Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias
Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias
复制标题
DOI：
10.1038/s41598-020-67658-3
复制
发表时间：
2020-07-03
影响因子：
4.6
通讯作者：
Pocock, Michael J. O.
中科院分区：
文献类型：
--
作者：
August, Tom;Fox, Richard;Pocock, Michael J. O.
世界各地的志愿者和非专业人士收集数据，作为环境公民科学项目的一部分，收集野生动物观察，水质测量等。然而，如果项目在如何、在何处和何时收集数据方面允许灵活性，参与者的行为就会发生变化，从而导致收集的数据集存在偏差。我们开发了一种方法来量化这种行为变化，描述了关键驱动因素，并提供了一种工具来解释使用这些数据的模型中的偏差。我们使用了一套指标来描述参与者的时间和空间行为，以及他们收集的数据的变化。这些应用于iRecord Butterflies移动的手机应用程序的5，268名用户，这是一个多物种环境公民科学项目。与以前的研究相比，在删除短暂的参与者（那些活跃在几天，谁贡献很少的记录），我们没有发现参与者的集群的证据;相反，参与者下降沿着四个连续轴描述参与者的行为变化：记录强度，空间范围，记录潜力和稀有记录。我们的研究结果支持远离标签的参与者属于一个行为组或另一个有利于把他们沿着轴的参与者行为，更好地代表个人之间的连续变化。通过考虑数据收集过程中的偏差，了解参与者行为可以支持更好地使用数据。
Around the world volunteers and non-professionals collect data as part of environmental citizen science projects, collecting wildlife observations, measures of water quality and much more. However, where projects allow flexibility in how, where, and when data are collected there will be variation in the behaviour of participants which results in biases in the datasets collected. We develop a method to quantify this behavioural variation, describing the key drivers and providing a tool to account for biases in models that use these data. We used a suite of metrics to describe the temporal and spatial behaviour of participants, as well as variation in the data they collected. These were applied to 5,268 users of the iRecord Butterflies mobile phone app, a multi-species environmental citizen science project. In contrast to previous studies, after removing transient participants (those active on few days and who contribute few records), we do not find evidence of clustering of participants; instead, participants fall along four continuous axes that describe variation in participants' behaviour: recording intensity, spatial extent, recording potential and rarity recording. Our results support a move away from labelling participants as belonging to one behavioural group or another in favour of placing them along axes of participant behaviour that better represent the continuous variation between individuals. Understanding participant behaviour could support better use of the data, by accounting for biases in the data collection process.