Rethinking leadership in an open science world


RDA_plenary1

I posted a blog on the AGU blog space today.

You can read the whole blog here:

Problematics for science leadership in a data-rich, open-science world

What I conclude is this:

“I would like to propose just two metrics for now:

  1. The sharing of data with full provenance and,
  2. The reuse of these data.

As global open science emerges, data resources will be added to a variety of open repositories, from individual and institutional collections to national and international repositories. These data—big and small—will be generated through ever-more ubiquitous collection methods by a still-growing number of scientists across the globe. Combined, these new resources enable entirely novel synthesis opportunities for new knowledge created from existing data. The network effect, which calculates how the value of networks multiply as they grow, holds true for data. Adding a single new data resource to an open repository multiplies its value for scientists everywhere. When everyone, including Top Chef scientists, share their recipes (their data), the internet opens up lateral learning potentials that can push science to a higher velocity of discovery.

Opening up your data requires a lot more than publishing a PDF of your spreadsheet. Remember, data is the pluperfect participle for the Latin verb do, “to give.” Data are “things that have been given.” The value of this gift is highly dependent on its provenance and the completeness of its description. Producing shareable data also means opening up and sharing workflows, methodologies, and software. Haec omnia in datis sunt: “It’s all in the data.” That includes the reputation of the team that created the data.

Science leadership for a data-rich academy

In the not-too-distant future, when it comes to choosing a scientist to lead your science organization, you might want to pick somebody who has a track record of sharing the data their team spent so much time and care to gather and describe, and whose data are actively used by others to create new knowledge; somebody who has shown integrity in their workflow and a concern not just for their own research but also for the wider science research realm. Using data sharing and reuse as metrics for science prizes, career decisions and leadership positions realigns global science with the promise of its digital future.”

 

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