A zooplankton researcher has ten years of monthly-sampled species counts data. They want to look for seasonal and inter-annual trends within their data, as well as any changes in relative species composition over time. They also want to compare these data with weekly-sampled in-situ chlorophyll data, irregularly-sampled nutrient data, and to somehow extract and include a subset of geographically-relevant satellite SST and chlorophyll data from the waters around their time series site. Finally, they are curious if there are any relationships between their variables and the local climate indices (e.g., the NAO or the PDO).
This may sound like it involves months of work, expensive software (or access to an "R" guru), and a lot of data reformatting and processing effort. In reality, it can be done with a spreadsheet and a web browser in a matter of minutes using a free online tool that will be demonstrated during this half day workshop. Further, the "Interactive Time-series Explorer" being used in this workshop uses the same analyses methods and visualization graphics featured in the ICES Zooplankton Status Report series and the International Group for Marine Ecological Time Series (IMGETS) studies.
This hands-on workshop will walk participants through a time series analysis, step-by-step, from raw data to temporal synchronization, visualization, correlation, and interpretation. Participants that bring their own laptop can try out the toolkit during the workshop, using the provided sample data set or their own data. Step-by-step documentation will be provided, allowing the participants to recreate the steps in class or to repeat and expand upon them later.
The goal of this workshop is to give researchers an introduction to basic time series analysis and visualization tools. The workshop will address and demonstrate the following topics:
- Basics of time series data preparation (temporal binning and synchronizing time periods, issues of numeric transformation (aka "to log or not to log"))
- Visualizing individual variables (seasonal patterns, inter-annual trends, month-based investigations, visual quality control / investigating outliers)
- Comparing multiple variables to each other (group plots, correlation plots)
- Linking to local climate indices (e.g., NAO/PDO) and larger regional trends in SST or satellite chlorophyll.
- Comparing data from/between different depths, sites or programs.
