The late Doreen Kimura, a renowned Canadian neuropsychologist, declared that
"...you [the individual] just have to go ahead and find things out for yourself. This is the mark of a good scientist.” (Science.ca, 2002).
The day when a principal investigator operates his own rig to run his own experiments (e.g. Hubel and Wiesel 1968; Schiller et al. 1976) is fast becoming a thing of the past. Big science is now considered the way of the future (e.g. Nicolelis 2012) and many aspiring neuroscientists equate success with being in charge of a large enterprise composed of many subordinates. This type of science, however, can create a serious disconnection between the principal investigator (who might never see how his data are being collected and processed) and the team of subordinates who supplies the final result for a scientific publication. The biggest fear a principal investigator of big science should have is that this disconnection may create contingencies that foster the fabrication of data.
How could such contingencies be created? First, a principal investigator must communicate a 'positive' experimental outcome to his underlings by promoting a grand theory. Second, by holding a weekly journal club the principal investigator can further inculcate his group in his thinking. Third, by having every scientific publication authorized and authored by the principal investigator this further enforces control by not permitting independent publications by subordinates. Finally, by holding a tight lease on salaries (via annual evaluations) and by reminding underlings about their future need for good letters of recommendation, the subordinates are further kept in check. If negative data conflicting with the grand theory should ensue, the subordinate investigator should be encouraged to return to his rig to find more positive data.
What should be done if the negative data continue to accumulate before finalizing a publication? A subordinate's self-doubts (due to career pressures) should not be discouraged by permitting notions such as: "the negative data were all collected on bad days, and so on." Hitherto, no explicit communication has been made between the principal investigator and the subordinate about the negative results. If the accumulation of negative results persists then the issue inevitably becomes public thereby triggering an intra-laboratory crisis. At this point, there are institutional mechanisms available to distance the principal investigator from the negative results. A committee can be formed within the institution to assess whether there was active suppression of the negative results by the principal investigator. Since the principal investigator 'just learned' about the accusation he is granted plausible deniability (as commonly occurs with leaders of great nations).
In order for the forgoing scenario to be realized, a principal investigator must create an organizational hierarchy that is under his total control. Rogues, free-thinkers, anarchists and so on would not be tolerated in such a hierarchy. Unfortunately, it is such individuals who disproportionately contribute to the advancement of science (e.g. Olds and Milner 1954; Hubel and Wiesel 1968; Neuenschwander and Singer 1996; Schiller and Tehovnik 2001). When thinking of hierarchies and their perverse outcomes we just need to heed the words of Stanley Milgram:
"Could it be that Eichmann and his million accomplices in the Holocaust were just following orders? Could we call them all accomplices?” (Schulweis 2010, pp. 106).
If the big research programs are well organized, structured, with intermediate level research question and middle level management the big science and data fabrication are not necessarily related. To say that they are related is like assuming that computers produced by IBM must be worse than those produce by a small business family.
So is a matter of management. To the extent that high class management is missing in big science it may be true that the data quality is weaker compared to small, usual project based science. In my field of research for instance holistic ecology might have produce lower quality data, as one can judge from the relatively small number of holistic ecology publications in the ISI journal.
One can fail to be a good scienties by tackling too complex problems, requesting too many resources, including here the high class human resource for managing large programs.
I am afraid that, indeed, Big Science and data manipulation are related.
I would hope their relationship to be linear, if I were not afraid that it is exponential.
It is similar to when big corporations pushed out small business. The bigger the corporation, the easier it is for its directors to behave in irresponsible and arrogant manner; the easier for its employees to care more about their personal advancement and short-term individualistic benefits than any long-term common benefits resulting from sticking with a moral code; and the easier for this culture to both produce waste and breed corruption.
Let's hope the "too-big-to-fail" does not translate into "too-big-to-falsify" for the future of Science.
Thanks for the question. Part of it says 'Rogues, free-thinkers, anarchists would not be tolerated. .. it is such individuals who disproportionately contribute to the advancement of science.' It has omitted saying that the truth that they were brave to state against popular belief made them to be considered as rogues. So Galileo was a rogue who was forced to suffer, to recant and to lose his freedom. But truth cannot be chained. How wonderful that house arrest that was to chain the truth, actually provided the time for Galileo to write his best works, so that the truth could be preserved in writing.
But this is the 21st century, we are more tolerant, even of negative results. Negative results are even published. If a big science organization must fabricate data, it should be held responsible. People will be affected. If a big pharmaceutical company fabricates data, they should be held responsible. And yes, in a world that is gripped by a lust for money and power, the temptation will be towards big science to be related to fabrication of data.
Big Science is a new idea that changes the human world, and so-called bisness-like science is create" food" for great science.
Here is quote by Einstein on science (from "The New Quotable Einstein," Princeton University Press, 2005]:
"One can organize to apply a discovery already made, but not to make one. Only a free individual can make a discovery…. Can you imagine an organization of scientists making the discoveries of Darwin?" [From an interview with Raymond Swing, “Einstein on the Atomic Bomb,” part 1, Atlantic Monthly, November 1945]
I love the way the question was asked and justified. Big science is defintely about the fabrication of data, indeed. And yes, the farmaceutical industry, i.e. research is indeed a very good example (hence, not the only one.
However, yesterday and today, the very core of the scientific endeavour and passion lies not so much on the data as on the interpretation of the data. Roughly said: the intelligence (or dumbness) of a researcher consits in his or her capacity to interpret the data.
Just to mentin a classical example: both Einstein and Bohr -in the context of the Copenhaguen debate- agreed on the very data. Their differences arose and were fed by the very interpretation of those data. Well, I think the case remains the same, whether in the dimension of little or big science. This is, I believe, the point where the call for a "grand theory" probably developed by the cheif researcher could anker.
Not to mention that the administration of research and the very freedom of the researchers should (and this is both an ethical and political demand) be stated in some sort of symmetry. For, the relationship between admnistration and research becomes a most sensitive question precisely in the realm of big science, rather than inthe case of little science).
Longtang (Lewis) Chen from the University of Mississippi Medical Center discusses the issue:
“Totally agree. Excellent discussion on human nature.
The story of Isaac Newton is useful here. It provides a hint as to what “bigness (i.e. power and corruption)” can do to a fellow scientist.
According to historian account (in PBS and Wikipedia), Isaac Newton was bitter over the critiques and disputes from Robert Hooke, who presided over the Royal Society. Hooke was guilty of jealousy over Newton’s intellectual success in every way. But he did provide some good (stimulating) criticisms and suggestions to provoke Newton’s creativity. Some of Newton’s solutions to gravity actually came from Hooke’s contribution.
Things took a different turn. Hooke died. Newton took over the Royal Society. Newton started re-writing history. One and the only one portray of Robert Hooke in the Society went missing. It has never been found. If this is incredible, see what Newton became.
What’s important is Newton started to “suppress” other physics scientists from publishing their discoveries. On record, some accused Newton stole the credit of their discoveries. These younger fellows had nowhere to turn to. Nobody would believe them—because everybody thought Newton had solved all of the physics questions. One physics professor even wrote that he could retire, because “there is no more physics to study; it is all done". What a powerful, enduring perception this guy must have received!!!
The bottom line is the following. Even a genius like Isaac Newton shows jealousy over other fellow physicists. He did everything he could while he could (as the President of Royal Society). This picture was totally the opposite when he was under the shadow of someone else.
“If I can, why not?!” may be the symptoms most of us share. It came from the selfish gene we inherited from millions of years of evolution. We can only talk about it. Can’t get rid of it.
The conclusion:
Becoming the President of the Royal Society added nothing to Newton’s contribution to physics. It actually stifled the contribution of the other scientists in the field. On the other hand, if Newton was never appointed to be the President of the Society, he would still be a genius. Other scientists would probably have benefited more from his contribution and pushed the progress of physics further and faster.
To keep the human species healthy and creative, it is always good to take the power away from the big guys. This is especially true for science.”
-- Longtang Chen, Jan 17, 2014, personal e-mail to EJ Tehovnik.
Although "underlings" may collect data in neuroscience, that is often done by machines run by computers (after you press the button). The keys are the design of the paradigm and the interpretation of the data. But it seems to me that it is quite common for teams to be made up of colleagues, often from different disciplines or with different expertise, who will have a variety of opinions both on the design of the paradigm and the interpretation of the data. Mistakes are thus more likely to be found, and qualifications to be added at the time of drawing conclusions and writing the report.
Dear Edward, I like your refernce to the story by and about Newton. With one singular circumstance (taht you are aware of): in retrospective, Newton's science was little science. (A scandal, as it appears).
The point then is shifting to the difficult relationship between scientists (researchers) and managers,.i.e. politicians. This remainds me of Plato's quote regarding the dream of the king-philosopher (or the philosopher-king). He says: "We will have or we can have a king-philosopher epekeine". And the term "epekeine" means: "by chance", or "as a miracle".
This is with regards to interpretation of data with theory. According to Einstein, there are two types of scientific theories - constructive and principle theories.
From "The Nature of Scientific Theory" (http://www.currentscience.ac.in/php/toc.php?vol=102&issue=03):
"A majority of scientific theories belong to the constructive type whose axiomatic foundation consists of empirically observed principles and imaginary or speculative hypotheses. On this base is erected a logical superstructure (e.g. calculus) whose object is to make concrete predictions which can be tested against experiential data. A favourable comparison will point to validity of the hypotheses while an unfavourable one will require their revision. A constructive theory holds temporarily and will eventually be replaced with a more comprehensive approach. In contrast, in a theory of principle, the axiomatic foundation consists solely of principles of nature that are derived from human experience. Unless these principles are found to be false in the future, the security of such a theory is guaranteed. An example of a constructive theory is the kinetic theory of gases while classical thermodynamics and the theory of relativity belong to the class of principle theories."
----------------
"Another feature of a constructive theory is that there can be more than one such theory based on different speculative constructions or premises that agree with experimental data taken at a particular level to a prescribed degree of tolerance. Since direct verification of the various hypotheses of the theory or measurement at a
finer level is often not possible due to limitations in technology, there is no way
of privileging one theory over the other., which are thus essentially of a fictitious
character."
Talk about big science fabricating data: big pharmaceuticals day by day fabricate data about insufficiently tested products. It is about time to advocate bringing an end to this irresponsibility in a forum like RG, where we can inveigh against the injustices committed in the name of science.
@Nelson et al.,
Please see the comments from Max Chartrand on my thread about this:
https://www.researchgate.net/post/What_practical_things_can_we_do_to_bring_RESEARCH_back_to_its_original_intention_of_obtaining_knowledge_for_the_good_of_man_and_environment
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