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

Abstract (Expand)

Abstract Nuclear astrophysics is a field at the intersection of nuclear physics and astrophysics, which seeks to understand the nuclear engines of astronomical objects and the origin of the chemicalthe origin of the chemical elements. This white paper summarizes progress and status of the field, the new open questions that have emerged, and the tremendous scientific opportunities that have opened up with major advances in capabilities across an ever growing number of disciplines and subfields that need to be integrated. We take a holistic view of the field discussing the unique challenges and opportunities in nuclear astrophysics in regards to science, diversity, education, and the interdisciplinarity and breadth of the field. Clearly nuclear astrophysics is a dynamic field with a bright future that is entering a new era of discovery opportunities.

Authors: H Schatz, A D Becerril Reyes, A Best, E F Brown, K Chatziioannou, K A Chipps, C M Deibel, R Ezzeddine, D K Galloway, C J Hansen, F Herwig, A P Ji, M Lugaro, Z Meisel, D Norman, J S Read, L F Roberts, A Spyrou, I Tews, F X Timmes, C Travaglio, N Vassh, C Abia, P Adsley, S Agarwal, M Aliotta, W Aoki, A Arcones, A Aryan, A Bandyopadhyay, A Banu, D W Bardayan, J Barnes, A Bauswein, T C Beers, J Bishop, T Boztepe, B Côté, M E Caplan, A E Champagne, J A Clark, M Couder, A Couture, S E de Mink, S Debnath, R J deBoer, J den Hartogh, P Denissenkov, V Dexheimer, I Dillmann, J E Escher, M A Famiano, R Farmer, R Fisher, C Fröhlich, A Frebel, C Fryer, G Fuller, A K Ganguly, S Ghosh, B K Gibson, T Gorda, K N Gourgouliatos, V Graber, M Gupta, W C Haxton, A Heger, W R Hix, W C G Ho, E M Holmbeck, A A Hood, S Huth, G Imbriani, R G Izzard, R Jain, H Jayatissa, Z Johnston, T Kajino, A Kankainen, G G Kiss, A Kwiatkowski, M La Cognata, A M Laird, L Lamia, P Landry, E Laplace, K D Launey, D Leahy, G Leckenby, A Lennarz, B Longfellow, A E Lovell, W G Lynch, S M Lyons, K Maeda, E Masha, C Matei, J Merc, B Messer, F Montes, A Mukherjee, M R Mumpower, D Neto, B Nevins, W G Newton, L Q Nguyen, K Nishikawa, N Nishimura, F M Nunes, E O’Connor, B W O’Shea, W-J Ong, S D Pain, M A Pajkos, M Pignatari, R G Pizzone, V M Placco, T Plewa, B Pritychenko, A Psaltis, D Puentes, Y-Z Qian, D Radice, D Rapagnani, B M Rebeiro, R Reifarth, A L Richard, N Rijal, I U Roederer, J S Rojo, J S K, Y Saito, A Schwenk, M L Sergi, R S Sidhu, A Simon, T Sivarani, Á Skúladóttir, M S Smith, A Spiridon, T M Sprouse, S Starrfield, A W Steiner, F Strieder, I Sultana, R Surman, T Szücs, A Tawfik, F Thielemann, L Trache, R Trappitsch, M B Tsang, A Tumino, S Upadhyayula, J O Valle Martínez, M Van der Swaelmen, C Viscasillas Vázquez, A Watts, B Wehmeyer, M Wiescher, C Wrede, J Yoon, R G T Zegers, M A Zermane, M Zingale

Date Published: 15th Nov 2022

Publication Type: Journal

Abstract (Expand)

This document specifies requirements for the consistent formatting and documentation of data and corresponding metadata (i.e. data describing the data and its context) in the life sciences, including biotechnology, and biomedical, as well as non-human biological research and development. It provides guidance on rendering data in the life sciences findable, accessible, interoperable and reusable (F-A-I-R). This document is applicable to manual or computational workflows that systematically capture, record or integrate data and corresponding metadata in the life sciences for other purposes. This document provides formatting requirements for both primary experimental or procedural data obtained manually and machine derived data. This document also describes requirements for storing, sharing, accessing, interoperability and reuse of data and corresponding metadata in the life sciences. This document specifies requirements for large quantities of data systematically obtained from automated high throughput workflows in the life sciences, as well as requirements for large-scale and small-scale data sets obtained by other life science technologies and manual data capture. This document is applicable to many domains in biotechnology and the life sciences including, but not limited to: basic/applied research in all domains of the life sciences, and industrial, medical, agricultural, or environmental biotechnology (excluding for diagnostic or therapeutic purposes), as well as methodology-driven domains, such as genomics (including massive parallel sequencing, metagenomics, epigenomics and functional genomics), transcriptomics, translatomics, proteomics, metabolomics, lipidomics, glycomics, enzymology, immunochemistry, synthetic biology, systems biology, systems medicine and related fields.

Author: Martin Golebiewski

Date Published: 4th Nov 2022

Publication Type: Manual

Abstract

Not specified

Authors: Melvin M. Moreno, Fabian R. N. Schneider, Friedrich K. Röpke, Sebastian T. Ohlmann, Rüdiger Pakmor, Philipp Podsiadlowski, Christian Sand

Date Published: 1st Nov 2022

Publication Type: Journal

Abstract (Expand)

Abstract Summary The evaluation of phylogenetic inference tools is commonly conducted on simulated and empirical sequence data alignments. An open question is how representative these alignments aretion is how representative these alignments are with respect to those, commonly analyzed by users. Based upon the RAxMLGrove database, it is now possible to simulate DNA sequences based on more than 70, 000 representative RAxML and RAxML-NG tree inferences on empirical datasets conducted on the RAxML web servers. This allows to assess the phylogenetic tree inference accuracy of various inference tools based on realistic and representative simulated DNA alignments. We simulated 20, 000 MSAs based on representative datasets (in terms of signal strength) from RAxMLGrove, and used 5, 000 datasets from the TreeBASE database, to assess the inference accuracy of FastTree2, IQ-TREE2, and RAxML-NG. We find that on quantifiably difficult-to-analyze MSAs all of the analysed tools perform poorly, such that the quicker FastTree2, can constitute a viable alternative to infer trees. We also find, that there are substantial differences between accuracy results on simulated and empirical data, despite the fact that a substantial effort was undertaken to simulate sequences under as realistic as possible settings. Contact Dimitri Höhler, dimitri.hoehler@h-its.org

Authors: Dimitri Höhler, Julia Haag, Alexey M. Kozlov, Alexandros Stamatakis

Date Published: 1st Nov 2022

Publication Type: Journal

Abstract

Not specified

Authors: Lukas Hubner, Demian Hespe, Peter Sanders, Alexandros Stamatakis

Date Published: 1st Nov 2022

Publication Type: Journal

Abstract

Not specified

Authors: Melvin M. Moreno, Fabian R. N. Schneider, Friedrich K. Röpke, Sebastian T. Ohlmann, Rüdiger Pakmor, Philipp Podsiadlowski, Christian Sand

Date Published: 1st Nov 2022

Publication Type: Journal

Abstract

Not specified

Authors: Johannes Bracher, Daniel Wolffram, Jannik Deuschel, Konstantin Görgen, Jakob L. Ketterer, Alexander Ullrich, Sam Abbott, Maria V. Barbarossa, Dimitris Bertsimas, Sangeeta Bhatia, Marcin Bodych, Nikos I. Bosse, Jan Pablo Burgard, Lauren Castro, Geoffrey Fairchild, Jochen Fiedler, Jan Fuhrmann, Sebastian Funk, Anna Gambin, Krzysztof Gogolewski, Stefan Heyder, Thomas Hotz, Yuri Kheifetz, Holger Kirsten, Tyll Krueger, Ekaterina Krymova, Neele Leithäuser, Michael L. Li, Jan H. Meinke, Błażej Miasojedow, Isaac J. Michaud, Jan Mohring, Pierre Nouvellet, Jedrzej M. Nowosielski, Tomasz Ożański, Maciej Radwan, Franciszek Rakowski, Markus Scholz, Saksham Soni, Ajitesh Srivastava, Tilmann Gneiting, Melanie Schienle

Date Published: 31st Oct 2022

Publication Type: Journal

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