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19 results for “Process metrics”
Research data, sources and documents for thesis on Exploring Complexity Metrics for Artifact-Centric Business Process Models
<p>Research data, sources and documents for thesis on Exploring Complexity Metrics for Artifact-Centric Business Process Models This repository contains the supplemental material for the <a href="https://pqdtopen.proquest.com/pubnum/10759956.html">thesis "Exploring Complexity Metrics for Artifact-Centric Business Process Models" by Marin, Mike A., Ph.D., University of South Africa (South Africa), 2017.</a></p>
California Wildfire Resilience Core Metrics Rating Process and Results
The California Wildfire & Forest Resilience Task Force (Task Force) is producing a toolkit that can support organizations to prioritize, plan and implement actions to lessen wildfire risk to communities and improve broader statewide ecosystem resilience. As part of this effort, a core set of metrics needed to be identified to report resilience progress. The Task Force's Science Advisory Panel engaged in a rapid Delphi process, collecting expert opinion via surveys to provide content knowledge and science support for this process. This data archive provides content 1) for transparency, to share as much of our workflow as is feasible; 2) for others to pull from as an example if they want to do a similar process; 3) to provide all the detailed results on metrics if a reader wants to look into the ratings for and definition of a particular metric. The archive supports a report and paper summarizing and describing our Delphi method and the results regarding the specific metrics considered by our experts.
Data from: Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics
Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. Here we publish pipelines to process raw time-lapse imagery, resulting in count data (number of penguins per image) and 'nearest neighbour distance' measurements. The latter provide useful summaries of colony spatial structure (which can indicate phenological stage) and can be used to detect movement – metrics which could be valuable for a number of different monitoring scenarios, including image capture during aerial surveys. We present two alternative pathways for producing counts: 1) via the Zooniverse citizen science project Penguin Watch and 2) via a computer vision algorithm (Pengbot), and share a comparison of citizen science-, machine learning-, and expert- derived counts. We provide example files for 14 Penguin Watch cameras, generated from 63,070 raw images annotated by 50,445 volunteers. We encourage the use of this large open-source dataset, and the associated processing methodologies, for both ecological studies and continued machine learning and computer vision development.
Dataset: Evolution Of Computational Ontologies: Assessing Development Processes Using Metrics
<p>Ontologies facilitate meaning between human and computational actors. On the one hand, the underlying technology can be considered mature. It has a standardized language, established tools for editing and sharing, and broad adoption in practice and research. On the other hand, we still know little about how these artifacts evolve over their lifetime, even though knowledge of the development process could influence quality control. It would enable us to give knowledge engineers better modeling or selection guidelines.</p> <p>This paper examines the evolution of computational ontologies using ontology metrics. First, we gathered hypotheses on the ontology development process. We assume that groups of ontologies follow a similar development pattern and that a stereotypical development process exists. Afterward, these hypotheses are tested against historical metric data from 7053 versions from 69 dormant ontologies.</p> <p>We will show that ontology development processes are highly heterogeneous. While the made hypotheses are partly true for a slight majority of ontologies, concluding the bigger picture of ontology development down to the individual ontologies is mostly not possible. Further, the data revealed that most ontologies have disruptive change events for most of the measures attributes. These disruptive events are further examined regarding their occurrences, combinations, and sizes.</p>
Data from: Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics
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Data from: Quantifying species contributions to ecosystem processes: a global assessment of functional trait and phylogenetic metrics across avian seed-dispersal networks
Quantifying the role of biodiversity in ecosystems not only requires understanding the links between species and the ecological functions and services they provide, but also how these factors relate to measurable indices, such as functional traits and phylogenetic diversity. However, these relationships remain poorly understood, especially for heterotrophic organisms within complex ecological networks. Here, we assemble data on avian traits across a global sample of mutualistic plant–frugivore networks to critically assess how the functional roles of frugivores are associated with their intrinsic traits, as well as their evolutionary and functional distinctiveness. We find strong evidence for niche complementarity, with phenotypically and phylogenetically distinct birds interacting with more unique sets of plants. However, interaction strengths—the number of plant species dependent on a frugivore—were unrelated to evolutionary or functional distinctiveness, largely because distinct frugivores tend to be locally rare, and thus have fewer connections across the network. Instead, interaction strengths were better predicted by intrinsic traits, including body size, gape width and dietary specialization. Our analysis provides general support for the use of traits in quantifying species ecological functions, but also highlights the need to go beyond simple metrics of functional or phylogenetic diversity to consider the multiple pathways through which traits may determine ecological processes.
Data from: Linking functional diversity and ecosystem processes: a framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species
1.Functional diversity (FD) metrics are widely used to assess invasion ecosystem impacts, but we have limited theory to predict how FD should respond to invasion. A key challenge to effectively using FD metrics is the complexity of conceptualizing alterations to multi-dimensional trait space, making it difficult to select a priori the most appropriate metric for specific ecological questions. 2.Here, we provide expectations on how invasion should change four commonly used FD metrics—functional richness (FRic), evenness (FEve), divergence (FDiv), and dispersion (FDis)—and then test these expectations in a lab decomposition experiment. We simulate invasion of a forest by understory plants by adding leaf litter from 18 natives and nonnatives to a representative canopy tree litter mixture to test changes in FD and decomposition. 3.All four metrics changed predictably with invasion. Species that were more functionally unique or when added at greater proportions had larger impacts on FD. Overall, FRic, FEve, and FDiv were poor choices for understanding impacts of nonnative species. FDis was the only metric that both changed predictably with addition of understory litter and correlated intuitively with changes in carbon mineralization. Furthermore, ranking species based upon how much they changed FDis of the litter mixture provided a fair assessment of which species had the largest impact on decomposition. As such, functional dispersion may be a key tool for predicting a priori which nonnatives will have the greatest impact on ecosystem processes. 4.Synthesis: We highlight the need to assess the suitability of each FD metric for the specific ecological question at hand. Our work reveals the pitfalls of considering multiple metrics or randomly choosing a single metric without suitability assessments. At the same time, it suggests a framework for metric assessment that should help lead to selection of a metric or metrics that provide robust a priori insights into how invasion by nonnative species can impact ecosystem processes.
Postural stability metrics associated to the publication: Additive manufacturing of spinal braces: evaluation of production process and postural stability in patients with scoliosis
<p>Data was collected for each condition (3D-printed brace, conventional brace, unbraced) for 60 seconds with patients in a standing posture, open eyes and both feet together.</p>
Processed data for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This is the data used to reproduce the results from "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Scatter plots for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains the test-score-vs-metric plots generated by the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Generalization metrics for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains all the generalization metrics that can be used to reproduce the results of "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Rank correlation results for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains the rank correlation results from the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Revisiting process versus product metrics: A large scale analysis
<p>Numerous methods can build predictive models from software data. However, what methods and conclusions should we endorse as we move from analytics in-the-small (dealing with a handful of projects) to analytics in-the-large (dealing with hundreds of projects)? To answer this question, we recheck prior small-scale results (about process versus product metrics for defect prediction and the granularity of metrics) using 722,471 commits from 700 Github projects. We find that some analytics in-the-small conclusions still hold when scaling up to analytics in-the-large. For example, like prior work, we see that process metrics are better predictors for defects than product metrics (best process/product-based learners respectively achieve recalls of 98%/44% and AUCs of 95%/54%, median values).</p> <p>That said, we warn that it is unwise to trust metric importance results from analytics in-the-small studies since those change dramatically when moving to analytics in-the-large. Also, when reasoning in-the-large about hundreds of projects, it is better to use predictions from multiple models (since single model predictions can become confused and exhibit a high variance).</p>
Data from: Quantifying species contributions to ecosystem processes: a global assessment of functional trait and phylogenetic metrics across avian seed-dispersal networks
Open the record for dataset details and reuse information.
Data from: Linking functional diversity and ecosystem processes: a framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species
Open the record for dataset details and reuse information.
Code, Quality, and Process Metrics in Graduated and Retired ASFI Projects
<p>Data for the Code, Quality, and Process Metrics in Graduated and Retired ASFI Projects paper.</p>
Contribution and Quality Metrics for Quantifying the Software Development Process Dataset
<p>This dataset contains the Contributions and Quality data regarding the 3,000 most starred GitHub Java projects towards Quantifying<br> the Software Development Process.</p> <p>You can use the dataset simply with the following steps:</p> <p> 1. Download the data.</p> <p> 2. Navigate to the download folder and use the mongorestore (<a href="https://docs.mongodb.com/manual/reference/program/mongorestore/">https://docs.mongodb.com/manual/reference/program/mongorestore/</a>) command. (Have in mind to use the --gzip flag)</p>
Process-sensitive sentinel genes as novel cell culture comparability metrics
GEO Series GSE33063. Mus musculus. 23 samples. Type: Expression profiling by array.
Process Metrics Collected using Commit Guru
<p>This data contains file-level process metrics collected using the commit guru tool.</p>
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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.