Anthony Juehne, MPH
Sr. Implementation and Evaluation Analyst
The Research Data Alliance U.S
Rensselaer Polytechnic Institute

ABSTRACT:

The scientific method depends on the verifiability and reproducibility of findings crucial to the construction of a “scientific heritage”¹ wherein researchers with the appropriate skills and understanding “can validate the work of others and build upon it.” In 2015 alone, an estimated $28 billion³ (24% of total funding) of publicly and privately funded biomedical research failed to reproduce. High-profile research integrity, data-quality, and replication disputes have plagued many scientific disciplines including psychology, climate science, and clinical oncology. Non-replicable and non-transparent research not only wastes resources, it limits reliability when translating research results to clinical treatment and population interventions.

Increased research transparency and reproducibility provides a context for retracing one’s steps in the research workflow, expanding intervention criteria, and altering clinical assumptions. This granularity allows for clear insight into where and how new discoveries could occur as well as reliability when scaling methods or interventions to unique populations. If we are to break ground in advancing health research and policy, we ought to leave more than a trail of breadcrumbs documenting where we have been; we should be clear on what has worked in the past and how.

Constructing a robust and reliable scientific heritage requires agreed upon terminology and modes of documenting, archiving, and communicating methods throughout the initial conception of a scientific hypothesis, the collection of data to operationalize a testable question, an analysis protocol and toolkit to test the question, and the production of reportable findings. Current policies defining and promoting methods for ensuring the reproducibility of clinical and health research are limited in clarity, implementation, and enforcement at multiple levels within the scientific field. The scientific community also needs faster and more scalable means to assess and improve reporting practices to reduce the barriers in re-using scientific work, verifying scientific outcomes, and assessing scientific quality.

This colloquium presentation will discuss the work and recommendations of Anthony Juehne and his colleagues within the Research Data Alliance to address the following:

• How non-reproducible research emerges from limitations within current research workflows and data lifecycles.
• Tools and techniques for enhancing the transparency, accessibility, and reproducibility of research workflows.

• Multi-tier policies that can support and guide reproducible research for stakeholders at multiple levels of the scientific landscape: from research funders and publishers to principle investigators, educators, and researcher staff.