHD equilibria of stellarators with a flexible coordinate frame"
<p>Data for the revised paper "<span>Computing MHD equilibria of stellarators with a </span><span>flexible coordinate frame"</span></p> <p>Thetitle has changed from the submission title: "A generalized Frenet frame for computing MHD equilibria in stellarators"</p> <p>We provide the input and output files for all GVEC simluations presented at the "JOINT VARENNA - LAUSANNE INTERNATIONAL WORKSHOP: THEORY OF FUSION PLASMAS, 2024" and to be published in PPCF.</p> <p>An ipython script that generates the postprocessing /plots is also provided.</p> <p>New content computing the frame from a boundary surface obtained from quasr is now also part of this compilation.</p> <p>See the README.md file for details.</p>
Computational Design Dataset
<p><strong>Computational Design Dataset</strong><br> This dataset contains data and scripts for the book <em>Computational Design for Landscape Architects</em>. Code includes Python scripts and Grasshopper definitions. This dataset includes laser scanned plants, lidar and raster data for Governor's Island, New York City, USA, and lidar and raster data for White Sands National Monument, New Mexico, USA. The CRS for the Governor's Island data is NAD83 / New York Long Island (ftUS) with the EPSG code 2263. The CRS for the White Sands data is NAD83 / UTM Zone 13N with EPSG code 26913. </p> <p><strong>Data Sources</strong></p> <ul> <li><a href="https://orthos.dhses.ny.gov">https://orthos.dhses.ny.gov</a></li> <li><a href="https://data.cityofnewyork.us">https://data.cityofnewyork.us</a></li> <li><a href="https://opentopography.org">https://opentopography.org</a></li> <li><a href="https://xyz.cct.lsu.edu">https://xyz.cct.lsu.edu</a></li> </ul> <p><strong>License</strong><br> The data in this dataset is licensed under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0 Universal Public Domain Dedication</a>, while the code in this dataset is licensed under <a href="https://opensource.org/license/mit/">The MIT License</a> by Brendan Harmon.<br> </p>
Automating Computational Chemistry in Multiscale Catalysis Electronic Data Compendium
<p>Electronic data compendium containing supplemental datasets and figures, primarily detailing lateral interactions and adlayer properties at catalytic surfaces.</p> <p>Part of the physical print of the thesis "Automating Computational Chemistry in Multiscale Catalysis" by B. Klumpers.</p>
Computer code accompanying Schraivogel, D. et al. "High-speed fluorescence image-enabled cell sorting" Science, 2022. doi: 10.1126/science.abj3013
<p>Computer code accompanying Schraivogel et al. "High-speed fluorescence image-enabled cell sorting". Details are provided in the manuscript's data and materials availability section and table 3.</p> <p> </p> <p>We provide three directories:</p> <p>(1) R code to reproduce figures (ICS2021_0.1.0.tar.gz)</p> <p>(2) Python code to reproduce figures (ICS_Fiji_Plugin.zip)</p> <p>(3) Code for ICS/CellView Fiji plugins (ICSPython.zip)</p> <p> </p> <p>Code for (1) and (3) has also been shared via Github:</p> <p>https://github.com/benediktrauscher/ICS</p> <p>https://github.com/embl-cba/ICS</p> <p> </p> <p>We recommend downloading the ICS Fiji plugins via Github or to install them using the Fiji update site to ensure you're using the most recent version.</p>
Personal Online Dosimetry Using Computational Methods: The PODIUM Project and the Future of Active Dosimetry
<p>Individual monitoring of workers exposed to external ionizing radiation is essential to allow application of the ALARA principle and follow up of the official dose limits. However, large uncertainties still exist in personal dosimetry. Also, many practical problems exist for personal dosimetry, with many dosemeters getting lost and the reluctance of many workers to wear one or more dosemeters.</p> <p>Most legal dosimetry is done with passive dosemeters, which are analyzed after the wearing period in an accredited lab. Active dosemeters are also widely used, although mostly only for ALARA purposes or for specific exposure situations. Although the active dosemeters are dosimetrically and technically at least equivalent to passive dosemeters, their higher cost limits their use as only legal dosemeters.</p> <p>In an attempt of reinventing dosimetry by using the modern evolutions in simulations, artificial intelligence and computer vision, the PODIUM project was set up. PODIUM was a short feasibility project, funded by the EC CONCERT programme.</p> <p>The objective of the PODIUM project was to improve personal dosimetry by an innovative approach: the development of an online dosimetry application based on computer simulations without the use of physical dosemeters. Operational quantities, protection quantities and radiosensitive organ doses (e.g. eye lens, brain, heart, extremities) can be calculated based on the use of modern technology such as personal tracking devices, flexible individualized phantoms and scanning of geometry set-up. When combined with fast simulation codes, the aim was to perform personal dosimetry in real-time.</p> <p>We applied and validated the methodology for two situations where improvements in dosimetry are urgently needed: neutron workplaces and interventional radiology. Several validation and test measurements were done in different hospitals, and in 2 workplace fields with significant neutron exposure. Personal doses could be calculated within acceptable simulation times, just based on captured movements of the workers and information of the radiation fields. These doses agreed with the results from physical dosemeters within the standard uncertainties that are accepted in personal dosimetry.</p> <p>This PODIUM dosimetry method can also be used to visualize the radiation in near real time. This will increase awareness of radiation protection among workers and will improve the application of the ALARA principle, and it can also be used in training modules. The use of neural networks and big data will help in further reducing simulation time, making real time simulations and dosimetry without physical dosemeters possible in the near future.</p>
Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks
<p>During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer-aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle-to-tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. The average testing accuracy in tissue classification was 98.53±0.39%, and the average testing relative error in distance estimation reached 4.42±0.56% (36.09±4.92 μm).</p> <p>The dataset is split into two parts:<br> (1) <strong>Classification</strong>. The zip file <em>veress_classification_raw_images.zip</em> contains 40K images from 8 swine samples where there are 1K images per layer (skin, fat, muscle, abdominal space, and small intestine)<br> (2) <strong>Regression</strong>. The zip file <em>veress_regression_raw_images.zip</em><strong> </strong>contains 8K images of the abdominal space from the same 8 swine samples, and the ground truth distance labels for each sample are found in the Excel files <em>S[1-8]_distance_measurement_20210803.xlsx.</em></p>
A Bibliometrics Analysis of Australian Computing Education Conference Proceedings
<p>The bib file includes the bib records of all research publications of Australasian Computing Education Conference prior to 2022.</p>
Data of "Using current research information systems to investigate data acquisition and data sharing practices of computer scientists"
<p>This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data.</p> <p>Without sufficient information about researchers’ data sharing, there is a risk of mismatching FAIR data service efforts with the needs of researchers. This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data. The advancement of FAIR data would benefit from novel methodologies that reliably examine data sharing at the level of multidisciplinary research organisations. Studies that use CRIS publication data to elicit insight into researchers’ data sharing may therefore be a valuable addition to the current interview and questionnaire methodologies.</p> <p><strong>Data was collected from the following sources:</strong></p> <p>All journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a coding framework was developed to capture the key elements of acquiring and sharing research data. Article DOIs are included in the research data.</p> <p>The scientific journal articles and theirs DOIs are used in this study for the purpose of academic expression.</p> <p>The raw data is compiled into a single CSV file. Rows represent specific articles and columns are the values of the data points described below. Author names and affiliations were not collected and are not included in the data set. </p> <p> </p> <p>The following data points were used in the analysis:</p> <p><strong>Data points</strong></p> <ul> <li><strong>Main study types</strong></li> <li>Literature-based study (e.g. literature reviews, archive studies, studies of social media)</li> <li>yes/no</li> <li>Novel computational methods (e.g. algorithms, simulations, software)</li> <li>yes/no</li> <li>Interaction studies (e.g, interviews, surveys, tasks, ethnography)</li> <li>yes/no</li> <li>Intervention studies (e.g., EEG, MRI, clinical trials)</li> <li>yes/no</li> <li>Measurement studies (e.g. astronomy, weather, acoustics, chemistry)</li> <li>yes/no</li> <li>Life sciences (e.g. “omics”, ecology)</li> <li>yes/no</li> <li><strong>Data acquisition</strong></li> <li>Article presents a data availability statement</li> <li>yes/no</li> <li>Article does not utilise data</li> <li>yes/no</li> <li>Original data was collected</li> <li>yes/no</li> <li>Open data from prior studies were used</li> <li>yes/no</li> <li>Open data from public authorities, companies, universities and associations</li> <li>yes/no</li> <li><strong>Data sharing</strong></li> <li>Article does not use original data</li> <li>yes/no</li> <li>Data of the article is not available for reuse</li> <li>yes/no</li> <li>Article used openly available data</li> <li>yes/no</li> <li>Authors agree to share their data to interested readers</li> <li>yes/no</li> <li>Article shared data (or part of) as supplementary material</li> <li>yes/no</li> <li>Article shared data (or part of) via open deposition</li> <li>yes/no</li> <li>Article deposited code or used open code</li> <li>yes/no</li> </ul>
Multi-Scale Computational Screening to Accelerate Discovery of IL/COF Composites for Flue Gas Separation
<p>Covalent organic frameworks (COFs) have emerged as novel adsorbents and membranes for gas separation. Incorporation of ionic liquids (ILs) into COFs is important to exceed the current performance limits of COFs. However, synthesis and testing of a nearly unlimited number of IL/COF combinations are simply impractical. Herein, we used a multi-scale computational screening approach combining COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, Grand Canonical Monte Carlo (GCMC), molecular dynamics (MD) simulations, and density functional theory (DFT) calculations to unlock both the adsorption- and membrane-based CO<sub>2</sub>/N<sub>2 </sub>separation performances of IL/COF composites. Several adsorbent and membrane performance assessment metrics including selectivity, working capacity, regenerability, adsorbent performance score, and permeability were computed. Our results revealed that IL-incorporation into COFs significantly improved CO<sub>2</sub>/N<sub>2</sub> adsorption selectivities (from 12 to 26) and adsorbent performance scores (from 3.7 to 12 mol/kg). By performing DFT calculations, the nature of the interactions between CO<sub>2</sub>, N<sub>2</sub>, COFs and their IL-incorporated composites were evaluated. The high CO<sub>2</sub> selectivity of IL/COF composites was attributed to the cooperative intermolecular effects induced by the COF and the IL. Finally, IL/COF membranes were studied, and results showed that they achieve significantly higher CO<sub>2</sub> permeabilities (2.4 10<sup>4</sup>-9.4 10<sup>5</sup> Barrer) than polymeric and zeolite membranes and comparable selectivities (up to 15.7), which hold great promise to replace conventional materials in membrane-based flue gas separation applications. Our results will be useful in accelerating experimental efforts to design new IL/COF composites that can achieve high-performance CO<sub>2</sub> separation.</p>
Computation Time, Searched Nodes and Path Length for Navigation Using Improved A-Star, Directional ORCA and FLC-ORCA
<p>Naivation simulation using Improved A*, Directional ORCA and Novel FLC-ORCA method. The computational times, searched nodes and path length are recorded for comparison. A simulation study for Robotic Navigation Aid (RNA)</p>
Dataset for quantifying avian inertial properties using calibrated computed tomography
<p>Estimating centre of mass and mass moments of inertia is an important aspect of many studies in biomechanics. Characterising these parameters accurately in three dimensions is challenging with traditional methods requiring dissection or suspension of cadavers. Here, we present a method to quantify the three-dimensional centre of mass and inertia tensor of birds of prey using calibrated computed-tomography (CT) scans. The technique was validated using several independent methods, providing body segment mass estimates within approximately 1% of physical dissection measurements and moment of inertia measurements with a 0.993 R<sup>2</sup> correlation with conventional trifilar pendulum measurements. Calibrated CT offers a relatively straightforward, non-destructive approach that yields highly detailed mass distribution data that can be used for three-dimensional dynamics modelling in biomechanics. Although demonstrated here with birds, this approach should work equally well with any animal or appendage capable of being CT scanned.</p>
Comparative analysis of patient-specific aortic dissections through computational fluid dynamics suggests increased likelihood of degeneration in partially thrombosed aorta
<p>Aortic dissection is a life-threatening cardiovascular disease associated with high rates of morbidity and mortality, especially in medically under-served communities. It compromises the hemodynamics of the arteries that originate from the aorta, and its outcomes include visceral ischemia and aortic rupture in the acute phase and aneurysmatic degeneration in the chronic phase. Understanding patients’ blood flow patterns is pivotal for non-invasive evidence-based treatment as they greatly influence both the disease onset and its outcome. In this paper, we combine diagnostic imaging techniques and computational fluid dynamics to analyze the flow patterns of three aorta dissections (fully perfused, partially thrombosed, and fully thrombosed), and compare them to a healthy aorta. Besides flow kinematics, we focus on time averaged wall shear stress and oscillatory shear index that are recognized risk factors for aneurysm and rupture. Our analysis shows that partially thrombosed dissection is the most prone to false lumen degeneration. In all dissections, the arteries connected to the false lumen are generally poorly supplied with blood. Further, both true and false lumens present higher turbulence levels than the healthy aorta, and critical stagnation points. Mesh sensitivity and a thorough comparison against literature data together support the methodology robustness.</p>
Transprecision Computing (Micro-benchmarks)
<p>The micro-benchmarks involve multiple Floating-Point variables whose precision, that is the number of bits used for the mantissa and the exponent, can be tuned using the FlexFloat SW library (see "A Transprecision Floating-Point Platform for Ultra-Low Power Computing", by G. Tagliavini et al., in 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) 2018 Mar 19 (pp. 1051-1056). IEEE). </p> <p>The data set comprises 7 different micro-benchmarks run under different precision configurations; this data set was created during the 2020 FET project OPRECOMP (g.a. 732631).</p> <p>The data set is fully described in the research paper: "Combining learning and optimization for transprecision computing", by authors Borghesi A, Tagliavini G, Lombardi M, Benini L, Milano M. In Proceedings of the 17th ACM International Conference on Computing Frontiers 2020 May 11 (pp. 10-18). See here the full paper: https://arxiv.org/pdf/2002.10890.pdf</p>
Patient breast MRI images and computational breast phantom data for research in patient-derived realistic breast modelling
<p>The data is comprised of two parts: 1) patient DICOM MRI images and 2) 3D matrix of a computational breast phantom.</p> <ol> <li>The DICOM images are anonymised patient breast MRI images of a female patient diagnosed with invasive ductal carcinoma. The obtaining of the patients’ DICOM images is approved by the Ethics Committee of Medical University of Varna. The acquisition was performed with GE Signa HDxt MRI scanner. The images are from a T1-weigthed Axial multi-phase VIBRANT (3-phase) sequence and with voxel size of 0.7 mm x 0.7 mm x 0.8 mm. Contrast agent is present. The image set can be opened with any standard DICOM reader.</li> <li>The computational breast phantom is derived from the above mentioned dataset. The phantom is in the form of a 3D matrix saved as a MATLAB data file (.mat file). Each voxel has an assigned Hounsfield Unit value depending on its classification: air = 0, adipose tissue = -152, glandular tissue = 42, tumour = 64, skin = 108. The data file can be opened with MATLAB or Octave.</li> </ol>
ScienceDex guides
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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.