Research – an important role in reproducible data science

Research – and important role in reproducible data science

Every machine learning project starts with research. Whether you are working in a corporation or in academia, it’s likely you are already familiar with the research phase of data science. And, if you’ve embarked on this research journey before, you may have started with a single paper, which lead you to numerous other papers, of which you gathered a relevant subsection which lead you to a dead end – but then, after a week or so brought you to a dozen other relevant papers, a heap of web searches leading you to some new ideas about the topic.


A typical view of a data scientist’s browser while conducting research

Two weeks later, you’re able to proceed with building your machine learning or deep learning models, quite possibly forgetting the bathroom break in which you rediscovered article #1 that prompted your breakthrough machine learning model to begin with.

Needless to say, the research tunnel is a vibrant and unpredictable one, leading in many directions, and provoking endless thought. The research process – unexpected as it is – can often be a hard one to retrace, let alone to reproduce.

The ugly-beautiful research phase

When discussing the reproducibility of data science, most often you’ll hear about the importance of documenting experiments, hyperparameters, metrics, or how to track models and algorithms to prepare for someone who would replicate it. Unfortunately, a major process in the data science pipeline that is completely overlooked in reproducibility, is research. Research is the ugly-beautiful practice that consumes 2 weeks – prior to any coding or experimentation – where you sit down and understand former attempts or learn from previously successful solutions.

As Jon Claerbout describes: “An article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code and data that produced the result.” What really makes it scholarship over advertising is the research that got you there to begin with.”

Why make research reproducible?

As stated in the rOpenSci Project’s Reproducibility Guide there are two main reasons to make research reproducible. One, is to show evidence of the correctness of your results. The other is to enable others to make use of your methods and results. This simple reasoning might seem trivial, but it holds true in any scientific endeavor, whether you aspire to advance science as a whole, or advance your team or company.

One might argue that it is redundant to do research for a problem you have already solved before. It can especially be overlooked when working in a fast-paced corporate environment. But, it’s likely that there are some exciting innovative solutions that you wouldn’t have encountered without research. Take for instance text classification – a rather simple and common machine learning task, where only in the past 30 days there were over 52 new papers published on Arxiv. Admittedly, not all of them will be related to the problem being solved, or even of superior quality, but they can spark new ideas and inspire you to try new approaches to solve your challenges.

Standardizing research reproducibility

Preparing data science research for reproducibility is easier said than done. There is no standardized way to document research, and the degree of documentation of research can vary between data scientists. Before starting cnvrg.io, we assisted companies in various data science projects. Time and time again, we continued to assist clients approaching the same problems, or had to reproduce projects that had been done before. We saw the important role research had in reproducing a project, and how much time could have been saved if proper documentation was available.

When cnvrg.io came to be, we integrated research deeply in the product, and created ways to standardize research documentation to make research reproducibility less daunting. The research center in cnvrg.io makes documentation of papers, discussions and ideas possible, allowing data scientists to research freely without preemptive thought of reproducibility. Such a simple solution can make research reproducibility a problem of the past, and help data scientists build a comprehensive and organized knowledge base of machine learning research.

As data scientists continue to discover breakthroughs in machine learning, it’s important to stick to our scientific roots. Embrace the power of research, and document every detail so that others can build from your well investigated conclusions. If anything, don’t you want your coworkers to experience the same trippy research journey you had the pleasure to embark on?

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