Results 171 to 180 of about 6,236,388 (328)

Retractions in rheumatology: trends, causes, and implications for research integrity

open access: yesArthritis Care &Research, Accepted Article.
Objective We aimed to describe the trends and main reasons for study retraction in rheumatology literature. Methods We reviewed the Retraction Watch database to identify retracted articles in rheumatology. We recorded the main study characteristics, authors’ countries, reasons for retraction, time from publication to retraction, and trends over time ...
Anna Maria Vettori, Michele Iudici
wiley   +1 more source

Influential variables in the Journal Impact Factor of Dentistry journals. [PDF]

open access: yesHeliyon, 2020
Valderrama P   +4 more
europepmc   +1 more source

Journal impact factor

open access: yesIndian Journal of Dermatology, Venereology and Leprology, 2006
openaire   +2 more sources

Impact factor and journal standard

open access: yesJournal of Saudi Chemical Society, 2015
A.M. AlMayouf
doaj   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

open access: yesAdvanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi   +4 more
wiley   +1 more source

Prospects of Electric Field Control in Perpendicular Magnetic Tunnel Junctions and Emerging 2D Spintronics for Ultralow Energy Memory and Logic Devices

open access: yesAdvanced Functional Materials, EarlyView.
Electric control of magnetic tunnel junctions offers a path to drastically reduce the energy requirements of the device. Electric field control of magnetization can be realized in a multitude of ways. These mechanisms can be integrated into existing spintronic devices to further reduce the operational energy.
Will Echtenkamp   +7 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

open access: yesAdvanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore   +7 more
wiley   +1 more source

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