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753 results for “metrics”
Metrics: NCBI data coverage
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Metrics: EOL core metrics 2015
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Metrics: iNaturalist data coverage
For species, genus and family level taxa.
Metrics: BHL data coverage
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Metrics: GGBN data coverage
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Clustering metrics over multiple configuration of the BERTopic pipeline for two datasets.
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Concept relevance metrics in the Medical Subject Headings (MeSH)
<p>In the MeSH concept relevance dataset, we define metrics to quantify relevance within this knowledge organization system and have applied them to the MeSH hierarchy. The research uses the 2022 PubMed collection, which contains over 33 million articles, and builds citation networks from January 2014 to December 2021. We quantify the relevance of MeSH concepts based on disruptiveness, influence, informativeness, usefulness, and concept relevance, using metrics such as disruption, graph centrality, entropy, category utility, and reciprocal rank fusion.</p>
ProteinInvBench: Benchmarking Protein Design on Diverse Tasks, Models, and Metrics
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EcoAdvDepression Post-Intervention Tracked, Metrics, and Clean Tracked Centres
<p>Tracked files include the raw frame-by-frame predictions of the YOLOv8 Model over the Resized video files for 26 fish across 52 trials stored as CSV files. Clean Tracked Centres include the cleaned predictions consisting of the centres of the predicted bounding boxes, additionally accounting for incorrect predictions for 26 fish across 52 trials stored as CSV files. Metrics consist of the analysed inferences of all the clean tracked centres producing various movement and social interaction parameters in a single CSV file. Data for the post-intervention stage.</p>
EcoAdvDepression Pre-Intervention Tracked, Metrics, and Clean Tracked Centres
<p>Tracked files include the raw frame-by-frame predictions of the YOLOv8 Model over the Resized video files for 26 fish across 52 trials stored as CSV files. Clean Tracked Centres include the cleaned predictions consisting of the centres of the predicted bounding boxes, additionally accounting for incorrect predictions for 26 fish across 52 trials stored as CSV files. Metrics consist of the analysed inferences of all the clean tracked centres producing various movement and social interaction parameters in a single CSV file. Data for the pre-intervention stage.</p>
Trained Surface Layer Models and Metrics for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
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Metrics: GBIF data coverage
<p>Includes species, genus and family level taxa.</p>
S2 Characteristics of rain events used for calculating performance metrices
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Data for Methane Emission Metrics Assessment: Sum44
<h1>Synthetic Datasets</h1> <p>Synthetic datasets created for "Assessing alternative methane emission metrics conducive to quantifying global warming and setting near-term climate goals". All datasets were uploaded as .ZIP files and data is in .TXT format.</p> <p>1) Synthetic Dataset - contains 1000 methane emissions pathways and their AGTP-derived temperature outputs used in the main text. Data generated according to methods detailed in the article text. </p> <p>2) Balanced Synthetic Dataset - contains 1000 methane emissions pathways and their AGTP-derived temperature outputs used in the Supplemental Materials Section 1.1. Pathways balanced for increases and decreases.</p> <p>3) Idealized Pathways - contains the step and pulse methane emissions pathways and their AGTP-derived temperature outputs. </p>
Data from: Assessing restoration success by predicting time to recovery – but by which metric?
<p><b>1. </b>Restoration of<b> </b>degraded ecosystems may take decades or even centuries. Accordingly, information about the current direction and speed of recovery provided by methods for predicting time to recovery may give important feedback to restoration schemes. While predictions of time to recovery have so far been based mostly upon change in species richness and other univariate predictors, the novel ordination-regression based approach (ORBA) affords a multivariate approach based upon species compositional change.</p> <p><b>2.</b> We used species composition data from four alpine spoil heaps in western Norway, recorded at three time points, to predict time to recovery using ORBA. This approach uses distances between restored plots and reference plots along a successional gradient, represented by a vector in ordination space, to model linear or asymptotic relationships of compositional change as a function of time. Results from ORBA were compared with results from models of more generic univariate attributes, i.e. total cover, species richness and properties of the physical environment as functions of time.</p> <p><b>3.</b> ORBA predictions of time to species compositional recovery varied from less than 60 years with linear models to 115–212 years with asymptotic models. The long estimated time to recovery suggests that the restoration schemes adopted for these spoil heaps are likely to be suboptimal.</p> <p><b>4. </b>Much shorter<b> </b>time to recovery was predicted<b> </b>from some of the more generic univariate attributes, i.e. species richness and total cover, than from species composition. Given the current rates of recovery, most spoil heaps will reach reference levels for total cover and species richness within 50 years while predictions indicate that 67–111 years are needed to restore levels of soil organic matter and pH.</p> <p><b>5.</b> <i>Synthesis and applications</i>. Species composition and soil conditions provide information of generally higher relevance for evaluation of ecosystem recovery processes than the most commonly used metric to assess restoration success, species richness. Species richness is decoupled from species compositional recovery, and likely to be a generally poor measure of restoration success. We therefore encourage further improvement of methods like the ordination-regression based approach that use species compositional data to predict time to recovery.09-Oct-2019</p>
Data from: Beyond thermal limits: comprehensive metrics of performance identify key axes of thermal adaptation in ants
How species respond to temperature change depends in large part on their physiology. Physiological traits, such as critical thermal limits (CTmax and CTmin), provide estimates of thermal performance but may not capture the full impacts of temperature on fitness. Rather, thermal performance likely depends on a combination of traits—including thermal limits—that vary among species. Here we examine how thermal limits correlate with the main components that influence fitness in ants. First, we compare how temperature affected colony survival and growth in two ant species that differ in their responses to warming in the field—Aphaenogaster rudis (heat-intolerant) and Temnothorax curvispinosus (heat-tolerant). We then extended our study to compare CTmax, thermal requirements of brood, and yearly activity season among a broader set of ant species. While thermal limits were higher for workers of T. curvispinosus than A. rudis, T. curvispinosus colonies also required higher temperatures for survival and colony growth. This pattern generalized across 17 ant species, such that species whose foragers had a high CTmax also required higher temperatures for brood development. Finally, species whose foragers had a high CTmax had relatively short activity seasons compared with less heat-tolerant species. The relationships between CTmax, thermal requirements of brood, and seasonal activity suggest two main strategies for growth and development in changing thermal environments: one where ants forage at higher temperatures over a short activity season, and another where ants forage at lower temperatures for an extended activity season. Where species fall on this spectrum may influence a broad range of life-history characteristics and aid in explaining the current distributions of ants as well as their responses to future climate change.
Alert burden in pediatric hospitals: A cross-sectional analysis of six academic pediatric health systems using novel metrics
<p class="Pediatrics">Background: Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.</p> <p class="Pediatrics">Objective: (1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics.</p> <p class="Pediatrics">Materials and Methods: We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per patient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least one EHR log in the system). We assessed intra- and inter-institutional variation and how alert burden rankings differed based on the chosen metric.</p> <p class="Pediatrics">Results: Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden.</p> <p>Conclusion: Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.</p>
Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions
<p>We uploaded the 15 morphological features of 91 samples of 85 patients analyzed in the manuscript: Fusco, Roberta, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria L. Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti, and Antonella Petrillo. 2021. "Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions" Applied Sciences 11, no. 4: 1880. https://doi.org/10.3390/app11041880</p>
Cognitive maps in the wild: Revealing the use of metric information in black howler monkeys' route navigation
<p>When navigating, wild animals rely on internal representations of the external world to take movement decisions – called "cognitive maps". As a rule, flexible navigation is hypothesized to be supported by sophisticated spatial skills (i.e., Euclidean cognitive maps); however, constrained movements along habitual routes is the most commonly reported navigation strategy. Even though incorporating metric information (i.e., distances and angles between locations) in route-based cognitive maps would likely enhance an animal's navigation efficiency, there has been no evidence of this strategy reported for non-human animals to date. Here, we examine the properties of the cognitive map used by a wild population of primates by testing a series of cognitive hypotheses against spatially-explicit movement simulations. We collected 3104 hours of ranging and behavioural data on five groups of black howler monkeys (<i>Alouatta pigra</i>) at Palenque National Park, Mexico, from September 2016 through August 2017. We simulated correlated-random walks mimicking the ranging behaviour of the study subjects and tested for differences between observed and simulated movement patterns. Our results indicated that black howler monkeys engaged in constrained movement patterns characterized by a high path recursion tendency, which limited their capacity to travel in straight lines and approach feeding trees from multiple directions. In addition, we found that the structure of observed route networks was more complex and efficient than simulated route networks, suggesting that black howler monkeys incorporate metric information into their cognitive map. Our findings not only expand the use of metric information during route navigation to non-human animals but also highlight the importance of considering efficient route-based navigation as a cognitively demanding mechanism.</p>
A parasite reduction conservation intervention does not improve fledging success or most condition metrics for purple martins
<p>Purple Martins (<i>Progne subis subis</i>) have an unusually close relationship with humans, as they nest exclusively in man-made nest boxes. Current conservation policy directly promotes further interaction with this species by advocating regular replacement of nest materials during the nestling phase to reduce ectoparasite load and increase nestling fitness. We conducted the first test of the efficacy of this recommendation and found that it was partially effective in reducing parasite abundance, but had no effect on nestling fledging success, body mass, leukocyte count, or triglyceride or uric acid concentration. We found a small but significant increase in nestling hematocrit associated with nest material replacement, implying that parasites may induce nestling anemia. Contrary to our expectations, we also found elevated heterophil/lymphocyte ratios in nestlings with replacements, possibly indicating elevated physiological stress associated with nest replacements. Based on our results, we do not recommend nest material replacements to combat routine parasite infestations.</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.