More Like this

1. Key challenges facing data-driven multicellular systems biology

   https://doi.org/10.1093/gigascience/giz127  

   Macklin, Paul (October 2019, GigaScience)

   Abstract Increasingly sophisticated experiments, coupled with large-scale computational models, have the potential to systematically test biological hypotheses to drive our understanding of multicellular systems. In this short review, we explore key challenges that must be overcome to achieve robust, repeatable data-driven multicellular systems biology. If these challenges can be solved, we can grow beyond the current state of isolated tools and datasets to a community-driven ecosystem of interoperable data, software utilities, and computational modeling platforms. Progress is within our grasp, but it will take community (and financial) commitment. 

   more » « less
2. Why Idealized Models Are More Important Than Ever in Earth System Science

   https://doi.org/10.1029/2025AV001716  

   Reed, Kevin A; Medeiros, Brian; Jablonowski, Christiane; Simpson, Isla R; Voigt, Aiko; Wing, Allison A (August 2025, AGU Advances)

   Abstract Simulating the Earth system is crucial for studying Earth's climate and how it changes. Modeling approaches that simplify the Earth system while retaining key characteristics are important tools to advance understanding. The simplicity and flexibility of idealized models enables imaginative science and makes them powerful educational tools. Evolving scientific community needs and increasing model complexity, however, makes it challenging to maintain and support idealized configurations in cutting‐edge Earth system modeling frameworks. We call on the scientific community to re‐emphasize model hierarchies within these frameworks to aid in understanding the Earth system, advancing model development, and developing the future workforce. 

   more » « less
3. Credibility via Coupling: Institutions and Infrastructures in Climate Model Intercomparisons

   https://doi.org/10.17351/ests2021.769  

   Mayernik, Matthew S. (December 2021, Engaging Science, Technology, and Society)

   This study investigates Model Intercomparison Projects (MIPs) as one example of a coordinated approach to establishing scientific credibility. MIPs originated within climate science as a method to evaluate and compare disparate climate models, but MIPs or MIP-like projects are now spreading to many scientific fields. Within climate science, MIPs have advanced knowledge of: a) the climate phenomena being modeled, and b) the building of climate models themselves. MIPs thus build scientific confidence in the climate modeling enterprise writ large, reducing questions of the credibility or reproducibility of any single model. This paper will discuss how MIPs organize people, models, and data through institution and infrastructure coupling (IIC). IIC involves establishing mechanisms and technologies for collecting, distributing, and comparing data and models (infrastructural work), alongside corresponding governance structures, rules of participation, and collaboration mechanisms that enable partners around the world to work together effectively (institutional work). Coupling these efforts involves developing formal and informal ways to standardize data and metadata, create common vocabularies, provide uniform tools and methods for evaluating resulting data, and build community around shared research topics. 

   more » « less
4. HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards

   https://doi.org/10.1109/BigData47090.2019.9005648  

   Tritt, Andrew J.; Rubel, Oliver; Dichter, Benjamin; Ly, Ryan; Kang, Donghe; Chang, Edward F.; Frank, Loren M.; Bouchard, Kristofer (December 2019, 2019 IEEE International Conference on Big Data (Big Data))

   A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0, a modern data standard for collaborative science across the neurophysiology community. 

   more » « less
5. Extreme Drought in Grassland Ecosystems (EDGE) Net Primary Production Quadrat Data at the Sevilleta National Wildlife Refuge, New Mexico

   https://doi.org/10.6073/pasta/4b6fca5328fcdfa8bc644a3d6d9727f7  

   Baur, Lauren; Collins, Scott; Muldavin, Esteban; Rudgers, Jennifer A; Pockman, William T. (January 2021, Environmental Data Initiative)

   EDGE is located at six grassland sites that encompass a range of ecosystems in the Central US - from desert grasslands to short-, mixed-, and tallgrass prairie. We envision EDGE as a research platform that will not only advance our understanding of patterns and mechanisms of ecosystem sensitivity to climate change, but also will benefit the broader scientific community. Identical infrastructure for manipulating growing season precipitation will be deployed at all sites. Within the relatively large treatment plots (36 m2), we will measure with comparable methods, a broad spectrum of ecological responses particularly related to the interaction between carbon fluxes (NPP, soil respiration) and species response traits, as well as environmental parameters that are critical for the integrated experiment-modeling framework, as well as for site-based analyses. By designing EDGE as a research platform open to the broader scientific community, with subplots in all replicates (n = 180 plots) set-aside for additional studies, and by making data available to the broader ecological community EDGE will have value beyond what we envision here. 

   more » « less