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Archival Research

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...

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Ten recommendations for organising bioimaging data for archival.

Paul K Korir¹, Andrii Iudin¹, Sriram Somasundharam¹

¹EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.

|March 15, 2024

Summary

This guide provides recommendations for improving bioimage data organization for better archival and usability. It introduces bandbox, a Python tool to identify and fix data organization issues before submission.

Keywords:

BioImage Archive BioStudies EMPIAR Organising data bioimaging data deposition open data public archiving

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Area of Science:

Bioimaging
Data Science
Scientific Archiving

Background:

Rapid advancements in bioimaging instrumentation and techniques have led to massive data volumes, making organized data difficult to achieve.
Well-organized data is crucial for effective use by researchers, clinicians, and industry partners.
Current data deposition practices often result in data that is challenging to archive and utilize.

Purpose of the Study:

To propose strategies for improving the organization and usability of bioimage data for archival.
To provide recommendations for bioimage depositors, analysts, and software developers.
To enhance the accessibility and clarity of large bioimage datasets for a diverse community.

Main Methods:

Based on experience archiving large datasets in EMPIAR, BioImage Archive, and BioStudies.
Development of the 'bandbox' Python package to assess and flag data organization issues.
Formulation of recommendations for data depositors and developers.

Main Results:

Identification of key strategies to improve data usability, including clarity, orderliness, and self-documentation.
The 'bandbox' tool automates the assessment of data organization, flagging issues like redundant directories and invalid file names.
Recommendations are provided to enhance data coherence, consistency, and accessibility for various stakeholders.

Conclusions:

Implementing the proposed recommendations and utilizing tools like 'bandbox' can significantly improve bioimage data organization.
Enhanced data organization facilitates better archival, accessibility, and usability for the broader bioimaging community.
These strategies aim to foster more substantial conversations and collaborations in data-intensive scientific disciplines.