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How can we get data into a network format? This chapter briefly describes the basics and introduces the main kinds of networks we encounter in network science. It also shows how to take data that may not obviously present itself as a network and transform it into a network format.
For a book that attempts to explain how to understand visuals in life sciences, it seems prudent to first explain what we mean by “visual,” even if it may seem quite a common word.
In everyday conversation, “visual” is often used as an adjective and means “relating to seeing or sight,” as in “visual impression” or “visual effect.” In the context of this book, “visual” is used similarly as an adjective, but in addition, and more often, it is used as a noun. As a noun, it refers to the variety of images used in life science communication. For example, photographs are a type of visual commonly used in life science communication, and so are drawings.
Illustrations are a visual staple in life science communication. Despite being commonplace, they are in many ways a blackbox. They mask the creative – and scientific – decisions that go into making them. They present an end product that says, as it were, “this is how you look through life to its essence.” The use of precise lines and explicit shapes helps to convey this scientific authority. In contemporary illustrations, pseudo-details such as colors and dimensions further prove that “this is what life looks like.”
Micrographs, like the little (pun intended) cousin of photographs, are considered by some as an objective portrayal of nature. Why, they are photographs of the microscopic world invisible to the naked human eye. As such, what you see is what you get, and what you get is nature unveiled.
Particularly because the microscopic world is invisible to us in everyday life, we find it even more urgent to behold that world. We assume that if and when we see, we will automatically understand. If and when we observe microorganisms in their smallest components, we will be able to “get” them and conquer them.
Contemporary life sciences are big data sciences. The human genome, for example, contains about three billion DNA base pairs and an estimated 20,000 protein-coding genes. Public health data, as another example, are endlessly enormous and encompass electronic medical records, health monitoring data, environmental data, and more. When it comes to analyzing and presenting these big data, interactive online visuals – maps, graphs, three-dimensional models, even computer games – have inherent advantages. They are dynamic and easily updated. They support user interaction and allow users to create displays that make sense to them. Being “hands-on” also makes these visual displays more interesting. As computer visualization technologies continue to advance, we are guaranteed to see faster, more fluid, more ingenious interactive displays.
As we have seen throughout this book, standalone visuals like photographs and illustrations are promising ways to communicate science to the public – and they carry their fair share of misconceptions and complications. These promises – as well as challenges – are multiplied in infographics.
The word “infographic” comes from the phrase “information graphic.” Originally, the term referred to the production of graphics for print media such as newspapers and magazines. Today it refers to a unique multimodal genre that combines data visualizations (i.e., graphs such as lines, pies, bars, and pictographs), illustrations (such as icons and drawings), photographs, and small amounts of text. When designed for online use, infographics can also have interactive components. For example, putting the mouse cursor somewhere on the infographic may reveal a small pop-up window with additional information. Some infographics are also animated: bars in a bar chart may grow, colors may change, or characters may move. This is often achieved by using animated GIF files that display a sequence of static images in a repeating loop, which creates the illusion of motion.
Graphs – such as line graphs or bar graphs – convey numerical data. They are commonly used in life science communication as well as other communication contexts, such as when conveying stock market data, crime statistics, or real estate trends. The prevalence of these graphs doesn’t mean, as some may assume, that they are always easy to understand. Depending on design choices, some graphs will be able to shed light on important numerical data for public understanding of science, while others are likely to confuse or leave readers with a heightened conviction that science is an inaccessible enterprise.
Photographs are often considered an “easy” and accessible type of scientific visual. After all, they are commonplace in everyday life and not exclusive to scientific research. Everyone takes photographs and knows what photographs are. As long as one can physically see, one (so it is thought) can get what a photograph is about. Unfortunately, when it comes to life science photographs, much of this is misconception. This chapter explains why.
From photographs to micrographs, from the various types of graphs to fun, interactive visuals and games, there are many different forms in which science can be visualised. However, all of these forms of visualisation in the Life Sciences are susceptible to misunderstandings and misinformation. This accessible and concise book demonstrates the misconceptions surrounding the visuals used in popular life science communication. Richly illustrated in colour, this guide is packed with examples of commonly used visual types: photographs, micrographs, illustrations, graphs, interactive visuals, and infographics allowing visual creators to produce more effective visuals that aspire to being both attractive and informative for their target audience. It also encourages non-specialist readers to be more empowered and critical, to ask difficult questions, and to cultivate true engagement with science. This book is an invaluable resource for life scientists and science communicators, and anyone who creates visuals for public or non-specialist readers.
This chapter describes the basics of scientific figures. It provides tips for identifying different types of figures, such as experimental protocol figures, data figures, and summary figures. There is a description of ways to compare groups and of different types of variables. A short discussion of statistics is included, describing elements such as central tendency, dispersion, uncertainty, outliers, distributions, and statistical tests to assess differences. Following that is a short overview of a few of the more common graph types, such as bar graphs, boxplots, violin plots, and raincloud plots, describing the advantages that each provides. The end of the chapter is an “Understanding Graphs at a Glance” section which gives the reader a step-by-step outline for interpreting many of the graphs commonly used in neuroscience research, applicable independently of the methodology used to collect those data.
The spatial arrangement of objects in residential environments is a crucial indicator of occupant behavior, shedding light on the complex dynamics of their interaction with the interior. This study introduces an object-based graph method for decoding urban home interiors, examining the co-presence of objects to uncover domestic behavioral patterns through indoor imagery analysis. By integrating centrality metrics with objects in graphs, we gain deeper insights into household behaviors, which provide empirical evidence for future interior design.
The Digital Thread is a system that connects different phases of the product lifecycle and the related data across one or more companies in the supply chain. This work aims to develop a graph data model of the Digital Thread, in the context of the vision of polyglot persistence, that interconnects the different phases of the lifecycle and their corresponding data models, processes, and IT systems. This work proposes a Digital Thread Graph that integrates a Digital Model and a derived Digital Twin, using object and relation attributes for view creation and filtering while minimizing redundancy.
We study the position of the computable setting in the “common theory of locality” developed in [4, 5] for local problems on $\Delta $-regular trees, $\Delta \in \omega $. We show that such a problem admits a computable solution on every highly computable $\Delta $-regular forest if and only if it admits a Baire measurable solution on every Borel $\Delta $-regular forest. We also show that if such a problem admits a computable solution on every computable maximum degree $\Delta $ forest then it admits a continuous solution on every maximum degree $\Delta $ Borel graph with appropriate topological hypotheses, though the converse does not hold.
The clustered chromatic number of a class of graphs is the minimum integer
$k$
such that for some integer
$c$
every graph in the class is
$k$
-colourable with monochromatic components of size at most
$c$
. We determine the clustered chromatic number of any minor-closed class with bounded treedepth, and prove a best possible upper bound on the clustered chromatic number of any minor-closed class with bounded pathwidth. As a consequence, we determine the fractional clustered chromatic number of every minor-closed class.
It is standard in chemistry to represent a sequence of reactions by a single overall reaction, often called a complex reaction in contrast to an elementary reaction. Photosynthesis
$6 \text{CO}_2+6 \text{H}_2\text{O} \longrightarrow \text{C}_6\text{H}_{12}\text{O}_6 + 6 \text{O}_2$
is an example of such complex reaction. We introduce a mathematical operation that corresponds to summing two chemical reactions. Specifically, we define an associative and non-communicative operation on the product space
${\mathbb{N}}_0^n\times {\mathbb{N}}_0^n$
(representing the reactant and the product of a chemical reaction, respectively). The operation models the overall effect of two reactions happening in succession, one after the other. We study the algebraic properties of the operation and apply the results to stochastic reaction networks (RNs), in particular to reachability of states, and to reduction of RNs.
Given a fixed graph H that embeds in a surface
$\Sigma$
, what is the maximum number of copies of H in an n-vertex graph G that embeds in
$\Sigma$
? We show that the answer is
$\Theta(n^{f(H)})$
, where f(H) is a graph invariant called the ‘flap-number’ of H, which is independent of
$\Sigma$
. This simultaneously answers two open problems posed by Eppstein ((1993) J. Graph Theory17(3) 409–416.). The same proof also answers the question for minor-closed classes. That is, if H is a
$K_{3,t}$
minor-free graph, then the maximum number of copies of H in an n-vertex
$K_{3,t}$
minor-free graph G is
$\Theta(n^{f'(H)})$
, where f′(H) is a graph invariant closely related to the flap-number of H. Finally, when H is a complete graph we give more precise answers.
A graph G is called a
$(P_{\geq n},k)$
-factor-critical covered graph if for any
$Q\subseteq V(G)$
with
$|Q|=k$
and any
$e\in E(G-Q)$
,
$G-Q$
has a
$P_{\geq n}$
-factor covering e. We demonstrate that (i) a
$(k+1)$
-connected graph G with at least
$k+3$
vertices is a
$(P_{\geq 3},k)$
-factor-critical covered graph if its toughness
$t(G)>{(2+k)}/{3}$
; (ii) a
$(k+2)$
-connected graph G is a
$(P_{\geq 3},k)$
-factor-critical covered graph if its isolated toughness
$I(G)>{(5+k)}/{3}$
. Furthermore, we show that the conditions on
$t(G)$
and
$I(G)$
are sharp.
This chapter provides an overview of mixed-integer programming (MIP) modeling and solution methods.In Section 2.1, we present some preliminary concepts on optimization and mixed-integer programming. In Section 2.2, we discuss how binary variables can be used to model features commonly found in optimization problems. In Section 2.3, we present some basic MIP problems and models. Finally, in Section 2.4, we overview the basic approaches to solving MIP models and present some concepts regarding formulation tightness and decomposition methods.Finally, we discuss software tools for modeling and solving MIP models in Section 2.5.
The chapter presents and discusses the graph conception of set. According to the graph conception, sets are things depicted by graphs of a certain sort. The chapter begins by presenting four set theories, due to Aczel, which are formulated by using the notion of a graph. The graph conception is then introduced, and a historical excursion into forerunners of the conception is also given. The chapter continues by clarifying the relationship between the conception and the four theories described by Aczel. It concludes by discussing four objections to the graph conception: the objection that set theories based on graphs do not introduce new isomorphism types; the objection that the graph conception does not provide us with an intuitive model for the set theory it sanctions; the objection that the graph conception cannot naturally allow for Urelemente; and the objection that a set theory based on the graph conception cannot provide an autonomous foundation for mathematics. It is argued that whilst the first two objections fail, the remaining two retain their force.
We give the crossing number of the join product $W_{4}+D_{n}$, where $W_{4}$ is the wheel on five vertices and $D_{n}$ consists of $n$ isolated vertices. The proof is based on calculating the minimum number of crossings between two different subgraphs from the set of subgraphs which do not cross the edges of the graph $W_{4}$ and from the set of subgraphs which cross the edges of $W_{4}$ exactly once.