EARLY HEARING DETECTION AND INTERVENTION VIRTUAL CONFERENCE
MARCH 2-5, 2021
(Virtually the same conference, without elevators, airplane tickets, or hotel room keys)
6/08/2017 | 11:00 AM - 11:15 AM | DON'T DROWN IN THE DATA DELUGE: SUPPORTING THE ANALYSIS OF BIG FRESHWATER DATA | 302B
DON'T DROWN IN THE DATA DELUGE: SUPPORTING THE ANALYSIS OF BIG FRESHWATER DATA
Data availability no longer limits many advances in freshwater science. Instead, researchers are challenged to efficiently wrangle, analyze, and share complex time-series data. For example, the StreamPULSE project is expanding the availability of stream metabolism predictions but we face multiple data challenges, including 1.) assimilation and homogenization from many sources, 2.) consistent quality checks, and 3.) network-wide data access and modeling. These issues are not unique to our project. Here we present an approach for managing these challenges with an open-source web-based software stack that we have developed. We use this software to easily preview time-series data, rapidly flag problem data with machine learning, and download data and model output. We outline how these tools help us analyze the data from our network and discuss how they can be useful to researchers with similar challenges.
- S07 Conducting freshwater science with open-source, inexpensive technologies
- C21 Communicating Science
- S24 Towards a predictive freshwater ecology: using time-series data to understand and forecast responses to a changing environment
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Presenters/Authors
Aaron Berdanier
(), Duke University, aaron.berdanier@gmail.com;
ASHA DISCLOSURE:
Financial -
Nonfinancial -
StreamPULSE Network
(), Multiple Institutions, abb30@duke.edu;
ASHA DISCLOSURE:
Financial -
Nonfinancial -