Resource post: Tools for better colors

Looking through my list of bookmarks, it’s apparent that something I like to read about (other than food) is design. The reason? I believe that well-made, attractive visuals are worth the effort because they can help make a point clearer and more memorable. In a sea of plots and diagrams (or in a room full of posters), you want people to notice, understand, and remember yours. One aspect of this process is finding the right color scheme to get your message across, which can involve several questions: How much (if any) color is necessary? What do I want these colors to convey? Will viewers be able to distinguish every color? When answering these questions, I've found a couple of links to be useful.

Getting inspired/informed: 

• Dribbble: Although (or perhaps because) this design gallery isn't science-centric, browsing it can trigger new ideas for how to use color and other design elements. 
• Color Universal Design: How to make figures that are colorblind-friendly, with examples. 
• The subtleties of color: An introduction to using color in data visualization. 

Tools:  

• Adobe Kuler: Generate color schemes based on color rules or images, browse schemes created by other users, or put together your own. This is nifty for seeing how different colors will look when they are side by side. 
  
• Colorbrewer: A popular tool among scientists in part because it offers different color schemes for different data types. 
• Colorzilla: A browser plug-in that makes it easy to pick and analyze color from webpages, for when you see a color that you really want to use. 

Do you have any favorite tools or approaches for using color? Or is it something that you'd rather not emphasize?

Being (and keeping) a collaborator

Recently, a paper of ours was accepted for publication (stay tuned for more about that!). It grew out of a long, trans-atlantic collaboration. It was the first collaboration that I was part of, and I was "spoiled" by the experience because of how productive and fun it was (and continues to be). I remember the first time that my side of project yielded a useful clue. Much to my surprise and delight, our collaborators took that clue to their lab and followed up on it right away. 

Collaborations can be awesome. They're also becoming increasingly prevalent as connections grow between different fields. There are lots of potential benefits for everyone involved: you get to learn about techniques outside your own specialization, your can develop a unique new perspective, and you may find yourself having some friends to visit in faraway places. 

Good memories of great science-friends in Odense, Denmark.

However, I've noticed since then, through observation and experience, that not all collaborations reach their best potential. So I have been thinking about what qualities are possessed by a good collaborator so that I know what to look for and what I should try to be. 

1. Finding a problem that you can tackle together. It goes without saying, but it's key to pick a problem that all participants care about and can actually work on. Bonus points if it's a problem that can only be addressed by combining the complementary skills of everyone involved. (Otherwise, are you collaborating just for show?)
2. Reliability and communication. When you and your collaborator work in different offices (or countries), it can be easy to fall off each other's radar and let the project fizzle out. To avoid this outcome, demonstrate that you're serious about the project (even if you don't have spectacular results yet) and that you want to interact with them occasionally. 
3. Openness to feedback. A big part of collaboration is giving each other feedback. When the person giving you feedback is not in your field, it may feel like they're impinging on your space. When this happens, pause for a minute - they might be giving you a fresh, valid perspective. Or, they might just need you to better clarify/justify what you're doing, which can be a preview of how an outside audience might respond. 
4. Understanding capabilities and limitations. Everyone has some things (experiments, simulations, etc) that they can do routinely, other things that take more time/money/pain, and some things that would be desirable but are unfeasible. These things may be obvious to someone in your field, but you and your collaborator may need to discuss them to ensure that you both have a realistic picture of what the other can do. 

Have you been, or do you want to be, part of a collaboration? What did you get (or want to get) from the experience?