Data-Intensive Ecological Research Is Catalyzed by Open Science and Team Science

被引:41
|
作者
Cheruvelil, Kendra Spence [1 ,2 ]
Soranno, Patricia A. [2 ]
机构
[1] Michigan State Univ, Lyman Briggs Coll, E Lansing, MI 48824 USA
[2] Michigan State Univ, Dept Fisheries & Wildlife, E Lansing, MI 48824 USA
基金
美国食品与农业研究所;
关键词
data-intensive science; open science; team science; ecology; science culture; gradient of adoption; BIG-DATA; MACROSYSTEMS ECOLOGY; CHALLENGES; COLLABORATION; DIVERSITY; EVOLUTION; NETWORK; ETHICS; FUTURE; MODEL;
D O I
10.1093/biosci/biy097
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Many problems facing society and the environment need ecologists to use increasingly larger volumes and heterogeneous types of data and approaches designed to harness such data-that is, data-intensive science. In the present article, we argue that data-intensive science will be most successful when used in combination with open science and team science. However, there are cultural barriers to adopting each of these types of science in ecology. We describe the benefits and cultural barriers that exist for each type of science and the powerful synergies realized by practicing team science and open science in conjunction with data-intensive science. Finally, we suggest that each type of science is made up of myriad practices that can be aligned along gradients from low to high level of adoption and advocate for incremental adoption of each type of science to meet the needs of the project and researchers.
引用
收藏
页码:813 / 822
页数:10
相关论文
共 50 条
  • [21] Data Grids: a new computational infrastructure for data-intensive science
    Avery, P
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY OF LONDON SERIES A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2002, 360 (1795): : 1191 - 1209
  • [22] Openness and trust in data-intensive science: the case of biocuration
    Gabrielsen, Ane Moller
    MEDICINE HEALTH CARE AND PHILOSOPHY, 2020, 23 (03) : 497 - 504
  • [23] Data-intensive Science: A New Paradigm for Biodiversity Studies
    Kelling, Steve
    Hochachka, Wesley M.
    Fink, Daniel
    Riedewald, Mirek
    Caruana, Rich
    Ballard, Grant
    Hooker, Giles
    BIOSCIENCE, 2009, 59 (07) : 613 - 620
  • [24] Data-Intensive Science: Problems and Development of the Fourth Paradigm
    Erkimbaev, A. O.
    Zitserman, V. Yu.
    Kobzev, G. A.
    AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS, 2024, 58 (03) : 159 - 171
  • [25] Globus toolkit support for distributed data-intensive science
    Allcock, W
    Chervenak, A
    Foster, I
    Pearlman, L
    Welch, V
    Wilde, M
    PROCEEDINGS OF CHEP 2001, 2001, : 692 - 695
  • [26] From Cosmos to Connectomes: The Evolution of Data-Intensive Science
    Burns, Randal
    Vogelstein, Joshua T.
    Szalay, Alexander S.
    NEURON, 2014, 83 (06) : 1249 - 1252
  • [27] EPOS: a novel use of CERIF for data-intensive science
    Bailo, Daniele
    Jeffery, Keith G.
    12TH INTERNATIONAL CONFERENCE ON CURRENT RESEARCH INFORMATION SYSTEMS (CRIS 2014): MANAGING DATA INTENSIVE SCIENCE: THE ROLE OF RESEARCH INFORMATION SYSTEMS IN REALISING THE DIGITAL AGENDA, 2014, 33 : 3 - +
  • [28] APPLICATIONS OF COMPUTATIONAL SCIENCE: DATA-INTENSIVE COMPUTING FOR STUDENT PROJECTS
    Howard, Jessica
    Padron, Omar
    Morreale, Patricia
    Joiner, David
    COMPUTING IN SCIENCE & ENGINEERING, 2012, 14 (02) : 84 - 89
  • [29] Science in the Cloud: Allocation and Execution of Data-Intensive Scientific Workflows
    Claudia Szabo
    Quan Z. Sheng
    Trent Kroeger
    Yihong Zhang
    Jian Yu
    Journal of Grid Computing, 2014, 12 : 245 - 264
  • [30] Scalable Programming and Algorithms for Data-Intensive Life Science Applications
    Qiu, Judy
    OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY, 2011, 15 (04) : 235 - 237