检索结果-内蒙古大学图书馆

Surgical data science-from concepts toward clinical translation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Maier-Hein, Lena Eisenmann, Matthias Sarikaya, Duygu März, Keno Collins, Toby Malpani, Anand Fallert, Johannes Feussner, Hubertus Giannarou, Stamatia Mascagni, Pietro Nakawala, Hirenkumar Park, Adrian Pugh, Carla Stoyanov, Danail Vedula, Swaroop S. Cleary, Kevin Fichtinger, Gabor Forestier, Germain Gibaud, Bernard Grantcharov, Teodor Hashizume, Makoto Heckmann-Nötzel, Doreen Kenngott, Hannes G. Kikinis, Ron Mündermann, Lars Navab, Nassir Onogur, Sinan Sznitman, Raphael Taylor, Russell H. Tizabi, Minu D. Wagner, Martin Hager, Gregory D. Neumuth, Thomas Padoy, Nicolas Collins, Justin Gockel, Ines Goedeke, Jan Hashimoto, Daniel A. Joyeux, Luc Lam, Kyle Leff, Daniel R. Madani, Amin Marcus, Hani J. Meireles, Ozanan Seitel, Alexander Teber, Dogu Ückert, Frank Müller-Stich, Beat P. Jannin, Pierre Speidel, Stefanie Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Medical Faculty Heidelberg University Heidelberg Germany Department of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey LTSI Inserm UMR 1099 University of Rennes 1 Rennes France IRCAD Strasbourg France The Malone Center for Engineering in Healthcare The Johns Hopkins University BaltimoreMD United States KARL STORZ SE and Co. KG Tuttlingen Germany Department of Surgery Klinikum Rechts der Isar Technical University of Munich Munich Germany The Hamlyn Centre for Robotic Surgery Imperial College London London United Kingdom ICube University of Strasbourg CNRS France IHU Strasbourg Strasbourg France Altair Robotics Lab University of Verona Verona Italy Department of Surgery Anne Arundel Health System AnnapolisMD United States Johns Hopkins University School of Medicine BaltimoreMD United States Department of Surgery Stanford University School of Medicine StanfordCA United States Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany The Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital WashingtonDC United States The Perk Lab Queen's University KingstonON Canada University of Haute-Alsace Mulhouse France Faculty of Information Technology Monash University ClaytonVIC Australia University of Toronto TorontoON Canada The Li Ka Shing Knowledge Institute of St. Michael's Hospital TorontoON Canada Kyushu University Fukuoka Japan Kitakyushu Koga Hospital Fukuoka Japan Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Computer Aided Medical Procedures Technical University of Munich Munich Germany Department of Computer Science The Johns Hopkins University Baltim

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process. © 2020, CC BY.

关键词： Deep learning

Gaia data Release 3: Astrophysical parameters inference system (Apsis) I - methods and content overview

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Creevey, Orlagh L. Sordo, R. Pailler, F. Frémat, Y. Heiter, U. Thévenin, F. Andrae, R. Fouesneau, M. Lobel, A. Bailer-Jones, C.A.L. Garabato, D. Bellas-Velidis, I. Brugaletta, E. Lorca, A. Ordenovic, C. Palicio, P.A. Sarro, L.M. Delchambre, L. Drimmel, R. Rybizki, J. Torralba Elipe, G. Korn, A.J. Recio-Blanco, A. Schultheis, M.S. De Angeli, F. Montegriffo, P. Abreu Aramburu, A. Accart, S. Álvarez, M.A. Bakker, J. Brouillet, N. Burlacu, A. Carballo, R. Casamiquela, L. Chiavassa, A. Contursi, G. Cooper, W.J. Dafonte, C. Dapergolas, A. de Laverny, P. Dharmawardena, T.E. Edvardsson, B. Le Fustec, Y. García-Lario, P. García-Torres, M. Gomez, A. González-Santamaría, I. Hatzidimitriou, D. Jean-Antoine Piccolo, A. Kontizas, M. Kordopatis, G. Lanzafame, A.C. Lebreton, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Magdaleno Romeo, A. Manteiga, M. Marocco, F. Marshall, D.J. Mary, N. Nicolas, C. Pallas-Quintela, L. Panem, C. Pichon, B. Poggio, E. Riclet, F. Robin, C. Santoveña, R. Silvelo, A. Slezak, I. Smart, R.L. Soubiran, C. Süveges, M. Ulla, A. Utrilla, E. Vallenari, A. Zhao, H. Zorec, J. Barrado, D. Bijaoui, A. Bouret, J.-C. Blomme, R. Brott, I. Cassisi, S. Kochukhov, O. Martayan, C. Shulyak, D. Silvester, J. Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Bd de l'Observatoire CS 34229 Cedex 4 Nice06304 France INAF - Osservatorio astronomico di Padova Vicolo Osservatorio 5 Padova35122 Italy CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Cedex 9 Toulouse31401 France Royal Observatory of Belgium Ringlaan 3 Brussels1180 Belgium Observational Astrophysics Division of Astronomy and Space Physics Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Dpto. de Inteligencia Artificial UNED c/Juan del Rosal 16 Madrid28040 Spain Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium INAF - Osservatorio Astrofisico di Torino via Osservatorio 20 TO Pino Torinese10025 Italy Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna via Piero Gobetti 93/3 Bologna40129 Italy Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Cedex 9 Toulouse31401 France Dpto. de Matemática Aplicada y C

Gaia data Release 3 contains a wealth of new data products for the community. Astrophysical parameters are a major component of this release. They were produced by the Astrophysical parameters inference system (Apsis) within the Gaia data Processing and Analysis Consortium. The aim of this paper is to describe the overall content of the astrophysical parameters in Gaia DR3 and how they were produced. In Apsis we use the mean BP/RP and mean RVS spectra along with astrometry and photometry, and we derive the following parameters: source classification and probabilities for 1.6 billion objects, interstellar medium characterisation and distances for up to 470 million sources, including a 2D total Galactic extinction map, 6 million redshifts of quasar candidates and 1.4 million redshifts of galaxy candidates, along with an analysis of 50 million outlier sources through an unsupervised classification. The astrophysical parameters also include many stellar spectroscopic and evolutionary parameters for up to 470 million sources. These comprise Teff, log g, and [M/H] (470 million using BP/RP, 6 million using RVS), radius (470 million), mass (140 million), age (120 million), chemical abundances (up to 5 million), diffuse interstellar band analysis (0.5 million), activity indices (2 million), Hα equivalent widths (200 million), and further classification of spectral types (220 million) and emission-line stars (50 thousand). This paper is part of a series of three papers: this Paper I focusses on describing the global content of the parameters in Gaia DR3. The accompanying papers II and III focus on the validation and use of the stellar and non-stellar products, respectively. This catalogue is the most extensive homogeneous database of astrophysical parameters to date, and it is based uniquely on Gaia data. It will only be superseded in Gaia data Release 4. This catalogue will therefore remain a key reference over the next four years, providing astrophysical parameters independ

关键词： Stars

5G ENTERING INTO A NEW ERA

学校读者我要写书评

暂无评论

China Communications 2017年第12期14卷 I0002-I0004页

作者： Feifei Gao Changle Li Jun Fang Kaizhi Huang Zhi Quan Chengwen Xing Jianhua Zhang Zhongshan Zhang Liqiang Zhao Yulong Zou Department of Automation Tsinqhua UniversityBeiiingChina the State Key Laboratory of Integrated Services Networks Xidian University china communications ieee communications magazine ieee signal processing letters national digital switching system engineering&technological research center the college of information engineering shenzhen university the school of information and electronics beijing institute of technology bupt itu-r imt-2020 channel model ieee comm the school of computer and communication engineering in the university of science and technology beijing(ustb) state key lab.of integrated ser vice networks xidian university the nanjing university of posts and telecommunications(nupt)

The ever increasing wireless data services, such asimaging, video, audio, multimedia, etc., have de-mands for the very high speed wireless communicationsand network, which are unfortunately a bottleneck whencombining with the wireline core network. Users＇ arenow expecting high quality of experience with low-costdevices, ubiquitous connectivity, energy efficiency, highreliability, or even ultra-low latency if a vehicle terminalis applied.

关键词： 5G ENTERING A NEW ERA

Whole-Genome Sequencing and Analysis of the Chinese Herbal Plant Panax notoginseng

学校读者我要写书评

暂无评论

Molecular Plant 2017年第6期10卷 899-902页

作者： Wei Chen Ling Kui Guanghui Zhang Shusheng Zhu Jing Zhang Xiao Wang Min Yang Huichuan Huang Yixiang Liu Yong Wang Yahe Li Lipin Zeng Wen Wang Xiahong He Yang Dong Shengchao Yang Biological Big Data College Yunnan Agricultural University yunnan Research Institute for Local Plateau Agriculture and Industry Kunming 650201 China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming 650223 China University of Chinese Academy of Sciences Beijing 100049 China National-Local Joint Engineering Research Center on Germplasm Utilization and Innovation of Chinese Medicinal Materials in Southwest China Yunnan Agricultural University Kunming 650201 China State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan Key Laboratory for Agro-biodiversity and Pest Control of Ministry of Education Yunnan Agricultural University Kunming 650201 China NOWBIO Technology Co. Ltd Kunming 650202 China Institute of Panax notoginseng Wenshan University Wenshan 663000 China Longjing Pharmaceutical Co. Ltd Kunming 650228 China Farlong Pharmaceutical Walnut CA 91789 USA College of Life Science Kunming University of Science and Technology Kunming 650504 China

Dear Editor Panax notoginseng （Burk.） F.H. Chen （2n = 2x = 24, common name sanqi or tianql）, belonging to the Araliaceae family, is a slow-growing plant species documented in the ancient Chinese medical literatures for its ability to ameliorate hemostasis and improve blood circulation （Wang et al., 2016）. After decades of pharmacological research, a variety of P. notoginseng-specific secondary metabolites （notably ginsenosides, notoginsenosides and gypenosides） were isolated, identified, and implicated in conferring medicinal properties （Wang et al., 2016）.

关键词： Whole-Genome Sequencing Chinese Herbal Plant Panax notoginseng

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

Constraint Free Preference Preserving Hashing for Fast Recommendation

学校读者我要写书评

暂无评论

Constraint Free Preference Preserving Hashing for Fast Recom...

IEEE Global Communications Conference

作者： Yan Zhang Guowu Yang Defu Lian Hong Wen Jinsong Wu Center for Cyber Security University of Electronic Science and Technology of China Big Data Research Center University of Electronic Science and Technology of China National Key Lab of Commun. University of Electronic Science and Technology of China Department of Electrical Engineering Universidad de Chile

ISBN: (纸本)9781509013296

Recommender systems have been widely used to deal with information overload, by suggesting relevant items that match users' personal interest. One of the most popular recommendation techniques is matrix factorization (MF). The inner products of learned latent factors between users and items can estimate users' preferences for items with high accuracy, but the preferences ranking is time consuming. Thus, hashing-based fast search technologies were exploited in recommender systems. However, most previous approaches consist of two stages: continuous latent factor learning and binary quantization, but they didn't well deal with the change of inner product arising from quantization. To this end, in this paper, we propose a constraint free preference preserving hashing method, which quantizes both norm and similarity in dot product. We also design an algorithm to optimize the bit length for norm quantization. The performance of our method is evaluated on three real world datasets. The results confirm that the proposed model can improve recommendation performance by 11%-15%, as compared with the state-of-the-art hashing approaches.

关键词： Recommender system Preference ranking Nor-m quantization Optimal bit Recommender systems Dot product Quantization hashing Preferences User preferences preference Consumers

Promoting Business Analytics and Quantitative Management of technology: Preface for ITQM 2016

学校读者我要写书评

暂无评论

Procedia Computer Science 2016年 91卷 1-8页

作者： Lee, Heeseok Shi, Yong Lee, Jongwon Cordova, Felisa Dzitac, Ioan Kou, Gang Li, Jaiping College of Business Korea Advanced Institute Science and Technology Dongdaemum-gu Seoul130-722 Korea Republic of Research Center on Fictitiotus Economy and Data Science Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Business Administration Hoseo University Asan-si Chungcheongnam-do336-795 Korea Republic of Faculty of Engineering and Business University Finis Terrae Pedro de Valdivia 1509. Providencia Santiago Chile Universitatea Agora Agora University Piata Tineretului 8 Oradea410526 Romania School of Business Administration Southwestern University of Finance and Economics Chengdu Sichuan61130 China Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China

A hybrid-integrated external cavity laser with ultra-wide wavelength tuning range and high side-mode suppression

学校读者我要写书评

暂无评论

A hybrid-integrated external cavity laser with ultra-wide wa...

Optical Fiber Communication Conference, OFC 2016

作者： Guo, Yuyao Li, Xinhang Xu, Weihan Liu, Chuxin Jin, Minhui Lu, Liangjun Xie, Jingya Stroganov, Anton Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China LIGENTEC SA EPFL Innovation Park Bâtiment L Ecublens1024 Switzerland

We present a III-V/Si3N4 hybrid-integrated tunable laser. The laser shows a record of ~170-nm tuning range with a side mode suppression ratio above 64 dB and an intrinsic linewidth below 2.8 kHz. © 2022 The Autho...

ISBN: (纸本)9781943580071

关键词：

Journal of Open Source Software (JOSS): Design and first-year review

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Smith, Arfon M. Niemeyer, Kyle E. Katz, Daniel S. Barba, Lorena A. Githinji, George Gymrek, Melissa Huff, Kathryn D. Madan, Christopher R. Mayes, Abigail Cabunoc Moerman, Kevin M. Prins, Pjotr Ram, Karthik Rokem, Ariel Teal, Tracy K. Guimera, Roman Valls Vanderplas, Jacob T. Data Science Mission Office Space Telescope Science Institute BaltimoreMD United States School of Mechanical Industrial and Manufacturing Engineering Oregon State University CorvallisOR United States National Center for Supercomputing Applications & Department of Computer Science Department of Electrical and Computer Engineering School of Information Sciences University of Illinois at Urbana–Champaign UrbanaIL United States Department of Mechanical & Aerospace Engineering George Washington University WashingtonDC United States KEMRI–Wellcome Trust Research Programme Kilifi Kenya Departments of Medicine & Computer Science and Engineering University of California San DiegoCA United States Department of Nuclear Plasma and Radiological Engineering University of Illinois at Urbana–Champaign UrbanaIL United States School of Psychology University of Nottingham Nottingham United Kingdom Mozilla Foundation TorontoON Canada MIT Media Lab Massachusetts Institute of Technology CambridgeMA United States Trinity Centre for Bioengineering Trinity College University of Dublin Dublin Ireland University of Tennessee Health Science Center MemphisTN United States University Medical Centre Utrecht Utrecht Netherlands Berkeley Institute for Data Science University of California Berkeley BerkeleyCA United States eScience Institute University of Washington SeattleWA United States Data Carpentry DavisCA United States University of Melbourne Centre for Cancer Research University of Melbourne Melbourne Australia

This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision;while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a digital object identifier (DOI), deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative (OSI). Copyright © 2017, The Authors. All rights reserved.

关键词： Abstracting