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Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric applications. Driven by (i) rising demands for…

The complexity of human biology and its intricate systems holds immense potential for advancing human health, disease treatment, and scientific discovery. However, traditional manual methods for studying biological interactions are often…

Ecosystem restoration is a paramount policy priority for this decade, with ambitious global and European targets requiring unprecedented levels of data-driven implementation. Achieving effective and equitable restoration, particularly for…

Data has become a critical resource for organizations and society. Yet, it is not always as valuable as it could be since there is no well-defined approach to managing and using it. This article explores the increasing importance of global…

Nowadays, we have the emergence and abundance of many different data repositories and archival systems for scientific data discovery, use, and analysis. With the burgeoning data sharing platforms available, this study addresses how natural…

In recent years, data science has become an indispensable part of our society. Over time, we have become reliant on this technology because of its opportunity to gain value and new insights from data in any field - business, socializing,…

In recent years, cloud service providers have been building and hosting datacenters across multiple geographical locations to provide robust services. However, the geographical distribution of datacenters introduces growing pressure to both…

Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches. We propose AutoKaggle, a powerful and user-centric framework that assists data scientists in completing daily data…

The field of data science currently enjoys a broad definition that includes a wide array of activities which borrow from many other established fields of study. Having such a vague characterization of a field in the early stages might be…

Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional data-driven methods face challenges in capturing inherently…

Earth system science is producing increasingly large, high-dimensional datasets from physics based Earth system models to AI-based weather and climate models. Embedding-based representations can make these data searchable through similarity…

The development history of landscape architecture (LA) reflects the human pursuit of environmental beautification and ecological balance. With the advancement of artificial intelligence (AI) technologies that simulate and extend human…

Computational experiments have become essential for scientific discovery, allowing researchers to test hypotheses, analyze complex datasets, and validate findings. However, as computational experiments grow in scale and complexity, ensuring…

This paper explores the evolving landscape of data spaces, focusing on key concepts, practical applications, and emerging future directions. It begins by introducing the foundational principles that underpin data space architectures,…

Biomedical research centers can empower basic discovery and novel therapeutic strategies by leveraging their large-scale datasets from experiments and patients. This data, together with new technologies to create and analyze it, has ushered…

In this paper we take the common position that AI systems are limited more by the integrity of the data they are learning from than the sophistication of their algorithms, and we take the uncommon position that the solution to achieving…

The automation of scientific discovery represents a critical milestone in Artificial Intelligence (AI) research. However, existing agentic systems for science suffer from two fundamental limitations: rigid, pre-programmed workflows that…

Life cycle assessment (LCA) plays a critical role in assessing the environmental impacts of a product, technology, or service throughout its entire life cycle. Nonetheless, many existing LCA tools and methods lack adequate metadata…

In materials science and manufacturing, vast amounts of heterogeneous data (e.g., measurement and simulation logs, process data, publications) serve as the bedrock of valuable knowledge for various engineering applications. However,…

The rapid advancement of AI is transforming human-centered systems, with profound implications for human-AI interaction, human-data interaction, and visual analytics. In the AI era, data analysis increasingly involves large-scale,…