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arXiv 2022年

作者： Recio-Blanco, Alejandra de Laverny, Patrick Palicio, P.A. Kordopatis, G. Álvarez, M.A. Schultheis, M. Contursi, G. Zhao, H. Torralba Elipe, G. Ordenovic, C. Manteiga, M. Dafonte, C. Oreshina-Slezak, I. Bijaoui, A. Frémat, Y. Seabroke, G. Pailler, F. Spitoni, E. Poggio, E. Creevey, O.L. Abreu Aramburu, A. Accart, S. Andrae, R. Bailer-Jones, C.A.L. Bellas-Velidis, I. Brouillet, N. Brugaletta, E. Burlacu, A. Carballo, R. Casamiquela, L. Chiavassa, A. Cooper, W.J. Dapergolas, A. Delchambre, L. Dharmawardena, T.E. Drimmel, R. Edvardsson, B. Fouesneau, M. Garabato, D. García-Lario, P. García-Torres, M. Gavel, A. Gomez, A. González-Santamaría, I. Hatzidimitriou, D. Heiter, U. Jean-Antoine Piccolo, A. Kontizas, M. Korn, A.J. Lanzafame, A.C. Lebreton, Y. Le Fustec, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Lobel, A. Lorca, A. Magdaleno Romeo, A. Marocco, F. Marshall, D.J. Mary, N. Nicolas, C. Pallas-Quintela, L. Panem, C. Pichon, B. Riclet, F. Robin, C. Rybizki, J. Santoveña, R. Silvelo, A. Smart, R.L. Sarro, L.M. Sordo, R. Soubiran, C. Süveges, M. Ulla, A. Vallenari, A. Zorec, J. Utrilla, E. Bakker, J. Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Nice France CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain CIGUS CITIC Department of Nautical Sciences and Marine Engineering University of A Coruña Paseo de Ronda 51 A Coruña15071 Spain Royal Observatory of Belgium 3 avenue circulaire Brussels1180 Belgium Mullard Space Science Laboratory University College London Holmbury St Mary Dorking SurreyRH5 6NT United Kingdom CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Dpto. de Matemática Aplicada y Ciencias de la Computación Univ. de Cantabria ETS Ingenieros de Caminos Canales y Puertos Avda. de los Castros s/n Santander39005 Spain GEPI Observatoire de Paris Université PSL CNRS 5 Place Jules Janssen Meudon92190 France Centre for Astrophysics Research University of Hertfordshire College Lane HatfieldAL10 9AB United Kingdom 10025 Italy Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium Theoretical Astrophysics Division of Astronomy and Space Physics Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Data Science and

Context. The chemo-physical parametrisation of stellar spectra is essential for understanding the nature and evolution of stars and of Galactic stellar populations. A worldwide observational effort from the ground has provided, in one century, an extremely heterogeneous collection of chemical abundances for about two million stars in total, with fragmentary sky coverage. Aims. This situation is revolutionised by the Gaia third data release (DR3), which contains the parametrisation of Radial Velocity Spectrometer (RVS) data performed by the General Stellar Parametriser-spectroscopy, GSP-Spec, module. Here we describe the parametrisation of the first 34 months of Gaia RVS observations. Methods. GSP-Spec estimates the chemo-physical parameters from combined RVS spectra of single stars, without additional inputs from astrometric, photometric, or spectro-photometric BP/RP data. The main analysis workflow described here, MatisseGauguin, is based on projection and optimisation methods and provides the stellar atmospheric parameters;the individual chemical abundances of N, Mg, Si, S, Ca, Ti, Cr, Fe i, Fe ii, Ni, Zr, Ce and Nd;the differential equivalent width of a cyanogen line;and the parameters of a diffuse interstellar band (DIB) feature. Another workflow, based on an artificial neural network (ANN) and referred to with the same acronym, provides a second set of atmospheric parameters that are useful for classification control. For both workflows, we implement a detailed quality flag chain considering different error sources. Results. With about 5.6 million stars, the Gaia DR3 GSP-Spec all-sky catalogue is the largest compilation of stellar chemo-physical parameters ever published and the first one from space data. Internal and external biases have been studied taking into account the implemented flags. In some cases, simple calibrations with low degree polynomials are suggested. The homogeneity and quality of the estimated parameters enables chemo-dynamical studies of G

关键词： Stars

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Correction to: Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease

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Journal of neuroengineering and rehabilitation 2019年第1期16卷 98页

作者： An Nguyen Nils Roth Nooshin Haji Ghassemi Julius Hannink Thomas Seel Jochen Klucken Heiko Gassner Bjoern M Eskofier Machine Learning and Data Analytics Lab Department of Computer Science Friedrich-Alexander-University Erlangen-Nürnberg (FAU) Carl-Thiersch-Straße 2b 91052 Erlangen Germany. an.nguyen@fau.de. Machine Learning and Data Analytics Lab Department of Computer Science Friedrich-Alexander-University Erlangen-Nürnberg (FAU) Carl-Thiersch-Straße 2b 91052 Erlangen Germany. Control Systems Group Department of Electrical Engineering and Computer Science Technische Universität Berlin (TUB) Einsteinufer 17 10587 Berlin Germany. Department of Molecular Neurology University Hospital Erlangen Friedrich-Alexander University Erlangen-Nürnberg (FAU) Schwabachanlage 6 91054 Erlangen Germany.

The original article [1] contained an error whereby Fig. 6 contained a minor shading glitch affecting its presentation. This has now been corrected.

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Small P values may not yield robust findings:an example using REST-meta-PD

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science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise

arXiv

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arXiv 2017年

作者： Qian, Chao Bian, Chao Jiang, Wu Tang, Ke Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Shenzhen Key Lab of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

In many real-world optimization problems, the objective function evaluation is subject to noise, and we cannot obtain the exact objective value. Evolutionary algorithms (EAs), a type of general-purpose randomized optimization algorithm, have been shown to be able to solve noisy optimization problems well. However, previous theoretical analyses of EAs mainly focused on noise-free optimization, which makes the theoretical understanding largely insufficient for the noisy case. Meanwhile, the few existing theoretical studies under noise often considered the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation;while in many realistic applications, several bits of a solution can be changed simultaneously. In this paper, we study a natural extension of one-bit noise, the bit-wise noise model, which independently flips each bit of a solution with some probability. We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds. The analysis on LeadingOnes under bit-wise noise can be easily transferred to one-bit noise, and improves the previously known results. Since our analysis discloses that the (1+1)-EA can be efficient only under low noise levels, we also study whether the sampling strategy can bring robustness to noise. We prove that using sampling can significantly increase the largest noise level allowing a polynomial running time, that is, sampling is robust to noise. Copyright © 2017, The Authors. All rights reserved.

关键词： Computational complexity

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A fast converging and error reducing phase retrieval algorithm for Fourier Ptychographic microscopy

A fast converging and error reducing phase retrieval algorit...

引用

Digital Holography and Three-Dimensional Imaging, DH 2017

作者： Muniraj, Inbarasan Guo, Changliang Malallah, Ra'ed Cassidy, Derek Zhao, Liang Ryle, James P. Healy, John J. Sheridan, John T. School of Electrical and Electronic Engineering IoE2 Lab University College Dublin Belfield Dublin 4 Ireland Department of Biomedical Engineering Stony Brook University State University of New York NY United States Physics Department Faculty of Science University of Basrah Garmat Ali Basrah Iraq The Insight center for data analysis University College Dublin Belfield Dublin 4 Ireland

Fourier ptychography is a recently developed computational framework for wide-field, high-resolution imaging. Here, we propose to use a fast converging and error reducing phase retrieval algorithm to retrieve the comp... 详细信息

ISBN: (纸本)9781943580286

关键词： Aperture synthesis Image resolution LED lighting Optical transfer functions Phase retrieval Synthetic aperture imaging Fourier optics and signal processing (070.0070) Phase retrieval (100.5070) Microscopy (180.0180)

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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Fermi-GBM Discovery of GRB 221009A: An Extraordinarily Bright GRB from Onset to Afterglow

arXiv

引用

arXiv 2023年

作者： Lesage, S. Veres, P. Briggs, M.S. Goldstein, A. Kocevski, D. Burns, E. Wilson-Hodge, C.A. Bhat, P.N. Huppenkothen, D. Fryer, C.L. Hamburg, R. Racusin, J. Bissaldi, E. Cleveland, W.H. Dalessi, S. Fletcher, C. Giles, M.M. Hristov, B.A. Hui, C.M. Mailyan, B. Malacaria, C. Poolakkil, S. Roberts, O.J. von Kienlin, A. Wood, J. Ajello, M. Arimoto, M. Baldini, L. Ballet, J. Baring, M.G. Bastieri, D. Becerra Gonzalez, J. Bellazzini, R. Bissaldi, E. Blandford, R.D. Bonino, R. Bruel, P. Buson, S. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Chiaro, G. Cibrario, N. Ciprini, S. Cristarella Orestano, P. Crnogorcevic, M. Cuoco, A. Cutini, S. D’Ammando, F. De Gaetano, S. Di Lalla, N. Di Venere, L. Domínguez, A. Fegan, S.J. Ferrara, E.C. Fleischhack, H. Fukazawa, Y. Funk, S. Fusco, P. Galanti, G. Gammaldi, V. Gargano, F. Gasbarra, C. Gasparrini, D. Germani, S. Giacchino, F. Giglietto, N. Gill, R. Giroletti, M. Granot, J. Green, D. Grenier, I.A. Guiriec, S. Gustafsson, M. Hays, E. Hewitt, J.W. Horan, D. Hou, X. Kuss, M. Latronico, L. Laviron, A. Lemoine-Goumard, M. Li, J. Liodakis, I. Longo, F. Loparco, F. Lorusso, L. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. McEnery, J.E. Mereu, I. Meyer, M. Michelson, P.F. Mizuno, T. Monzani, M.E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Omodei, N. Orlando, E. Ormes, J.F. Paneque, D. Panzarini, G. Persic, M. Pesce-Rollins, M. Pillera, R. Piron, F. Poon, H. Porter, T.A. Principe, G. Rainò, S. Rando, R. Rani, B. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Ryde, F. Sánchez-Conde, M. Saz Parkinson, P.M. Scotton, L. Serini, D. Sgrò, C. Sharma, V. Siskind, E.J. Spandre, G. Spinelli, P. Tajima, H. Torres, D.F. Valverde, J. Venters, T. Wadiasingh, Z. Wood, K. Zaharijas, G. Department of Space Science University of Alabama in Huntsville 320 Sparkman Drive HuntsvilleAL35899 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States Science and Technology Institute Universities Space Research Association HuntsvilleAL35805 United States ST12 Astrophysics Branch NASA Marshall Space Flight Center HuntsvilleAL35812 United States Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States SRON Netherlands Institute for Space Research Niels Bohrweg 4 Leiden2333CA Netherlands Center for Non Linear Studies Los Alamos National Laboratory Los AlamosNM87545 United States Université Paris-Saclay CNRS IN2P3 IJCLab Orsay91405 France Astrophysics Science Division NASA Goddard Space Flight Center GreenbeltMD20771 United States Dipartimento Interateneo di Fisica dell’Università e Politecnico di Bari Via E. Orabona 4 Bari70125 Italy Istituto Nazionale di Fisica Nucleare - Sezione di Bari Via E. Orabona 4 Bari70125 Italy Jacobs Space Exploration Group HuntsvilleAL35806 United States Department of Aerospace Physics and Space Sciences Florida Institute of Technology MelbourneFL32901 United States International Space Science Institute Hallerstrasse 6 Bern3012 Switzerland Max-Planck-Institut für extraterrestrische Physik Giessenbachstrasse 1 GarchingD-85748 Germany Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Faculty of Mathematics and Physics Institute of Science and Engineering Kanazawa University Kakuma Ishikawa Kanazawa920-1192 Japan Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-YvetteF-91191 Cedex France Rice University Department of Physics and Astronomy MS-108 P. O. Box 1892 HoustonTX77251 United States Istituto Naziona

We report the discovery of GRB 221009A, the highest flux gamma-ray burst ever observed by the Fermi Gamma-ray Burst Monitor (Fermi-GBM). This GRB has continuous prompt emission lasting more than 600 seconds which smoothly transitions to afterglow visible in the Fermi-GBM energy range (8 keV–40 MeV), and total energetics higher than any other burst in the Fermi-GBM sample. By using a variety of new and existing analysis techniques we probe the spectral and temporal evolution of GRB 221009A. We find no emission prior to the Fermi-GBM trigger time (t0;2022 October 9 at 13:16:59.99 UTC), indicating that this is the time of prompt emission onset. The triggering pulse exhibits distinct spectral and temporal properties suggestive of the thermal, photospheric emission of shock-breakout, with significant emission up to ∼15 MeV. We characterize the onset of external shock at t0+600 s and find evidence of a plateau region in the early-afterglow phase which transitions to a slope consistent with Swift-XRT afterglow measurements. We place the total energetics of GRB 221009A in context with the rest of the Fermi-GBM sample and find that this GRB has the highest total isotropic-equivalent energy (Eγ,iso = 1.0 × 1055 erg) and second highest isotropic-equivalent luminosity (Lγ,iso = 9.9 ×1053 erg/s) based on redshift of z = 0.151. These extreme energetics are what allowed us to observe the continuously emitting central engine of Fermi-GBM from the beginning of the prompt emission phase through the onset of early afterglow. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

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Informative data projections: A framework and two examples 24

Informative data projections: A framework and two examples

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24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016

作者： De Bie, Tijl Lijffijt, Jefrey Santos-Rodríguez, Raúl Kang, Bo Data Science Lab Ghent University Belgium Dept. of Engineering Mathematics University of Bristol United Kingdom

ISBN: (纸本)9782875870278

Projection Pursuit aims to facilitate visual exploration of high-dimensional data by identifying interesting low-dimensional projections. A major challenge in Projection Pursuit is the design of a projection index-a suitable quality measure to maximise. We introduce a strategy for tackling this problem based on quantifying the amount of information a projection conveys, given a user's prior beliefs about the data. The resulting projection index is a subjective quantity, explicitly dependent on the intended user. As an illustration, we developed this principle for two kinds of prior beliefs;the first leads to PCA, the second leads to a novel projection index, which we call t-PCA, that can be regarded as a robust PCA-variant. We demonstrate t-PCA's usefulness in comparative experiments against PCA and FastICA, a popular PP method.

关键词： Clustering algorithms

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Ten simple rules for reproducible research in jupyter notebooks

arXiv

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arXiv 2018年

作者： Rule, Adam Birmingham, Amanda Zuniga, Cristal Altintas, Ilkay Huang, Shih-Cheng Knight, Rob Moshiri, Niema Nguyen, Mai H. Rosenthal, Sara Brin Pérez, Fernando Rose, Peter W. Design Lab UC San Diego San DiegoCA United States Center for Computational Biology and Bioinformatics UC San Diego San DiegoCA United States Department of Pediatrics UC San Diego San DiegoCA United States Data Science Hub San Diego Supercomputer Center UC San Diego San DiegoCA United States Departments of Bioengineering and Computer Science and Engineering Center for Microbiome Innovation UC San Diego San DiegoCA United States Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA United States Department of Statistics Berkeley Institute for Data Science UC Berkeley Lawrence Berkeley National Laboratory BerkeleyCA United States Biomedical Informatics Graduate Program Stanford University StanfordCA United States

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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