# Doing Things Twice (Or Differently): Strategies to Identify Studies for Targeted Validation

## TL;DR

This paper reviews strategies for selecting research studies for targeted validation to address reproducibility issues, emphasizing the potential of scientific data science within open-access publishing to identify high-value studies.

## Contribution

It highlights scientific data science as a promising approach for prioritizing research validation, advocating for its increased focus in reproducibility efforts.

## Key findings

- Scientific data science can effectively identify high-value studies.
- Open-access publishing enhances opportunities for targeted validation.
- Prioritization strategies can improve reproducibility efforts.

## Abstract

The "reproducibility crisis" has been a highly visible source of scientific controversy and dispute. Here, I propose and review several avenues for identifying and prioritizing research studies for the purpose of targeted validation. Of the various proposals discussed, I identify scientific data science as being a strategy that merits greater attention among those interested in reproducibility. I argue that the tremendous potential of scientific data science for uncovering high-value research studies is a significant and rarely discussed benefit of the transition to a fully open-access publishing model.