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

被引:42
|
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
  • [31] Open science considerations for descriptive research in developmental science
    Kosie, Jessica E.
    Lew-Williams, Casey
    INFANT AND CHILD DEVELOPMENT, 2024, 33 (01)
  • [32] ADON: Application-Driven Overlay Network-as-a-Service for Data-Intensive Science
    Bazan Antequera, Ronny
    Calyam, Prasad
    Debroy, Saptarshi
    Cui, Longhai
    Seetharam, Sripriya
    Dickinson, Matthew
    Joshi, Trupti
    Xu, Dong
    Beyene, Tsegereda
    IEEE TRANSACTIONS ON CLOUD COMPUTING, 2018, 6 (03) : 640 - 655
  • [33] Differentiated Network Services for Data-intensive Science using Application-aware SDN
    Anantha, Deepak Nadig
    Ramamurthy, Byrav
    Bockelman, Brian
    Swanson, David
    2017 IEEE INTERNATIONAL CONFERENCE ON ADVANCED NETWORKS AND TELECOMMUNICATIONS SYSTEMS (ANTS), 2017,
  • [34] Asterism: Pegasus and dispel4py hybrid workflows for data-intensive science
    Filgueira, Rosa
    da Silva, Rafael Ferreira
    Krause, Amrey
    Deelman, Ewa
    Atkinson, Malcolm
    PROCEEDINGS OF 7TH INTERNATIONAL WORKSHOP ON DATA-INTENSIVE COMPUTING IN THE CLOUDS (DATACLOUD 2016), 2016, : 1 - 8
  • [35] Collaborative Research: Techniques for Conducting Collaborative Research From the Science of Team Science (SciTS)
    Turner, John R.
    Baker, Rose
    ADVANCES IN DEVELOPING HUMAN RESOURCES, 2020, 22 (01) : 72 - 86
  • [36] OPEN SCIENCE DATA CATALOGUE
    Schindler, F.
    Pari, S.
    Meissl, S.
    Smith, G.
    Dobrowolska, E.
    Anghelea, A.
    GEOSPATIAL WEEK 2023, VOL. 48-1, 2023, : 997 - 1003
  • [37] Accelerating AI for science: open data science for science
    Lawrence, Neil D.
    Montgomery, Jessica
    ROYAL SOCIETY OPEN SCIENCE, 2024, 11 (08):
  • [38] Data-intensive research in physics: challenges and perspectives
    Meera, B. M.
    Hiremath, Vani
    ANNALS OF LIBRARY AND INFORMATION STUDIES, 2018, 65 (01) : 43 - 49
  • [39] A Need for Exploratory Visual Analytics in Big Data Research and for Open Science
    Tanaka, Yuzuru
    Sjobergh, Jonas
    Takahashi, Keisuke
    PROCEEDINGS 2016 20TH INTERNATIONAL CONFERENCE INFORMATION VISUALISATION IV 2016, 2016, : 261 - 270
  • [40] Research Data and Open Science in the Russian University Environment
    Balashova, Yuliya B.
    OPEN SCIENCE ENCOMPASSES NEW FORMS OF GREY LITERATURE, 2020, 21 : 113 - 116