From Reductionism to Reintegration: Solving society’s most pressing problems requires building bridges between data types across the life sciences

Abstract

Decades of reductionist approaches in biology have achieved spectacular progress, but the proliferation of subdisciplines, each with its own technical and social practices regarding data, impedes the growth of the multidisciplinary and interdisciplinary approaches now needed to address pressing societal challenges. Data integration is key to a reintegrated biology able to address global issues such as climate change, biodiversity loss, and sustainable ecosystem management. We identify major challenges to data integration and present a vision for a “Data as a Service”-oriented architecture to promote reuse of data for discovery. The proposed architecture includes standards development, new tools and services, and strategies for career-development and sustainability.

A literature study and public survey on mass cytometry dataset release and reuse

“…The time is right for journals and funding agencies to institute such a mandate for public deposition of cytometry data at the time of initial publication as they do for other data types [14]. First, this will benefit science by enabling reanalysis for reproducibility [15], confirmation of new results with previous independent datasets, and general dataset reuse for future discovery [16]. Second, this will also supply a larger selection of datasets for benchmarking algorithms for cytometry analysis, which are currently highly focused on only a handful of datasets (Figure S7; Table S2).…”

“…Based on the best‐practices in other fields such as SeqData, the general movement toward data sharing and findability, accessibility, interoperability, and reusability (FAIR) principles [13], and the announcement of policies like the recent finalized US NIH data management and sharing policy beginning in January 2023 [4] we argue the following: The time is right for journals and funding agencies to institute such a mandate for public deposition of cytometry data at the time of initial publication as they do for other data types [14]. First, this will benefit science by enabling reanalysis for reproducibility [15], confirmation of new results with previous independent datasets, and general dataset reuse for future discovery [16]. Second, this will also supply a larger selection of datasets for benchmarking algorithms for cytometry analysis, which are currently highly focused on only a handful of datasets (Figure S7; Table S2).…”

A synthetic synthesis to explore animal evolution and development

“…Phenomics approaches will produce huge datasets that vary significantly in content as well as the method and framework of acquisition. How to integrate information and extract meaningful conclusions from diverse, high-dimensional datasets may become one of the most urgent biological challenges in the near future (reviewed in [ 172 , 173 ]). Addressing this challenge will require (i) developing standard methods for acquiring and processing different types of data, which will enable future automation; (ii) stimulating data sharing and data reuse in order to avoid duplication of efforts; and (iii) developing next-generation and community-oriented data platforms to facilitate data accessibility and standardization of processing methods [ 174 ].…”

Past and future uses of text mining in ecology & evolution

“…Integrating data from across the life sciences is currently a major challenge, but will likely foster the interdisciplinary research needed to address pressing global issues [43]. Applying NLP approaches, unstructured texts can be transformed into structured data commonly analyzed in ecological and evolutionary studies.…”