Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
753
datasets available to search
ShareScore release 0.9.0
Dataset results
753 results for “metrics”
Deliberate Practice With Validated Metrics Improves Skills Acquisition
ClinicalTrials.gov study NCT03154372. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Bevacizumab Versus Docetaxel/Erlotinib on Tumor Metrics in Patients With Previously Untreated Advanced Non Small Cell Lung Cancer
ClinicalTrials.gov study NCT04257864. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Whole-Brain Tumor Burden Metrics with Inflammatory and Molecular Markers to Predict Postoperative Neurocognitive Decline in Glioma
ClinicalTrials.gov study NCT06885333. IPD Sharing: YES. Countries: 0. Publications: 0.
Glucose Disposal Rate as a Metric for Detection of Metabolic Associated Fatty Liver
ClinicalTrials.gov study NCT06923215. IPD Sharing: NO. Countries: 0. Publications: 0.
Investigating Trends in Compliance With Quality Assurance Metrics
ClinicalTrials.gov study NCT03119532. IPD Sharing: NO. Countries: 0. Publications: 0.
Oak woodland health assessment in Orange County metrics
Open the record for dataset details and reuse information.
A quantitative metric of pioneer activity reveals that HNF4A has stronger in vivo pioneer activity than FOXA1
GEO Series GSE204726. Homo sapiens. 24 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
A Survey of Health Management User Objectives in Aerospace Systems Related to Diagnostic and Prognostic Metrics
One of the most prominent technical challenges to effective deployment of health management systems is the vast difference in user objectives with respect to engineering development. In this paper, a detailed survey on the objectives of different users of health management systems is presented. These user objectives are then mapped to the metrics typically encountered in the development and testing of two main systems health management functions: diagnosis and prognosis. Using this mapping, the gaps between user goals and the metrics associated with diagnostics and prognostics are identified and presented with a collection of lessons learned from previous studies that include both industrial and military aerospace applications.*
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.
Prognostic Performance Metrics
This chapter presents several performance metrics for offline evaluation of prognostics algorithms. A brief overview of different methods employed for performance evaluation is also included. Because metrics developed in the context of forecasting differ from prognostics in the systems health management context and to account for the additional considerations, metrics specialized for prognostics (but not necessarily for the applica- tion) are needed. These metrics were introduced, and their use was illus- trated with recommendations.
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.
Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics
Structural damage to ball grid array interconnects incurred during vibration testing has been monitored in the prefailure space using resistance spectroscopy-based state space vectors, rate of change of the state variable, and acceleration of the state variable. The technique is intended for condition monitoring in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss of functionality are too high to bear. Future state of the system has been estimated based on a second-order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of the prognostic health management algorithm during the vibration test has been quantified using performance evaluation metrics. The method- ology has been demonstrated on leadfree area-array electronic assemblies subjected to vibration. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including α − λ precision, β accuracy, and relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness.
On Applying the Prognostic Performance Metrics
Prognostics performance evaluation has gained significant attention in the past few years. *As prognostics technology matures and more sophisticated methods for prognostic uncertainty management are developed, a standardized methodology for performance evaluation becomes extremely important to guide improvement efforts in a constructive manner. This paper is in continuation of previous efforts where several new evaluation metrics tailored for prognostics were 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. Several shortcomings identified, while applying these metrics to a variety of real applications, are also summarized along with discussions that attempt to alleviate these problems. Further, these metrics have been enhanced to include the capability of incorporating probability distribution information from prognostic algorithms as opposed to evaluation based on point estimates only. Several methods have been suggested and guidelines have been provided to help choose one method over another based on probability distribution characteristics.
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.
Metrics for Evaluating Performance of Prognostics Techniques
Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. Along with 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 maintenance have 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.
Tropical CPR identification data for Organization Metric
There are three kinds of files are in one zip file. 1) Cloud-Precipitation (hybrid) regime information projected onto hourly IMERG precipitation data (Version Pr6x7 in 15S-15N, 2000.06.01-2021.06.29 (7699 days), 24 steps per day, -24.5 to 24.5N (nlat=50), and -179.5 to 179.5 (nlon=360); Binary, 2-byte integer) 2) Example python codes to calculate ABCOP and other organization metrics 3) Calculated organization metrics in select domain from CPR data (#1) [Reference: 2022JD036665 (AGU JGR-A submitted)] [See also https://github.com/DJ4seasons/Organization-Metric-ABCOP]
Metrics for Evaluating Performance of Prognostic Techniques
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.
Comparison of Two Probabilistic Fatigue Damage Assessment Approaches Using Prognostic Performance Metrics
A general framework for probabilistic prognosis using maximum entropy approach, MRE, is proposed in this paper to include all available information and uncertainties for RUL prediction. Prognosis metrics are used for model comparison and performance evaluation. Several conclusions can be drawn based on the results in the current investigation: The proposed MRE updating approach results in more accurate and precise prediction compared with the classical Bayesian method. The classical Bayesian method is a special case of the proposed MRE approach and MRE approach is more flexible to include additional information for inference, which cannot be handled by the classical Bayesian method. The prognosis metrics can be successfully used for algorithm comparison and can give quantitative values in model (algorithm) performance evaluation. A robustness metric measuring the updating algorithmic sensitivity to prior uncertainty is proposed and applied to both Bayesian and MRE updating approaches. The application examples show that MRE exhibits more robustness against the uncertainty introduced by parameter distribution priors in the sense of prognosis performance. It is important to realize when to apply these metrics to arrive at meaningful interpretations. For instance, use of the convergence metric makes sense only when the algorithm predictions converge (get better) with time.
Comparison of cortisol metrics and total RNA profiles from Healthy Individuals
GEO Series GSE134637. Homo sapiens. 80 samples. Type: Expression profiling by high throughput sequencing.
Inherent Specificity and Mutational Sensitivity as Quantitative Metrics for RBP Binding
GEO Series GSE291358. Homo sapiens. 20 samples. Type: Other.
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.