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753 results for “metrics”
Pareto Fronts and metrics for the optimization design water distribution networks (Hanoi, Fossolo, Modena, and Balerma)
<p>Approximations to the Best-known PFs using NSGA-II retrofitted by OPUS for Hanoi, Fossolo, Modena, and Balerma, Approximations to the Best-known PFs using NSGA-II for Hanoi, Fossolo, Modena and Balerma, Hypervolume (HV) and Modified Inverted Generational Distance (IGD+) for Hanoi, Fossolo, Modena, and Balerma.</p> <ul> <li>Hanoi (30 simulations)</li> <li>Fossolo (30 simulations)</li> <li>Modena (1 simulation)</li> <li>Balerma (1 simulation)</li> </ul>
Raw sequencing metrics from two different prep methods for obtaining SARS-CoV2 genomes
<p>Sequencing_metrics_IlluminaDNAprep.xlsx contains metrics used for the determination of SARS-CoV2 genome assembly success using Illumina's DNA prep adapter-tagmentation method, coupled with the ARTIC protocol for gene-specific amplification of SARS-CoV2</p> <p>Sequencing_metrics_Seqwellprep.xlsx contains metrics used for the determination of SARS-CoV2 genome assembly success using Seqwell's Plexwell384 adapter-tagmentation method, coupled with the ARTIC protocol for gene-specific amplification of SARS-CoV2</p>
Data from: The shape of avian eggs: assessment of a novel metric for quantifying eggshell conicality
<p>Studying avian egg shape and other aspects of its morphology has recently undergone a renaissance. Yet, most studies rely solely on two metrics for the quantification of egg shape: elongation and asymmetry. The difficulty of additionally quantifying the curvature of an eggshell has yielded many attempts including those with complex equations and spatial modeling techniques based on digitized images. These have lacked an independent single-variable metric, hampering comparative studies. We propose a metric for one common quality of egg shape, conicality, which is notably variable in diverse species' calcareous shells including shorebirds and non-avian theropods. This metric utilizes multiple measurements of the slope along an eggs profile to produce a distribution of angular measurements, which can be analyzed with a Kurtosis (K) value. This metric was tested with sets of computer modeled and 3D printed egg forms, where elongation, the percentage of conicality, and the relative curvature of the shell profile were controlled. For applicability to natural eggs and their diversity across taxa, the Kurtosis value was used to quantify the gradient of conicality across a focal avian family, Alcidae, where the Kurtosis value successfully identified the most conical eggs using qualitative descriptions from well-established literature. Given the significance of egg morphology and profile curvature to structural integrity, surface-area to volume ratio, egg mobility/stability, nesting behavior, and embryonic development, our proposed measure of conicality could prove a useful variable to the study of avian and non-avian egg-producing species. </p>
Data from: Energetic fitness: field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics
1) Evaluating the fitness of organisms is an essential step towards understanding their responses to environmental change. Connections between energy expenditure and fitness have been postulated for nearly a century. However, testing this premise among wild animals is constrained by difficulties in measuring energy expenditure while simultaneously monitoring conventional fitness metrics such as survival and reproductive output. 2) We addressed this issue by exploring the functional links between field metabolic rate (FMR), body condition, sex, age and reproductive performance in a wild population. 3) We deployed 3D accelerometers on 115 Adélie penguins (Pygoscelis adeliae) during four breeding seasons at one of the largest colonies of this species, Cape Crozier, on Ross Island, Antarctica. The demography of this population has been studied for the past 18 years. From accelerometry recordings, collected for birds of known age and breeding history, we determined the vector of the dynamic body acceleration (VeDBA) and used it as a proxy for FMR. 4) This allowed us to demonstrate relationships between FMR, a breeding quality index (BQI), and body condition. Notably, we found a significant quadratic relationship between mean VeDBA during foraging and BQI for experienced breeders, and individuals in better body condition showed lower rates of energy expenditure. 5) We conclude that using FMR as a fitness component complementary to more conventional fitness metrics will yield greater understanding of evolutionary and conservation physiology.
Metrics of diabetes risk are only minimally improved by exercise training in postmenopausal breast cancer survivors
<p>Context</p> <p>Insulin resistance is a risk factor for breast cancer recurrence. How exercise training changes fasting and post-glucose insulin resistance in breast cancer survivors is unknown.</p> <p>Objective</p> <p>To evaluate exercise-induced changes in post-glucose ingestion insulin concentrations, insulin resistance, and their associations with cancer-relevant biomarkers in breast cancer survivors.</p> <p>Setting</p> <p>The University of Massachusetts Kinesiology Department.</p> <p>Participants</p> <p>Fifteen postmenopausal breast cancer survivors not meeting the physical activity guidelines (150 minutes/week of exercise).</p> <p>Intervention</p> <p>a supervised 12-week aerobic exercise program (60 min/day, 3-4 days/week).</p> <p>Main outcome measures</p> <p>Post-glucose ingestion insulin was determined by peak insulin and area under the insulin curve (iAUC) during a five-sample oral glucose tolerance test. Insulin sensitivity was estimated from the Matsuda composite insulin sensitivity index (C-ISI). Changes in fitness and body composition were determined from submaximal VO<sub>2peak</sub> and dual energy X-ray absorptiometry (DEXA).</p> <p>Results</p> <p>Participants averaged 156.8±16.6 minutes/week of supervised exercise. Estimated VO<sub>2peak</sub> significantly increased (+2.8±1.4 ml/kg/min, p<0.05) and body weight significantly decreased (-1.1±0.8 kg, p<0.05) following the intervention. There were no differences in fasting insulin, iAUC, C-ISI or peak insulin following the intervention. Insulin was only significantly lower 120 minutes following glucose consumption (68.8 ± 34.5 vs. 56.2 ± 31.9 uU/ml, p<0.05), and there was a significant interaction with past/present aromatase inhibitor (AI) use for peak insulin (-11.99 (non-AI) vs +13.91 (AI) uU/mL) and iAUC (-24.03 (non-AI) vs +32.73 (AI) uU/mL).</p> <p>Conclusions</p> <p>Exercise training had limited overall benefits on insulin concentrations following glucose ingestion in breast cancer survivors but was strongly influenced by AI use.</p>
Towards the formal verification of data-intensive applications through metric temporal logic
p>The dataset consists of a set of model descriptions representingnbsp;span>Storm topologies. It is designed on purpose to show the approach presented in the paper quot;/span>span>Towards the formal verification of data-intensive applications through m/span>span>etric/span>span>nbsp;temporalnbsp;/span>span>logicquot; (F. Marconi, M.M. Bersani, M. Erascu and M. Rossi) which focuses on the analysisnbsp;/span>span>of bottleneck nodes of data intensive applications implemented with Storm./span>/p>
Job metrics for Big Data Technologies
<p>This dataset contains metrics for a 10 minute job running on YARN. More specifically it contains metrics for:</p> <ul> <li>YARN</li> <li>HDFS</li> <li>System Metrics (CPU, RAM, HDD, Interface etc.)</li> </ul> <p> </p> <p> </p>
Aging-related bugs and software complexity metrics
<p>This dataset contains information on aging-related bugs found in two open-source projects (the Linux kernel and the MySQL DBMS). This dataset has been used to investigate defect prediction approaches for aging-related bugs, by using software complexity metrics and machine learning techniques. New software complexity metrics were proposed in this study to support defect prediction ("aging-related" metrics).</p> <p>The dataset contains an ARFF file for each subsystem of the open-source projects. Each row of the ARFF file contains:</p> <p>- The name of a file in the project;</p> <p>- "Program size" metrics for the file (columns from 2 to 50);</p> <p>- "McCabe's cyclomatic complexity" metrics for the file (columns from 51 to 68);</p> <p>- "Halstead" metrics for the file (columns from 69 to 77);</p> <p>- "Aging-related" metrics for the file (columns from 78 to 83);</p> <p>- The number of aging-related bugs found in the file.<br> </p>
Supplementary material for: Phylogenetic biodiversity metrics should account for both accumulation and attrition of evolutionary heritage
<p>Phylogenetic metrics are essential tools used in the study of ecology, evolution and conservation. Phylogenetic diversity (PD) in particular is one of the most prominent measures of biodiversity, and is based on the idea that biological features accumulate along the edges of phylogenetic trees that are summed. We argue that PD and many other phylogenetic biodiversity metrics fail to capture an essential process that we term attrition. Attrition is the gradual loss of features and other sources of variety through causes other than extinction. Here we introduce `EvoHeritage', a generalisation of PD that is founded on the joint processes of accumulation and attrition of features. We argue that whilst PD measures evolutionary history, EvoHeritage is required to capture a more pertinent subset of evolutionary history including only components that have survived attrition. We show that EvoHeritage is not the same as PD on a tree with scaled edges; instead, accumulation and attrition interact in a more complex non-monophyletic way that cannot be captured by edge lengths alone. This leads us to speculate that the one-dimensional edge lengths of classic trees may be insufficiently flexible to capture the nuances of evolutionary processes. We derive a measure of EvoHeritage and show that it elegantly reproduces species richness and PD at opposite ends of a continuum based on the intensity of attrition. We demonstrate the utility of EvoHeritage in ecology as a predictor of community productivity compared with species richness and PD. We also show how EvoHeritage can quantify living fossils and resolve their associated controversy. We suggest how the existing calculus of PD-based metrics and other phylogenetic biodiversity metrics can and should be recast in terms of EvoHeritage accumulation and attrition.</p>
Induced pluripotent stem cell-derived cardiomyocyte in vitro models: tissue fabrication protocols, assessment methods, and quantitative maturation metrics for benchmarking progress
<p>The advent of human induced pluripotent stem cells (hiPSCs) and techniques to differentiate cardiomyocytes from them has opened a viable path to creating <em>in vitro</em> models of normal and diseased hearts, accelerating more predictive drug screening and therapeutic strategies for cardiac pathologies. Currently, hiPSC-derived cardiomyocytes (hiPSC-CMs) are more similar to fetal than adult cardiomyocytes, leading many in the field to explore approaches to enhance cell and tissue maturation. There are over 2,000 studies utilizing hiPSC-CMs in models composed of various combinations of cell and extracellular matrix components, using a plethora of differentiation protocols, culture formats, and methods for quantifying cardiomyocyte function. To assess the current state of this rapidly growing area, we systematically analyzed 300 studies using hiPSC-CM models for their selection of hiPSC lines, hiPSC-CM differentiation protocols, types of <em>in vitro </em>models, maturation techniques, and metrics used to assess cardiomyocyte functionality and maturity. Here, we provide the data compiled from our analysis of these papers so others in the field can utilize it to inform their research.</p> <p>Based on this analysis, we highlight the diversity of, and current trends in, <em>in vitro</em> model designs and highlight the most common and promising practices for functional assessments. We further analyzed outputs spanning structural maturity, contractile function, electrophysiology, and gene expression and note field-wide improvements over time. Finally, we observe that a persistent lack of coordination amongst investigators is limiting the field's ability to benchmark and advance hiPSC-CM function against previous studies. We discuss opportunities to collectively pursue the common goal of hiPSC-CM model development, maturation, and assessment that we believe are critical to drive the entire community forward in engineering mature cardiac tissue.</p>
Calculating functional diversity metrics using neighbor-joining trees
<p>The study of functional diversity (FD) provides ways to understand phenomena as complex as community assembly or the dynamics of biodiversity change under multiple pressures. Different frameworks are used to quantify FD, either based on dissimilarity matrices (e.g., Rao entropy, functional dendrograms) or multidimensional spaces (e.g., convex hulls, kernel-density hypervolumes), each with their own strengths and limits. Frameworks based on dissimilarity matrices either do not enable the measurement of all components of FD (i.e., richness, divergence, and regularity), or result in the distortion of the functional space. Frameworks based on multidimensional spaces do not allow for comparisons with phylogenetic diversity (PD) measures and can be sensitive to outliers.</p> <p>We propose the use of neighbor-joining trees (NJ) to represent and quantify FD in a way that combines the strengths of current frameworks without many of their weaknesses. Importantly, our approach is uniquely suited for studies that compare FD with PD, as both share the use of trees (NJ or others) and the same mathematical principles.</p> <p>We test the ability of this novel framework to represent the initial functional distances between species with minimal functional space distortion and sensitivity to outliers. The results using NJ are compared with conventional functional dendrograms, convex hulls, and kernel-density hypervolumes using both simulated and empirical datasets.</p> <p>Using NJ, we demonstrate that it is possible to combine much of the flexibility provided by multidimensional spaces with the simplicity of tree-based representations. Moreover, the method is directly comparable with taxonomic diversity (TD) and PD measures, and enables quantification of the richness, divergence and regularity of the functional space.</p>
STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping
<p>Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at <a href="https://github.com/JEFworks-Lab/STalign">https://github.com/JEFworks-Lab/STalign</a> and as supplementary software with additional documentation and tutorials available at <a href="https://jef.works/STalign">https://jef.works/STalign</a>.</p> <p>Here we have included alignment results that were used in performance analysis of STalign:</p> <p>We aligned Slice 2 Replicate 3 to Slice 2 Replicate 2 of the MERFISH mouse coronal brain sections available from Vizgen Data Release V1.0. May 2021 (<a href="https://info.vizgen.com/mouse-brain-map">https://info.vizgen.com/mouse-brain-map</a>).</p> <ul> <li>STalign_S2R3_to_S2R2.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to S2R2 with STalign, cell positions of S2R3 after supervised affine alignment to S2R2, and counts for genes and blanks.</li> <li>STalign_S2R2.csv.gz contains cell ids, cell centroid positions of S2R2 and counts for genes and blanks.</li> </ul> <p>Additionally, we aligned Slice 2 Replicate 3 to a Visium dataset of an FFPE preserved adult mouse brain were obtained from the 10X Datasets website for <em>Spatial Gene Expression Dataset by Space Ranger 1.3.0</em> (<a href="https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0">https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0</a>).</p> <ul> <li>STalign_S2R3_to_Visium.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to Visium H&E staining with STalign, and counts for genes and blanks.</li> </ul> <p>Furthermore, we performed alignments with the 50um resolution 3D Allen Reference Atlas Nissl common coordinate framework, CCF (<a href="https://help.brain-map.org/display/mouseconnectivity/API">https://help.brain-map.org/display/mouseconnectivity/API</a>). We applied STalign to align the Allen CCF to each of the 9 MERFISH slices (3 slice locations with 3 biological replicates) provided by Vizgen. Because the Allen CCF has annotated brain regions, we were able to lift over those brain region annotations to label all cells in the MERFISH datasets.</p> <p>Also, since the STalign mappings from the Allen CCF to the MERFISH slices are invertible, for each slice we can apply the inverse of the mapping to get cell positions in the Allen CCF coordinates.</p> <ul> <li>STalign_SXRX_with_structure_id_name.csv.gz contains cell ids for Slice X Replicate X, original cell centroid positions, cell xyz-coordinates in Allen CCF, brain structure id per cell, brain structure acronym</li> </ul> <p>To evaluate the 3D CCF alignment, we performed unified transcriptional clustering analysis and cell-type annotation. All MERFISH datasets were combined. Transcriptional clustering analysis and cell type annotation was performed using the SCANPY package [version 1.9.1]. Data were normalized to counts per million (scanpy: normalize_total) and log transformed (scanpy: log1p). PCA (scanpy: pca) was computed on the cell by gene matrix. A neighborhood graph of cells using the top 10 PCs and 10 nearest neighbors was created (scanpy: neighbors), and Leiden clustering was performed on this graph (scanpy: leiden) to identify 29 clusters. Differentially expressed genes were extracted from each cluster (scanpy: rank_genes_groups), and cell-types were annotated based on marker genes in each cluster.</p> <ul> <li>STalign_celltypeannotations_merfishslices_v2.csv.gz contains for all nine slices cell ids and cell type annotations</li> </ul> <p>This updated (v2) cell-type annotation file contains a new column with simplified cell-types. Briefly, we fixed typos, standardized lower case/upper case formats, merged subclasses of each cell-types. For example, subclasses of astrocytes­­, which are originally labeled as “Astrocytes”, “Astrocytes(1)”, “Astrocytes(2)”, “Astrocytes(3)”, are all labeled as “Astrocytes” in the added column.</p> <p>Note: Cell ids may have been mutated from original string of numbers through reading and writing across programming languages that handle numbers with different precision. If using R to read the files shared here, one can find the cells in STalign_celltypeannotations_merfishslices_v2.csv.gz that correspond with STalign_SXRX_with_structure_id_name.csv.gz when cell ids are formatted as a double in scientific notation, which is how R will read the file automatically.</p>
Data from: Warm-up and metrics of song performance: a commentary on Vazquez-Cardona et al
<p>Interactive plots that re-generate random data in Figure 2 of Cardoso (2024) "Warm-up and metrics of song performance: a commentary on Vazquez-Cardona et al." Behavioral Ecology.</p>
DeviantArt Image and Metric Dataset: results by topic
<p>Dataset of images stored by deviantart.com website, as a result of the search for 20 topics and the images included in the first 15 pages of each topic, downloaded on 2024-04-14. The topics searched are: </p> <p>"Fantasy art", "Science fiction art", "Anime and manga art", "Fan art (for specific fandoms)", "Digital paintings", "Traditional drawings", "Character designs", "Creature concepts", "Landscape art", "Abstract art", "Surrealism", "Steampunk art", "Cyberpunk art", "Gothic art", "Horror art", "Cosplay photography", "Pixel art", "Concept art", "Comics and graphic novels", "Street art and graffiti".</p> <p>The dataset contains 7067 image records related to these topics.</p> <p>The fields included in the dataset are:<br>- <strong>search_topic</strong>: the image is a search result for this topic<br>- <strong>page_num</strong>: the image appears on this search page number<br>- <strong>image_page</strong>: link to the page with the image information<br>- <strong>image_url</strong>: link to the image<br>- <strong>image_title</strong>: image title<br>- <strong>image_author</strong>: author of the image<br>- <strong>image_favs</strong>: number of times the image has been “liked”<br>- <strong>image_com</strong>: number of comments that the image has<br>- <strong>image_views</strong>: number of views to the image<br>- <strong>private_collections</strong>: number of times it has been included in a private collection<br>- <strong>tags</strong>: tags that have been assigned to the image to facilitate its discovery<br>- <strong>location</strong>: country or geographical location, if the author wants to identify it<br>- <strong>description</strong>: open text field created by the author. It may include technical details or links to the author's social networks.<br>- <strong>image_px</strong>: dimensions of the image, in pixels.<br>- <strong>image_size</strong>: image weight in MB.<br>- <strong>published_date</strong>: image publication date.<br>- <strong>last_comment</strong>: the last comment added to the image.<br>- <strong>license</strong>: image license.</p> <p>Individual image licenses must be respected by users of this dataset.</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y13 & Y14
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 2008 (Y13) and 2009 (Y14).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y17 & Y18
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 2012 (Y17) and 2013 (Y18).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y7 & Y8
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 2002 (Y7) and 2003 (Y8).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y17 & Y18
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2012 (Y17) and 2013 (Y18).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y3 & Y4
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 1998 (Y3) and 1999 (Y4).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y15 & Y16
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2010 (Y15) and 2011 (Y16).</p>
ScienceDex guides
Understand access before you commit
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.