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7,507 results for “generate”
Automated Programming Exercise Generation in the Era of Large Language Models
<p>Lecturers are increasingly attempting to use large language models (LLMs) to simplify and make the creation of exercises for students more efficient. Efforts are also being made to automate the exercise creation process in software engineering (SE) education. This study explores the use of advanced LLMs, including GPT-4 and LaMDA, for automated programming exercise creation in higher education and compares the results with related work using GPT-3.5-turbo. Utilizing applications such as ChatGPT, Bing AI Chat, and Google Bard, we identify LLMs capable of initiating different exercise designs. However, manual refinement is crucial for accuracy. Common error patterns across LLMs highlight challenges in complex programming concepts, while specific strengths in various topics showcase model distinctions. This research underscores LLMs' value in exercise generation, emphasizing the critical role of human supervision in refining these processes. Our concise insights cater to educators, practitioners, and other researchers seeking to enhance SE education through LLM applications.</p>
ChatGPT's Aptitude in Utilizing UML Diagrams for Software Engineering Exercise Generation
<p>The integration of Artificial Intelligence (AI) technologies into educational settings has paved the way for innovative teaching and learning approaches. In Software Engineering (SE) education, using Unified Modeling Language (UML) diagrams is a fundamental teaching element for understanding complex software systems. This research addresses the ability of ChatGPT to utilize UML class and sequence diagrams for creating SE modeling exercises. We use ChatGPT to generate exercises based on the information from uploaded UML diagrams by analyzing textual UML representations such as Mermaid and graphical diagrams. The research explores ChatGPT's ability to synthesize UML-specific information from class and sequence diagrams, enabling the generation of various exercises tailored to strengthen conceptual understanding and practical application. Furthermore, we investigate generating graphical UML class and sequence diagrams based on natural language as input. By bridging the gap between AI-driven natural language understanding and the comprehension of UML diagrams, this study highlights the potential of ChatGPT to improve SE education. Our concise findings address educators, practitioners, and other researchers engaged in the field of SE education with a special focus on UML.</p>
ERA5 overviews complementing temperature measurements of ground-based Rayleigh lidars for the investigation of gravity waves generated by moving sources
<p>ERA5 overviews to associate stratospheric gravity waves in temperature measurements from vertically staring (zenith-pointing) ground-based Rayleigh lidars with atmospheric processes. Animations are for a virtual lidar location over the Southern Ocean during research flight RF25 of the DEEPWAVE campaign (July 17 to 19, 2014) and for the location of the COmpact Rayleigh Autonomous Lidar (CORAL) in the lee of the southern Andes. Here, the first overview is for the CORAL measurement from June 22 to 23, 2018. The second one is for the nightly measurements between August 7 and 9, 2020.</p> <p>(a) and (b) emulate the measurement of a vertically staring ground-based lidar and show temperature perturbations after subtracting a temporal running mean of 12h (a) and the mean absolute temperature profile (b). Panels (c) and (d) are vertical sections of stratospheric 𝑇′ along sectors of the latitude circle (c) and meridian (d) of the virtual lidar location. (e) and (f) are corresponding vertical sections of thermal stability 𝑁2 (10−4 s−2, color-coded), potential temperature (K, thin grey lines), and potential vorticity (1, 2, 4 PVU: black, 2 PVU: green). Thin black lines in the vertical sections are zonal (d, f) and meridional (c, e) wind components (solid: positive, dashed: negative). Panel (g) is a horizontal section of the height of the 2 PVU surface (km, color-coded), geopotential height (m, solid lines) and wind barbs at the 850 hPa level. The black vertical line in (a) marks the time for (c)-(g) and dashed lines in (c)-(g) highlight the location of the virtual lidar and profiles in (a) and (b).</p> <p>The provided NETCDF files contain the corresponding CORAL temperature measurements for the two periods with CORAL measurements in 2018 and 2020.</p>
Generation of a network slicing dataset: the foundations for AI-based B5G resource management
<p><span>This paper introduces a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and other data-based resource management and network performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the RAN infrastructure.</span></p> <p><span>Each sample consists of pairs of (network scenario, performance metrics). The network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of realistic scenarios without violating SLAs.</span></p> <p><span>This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.</span></p>
Dataset generated and/or analyzed in the paper "Volcanic unrest after the 2021 eruption of La Palma"
<p>Data generated and/or analyzed in the paper "Volcanic unrest after the 2021 eruption of La Palma" by Jose Fernandez, Joaquin Escayo, Juan F. Prieto, Kristy F. Tiampo, Antonio G. Camacho, and Eumenio Ancochea, submitted to Geophysical Research Letters. Also readme files are included describing the data files.</p>
Quantitative results of the analysis of human native and bioengineered tissues corresponding to the work "Histological, histochemical and immunohistochemical characterization of NANOULCOR nanostructured fibrin-agarose human cornea substitutes generated by tissue engineering"
<p>Dataset containing the quantitative results of the histochemical and immunohistochemical analysis of the following human tissues:</p> <ul> <li>Control native cornea (CTR-C)</li> <li>Control native limbus (CTR-L)</li> <li>Artificial cornea generated by tissue engineering (HAC)</li> </ul> <p>Each tissue type was subjected to histochemical and immunohistochemical analyses and results were quantified using ImageJ software to determine average intensities and area fractions corresponding to positive staining signal for each marker.</p>
Experimental data from laboratory studies on the generation and evolution of internal tides under various Coriolis parameters
<p>The dataset includes experimental data from laboratory studies on the generation and evolution of internal tides under various Coriolis parameters.</p> <p>"uu" and "uh" are horizontal velocities. (unit: m/s)<br>"vv" and "vh" are vertical velocities. (unit: m/s)<br>"xx" and "yy" are the horizontal and vertical coordinates, respectively. (unit: m)</p> <p>The frequency of internal tide is 0.68 rad/s.<br>The Coriolis parameters are 0, 0.13, 0.17, 0.21, 0.25, 0.29, 0.335, 0.38, 0.42, 0.46, 0.54 rad/s for f00 to f26.<br>The time interval is 0.2s.</p>
A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics
<p>This dataset includes measured photovoltaic (PV) power generation data and on-site weather data collected from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using Brick schema was developed, which simplifies the data comprehension and the development of smart analytics applications. The detailed Brick model is stored in the .ttl file format, which can be accessed for retrieving metadata through the use of SPARQL queries.This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems.</p>
Time dependent interaction modification generated from plant-soil feedback
<p>Pairwise interactions between species can be modified by other community members, leading to emergent dynamics contingent on community composition. Despite the prevalence of such higher-order interactions, little is known about how they are linked to the timing and order of species' arrival. We generate population dynamics from a mechanistic plant-soil feedback model, then apply a general theoretical framework to show that the modification of a pairwise interaction by a third plant depends on its germination phenology. These time-dependent interaction modifications emerge from concurrent changes in plant and microbe populations and are strengthened by higher overlap between plants' associated microbiomes. The interaction between this overlap and the specificity of microbiomes further determines plant coexistence. Our framework is widely applicable to mechanisms in other systems from which similar time-dependent interaction modifications can emerge, highlighting the need to integrate temporal shifts of species interactions to predict the emergent dynamics of natural communities.</p>
Figure 3 in The effects of maize pollen on development and population growth potential of Amblyseius swirskii and Cydnoseius negevi (Acari: Phytoseiidae) in subsequent generations
Figure 3. Age-stage specific reproductive value (vxj) of Amblyseius swirskii and Cydnoseius negevi reared on pollen grains of maize for 11 generations.
Figure 2 in The effects of maize pollen on development and population growth potential of Amblyseius swirskii and Cydnoseius negevi (Acari: Phytoseiidae) in subsequent generations
Figure 2. Age-specific survival rate (lx), age-stage specific fecundity of female (fxj) and age-specific fecundity rate (mx) of Amblyseius swirskii and Cydnoseius negevi reared on pollen grains of maize for 11 generations.
Figure 1 in The effects of maize pollen on development and population growth potential of Amblyseius swirskii and Cydnoseius negevi (Acari: Phytoseiidae) in subsequent generations
Figure 1. Age-stage specific survival rate (sxj) of Amblyseius swirskii and Cydnoseius negevi reared on pollen grains of maize for 11 generations.
A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation
<h1>Dataset Description for "A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</h1> <p>This dataset accompanies the research paper titled <strong>"A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</strong>, currently under review for the AGU Journal JAMES. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/A-Robust-Generative-Adversarial-Network-Approach-for-Climate-Downscaling" target="_new">GitHub repository</a>.</p> <h2>Aims</h2> <p>Our study's overarching goal was to assess the effectiveness of Generative Adversarial Networks (GANs) in a climate downscaling context and is structured around two aims. The first aim of our study is to examine whether GANs can overcome several important limitations of regression-based climate downscaling algorithms (i.e. underestimating the magnitude of extreme events). The second and most important aim of our study is to assess the robustness GAN performance to different training hyperparameters. Our robustness assessment thoroughly scrutinizes GANs for their application in climate downscaling contexts, ensuring that they can learn and capture regional climate processes</p> <h2>Geographic Focus</h2> <p>Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).</p> <p> </p> <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> <p>The training data used in this study (for our RCM emulator) only spans the historical period of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables. </p> <ul> <li> <p><strong>Resolution:</strong> The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation. </p> </li> <li> <p><strong>Period Coverage:</strong></p> <ul> <li>Training Data: 1960-2014</li> <li>Validation Data: 1986-2005</li> </ul> </li> <li> <p><strong>Models:</strong></p> <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM</li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> <p><strong>Training Data:</strong></p> <ul> <li>Target/Ground Truth (Y): <code>predictor_ACCESS-CM2_hist.nc</code></li> <li>Predictor (X): <code>pr_ACCESS-CM2_hist.nc</code></li> </ul> </li> <li> <p><strong>Evaluation Data:</strong></p> <ul> <li><strong>NorESM2-MM:</strong> <ul> <li>Target (Y): <code>NorESM2-MM_historical_precip_compressed.nc</code></li> <li>Predictor (X): <code>NorESM2-MM_histupdated_compressed.nc</code></li> </ul> </li> <li><strong>EC-Earth3:</strong> <ul> <li>Target: <code>EC-Earth3_historical_precip_compressed.nc</code></li> <li>Predictor: <code>EC-Earth3_histupdated_compressed.nc</code></li> </ul> </li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> <p><strong>Regional Climate Model</strong>, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>.</p> </li> <li> <p><strong>Predictor and Target Variables:</strong> Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository.</p> </li> <li> <p><strong>Training Framework:</strong> Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a></p> </li> </ul>
From Generalist to Specialist: Incorporating Domain-Knowledge into Flamingo for Chest X-Ray Report Generation
<p>This subset of the MIMIC-CXR split file contains the study identifiers and paths to the chest X-ray images used for training, validation and testing of all models presented in the paper: "From Generalist to Specialist: Incorporating Domain-Knowledge into Flamingo for Chest X-Ray Report Generation".</p>
The NGDEEP NIRIS calibration files for 'The Next Generation Deep Extragalactic Exploratory Public Near-Infrared Slitless Survey Epoch 1 (NGDEEP-NISS1): Extra-Galactic Star-formation and Active Galactic Nuclei at 0.5 < z < 3.6
<p>GRISMCONF configurations files used in Pirzkal et al. 2024. These contain the full field calibrated solution for the dispersion solution, trace as well as wavelength calibration. They provide a mean to extract NIRISS WFSS spectra obtained using the F115W, F150W, or F200W to within an acccuracy better than 0.25 pixel over most of the field of view. Wavelength calibration of both grism was verified to be accurate to within 15A over most of the field of view. Details can be found in Appendix A of Pirzkal et al. 2024.</p>
Code for generating figures and analyzing amplicon sequencing of human mRNA and reporter mRNA targeted with type III-A CRISPR complex from Streptococcus thermophiles
<p>This dataset contains code for analyzing amplicon sequencing data and generating figures in the manuscript by Anna Nemudraia, Artem Nemudryi, and Blake Wiedenheft (2024), "Repair of CRISPR-guided RNA breaks enables site-specific RNA excision in human cells." </p> <p>Amplicon sequencing data has been deposited to NCBI Sequence Read Archive (SRA) under BioProject PRJNA1099688. The description of read files deposited to SRA can be found in the spreadsheet ./code_for_sequencing_data_analysis/SRA_read_files_description.xlsx</p> <p>The code for analyzing amplicon sequencing data can be found in the archive "code_for_sequencing_data_analysis.tar.gz." Output files from this analysis were used to generate figures. Figures were generated using the ggplot2 package in R and finalized in CorelDRAW.</p> <p>Code for generating figures can be found in the archive "code_for_generating_figures.tar.gz". </p> <p>Any questions or requests regarding the data or the code should be addressed to Dr. Artem Nemudryi at artem.nemudryi@gmail.com.</p>
Data generated for the publication: Keeping it in the family: Using protein family templates to rescue poor AlphaFold models unliked
<p>Data and manuscript of:</p> <p>Keeping it in the family: Using protein family templates to rescue low confidence AlphaFold2 models</p> <p>Francesco Costa1, Matthias Blum1 and Alex Bateman1</p> <ol> <li>European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton. CB10 1SD. UK</li> </ol> <ul> <li>results contains the workflow results;</li> <li>AF2_seed contains results of the comparison with AF2 run with multiple seeds;</li> </ul>
Generation of Ammonia in a Pulsed Hollow Cathode Discharge
<p>A hollow cathode discharge with a copper nickel cathode (Cu50Ni50) was operated in an Ar/H2/N2 gas mixture. Optical emission spectroscopy revealed the formation of NH radicals, which serve as precursors for NH3 formation. Ion mass spectrometry showed the formation of NH3+ and NH4+ ions indicating NH3 formation. Gas samples taken at the exhaust of the vacuum system were analyzed by Fourier transform infrared spectroscopy. Clear evidence for NH3 formation was obtained from these measurements</p>
Next-Generation Sequencing Dataset of Adult Pilocytic Astrocytomas
<p>Next-Generation Sequencing Dataset of Adult Pilocytic Astrocytomas</p> <p>Pilocytic astrocytoma (PA) is a benign grade 1 glioma according to the World Health Organization (WHO), common in children but rare in adults, where it may have a worse prognosis. Pediatric PA is usually associated with dysregulation of the MAPK pathway, often involving BRAF alterations such as the KIAA1549::BRAF (K-B) fusion or the V600E mutation. This dataset contains molecular data of 28 cases of adult PA obtained by using gene-targeted next-generation sequencing (NGS).</p>
The experiment data for Photonics Diffraction Generator
<p>Three h5 compiled files are represent following experimental data:<br>cas_opt: The experimentally generated handwritten digit from a cascaded PDG<br>par_opt: The experimentally generated handwritten digit from a parallelPDG<br>speckle_opt: The experimentally collected speckles</p>
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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.