Dataset Information

Stress relaxation in the presence of sudden strain bursts: Methodology and stress relaxation data of microcast aluminium microwires.

ABSTRACT: This data article presents a methodology and the corresponding code developed to perform and process stress relaxation tests where samples display superimposed (i) classical, continuous logarithmic relaxation together with (ii) sudden displacements manifest as abrupt stress decreases. The method extracts the activation area characteristic of the thermally activated mechanism that drives continuous plastic deformation in the material. We report stress relaxation data appertaining to as-cast (27) and annealed (2) aluminium microwires produced through a microcasting process. For an interpretation and discussion of the data on annealed microwires the reader is referred to " The effect of size on the plastic deformation of annealed cast aluminium microwires" (Verheyden et al., In Press) [1]. For full descriptions of the production process of aluminium microwires or of the tensile testing equipment and procedure the reader is referred to Krebs et al. (2017) [2].

SUBMITTER: Verheyden S

PROVIDER: S-EPMC6265499 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Stress relaxation in the presence of sudden strain bursts: Methodology and stress relaxation data of microcast aluminium microwires.

Verheyden S S Deillon L L Mortensen A A

Data in brief 20181114

This data article presents a methodology and the corresponding code developed to perform and process stress relaxation tests where samples display superimposed (i) classical, continuous logarithmic relaxation together with (ii) sudden displacements manifest as abrupt stress decreases. The method extracts the activation area characteristic of the thermally activated mechanism that drives continuous plastic deformation in the material. We report stress relaxation data appertaining to as-cast (27) ...[more]

PMID: 30533464

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Project description:ObjectiveInformative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues. This review aims to synthesize such existing methodology, thereby helping identify an agenda for future methodological work.Materials and methodsA systematic literature search was conducted by 2 independent reviewers using prespecified keywords.ResultsThirty-six articles were included. We categorized the methods presented within as derived predictors (including some representation of the measurement process as a predictor in the model), modeling under IP, and latent structures. Including missing indicators or summary measures as predictors is the most commonly presented approach amongst the included studies (24 of 36 articles).DiscussionThis is the first review to collate the literature in this area under a prediction framework. A considerable body relevant of literature exists, and we present ways in which the described methods could be developed further. Guidance is required for specifying the conditions under which each method should be used to enable applied prediction modelers to use these methods.ConclusionsA growing recognition of IP and IO exists within the literature, and methodology is increasingly becoming available to leverage these phenomena for prediction purposes. IP and IO should be approached differently in a prediction context than when the primary goal is explanation. The work included in this review has demonstrated theoretical and empirical benefits of incorporating IP and IO, and therefore we recommend that applied health researchers consider incorporating these methods in their work.

Dataset Information

Stress relaxation in the presence of sudden strain bursts: Methodology and stress relaxation data of microcast aluminium microwires.

Publications

Stress relaxation in the presence of sudden strain bursts: Methodology and stress relaxation data of microcast aluminium microwires.

Similar Datasets

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