# Sharing Begins at Home

arXiv: https://arxiv.org/abs/2201.06564 · HTML: https://mlsanity.com/paper/2201.06564/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [FAIR for AI: An interdisciplinary and international community building perspective](https://mlsanity.com/paper/2210.08973/) (2022)
- [Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Models](https://mlsanity.com/paper/2407.01608/) (2024)
- [From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eScience](https://mlsanity.com/paper/2506.16051/) (2025)

## Abstract (arXiv)

The broad sharing of research data is widely viewed as of critical importance for the speed, quality, accessibility, and integrity of science. Despite increasing efforts to encourage data sharing, both the quality of shared data, and the frequency of data reuse, remain stubbornly low. We argue here that a major reason for this unfortunate state of affairs is that the organization of research results in the findable, accessible, interoperable, and reusable (FAIR) form required for reuse is too often deferred to the end of a research project, when preparing publications, by which time essential details are no longer accessible. Thus, we propose an approach to research informatics that applies FAIR principles continuously, from the very inception of a research project, and ubiquitously, to every data asset produced by experiment or computation. We suggest that this seemingly challenging task can be made feasible by the adoption of simple tools, such as lightweight identifiers (to ensure that every data asset is findable), packaging methods (to facilitate understanding of data contents), data access methods, and metadata organization and structuring tools (to support schema development and evolution). We use an example from experimental neuroscience to illustrate how these methods can work in practice.