Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering , Brooklyn, New York.
Zebrafish. 2018 Jun;15(3):310-313. doi: 10.1089/zeb.2017.1542. Epub 2018 Feb 22.
The advent of automated tracking software has significantly reduced the time required to record movement trajectories, thereby facilitating behavioral studies of zebrafish. However, results are substantially influenced by tracking errors, such as loss and misidentification of individuals. In this study, we present the development of an online citizen science platform, Tracking Nemo, to improve data accuracy on swimming trajectories of zebrafish groups. As an online extension of software for tracking the position of zebrafish from video recordings, Tracking Nemo offers volunteers the opportunity to contribute to science by manually correcting tracked trajectory data from their personal computers. Researchers can upload their videos that require human intervention for correcting and validating the data. Citizen scientists can monitor their contributions through a leaderboard system, which is designed to strengthen participant retention and contribution by tapping into intrinsic and extrinsic motivations. Tracking Nemo is expected to help scientists improve data accuracy through the involvement of citizen scientists, who, in turn, engage in an authentic research activity and learn more about the behavior of zebrafish.
自动化跟踪软件的出现大大减少了记录运动轨迹所需的时间，从而促进了斑马鱼的行为研究。然而，结果受到跟踪误差的显著影响，例如个体的丢失和错误识别。在这项研究中，我们提出了一个在线公民科学平台的开发，即 Tracking Nemo，以提高斑马鱼群体游泳轨迹数据的准确性。作为用于从视频记录中跟踪斑马鱼位置的软件的在线扩展，Tracking Nemo 为志愿者提供了通过个人电脑手动纠正跟踪轨迹数据来为科学做出贡献的机会。研究人员可以上传他们需要人工干预来纠正和验证数据的视频。公民科学家可以通过排行榜系统监控他们的贡献，该系统旨在通过利用内在和外在动机来增强参与者的保留率和贡献度。预计 Tracking Nemo 将通过公民科学家的参与帮助科学家提高数据的准确性，而公民科学家反过来又会参与到真实的研究活动中，并更多地了解斑马鱼的行为。
