29 papers · ranked by Valyu relevance
Paul Slater, Felicity Hasson
Quantitative data quality assurance is the systematic process and procedures used to ensure the accuracy, consistency, reliability, and integrity of data throughout the research process. Effective quality assurance helps identify and correct errors, reduce biases, and ensure the data meets the standards needed for…
Bob Mash, Gboyega A. Ogunbanjo
This article is part of a series on Primary Care Research Methods. The article describes types of continuous and categorical data, how to capture data in a spreadsheet, how to use descriptive and inferential statistics and, finally, gives advice on how to present the results in text, figures and tables. The article…
Michael C. Schatz
The last 20 years have been a remarkable era for biology and medicine. One of the most significant achievements has been the sequencing of the first human genomes, which has laid the foundation for profound insights into human genetics, the intricacies of regulation and development, and the forces of evolution.…
Matthias Borgstede, Marcel Scholz
In this paper, we provide a re-interpretation of qualitative and quantitative modeling from a representationalist perspective. In this view, both approaches attempt to construct abstract representations of empirical relational structures. Whereas quantitative research uses variable-based models that abstract from…
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…
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…
Elizaveta I. Shestoperova, Daniil G. Ivanov, Eric R. Strieter
The diversity of ubiquitin modifications calls for methods to better characterize ubiquitin chain linkage, length, and morphology. Here, we use multiple linear regression analysis coupled with ion mobility mass spectrometry (IM-MS) to quantify the relative abundance of different ubiquitin dimer isomers. We demonstrate…
Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen + 9 more
Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous.…
Alisa Bokulich, Wendy Parker
We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational (PR) view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness…
Rodolfo Blanco-Rodriguez, Tanya A. Miura, Esteban Hernandez-Vargas
The integration of computational models with experimental data is a cornerstone for gaining insight into biomedical applications. However, parameter fitting procedures often require an immense availability and frequency of data that are challenging to obtain from a single source. Here, we present a novel methodology…
Chris Brunsdon, Alexis Comber
In this paper we consider some of the issues of working with big data and big spatial data and highlight the need for an open and critical framework. We focus on a set of challenges underlying the collection and analysis of big data. In particular, we consider 1) the issues related to inference when working with…
Claire Glenton, Benedicte Carlsen, Simon Lewin, Heather Munthe-Kaas + 9 more
Background The GRADE-CERQual (Confidence in Evidence from Reviews of Qualitative research) approach has been developed by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) working group. The approach has been developed to support the use of findings from qualitative evidence syntheses in…
Louis J. Gross, Rachel Patton McCord, Sondra LoRe, Vitaly V. Ganusov + 5 more
Substantial guidance is available on undergraduate quantitative training for biologists, including reports focused on biomedical science, but far less attention has been paid to the graduate curriculum. In this setting, we propose an innovative approach to quantitative education that goes beyond recommendations of a…
A. Carvajal-Rodríguez
Sexual selection theory is a multifaceted area of evolutionary research that has profound implications across various disciplines, including population genetics, evolutionary ecology, animal behavior, sociology, and psychology. It explores the mechanisms by which certain traits and behaviors evolve due to mate choice…
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…
James Wellnitz, Sankalp Jain, Joshua Hochuli, Travis Maxfield + 3 more
Traditional best practices for Quantitative Structure Activity Relationship (QSAR) modeling recommend dataset balancing and balanced accuracy (BA) as the key desired objective of model development. This study challenges the conventional norms by recommending the use of models with the highest positive predictive value…
Alex Scharaschkin
Psychometrics conceptualizes a person's proficiency (or ability, or competence), in a cognitive or educational domain, as a latent numerical quantity. Yet both conceptual and empirical studies have shown that the assumption of quantitative structure for such phenomena is unlikely to be tenable. A reason why most…
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…
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.…
Suneth Ranasinghe, Horst Pichler, Johann Eder
This report discusses the issues of data quality in biobanks. It presents the state-of-the-art in data quality: the definition of data quality, the dimensions of data quality, and the quality management system for achieving or describing the aspired data quality characteristics and we present and discuss all elements…
C. Wild
"An unexamined life is not worth living," said Socrates as his own life drew to a tragic close. Even more emphatically, an unexamined education is not worth delivering. Periodically, we need to examine what we are doing and why — starting from the most fundamental of fundamentals. Ograjenšek and Gal (2015, hereafter…
Firas Bayram, Bestoun S. Ahmed, Erik I. Hallin, Anton Engman
Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world applications generate huge volumes of data at speeds. These data streams require analysis and preprocessing before being permanently stored…
Sezal Chug, Priya Kaushal, Ponnurangam Kumaraguru, Tavpritesh Sethi
Data is expanding at an unimaginable rate, and with this development comes the responsibility of the quality of data. Data Quality refers to the relevance of the information present and helps in various operations like decision making and planning in a particular organization. Mostly data quality is measured on an…
Authors not listed
The discoverability and reusability of data is critical for machine learning to drive new discovery in the chemical sciences, and the ‘FAIR Guiding Principles for scientific data management and stewardship’ provide a measurable set of guidelines that can be used to ensure the accessibility of reusable data. We…
Guido Pauli, G. Joseph Ray, Anton Bzhelyansky, Birgit Jaki + 18 more
Classical 1D 1H NMR spectra are prototypic for NMR spectroscopy in that they represent a wealth of chemical information encoded into convoluted graphs or patterns that contain complex features (aka multiplets), even for seemingly simple molecules. Accordingly, the utility of NMR depends on the theoretical and visual…
José Fausto de Morais, Gecilmar Pereira Borges, Gilmar Fernandes do Prado
The statistical analysis is an important part of the process of assessing the quality of randomized controlled trials, unfortunately it tends to be underestimated or even omitted by editors, in this sense, scales that cover this gap becomes necessary. To build a definition and a scale for assessing the quality 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…
Anastasija Nikiforova
Nowadays open data is entering the mainstream - it is free available for every stakeholder and is often used in business decision-making. It is important to be sure data is trustable and error-free as its quality problems can lead to huge losses. The research discusses how (open) data quality could be assessed. It also…