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
  • [21] Enabling Interdisciplinary Research in Open Science: Open Science Data Network
    Dang, Vincent-Nam
    Aussenac-Gilles, Nathalie
    Megdiche, Imen
    Ravat, Franck
    RESEARCH CHALLENGES IN INFORMATION SCIENCE, PT I, RCIS 2024, 2024, 513 : 19 - 34
  • [22] Qualitative research at the crossroads of open science and big data: Ethical considerations
    Stegenga, Sondra M.
    Steltenpohl, Crystal N.
    Lustick, Hilary
    Meyer, Melanie S.
    Renbarger, Rachel
    Reyes, Laurel Standiford
    Lee, Lindsay Ellis
    SOCIAL AND PERSONALITY PSYCHOLOGY COMPASS, 2024, 18 (01)
  • [23] Data Science in Open-Access Research On-line Resources
    Lande, Dmytro
    Andrushchenko, Valentyna
    Balagura, Iryna
    2018 IEEE SECOND INTERNATIONAL CONFERENCE ON DATA STREAM MINING & PROCESSING (DSMP), 2018, : 17 - 20
  • [24] Skills and Knowledge for Data-Intensive Environmental Research
    Hampton, Stephanie E.
    Jones, Matthew B.
    Wasser, Leah A.
    Schildhauer, Mark P.
    Supp, Sarah R.
    Brun, Julien
    Hernandez, Rebecca R.
    Boettiger, Carl
    Collins, Scott L.
    Gross, Louis J.
    Fernandez, Denny S.
    Budden, Amber
    White, Ethan P.
    Teal, Tracy K.
    Labou, Stephanie G.
    Aukema, Juliann E.
    BIOSCIENCE, 2017, 67 (06) : 546 - 557
  • [25] OPEN SCIENCE, OPEN RESEARCH DATA AND THE ROLE OF IOSSG
    Gargiulo, Paola
    SCIRES-IT-SCIENTIFIC RESEARCH AND INFORMATION TECHNOLOGY, 2020, 10 : 53 - 58
  • [26] Open Science, Open Research Data and some Open Questions
    Novotny, Jakub
    HRADEC ECONOMIC DAYS, PT II, 2019, 2019, 9 : 174 - 181
  • [27] From Open Data to Open Science
    Ramachandran, Rahul
    Bugbee, Kaylin
    Murphy, Kevin
    EARTH AND SPACE SCIENCE, 2021, 8 (05)
  • [28] Schizophrenia research in the era of Team Science and big data
    Senthil, Geetha
    Lehner, Thomas
    SCHIZOPHRENIA RESEARCH, 2020, 217 : 13 - 16
  • [29] A LNS-based data placement strategy for data-intensive e-science applications
    Zhang, Tiantian
    Cui, Lizhen
    Xu, Meng
    INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING, 2014, 5 (04) : 249 - 262
  • [30] The Promise of the Open Science Movement for Research on Identity
    Syed, Moin
    IDENTITY-AN INTERNATIONAL JOURNAL OF THEORY AND RESEARCH, 2020, 20 (03): : 143 - 156