The Future of Experimentalists and Data Scientists.

I observe two contradictory trends in academia these days. On one hand, experimental researchers flock together into bigger and bigger consortia.

Examples include the Enigma Network in neuroimaging. Another is an international team of physicists at the Large Hadron Collider (LHC) in Geneva, Switzerland. 

The main purpose of these consortia is to collaborate in large groups and conduct complex experiments. These experiments are so labour- and know-how-expensive that it would not be possible to approach them in a more traditional, individualistic way.

According to Wuchty, Jones and Uzzi (2007), a huge analysis of research papers found a clear trend. Team-authored research has come to dominate solo-authored work across virtually every field, including in the production of the highest-impact studies.

One example of an institution built on this concept of scaling projects to a whole new level is the Allen Institute in Seattle. It is a non-profit research institution that carries out large research projects and releases the results to the public. 

Allen Institute does not consist of research groups; it is rather an army of researchers who gave up on their careers in the name of a greater good. Together, they form a collaborative effort to produce new knowledge in experiments so complex that it would not be possible to complete them otherwise.

What Is Happening on the Data Science Side?

On the other hand, I observe quite the opposite and even a bit contradictory trend. The aforementioned consortia release new, large, high-quality datasets, such as Human Connectome Project data, which are open to the public. As a result, everyone worldwide can, in principle, conduct research on those datasets. 

Furthermore, there are also sources of computational power, such as the NSG gateway, openly available for researchers. Therefore, conducting these private, individualistic studies does not need to be limited to the capacity of a single personal computer. 

Finally, posting preprints online on websites such as arXiv or bioRxiv, and free networking through social media like Twitter, are also openly available to anyone. In principle, there are no limits on data scientists in terms of what, where, and with whom to publish.

How Do Individual Scientists Gain Visibility?

Anyone connected and talented enough can formulate a research question. They can search for open datasets and conduct a computational study. They can then report the results, post them as a preprint, and spread the news through social media.

According to Abdill and Blekhman (2019), uploads to the biology preprint server bioRxiv kept climbing every year after its launch. By late 2018 alone, the site saw well over 2,000 new preprints and more than a million downloads in a single month. 

If the article achieves enough publicity, the editors of the scientific journals will write to the author on their own. They will ask for that manuscript. All the boundaries have just disappeared for the computational people.

Where Is This Heading in the Next Few Years?

Now, my prediction is that in the next few years, science will be further forking into these two trends. Experimentalists will become more and more like anonymous craftsmen, working in perfectly organised and tightly managed communities. Computational people, in contrast, will become much more like free spirits, performing individualistic research in the same fashion as artists.

They will come up with their own private ideas for a study and conduct these studies on their own. Then they will present the results online. The crowd decides whether the new research is hot or not. 

I am a computational person myself and a fan of equalising chances in academia. Therefore, I look into the future with hope.

I am really glad to live in the times of open data and knowledge exchange. Every smart, proactive person can define their own goals in life and figure out their path to get there. The future was never brighter than today.