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753 results for “metrics”
Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory"
<p>Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory" published in the Journal of Geophysical Research Oceans. <br>URL of the manuscript: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JC020101<br>DOI of the manuscript: https://doi.org/10.1029/2023JC020101</p>
A Stable Software Architecture Modularity Metric
<p>Anonymous Replication Package for A Stable Software Architecture Modularity Metric</p>
Example dataset to illustrate metric learning for ReID in maDLC
<p>Example data to illustrate ReID transformer in Lauer et al. Nature Methods 2022. </p><p>Link: https://www.nature.com/articles/s41592-022-01443-0</p><p> </p><p><br><strong>NOTE: This same data is also here: https://zenodo.org/records/7883589 (we realized this only afterwards)</strong></p>
Figure 5. Pimplinae wasp community metrics against mean monthly temperature across 15 in Variation in a Darwin Wasp (Hymenoptera: Ichneumonidae) Community along an Elevation Gradient in a Tropical Biodiversity Hotspot: Implications for Ecology and Conservation
Figure 5. Pimplinae wasp community metrics against mean monthly temperature across 15 sites. (a) Abundance; (b) Log10 Species Richness; (c) Log10 Simpson's Index (1/D); and (d) Shannon Index. Lines are the equations of the model in Table 3 ± 95%CI. (a) cubic model; (b,d) quadratic models; and (c): linear model.
Technical Debt Awareness: Definitions, Methods, and Metrics
<p>The additional material for the paper "Technical Debt Awareness: Definitions, Methods, and Metrics" comprises the following documents:</p><p> </p><p>* Guideline_to_Use_TD-SAGAT.docx (.pdf)</p><p> A short guideline on how the use the TD-SAGAT method.</p><p>* Semi_SLR_papers.xlsx</p><p> All excluded and included papers of our smei-SLR whit resons for exclusion and extracted data for included papers.</p><p>* Participant_and_Meetings.xslx</p><p> overview of all participants with role and years of experience </p><p> overview of all meetings, we used for analysis</p><p>* Survey_and_Observation_Template_plus_Mappings.xlsx</p><p> survey questions for workshop survey, TD-SAGAT survey and observation protocol</p><p> mapping of all questions and aspects between the surveys and the obersvation aspects</p><p>* SAGAT_for_TD_from_Goals_to_Queries.pdf</p><p> Determination of survey questions by following the SAGAT approach of following the hierarchy of goals, subgoals, decisions, requirements, queries </p><p>* TD-SAGAT_Analysis.xlsx</p><p> analysis data for the TD-SAGAT analysis, incl. chart</p><p>* Observations.xslx</p><p> observation protocols (anonymized) for all observed meetings</p><p> </p>
DEDICAT 6G Robot metrics for different levels of intelligence (computation) distribution
<p>Battery consumption, CPU and RAM metrics for locobots used in testing of intelligence distribution. </p>
Programs and data used for Extracting latent variables from forecast ensembles and advancements in similarity metric utilizing optimal transport
<p>This is compiled from the program and output data using in Nishizawa (2024).</p> <p> </p> <p>Nishizawa, 2024: Extracting latent variables from forecast ensembles and advancements in similarity metric utilizing optimal transport. submitted to JGR: Machine Learning and Computation.</p>
A data set from a survey investigating the SMART approach to develop good cyber security metrics
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Shallow subsurface water-ice distribution in the lunar south pole: Analysis based on Mini-RF and multi-metrics
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Metrics Datasets
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CellMet: Extracting 3D shape metrics from cells and tissues
<p>This dataset supports the manuscript title "CellMet: Extracting 3D shape metrics from cells and tissues". It contains segmented images (mask.tif) of severals tissues. </p>
Performance Evaluation of Seven Remote Sensing Datasets for TRB Cropland Area Estimation (Accuracy Metrics and Temporal Trends)
<p>This dataset contains accuracy assessments and trend analyses for cropland area estimation using seven remote sensing datasets in the TRB region. The data is organized into the following structure:<br>1. Accuracy Evaluation ("RMSE+Rt+MPE" directory)</p> <p>Contains individual evaluation files for each of the seven remote sensing products<br>File naming convention: [DatasetName]_Evaluation.csv<br>Each file contains four columns:</p> <p>County: Administrative region<br>Rt: Temporal correlation<br>RMSE: Root Mean Square Error<br>MPE: Mean Percentage Error</p> <p>2. Area Change Trends ("Trend" directory)</p> <p>Contains trend analysis files for seven remote sensing products and reference observations (OBS)<br>File naming convention: [DatasetName/OBS]_trend.csv<br>Each file contains three columns:</p> <p>County: Administrative region<br>Slope: Trend slope coefficient<br>P_value: Statistical significance value</p> <p>3. Spatial Correlation (Rs.csv)</p> <p>Single file containing annual spatial correlation coefficients (Rs) for all seven remote sensing datasets.</p>
Microservice Metrics Dataset
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LO2: Microservice Dataset of Logs and Metrics
<div> <h2>LO2 dataset</h2> <p>This is the data repository for the LO2 dataset.</p> <p>Here is an overview of the contents.</p> <p><strong>lo2-data.zip</strong></p> <p>This is the main dataset. This is the completely unedited output of our data collection process. Note that the uncompressed size is around 540 GB. For more information, see the paper and the data-appendix in this repository.</p> <p><strong>lo2-sample.zip</strong></p> <p>This is a sample that contains the data used for preliminary analysis. It contains only service logs and the most relevant metrics for the first 100 runs. Furthermore, the metrics are combined on a run level to a single csv to make them easier to utilize. </p> <p><strong>data-appendix.pdf</strong></p> <p>This document contains further details and stats about the full dataset. These include file size distributions, empty file analysis, log type analysis and the appearance of an unknown file.</p> <p><strong>lo2-scripts.zip</strong></p> <p>Various scripts for processing the data to create the sample, to conduct the preliminary analysis and to create the statistics seen in the data-appendix.</p> <ul> <li><strong>csv_generator.py, csv_merge*.py: </strong>These scripts create and combine the metrics into csv files. They need to be run in order. Merging runs to global is very memory intensive.</li> <li><strong>findempty.py: </strong>Finds empty files in the folders. As some are expected to be empty, it also counts the unexpected ones. Used in data-appendix.</li> <li><strong>loglead_lo2.py: </strong>Script for the preliminary analysis of the logs for error detection. Requires LogLead version 1.2.1.</li> <li><strong>logstats.py: </strong>Counts log lines and their type. Used for creating the figure of number of lines per type and service.</li> <li><strong>node_exporter_metrics.txt: </strong>Metric descriptions exported from Prometheus (text file).</li> <li><strong>pca.py: </strong>The Principal Component Analysis script used for preliminary analysis.</li> <li><strong>reduce_logs.py: </strong>Very important for fair analysis as in the beginning of the files there are some initialization rows that leak information regarding correctness.</li> <li><strong>requirements.txt: </strong>Required Python libraries to run the scripts.</li> <li><strong>sizedist.py: </strong>Creating distributions of file sizes per filename for the data-appendix.</li> </ul> <p><em>Version v3: Updated data appendix introduction, added another stage in the log analysis process in loglead_lo2.py</em></p> </div>
Temporal validity of software datasets for code metrics: an empirical assessment of sampling strategies
<p>This is the repository for the scripts and data of the study "Building and updating software datasets: an empirical assessment".</p> <h2>Data collected</h2> <p>The data generated for the study it can be downloaded as a zip file. Each folder inside the file corresponds to one of the datasets of projects employed in the study (qualitas, currentSample and qualitasUpdated). Every dataset comprised three files "class.csv", "method.csv" and "sample.csv", with class metrics, method metrics and repository metadata of the projects respectively. Here is a description of the datasets:</p> <ul> <li>qualitas: includes code metrics and repository metrics from the projects in the release 20130901r of the Qualitas Corpus.</li> <li>currentSample: includes code metrics and repository metrics from a recent sample collected with our sampling procedure.</li> <li>qualitasUpdated: includes code metrics and repository metrics from an updated version of the Qualitas Corpus applying our maintenance procedure.</li> </ul> <h2>Plot graphics</h2> <p>To plot the results and graphics in the article there is a Jupyter Notebook "Experiment.ipynb". It is initially configured to use the data in "datasets" folder.</p> <h2>Replication Kit</h2> <p>For replication purposes, the datasets containing recent projects from Github can be re-generated. To do so, the virtual environment must have installed the dependencies in "requirements.txt" file, add Github's tokens in "./token" file, re-define or leave as is the paths declared in the constants (variables written in caps) in the main method, and finally run "main.py" script. The portable versions of the source code scanner <a href="https://sourcemeter.com/" target="_blank" rel="noopener">Sourcemeter</a> are located as zip files in "./Sourcemeter/tool" directory. To install Sourcemeter the appropriate zip file must be decompressed excluding the root folder "SourceMeter-10.2.0-x64-<OS>".</p> <p>The script comprise 5 steps:</p> <ol> <li>Project retrieval from Github: at first the sampling frame with projects complying with a specific quality criteria are retrieved from Github's API.</li> <li>Create samples: with the sampling frame retrieved, the current samples are selected (currentSample and qualitasUpdated). In the case of qualitasUpdated, it is important to have first the "sample.csv" file inside the qualitas folder of the dataset originally created for the study. This file contains the metadata of the projects in Qualitas Corpus.</li> <li>Project download and analysis: when all the samples are selected from the sampling frame (currentSample and qualitasUpdated), the repositories are downloaded and scanned with SourceMeter. In the cases in which the analysis is not possible, the projects are replaced with another one with similar size.</li> <li>Outlier detection: once the datasets are collected, it is necessary to manually look for possible outliers in the code metrics under study. In the notebook "Experiment.ipynb" there are specific sections dedicated for it ("Outlier detection (Section 4.2.2)").</li> <li>Outlier replacement: when the outliers are detected, in the same notebook there is also a section for outlier replacement ("Replace Outliers") where the outliers' url have to be listed to find the appropriate replacement.</li> </ol> <ul> <li>If it is required, the metrics from the Qualitas Corpus can also be re-generated. First, it is necessary to download the release 20130901r from its <a href="http://www.qualitascorpus.com/download/" target="_blank" rel="noopener">official webpage</a>. Second, decompress the .tar files downloaded. Third, make sure that the compressed files with source code from the projects (.java files) are placed in the "compressed" folder, in some cases it is necessary to read the "QC_README" file in the project's folder. Finally, run the original main script "Generate metrics for the Qualitas Corpus (QC) dataset" part of the code. </li> </ul>
Simulation codes and data from: Efficient Flocking: metric versus topological interactions
<p>Flocking is a fascinating phenomenon observed across a wide range of living organisms. We investigate, based on a simple theoretical particle model, how the emergence of ordered motion in a collectively moving group is influenced by the local rules of interactions among the individuals, namely, metric versus topological interactions as debated over in the current literature.</p>
FAIREST Metrics and Assessment Data
<p>This data supplements the article “FAIREST: A Framework for Assessing Research Repositories”.</p> <p>In the article, we introduce the FAIREST principles, an extension of the well-known FAIR principles. Along these principles, we provide comprehensive metrics for assessing and selecting solutions for building digital repositories for research artefacts. The metrics are based on two pillars:</p> <p>(1) an analysis of established features and functionalities, drawn from existing solutions, <br> (2) a literature review on general requirements for digital repositories for research artefacts and related systems.</p> <p>We further describe an assessment of 11 widespread solutions, with the goal to provide an overview of the current landscape of research data repository solutions, identifying gaps and research challenges to be addressed:</p> <p>The solutions are:</p> <ul> <li>ResearchGate</li> <li>Academia.edu</li> <li>Zenodo</li> <li>arXiv</li> <li>Bibsonomy</li> <li>Figshare</li> <li>CKAN</li> <li>DSpace</li> <li>Invenio</li> <li>Dataverse</li> <li>EPrints</li> </ul> <p><strong>Overview of the data:</strong></p> <p><em>01 FAIREST Assessment Metrics and Solutions (All-in-one).xlsx</em><br> This Excel file includes both the assessment metrics and the results for the 11 solutions</p> <p><em>02 FAIREST Assessment Metrics.csv</em><br> The assessment metrics as CSV</p> <p><em>XX FAIREST Assessment XXX.csv</em><br> Assessment result for the respective solution</p> <p><em>14 FAIREST Assessment Template.xlsx</em><br> A template to apply the metrics to an individual solution<br> Note: Fill in your assessment in column F and get the result at the bottom of the sheet</p> <p> </p>
Metrics of Social Curiosity: The WhatsApp Case
<p>We provide a database file with the anonymized messages shared in WhatsApp. </p>
Build Prediction in Continuous Integration Using Textual Analysis of Source Code and Traditional Software Metrics
<p>Continuous Integration (CI) systems integrate code changes committed by software developers, tests the results of the integration, and feed developers with information about the outcome of the integration and testing. Predicting the outcome of the integration is important since it reduces the feedback time between the CI system and the developers. This data-set comprises of historical code changes extracted from the TravisTorrent data-set (found in the train-lines folder) and their corresponding feature vectors (found in the train-bag-of-words folder) for Java projects. It also includes a set of files that contains historical build records and a set of traditional software metrics.</p>
Dataset used in the assessment of the article "Basic urban services fail to neutralise environmental determinants of 'rattiness', a composite metric of rat abundance"
<p>This data was collected in four Brazilian informal settlements located in the city of Salvador - the third largest city of Brazil - in the wet season of the year 2018. The data includes the collection of three rat abundance metrics - namely, rat marks on track plates, rats caught in live traps and removed, and presence of rat signs (faecal droppings, trails and active burrows) - in a total of 529 independent sampling locations. In addition to the single rat metrics, which were also used to compose the rattiness outcome, the data contain the baseline environmental and socioeconomic (surveyed and mapped) variables and the BUS variables considered in Carvalho-Pereira et al., 2023. (the data will be available soon)</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.