Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication

Nowadays color in scientific visualizations is standard and extensively used to group, highlight or delineate different parts of data in visualizations. The rainbow color map (also known as jet color map) is famous for its appealing use of the full visual spectrum with impressive changes in chroma and luminance. Besides attracting attention, science has for decades criticized the rainbow color map for its non-linear and erratic change of hue and luminance along the data variation. The missed uniformity causes a misrepresentation of data values and flaws in science communication. The rainbow color map is scientifically incorrect and hardly decodable for a considerable number of people due to color vision deficiency (CVD) or other vision impairments. Here we aim to raise awareness of how widely used the rainbow color map still is in hydrology. To this end, we perform a paper survey scanning for color issues in around 1000 scientific publications in three different journals including papers published between 2005 and 2020. In this survey, depending on the journal, 16 %–24 % of the publications have a rainbow color map and around the same ratio of papers (18 %–29 %) uses red–green elements often in a way that color is the only possibility to decode the visualized groups of data. Given these shares, there is a 99.6 % chance to pick at least one visual problematic publication in 10 randomly chosen papers from our survey. To overcome the use of the rainbow color maps in science, we propose some tools and techniques focusing on improvement of typical visualization types in hydrological science. We give guidance on how to avoid, improve and trust color in a proper and scientific way. Finally, we outline an approach how the rainbow color map flaws should be communicated across different status groups in science.

Rainbow colors distort and mislead research in hydrology – guidance for better visualizations and science communication

Nowadays color in scientific visualizations is standard and extensively used to group, highlight or delineate different parts of data in visualizations. The rainbow color map (also known as jet color map) is famous for its appealing use of the full visual spectrum with impressive changes in chroma and luminance. Beside attracting attention, science has for decades criticized the rainbow color map for its non-linear and erratic change of hue and luminance along the data variation. The missed uniformity causes a misrepresentation of data values and flaws in science communication. The rainbow color map is scientifically incorrect and hardly decodable for a considerable number of people due to color-vision deficiency (CVD) or other vision impairments. Here we aim to raise awareness how widely used the rainbow color maps still is in hydrology. To this end we perform a paper survey scanning for color issues in around 1000 scientific publications in three different journals including papers published between 2005 and 2020. In this survey, depending on the journal, 16–24 % of the publications have a rainbow color map and around the same ratio of papers (18–29 %) use red-green elements often in a way that color is the only possibility to decode the visualized groups of data. Given these shares, there is a 99.6 % chance to pick at least one visual problematic publication in 10 randomly chosen papers from our survey. To overcome the use of the rainbow color maps in science, we propose some tools and techniques focusing on improvement of typical visualization types in hydrological science. Consequently, color should be used with more care to highlight most important aspects of a visualization and the identification of correct data types such as categorical or sequential data is essential to pick appropriate color maps. We give guidance how to avoid, improve and trust and color in a proper and scientific way. Finally, we sketch a way to improve the communication of rainbow flaws between different status groups in science, publishers, and the media.

Rainbow connection number of Cm o Pn and Cm o Cn

Let <em>G </em>= (<em>V</em>(<em>G</em>),<em>E</em>(<em>G</em>)) be a nontrivial connected graph. A rainbow path is a path which is each edge colored with different color. A rainbow coloring is a coloring which any two vertices should be joined by at least one rainbow path. For two different vertices, <em>u,v</em> in <em>G</em>, a geodesic path of <em>u-v</em> is the shortest rainbow path of <em>u-v</em>. A strong rainbow coloring is a coloring which any two vertices joined by at least one rainbow geodesic. A rainbow connection number of a graph, denoted by <em>rc</em>(<em>G</em>), is the smallest number of color required for graph <em>G</em> to be said as rainbow connected. The strong rainbow color number, denoted by <em>src</em>(<em>G</em>), is the least number of color which is needed to color every geodesic path in the graph <em>G</em> to be rainbow. In this paper, we will determine the rainbow connection and strong rainbow connection for Corona Graph <em>Cm</em> o <em>Pn</em>, and <em>Cm</em> o <em>Cn</em>.

Face recognition methods analysis

Face Recognition is one of the most important issues in Image processing tasks. It is important because it uses for various purposes in real world such as Criminal detection or for detecting fraud in passport and visa check in airports. Face book is a nice example of Face recognition application, when it sends notification to one user’s friends who are recognized by their images that user uploaded in face book page. To solve Face Recognition problem different methods are introduced such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Hidden Markov Models (HMM) which are explained and analyzed. Also algorithms like; Eigen face, Fisher face and Local Binary Pattern Histogram (LBPH) which are simplest and most accurate methods are implemented in this project for AT&T dataset to recognize the most similar face to other faces in this data set. To this end these algorithms are explained and advantages and disadvantages of each one are analyzed as well. Consequently, the best method is selected with comparison between the results of face reconstruction by Engine face, Fisher face and Local binary pattern histogram methods. In this project Eigen face method has best result. It should be noted that for implementing face recognition algorithms color map methods are used to distinguish the facial features more precisely. In this work Rainbow color map in Eigen Face algorithm and HSV color map in Fisher Face algorithm are utilized and results shows that HSV color map is more accurate than rainbow color map.

Whole-rainbow-color organic solid fluorophores from subtle modification of thiazolo[5,4-b]thieno[3,2-e]pyridines (TTPs)

A novel whole-rainbow-color (403 ≤ λmax ≤ 655 nm) organic solid fluorophore system was designed and synthesised.