# Unlocking the Next Decade of Proteomics with Standardized, Structured Metadata

## TL;DR

This paper advocates for better metadata standards in proteomics to improve data reuse and long-term value of public datasets.

## Contribution

A coordinated plan is proposed to enhance metadata annotation through collaboration among stakeholders in the proteomics community.

## Key findings

- Limited metadata adoption hinders large-scale data reuse in proteomics.
- Standardized metadata infrastructure can significantly increase the value of public proteomics data.
- Collaboration among funders, researchers, and journals is essential for metadata improvement.

## Abstract

The proteomics community has fully embraced data sharing,
yet data
set metadata provision remains limited, especially at the level of
the biological samples and experimental design. This hampers large-scale
data reuse, as comprehensive and structured sample context and study
design information are often essential for confident, automatic reuse,
and (re)­interpretation. Although standards such as Sample and Data
Relationship Format for Proteomics (SDRF-Proteomics) and supporting
tools are already available, their adoption remains limited. Many
researchers lack incentives, and enforcement by journals and repositories
remains challenging in practice. Still, metadata defines a data set’s
long-term value. We propose a coordinated plan to dramatically improve
metadata annotation of publicly disseminated proteomics data. Funders
can drive progress by investing in a sustainable, scalable metadata
infrastructure. HUPO-PSI plays a central role in setting community
standards and enabling validation. ProteomeXchange repositories are
key to implementing and supporting metadata adoption. Data producers
must treat metadata as a part of their scientific output. Instrument
vendors can contribute by enabling the automatic capture of technical
metadata. Software developers should embed SDRF-Proteomics metadata
into analysis workflows. Finally, journals and reviewers are well
positioned to shape expectations and enforce compliance. By aligning
efforts across these stakeholders, we can build the road to large-scale,
context-aware reuse and unlock the full value of public proteomics
data sets.