25 papers · ranked by Valyu relevance
Kristin Wulff, Hanne Finnestrand
Because of the increase in data and the possibilities created by machine learning, organizations are now looking to become more data-driven. In sociotechnical systems design there has been a focus on designing information for action to support decentralized organizations. The purpose of this article, published in…
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Sanchaita Hazra + 2 more
'Sanchaita Hazra' 'Ashish Sabharwal' 'Peter E. Clark'] With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially. This position paper urges the Machine Learning (ML) community to exploit the capabilities of large generative models (LGMs) to develop…
Johannes Jakubik, Michael Vössing, Niklas Kühl, Jannis Walk + 1 more
Over the past decades, researchers and practitioners in artificial intelligence (AI) have focused on improving ML models in AI-based systems (model-centric AI paradigm). However, the provision and selection of suitable data also impact model effectiveness (e.g., performance) and efficiency (e.g., costs for labeling or…
Andrea Gauthier, Saman Rizvi, Mutlu Cukurova, Manolis Mavrikis
Data-driven technologies for education, such as artificial intelligence in education (AIEd) systems, learning analytics dashboards, open learner models, and other applications, are often created with an aspiration to help teachers make better, evidence-informed decisions in the classroom. Addressing gender, racial, and…
Michael L. Brodie
Modern data science is in its infancy. Emerging slowly since 1962 and rapidly since 2000, data science is a fundamentally new field of inquiry, one of the most active, powerful, and rapidly evolving innovations of the 21st century. Due to its value, power, and scope of applicability, it is emerging in over 40…
Xuming He, David Madigan, Bin Yu, Jon Wellner
| EXECUTIVE SUMMARY | 4 | | --- | --- | | SECTION 1: ROLE/VALUE OF STATISTICS AND DATA SCIENCE | 6 | | SECTION 2: CHALLENGES IN SCIENTIFIC AND SOCIAL APPLICATIONS | 10 | | SECTION 3: FOUNDATIONAL RESEARCH | 16 | | SECTION 4: PROFESSIONAL CULTURE & COMMUNITY RESPONSIBILITIES | 20 | | SECTION 5: DOCTORAL EDUCATION | 23 |…
Suguru Fujita, Yasuaki Karasawa, Ken-ichi Hironaka, Y-h. Taguchi + 1 more
High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually…
Ricardo Stefani
The use of data science, artificial intelligence, and big data in the field of chemistry has recently grown to speed up the discovery of new materials, drugs, and synthetic substances and the identification of automated compounds. Machine learning and data science are commonly used in organic chemistry to predict…
Sebastian Zieglmeier, Mathias Hudoba de Badyn, Narada D. Warakagoda, Thomas R. Krogstad + 1 more
'Thomas R. Krogstad' 'Paal Engelstad'] Abstract— Data-enabled predictive control (DeePC) has emerged as a powerful technique to control complex systems without the need for extensive modeling efforts. However, relying solely on offline collected data trajectories to represent the system dynamics introduces certain…
Robert Mieth, Juan M. Morales, H. Vincent Poor
—With the ongoing investment in data collection and communication technology in power systems, data-driven optimization has been established as a powerful tool for system operators to handle stochastic system states caused by weatherand behavior-dependent resources. However, most methods are ignorant to data quality…
Jennifer Fouquier, Maggie Stanislawski, John O’Connor, Ashley Scadden + 1 more
The potential for disease treatment through gut microbiome modification has contributed to an increase in longitudinal microbiome studies (LMS). Gut microbiome modification can occur through factors such as diet, probiotics, or fecal transplants. Scientific data often motivates researchers to perform exploratory…
Authors not listed
Despite the promise of self-driving laboratories to accelerate discovery, their widespread implementation is hindered by prohibitive cost and technical complexity. We introduce BrickSDLab, a fully functional self-driving lab platform built entirely from LEGO® components, designed to bridge this accessibility gap.…
Stefaan Verhulst, Andrew Young
As a society, we need to become more sophisticated in assessing and addressing data asymmetries-and their resulting political and economic power inequalities-particularly in the realm of open science, research, and development. This article seeks to start filling the analytical gap regarding data asymmetries globally…
August George, Damon Leach, Conner Phillips, Joshua Brown + 11 more
Spatially distributed environmental sampling generates highly complex and multidimensional datasets illuminating key insights into microbial diversity, evolutionary-coevolutionary processes, and host-pathogen interactions. While these sampling methods generate high value datasets, dataset size, the dataset integration…
Bruce S. Kristal, Melissa D. McCradden, Jeyan Thiyagalingam, Zijun Zhang + 1 more
In this People of Data piece, our advisory board members select and discuss papers that they consider to be foundational to data science.
Michael Statt, Brian Rohr, Dan Guevarra, Ja'Nya Breeden + 2 more
Materials knowledge is inherently hierarchical. While high-level descriptors such as composition and structure are valuable for contextualizing materials data, the data must ultimately be considered in the context of its low-level acquisition details. Graph databases offer an opportunity to represent hierarchical…
Roman Lukyanenko
Data Management Authors: ['Roman Lukyanenko'] In an era dominated by information technology, the critical discipline of data management remains undervalued compared to the innovations it enables, such as artificial intelligence and social media. The ambiguity surrounding what constitutes data management and its…
Connor Bernard, Gabriel Silva Santos, Jacques Deere, Roberto Rodriguez-Caro + 5 more
The ecological sciences have joined the big data revolution. However, despite exponential growth in data availability, broader interoperability amongst datasets is still needed to unlock the potential of open access. The interface of demography and functional traits is well-positioned to benefit from said…
Feng Feng, Zhenru Chen, Jianyuan Ni, Yuanxun Zhang + 3 more
Drinking water is essential to public health and socioeconomic growth. Therefore, assessing and ensuring drinking water supply is a critical task in modern society. Conventional approaches to analyzing and controlling drinking water quality are labor-intensive and costly with a low throughput. Machine learning (ML) is…
Authors not listed
The rapid progress in Artificial Intelligence (AI) has led to extraordinary achievements across various domains, significantly impacting every aspect of daily life. This advancement is also revolutionizing research in numerous scientific areas, particularly within bioinformatics, chemistry, pharmaceuticals, and…
Tobias K. Mildenberger, Federico Maioli, Casper W. Berg
Scientific bottom-trawl surveys provide essential fisheries-independent data for fisheries and ecosystem research. In the Northeast Atlantic, the ICES Database of Trawl Surveys (DATRAS) compiles haul-level information, species- and length-specific catch data, and individual biological observations across multiple…
Jacquelyn P. Boerman, Luiz F. Brito, Maria E. Montes, Jacob M. Maskal + 2 more
'Jacob M. Maskal' 'Jarrod Doucette' 'Kirby Kalbaugh'] Title: Graphical Abstract
Pedro Matos-Filipe, Juan Manuel García-Illarramendi, Guillem Jorba, Baldo Oliva + 2 more
Randomized clinical trials (RCT) are limited in reflecting observable results out of controlled settings, which requires the execution of further lengthy observational studies. The usage of real-world data (RWD) has been recently considered to be a viable alternative to overcome these issues and complement certain…
Rebecca Brunk, Kriti Shukla, Bryant Hutson, Yue Wang + 7 more
Genomic sequencing and other big biological data is unquestionably of paramount value, however the success in recruiting highly skilled individuals with diverse backgrounds has been limited. A main reason for this deficiency could be due to the lack of educational resources and early exposure to the field. With the…
Candace Savonen, Carrie Wright, Ava Hoffman, Elizabeth Humphries + 3 more
Data science education provides tremendous opportunities but remains inaccessible to many communities. Increasing the accessibility of data science to these communities not only benefits the individuals entering data science, but also increases the field's innovation and potential impact as a whole. Education is the…