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  • Palgrave Communications
Raiders of the lost HARK: a reproducible inference framework for big data science
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CC BY
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Hypothesizing after the results are known (HARK) has been disparaged as data dredging, and safeguards including hypothesis preregistration and statistically rigorous oversight have been recommended. Despite potential drawbacks, HARK has deepened thinking about complex causal processes. Some of the HARK precautions can conflict with the modern reality of researchers’ obligations to use big, ‘organic’ data sources—from high-throughput genomics to social media streams. We here propose a HARK-solid, reproducible inference framework suitable for big data, based on models that represent formalization of hypotheses. Reproducibility is attained by employing two levels of model validation: internal (relative to data collated around hypotheses) and external (independent to the hypotheses used to generate data or to the data used to generate hypotheses). With a model-centered paradigm, the reproducibility focus changes from the ability of others to reproduce both data and specific inferences from a study to the ability to evaluate models as representation of reality. Validation underpins ‘natural selection’ in a knowledge base maintained by the scientific community. The community itself is thereby supported to be more productive in generating and critically evaluating theories that integrate wider, complex systems.

Subject:
Applied Science
Health, Medicine and Nursing
Material Type:
Reading
Provider:
Palgrave Communications
Author:
Iain E. Buchan
James S. Koopman
Jiang Bian
Matthew Sperrin
Mattia Prosperi
Mo Wang
Date Added:
08/07/2020
A reputation economy: how individual reward considerations trump systemic arguments for open access to data
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CC BY
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Open access to research data has been described as a driver of innovation and a potential cure for the reproducibility crisis in many academic fields. Against this backdrop, policy makers are increasingly advocating for making research data and supporting material openly available online. Despite its potential to further scientific progress, widespread data sharing in small science is still an ideal practised in moderation. In this article, we explore the question of what drives open access to research data using a survey among 1564 mainly German researchers across all disciplines. We show that, regardless of their disciplinary background, researchers recognize the benefits of open access to research data for both their own research and scientific progress as a whole. Nonetheless, most researchers share their data only selectively. We show that individual reward considerations conflict with widespread data sharing. Based on our results, we present policy implications that are in line with both individual reward considerations and scientific progress.

Subject:
Applied Science
Information Science
Material Type:
Reading
Provider:
Palgrave Communications
Author:
Benedikt Fecher
Marcel Hebing
Sascha Friesike
Stephanie Linek
Date Added:
08/07/2020