Small data variations can indeed be more clearly visible in some parts along an unscientific colour map but are, in turn, made invisible in other parts (e.g., greenish vs. reddish parts in jet). It is exactly these unequal colour contrasts along the colour map that perceptually distort the underlying data (see supplementary note of Crameri et al., 2020, Nature Comms. for various examples).
No. Using unscientific colour maps is factually a blind interpretation of the data, and not - as it should be - a simple representation of data.
No. There are several Scientific colour maps like batlow that contain variations of multiple colours and therefore feature a multitude of discernible colours.
No. While there are of course some that do that (for good reason), there are also perceptually uniform colour maps like the Scientific colour maps that contain more than just one or two hues.
To make it easier to read out individual data values, a Scientific colour map in a discrete type (Crameri et al., 2020, Nature Comms., Section 5.2.2) should be used, or alternatively separate indicators (like superposed contour lines) to interpret the data.
Even though some are (e.g., buda and imola), it is more effective to use more subtle - and indeed more natural - colours to represent data (Cairo 2012, New Riders). Glary and somewhat alarmistic pure colours can be a distraction and should be used only for the most important aspect of the graphics.
The colour maps listed in Crameri et al. (2020, Nature Comms.), Box 2, are science-proof. To recognise if a colour map is unscientific, follow the guideline given in Crameri et al. (2020, Nature Comms.), Section 3.7.
Using science-proof colour maps is straightforward. Serious visualisation software provide scientifically-derived colour maps as built-in options (or even as default). Otherwise, the Scientific colour maps come with clear and easy-to-follow instructions on how to use them (see www.fabiocrameri.ch/colourmaps).
The Scientific colour maps (www.fabiocrameri.ch/colourmaps) are provided in a myriad of formats and can be read in with any good visualisation program. In addition, the Scientific colour maps are included in many additional packages (e.g., scico for R or crameri for MatLab) to be used conveniently within certain software. Finally, it is always helpful to remind software developers to take visualisation seriously, if they do not.
We suggest the following: "The current colour map implemented in this study distorts the data and is not accessible to people with colour-vision deficiencies. I encourage you to use one of the freely available Scientific colour maps provided at www.fabiocrameri.ch/colourmaps or see Crameri et al. (2020, Nature Comms.) for more details."