There has been a huge amount of attention focused on “open data.” A casual reading of the blogosphere is that Open Data is good, Secret Data is bad.
Remarkably, there has been very little discussion given to the property right issues associated with open data. The Open Data Movement wants to turn a private good (datasets) into a public good. Economists know something about public goods. They tend to get under-produced. This introduces a trade-off between the propagation of data for use by multiple researchers, a social good (though see here for a discussion where this is not necessarily so), versus the disincentive this causes for producing data, a social bad. How best to make this trade-off is unclear.
In a recent blog entitled “Open data, authorship, and the early career scientist”, MARGARET KOSMALA, a postdoctoral fellow at Harvard University, argues that making one’s data available to others hurts the data-producing scholar, particularly younger scholars. The argument is not so much that the data-producing scholar will be scooped by other scientists on the associated research. Rather, it is that subsequent research projects that could have resulted in publications for the data-producing scholar will end up being undertaken by other scientists. And while Kosmala does not make this point explicitly, this serves as a disincentive for scientists to produce data, if only because younger scholars may not be able to produce sufficient publications to get the funding and tenure they need to continue their careers.
What is really interesting about this blog is that it led to a discussion between a reader and the author about the ethics of “requiring co-authorship” when authors use data produced by another scientist. Missing from the discussion was the recognition that “requiring co-authorship” provides a potential solution to the problem of open data. It is a way for the data-producing scientist to reap the rewards of data production, while still allowing other authors to use it.
Of course, there are issues associated with implementing a policy like this. Once data are released, how will the data-producing author be able to ensure that others who use the data will extend co-authorship to him/her? And suppose the data-producing author does not wish to have their data used in a certain way. Should they have the right to restrict its use? While the answers are debatable, the questions are illuminating, because they make us realise that the debate over open data is just another application of the larger subject of intellectual property rights.
So another study finds that X affects Y, and you are a sufficiently cynical TRN reader that you wonder if the authors have p-hacked their way to get their result. Don’t have time (or the incentive) to do a replication? You might consider using a “p-curve” analysis to determine whether the effect has “evidentiary value.” How does one do that? Let’s take as a given that most journals will not publish a result unless it is statisically significant. Even if the journals only report significant results, one can examine the distribution of p-values to determine whether or not the effect is true. Want to learn more about “p-curves?” The original article by Simonsohn, Nelson, & Simmons (2011), “P-Curve: A Key to the File Drawer” can be found here. A straightforward explanation of the technique by Will Gervais can be found here. A critique by Bruns & Ioannidis can be found here. And an excellent response by the original authors can be found here. P-curves are definitely worth a look!
[From the article “Muddled meanings hamper efforts to fix reproducibility crisis” in Nature] “A semantic confusion is clouding one of the most talked-about issues in research. Scientists agree that there is a crisis in reproducibility, but they can’t agree on what ‘reproducibility’ means.” To read more, click here.
[From the podcast “When Great Minds Think Unlike: Inside Science’s ‘Replication Crisis” from NPR’s Hidden Brain series] This podcast is distinguished by its discussion of what it means – and what it doesn’t mean – when a replication “fails.” It is about 30 minutes long.
It is self-described as follows: “This week, Hidden Brain looks at the “replication crisis” through zooming in on one seminal paper that was the focus of two replication efforts: one succeeded in replicating the original finding, the other failed.
The original study, authored by Margaret Shih, Todd Pittinsky, and Nalini Ambady in 1999, found that Asian women performed worse on a math test when primed to think about their female identity, but better when they were primed to think about their Asian identity.
Nearly two decades later, Nosek and the Reproducibility Project noticed that this study, which by then had been widely disseminated in textbooks and psychology education, had never itself been replicated. So he assigned two teams to run it again—one in Georgia and the other in California. They came back with different results. And this gets at one of the biggest questions explored in this episode: when scientific studies come to different conclusions, what should we think of as true?” To listen to the podcast, click here.
From the article “1500 Scientists lift the lid on reproducibility” published in Nature: “More than 70% of researchers have tried and failed to reproduce another scientist’s experiments, and more than half have failed to reproduce their own experiments. Those are some of the telling figures that emerged from Nature‘s survey of 1,576 researchers who took a brief online questionnaire on reproducibility in research.”
Other questions explored in the survey included: (i) Is there a reproducibility crisis in science?”, (ii) “How much published work in your field is reproducible?”, (iii) “Have you failed to reproduce an experiment”, and (iv) “Have you ever tried to publish a reproduction attempt?”. Respondents came from a wide variety of subject areas including chemistry, biology, physics and engineering, medicine, and earth and environmental sciences. To read more, click here.
Earlier this month, the Psychonomic Society meetings held a session on Open Science. The session was recorded and is available on YouTube (click here). It consisted of four presentations.
— “The Peer Reviewers’ Openness Initiative” by RICHARD MOREY of Cardiff University (1:11)
— “The Availability of Psychological Research Data” by WOLF VANPAEMEL of KU Leuven (23:20)
— “On Knowing How the Sausage is Made” by ROLF ZWAAN of Erasmus University Rotterdam (43:50)
— “The Dark Side of Open Science: Weaponizing Transparency” by STEPHAN LEWANDOWSKY (1:01:23)
The times at which the talks appear in the video are given in parentheses above. The last talk makes a number of arguments that caution that data sharing may not be an unambiguously good thing. All of the talks are interesting and recommended. But, then again, TRN may be a little biased.
In an article entitled “Why Do So Many Studies Fail to Replicate,” Jay Van Bavel, an associate professor of psychology at NYU, writes: “In a paper published on Monday in the Proceedings of the National Academy of Sciences, my collaborators and I shed new light on this issue. Our results suggest that many of the studies failed to replicate because it was difficult to recreate, in another time and place, the exact same conditions as those of the original study.” The example the author gives is a study he did in Canada in 2006 on people’s emotional responses to famous people. The replication was to be done in 2016, in the US. Likely the “famous people” used in the Canadian study (e.g., Jean Chretien, Don Cherry and Karla Homolka) would not carry over the geographical and time divide to the replication. To read more, click here.
In a recent interview on Retraction Watch, Andrew Gelman reveals that what keeps him up at night isn’t scientific fraud, it’s “the sheer number of unreliable studies — uncorrected, unretracted — that have littered the literature.” He then goes on to argue that retractions cannot be the answer. His argument is simple. The scales don’t match. “Millions of scientific papers are published each year. If 1% are fatally flawed, that’s thousands of corrections to be made. And that’s not gonna happen.”
Actually, if 1% of studies are fatally flawed, the problem is probably manageable. Assuming a typical journal publishes 10 articles an issue, 4 issues a year, that means one retraction every two and a half years, which is certainly feasible for a journal. Problems arise only when the percent substantially rises. Gelman goes on to say that he personally thinks the error rate to be large as 50% in some journals, where “half the papers claim evidence that they don’t really have.” At that point retractions are not the solution.
If revealed preference is any indication, hopes for a solution appear centered on “data transparency.” Data transparency means different things to different people, but a common core is that researchers make their data and programming code publicly available.
The Center for Open Science, Dataverse, and EUDAT are but a few examples of the high-profile explosion in efforts to make research data more “open,” transparent and shareable. In a recent guest blog at The Replication Network (reblogged from BITSS), Stephanie Wykstra promotes the related topic of data re-use.
In an encouraging sign, these efforts appear to have had an impact. A recent survey article by Duvendack et al. report that, of 333 journals categorized as “economics journals” by Thompson Reuter’s Journal Citation Reports, 27, or a little more than 8 percent, regularly published data and code to accompany empirical research studies. As some of these journals are exclusively theory journals, the effective rate is somewhat higher.
Noteworthy is that many of these journals only recently instituted a policy of publishing data and code. So while one can argue whether the glass is, say, 20 percent full or 80 percent empty, the fact is that the glass used to contain virtually nothing. That is progress.
But making data more “open” does not, by itself, address the problem of scientific unreliability. Researchers have to be motivated to go through these data, examine them carefully, and determine if they are sufficient to support the claims of the original study. Further, they need to have an avenue to publicize their findings in a way that informs the literature.
This is what replications are supposed to do. Replications provide a way to confirm/disconfirm the results of other studies. They are scalable to fit the size of the problem. With so many studies potentially unreliable, researchers would prioritize the most important findings that are worthy of further analysis. The self-selection mechanism of researchers’ time and interests would insure that the most important, most influential studies are appropriately vetted.
But after obtaining their results, researchers need a place to publicize their findings.
Unfortunately, on this dimension, the Duvendack et al. study is less encouraging. They report that only 3 percent of “economics” journals explicitly state that that they publish replications. Most of these are specialty/field journals, so that an author of a replication study only has a very few outlets, maybe as few as one or two, in which they can hope to publish their research.
And just because a journal states that it publishes replication studies, doesn’t mean that it does it very often. Duvendack et al. report that 6 journals account for 60 percent of all replication studies ever published in Web of Science “economics” journals. Further, only 10 journals have ever published more than 3 replication studies. In their entire history.
Without an outlet to publish their findings, researchers will be unmotivated to spend substantial effort re-analysing other researchers’ data. Or to put it differently, the open science/data sharing movement only addresses the supply side of the scientific market. Unless the demand side is addressed, these efforts are unlikely to be successful in providing a solution to the problem of scientific unreliability.
The irony is this: The problem has been identified. There is a solution. The pieces are all there. But in the end, the gatekeepers of scientific findings, the journals, need to open up space to allow science to be self-correcting. Until that happens, there’s not much hope of Professor Gelman getting any more sleep.
Bob Reed is Professor of Economics at the University of Canterbury in New Zealand, and co-organizer of The Replication Network.
[THIS BLOG ORIGINALLY APPEARED ON THE BITSS WEBSITE] As advocates for open data, my colleagues and I often point to re-use of data for further research as a major benefit of data-sharing. In fact there are many cases in which shared data was clearly very useful for further research. Take the Sloan Digital Sky Survey (SDSS) data, which researchers have used for nearly 6,000 papers. Or take Genbank, within bioinformatics, which is a widely used database of nucleotide and protein sequence data. Within social science, large-scale surveys such as the Demographic and Health Survey (DHS) are used by many, many researchers as well as policy-makers.
Research data re-use: where are the cases?
In spite of the obviousness of the value of data-sharing in general, I realized that we didn’t have many cases of re-use of research data. By “research data” here, I have in mind data which were collected by an individual researcher or research team for their own project (e.g. from a field experiment), and then shared along with the publication. This differs from the databases like SDSS, Genbank and DHS in a few different ways:
— The data are often much smaller scale than DHS or SDSS; they are often studies of a few hundred to a few thousand subjects.
— They are not part of a unified data-gathering effort using common measures (as are SDSS and DHS), but rather use their own instruments, often with their own non-standardized measures.
— While it’s fairly clear that researchers can use SDSS data for their own research, and bioinformaticists can use Genbank data, it’s less clear how social scientists would re-use data that other researchers collected for the purpose of their own study. In general, they could use data for secondary analysis or meta-analysis; however, we haven’t seen numerous examples.
A call for cases studies of data re-use
After a brainstorming session with Stephanie Wright, a colleague at Mozilla Science Lab, we decided to put out a call for cases of data re-use. For this project, we were particularly interested in cases of re-use within economics or political science. Since we support data-sharing among researchers and research staff, we want to be able to point to cases of real world re-use, and to delve into what made the data particularly useful. We wrote a post on our project, along with a survey on data re-use, and shared in venues such as IASSIST, Polmeth, Berkeley Initiative for Transparency in the Social Sciences’ blog, Open Science Collaboration’s discussion board, Mozilla Science Lab’s blog and various data librarian email lists.
What did we find through our call?
We received 14 responses to our call, including 10 responses to our survey and 4 emailed responses. While the number and quality of responses isn’t sufficient for us to learn a great deal, we want to share what we found in any case, for two reasons: (1) This call and response could be informative to those who are considering putting out a similar survey and (2) we think our findings do provide some evidence which confirms our initial feeling, which is that this is an area which warrants further work and research.
Our 10 survey respondents are in a variety of fields: one in political science, two in psychology, one in education and most of the rest in biochemistry. While all respondents did mention some data that were re-used, only three gave examples of the kind that we had requested e.g. data that had been collected by other researchers for their study, and then re-used for further research. The three cases included:
— Re-use of data from a collaboration of Psychology instructors, which collected data on emerging adulthood and politics. The data were not initially used for a publication, as intended, but were archived and were used for nine published articles later on.
— A researcher in political science gave several of his own research re-use cases in which publicly available data were used for a) a replication to “illustrate the usefulness of a new fit assessment technique for binary DV [dependent variable] models,” b) for pedagogical purposes in a book on causal inference and c) to test a new theory.
— Researchers in psychology used data from two large-scale studies on the benefits and transfer effects of a cognitive training for older adults. The data were used to test whether a subset of one test (the Useful Field of View test) were able to predict scores on another test (the Instrumental Activities of Daily Living test).
Beyond the cases above, we heard about re-use of protein sequence data and genomics data from databases such as ArrayExpress and Protein Data Bank, as well as government data from Open Data Toronto and Statistics Canada. See our spreadsheet for further details (we asked for permission to share responses).
In addition to the cases gathered through our survey, we received four emails with tips about where to find additional cases. One of the suggestions mentioned the Global Biodiversity Information Facility (GBIF), a database on global biodiversity, as well as International Polar Year (IPY), a coordination of research on the Polar regions. A second suggestion from a political scientist pointed to several sites, Uppsala Conflict Data Program and the Correlates of War Program. Both sites offer data which are widely used by scholars within international relations, and include variables which are constructed by scholars for their own research, and then submitted to the databases for others to re-use.
Finally, we received several suggestions from fellow open data advocates, of places to look for cases of re-use. The first source, Dissemination Information Packages for Information Re-use (DIPIR) is a study of data re-use in three communities (quantitative social scientists, archaeologists, and zoologists). The second is ICPSR’s bibliography of data-related literature, which is a searchable database of “over 70,000 citations of published and unpublished works resulting from analyses of data held in the ICPSR archive.” The third is UK Data Archive’s list of case studies of data re-use.
Data re-use: key for rewarding data-sharing
The data-sharing movement is gaining steam. From funders requiring data-sharing to new guidelines for journals (TOP guidelines) and journal requirements, to the rise of many data repositories, there is plenty of effort going into requiring and supporting data-sharing. Yet there are huge issues to confront, as we move forward. One of the biggest is how to change from a culture in which data-sharing is not a norm among researchers (as is still the case in many scientific fields) to one in which it is.
Researchers are rewarded for publishing, not for sharing data, and many researchers cite barriers to sharing data such as lack of time and lack of support (Tenopir et al. 2011). How will we shift to rewarding researchers for sharing their data, so that they have professional incentives to take the time to prepare and share data? One of the most-discussed ways is to develop good data-citation norms, and then to reward researchers (via tenure committee decisions) when others re-use and cite their data.
So, the question of how to promote and encourage data re-use is of clear importance. Yet, as practitioners in the open science movement, we have many questions. When it comes to re-using data from colleagues’ studies, particularly in the social sciences, what factors make datasets particularly helpful to researchers? What challenges arise in re-using data? As data curators and open data advocates, what could we do better to facilitate re-use? Is there something we can do to encourage others to look at and reuse existing data when they are considering new research projects? How can we increase opportunities for re-using data and decrease barriers?
Next steps
Particularly when it comes to data shared by researchers in the social sciences, we still need more examples of re-use. We also need much more investigation into what would make researchers more likely to re-use data from colleagues for their own research. We can think of a couple of interesting projects that we could undertake:
— Delving into the archives from ICPSR and UK Data Archive, as well as others mentioned above, and attempting to glean lessons from specific cases of re-use found there.
— Contacting researchers that have downloaded data from archives such as IPA’s data repository (we track data users and ask them permission to contact them, when they download data). We could gather more detailed information about what was or wasn’t helpful for re-use about the data and other materials as presented in the repository. We could also try to gather more information on whether data were re-used for further research (and if so, what made them particularly attractive for re-use).
We’re certainly open to further suggestions, so please get in touch if you have ideas!
Stephanie Wykstra directs the Research Transparency Initiative at Innovations for Poverty Action, and also works as an independent research consultant. She may be contacted at stephanie.wykstra@gmail.com.
Welcome to the tale of Emil Kirkegaard, a Danish postgraduate student, who has achieved worldwide notoriety for publishing data from the dating site, OkCupid. The story is well-told in a Vox article by Brian Resnick (click here). In addition to a committing a number of ethical sins, both mortal and venal, the case of Mr. Kirkegaard raises important issues for the data transparency movement. Mr. Kirkegaard stated that he needed to make his data — which was collated from the OkCupid website and “publicly” accessible to OkCupid users — available because that was the condition for submission to an open access journal where he was hoping to publish his research (hopes that were no doubt encouraged by the fact that he was the editor of the journal).
Perhaps more interesting are the ethical issues this case raises. Some of these issues are discussed in a blog by Oliver Keyes (click here). OkCupid will likely file a legal complaint which may involve Open Science Framework (OSF), the online host of Kirkegaard’s data. Given the open hosting nature of OSF, it seems unlikely that OSF will be at much legal risk. But how about journals (such as Economics Letters) that encourage submitters to post their data on open access data sites like OSF and Dataverse? Are they legally liable for data improprieties? Or how about journals that post article supplementary material such as data and code on the journal website? Are they legally responsible if the data violates copyright or other legal requirements? And if this is a legal grey area, will this have a chilling effect on the data transparency movement? Stay tuned.
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