Managing and Mapping the Data: Hurdles, Impacts & Solutions

By Zareh Zurabyan 4 min read 11 Apr 2024

In life science and biotech laboratories, “big data” has gotten bigger than ever before and shows no signs of stopping. The pool of data in most labs is highly diverse (think ‘omics, imaging, etc.), large-scale, and ever-increasing.

This massive amount of diverse data requires constant wrangling. When properly orchestrated, this diverse data can be fully harmonised according to FAIR guidelines, delivering insights that drive scientific breakthroughs. Yet, there are notable challenges and unique hurdles to managing and shaping the challenging topography of the data landscape.

In the following blog, we’ll discuss the impact of these various challenges and provide a solution.

Diversity of Data Types

  • The Challenge: Life science laboratories are prolific generators of diverse data types, including genomics, proteomics, metabolomics, and imaging data. The intricate challenge lies in seamlessly integrating and structuring this heterogeneous data into a cohesive framework. Furthermore, the complexity and heterogeneity of this data create issues with integration.
  •  The Impact: The incompatibility between various data types acts as a stumbling block, impeding comprehensive analysis and hindering the extraction of profound insights from these multifaceted datasets.

Volume and Scale

  • The Challenge: The relentless generation of data in life science and biotech experiments, fueled by advancements like high-throughput technologies, introduces an overwhelming volume that can surpass the capacities of traditional data structuring methods. Genomics alone will generate 2 to 40 exabytes in 2025.
  •  The Impact: The sheer magnitude of data becomes a resource-intensive burden, slowing down the analysis process and potentially creating bottlenecks in accessing critical information. As a result, there’s a “data storage crisis” looming over the industry.

Lack of Standardisation

  • The Challenge: The absence of standardised data formats and structures across laboratories and research institutions presents a formidable challenge, introducing hurdles in data interoperability.
  •  The Impact: The resultant lack of harmony in data standards complicates data sharing and collaborative efforts, which is now required by all labs receiving NIH funding. Researchers grapple with integrating and deciphering datasets produced under disparate standards, impeding seamless collaboration and insights extraction.

Temporal and Longitudinal Data

  • The Challenge: Longitudinal studies and time-course experiments introduce a temporal dimension, necessitating the structuring of data points across different time intervals.
  •  The Impact: The intricate task of structuring temporal data becomes pivotal. Misalignment or improper representation of time-dependent data compromises the accuracy of analyses and poses challenges in identifying dynamic patterns critical for scientific interpretation.

Metadata Complexity

  • The Challenge: Many laboratory scientists, particularly those who manage samples using manual, paper-based record-keeping, find capturing and organising metadata, including experimental conditions, sample details, and procedural information, challenging.
  •  The Impact: The completeness and consistency of metadata emerge as linchpins for contextualising experimental data. Incomplete or inconsistent metadata creates hurdles in reproducing experiments and comparing study results.

Data Security and Compliance

  • The Challenge: Ensuring data security and compliance with stringent regulatory requirements, such as GDPR in Europe or HIPAA in the United States, adds additional complexity.
  •  The Impact: Unfortunately, cyberattacksnatural disasters, and other calamities can threaten your data. The consequences of inadequate data security measures loom large, with potential breaches jeopardising the confidentiality of sensitive information and compromising adherence to regulatory standards.

Evolution of Analytical Techniques

  • The Challenge: The rapid evolution of analytical techniques and technologies outpaces existing data structures, rendering them outdated.
  •  The Impact: Laboratories struggle to adapt data structuring methodologies to accommodate emerging analytical approaches. The lag in adaptation results in inefficiencies and missed opportunities to harness the full potential of cutting-edge technologies.

User Adoption and Training

  • The Challenge: Researchers may resist the adoption of standardised data structuring practices due to unfamiliarity or a lack of training.
  •  The Impact: The consequential inconsistencies in data structuring hinder collaborative efforts, impede effective data sharing, and disrupt the implementation of standardised analyses. Bridging this gap demands targeted training initiatives and a cultural shift towards embracing structured data methodologies.

Integrating and Mapping the Data

  • The Challenge: Mapping biological knowledge from data involves representing complex biological concepts, relationships, and processes in a computationally tractable format. Developing interpretable and semantically rich knowledge representations requires domain expertise, ontological frameworks, and natural language processing techniques to effectively capture and formalise biological knowledge.
  •  The Impact: Resolving challenges in mapping life sciences data leads to increased efficiency in data analysis, standardisation of methodologies, and improved access to diverse datasets, fostering accelerated scientific discovery and collaboration.

The Solution to Data Management and Mapping Challenges

A strategic blend of technological solutions, standardisation efforts, and targeted training initiatives becomes imperative to tackle these challenges. Only through meticulous data structuring can laboratories unlock the full potential of their research endeavours, paving the way for new horizons in life science and biotechnology. This comprehensive approach ensures that data in life science and biotech laboratories is structured optimally, fostering meaningful interpretation, collaboration, and innovation.

Digital lab platforms, such as those offered by eLabNext, enable researchers to take a comprehensive approach to data structuring, integration, and management. Contact us today to learn more!

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