Color is a powerful tool. The right use of color can quickly highlight details in your data, clearly identify differences in groups of data, and illustrate changing trends. As sites like FlowingData show, many artists use color and data effectively to create works of art that also carry information. Use of color can also elicit emotion; advertising companies have known about the connection between our emotions and color for a long time and have effectively used color to elicit specific responses to their products or brands (Hupka et al. 1997;Soriano & Valenzuela 2009). Websites, including library websites, have also used color to effectively reach audiences (Cyr et al. 2010).But using color can have unintended consequences. Emotions connected to colors are not universal. This means that different cultures might associate different emotions with a color you choose. The use of multiple colors in data visualizations might cause a variety of problems, from inaccessibility for users with different color impairments to difficulty in accurately interpreting the data. The now notorious redgreen schema (also known as the rainbow schema) often used in weather forecasts prevents those with deuteranopia from clearly understanding the visualization, making it nearly impossible to interpret (Borland & Taylor li 2007;Hall 2017). Color schemes with too many classes lead to illegible visualizations, or worse, visualizations that are easily misinterpreted.Ultimately, this means that color choices are important. Choosing to add color to your visualization gives you power to make your data stand out. But it's important to remember that your color choices may influence the decisions that are made based on your data; if decision makers can't accurately read your visualization, they may make misinformed choices. No one wants that. Luckily, there are tools that make choosing colors relatively painless. In this column, I will focus on a tool, ColorBrewer 2.0, that I have found particularly useful. I'll start by talking about why color is an important consideration in data visualization, take a quick look at the ColorBrewer tool, and then dive into some basic color guidelines to help you with your next visualization. Overview of ColorBrewer and ColorColorBrewer 2.0 is a tool created by The Pennsylvania State University to help folks choose effective colors for data maps. While the examples displayed in the tool focus on cartography, I've found their color schemas work wonders with other data visualizations (Figure 1). The creators of the code, Cynthia Brewer and Mark Harrower, have also made it freely available, letting others create similar tools and expand ColorBrewer's capabilities.

As science and technology libraries continue to evolve, specialized research support services are developed and offered at academic institutions or research organizations. Making sense of this changing landscape and determining the best programs for an institution can be a daunting task, especially for early-career librarians. This article aims to provide an overview of various small to medium size non-traditional or specialized research support services in academic and special libraries serving Science, Technology, Engineering and Mathematics (STEM) disciplines. A systematic search of five databases was conducted for articles that described the development and implementation of research support services. Non-traditional or specialized research support services identified in this scoping review fall in the following areas: bibliometrics/altmetrics, data management services, geographic information systems, patents, and systematic reviews. The paper provides a detailed foundation for novice and experienced STEM librarians to offer innovative library services or enhance existing research support services.

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