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42 results for “Evaluation Metrics”
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>
Online program metrics and evaluation for FMNP and NATA
<p>Restrictions on public gatherings in early 2020 due to the COVID-19 pandemic resulted in cancellation of in-person outreach programs offered by the Florida Master Naturalist Program and Natural Areas Training Academy, two successful University of Florida extension programs that provide natural history and resource management training to lay and professional audiences. In response, both programs rapidly transitioned to blended or 100% online educational methods to continue offering courses and maintain program operations. To assess participant responses to these changes, we used surveys and course registry data to evaluate and compare course enrollment, satisfaction, and outcomes among courses with new online formats to courses offered prior to the COVID-19 pandemic. We also examined logistical challenges and key programmatic elements that facilitated the transition of both programs to increased reliance on online education. Course participants responded favorably to classes offered online. Our results revealed an audience exists for online programming, that satisfaction with online courses was high and comparable to that measured for in-person courses, and that online approaches effectively transferred knowledge and promoted behavior change in participants. The transition to online programming required investments of time, energy, and in some cases, direct costs. However, this transition was greatly facilitated by the existence of well-defined program protocols, educational curricula, strong partnerships, and feedback mechanisms for both programs. Long-term investments in program structure, partnerships, and support systems enabled both programs to be resilient and adaptable and successfully implement online programming in response to the COVID-19 pandemic.</p>
Can Proposed Service Interface Metrics Effectively Evaluate the Quality of RESTful APIs? Dataset and Filter Criteria
<p>The csv datasets contains repositories with API metrics and software quality metrics.</p> <p>We use linear regression to predict a software quality metric with the API metrics.</p> <p> </p> <p>The .txt files contain the lists for the filter criteria to filter out unsuitable repositories.</p>
Evaluation metrics for eight types of gap-filled snow cover products in China and four schemes of combining multiple products
<p>This dataset contains the station-based evaluation metrics in terms of CK, R of SCD, and CWR values of SSD and SED for all the eight types of gap-filled products and the proposed schemes for combining multiple products. "TP" means the Tibetan Plateau.</p>
Evaluation metrics for eight types of gap-filled snow cover products in China and four schemes of combining multiple products
<p>This dataset contains the station-based evaluation metrics in terms of CK, R of SCD, and CWR values of SSD and SED for all the eight types of gap-filled products and the proposed schemes for combining multiple products. "TP" means the Tibetan Plateau.</p>
Evaluation of the Glucose Metrics After Eating Pizza Margherita in Hybrid Closed Loop Users
ClinicalTrials.gov study NCT05729776. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Improving Competency and Metrics for PoLypectomy Skills Using Evaluation Tools and VidEo Feedback (COMPLETE)
ClinicalTrials.gov study NCT03115008. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Tandem Control-IQ Evaluation Regarding Glucose Metrics, Sleep, and Health Economics
ClinicalTrials.gov study NCT05969106. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluating restoration success using metric-based indicators of ecosystem recovery in tidal marshes along the northern Gulf of Mexico
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Online program metrics and evaluation for FMNP and NATA
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Data from: Measuring and interpreting sexual selection metrics - evaluation and guidelines
(1) Routine assessments of overall sexual selection, including comparisons of its direction and intensity between sexes or species, rely on summary metrics that capture the essence of sexual selection. Nearly all currently employed metrics require population-wide estimates of individual mating success and reproductive success. The resulting sexual selection metrics, however, can heavily and systematically vary with the chosen approaches in terms of sampling, measurement, and analysis. (2) Our review illustrates this variation using the Bateman gradient, a particularly prominent sexual selection metric. It represents the selection gradient on mating success and – given the latter's pivotal role in defining sexual selection – reflects a trait-independent integrative proxy for the maximum strength of sexual selection. Drawing from a recent meta-analysis, we evaluate potential biases arising from study design, data collection, and parameter estimation, and provide suggestions to mitigate such biases in future studies. (3) With respect to study design, we argue that currently almost inexistent manipulative studies must complement the dominating correlative studies to inform us about causality in sexual selection. With respect to data collection, we outline how different measures of mating and reproductive success affect the components of sexual (and natural) selection that are reflected in standard summary metrics. With respect to parameter estimation, we show the potential impact of decisions about data inclusion and the chosen quantitative approach on inferences of sexual selection and its sex difference. (4) We expect this meta-analytical review to aid future studies in providing less biased and more informative estimates of sexual selection.
Metabolism Evaluation Through Resting Indirect Calorimetry in Bladder Cancer (METRICS)
ClinicalTrials.gov study NCT06721260. IPD Sharing: Not stated. Countries: 1. Publications: 0.
To Evaluate the Best Metric for Inspection Time During a Diagnostic and/or Surveillance Upper Gastrointestinal Endoscopy (Age ≥ 18 Years, Informed Consent), Between Total Inspection Time Versus Withdr
ClinicalTrials.gov study NCT06696209. IPD Sharing: YES. Countries: 1. Publications: 0.
EVA: Evaluation of EVOKE Therapy Metrics Generated Using the Evoke System With EVA
ClinicalTrials.gov study NCT07267715. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Measuring and interpreting sexual selection metrics - evaluation and guidelines
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A Survey of Metrics for Performance Evaluation of Prognostics
Prognostics is an emerging concept in condition basedmaintenance(CBM)ofcriticalsystems.Alongwith developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenancehave been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.
Metrics for Offline Evaluation of Prognostic Performance
Prognostic performance evaluation has gained significant attention in the past few years.*Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end- user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements. Very little attention has been focused on establishing a standardized approach to compare different efforts. This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. These metrics have the capability of incorporating probabilistic uncertainty estimates from prognostic algorithms. In addition to quantitative assessment they also offer a comprehensive visual perspective that can be used in designing the prognostic system. Several methods are suggested to customize these metrics for different applications. Guidelines are provided to help choose one method over another based on distribution characteristics. Various issues faced by prognostics and its performance evaluation are discussed followed by a formal notational framework to help standardize subsequent developments.
Evaluating Algorithm Performance Metrics Tailored for Prognostics
Prognostics has taken center stage in Condition Based Maintenance (CBM) where it is desired to estimate Remaining Useful Life (RUL) of a system so that remedial measures may be taken in advance to avoid catastrophic events or unwanted downtimes. Validation of such predictions is an important but difficult proposition and a lack of appropriate evaluation methods renders prognostics meaningless. Evaluation methods currently used in the research community are not standardized and in many cases do not sufficiently assess key performance aspects expected out of a prognostics algorithm. In this paper we introduce several new evaluation metrics tailored for prognostics and show that they can effectively evaluate various algorithms as compared to other conventional metrics. Four prognostic algorithms, Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Polynomial Regression (PR), are compared. These algorithms vary in complexity and their ability to manage uncertainty around predicted estimates. Results show that the new metrics rank these algorithms in a different manner; depending on the requirements and constraints suitable metrics may be chosen. Beyond these results, this paper offers ideas about how metrics suitable to prognostics may be designed so that the evaluation procedure can be standardized.
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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.