Anita de Waard, VP Research Data Collaborations, Elsevier

Optimising Scientific Knowledge Transfer: On Collective Sensemaking, and the Radical Idea

350 years ago, science was an individual enterprise:

Today, science is done by big groups:

Or is it?

But that he was much surprised when he saw the Micrography of Mr. Hook, and found there, that his engine was published on a mere Theory, without having made any Experiment, though that might have been made with little charge and great speed; expense of Money and Time being the only thing that can excuse those in matter of Engines impart their inventions to the public without having tried them, to excite others to make trial thereof.

In the rest of this note, we show that while these are some beautiful ideas, the above analysis hides some important subtleties.

How do we unify the needs of the collective and the individual scientist?

“Let us endeavor to build systems that allow a kid in Mali who wants to learn about proteomics to not be overwhelmed by the irrelevant and the untrue.”

- John Perry Barlow, iAnnotate 2014

We need to create systems of knowledge management: • That make it easy to find what is known and found,• Anchored in a substrate of integrated facts,• Allowing attribution to previous claims and evidence,• To support the development of radical new thoughts.

One problem: the ‘paper’ is still the overarching modus operandi:

Citations are to papers, not thoughts

Data is presented as pictures, not - data

We need better ways of citing and connecting knowledge:

To create claim-evidence networks:• Given that papers are ‘stories, that persuade with data’ • We need better ways of citing previous claims where the

claim can be linked to directly• And we should link all claims to the corresponding evidence

for a particular claim• Thus constructing chains of evidence that can be traced to

their source.

For this, we need: • Tools that enable us to do this easily (using e.g. Xlink);• To build these habits of citing evidence into all aspects of our

educational systems.

Another problem: data cannot be found – or integrated…

… and manual curation cannot keep up!

Data storage systems are not aimed at knowledge integration….

We need better ways to access and integrate data:

• Putting the data out there and making it accessible is great, but not enough

• It needs to be possible to find, understand, cite, and where possible reuse the date

• Systems to add appropriate descriptive metadata need to be integrated into the scientific workflow (upstream, cf. Matt Cockerill)

• An understanding needs to evolve (and be taught) that in any science, you are not off in your own world, but contributing a component to a larger whole – you are contributing to a collective endeavor, for the good of mankind.

The tools we use to explore scientific/technical knowledge are greatly outdated…

A third problem: scientific software is not up to snuff…

…while the tools we use our daily lives are getting smarter all the time.

We collectively need to buildsuperior scientific software:

• It cannot be that the software that is used to sell me shoes is light years ahead of the software an oncologist can use to decide on a treatment!

• Software development is a profession that requires different training, practices and salaries than doing research (cf Tony Hey)

• One key component is a constant focus on usability and user testing

• Another component is need to do constant evaluations and maintain performance metrics, which competing designs are tested against

• Everything must constantly be overhauled to stay up to date – this is not a one-time investment!

• We collectively need to find a way to practice and fund this –(even if it means working with companies!)

To enable the individual to harness the power of the collective, we need:

Goal Requirement Action Agent

To see the heritage of a particular idea:

Need better tools to cite claims/evidence, not papers

Build tools Software creators

Need requirements to trace ideas – not papers

Issue mandates, recommendations

Funders, societies, journals?

To see all data on a specific object:

Need systems/tools to find and integrate data, methods

Build connections, search engines

Software creators

Need standards, habits, requirements, mandates to post data, methods, analyses

Mandates, standards Funding bodies, learned societies, journals

To have scientific tools catch up with consumer software:

Need better metrics for assessing software quality, usability, search, etc.

Develop metrics for scientific software performance

Software creators, funding agencies, all

Need robust funding models for sustainable software development

Develop models for long-term development

Funding agencies, governments, i.c.w. industry if needed