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2,453 results for “Architecture”
Data for: Landscape diversity promotes stable food web architectures in large rivers
<p>Uncovering relationships between landscape diversity and species interactions is crucial for predicting how ongoing land-use change and homogenization will impact the stability and persistence of communities. However, such connections have rarely been quantified in nature. We coupled high-resolution river sonar imaging with annualized energetic food webs to quantify relationships between habitat diversity, energy flux, and trophic interaction strengths in large-river food web modules that support the endangered Pallid Sturgeon. Our results demonstrate a clear relationship between habitat diversity and species interaction strengths, with more diverse foraging landscapes containing higher production of prey and a greater proportion of weak and potentially stabilizing interactions. Additionally, rare patches of large and relatively stable river sediments intensified these effects and further reduced interaction strengths by increasing prey diversity. Our findings highlight the importance of landscape characteristics in promoting stabilizing food-web architectures and provide direct relevance for future management of imperiled species in a simplified and rapidly changing world.</p>
GNN Models and results for the paper "Band-gap regression with architecture-optimized message-passing neural networks"
<p>Contains files with model parameters for random search and reference models, as well as the converted AFLOW dataset, in graphs form. Corresponds to results in <a href="https://arxiv.org/pdf/2309.06348.pdf">https://arxiv.org/pdf/2309.06348.pdf</a>.</p> <p>Model predictions along with AUID identifiers are located in result_combined.zip, band gap (egap) and formation energy (ef) predictions are from the PaiNN ensemble, band gap classification is done by MPEU model.</p> <p>New results include PaiNN NAS models.</p> <p>Compatible source code can be found at <a href="https://github.com/tisabe/jraph_MPEU/tree/v1.0.0">jraph_MPEU GitHub repository</a>.</p>
Sensory-Informed Architectural Design Qualities in Autism
<p><span>This dataset provides a collection of design qualities for autism-friendly designs. The collected data relies on the current literature, including various guidelines and research papers.</span></p>
Dataset: an empirical study on architectural smells through a pipeline for continuous technical debt assessment
<h2><strong>Dataset of the study "An empirical study on architectural smells through a pipeline for continuous technical debt assessment"</strong></h2> <h3><strong>Abstract</strong></h3> <p>In recent years, researchers spent an increasing amount of effort investigating technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.</p> <p>However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.<br>In response to this concern, researchers strive to expand the scope of their studies by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.</p> <p>To address this issue, this paper presents an approach for tackling this problem and enabling researchers to study architectural smells, a well-known indicator of architectural technical debt, at a large scale. Specifically, we introduce a novel approach to a data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets with any static analysis tool.</p> <p>Finally, we use the data collected through the pipeline to study the correlation between architectural smells and logical coupling in order to understand how smells influence maintenance efforts.</p>
Probing Ion Channel Functional Architecture and Domain Recombination Compatibility by Massively Parallel Domain Insertion Profiling
<p>Supplementary Data for a large insertional profiling study described in Coyote-Maestas et al. (2021) Nature Communications.</p>
Reference data and analysis software for "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane"
<p>Reference data set for the single molecule co-tracking analysis presented in "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane". Corresponding author for further inquiries:</p> <p>Prof. Dr. Jacob Piehler</p> <p>University of Osnabrück, Department of Biology/Chemistry, Division of Biophysics, Barbarastr. 11, 49076 Osnabrück, Germany</p> <p>https://www.biophysik.uni-osnabrueck.de/</p>
Fig. 4 in Morphology and architecture of the threatened Florida palm Acoelorrhaphe wrightii (Arecaceae: Coryphoideae)
Fig. 4. - Two perpendicular diameters (diam. 1 and 2) for 31 genets of Acoelorrhaphe wrightii of different sizes at Fairchild Tropical Botanic Garden and Montgomery Botanical Center plants in Miami FL; data from plants measured in Nov. 2013.
Fig. 6 in Morphology and architecture of the threatened Florida palm Acoelorrhaphe wrightii (Arecaceae: Coryphoideae)
Fig. 6. - Exponential clonal growth model estimations for different growth rates (R), given different levels of reproduction (r) and survival rates (s) for clonal palm, Acoelorrhaphe wrightii. Model 1: r = 3, s = 0.3, R = 0.9. Model 2: r = 3, s = 0.5, R = 1.5. Model 3: r = 3, s = 0.8, R = 2.4. Model 4: r = 6, s = 0.3, R = 1.8. Model 5: r = 6, s = 1.5, R = 3.0. Model 6: r = 6, s = 4.8, R = 4.8. Dashed line represents values from genets measured in the gardens. Selected model (Model 4) fits data to within 1 ramet.
Fig. 5 in Morphology and architecture of the threatened Florida palm Acoelorrhaphe wrightii (Arecaceae: Coryphoideae)
Fig. 5. - Architectural relationships in Acoelorrhaphe wrightii. A. Number of ramets vs. genet circumference; B. Number of tiers vs. circumference; C. Number of tiers vs. number of ramets in 31 genets of A. wrightii in Fairchild Tropical Botanic Garden and Montgomery Botanical Center plants in Miami FL; data from plants growing in full sun and measured in Nov. 2013.
Fig. 3. - Acoelorrhaphe wrightii. A in Morphology and architecture of the threatened Florida palm Acoelorrhaphe wrightii (Arecaceae: Coryphoideae)
Fig. 3. - Acoelorrhaphe wrightii. A. Absence of the protoclone, which results in empty-centered ring of ramets; B. Basal node branching occurs when a basal axillary bud grows out to form a new ramet without any horizontal elongation; C. Rhizomatous branching occurs when a basal axillary bud grows out to form a new ramet through horizontal elongation before turning upward; D. Tiers are present in all observed A. wrightii individuals and decrease in height from inner to outer tiers. [Photos: S. Edelman]
Fig. 2 in Morphology and architecture of the threatened Florida palm Acoelorrhaphe wrightii (Arecaceae: Coryphoideae)
Fig. 2. - Acoelorrhaphe wrightii leaf production on ramets of different heights in Fairchild Tropical Botanic Garden and Montgomery Botanical Center plants in Miami FL, measured from Nov. 2012 through Dec. 2014. Data divided into leaves from establishing ramets (ramet height ≤ 0.3 m) and established ramets (ramet height> 0.3 m). Error bars = standard error.
Architectural Design Decisions for the Machine Learning Workflow: Dataset and Code
<p><strong>Title:</strong> Architectural Design Decisions for the Machine Learning Workflow: Dataset and Code</p> <p><strong>Authors:</strong> Stephen John Warnett; Uwe Zdun</p> <p><strong>About:</strong> This is the dataset and code artifact for the article entitled "Architectural Design Decisions for the Machine Learning Workflow".</p> <p><strong>Contents:</strong> The "_generated" directory contains the generated results, including latex files with tables for use in publications and the Architectural Design Decision model in textual and graphical form. "Generators" contains Python applications that can be run to generate the above. "Metamodels" contains a Python file with type definitions. "Sources_coding" contains our source codings and audit trail. "Add_models" contains the Python implementation of our model and source codings. Finally, "appendix" contains a detailed description of our research method.</p> <p><strong>Article Abstract: </strong>Bringing machine learning models to production is challenging as it is often fraught with uncertainty and confusion, partially due to the disparity between software engineering and machine learning practices, but also due to knowledge gaps on the level of the individual practitioner. We conducted a qualitative investigation into the architectural decisions faced by practitioners as documented in gray literature based on Straussian Grounded Theory and modeled current practices in machine learning. Our novel Architectural Design Decision model is based on current practitioner understanding of the topic and helps bridge the gap between science and practice, foster scientific understanding of the subject, and support practitioners via the integration and consolidation of the myriad decisions they face. We describe a subset of the Architectural Design Decisions that were modeled, discuss uses for the model, and outline areas in which further research may be pursued.</p> <p><strong>Objective:</strong> This article aims to study current practitioner understanding of architectural concepts associated with data processing, model building, and Automated Machine Learning (AutoML) within the context of the machine learning workflow.</p> <p><strong>Method:</strong> Applying Straussian Grounded Theory to gray literature sources containing practitioner views on machine learning practices, we studied methods and techniques currently applied by practitioners in the context of machine learning solution development and gained valuable insights into the software engineering and architectural state of the art as applied to ML.</p> <p><strong>Results:</strong> Our study resulted in a model of Architectural Design Decisions, practitioner practices, and decision drivers in the field of software engineering and software architecture for machine learning.</p> <p><strong>Conclusions:</strong> The resulting Architectural Design Decisions model can help researchers better understand practitioners' needs and the challenges they face, and guide their decisions based on existing practices. The study also opens new avenues for further research in the field, and the design guidance provided by our model can also help reduce design effort and risk. In future work, we plan on using our findings to provide automated design advice to machine learning engineers.</p>
LEXIS Platform Architecture Scheme
<p>This image contains current version of the <a href="https://lexis-project.eu/web/lexis-platform/">LEXIS platform architecture</a>, which has been created during the LEXIS project.</p> <p>The architecture is divided in three top-level layers:</p> <ol> <li> <p>The LEXIS Portal Layer, providing easy access to the LEXIS platform for Pilots and possible external users,</p> </li> <li> <p>The LEXIS Services Layer, running on top of the infrastructure layer. It includes federated security Infrastructure (Authentication & Authorization Infrastructure, AAI), data management (Data Distribution Infrastructure, DDI), and orchestration services (Orchestrator),</p> </li> <li> <p>The HPC/Cloud Infrastructure Layer, focusing on the interactions among HPC and Cloud hardware systems to provide the computing power and data storage space to the upper layers. It is implemented as a federation of multiple HPC providers and data centres.</p> </li> </ol> <p>Please refer to the project WP2 (co-design) deliverables at <a href="https://lexis-project.eu/web/outcomes/deliverables/">https://lexis-project.eu/web/outcomes/deliverables/</a> for more information.</p>
Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.
<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in PNML-file)</li> </ol>
Architectural Design Decisions for Machine Learning Deployment: Dataset and Code
<p><strong>Title:</strong> Architectural Design Decisions for Machine Learning Deployment: Dataset and Code</p> <p><strong>Authors:</strong> Stephen John Warnett; Uwe Zdun</p> <p><strong>About:</strong> This is the dataset and code artefact for the paper entitled "Architectural Design Decisions for Machine Learning Deployment".</p> <p><strong>Contents:</strong> The "_generated" directory contains the generated results, including latex files with tables for use in publications and the Architectural Design Decision model in textual and graphical form. "Generators" contains Python applications that can be run to generate the above. "Metamodels" contains a Python file with type definitions. "Sources_coding" contains our source codings and audit trail. "Add_models" contains the Python implementation of our model and source codings. Finally, "appendix" contains a detailed description of our research method.</p> <p><strong>Paper Abstract:</strong> Deploying machine learning models to production is challenging, partially due to the misalignment between software engineering and machine learning disciplines but also due to potential practitioner knowledge gaps. To reduce this gap and guide decision-making, we conducted a qualitative investigation into the technical challenges faced by practitioners based on studying the grey literature and applying the Straussian Grounded Theory research method. We modelled current practices in machine learning, resulting in a UML-based architectural design decision model based on current practitioner understanding of the domain and a subset of the decision space and identified seven architectural design decisions, various relations between them, twenty-six decision options and forty-four decision drivers in thirty-five sources. Our results intend to help bridge the gap between science and practice, increase understanding of how practitioners approach the deployment of their solutions, and support practitioners in their decision-making.</p> <p><strong>Objective:</strong> This paper aims to study current practitioner understanding of architectural concepts associated with machine learning deployment.</p> <p><strong>Method:</strong> Applying Straussian Grounded Theory to gray literature sources containing practitioner views on machine learning practices, we studied methods and techniques currently applied by practitioners in the context of machine learning solution development and gained valuable insights into the software engineering and architectural state of the art as applied to ML.</p> <p><strong>Results:</strong> Our study resulted in a model of Architectural Design Decisions, practitioner practices, and decision drivers in the field of software engineering and software architecture for machine learning.</p> <p><strong>Conclusions:</strong> The resulting Architectural Design Decisions model can help researchers better understand practitioners' needs and the challenges they face, and guide their decisions based on existing practices. The study also opens new avenues for further research in the field, and the design guidance provided by our model can also help reduce design effort and risk. In future work, we plan on using our findings to provide automated design advice to machine learning engineers.</p>
Dataset used by the paper MADE: Learning to Detect and Explain Chaos in Microservice Architectures
<p>Dataset used by the paper MADE: Learning to Detect and Explain Chaos in Microservice Architectures</p> <p>Include the raw dataset of 10 chaos</p>
IHTApark. Multi-detailed 3D architectural model for sound perception research in Virtual Reality
<p><strong>IHTApark – Multi-detailed 3D architecture model</strong></p> <p>This dataset describes visual and acoustic 3D architectural models of the park next to the IHTA.</p> <p>Institute of Hearing Technology and Acoustics (IHTA), RWTH Aachen, 52056 Aachen, Germany</p> <p>Files are stored in FBX format for geometry, JPEG format for visual textures, and Unreal Engine for the virtual reality scenes.</p> <p><strong>VERSION 1: Visual photogrammetry + Acoustic model</strong></p> <p>As used in the publication:</p> <p>[1] Llorca-Bofí, J. and Vorländer, M. (2021). Multi-Detailed 3D Architectural Framework for Sound Perception Research in Virtual Reality. Front. Built Environ. 7:687237.doi: https://doi.org/10.3389/fbuil.2021.687237</p> <p>Data is available separately for each definition, and for each visual and acoustic cue. The level of detail for each definition is shown here:</p> <ul> <li>Visual cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> <li>Acoustic cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> </ul> <p>This version of the model includes only the modules used for the description of the referenced paper. The authors reserve the right to complete other levels of detail if future applications require them.</p> <p>An additional data file contains a unique file in [IHTApark_UnrealEngine] Unreal Engine format, with the set up scenario. The instructions to open the final scenario are described here:</p> <ol> <li>Download the [IHTApark_UnrealEgine] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_UnrealEngine]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a <strong>Content Browser</strong> tab to navigate through the folder hierarchy. You can open the <strong>Content Browser</strong> under the tabs <strong>Window > Content Browser</strong></li> <li>Open the <strong>IHTApark</strong> map under the folder <strong>Content > Maps</strong> by double clicking on it.</li> <li>The scenario will be visible in the <strong>Viewport 1</strong> tab. Go to <strong>Window > Viewports > Viewport 1</strong> to open the tab.</li> <li>Press key <strong>G</strong> to hide or unhide the helpers and editor actors.</li> <li>Press keys <strong>0,</strong> <strong>1</strong>, <strong>2</strong>, <strong>3</strong>… <strong>9</strong> to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key <strong>W</strong> to navigate through the scenario.</li> </ol> <p><strong>VERSION 2: Object-based visualization in three different weather conditions</strong></p> <p>As used and described in the publication:</p> <p>[2] Submitted to journal.</p> <p>The file [IHTApark_3weath_comp] Unreal Engine format contains the set up scenario. The instructions to open the final scenario are described here:</p> <ol> <li>Download the [IHTApark_3weath_comp] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_3weath_comp]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a <strong>Content Browser</strong> tab to navigate through the folder hierarchy. You can open the <strong>Content Browser</strong> under the tabs <strong>Window > Content Browser</strong></li> <li>Open the <strong>IHTApark_warm</strong>, <strong>IHTApark_wet </strong>or<strong> IHTApark_snowy</strong> maps under the folder <strong>Content > Maps</strong> by double clicking on it to visualize each weather condition.</li> <li>The scenario will be visible in the <strong>Viewport 1</strong> tab. Go to <strong>Window > Viewports > Viewport 1</strong> to open the tab.</li> <li>Press key <strong>G</strong> to hide or unhide the helpers and editor actors.</li> <li>Press keys <strong>0,</strong> <strong>1</strong>, <strong>2</strong>, <strong>3</strong>… <strong>9</strong> to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key <strong>W</strong> to navigate through the scenario.</li> </ol> <p>The folder [IHTApark_3weathers_audio] contains the sound signals, as .wav files, in fist order ambisonics format (B-format).</p> <p> </p>
Replication Package for ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems
<p><strong>Replication Package for ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems</strong></p> <p>This is the replication package for the paper, ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems, which has been accepted at the International Conference on Software Architecture (ICSA), 2021. A preprint of the paper is included in this replication package (paper.pdf).</p> <p>This artifact is archived on Zenodo with the following DOI: <a href="https://doi.org/10.5281/zenodo.5834633">https://doi.org/10.5281/zenodo.5834633</a></p> <p>The study associated with this artifact was carried out by the following investigators:</p> <ul> <li><a href="http://christimperley.co.uk">Christopher S. Timperley</a> (Carnegie Mellon University)</li> <li><a href="https://tobiasduerschmid.github.io">Tobias Dürschmid</a> (Carnegie Mellon University)</li> <li><a href="https://www.cs.cmu.edu/~schmerl">Bradley Schmerl</a> (Carnegie Mellon University)</li> <li><a href="http://www.cs.cmu.edu/~garlan">David Garlan</a> (Carnegie Mellon University)</li> <li><a href="https://clairelegoues.com">Claire Le Goues</a> (Carnegie Mellon University)</li> </ul> <p>If you have any questions regarding the research or the replication package, you should contact Christopher, Tobias, or Bradley.</p> <p><strong>Abstract</strong></p> <p>Robot systems are growing in importance and complexity. Ecosystems for robot software, such as the Robot Operating System (ROS), provide libraries of reusable software components that can be configured and composed into larger systems. To support compositionality, ROS uses late binding and architecture configuration via “launch files” that describe how to initialize the components in a system. However, late binding often leads to systems failing silently due to misconfiguration, for example by misrouting or dropping messages entirely.</p> <p>In this paper we present ROSDiscover, which statically recovers the run-time architecture of ROS systems to find such architecture misconfiguration bugs. First, ROSDiscover constructs component level architectural models (ports, parameters) from source code. Second, architecture configuration files are analyzed to compose the system from these component models and derive the connections in the system. Finally, the reconstructed architecture is checked against architectural rules described in first-order logic to identify potential misconfigurations.</p> <p>We present an evaluation of ROSDiscover on real world, off-the-shelf robotic systems, measuring the accuracy, effectiveness, and practicality of our approach. To that end, we collected the first data set of architecture configuration bugs in ROS from popular open-source systems and measure how effective our approach is for detecting configuration bugs in that set.</p>
ARCHIMED-φ simulation files for the simulation of Design A from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal
<p>Input files for the simulation of Design A in ARCHIMED-φ from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal.</p> <p>See https://archimed-platform.github.io/archimed-phys-user-doc/ for more details on the model.</p> <p>Make a simulation by opening a terminal at the root of the folder and type: `java -jar .\archimed-phys.jar .\DesignA_MockUpA_seed1_MAP_72.yml`.</p>
On the genetic architecture of rapidly adapting and convergent life history traits in guppies
<p>The genetic basis of traits shapes and constrains how adaptation proceeds in nature; rapid adaptation can be facilitated by polygenic traits, which subsequently provide multiple, redundant, genetic routes to adaptive phenotypes, reducing re-use of the same genes (genetic convergence). Guppy life history traits evolve rapidly and convergently among natural high- (HP) and low-predation (LP) environments in northern Trinidad. This system has been studied extensively at the phenotypic level, but little is known about the underlying genetic architecture. Here, we use an F2 QTL design to examine the genetic basis of seven (five female, two male) guppy life history phenotypes to assess whether the genetic architecture of these traits reflects theoretical predictions. We use RAD-sequencing data (16,539 SNPs) from 370 male and 267 female F2 individuals. We perform linkage mapping, estimates of genome-wide and per-chromosome heritability (multi-locus associations), and QTL ma pping (single-locus associations). Our results are consistent with architectures of many-loci of small effect for male age and size at maturity and female interbrood period. Male trait associations are clustered on specific chromosomes, but female interbrood period exhibits a weak genome-wide signal suggesting a potentially highly polygenic component. Offspring weight and female size at maturity are also associated with a single significant QTL each. These results suggest rapid phenotypic evolution of guppies may be facilitated by polygenic trait architectures, but these could fuel redundancy and limit gene re-use across populations, in agreement with an absence of strong signatures of genetic convergence from recent population genomic analyses of wild HP-LP guppies.</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.