Equitable Accessibility

About 16% of the population of the world currently experiences significant disability. The figures below speak to the specifics.

The way in which we perceive and interact with the world is like a multi-modal modal dataset, sound, vision, touch, movement, taste, smell, etc. For researchers with disabilities, their dataset may have noise in a given mode, or missing data in a given mode, or a mode entirely missing. However, when we do multi-modal data analysis, removing certain modes and deeply exploring others often reveals totally new insights into the data – ones it is difficult to get looking at all modes. There are numerous studies into this topic, including:

• Abdolrahmani, A., Storer, K. M., Roy, A. R. M., Kuber, R., & Branham, S. M. (2020). Blind leading the sighted: drawing design insights from blind users towards more productivity-oriented voice interfaces. ACM Transactions on Accessible Computing (TACCESS), 12(4), 1-35.
• Dobel, C., Nestler-Collatz, B., Guntinas-Lichius, O., Schweinberger, S. R., & Zäske, R. (2020). Deaf signers outperform hearing non-signers in recognizing happy facial expressions. Psychological research, 84, 1485-1494.
• Han, C., Mitra, P., & Billah, S. M. (2024, May). Uncovering Human Traits in Determining Real and Spoofed Audio: Insights from Blind and Sighted Individuals. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-14).
• Pang, W., Xing, H., Zhang, L., Shu, H., & Zhang, Y. (2020). Superiority of blind over sighted listeners in voice recognition. The Journal of the Acoustical Society of America, 148(2), EL208-EL213.
• Grant, A., & Kara, H. (2021). Considering the Autistic advantage in qualitative research: the strengths of Autistic researchers. Contemporary Social Science, 16(5), 589-603.
• Taylor, H., & Vestergaard, M. D. (2022). Developmental dyslexia: disorder or specialization in exploration?. Frontiers in psychology, 13, 889245.
• Schippers, L. M., Horstman, L. I., Velde, H. V. D., Pereira, R. R., Zinkstok, J., Mostert, J. C., … & Hoogman, M. (2022). A qualitative and quantitative study of self-reported positive characteristics of individuals with ADHD. Frontiers in Psychiatry, 13, 922788.
• Bury, S. M., Hedley, D., Uljarević, M., & Gal, E. (2020). The autism advantage at work: A critical and systematic review of current evidence. Research in Developmental Disabilities, 105, 103750.
• Hatak, I., Chang, M., Harms, R., & Wiklund, J. (2021). ADHD symptoms, entrepreneurial passion, and entrepreneurial performance. Small business economics, 57, 1693-1713.

We want to include everyone in data science and beyond!

File formats were previously presented on the Data Cleaning and Standardization page. To recall, these are the main questions to consider about your file formats:

• Open or proprietary?
• Common or low use?
• Supported by many software platforms or only one?
• Freestanding or reliant on embedded programs, files, or scripts?
• Lossless or lossy?

Common file formats that are far more likely to have screen reader integration and other support systems. The more software platforms support a file type, the more likely at least one form of access can zoom on visual data, change the font for text data, etc. High-use, open data formats are more likely to be supported by state-of-the-art tools from accommodations research.

We exercise the power to name a lot. To list a few, we name:

• Projects
• Papers
• Headings
• Functions
• Variables
• Files

The names we use in our data, code, and associated artifacts can invite people in or push them away.

A particularly prolific example you may heard of is the prevalence of “master/slave” in code, data files, documentation, and algorithms. It’s used in numerous contexts including: particle in cell flow computation, parallel runtime execution models, periodic boundary conditions, file systems, hard drive technology, grid computing, and graph traversal. •Public discourse advocating its replacement dates back to mid-1990’s.

What are some alternatives to commonly used phrases to promote inclusive language?

Problem words	Suggested Alternative(s)
minority	traditionally underserved or underrepresented community historically excluded
black list (blacklist, black-list)	deny/denied list
white list (whitelist, white-list)	allow list
blackbox	closed system opaque glassbox frosted glass box mystery box unknown origin obfuscated
whitebox	open box open system glass box clear box
normal, healthy (to denote people without disabilities)	nondisabled person sighted person hearing person person without disabilities neurotypical person desired state
master/slave (relationship)	hierarchical parent-child primary-replica primary-secondary active-passive leader-follower origin-clone local-remote
dummy value	placeholder value sample value est data/test value pseudo value
man hours	person hours engineer hours hours of effort work hours labor hours

Reading large blocks of uninterrupted test is overwhelming for everyone, but is particularly overwhelming for neurodiverse researchers. Formatting can be particularly important for this subset of researchers. Some principles to follow are:

• Separate ideas with whitespace
• Use bullets
• Bold rather than italicize important words
• Be consistent

There are numerous naming standards in existence. In particular, there is a common set of machine-interoperable naming conventions in use:

Common Machine-interoperable naming conventions:

• camelCase: First word lower case, first letter of each subsequent word upper case.
• PascalCase: First letter of every word upper case.
• snake_case: All lower case, words separated by underscore.
• SCREAMING_SNAKE_CASE: All upper case, words separated by underscore

Of these four, snake_case and SCREAMING_SNAKE_CASE are the most accessible for researchers with dyslexia, low vision, or using screen readers.

SCREAMING_SNAKE_CASE, however, can be overwhelming and overstimulating for neurodiverse researchers.

Our recommendation? snake_case!

Text Components

Use a sans serif font of at least 12-14 pt to make your figure easier to read for people with dyslexia. Larger font is also important for researchers with low vision! You can do this in matplotlib:

plt.rcParams.update({'font.size': 12})

Note that the default font is already sans serif in matplotlib.

An additional low vision recommendation is set your resolution high enough for zooming in to still be clear. Which of these images is more clear to you?

You can change the default in matplotlib as well one of these two ways:

plt.savefig(f'data_image.png', dpi=300)
plt.savefig(f'data_image.svg')

Color

There are multiple types of color vision deficiency (CVD):

• Protanopia – Reduces sensitivity to red light
• Deuteranopia – Reduces sensitivity to green light
• Tritanopia – Reduces sensitivity to blue light
• Achromatopsia -A total loss or reduction of all three colors

It is important to design data visualizations, presentations, etc., with color deficiency in mind. For example:

The use of symbols and patterns can make otherwise inaccessible images available to those with CVD. The two plots below show how small changes in colors and line patterns can make data significantly more accessible.

This is a particularly pervasive problem in generated images for data using target boxes. While the standard vision view of the plots below may be accessible to most, those with deuteranopia will not be able to see the target boxes at all.

A better standard is to employ a perceptually uniform colormap. According to matplotlib‘s documentation, “For many applications, a perceptually uniform colormap is the best choice; i.e. a colormap in which equal steps in data are perceived as equal steps in the color space. Researchers have found that the human brain perceives changes in the lightness parameter as changes in the data much better than, for example, changes in hue. Therefore, colormaps which have monotonically increasing lightness through the colormap will be better interpreted by the viewer.”

While the above figure displays several CVD-friendly options, we recommend cividis. You can set the default tableau to be CVD friendly and the default colormap to cividis using the code below.

plt.style.use('tableau-colorblind10')
plt.rcParams['image.cmap'] = 'cividis'

Large blocks of color can be painful for data users with visual hypersensitivities or chronic migraines. An example can be seen in the collapsible menu below, but please use caution if you have visual hypersensitivities.

Alternative Text

Have you ever attempted to load a webpage with lots of images on your phone in a bad service area? When the images do not fully load, they will frequently show a “holder” location with a small icon and some words. Which of these images would be more helpful to you?

Alternative text (alt text) is a written alternative to an image, video, audio captions, link, etc. It is normally a short description of the content or information associated with that piece of media. Alt text is particularly helpful for screen readers, those with low internet bandwidth, or mobile device users.

When writing alt text, consider the following:

• Convey the content
• Mention color (if it is important to understanding the image)
• Share humor
• Transcribe text

Have you ever gotten code from another researcher that looks like this?

class thisdoessomething:
    def func(self, x, y):
        return y**2 + y*x + x**3
    def next(self, y, x, h):
        y_j = y + h*self.func(x, y)
        x_j = x + h
        return y_j, x_j

This is not very accessible. If you know the formula for Euler’s Method, you may recognize what this is doing, but how could it be made more approachable?

class EULERS_METHOD:
    def derivative_function(self, x, y):
        return y**2 + y*x + x**3
    def numerical_approximation(self,
        current_function_value,
        current_step_value, step_size):
        approximate_value = current_function_value + step_size *
                            self.derivative_function(current_step_value,
                                                     current_function_value)
        next_step_value = current_step_value + step_size
        return approximate_value, next_step_value

This code is much more explicit! Not only do we have a clear name for the class and its purpose, we also now know exactly what each variable is intended to do.