Parallel Session 6: Wednesday 10 April 10:15 - 12:00
Over the past decade we have seen a meteoric rise in the amount of data generated by the scientific community. Alongside this, open science and open data policies are encouraging more and more scientists to make their data more accessible. Not only that, there are more tools becoming available to aggregate, analyse and visualise data, for both scientists and the wider community. This includes tools for evaluating scientific impact.
As research facilities we are custodians of huge amounts of data, much of which is becoming publicly accessible. But are we making the most of it? Citizen science projects such as Galaxy Zoo have mobilised the public into analysing data by pattern recognition, where humans have traditionally excelled against computers, but will the rise of machine learning change all this?
Then there are tools to support impact metrics. For several years now DOI’s have provided unique references to scientific papers. Projects like ORCID could mean a similar unique identifier for researchers. Other initiatives aim to do the same for institutions, and possibly tools. Journals are starting to require data DOIs for publications, which could allow us to automatically harvest papers based on our data. Then tools like Altmetric allow us to track where these papers are read, talked about and used.
Can we work together on a joined up approach to capture and evaluate the impact of our science from proposal to publication and (for example) patent, the holy grail of research metrics? And more philosophically, should we?
