Big data uncertainties

Maugis, Pierre-André

doi:10.1016/j.jflm.2016.09.005

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…If the data is unrefined, AI will not produce any good results, as exemplified by Microsoft's Tay Chatbot [14], which learned nonsense when it was taught nonsense. Various retrospective studies further demonstrated how undesirable phenomena produced different results, depending on the type of data and the research method [1516]. Such results support the importance of data rather than methodology, and of quality over quantity.…”

Section: Introductionmentioning

confidence: 91%

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Kim

Yoon

2019

Endocrinol Metab

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Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.

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Section: Introductionmentioning

confidence: 91%

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Kim

Yoon

2019

Endocrinol Metab

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“…Data's intrinsic uncertainty grows along with its volume, diversity, and speed, which makes the subsequent analytics process and its conclusions less reliable. Artificial intelligence approaches, such as machine learning, natural language processing, and computational intelligence, produce outcomes in big data analytics that are more precise, swifter, more scalable when compared to traditional data methodologies and platforms [91][92][93][94].…”

Section: Advanced Data Analytic Techniquesmentioning

confidence: 99%

Recent Trends of Quantitative Approaches in Different Sectors: A Concise Review

Vijayendra

Fantone²,

Shehryar³

2023

AJESS

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The approaches and methods used in qualitative and quantitative research represent various research strategies and have diverse theoretical, epistemological, and ontological concerns. Every strategy will depend on how the researchers gather and analyze their data. It is important to use caution when doing research on problem-solving lessons in secondary scientific education to prevent bias in the collection and interpretation of data. Despite the fact that both methods are employed by academics, the quantitative technique is more popular because of its strengths and qualities. To measure actions, views, attitudes, and other characteristics and draw generalizations from a larger population, quantitative research is utilized. In order to express facts and identify trends in study, quantitative research uses quantifiable data. When conducting this kind of research, results are derived using statistical and mathematical tools. This review article focuses on the basic terminologies of quantitative research, its applications in various sectors and reasons why quantitative research approaches surpass qualitative research methods.

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“…Combining the insights from these three perspectives demonstrates that the complexities and subtleties of biophysical modelling uncertainty in digital data-rich contexts cannot be reduced to single numbers or quantitative assessments alone (also [81]). Systematic reflections on modelling uncertainties should thus consider sociocultural, technical, ecological, biophysical and statistical aspects.…”

Section: An Engineering and Data Science Lensmentioning

confidence: 99%

Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization

et al. 2020

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Agricultural digitalization is providing growing amounts of real-time digital data. Biophysical simulation models can help interpret these data. However, these models are subject to complex uncertainties, which has prompted calls for interdisciplinary research to better understand and communicate modelling uncertainties and their impact on decision-making. This article develops two corresponding insights from an interdisciplinary project in a New Zealand agricultural research organization. First, we expand on a recent Royal Society Open Science journal article (van der Bles et al . 2019 Royal Society Open Science 6 , 181870 ( doi:10.1098/rsos.181870 )) and suggest a threefold conceptual framework to describe direct, indirect and contextual uncertainties associated with biophysical models. Second, we reflect on the process of developing this framework to highlight challenges to successful collaboration and the importance of a deeper engagement with interdisciplinarity. This includes resolving often unequal disciplinary standings and the need for early collaborative problem framing. We propose that both insights are complementary and informative to researchers and practitioners in the field of modelling uncertainty as well as to those interested in interdisciplinary environmental research generally. The article concludes by outlining limitations of interdisciplinary research and a shift towards transdisciplinarity that also includes non-scientists. Such a shift is crucial to holistically address uncertainties associated with biophysical modelling and to realize the full potential of agricultural digitalization.

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Big data uncertainties

Cited by 9 publications

References 28 publications

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Recent Trends of Quantitative Approaches in Different Sectors: A Concise Review

Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization

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