CODATA and global challenges in data-driven science

被引:3
|
作者
Rybkina, A. [1 ,2 ]
Hodson, S. [2 ]
Gvishiani, A. [1 ]
Kabat, P. [3 ]
Krasnoperov, R. [1 ]
Samokhina, O. [1 ]
Firsova, E. [1 ]
机构
[1] Russian Acad Sci, Geophys Ctr, 3 Molodezhnaya St, Moscow 119296, Russia
[2] Comm Data Int Council Sci CODATA, 5 Rue Auguste Vacquerie, F-75016 Paris, France
[3] IIASA, Schlosspl 1, A-2361 Laxenburg, Austria
来源
RUSSIAN JOURNAL OF EARTH SCIENCES | 2018年 / 18卷 / 04期
基金
俄罗斯科学基金会;
关键词
Big Data; Open Data; FAIR principles; data-driven science; system analysis methods; data mining; machine learning; pattern recognition; international conference; CODATA;
D O I
10.2205/2018ES000625
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
This synthesis report presents the scientific results of the international conference "Global Challenges and Data-Driven Science" which took place in St. Petersburg, Russian Federation from 8 October to 13 October 2017. This event facilitated multidisciplinary scientific dialogue between leading scientists, data managers and experts, as well as Big Data researchers of various fields of knowledge. The St. Petersburg conference covered a wide range of topics related to data science. It featured discussions covering the collection and processing of large amounts of data, the implementation of system analysis methods into data science, machine learning, data mining, pattern recognition, decision-making robotics and algorithms of artificial intelligence. The conference was an outstanding event in the field of scientific diplomacy and brought together more than 150 participants from 35 countries. It's success ensured the effective data science dialog between nations and continents and established a new platform for future collaboration.
引用
收藏
页数:11
相关论文
共 50 条
  • [31] Thoughts on Starting the CODATA Data Science Journal
    Rumble J.
    Data Science Journal, 2023, 22 (01)
  • [32] Maximizing the Science in the Era of Data-Driven Astronomy
    Aloisi, Alessandra
    ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXV, 2017, 512 : 3 - 12
  • [33] Data-driven modeling and learning in science and engineering
    Montans, Francisco J.
    Chinesta, Francisco
    Gomez-Bombarelli, Rafael
    Kutz, J. Nathan
    COMPTES RENDUS MECANIQUE, 2019, 347 (11): : 845 - 855
  • [34] 2022 Review of Data-Driven Plasma Science
    Anirudh, Rushil
    Archibald, Rick
    Asif, M. Salman
    Becker, Markus M.
    Benkadda, Sadruddin
    Bremer, Peer-Timo
    Bude, Rick H. S.
    Chang, C. S.
    Chen, Lei
    Churchill, R. M.
    Citrin, Jonathan
    Gaffney, Jim A.
    Gainaru, Ana
    Gekelman, Walter
    Gibbs, Tom
    Hamaguchi, Satoshi
    Hill, Christian
    Humbird, Kelli
    Jalas, Soeren
    Kawaguchi, Satoru
    Kim, Gon-Ho
    Kirchen, Manuel
    Klasky, Scott
    Kline, John L.
    Krushelnick, Karl
    Kustowski, Bogdan
    Lapenta, Giovanni
    Li, Wenting
    Ma, Tammy
    Mason, Nigel J.
    Mesbah, Ali
    Michoski, Craig
    Munson, Todd
    Murakami, Izumi
    Najm, Habib N.
    Olofsson, K. Erik J.
    Park, Seolhye
    Peterson, J. Luc
    Probst, Michael
    Pugmire, David
    Sammuli, Brian
    Sawlani, Kapil
    Scheinker, Alexander
    Schissel, David P.
    Shalloo, Rob J.
    Shinagawa, Jun
    Seong, Jaegu
    Spears, Brian K.
    Tennyson, Jonathan
    Thiagarajan, Jayaraman
    IEEE TRANSACTIONS ON PLASMA SCIENCE, 2023, 51 (07) : 1750 - 1838
  • [35] Data-Driven Multiscale Science for Tread Compounding
    Burkhart, Craig
    Jiang, Bing
    Papakonstantopoulos, George
    Polinska, Patrycja
    Xu, Hongyi
    Sheridan, Richard J.
    Brinson, L. Catherine
    Chen, Wei
    TIRE SCIENCE AND TECHNOLOGY, 2023, 51 (02) : 114 - 131
  • [36] Cloud computing for data-driven science and engineering
    Simmhan, Yogesh
    Ramakrishnan, Lavanya
    Antoniu, Gabriel
    Goble, Carole
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2016, 28 (04): : 947 - 949
  • [37] Stream processing in data-driven computational science
    Liu, Ying
    Vijayakumar, Nithya N.
    Plate, Beth
    2006 7TH IEEE/ACM INTERNATIONAL CONFERENCE ON GRID COMPUTING, 2006, : 160 - +
  • [38] Statistical Reliability of Data-Driven Science and Technology
    Takeuchi, Ichiro
    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2025,
  • [39] Reframing groundwater hydrology as a data-driven science
    Shapiro, Allen M.
    Day-Lewis, Frederick D.
    GROUNDWATER, 2022, 60 (04) : 455 - 456
  • [40] Data-Driven Computational Social Science: A Survey
    Zhang, Jun
    Wang, Wei
    Xia, Feng
    Lin, Yu-Ru
    Tong, Hanghang
    BIG DATA RESEARCH, 2020, 21