# Example: Data Science¶

The following describes a series of steps to bootstrap a data science paper that follows the Popper convention using the Popper-CLI tool. Popper in this scenario is followed so that datasets are properly referenced and analysis scripts used to process data (as well as any output data) are versioned and associated to an article. For more on the Popper convention, look at the [[Intro to Popper]] article.

While in this guide we use LATeX, Docker, dpm and Jupyter, any of these can be swapped for equivalent tools. To learn more about how to use other tools and how the Popper convention is toolchain-agnostic, see here.

Requirements:

Initialize a Popper Repository

Our Popper-CLI tool assumes a git repository exists. To create one:

mkdir mypaper
cd mypaper
git init
echo "# My Paper Repo" > README.md
git commit -m "First commit of my paper repo."


See here for a list of good resources for learning git. Once a git repo exists, we can invoke the popper-cli tool:

cd mypaper
popper init


The above creates a .popper.yml file that contains configuration options for the CLI tool. This file should be committed to the paper repository (git repo we create above). For an explanation on the folder structure of a Popper repo, see here.

The Popper convention outlines how to make it practical to generate reproducible experiments. As part of our effort, we maintain a list of experiment templates that have been "popperized" (see here for an explanation of what constitutes a Popper-compliant experiment). To see a list of available experiments:

popper experiment list


In order to add a new experiment, we refer to a template and assign a name to it. The general invocation form is the following:

popper experiment add <template> <experiment-name>


For example, assume we want to analyze data from an experiment in the area of meteorological sciences (a template created as part of the Big Weather Web project):

popper experiment jupyter-bww myexperiment


This data analysis experiment consists of one dataset and a jupyter notebook. To retrieve the dataset to the local machine:

cd experiments/myexperiment

docker run --rm -v pwd/datapackages:/datapackages \
ivotron/dpm install /datapackages/air-temperature

NOTE: The above makes use of the dpm tool for managing datapackages. The tool doesn't support file:/// URLs yet (until this issue gets resolved). In the meantime, to download the dataset from github, replace /datapackages/air-temperature with https://github.com/ivotron/air-temperature.

To visualize and interact with the data analysis of this experiment:

cd experimetns/myexperiment
./visualize


The above opens a browser and points it to the notebook. In this example, the dataset used by the notebook resides in the myexperiment/datapackages/ folder.

For this experiment we assume that input data has been externally generated, i.e. dataset creation is not part of the experiment. Also, the analysis runs on a single machine. Other types of data science projects might involve generating their input datasets and/or process data in a cluster of machines. Popper still can be followed in these scenarios (e.g. see [[Popper-Distributed-Systems]] and [[Popper-HPC]]).

Datasets are stored (or referenced) in the datapackages/ (or datasets/) folder of each experiment, with one subfolder for each dataset. For examples datasets see here. To add or reference a new dataset, one has to either provide a URL of the dataset, or inspect a the list of datapackages available in a data repository using the dpm tool. Available repositories are github, ckan and thredds.

NOTE: Support for THREDDS is not part of the official dpm client yet. Work is being done in this as part of the big weather web project.

Once a dataset URL is available, one can install a package by doing

docker run --rm -v pwd/datapackages:/datapackages \
ivotron/dpm install http://motherlode.ucar.edu:8080/thredds/bww/


To display the info for a package, use the info command of dpm. For more info on how to use dpm take a look at the official documentation.

Generating Image Files For Reference In Manuscripts

Assume we add a new type of analysis to the notebook and we want to generate an image. For the notebook of our example (xarray-tutorial.ipynb of the jupyter-bww experiment), we can generate a file for figure 2 (Line [45]). In Jupyter, we add a new cell below the figure and type the following line:

plt.savefig('air-temperature.png',bbox_inches='tight', dpi=300)


Since the experiment folder is available in the filesystem that Jupyter has available to it, the figure persists even after the Jupyter server exits. To automatically re-execute the analysis and re-generate figures from a notebook, one can use the run-notebook script contained in the jupyter-bww experiment:

cd myexperiment
./run-notebook


Documenting the Experiment

After we're done with our experiment, we might want to document it and add a paper. We can use the generic article latex template or other more domain-specific one (available here). To display the available templates we do popper paper list. In this example we'll use the latex template for articles that appear in the Bulletin of the American meteorological Society (BAMS):

popper paper add latex-ametsoc


Let's assume we will have a new section in the LATeX file where we describe our experiment. We will make use of the figure that we generated in the previous section. We can make the assumption that the experiments folder is available at the level of the latex file, so we can reference the image directly. For example:

\begin{figure}[t]
\includegraphics{experiments/myexperiment/air-temperature.png}\\
\caption{Air temperature.}\label{f1}
\end{figure}


And to re-generate the PDF containing the new image:

cd paper
./build


Documenting Changes to Experiments

The paper repository is the analogy to the lab notebook in experimental science. There are many ways in which these changes can be registered in the form of code repository commits. A couple of tips:

• Make changes small. Avoid having large commits since that makes it harder to document.
• Separate commits that change the logic of the experiment and analysis, from the ones that record changes to results.
• Commit messages should describe in as much detail as possible the changes to the experiment, or the new results being added to the repository.

For examples of Popperized repositories, see here. We are currently working with researchers in this domain to include more experiments to our templates repository. If you are interested in contributing one but are not certain on how to start, please feel free to email us, chat or open an issue.