27 papers · ranked by Valyu relevance
Nawsher Khan, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Zakira Inayat + 4 more
'Zakira Inayat' 'Waleed Kamaleldin Mahmoud Ali' 'Muhammad Alam' 'Muhammad Shiraz' 'Abdullah Gani'] Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2…
Rising Odegua
A large amount of data is produced every second from modern information systems such as mobile devices, the world wide web, Internet of Things, social media, and so on. Analysis and mining of these massive data require a lot of advanced tools and techniques. Therefore, big data analytics and mining is currently an…
Stephanie C. Hicks, Roger D. Peng
The data revolution has led to an increased interest in the practice of data analysis. For a given problem, there can be significant or subtle differences in how a data analyst constructs or creates a data analysis, including differences in the choice of methods, tooling, and workflow. In addition, data analysts can…
Shohel Sayeed, Abu Fuad Ahmad, Tan Choo Peng
The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities…
Kornelia Batko, Andrzej Ślęzak
The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the…
Lucy D’Agostino McGowan, Roger D. Peng, Stephanie C. Hicks
The data science revolution has led to an increased interest in the practice of data analysis. While much has been written about statistical thinking, a complementary form of thinking that appears in the practice of data analysis is design thinking – the problem-solving process to understand the people for whom a…
Ringyao Jajo, Shivani Kansal, Sonia Balyan, Saurabh Raghuvanshi
Data visualisation technique has greatly improved as technology has advanced. While representing the data through graph, it has made the underlying data structure become more transparent and interpretable. However, the informational scope of freely available generic visualisation tools is still limited since they only…
Changsoo Song, Resa Helikar, Wendy M. Smith, Tomáš Helikar
Discipline-Based Education Research (DBER) scientists repeatedly analyze assessment data to ensure question items’ reliability and examine the efficacy of a new educational intervention. Analyzing assessment data comprises multiple steps and statistical techniques that consume much of researchers’ time and are…
Stephanie C. Hicks, Roger D. Peng
A fundamental problem in the practice and teaching of data science is how to evaluate the quality of a given data analysis, which is different than the evaluation of the science or question underlying the data analysis. Previously, we defined a set of principles for describing data analyses that can be used to create a…
Muhammad Usman Tariq, Muhammad Babar, Marc Poulin, Akmal Saeed Khattak + 2 more
'Akmal Saeed Khattak' 'Mohammad Dahman Alshehri' 'Sarah Kaleem'] Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social…
Roger D. Peng, Hilary S. Parker
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 natural, but over time maintaining such a broad definition becomes unwieldy and…
Eric W. Deutsch, Roger Kramer, Joseph Ames, Andrew Bauman + 21 more
Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities.…
Gunther Eysenbach, Sanmitra Bhattacharya, Chun Hung Li, Carolyn McGregor + 5 more
'Carolyn McGregor' 'Anandhi Ramachandran' 'Robert Eugene Hoyt' 'Dallas Snider' 'Carla Thompson' 'Sarita Mantravadi'] Background We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data…
Guy Teichman, Dror Cohen, Or Ganon, Netta Dunsky + 3 more
Amongst the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further heightened for researchers who are not experienced in writing computer code…
Yi Hsiao, Haijian Zhang, Ginny Xiaohe Li, Yamei Deng + 5 more
The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an…
Gunther Eysenbach, Luca Toldo, Junfeng Gao, Weiqi Wang + 1 more
Background In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting…
Souvik Manna, Diptendu Roy, Sandeep Das, Biswarup Pathak
Application of data science and machine learning (ML) techniques in the domain of materials science has been increasing by leaps and bounds recently. With the help of ML, through input features derived from available databases we can rapidly screen materials based on our desired output. Capacity is one of the important…
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…
Aleksandar Jagličić, Torben Gädt, Matthias Hofmann
Isothermal heat flow calorimetry is a powerful method for studying chemical processes. In cement research, it has become indispensable for quantifying the heat release during cement hydration. It is used to study the reactivity of cementitious binders and the effect of admixture chemistry and dosage. Most isothermal…
Authors not listed
Raman spectroscopy is an increasingly powerful and fast-growing analytical technique across diverse disciplines, from materials science and chemistry to biology and medicine, thanks to advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However…
Matthias Scheffler, Stefan Bauer, Peter Benner, Tristan Bereau + 57 more
Matthias Scheffler 1 , Stefan Bauer 2 , Peter Benner 3 , Tristan Bereau 4 , Volker Blum 5 , Mario Boley 6 , Christian Carbogno 7 , C. Richard A. Catlow 8 , Gerhard Dehm 9 , Sebastian Eibl 10 , Ralph Ernstorfer 11 , Ádám Fekete 12 , Lucas Foppa 1 , Peter Fratzl 13 , Christoph Freysoldt 9 , Baptiste Gault 9 , Luca M.…
Eftychia Eva Kontou, Axel Walter, Oliver Alka, Julianus Pfeuffer + 5 more
Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for…
Julia Keizer, Christian F. Luz, Bhanu Sinha, Lisette van Gemert-Pijnen + 3 more
Data and data visualization are integral parts of (clinical) decision-making in general and stewardship (antimicrobial stewardship, infection control, and institutional surveillance) in particular. However, systematic research on the use of data visualization in stewardship is lacking. This study aimed at filling this…
Eric Deutsch, Luis Mendoza, David Shteynberg, Michael Hoopmann + 3 more
The Trans-Proteomic Pipeline mass spectrometry data analysis suite has been in continual development and refinement since its first tools PeptideProphet and ProteinProphet were published twenty years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search…
Authors not listed
Traditional and non-classical machine learning models for solid-state structure prediction have predominantly relied on compositional features (derived from properties of constituent elements) to predict the existence of structure and its properties. However, the lack of structural information can be a source of…
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…
Jason David Wark, Ori Pomerantz, Kristine Coleman
Simple Summary Monitoring animal behavior over time is important for zoos and aquariums seeking to continually evaluate animal welfare. Although new digital tools are making behavior monitoring more accessible, analyzing behavior data in a timely manner to draw meaningful insights can be challenging. Business…