Publications

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1579 Publications visible to you, out of a total of 1579

Abstract

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Authors: Nikos I. Bosse, Sam Abbott, Anne Cori, Edwin van Leeuwen, Johannes Bracher, Sebastian Funk

Date Published: 29th Aug 2023

Publication Type: Journal

Abstract

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Author: Michel Tarnow

Date Published: 22nd Aug 2023

Publication Type: Bachelor's Thesis

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Authors: Lucas G. Viviani, Daria B. Kokh, Rebecca C. Wade, Antonia T.-do Amaral

Date Published: 14th Aug 2023

Publication Type: Journal

Abstract (Expand)

The Metadata Schema of the NFDI4Health and the NFDI4Health Task Force COVID-19 (Metadata Schema) contains a list of properties that describe a resource to be registered in the German Central Health Study Hub. Currently, two main types of resources are distinguished: a) study descriptions (i.e., metadata set describing a study) and b) study documents. However, due to the generic character of the Metadata Schema, other types of resources may also be described and registered. The metadata properties are divided into mandatory and recommended ones. Along with bibliographic information such as title and description of the resource, the related persons and organizations contributing to the development of the resource can also be specified. The results of studies published in journal articles or other text publications can be linked too. For studies, information about study design and accessibility of the collected data should be additionally provided. The Metadata Schema consists mainly of properties adapted from established standards and models such as DataCite Metadata Schema 4.4, data models of the ClinicalTrials.gov, German Clinical Trials Register, International Clinical Trials Registry, HL7® FHIR, MIABIS, Maelstrom Research cataloguing toolkit and DDI Controlled Vocabularies. This is an updated version V3_2 of the Metadata Schema, which improves the modules of the previous version via refined description texts and added, deleted, moved, or renamed items. Additional use case-specific requirements, particularly for the chronic diseases and record linkage modules, have also been considered in this new version along with updating the list of sources. The undertaken changes are described within the document.

Authors: Haitham Abaza, A. Shutsko, Martin Golebiewski, Sophie Klopfenstein, Carsten Oliver Schmidt, Carina Vorisek, NFDI4Health Task Force COVID-19, NFDI4Health, C. Brünings-Kuppe, V. Clemens, J. Darms, S. Hanß, T. Intemann, F. Jannasch, E. Kasbohm, Birte Lindstädt, Matthias Löbe, E. Orban, I. Perrar, M. Peters, U. Sax, M. Schulze, C. Schupp, F. Schwarz, C. Schwedhelm, S. Strathmann, Dagmar Waltemath, H. Wünsche, A. Zeleke

Date Published: 14th Aug 2023

Publication Type: Misc

Abstract

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Authors: Daniel Wolffram, Sam Abbott, Matthias an der Heiden, Sebastian Funk, Felix Günther, Davide Hailer, Stefan Heyder, Thomas Hotz, Jan van de Kassteele, Helmut Küchenhoff, Sören Müller-Hansen, Diellë Syliqi, Alexander Ullrich, Maximilian Weigert, Melanie Schienle, Johannes Bracher

Date Published: 11th Aug 2023

Publication Type: Journal

Abstract (Expand)

Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.

Authors: Rostislav Fedorov, Ganna Gryn’ova

Date Published: 8th Aug 2023

Publication Type: Journal

Abstract

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Authors: Shubham Srivastav, T. Moore, M. Nicholl, M. R. Magee, S. J. Smartt, M. D. Fulton, S. A. Sim, J. M. Pollin, L. Galbany, C. Inserra, A. Kozyreva, Takashi J. Moriya, F. P. Callan, X. Sheng, K. W. Smith, J. S. Sommer, J. P. Anderson, M. Deckers, M. Gromadzki, T. E. Müller-Bravo, G. Pignata, A. Rest, D. R. Young

Date Published: 1st Aug 2023

Publication Type: Journal

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