We've been working on mapping science progress, and collaborations, geo-location of research contributions, and the rise (and fall) of institutions (labs etc) over 50 years of bibliometric data.
This is easy as we;re all so eager to be seen to publish that the meta-data about our work is all freely available through plenty of online databases and can be extracted in a very small number of accepted standard formats for magic processing - so we started here with a basic 5 year set of data (we've also looked at the Turing institute's publication data over the last 3 years, and now we're working on all of sigcomm's half a century of data. it would be easy to do more subjects and also more analyses.
First of two we're just adding is mentioned above, which is to look at the trajectory of research labs. So most people will be aware that Bell Labs is no longer the thing of beauty it once was. Nor is Xerox PARC. And some labs simply disappeared due to their corporate hq discorporating (Sun Microsystems, Digital Equipment Corporation) or losing the plot (HP?).
So we can see his effect over time. We can use the data + other information, to train algorithms to predict the demise of labs too (in the same way this Euro S&P paper from oxford built an algorithm for mergers and treaties out of airplane tracking datasets and external news reports.
We can also see geographic variation in success at research outputs.
But we can go further - a second thing we can add is gender data, inferred from names (or by using data from web lookup of author name + institution or other public sources. And this could be useful for combating bias.
However, what happens when we start to relate overall trends of institutions to the arrival (or departure) of particular researchers? This could be turned into a recruitment (or promotion or dismissal) tool. That could be quite a bad thing (given there are natural reasons why there's variation in peoples' output or influence on colleagues output, which can vary over time, and are probably going to result in short termism killing more long term strategic type thinking).
what to do?
This is easy as we;re all so eager to be seen to publish that the meta-data about our work is all freely available through plenty of online databases and can be extracted in a very small number of accepted standard formats for magic processing - so we started here with a basic 5 year set of data (we've also looked at the Turing institute's publication data over the last 3 years, and now we're working on all of sigcomm's half a century of data. it would be easy to do more subjects and also more analyses.
First of two we're just adding is mentioned above, which is to look at the trajectory of research labs. So most people will be aware that Bell Labs is no longer the thing of beauty it once was. Nor is Xerox PARC. And some labs simply disappeared due to their corporate hq discorporating (Sun Microsystems, Digital Equipment Corporation) or losing the plot (HP?).
So we can see his effect over time. We can use the data + other information, to train algorithms to predict the demise of labs too (in the same way this Euro S&P paper from oxford built an algorithm for mergers and treaties out of airplane tracking datasets and external news reports.
We can also see geographic variation in success at research outputs.
But we can go further - a second thing we can add is gender data, inferred from names (or by using data from web lookup of author name + institution or other public sources. And this could be useful for combating bias.
However, what happens when we start to relate overall trends of institutions to the arrival (or departure) of particular researchers? This could be turned into a recruitment (or promotion or dismissal) tool. That could be quite a bad thing (given there are natural reasons why there's variation in peoples' output or influence on colleagues output, which can vary over time, and are probably going to result in short termism killing more long term strategic type thinking).
what to do?
2 comments:
You might be confusing cause and effect. That is, the decline in prestige or influence of the researchers joining a corporate lab might not be the root cause of its ultimate demise, but rather a consequence of the prior arrival of poor managers (either of the lab itself or at higher levels). In my experience, good leaders can attract great researchers or help nurture them into greatness. Mediocre leaders either can’t attract good staff, or simply hire people for the wrong reasons.
Running a corporate research lab is hard. You don’t have to be a jerk to destroy one (although it helps).
true, not sure I'm inferring cause, but correlation -but if i get enough dimensionality in the data, we can do causal inference too:-)
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