24 papers · ranked by Valyu relevance
Lauri Himanen, Amber Geurts, Adam Stuart Foster, Patrick Rinke
Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning-typically with the intent to discover new or improved materials or materials phenomena. Multiple…
Longbing Cao
The twenty-first century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science…
Fatima Samea, Farooque Azam, Muhammad Rashid, Muhammad Waseem Anwar + 3 more
'Wasi Haider Butt' 'Abdul Wahab Muzaffar' 'Haoran Xie'] In a serverless cloud computing environment, the cloud provider dynamically manages the allocation of resources whereas the developers purely focus on their applications. The data-driven applications in serverless cloud computing mainly address the web as well as…
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…
Longbing Cao
—Data science is creating very exciting trends as well as significant controversy. A critical matter for the healthy development of data science in its early stages is to deeply understand the nature of data and data science, and to discuss the various pitfalls. These important issues motivate the discussions in this…
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 |…
M. Shazmin Marikar, H. M. N. Dilum Bandara
Organizations are adopting data analytics and Business Intelligence (BI) tools to gain insights from the past data, forecast future events, and to get timely and reliable information for decision making. While the tools are becoming mature, affordable, and more comfortable to use, it is also essential to understand…
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…
Thomas Hartmann, Assaad Moawad, François Fouquet, Grégory Nain + 3 more
'Jacques Klein' 'Yves Le Traon' 'Jean-Marc Jézéquel'] Abstract—Gaining profound insights from collected data of today's application domains like IoT, cyber-physical systems, health care, or the financial sector is businesscritical and can create the next multi-billion dollar market. However, analyzing these data and…
Dominik Balazka, Dario Rodighiero
Starting from an analysis of frequently employed definitions of big data, it will be argued that, to overcome the intrinsic weaknesses of big data, it is more appropriate to define the object in relational terms. The excessive emphasis on volume and technological aspects of big data, derived from their current…
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…
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.…
Yi Luo, Saientan Bag, Orysia Zaremba, Jacopo Andreo + 3 more
Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis…
Timothée Poisot, Dominique Gravel, Shawn Leroux, Spencer A. Wood + 5 more
The increased availability of both open ecological data, and software to interact with it, allows to rapidly collect and integrate data over large spatial and taxonomic scales. This offers the opportunity to address macroecological questions in a cost-effective way. In this contribution, we illustrate this approach by…
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…
Ted Laderas, Nicole Vasilevsky, Bjorn Pederson, Melissa Haendel + 2 more
Our goal was to create a synthetic dataset and curricular materials to assist in teaching fundamentals of translational data science. A literature review was conducted to extract current cardiovascular risk score logic, data elements, and population characteristics. Then, clinical data elements in the models were…
Nathan Emery, Erika Crispo, Sarah R. Supp, Andrew J. Kerkhoff + 5 more
There is a clear and concrete need for greater quantitative literacy in the biological and environmental sciences. Data science training for students in higher education necessitates well-equipped and confident instructors across curricula. However, not all instructors are versed in data science skills or…
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…
Micaela S. Parker, Arlyn E. Burgess, Philip E. Bourne, Russell Schwartz
Data science has emerged as a new paradigm for research. Readers of this journal might be tempted to say this is the research we have been doing all along. However, we contest that there is something fundamentally different in terms of the dimensions of data, diversity of disciplines, as well as the role of the private…
Kimberly A Dill-McFarland, Steven J Hallam
We live in an increasingly data-driven world. This is strongly evident in the life sciences where high-throughput data generation platforms are transforming biology into information science. This has shifted major challenges in biological research from data generation to interpretation and knowledge translation.…
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…