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753 results for “metrics”
Software development output metrics for four industrial projects
<p>Data used for the paper "Benchmarking ongoing development output in real-life software projects"</p>
Supplementary material of Metrics for Quality Assessment in Blockchain-based Systems: A Systematic Mapping Study
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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>
Data from: Simple metrics to characterize inter-individual and temporal variation in habitat selection behaviour
<p>1. Individual variation in habitat selection and movement behavior is receiving growing attention, but primarily with respect to characterizing behaviors in different contexts as opposed to decomposing structure in behavior within populations. This focus may be limiting advances in understanding the diversity of individual behavior and its influence on population organization. We propose a framework for characterizing variation in space-use behavior with the aim of advancing interpretation of its form and function.</p> <p>2. Using outputs from integrated Step Selection Analyses of 20 years of telemetry data from African elephants (<em>Loxodonta Africana</em>), we developed four metrics characterizing differentiation in resource selection behavior within a population [specialization (magnitude of the response independent of direction), heterogeneity (inter-individual variation), consistency (temporal shift in response) and reversal (frequency of directional changes in the response)].</p> <p>3. We contrast insight from the developed metrics relative to the mean population response using an example focused on two covariates. We then expanded this contrast by evaluating if the metrics identify structurally important information on seasonal shifts in resource selection behaviors in addition to that provided by mean selection coefficients through Principal Component Analyses (PCAs) and a random forest classification.</p> <p>4. The simplified example highlighted that for some covariates focusing on the population average failed to capture complex individual variation in behaviors. The PCAs revealed that the developed metrics provided additional information in explaining the patterns in elephant selection beyond that offered by population average covariate values. For elephants, specialization and heterogeneity were informative, with specialization often being a better descriptor of differences in seasonal resource selection behavior than population average responses. Summarizing these metrics spatially and temporally, we illustrate how these metrics can provide insights on overlooked aspects of animal behavior.</p> <p>5. Our work offers a new approach in how we conceptualize variation in space-use behavior (i.e., habitat selection and movement) by providing ways of encapsulating variation that enables diagnoses of the drivers of individual level variability in a population. <span>The developed metrics explicitly distill how variation in a behavior is structured among individuals and over time which could facilitate comparative work across time, populations, or strata within populations.</span></p>
Sensorimotor Synchronization with Higher Metrical Levels in Music Shortens Perceived Time
<p>Data set for the study "Sensorimotor Synchronization with Higher Metrical Levels in Music Shortens Perceived Time" published in Music Perception.</p>
Data from: Song complexity - no correlation between standard deviation of frequency and traditionally used song complexity metrics in passerines: a comment on Pearse et al. (2018)
[No abstract entered]
Using phenome-wide association studies and the SF-12 quality of life metric to identify profound consequences of adverse childhood experiences on adult mental and physical health in a northern Nevadan population
<p>In this research, we examine and identify the implications of Adverse Childhood Experiences (ACEs) on a range of health outcomes, with particular focus on a number of mental health disorders. Many previous studies observed that traumatic childhood events are linked to long-term adult diseases using the standard Adverse Childhood Experience Questionnaire. The study cohort was derived from the Healthy Nevada Project, a volunteer-based population health study in which each adult participant is invited to take a retrospective questionnaire that includes the Adverse Childhood Experience Questionnaire, the 12-item Short Form Survey measuring quality of life, and self-reported incidence of nine mental disorders. Using participants' cross-referenced electronic health records, a phenome-wide association analysis of 1,703 phenotypes and the incidence of ACEs examined links between traumatic events in childhood and adult disease. These analyses showed that many mental disorders were significantly associated with ACEs in a dose-response manner. Similarly, a dose-response between ACEs and obesity, chronic pain, migraine, and other physical phenotypes was identified. An examination of the prevalence of self-reported mental disorders and incidence of ACEs showed a positive relationship. Furthermore, participants with less adverse childhood events experienced a higher quality of life, both physically and mentally. The whole-phenotype approach confirms that ACEs are linked with many negative adult physical and mental health outcomes. With the nationwide prevalence of ACEs as high as 67%, these findings suggest a need for new public health resources: ACE-specific interventions and early childhood screenings.</p>
Figure 1 from: Chen J, Bagga D (2017) Noise paradoxically increases reliability metrics. Research Ideas and Outcomes 3: e12641. https://doi.org/10.3897/rio.3.e12641
Figure 1 - A: The voxel-wise ICC values of RSFC with respect to a PCC seed under different SNR levels (SNR is defined as the ratio of the amplitude of fluctuations < 0.2 Hz to > 0.2 Hz, averaged across voxels in the slice, each column) and session numbers (by partitioning each subject's scan to multiple windows, each row). B: ICC values (a), between-subject (b) and inter-subject (c) variability averaged within all voxels of the displayed slice in A ('All', numbers in the parenthesis are the window number), and voxels significantly correlated with the PCC seed at the group level ('Active', evaluated across 10 subjects using the entire scan dataset filtered < 0.2 Hz, p < 0.05, uncorrected)
Metrics in Agile Software Development: Repository
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Are Biomass Feedstocks Sustainable? A Systematic Review of Three Key Sustainability Metrics
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Figure 2 from: Ávila MP, Carvalho RN, Casatti L, Simião-Ferreira J, de Morais LF, Teresa FB (2018) Metrics derived from fish assemblages as indicators of environmental degradation in Cerrado streams. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e12895
Figure 2 Stream sites grouped by their environmental characteristics by using UPGMA (Unweighted Pair Group Method with Arithmetic Mean). Height at y-axis refers to Euclidean distance.
Figure 3 from: Ávila MP, Carvalho RN, Casatti L, Simião-Ferreira J, de Morais LF, Teresa FB (2018) Metrics derived from fish assemblages as indicators of environmental degradation in Cerrado streams. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e12895
Figure 3 Box-and-Whisker plots of the three sensitive metrics. Rectangles represent the 1st and 3rd quartiles, small squares are medians, bars are maximum and minimum values. Different letters indicate statistically significant differences.
Figure 1 from: Ávila MP, Carvalho RN, Casatti L, Simião-Ferreira J, de Morais LF, Teresa FB (2018) Metrics derived from fish assemblages as indicators of environmental degradation in Cerrado streams. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e12895
Figure 1 Map of South America highlighting the Goiás State (Brazil) and the sampling sites located in the Santa Tereza sub-basin, Upper Tocantins River basin.
supporting data for 'categorical colour metric' publication
<p>sampledMT.txt are metric tensors for the 'categorical colour metric' describe in the paper of the same name (to be) published in PLOS One.</p> <p>The data is stored as a nested list of dimension 21*21*21*3*3; with the outer dimension being the R coordinate of where the tensor lies running from 0.00, 0.05,...,0.95,1.00; next dimension G; then Bl then the tensors themselves.</p> <p>It can be directly read into Mathematica using <<</p>
Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration - Synthetic data
<p>Analysis of dMRI of the optic chiasm using CSD revealed unexpectedly high AFD of the intact bundle in the experimental animals. To evaluate whether this finding is an artifact produced by CSD, we used analytic synthetic data. Using the multi-tensor model, two sets of synthetic dMRI data were created consisting of 100 voxels, each with two tensors crossing at right angles. For each voxel, one tensor was modelled as an “intact” fiber bundle, following the DTI metrics observed in our experimental data (λ∥=0.64 and λ⟂=0.12 ×10-3 mm²/s), while the other tensor was modelled as an “injured” fiber bundle (λ∥=0.42 and λ⟂=0.22 ×10-3 mm²/s). Volume fractions were set as f=0.5 for both tensors. The multi-shell scheme to generate the synthetic signals consisted of 20 b=0 s/mm², and 160 DWI volumes, consisting of 80 diffusion gradient orientations, each with two distinct b values, namely b=1000 and 3000 s/mm². Signal-to-noise ratio was set at 20. </p> <p>Diffusion-weighted parameters are shown in the corresponding *protocol.txt files, organized as in the MRtrix gradient scheme (https://mrtrix.readthedocs.io/en/latest/concepts/dw_scheme.html), namely one row per volume, each containing the vector [x y z b], where x, y, and z, correspond to the components of the diffusion gradient direction, and b is the b value in s/mm^2.</p> <p>Refers to:</p> <p>Rojas-Vite, Gilberto, Coronado-Leija, Ricardo, Narvaez-Delgado, Omar, Ramirez-Manzanares, Alonso, Marroquin, Jose-Luis, Noguez-Imm, Ramses, Aranda, Marcos L, Scherrer, Benoit, Larriva-Sahd, Jorge, Concha, Luis: Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration , Available at bioRxiv, doi.org/10.1101/571539, 2019.</p>
Replication package with data used in the study: The effect of code smells and design patterns on two change-related metrics: An exploratory study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells and design patterns on two change-related metrics: An exploratory study"</p> <p>This dataset contains the following folders:</p> <ul> <li>Aggregated Results Per System <ul> <li> For each subject system (AOI, Jedit, JHotDraw), we identify the following datasets: DP, nDP, S, nS ,SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</li> <li> Those csv files include raw data about every class in every release, the csv files also include columns which represent: <ul> <li>- CHURN (CLPLPR(C)*100): defined as the sum of added and deleted lines in a class in a release, adjusted to the size of the class and to the number of revisions in the release;</li> <li>- and FREQ (MTPR(C)*100): defined as the average number of changes made to a class in a release, adjusted to the number of revisions in the release</li> </ul> </li> </ul> </li> <li>Detailed Results Per Smell Or Pattern <ul> <li> For each specific code smell (S) in each public release (Rel) of all subject systems, we identify SDP and SnDP datasets. Each dataset is in a separate .csv file</li> <li> For each specific design pattern (DP) in each public release (Rel) of all subject systems, we identify SDP and nSDP </li> </ul> </li> <li>Plots<br> We also include QQ plots for CHURN, FREQ values for every dataset in every system, that could serve as a supplementary data for the paper.</li> </ul>
Text-fig. 1. Trigonid width characteristic of m2 in studied material. The Toringian sample tends to have relatively broader trigonid than the Biharian one. in Metric Characteristics Of Ursid Cheek Teeth From Za Hájovnou Cave (Javoříčko Karst, The Czech Republic) And Its Taxonomical Implication
Text-fig. 1. Trigonid width characteristic of m2 in studied material. The Toringian sample tends to have relatively broader trigonid than the Biharian one.
A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics
<p><strong>Motivation</strong></p> <p>Increasing heat stress due to climate change poses significant risks to human health and can lead to widespread social and economic consequences. Evaluating these impacts requires reliable datasets of heat stress projections. </p> <p><strong>Data Record</strong></p> <p><strong>CMIP6</strong></p> <p>We present a global dataset projecting future dry-bulb, wet-bulb, and wet-bulb globe temperatures under 1-4°C global warming scenarios (at 0.5°C intervals) relative to the preindustrial era, using outputs from 16 CMIP6 global climate models (GCMs) (Table 1). All variables were retrieved from the historical and SSP585 scenarios which were selected to maximize the warming signal.</p> <p>Wet-bulb and wet-bulb globe temperature are calculated using the Davies-Jones[1] and Liljegren[2] approach respectively.</p> <p>The dataset was bias-corrected against ERA5 reanalysis by incorporating the GCM-simulated climate change signal onto the ERA5 baseline (1950-1976) at a 3-hourly frequency. It therefore includes a 27-year sample for each GCM under each warming target.</p> <p>The data is provided at a fine spatial resolution of 0.25° x 0.25° and a temporal resolution of 3 hours, and is stored in a self-describing NetCDF format. Filenames follow the pattern "VAR_bias_corrected_3hr_GCM_XC_yyyy.nc", where:</p> <ul> <li> <p>"VAR" represents the variable (Ta, Tw, WBGT for dry-bulb, wet-bulb, and wet-bulb globe temperature, respectively),</p> </li> <li> <p>"GCM" denotes the CMIP6 GCM name,</p> </li> <li> <p>"X" indicates the warming target compared to the preindustrial period,</p> </li> <li> <p>"yyyy" represents the year index (0001-0027) of the 27-year sample</p> </li> </ul> <p><strong>Table 1 </strong>CMIP6 GCMs used for generating the dataset for Ta, Tw and WBGT.</p> <div> <table> <tbody> <tr> <td> <p>GCM</p> </td> <td> <p>Realization</p> </td> <td> <p>GCM grid spacing</p> </td> <td> <p>Ta</p> </td> <td> <p>Tw</p> </td> <td> <p>WBGT</p> </td> </tr> <tr> <td> <p>ACCESS-CM2</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>BCC-CSM2-MR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.1ox1.125o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CanESM5</p> </td> <td> <p>r1i1p2f1</p> </td> <td> <p>2.8ox2.8o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CMCC-CM2-SR5</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.94ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CMCC-ESM2</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.94ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CNRM-CM6-1</p> </td> <td> <p>r1i1p1f2</p> </td> <td> <p>1.4ox1.4o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> </td> </tr> <tr> <td> <p>EC-Earth3</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.7ox0.7o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>GFDL-ESM4</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.0ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>HadGEM3-GC31-LL</p> </td> <td> <p>r1i1p1f3</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>HadGEM3-GC31-MM</p> </td> <td> <p>r1i1p1f3</p> </td> <td> <p>0.55ox0.83o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>KACE-1-0-G</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>KIOST-ESM</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.9ox1.9o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MIROC-ES2L</p> </td> <td> <p>r1i1p1f2</p> </td> <td> <p>2.8ox2.8o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MIROC6</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.4ox1.4o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-HR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.93ox0.93o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-LR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.85ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> </tbody> </table> </div> <p><strong>ERA5</strong></p> <p>We also provide hourly Tw and WBGT derived from ERA5 reanalysis during 1950-2023 to enable analyses of heat stress changes from historical period to a warmer climate.</p> <p><strong> </strong></p> <p><strong>Data Access</strong></p> <p>An inventory of the dataset is available in this repository. The complete dataset, approximately 57 TB in size, is freely accessible via Purdue Fortress' long-term archive through Globus. The bias-corrected CMIP6 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?origin_id=6538f53a-1ea7-4c13-a0cf-10478190b901&origin_path=%2F">Globus Link1</a>, and the ERA5 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?destination_id=63242aea-d3e0-4aa4-9372-0e19dd0c6539&destination_path=%2F">Globus Link2</a>. After clicking the link, users may be prompted to log in with a Purdue institutional Globus account. You can switch to your institutional account, or log in via a personal Globus ID, Gmail, GitHub handle, or ORCID ID. Alternatively, the dataset can be accessed by searching for the universally unique identifier (UUID)—"6538f53a-1ea7-4c13-a0cf-10478190b901" for CMIP6, and “63242aea-d3e0-4aa4-9372-0e19dd0c6539” for ERA5 dataset—in Globus.</p> <p><strong>Dataset Validation</strong></p> <p>We validate the bias-correction method and show that it significantly enhances the GCMs' accuracy in reproducing both the annual average and the full range of quantiles for all metrics within an ERA5 reference climate state. This dataset is expected to support future research on projected changes in mean and extreme heat stress and the assessment of related health and socio-economic impacts.</p> <p>For a detailed introduction to the dataset and its validation, please refer to our data descriptor currently under review at Scientific Data. We will update this information upon publication.</p> <p><strong><br><br><br></strong></p>
The Influence of User Characteristics on Task Execution Metrics: Experimental material
<p>Material of paper: The Influence of User Characteristics on Task Execution Metrics</p>
Data from: Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics
Traditional means for scoring the effects of anti-cancer drugs on the growth and survival of cell lines is based on relative cell number in drug-treated and control samples and is seriously confounded by unequal division rates arising from natural biological variation and differences in culture conditions. This problem can be overcome by computing drug sensitivity on a per-division basis. The normalized growth rate inhibition (GR) approach yields per-division metrics for drug potency (GR50) and efficacy (GRmax) that are analogous to the more familiar IC50 and Emax values. In this work, we report GR-based, proliferation-corrected, drug sensitivity metrics for ~4,700 pairs of breast cancer cell lines and perturbagens. Such data are broadly useful in understanding the molecular basis of therapeutic response and resistance. Here, we use them to investigate the relationship between different measures of drug sensitivity and conclude that drug potency and efficacy exhibit high variation that is only weakly correlated. To facilitate further use of these data, computed GR curves and metrics can be browsed interactively at http://www.GRbrowser.org/.
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.