# Ten recommendations for organising bioimaging data for archival

## TL;DR

This paper provides ten practical recommendations for organizing bioimaging data to improve archival and future use.

## Contribution

The paper introduces bandbox, a Python tool for assessing and improving data organization before archival.

## Key findings

- Recommendations are based on experience from archiving large bioimaging datasets.
- bandbox helps identify potential issues in data organization prior to archival.

## Abstract

Organised data is easy to use but the growth of bioimaging, with improvements in instrumentation, detectors, software and experimental techniques has resulted in an explosion in the volumes of data being generated, making this an elusive goal. This guide offers a handful of recommendations whose implementation would contribute towards better organised data in preparation for archival. Based on our experience archiving large image datasets in EMPIAR, the BioImage Archive and BioStudies, we propose a number of strategies that we believe would make future data depositions more useful to the bioimaging community and that may also find use in other data-intensive disciplines. To facilitate the process of analysing data organisation, we present bandbox, a Python package that provides users with an assessment of their data by flagging potential issues that could be addressed before archival.