Data in My Science
Speaking at a client’s board meeting earlier this summer, one of the topics that came up was the scholarly communications arena and what’s happening in that sector. We’ve been saying the journal is giving way to data as the center of the innovation universe and that changes with funders, government agencies, research institutions, and end-users over the years are finally coming home to roost.
We did seminal work about open access going back years and in 2018 forecast scenarios in this report. The world is full of commentary about the sector and one thing we know for sure: Innovation, science, and discovery are not going away. What is changing is how it’s done and Covid spun that up in spades.
In 2019, we collaborated with CCC documenting the scholarly communications ecosystem. And at an event we co-hosted that revealed and discussed this work, Outsell said that the journals world was going to have its “newspaper moment” — another trend we chronicled when we forecast a scenario called “hitting the wall.” We were spot on in the worst possible way. While we don’t expect a catastrophic meltdown in the journals world, three things unfolded around that meeting. When strung together they point to cracks in the established ecosystem.
- Coalition S came down on Elsevier, Springer Nature, and ACS leading to a large proportion of their hybrid journals being struck from the Coalition S Listing.
- Research Integrity continued to take center stage with announcements such as the one by Digital Science’s launch of its Dimensions Research Integrity App, following others in the field such as STM’s Integrity Hub launched in 2022.
- Highwire promoted a best practices seminar focused on a “revolution” in peer review workflow with more and more third party peer review services in the field. We’ve long said when peer review is “crowd-sourced” or whatever “new model metaphor” one wishes to use, the field will change indelibly.
And as this occurs, it points to the importance of data in the field of scholarly communications — what we’ve come to call discovery and innovation as basic research gives way to new solutions in whatever field they’re invented and if we are lucky, commercialized. If the journal and article become less central, data becomes more so. But what is data in a discovery and innovation context? We hear the word so often as if it has universal meaning.
Two-plus decades ago a survey of scientists and engineers I oversaw for a previous employer asked about the data they’d like to see more of. Respondents, in mostly corporate R&D functions, gave an answer: Their top unmet need for what they’d like to see more of was “market data” — as in market research, customer insights, and the like. That’s data.
In the world of rigorous data trials on a drug’s efficacy peer-reviewed in rarified air over a long and arduous process, 100,000 people can report contra-indications of that same genre of drug in their particular ailments’ online support group. That too is data.
And what about third-party data? What if a scientist needs dog DNA data from Embark or something licensed or available on the Amazon marketplace? This too is “data” in the context of discovery and innovation.
And of course, let’s not forget research data as in data reproducibility — another key type of data — data used in the experiment, attached to an article file, exposed and reused with the intent to reproduce the findings.
And then there is data in the text of scholarly articles when they are rendered as searchable data.
And too reader and analytics data — usage by a publisher’s readers or members or own customers. Who is using what? For what? These “usage analytics” say a lot and tell a big story. They too are data.
And so on.
“Data” is now fundamentally important in the field and important distinctions about it are essential in order to get our hands around the issues. It’s a little word with loads of meaning packed into two consonants and two vowels.
