A surreal digital illustration of a winding data road through a biological landscape.

Decoding Data-Centric Biology: How to Make Data Work for You

Reese Marlowe in Science & Nature December 2025 • 4 min read.

"A philosophical dive into transforming raw biological data into valuable insights and discoveries."

In today's research landscape, 'big data' is a ubiquitous term, yet the fundamental question of data's true value often remains unexamined. Why is some data more valuable than others? How does data become useful, and what historical and philosophical implications do modern considerations of data in biology carry?

Sabina Leonelli's 'Data-Centric Biology: A Philosophical Study' delves into these critical questions, merging historical context with philosophical insights to illuminate the intricate processes hidden within big data biology. Rather than simply accepting data at face value, Leonelli provides a framework for understanding the journey data undertakes, from its initial collection to its eventual application.

This journey, as Leonelli explains, is not always straightforward. Much like any expedition, the path of data is fraught with unexpected turns and deviations. However, it is precisely these unforeseen challenges that make the journey epistemologically interesting and ultimately useful. This article will explore the core concepts of Leonelli's work, providing you with insights on how to effectively navigate the world of data-centric biology.

The Data Journey: Preparation, Travel, and the Unexpected

A surreal digital illustration of a winding data road through a biological landscape.

Leonelli uses the analogy of a 'data journey' to emphasize the importance of preparation and adaptability in working with data. Just as travelers must prepare for a trip, researchers must carefully plan for data collection, management, and analysis. However, the journey rarely goes exactly as planned. Unexpected results, errors, and biases can all derail the process.

The key, Leonelli argues, lies in embracing this inherent uncertainty. The 'lack of smoothness and predefined direction' is not a weakness but a strength. It is through navigating these unexpected challenges that researchers gain deeper insights into the data and its potential applications.

Preparation: Careful planning for data collection and management is essential.
Adaptability: Be prepared for unexpected results and deviations from the original plan.
Embrace Uncertainty: View challenges as opportunities for deeper understanding.

Leonelli's analysis extends beyond the technical aspects of data handling. She also explores the social and philosophical dimensions of data-centric biology. By examining the practices of data curators and the evolving roles of experiment and theory, she sheds light on the complex interplay between data, knowledge, and human interpretation.

From Decontextualization to Recontextualization: Making Data Valuable

Leonelli introduces the concepts of 'decontextualization' and 'recontextualization' to explain how data becomes valuable. Decontextualization involves removing data from its original setting, stripping away the specific material and epistemological circumstances in which it was produced.

This process allows data to travel and be reused in different contexts. However, to be truly useful, data must also be recontextualized, meaning it needs to be properly adapted and interpreted within its new setting. Data curators play a vital role in this process, enabling data to be both stable and dynamic, ensuring its continued relevance and utility.

Ultimately, Leonelli's work challenges us to rethink our relationship with data. By understanding the data journey, embracing uncertainty, and mastering the art of decontextualization and recontextualization, we can unlock the full potential of data-centric biology and drive groundbreaking discoveries. For scientists, historians, philosophers, and policymakers, Leonelli’s insights provide a crucial foundation for navigating the ever-evolving landscape of big data in biological research.

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Everything You Need To Know

How does Leonelli describe the process of working with biological data, and what key elements does this involve?

In "Data-Centric Biology," Leonelli uses the concept of a 'data journey' to highlight the necessity of preparation and adaptability when dealing with biological data. This involves careful planning for data collection, management, and analysis. However, researchers must also be ready for unexpected results, errors, and biases that can arise. Embracing this uncertainty, rather than seeing it as a weakness, allows for deeper insights into the data and its potential applications, making the journey epistemologically interesting.

Can you explain the concepts of 'decontextualization' and 'recontextualization' in the context of making data valuable in biological research?

Leonelli introduces 'decontextualization' and 'recontextualization' to explain how raw biological data gains value. 'Decontextualization' involves removing data from its original setting, stripping away the specific material and epistemological circumstances in which it was produced. This initial abstraction is crucial. The data is then 'recontextualized,' meaning it is re-embedded in new frameworks and applications, where it can contribute to broader scientific understanding. The recontextualization imbues the data with utility and value.

Beyond the technical aspects, what social dimensions of data-centric biology does Leonelli explore in her work?

Leonelli's work extends into the social dimensions of data-centric biology. She examines the practices of data curators and the evolving roles of experiment and theory. By exploring these aspects, she highlights the intricate interplay between data, knowledge, and human interpretation. These social dynamics influence how data is managed, interpreted, and ultimately used to generate knowledge, which is a central theme in her philosophical study.

What is the significance of the 'lack of smoothness and predefined direction' in Leonelli's perspective on data analysis?

The 'lack of smoothness and predefined direction' is a concept introduced by Leonelli to challenge the conventional view of data analysis. Instead of seeing unexpected results, errors, and biases as hindrances, Leonelli argues that these unforeseen challenges are opportunities for deeper understanding. Embracing uncertainty allows researchers to gain insights into the data's potential applications, making the research process epistemologically more interesting and fruitful.

What are the broader implications of data-centric biology for biological research and the production of scientific knowledge, according to Leonelli's philosophical study?

Data-centric biology, as explored by Sabina Leonelli, carries significant implications for how biological research is conducted and how knowledge is produced. By understanding the philosophical and historical dimensions of 'big data,' researchers can more effectively manage, analyze, and interpret biological data. This approach not only enhances the rigor and reproducibility of research but also fosters a deeper appreciation for the complexities and uncertainties inherent in the data journey. Moreover, it highlights the crucial role of human interpretation and social practices in shaping scientific knowledge.