ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science1

Abstract

Statistical graphics, and data visualization, play an essential but under-utilized, role for data analysis in animal science, and also to visually illustrate the concepts, ideas, or outputs of research and in curricula. The recent rise in web technologies and ubiquitous availability of web browsers enables easier sharing of interactive and dynamic graphics. Interactivity and dynamic feedback enhance human–computer interaction and data exploration. Web applications such as decision support systems coupled with multimedia tools synergize with interactive and dynamic graphics. However, the importance of graphics for effectively communicating data, understanding data uncertainty, and the state of the field of interactive and dynamic graphics is underappreciated in animal science. To address this gap, we describe the current state of graphical methodology and technology that might be more broadly adopted. This includes an explanation of a conceptual framework for effective graphics construction. The ideas and technology are illustrated using publicly available animal datasets. We foresee that many new types of big and complex data being generated in precision livestock farming create exciting opportunities for applying interactive and dynamic graphics to improve data analysis and make data-supported decisions.

Automated acquisition of top-view dairy cow depth image data using an RGB-D sensor camera

“…With the development of high-throughput technologies, animal science has become a large-scale, data-rich field ( Morota et al, 2021 ). Reducing labor costs and enhancing welfare are the primary goals that promote livestock productivity and operational sustainability.…”

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science

“…It is used as an exploratory graphical tool to obtain nonparametric statistical information and analyze the distribution ( Friedman and Stuetzle, 2002 ). Tukey (1977) insisted that “there is no excuse for failing to plot and look” because “the greatest value of a picture is when it forces us to notice what we never expected to see.” Data visualization is a must in data analytics, and nowadays, there are many ways to graphically analyze data ( Morota et al, 2021 ; Weissgerber et al, 2017 ). As shown in Figure 3 , the boxplot is a rectangle that contains five statistics of interest: the minimum and maximum values (excluding outliers), the first and third quartile, and the median (second quartile) of the data.…”

“…The expectations for DSS to solve all problems within a production context were too high, in part because data had limited availability, computational processes were still rustic to the desired outcome, and there was a lack of proper training of the workforce, more specifically the next generation of students that could have made the difference between success and failures in using this technology. Given the increased availability of data through precision livestock farming initiatives ( Tedeschi et al, 2021 ), improved data visualization ( Morota et al, 2021 ), and AI ( Wang et al, 2021 ), the expectations have been renewed with data analytics sparking new motivations to develop more powerful DSS by combining different tools to understand (and apply) the data.…”

DairyCoPilot—Automated data compilation and analysis tools for DairyComp data assets

“…Managing dairy cows has become a data-intensive practice [ 1 , 2 ]. Increased number of cows per farm combined with enhanced data recording has expanded the availability of data for producers and consultants to make informed decisions [ 3 ].…”