Published May 26, 2020 | Version 1.0
Dataset Open

Data Release: Aurorasaurus Science Products Inventory & Survey Results (2014-2019)

Authors/Creators

  • 1. Aurorasaurus, NASA

Description

Aurorasaurus is an eight-year-old project: the first and only citizen science initiative that tracks auroras around the world via reports on our website, mobile apps, and social media. (See Kosar et al., (2018). Aurorasaurus Real-Time Citizen Science Aurora Data (Version v1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1255196.)

At the American Geophysical Union Fall Meeting 2019, MacDonald and Brandt (2019) gave a talk entitled "Towards developing appropriate and diverse metrics for citizen science – a case study." In the presentation, they examined our 2015 user survey, used more recent metrics to assess Aurorasaurus' current status, and began to lay a framework for their next round of evaluation. 

In order to frame the process, MacDonald and Brandt (2019) utilized the Science Products and Data Practices inventories created by Wiggins et al., (2018). The authors constructed lists of items and practices that should be present in citizen science projects. While the Science Products and Data Practices inventories are excellent for quantitative analysis, MacDonald and Brandt (2019) wanted to include qualitative analysis of past evaluation as well. To that end, they informally and retrospectively mapped Aurorasaurus' 2015 survey questions to the BASIK framework.

Aurorasaurus is publishing this dataset as a case study for how the Wiggins et al. framework can be applied and combined with other systems.

Notes

Aurorasaurus is a research project that is a public-private partnership with the New Mexico Consortium supported by the National Science Foundation and NASA (NSSEC), and was designed by researchers from the New Mexico Consortium, NASA, Penn State University, Science Education Solutions, and Ideum. Space physics real-time data are provided by the Space Weather Prediction Center at NOAA, Thanks to Twitter for providing relevant tweets from their search API. The 2015 survey was conducted under IRB# 45896.

Files

MacDonald and Brandt — AGU 2019.pdf

Files (41.7 MB)