IAmSciComm | Sheeva Azma Profile picture
Takeover by @SheevaAzma, freelance science writer and founder of @FancyComma (https://t.co/uLL36ZJv4A). A @SciCrastina rocur, managed by @prksrng.
Sara Stojković Profile picture (*˘︶˘*).。✧*。 Profile picture 2 added to My Authors
28 Feb
Well, it's time for me to close out my week hosting @iamscicomm. Thanks for all of your questions and comments! I had so much fun chatting with folks from different fields and walks of life!

I'll leave you with my 5 general guidelines for creating more effective dataviz.
1. Show the Data.
Your reader can only grasp your point, argument, or story if they see the data. This doesn’t mean that all the data must be shown, but it does mean that you should highlight the values that are important to your argument.
2. Reduce the Clutter.
The use of unnecessary visual elements distracts your reader from the central data and clutters the page. Reduce/eliminate heavy tick marks, gridlines, textured gradients, too much text and labels. Focus on the data.
Read 7 tweets
27 Feb
I close out my week hosting the @iamscicomm account by sharing just a select few examples of #dataviz that don't follow the rules or templates or tried-and-true approaches. But they are beautiful and engaging and enlightening.
As you go forth and create your visualizations, continue to explore. Draw inspiration from all around you and from the amazing work these and other creators are generating.
Be sure to check out the amazing work from @NadiehBremer like this one on words translated into English. | visualcinnamon.com/portfolio/beau…

Her site: visualcinnamon.com
Read 6 tweets
27 Feb
Earlier in the week, I promised I'd put together a list of my favorite #dataviz tools. 🛠

These are by no means the only tools out there, but these are the ones I like and use regularly.

(Sigh, another thread....I know, I know....) 🧵
1. Excel. Yep, I use Excel for much of my #dataviz work. Excel is based on bars, lines, and dots, all in an X-Y space. Think of it that way, and you can create lots of stuff.

You can learn more in my step-by-step ebook: policyviz.com/product/a-guid…
2. #Rstats. If you code in SAS, Stata, SPSS, or other statistical packages, you won't have a hard time picking up #Rstats. Some great resources:
-r4ds.had.co.nz from @hadleywickham
-cedricscherer.com from @CedScherer
-r-graph-gallery.com from @R_Graph_Gallery
Read 11 tweets
27 Feb
I've written a bunch on how to create better data-rich tables. So prepare for a longish thread here. 🧵

Here's a 10-step summary of my "Ten Guidelines for Better Tables" in #BetterDataVisualizations and @benefitcost.
Before we get to the ten guidelines, recognize that just like in graphs and charts, there are a lot of pieces to tables. And, just like graphs and charts, we can control the look and design of all of these elements.
Rule 1. Offset the Heads from the Body
Make your column titles clear. Try using boldface type or lines to offset them from the numbers and text in the body of the table.
Read 13 tweets
26 Feb
Perhaps the most common #dataviz for qualitative data is the Word Cloud. ⛅️
In a word cloud, the size of each word is adjusted according to its frequency in a passage of text. Image
But here's the thing: The font, alignment, and color of the words in the word cloud can affect our perception of the data. Furthermore, it's hard to see the most important *concepts* in the text.
So, take the work @MartiHearst at Berkeley, who suggests breaking up the text into semantic groups before making the word cloud. | ischool.berkeley.edu/news/2019/word…
Read 4 tweets
26 Feb
We kick off today's subject of #dataviz for part-to-whole relationships and qualitative data with some of my favorite fun pie charts. I did not originally create these, and the original creators are lost to history.
Read 6 tweets
25 Feb
Flow maps are another kind of way to visualize your data. Maybe the most famous flow map is this one from Charles Joseph Minard in 1869. Tufte always touts this one as being the "best statistical chart ever made".

A quick 🧵 on the Minard map. Image
The famous Minard map shows 6 data values in a single view:
1. Number of troops (line thickness)
2. Distance traveled (scale)
3. Temperature (line at bottom)
4. Time (line at bottom)
5. Direction of travel (color)
6. Geography (cities, etc.)
But Tufte left out the fact that the Minard Napolean map was only one panel in a full spread. It also included the lesser-known map of Hannibal’s 218 BC march through the Alps to Rome. (This image from Ecole nationale des ponts et chaussées, which I include in my book.) Image
Read 4 tweets
25 Feb
There are other ways to visualize these election data.
-A Demers cartogram uses differently-sized squares.
-A tile grid map uses a single square for each geographic unit, be it states or countries. ImageImage
Again, you can use Legos! Image
Here are some other countries/areas that have been built using the tile grid map format. Here's London, from data.london.gov.uk/dataset/averag… Image
Read 4 tweets
24 Feb
So far today, we've talked about graphs that show distributions and uncertainty that in some ways *summarize* the data. But what about showing specific data points in the data set? There are a few:
-Strip charts 🥓
-Beeswarm charts 🐝
-Wheatplots 🌾
-Raincloud plots 🌧
In the strip plot, the data points are plotted along a single horizontal or vertical axis. You might get some overlapping here, but you can use color transparency to show the individual points, if that's important. Here are a few from the NYT.
The thing about the strip chart (sometimes called the stripe chart) is that you can use dots, points, or lines. And sometimes the important thing is to just your reader know that there are lots of points in some part of the distribution.
Read 7 tweets
24 Feb
There are (at least) two graphs that can be used to show distributions in your data that don't show specific percentile values.
-The violin chart 🎻uses kernel density estimates to generate a shape of the entire distribution. Here's one I made of earnings in industries.
The ridgeline plot is a series of histograms or density plots shown for different groups aligned along the same horizontal axis and presented with a slight overlap along the vertical axis. It's kind of a 'small multiples' histogram. This one from @hrbrmstr.
If you're really interested in the ridgeline plot, check out this awesome story by @ChristiansenJen from @sciam who dug up the original plot. | blogs.scientificamerican.com/sa-visual/pop-…
Read 4 tweets
24 Feb
For those who are interested, here's a list of some papers relating to uncertainty in data visualization:
S. Belia, F. Fidler, J. Williams, and G. Cumming, “Researchers misunderstand confidence intervals and standard error bars.”, Psychological methods, vol. 10, no. 4, p. 389, 2005.
Brodlie K, Osorio RA, Lopes A. A review of uncertainty in data visualization. Expanding the frontiers of visual analytics and visualization. 2012:81-109.
Read 5 tweets
24 Feb
One of the most common ways to visualize the distribution in your data is the histogram. It's basically a bar chart where the data are divided into bins. I like this one from @JustinWolfers about finishing times in the NY Marathon. | nytimes.com/2014/04/23/ups…
Again, while I think many people don't quite understand concepts like variance and percentiles, the histogram resembles a bar chart, so it may be a graph type folks can easily understand. You can also find histograms in Google when you look for restaurants or stores.
I thought this was a nice blog post on the difference between histograms and bar charts that appeared on the @storywithdata blog. | storytellingwithdata.com/blog/2021/1/28…
Read 4 tweets
23 Feb
Let's do one more: the connected scatterplot. The CS shows two time series simultaneously—one each along horizontal and vertical axes—and are connected by a line to show relationships of the points over time. It's a great possible alternative to the dreaded dual axis chart. Image
(The connected scatterplot in the previous tweet was from @hfairfield at @NYTScience.)
In general, I find that the connected scatterplot is 🎇awesome🎇 about 2/10 times--the rest of the time, I either get a straight line (e.g., spending and participation) or a hairball mess. But, there are lovely cases where it just works out.
Read 5 tweets
23 Feb
One variation on the area chart is the streamgraph. Bear with me here, as it's kind of a weird looking graph. A streamgraph stacks the data series, but the central horizontal axis does not necessarily signal a zero value. Instead, data can be positive on both sides of the axis. Image
The previous example from @Harry_Stevens when he was at the @HindustanTimes. | hindustantimes.com/static/padmas-…
This is a recent lovely streamgraph from @CedScherer, who has some awesome #RStats material on his website, cedricscherer.com. Image
Read 4 tweets
23 Feb
Another way to consider plotting changes over time is to not just use a single line in the entire graph and break things up over multiple graphs. There are a couple of options here.
1. Sparklines. Named by Edward Tufte, sparkles are “small intense, simple, word-sized graphics with typographic resolution.” They are typically embedded within tables, which can help make tables easier to read. Here's a basic example: grapecity.com/blogs/visualiz… Image
2. Cycle graphs. Cycle graphs typically compare small units of time, such as weeks or months, across a multiyear
time frame. They were introduced by Bell Labs in a 29182 paper. Here's an example from @kennelliott in @PostGraphics. Image
Read 4 tweets
23 Feb
We start with the LINE chart. Why? Because, just like the bar chart, it's familiar and easy to create. As simple as they are, there are a number of considerations to take into account, some of which are aesthetic, and some of which are substantive.
1. There is no limit to the number of lines you plot. This @FiveThirtyEight chart has 3,282 lines, but it's still easy to read, right? The key is not to worry about the sheer amount of data, but instead about the purpose of the graph and how you can direct your reader's focus. Image
2. You don't need to start the vertical axis at zero. There is still some debate about this, but the basic rule-of-thumb is that the axis dimensions in a line chart depend on the data and your communication goal.
Read 9 tweets
18 Feb
With full bias I'm going start with me 😅
I am the host of @RootofSciPod. Where I interview various #AfricansInSTEM across the globe to talk about their science and find out what got them into #STEM

Have a listen to the intro buzzsprout.com/809081/2632615…
This podcast series aims to create visible role models for the younger generation and guests have shared some great stories. One of the most memorable stories was with @TapokaM: buzzsprout.com/809081/4312232…

But there a 61 other episodes for you listen to here
linktr.ee/RootofScienceP…
I'm also part of the amazing team of @ViSTEM_Africa (I'll talk more about their work in detail later) and I'm responsible for the blog section.
Website:visibilitystemafrica.com
Read 5 tweets
18 Feb
Hello all its day 4! The most exciting day of the week 😁. Yesterday session was a brief one talking about #scicomm in Africa

Today we have ALOT to get through I'm going to be speaking about some awesome #AfricansInSTEM who are PHENOMENAL #scicommers!

Im soo EXCITED !
Do you know #AfricansInSTEM #scicommers?
If so please comment on this thread and tag also share what they do
Read 24 tweets
17 Feb
To talk about #scicomm in Africa I need to bring it back to me.
I only found out about this field late 2019 in the end of MSc year right here on Twitter= social media.
Similar to many of you in this poll.
1/n
Despite the recent dev in scientific output from Africa public understanding of science researchers in many parts of the continent remain low. This has been so obvious during this pandemic with mass misinformation flying all over social media
2/n
an opinion piece by Karikarl 2016 links this to the following:
1. Lack of awareness
2. Literacy rates
3. Multiplicity of language(this is so NB!!)
Yesterday I reminded u Africa has 54 countries with different languages. Which can make #scicomm difficult but not impossible.
3/n
Read 6 tweets
16 Feb
I asked which field you think contributes the most in terms of scientific research output by #AfricansInSTEM?
Reveal time 😁
This poll is right it's actually a close link btwn life sciences and earth & Env science!
I hope you didn't cheat 👀😅

1/n
This comes from a report by .The Next Generation of Scientists in Africa(2018) of a 4year study. Authors surveyed 5,700 African researchers 2016- 2017 & analysed papers listed in Web of Science that had African authors & were published btwn 2005- 2016.
Here's the list.
2/n Image
The question is why these topics?
Well @Aliens68 pointed out something important :FUNDING

big grants tend to be in fields favoured by foreign funders who favor topics such as agriculture and health sciences.
Here's a visual of some major funders of #AfricansInSTEM.
3/n Image
Read 4 tweets
16 Feb
Let's talk about scientific & #AfricansInSTEM.
Quick disclaimer: this info I report is from research from books, sites etc. I don't claim to know it all. So if I am wrong I am very willing to correct my mistakes & learn. Let's converse. That's exactly what today is about
1/n
Here's some stats for you. According to an article by Elsiver (bitly.com) Africa accounts for <1 percent% of global research output. Despite having 16.72% of global population.

I love this visual made by @Tasia1409 which gives us a visual understanding.

2/n Image
When some people think of Africa, they think it's a monolith. There are 54 countries. Let's see where this 1% comes from. In a @nature country research outputs report from
1 De'19 - 30 Nov'20 shows South Africa is leading. Here's the top 20.
🔗 :shorturl.at/hjtHK
3/n Image
Read 5 tweets