Big Data access and infrastructure for modern biology: case studies in data repository utility

Area of Science:

• * Computational Biology and Bioinformatics
• * Genomics and Epigenomics
• * Neuroscience and Systems Biology

Background:

• * Modern experimental techniques, including high-throughput sequencing, multichannel electrophysiology, and advanced imaging, generate massive datasets.
• * Accessibility of Big Data is increasing for researchers, moving beyond large institutions.
• * Challenges exist in data handling, storage, and analysis for individual researchers.

Purpose of the Study:

• * To assess the essential resources for engaging with Big Data in research.
• * To review current data analysis pipelines and their capabilities.
• * To highlight data repository case studies and support initiatives.

Main Methods:

• * Literature review of Big Data resources and tools.
• * Survey of contemporary data analysis pipelines.
• * Analysis of data repository case studies and support programs.

Main Results:

• * Significant data generation capabilities are now widely available.
• * Resource and skill limitations hinder individual researcher's data utilization.
• * A need for standardized pipelines and accessible repositories is evident.

Conclusions:

• * Effective utilization of Big Data requires strategic resource allocation and skill development.
• * Development of robust data management and analysis infrastructure is crucial.
• * Support from institutions and programs is vital for advancing Big Data research.