Opaque Transparency in the Promise to Share Data
Research integrity is a hot topic these days. People want to know that they can rely upon scientific publications to be accurate reflections of an honest inquiry to find the answer to a research question. Toward this end, data sharing is an important commitment. Increasingly, researchers pledge to share their data in the interest of transparency to enable other researchers to replicate their findings. Many research funders and publications now require this.
But it seems the commitment to share data can become an exercise in opaque transparency because, all too often, researchers don’t live up to their commitment. Empty promises become pernicious deceptions.
Common Pledges, Uncommon Adherence
Recognition that earning trust is essential for the credibility of science has led to many substantive changes that will improve the integrity of scientific and, especially, behavioral research. The commitment to data sharing is an important part of these changes.
Lately, though, several authors have noted that the commitment is not enough. It’s the practice that counts. Jasmine Jamshidi-Naeini and colleagues have examples of data sharing commitments that fall short. They describe them in a guest editorial for the Committee on Publication Ethics.
“We have found that promises of data sharing are often not upheld, even when the DAS (data availability statement) indicates data can be accessed upon [reasonable] request.
“The inclusion of a DAS in articles should promote actual data sharing, recognizing that data sharing is a fundamental step towards increasing the reproducibility and trustworthiness of science.”
In the preprint of an analysis earlier this year, Ian Hussey offers a rather pessimistic conclusion:
“The presence of Data Availability Statements was not associated with higher rates of data sharing (p = .55).”
More often than not, says Hussey, authors do not live up to their pledges for data sharing.
No Data, No Science
In 2020, the editor in chief of Molecular Brain described his experience with requesting data sharing during the editorial review process:
“I have handled 180 manuscripts since early 2017 and have made 41 editorial decisions categorized as ‘Revise before review,’ requesting that the authors provide raw data. Surprisingly, among those 41 manuscripts, 21 were withdrawn without providing raw data, indicating that requiring raw data drove away more than half of the manuscripts. I rejected 19 out of the remaining 20 manuscripts because of insufficient raw data. Thus, more than 97% of the 41 manuscripts did not present the raw data supporting their results when requested by an editor, suggesting a possibility that the raw data did not exist from the beginning, at least in some portions of these cases.”
He writes that the data is essential: “no raw data, no science.”
So clearly, data sharing is a good concept. But right now, we have more opacity than transparency in data sharing.
Open Window with Hills, painting by Juan Gris / WikiArt
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August 12, 2023
