26 papers · ranked by Valyu relevance
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…
Yang, Xunmo, Pospisil, Taylor + 4 more
This paper outlines a grammar of data analysis, as distinct from grammars of data manipulation, in which the primitives are metrics and dimensions. We describe a Python implementation of this grammar called Meterstick, which is agnostic to the underlying data source, which may be a DataFrame or a SQL database.
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…
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…
Blend Berisha, Endrit Mëziu, Isak Shabani
Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Every day a huge amount of data is produced from different sources. This data is so big in size that traditional processing tools are unable to deal with them. Besides being big, this data moves fast and has a lot…
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…
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 1 evolving innovations of the 21st century. Due to its value, power, and scope of applicability, it is emerging in over 40…
Annabelle Warner, Andrew McNutt, Paul Rosen, El Kindi Rezig
Preparing datasets—a critical phase known as data wrangling constitutes the dominant phase of data science development, consuming upwards of 80% of the total project time. This phase encompasses a myriad of tasks: parsing data, restructuring it for analysis, repairing inaccuracies, merging sources, eliminating…
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…
Jaka Kokošar, Cagatay Turkay, Luka Avsec, Miha Štajdohar + 1 more
We introduce a visual analytics methodology for survival analysis, and propose a framework that defines a reusable set of visualization and modeling components to support exploratory and hypothesis-driven biomarker discovery. Survival analysis—essential in biomedicine—evaluates patients’ survival rates and the onset of…
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…
Niklas Elmqvist
Data is collected everywhere in our increasingly instrumented world and people are increasingly wanting to access this data from anywhere in it. This kind of anywhere & everywhere data present new challenges and opportunities for data-driven sensemaking and decision-making that will require leveraging novel mobile…
Petar Radanliev, David De Roure
With the increased digitalisation of our society, new and emerging forms of data present new values and opportunities for improved data driven multimedia services, or even new solutions for managing future global pandemics (i.e., Disease X). This article conducts a literature review and bibliometric analysis of…
Lucina Hackman, Pauline Mack, Hervé Ménard
Data underpinning science have become one of the most precious assets in research, and while the principles of FAIR (Findable, Accessible, Interoperable and Reusable) have been put forward as a guide to how to approach data handling, data sharing and long-term storage still remain a challenge for many research areas…
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…
Rafael C. Alvarado
Consensus on the definition of data science remains low despite the widespread establishment of academic programs in the field and continued demand for data scientists in industry. Definitions range from rebranded statistics to data-driven science to the science of data to simply the application of machine learning to…
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…