Peer-reviewed publishing of results from Citizen Science projects

被引:12
作者
Gadermaier, Gabriele [1 ]
Doerler, Daniel [2 ,3 ,4 ,5 ]
Heigl, Florian [2 ,3 ,4 ,6 ]
Mayr, Stefan [7 ,8 ]
Ruedisser, Johannes
Brodschneider, Robert [9 ]
Marizzi, Christine [10 ]
机构
[1] Univ Salzburg, Mol Biol & Immunol, Salzburg, Austria
[2] Univ Nat Resources & Life Sci, Vienna, Austria
[3] Citizen Sci Network Austria, Vienna, Austria
[4] Platform Osterreich Forscht, Vienna, Austria
[5] Open Innovat Sci, Vienna, Austria
[6] Project Roadkill, Vienna, Austria
[7] Univ Innsbruck, Res Grp Ecophysiol, Innsbruck, Austria
[8] Univ Innsbruck, Dept Bot, Innsbruck, Austria
[9] Karl Franzens Univ Graz, Graz, Austria
[10] Cold Spring Harbor Lab, DNA Learning Ctr, Urban Barcode Project, POB 100, Cold Spring Harbor, NY 11724 USA
来源
JCOM-JOURNAL OF SCIENCE COMMUNICATION | 2018年 / 17卷 / 03期
关键词
Citizen science; Scholarly communication;
D O I
10.22323/2.17030101
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
Citizen science (CS) terms the active participation of the general public in scientific research activities. With increasing amounts of information generated by citizen scientists, best practices to go beyond science communication and publish these findings to the scientific community are needed. This letter is a synopsis of authors' personal experiences when publishing results from citizen science projects in peer-reviewed journals, as presented at the Austrian Citizen Science Conference 2018. Here, we address authors' selection criteria for publishing CS data in open-access, peer-reviewed scientific journals as well as barriers encountered during the publishing process. We also outline factors that influence the probability of publication using CS data, including 1) funding to cover publication costs; 2) quality, quantity and scientific novelty of CS data; 3) recommendations to acknowledge contributions of citizen scientists in scientific, peer-reviewed publications; 4) citizen scientists' preference of the hands-on experience over the product (publication) and 5) bias among scientists for certain data sources and the scientific jargon. These experiences show that addressing these barriers could greatly increase the rate of CS data included in scientific publications.
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页数:5
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