Complex data workflows contribute to the reproducibility crisis in science

Via O'Reilly Data Newsletter:

Complex data workflows contribute to the reproducibility crisis in science

70 independent teams, given the same data and a common analysis challenge, came to very different conclusions in a recent Stanford study that highlights the challenges of data analysis in an era of huge datasets and highly flexible processing workflows.

"Simply put, no two groups of researchers are necessarily crunching data the same way. And with so much data to get through and so many ways to process it, researchers can arrive at totally different conclusions."

Why Sharing Academic Publications Under “No Derivatives” Licenses is Misguided

From: ttps://creativecommons.org/2020/04/21/academic-publications-under-no-derivatives-licenses-is-misguided/


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In this blog post, we explain that applying restrictive licenses to academic publications is a misguided approach to addressing concerns over academic integrity. Specifically, we make it clear that using Creative Commons “No Derivatives” (ND) licenses on academic publications is not only ill-advised for policing academic fraud but also and more importantly unhelpful to the dissemination of research, especially publicly-funded research. We also show that the safeguards in place within truly open licenses (like CC BY or CC BY-SA) are well-suited to curbing malicious academic behavior, above and beyond other existing recourses for academic fraud and similar abuses. 

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Read the blog post: https://creativecommons.org/2020/04/21/academic-publications-under-no-derivatives-licenses-is-misguided/

Nature technology feature: Find a home for every imaging data set

Repositories let researchers store, share and access life‑science images — and maybe even extract new findings.

https://www.nature.com/articles/d41586-020-00594-4

"... researchers shouldn’t simply drop their data sets into small, project-specific archives or generic cloud storage. “Just dumping the data somewhere doesn’t mean people can use it,” Ellenberg explains. “You need to organize the data, you need to annotate it, and curate it.” "

What makes for good data "health"?

In a blog post on Towards Data Science, Barr Moses poses the question "How come we know everything about how well our data infrastructure is performing, but so little about whether the data is right?" and answers it by identifying five "data observability pillars" of "data health":

• Freshness: is the data recent? When was the last time it was generated? What upstream data is included/omitted?
• Distribution: is the data within accepted ranges? Is it properly formatted? Is it complete?
• Volume: has all the data arrived?
• Schema: what is the schema, and how has it changed? Who has made these changes and for what reasons?
• Lineage: for a given data asset, what are the upstream sources and downstream assets which are impacted by it? Who are the people generating this data, and who is relying on it for decision making?

https://towardsdatascience.com/good-pipelines-bad-data-e55d9ba17920