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1,069 results for “Data managment”
Data Mining in Systems Health Management
This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.
A Combined Model-Based and Data-Driven Prognostic Approach for Aircraft System Life Management
Failure prognosis - as a natural extension to the fault detection and isolation (FDI) problem - has become a key issue in a world where the economic impact of system reliability and cost-effective operation of critical assets is steadily increasing. Failure prognostic algorithms aim to characterize the evolution of incipient fault conditions in complex dynamic processes, thus allowing to estimate of the remaining useful life (RUL) of subsystems and components. Several examples can be used here to illustrate the range of possible applications for these algorithms: electro-mechanical systems, continuous-time manufacturing processes, structural damage analysis, and even fault tolerant software architectures. Most of them have in common the fact that they are highly complex, nonlinear, and affected by large-grain uncertainty. We introduce in this chapter an integrated failure prognosis architecture that is applicable to a variety of aircraft systems and industrial processes. We are targeting a specific rotorcraft system as a prototypical testbed for proof-of-concept. The overall architecture consists of an on-board and an off-board module for eventual on-platformimplementation purposes.
Introduction to curating and managing research data for re-use: natural gas infrastructure case study data set
<p>This data set underpins the report and data provided between 2004-2006 for registration in the restrictions application of Estonian Land Board.</p>
Data from: Management-induced micro-ecosystems in crop fields alter the functional trait composition of arable plant communities
<p>Data used for RLQ analysis. Include trait table, environment table and species table. </p><p>Traits/species table: <br>EQUAR, PLAMA and SPRAR were removed due to few observations. CHEPR and TAROF were removed due to lack of traits data.</p><p>Environmental table:<br>MD (mechanical disturbance) is a combination or location and a row-hoeing coefficient that assumes the impact of the hoe decrease with increasing distance from the centre of the hoe tine. cr hoed twice and ic hoed once both got a value of 2 and to distinguish them the latter was changed manually to 2.5</p>
Code and data from : Using a spatially explicit population model to evaluate cost-effective management scenarios for an invasive deer population
<p>This record contains the following:</p> <p>-"WoJ SEPM.Rmd": Script used to build our spatially explicit population model, run simulations for our scenarios and calculate population summary statistics</p> <p>-"WoJ cpue.Rmd": Script used to run our catch-per-unit-effort model that estimates relationship between deer density and number of deer shot per hour</p> <p>-"Cost estimates.Rmd": Script used to calculate costs for each scenario</p> <p>-"Costs functions.R": Functions that are called in the "Cost estimates" script.</p> <p> </p> <p>In addition, all datafiles required to run the scrips are included here.</p>
Introduction to curating and managing research data for re-use: North European Gas Pipeline case study data set
<p>The introductory data set illustrates briefly different relationships in the case study of the North European Gas Pipeline.</p>
Data set from Dellafiore F, Arrigoni C, Ghizzardi G, Baroni I, Conte G, Turrini F, Castiello G, Magon A, Pittella F, Caruso R. Development and validation of the pressure ulcer management self-efficacy scale for nurses. J Clin Nurs. 2019 Sep;28(17-18):3177-3188. doi: 10.1111/jocn.14875. Epub 2019 Apr 21. PMID: 30938908.
<p>Data set from Dellafiore F, Arrigoni C, Ghizzardi G, Baroni I, Conte G, Turrini F, Castiello G, Magon A, Pittella F, Caruso R. Development and validation of the pressure ulcer management self-efficacy scale for nurses. J Clin Nurs. 2019 Sep;28(17-18):3177-3188. doi: 10.1111/jocn.14875. Epub 2019 Apr 21. PMID: 30938908</p> <p> </p> <p>This is the abstract:</p> <p><strong>Background: </strong>Pressure ulcers (PUs) represent a current issue for healthcare delivery. Nurse self-efficacy in managing PUs could predict patients' outcome, being a proxy assessment of their overall competency to managing PUs. However, a valid and reliable scale of this task-specific self-efficacy has not yet been developed.</p> <p><strong>Objectives: </strong>To develop a valid and reliable scale to assess nurses' self-efficacy in managing PUs, that is, the pressure ulcer management self-efficacy scale for nurses (PUM-SES).</p> <p><strong>Methods: </strong>This study had a multi-method and multi-phase design, where study reporting was supported by the STROBE checklist (File S1). Phase 1 referred to the scale development, consisting in the items' generation, mainly based on themes emerged from the literature and discussed within a panel of experts. Phase 2 focused on a three-step validation process: the first step aimed to assess face and content validity of the pool of items previously generated (initial version of the PUM-SES); the second aimed to assess psychometrics properties through exploratory factorial analysis; the third step assessed construct validity through confirmative factorial analysis, while concurrent validity was evaluated describing the relationships between PUM-SES and an established general self-efficacy measurement. Reliability was assessed through the evaluation of stability and internal consistency.</p> <p><strong>Results: </strong>PUM-SES showed evidence of face and content validity, adequate construct and concurrent validity, internal consistency and stability. Specifically, PUM-SES had four domains, labelled as follows: assessment, planning, supervision and decision-making. These domains were predicted by the same second-order factor, labelled as PU management self-efficacy.</p> <p><strong>Conclusion: </strong>PUM-SES is a 10-item scale to measure nurses' self-efficacy in PU management. A standardised 0-100 scoring is suggested for computing each domain and the overall scale. PUM-SES might be used in clinical and educational research.</p> <p><strong>Relevance to clinical practice: </strong>Optimising nurses' self-efficacy in PU management might enhance clinical assessment, determining better outcomes in patients with PUs.</p> <p> </p>
Material: "University Library Hildesheim. Response to Covid-19, Research Data Management." München 2020. Munich 2020.
<p>BibliotheksLeiterTag2020.<br> Stark durch schwierige Zeiten.</p> <p>Materialien ...</p>
Repository of data collected on the different variables for the psychobiological effects of virtual reality and binaural tones on chronic pain management in paediatric patients with a rheumatic disease.
<p>Data set used to carry out the statistical analysis of the project <strong>Advances in the Cognitive Management of Chronic Pain in Children and Adolescents Through the Use of Virtual Reality Combined with Binaural Beats. </strong></p>
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.