Adding 'Real-Time' Data to the Black Rock Forest Digital Library to Enable Students to Engage in Predictive Investigations

9907689 Kastens该项目将（a）实施一个系统，用于在收集数据后立即（即接近“实时”）将数据从装有仪器的森林内的环境传感器传送到教室计算机，以及（B）证明数据的“实时性”使实现无法用存档数据实现的教育目标成为可能。 仪器森林是位于纽约哈德逊高地地区的黑岩森林（BRF）。 两个地面传感器站和一个河流传感器站自动监测和记录环境参数，如空气、水和土壤温度、太阳辐射、降水、相对湿度和河流流量。 该项目将把这些环境数据纳入一个专业管理的数字数据库，到万维网上，到一个易于使用的数据可视化工具，到学生的计算机上，并进入学生实习-所有这些都在几个小时内收集后。 此外，该项目将开发和测试一个示范性的本科生调查，这将展示实时数据对教育的价值，并在现实条件下练习实时数据链接。 学生将检查档案降水量和流量数据，开发一个模型连接两者，然后使用他们的模型来预测未来几天的流量。 大多数成年地球科学家花了大部分的问题，社会是摆在地球科学界与未来有关。 我们需要找到方法来训练我们的学生严谨而大胆地思考未来。 预测性调查是一种方法。

9907689KastensThe project will (a) implement a system for delivering data from environmental sensors within an instrumented forest to classroom computers immediately after the data are collected (i.e. in near "real-time"), and (b) demonstrate that the "real-time-ness" of the data makes it possible to accomplish educational objectives that could not be accomplished with archived data. The instrumented forest is the Black Rock Forest (BRF) in the Hudson Highlands region of New York. Two terrestrial sensor stations and one stream sensor station automatically monitor and record environmental parameters such as air, water, and soil temperature, solar radiation, precipitation, relative humidity and stream discharge. The project will bring these environmental data into a professionally managed digital data library, onto the World Wide Web, into an easy-to-use data visualization tool, onto student computers, and into student imaginations-all within a few hours after it is collected. In addition, the project will develop and test an exemplary undergraduate investigation, which will showcase the value of real-time data for education, and exercise the real-time data link under realistic conditions. Students will examine archival precipitation and stream discharge data, develop a model linking the two, and then use their model to predict what the stream discharge will be a few days into the future. Most adult geoscientists have spent most of the questions that society is posing to the geoscience community have to do with the future. We need to find ways to train our students to think rigorously but boldly about the future. Predictive investigations are one way to do this.