I am incredibly excited to announce that I am the recipient of one of the Moore Foundation’s Investigators in Data-Driven Discovery awards.
To quote Chris Mentzel, the Program Director of the Data-Driven Discovery Initiative:
Science is generating data at unprecedented volume, variety and velocity, but many areas of science don’t reward the kind of expertise needed to capitalize on this explosion of information. We are proud to recognize these outstanding scientists, and we hope these awards will help cultivate a new type of researcher and accelerate the use of interdisciplinary, data-driven science in academia.
I feel truly honored to have been selected. All the finalists that I met at the Moore Foundation in July were amazing as were all of the semi-finalists that I knew. I did not envy the folks making the final decisions.
So what will we be doing with this generous support from the Moore Foundation?
- Doing data-intensive prediction and forecasting in ecological systems: We’ll be focusing on population and community level forecasting as well as ecosystem level work where it interfaces with community level approaches. We’ll be using both process based ecological approaches with machine learning, with an emphasis on developing testable predictions and evaluating them with independent (out-of-sample) data. As part of this effort we’ll be making publicly available forecasts for large ecological datasets prior to the collection of the next round of data, following Brian McGill’s 6th P of Good Prediction (in fact we’ll be trying to follow all of his P’s as much as possible). There’s a lot of good work in this area and we’ll be building on it rather than reinventing any wheels.
- Increasing the emphasis on testable prediction and forecasting in ecology more broadly: Industry and other areas of science have improved their prediction/forecasting through competitions that provide data with held out values and challenge folks to see who can do the best job of predicting those values (most notable in Kaggle competitions). We’ll be helping put together something like this for ecology and hopefully integrating that with our advanced predictions to allow other folks to easily make predictions from their models public and have them evaluated automatically as new data is released.
- Tools for making data-intensive approaches to ecology easier: We’ll be continuing our efforts to make acquiring and working with ecological data easier. Our next big step is to make combining numerous ecological and environmental datasets easy so that researchers can focus on doing science rather than assembling data.
- Training: We’ll be helping build and grow Data Carpentry, a new training effort that is a sister project to Software Carpentry with a focus on data management, manipulation and analysis.
I’m very excited to be joined in this honor by my open science/computational training/data-intensive partner in crime C. Titus Brown(@ctitusbrown). I was also particularly thrilled to find out that I wasn’t the only investigator studying ecological systems. Laurel Larsen is in the Geography department at Berkeley and I can’t wait to interact with her more as we both leverage large amounts of ecological data to improve our understanding of ecological systems and our ability to forecast their states in the future. We are joined by astronomers, statisticians, computer scientists, and more. Check out the entire amazing group at the official Moore Foundation Investigators site and see the full press release for additional details about the program.
The award is being run through the University of Florida since we are in the process of relocating there, but I owe a huge dept of gratitude to the Biology Department and the Ecology Center at Utah State University for always supporting me while I spent time developing software, working on computational training initiatives, and generally building a data-intensive ecology program. Without their support I have no doubt that I wouldn’t be writing this blog post today.