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97 results for “code pattern”
Supplementary material for "can you tell me if it smells? A study on how developers discuss code smells and anti-patterns in Stack Overflow"
<p>This dataset contains all data and results from the "Can you tell me if it smells? A study on how developers discuss code smells and anti-patterns in Stack Overflow" paper that was accepted at the 22nd International Conference on Evaluation andAssessment in Software Engineering (EASE), Christchurch, New Zealand.</p>
Code and data to reproduce the results of the paper: "Land Use Patterns and Climate Change---A Modeled Scenario of the Late Bronze Age in Southern Greece"
<p>Code and data to reproduce the results of Knitter et al. (2019): Land Use Patterns and Climate Change---A Modeled Scenario of the Late Bronze Age in Southern Greece. ERL.</p>
Replication Package for "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis"
<p>This repository provides the data sets and scripts used in the paper "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis" by Sebastian Nielebock, Robert Heumüller, Kevin Michael Schott, and Frank Ortmeier from the Faculty of Computer Science of the Otto-von-Guericke University Magdeburg, Germany. This paper is published in Springer's "Automated Software Engineering - An International Journal" in August 2021. The article is available as open access at <a href="https://dx.doi.org/10.1007/s10515-021-00294-x">https://dx.doi.org/10.1007/s10515-021-00294-x</a>. A preprint is available under <a href="https://arxiv.org/abs/2008.00277">https://arxiv.org/abs/2008.00277</a>.</p> <p>All scripts and data sets are provided by the authors and come without any guarantee. For any issues regarding replication do not hesitate to contact us ({sebastian.nielebock,robert.heumueller, kevin.schott, frank.ortmeier} <at> ovgu.de)</p> <p>If you use or refer to these datasets, please cite our paper using the following BibTex entry.</p> <pre>@article{NielebockAPIFilterSearch2021, title = {Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis}, author = {Sebastian Nielebock and Robert Heum\"{u}ller and Kevin Michael Schott and Frank Ortmeier}, editor = {Springer}, journal = {Springer Automated Software Engineering - An International Journal}, number = {15}, pages = {1-48}, volume = {28}, url = {https://arxiv.org/abs/2008.00277}, doi = {10.1007/s10515-021-00294-x}, year = {2021}, } </pre>
Data and codes to replicate the analysis in: The spatial ecology of conflicts: Unravelling patterns of wildlife damage at multiple scales
<p><span><span>Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damages to agriculture and livestock. Spatial risk modelling is a useful tool to gain understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (<i>Ursus arctos</i>) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also favoured damages. Furthermore, integrated-scale risk maps resulted in more accurate predictions than single-scale models. Our results suggest that principles of resource selection by animals can be used to understand the occurrence of damages and help mitigate conflicts in a proactive and preventive manner. </span></span></p>
Code Review of Build System Specifications: Prevalence, Purposes, Patterns, and Perceptions (Replication Package)
<p>Online appendix for "Code Review of Build System Specifications: Prevalence, Purposes, Patterns, and Perceptions", in the Proceedings of the International Conference on Software Engineering (ICSE), 2023. </p>
Data and model code from: Tracing growth patterns in cod (Gadus morhua L.) using bioenergetic modelling
<p><span>Understanding individual growth in commercially exploited fish populations is key to successful stock assessment and informed ecosystem-based fisheries management. Traditionally, growth rates in marine fish are estimated using otolith age-reading in combination with age-length relationships from field samples, or tag-recapture field experiments. However, for some species, otolith-based approaches have been proven unreliable, and tag-recapture experiments suffer from high working effort and costs as well as low recapture rates. An important alternative approach for estimating fish growth is represented by bioenergetic modelling, which, in addition to pure growth estimation, can provide valuable insights into the processes leading to temporal growth changes resulting from environmental and related behavioural changes. We here developed an individual-based bioenergetic model for Western Baltic cod (<em>Gadus</em> <em>morhua</em>), traditionally a commercially important fish species that however collapsed recently and likely suffers from climate change effects. Western Baltic cod is an ideal case study for bioenergetic modelling because of recently gained in-situ process knowledge on spatial distribution and feeding behaviour based on highly resolved data on stomachs and fish distribution. Additionally, physiological processes such as gastric evacuation, consumption, net-conversion efficiency, and metabolic rates have been well studied for cod in laboratory experiments. Our model reliably reproduced seasonal growth patterns observed in the field. </span><span>Importantly, our bioenergetic modelling approach implementing depth-use patterns and food intake allowed us to explain the potentially detrimental effect summer heat periods have on growth of Western Baltic cod that likely will increasingly occur in the future. Hence our model simulations highlighted a potential mechanism of how warming due to climate change affects the growth of a key species that may apply for similar environments elsewhere. </span></p> <p><span>Here we provide access to the individual-based bioenergetic growth model which is set up to model the growth of cod in ages 2 to 4 (<em>Gadus</em> <em>morhua</em> L.) in the Belt Sea (western part of the Western Baltic Sea) on a daily basis within one year. The model incorporates contemporary in-situ process knowledge on food intake and seasonal- and temperate-related spatial distribution of cod and allows us to identify seasonal growth patterns. The model is written in the statistical and programming environment R.<br></span></p>
Code and data from: Global patterns in plant environmental breadths
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Code from: Costs of parasite generalism revealed by abundance patterns across mammalian hosts
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Data and code from: Alternative pathways into the deep sea: Patterns in Bivalvia
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Codes for simulation and data for: The relationship between local and regional extinction rates depends on species distribution patterns
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Computer code for a model describing the emergence of a long transient regular spatial pattern from interaction of competing aquatic macrophytes and a biocontrol agent
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Statistical code from: Passive acoustic monitoring with AI-based detection and identification reveal sooty grouse hooting patterns in western Oregon
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Data and R code from: Downscaling species to individual-level networks reveals the importance of population-level processes in mediating generalized community-wide interaction patterns
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Data and codes to replicate the analysis in: The spatial ecology of conflicts: Unravelling patterns of wildlife damage at multiple scales
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Data and model code from: Tracing growth patterns in cod (Gadus morhua L.) using bioenergetic modelling
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Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects"
<p>Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects" with README included</p>
Data and code for changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China
<p>Data and code for the paper "changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China" published in Science.</p>
Co-ocurrence patterns of marine catfishes in the Amazonian estuary: Data and coding
<p>Data and script to analyze the local co-occurrence patterns of ariids in the Amazonian estuary. These results are discussed in the manuscript "Environmental conditions promote local segregation of catfishes in the Amazonian estuary, but ecomorphological differences may allow aggregation", soon to be submitted as a preprint to EcoEvoRxiv Preprints.</p>
Dataset and code release for "Current population structure and pathogenicity patterns of Ascochyta rabiei in Australia"
<p>This upload contains the raw DArTseq data and code for reproducible analysis and production of output tables and plots to accompany the publication "Current population structure and pathogenicity patterns of <em>Ascochyta rabiei</em> in Australia".<br> The analysis is performed primarily in R and is run using the `A_rabiei_DArT.R` file.</p> <p>A version-controlled repository of this upload is maintained at GitHub at the following link: <a href="https://github.com/IdoBar/A_rabiei_DArT">https://github.com/IdoBar/A_rabiei_DArT</a></p> <p>The archived file is structured as follows:</p> <ul> <li>Main analysis code is in <strong>A_rabiei_DArT.R</strong></li> <li>Raw DArTseq data and <em>A. rabiei</em> isolate metadata can be found in in the <strong><em>data </em></strong>folder</li> <li>Output tables and plots in <em><strong>output</strong></em> folder</li> <li>General information (partial and slightly outdated) in the markdown <strong>A_rabiei_DArT.Rmd </strong>and knitted <strong>A_rabiei_DArT.html </strong>files</li> <li>Australian chickpea production stats and figures in <strong>Chickpea_production.R</strong></li> </ul>
Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America
<p>The presence of many pathogens varies in a predictable manner with latitude, with infections decreasing from the equator towards the poles. We investigated the geographic trends of pathogens infecting a widely distributed carnivore: the gray wolf (<i>Canis lupus</i>). We compiled a large serological dataset of nearly 2000 wolves from 17 study areas, spanning 80º longitude and 50º latitude. Generalized linear mixed models were constructed to predict the probability of seropositivity of four important viruses: canine adenovirus, herpesvirus, parvovirus, and distemper virus – and two parasites: <i>Neospora caninum </i>and <i>Toxoplasma gondii</i>.</p> <p>Canine adenovirus and herpesvirus were the most widely distributed pathogens, whereas <i>N. caninum</i> was relatively uncommon. Canine parvovirus and distemper had high annual variation, with western populations experiencing more frequent outbreaks than eastern populations. Seroprevalence of all infections increased as wolves aged, and denser wolf populations had a greater risk of exposure. Probability of exposure was positively correlated with human density, suggesting that dogs and synanthropic animals may be important pathogen reservoirs. Pathogen exposure did not appear to follow a latitudinal gradient, with the exception of <i>N. caninum</i>. Instead, clustered study areas were more similar: wolves from the Great Lakes region had lower odds of exposure to the viruses, but higher odds of exposure to <i>N. caninum</i> and <i>T. gondii</i>; the opposite was true for wolves from the central Rocky Mountains. Overall, mechanistic predictors were more informative of seroprevalence trends than latitude and longitude. Individual host characteristics as well as inherent features of ecosystems determined pathogen exposure risk on a large scale.</p> <p>Here we provide the serological dataset and the R code used in Brandell et al. 2021. See the README file for a description of the dataset and generalized linear mixed models (GLMM); see Brandell et al. 2021 main text and Supplementary Information for additional information about data collection and cleaning, research permits, and variable descriptions and rationales.</p>
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.