Sharing data and code speeds up the advancement of science. In the past, I have included the data from my papers in supplementaries, but I am moving towards sharing via online repositories such as Github and Zenodo. I will link to these resources here for whomever is interested. You are welcome to use these - for research, education, anything at all. If you do find them useful - or if you identify errors - I would appreciate it if you could let me know using the email ID on the contact page.
1) Code to clean and cluster flow cytometry (FCM) and scanning flow cytometry (SFCM) datasets, along with example dataset (updated August 30th, 2017)
FCM and SFCM are powerful tools to track the dynamics of microbial communities in the lab and field. However, for large, complex datasets, few tools are available to ensure rapid, repeatable analyses. While working with such a dataset in Switzerland, I developed a protocol to clean (i.e. remove signals that are not from live cells) and cluster (i.e. identify subgroups) SFCM data, as well as estimate the biovolumes of individual cells and colonies. The method has been published in PLoS one, and the code and an example dataset are available through Zenodo and Github:
Dataset (DOI: 10.5281/zenodo.977772)
Code (DOI: 10.5281/zenodo.999747)
Though this worked example is based on data from the Cytobuoy, it may be adapted for use with any FCM or SFCM data.
2) Data on phytoplankton temperature traits, and growth rates at different temperatures (updated Jan 29th, 2016).
I gathered >7000 published measurements of the population growth rates of individual phytoplankton species at different temperatures, and used these to estimate important traits (such as the optimum temperature for growth) that tell us how ocean and lake temperatures influence phytoplankton. I include both the original growth rates and the estimated traits (and associated metadata) in this dataset. An explanation of the dataset is included inside the attached .zip file. For more details about it, please look at the methods in Thomas et al. (2012) and Thomas et al. (2016), as well as their supplementaries. If you do use this dataset for a publication, I'd appreciate it if you would cite these papers as the data sources.
I do not actively update this dataset currently. But if you have data you would be willing to contribute to it, I will gladly include it with attribution.
Dataset
3) R code to fit thermal reaction norms to growth rate (or performance) measurements at multiple temperatures (updated Jan 29th, 2016)
To estimate the temperature traits in the dataset described above, my collaborator Colin Kremer developed an equation (building on work by Jon Norberg) to describe the left-skewed shape of a thermal reaction norm. We (mostly Colin) developed code to fit this equation to population growth rate measurements at multiple temperatures, which I share here, along with a dataset containing two example thermal reaction norms. This is not very efficient code (we developed it as R beginners) and it is offered without guarantees, but it works reasonably well. Feel free to use it for any purpose, and please let me know if you find it useful.
Code
A few additional points:
i) Though we developed this while studying population growth rates in phytoplankton, the equation is much more broadly applicable; it works in all ectotherms, and for a number of biological processes. For example, sprint speed in reptiles is also a left-skewed function of temperature.
ii) The equation remains flexible enough that it can also describe symmetric or even right-skewed curves, so it does not 'force' skewness on the data.
iii) For a full explanation of the equation, please take a look at the supplementary methods in Thomas et al. (2012).
FCM and SFCM are powerful tools to track the dynamics of microbial communities in the lab and field. However, for large, complex datasets, few tools are available to ensure rapid, repeatable analyses. While working with such a dataset in Switzerland, I developed a protocol to clean (i.e. remove signals that are not from live cells) and cluster (i.e. identify subgroups) SFCM data, as well as estimate the biovolumes of individual cells and colonies. The method has been published in PLoS one, and the code and an example dataset are available through Zenodo and Github:
Dataset (DOI: 10.5281/zenodo.977772)
Code (DOI: 10.5281/zenodo.999747)
Though this worked example is based on data from the Cytobuoy, it may be adapted for use with any FCM or SFCM data.
2) Data on phytoplankton temperature traits, and growth rates at different temperatures (updated Jan 29th, 2016).
I gathered >7000 published measurements of the population growth rates of individual phytoplankton species at different temperatures, and used these to estimate important traits (such as the optimum temperature for growth) that tell us how ocean and lake temperatures influence phytoplankton. I include both the original growth rates and the estimated traits (and associated metadata) in this dataset. An explanation of the dataset is included inside the attached .zip file. For more details about it, please look at the methods in Thomas et al. (2012) and Thomas et al. (2016), as well as their supplementaries. If you do use this dataset for a publication, I'd appreciate it if you would cite these papers as the data sources.
I do not actively update this dataset currently. But if you have data you would be willing to contribute to it, I will gladly include it with attribution.
Dataset
3) R code to fit thermal reaction norms to growth rate (or performance) measurements at multiple temperatures (updated Jan 29th, 2016)
To estimate the temperature traits in the dataset described above, my collaborator Colin Kremer developed an equation (building on work by Jon Norberg) to describe the left-skewed shape of a thermal reaction norm. We (mostly Colin) developed code to fit this equation to population growth rate measurements at multiple temperatures, which I share here, along with a dataset containing two example thermal reaction norms. This is not very efficient code (we developed it as R beginners) and it is offered without guarantees, but it works reasonably well. Feel free to use it for any purpose, and please let me know if you find it useful.
Code
A few additional points:
i) Though we developed this while studying population growth rates in phytoplankton, the equation is much more broadly applicable; it works in all ectotherms, and for a number of biological processes. For example, sprint speed in reptiles is also a left-skewed function of temperature.
ii) The equation remains flexible enough that it can also describe symmetric or even right-skewed curves, so it does not 'force' skewness on the data.
iii) For a full explanation of the equation, please take a look at the supplementary methods in Thomas et al. (2012).
