I present an alternative reading of George Michael’s 1990’s hit song Freedom! While many interpret this song as about Michael’s struggles with fame in an industry that constantly aimed to warp his true identity, it can also be interpreted as a researcher progressing in a field where data ownership and data ‘rights’ are still hotly contested.
Heaven knows I was just a young boy
Didn’t know what I wanted to be
I was every little hungry schoolgirl’s pride and joy
And I guess it was enough for me
In these first lines the researcher describes the heady days of early grad school, where most folks were still in their twenties, enjoying a cadre of new friends, and still not sure of their future.
To win the race, a prettier face
Brand new clothes and a big fat place
On your rock and roll TV
But today the way I play the game is not the same, no way
Think I’m gonna get me some happy
The researcher describes a few things here: moving on to their postdoc, the excitement of their first few conferences where they got to present their exciting new results, but also hints at a big change in how they are approaching science recently. A change that could bring great happiness.
I think there’s something you should know
(I think it’s time I told you so)
There’s something deep inside of me
(There’s someone else I’ve got to be)
Take back your picture in a frame
(Take back your singing in the rain)
I just hope you understand
Sometimes the clothes do not make the man
The researcher is nervous about what they will share. They know it is not the view of many in the field and they hope others will understand. Even though they may wear the tevas-with-socks and pleated shorts of an ecologist, they have views they fear will not be widely accepted by their community.
All we have to do now
Is take these lies and make them true somehow
All we have to see
Is that I don’t belong to you and you don’t belong to me
Here the researcher sings out their truth! They stare down the lies they have repeatedly heard, including:
- Data you collected are owned by you and you should hold onto them possessively.
- If you publish your data or don’t tightly guard it, it will be stolen by others, and then your career may be ruined.
- People build entire careers on reusing other people’s data and they get more fame and recognition than those who toil away collecting data and are not recognized for their efforts.
- Your data can never be fully understood without your presence, and thus should probably not be used without you around in some way.
- If we all publish the data we collected regularly we will never have good data again, and we will never ever have long-term data because people will stop collecting long-term data.
The researcher sings out to their colleagues that these are lies and that for science to progress, data should be free, that data don’t ‘belong’ to any of us. They encourage their colleagues to let go of possessiveness (it never makes you happy!) around data.
Freedom (I won’t let you down)
Freedom (I will not give you up)
Freedom (Gotta have some faith in the sound)
You’ve got to give what you take (It’s the one good thing that I’ve got)
Freedom (I won’t let you down)
Freedom (So please don’t give me up)
Freedom (‘Cause I would really)
You’ve got to give what you take (really love to stick around)
Here they sing out for data freedom (“Freedom!”), alternating with pulls they have felt from colleagues who believe data sharing may destroy the field (“I will not give you [data] up”).
Heaven knows we sure had some fun, boy
What a kick just a buddy and me
We had every big-shot goodtime band on the run, boy
We were living in a fantasy
The researcher again looks back fondly on their PhD, remembering days in the field when they pulled on their waders, grabbed their plastic bucket and collected data (for example, see opening images here), and then published their first exciting papers.
We won the race, got out of the place
Went back home, got a brand new face for the boys on MTV (Boys on MTV)
But today the way I play the game has got to change, oh yeah
Now I’m gonna get myself happy
The researcher remembers wrapping up their PhD, submitting a great Dance Your PhD (here’s a favorite example), and then returns to their refrain on realizing that their field must change. Both for the field and for personal happiness.
The chorus repeats ….
All we have to do now
Is take these lies and make them true somehow
All we have to see
Is that I don’t belong to you and you don’t belong to me
Freedom!
Freedom!
Freedom!
It’s the one good thing that I’ve got
Sing it with me! Data freedom! Data freedom! If you love ‘your’ data set it free!
And if you’re an ecologist or in any similar field with a contingent of folks who speak some of the lies mentioned above I encourage to ask for examples. Ask for the list of people whose careers have been ruined by data sharing, ask also for the list of happy people who publish data they collect—and try to actually figure out what the distribution of these ruined versus non-ruined people looks like. If they tell you someday your field will be destroyed, ask them for examples of other fields where people have been made to share data (think GenBank, parts of medicine, please help me expand this list!) and what actually happened.
Wow—it’s poetry corner on the blog today.
I know, I wrote it this morning before checking the blog and then checked and thought perhaps I shouldn’t post it. But then figured I will never post if I wait for the right spot. So, there it is.
Kipling and George Michael — obvious combination.
I assume you have seen McElreath’s talk on related matter:
https://www.youtube.com/watch?time_continue=2&v=zwRdO9_GGhY&feature=emb_logo
Worth a looksie. Also, in NOAA there is a concerted effort to make environment data publicly and easily available. For example, the very innocent looking web page/ web service at https://upwell.pfeg.noaa.gov/erddap is actually a federated serviced that links to data at some 20-30 different locations, literally petabytes of data, all can be subsetted and downloaded in a large variety of formats, and can be used by anything that can send an URL and receive a file, as any request is completely defined by an URL. Hidden in there are the complete CalCOFI dataset as well as data from several very important long-term fish surveys.
“People build entire careers on reusing other people’s data and they get more fame and recognition than those who toil away collecting data and are not recognized for their efforts.”
Calling it a lie implies that it never happens. Hopefully you did not mean it that way. Here is the tale of Donald Graybill, the guy who collected almost all the data but received little recognition, and Michael Mann, the guy who became wildly famous for butchering that data:
https://climateaudit.org/2018/10/24/pages2k-north-american-tree-ring-proxies/
“Mann’s hockey stick is merely an alter ego for Graybill’s stripbark bristlecone chronologies”
For the record, and despite this incident, I am a big supporter of data sharing and always made mine available if the decision was mine.
This type of behavior seems to just be emergent in academia and science, repeatedly happening in different ways in different areas at different times.
Using a metaphor of Zebra behavior (hopefully that is not too wrong biologically), Zebras with seemingly different stripes are moved to the edge of the herd to be picked off by predators. That apparently explains the persistence of the distinctive strip pattern.
Now, in academia, assurance of persistence a of pattern of distinctive scholarship being seen as best, different stripes have to be kept out as well as excluded if they get it or arise just within the in group. Not sharing the data is one way of keeping others out or excluding them by cutting off access and stop them from displaying any effectiveness of alternative scholarship.
For instance, I was watching an online talk from one in the group that I met with to address a topic in 2005. In the initial discussions arguments were given that effectively shut out other approaches and other researchers that had been ongoing but not yet well established. One was on banning the politically incorrect use of the term quality and the other was the need to block any quantitative analysis and keep it qualitative only.
In 2015, it became clear there had not been much progress given the group leaders response to how “their” approach was working out at an international meeting – “we are currently revising it”. After that, someone with different stipes somehow got in and had the expertise to move the group forward and not the “not exactly their approach but now presented as such” does offer real value.
So 15 years later they have finally provided some value, they still discourage the use of the word quality but no longer a total ban on it and quantitative methods are being suggested. These are essentially what was in one or more of the “excluded” pre-2005 papers, but unlikely they will be referenced.
p.s. it was much easier to write this up as if it could be fiction.
“If you publish your data or don’t tightly guard it, it will be stolen by others, and then your career may be ruined.
“People build entire careers on reusing other people’s data and they get more fame and recognition than those who toil away collecting data and are not recognized for their efforts.
“Your data can never be fully understood without your presence, and thus should probably not be used without you around in some way.”
It is, at best, premature to call these three principles lies. As for the first two, since people generally do guard their data closely, there is relatively little opportunity to see what happens with data that is openly available. It may well be that others will steal it and your career will suffer from that. It may well be that those who reuse the data will gather fame while those who created it will languish. We simply don’t know because there have been few opportunities to observe these phenomena. It seems likely, though, that in the absence of some changes in the current academic culture and norms, these adverse consequences of openness may well arise.
As for the third, it is clearly not a lie. It is true, or close to true. Much of my work involves the re-analysis of other people’s data (freely given). I find that even with the best documentation, there are always aspects of the data that are obscure, or even misunderstood on first approach. The support and involvement of those who created the data invariably proves helpful, if not essential, to using the data in appropriate ways. Similarly, when I share my data with others, despite my earnest attempts to provide good documentation, including code, the new users almost always encounter questions that require my response before they can use the data appropriately. And, of course, I freely make myself available to do that. But that, in turn, limits the number of people I can share my data with, as I have only so much time available to support their work with it.