Monthly Archives: March 2014

Buzzfeed’s infographic review of Beautiful Science

The British Library is running an exhibit entitled “Beautiful Science: Picturing Data, Inspiring Insight” from 20th February to 26th May 2014.

BL and St Panc by Jim Linwood

Picture: The British Library and St Pancras by Jim Linwood, Flickr Creative Commons

The exhibition explores how scientific stories are told by turning numbers into pictures – the story of infographics.

This historical review of infographics reveal how scientific understanding has developed together with people’s capacity to represent data in pictures and graphs. Buzzfeed have paid tribute to Beautiful Science with a look at “9 Glorious Infographics Through History”, which is both an appropriate and considerate choice. The display features a variety of designs spanning almost four centuries.

BF BS exhibit

Perhaps unsurprisingly, my favourite infographics were John Graunt’s “Bills of Mortality” from 1662 and Florence Nightingale’s “Rose Diagram” from 1854.

enhanced-buzz-wide-Graunt's Bills of Mortality

Picture: John Graunt’s Bills of Mortality (1662). From British Library.  Click to enlarge

Graunt’s table is one of the earliest publications of public health data. It was collated from early death notifications gathered by parish clerks in London at the turn of the 17th century, in an attempt to monitor deaths from plague.

Among the more interesting points were the three to four people per year who died from lethargy; the eight who died by “Wolf” between 1633 and 1636 (why none before or after – was there a cull of man-eaters?); and most sadly what appears to be 279 folk who died from grief over those 15-20 years.

John Graunt was a haberdasher by trade, although he is now considered to be one of the first epidemiologists.

The Rose Diagram by Florence Nightingale (below) also stands out as a fine public health infographic.

Nightingale is famous for looking after thousands of soldiers during the Crimean War (1853-6). But the Lady of the Lamp was also a splendid epidemiologist, who harnessed the power of the infographic and statistics to initiate change.

Flo N Rose Diagram

Picture: Florence Nightingale’s Rose Diagram (1854). From the British Library

Iconic may be too strong a word to describe Nightingale’s “rose diagram” but I think it is appropriate – this nineteenth century pie-chart is indeed a visual icon.

It shows seasonal variation in the cause of mortality of soldiers in the military field hospital.

At the end of the war, Nightingale wrote a report including this infographic, which carried a stark message: hospitals can kill. The majority of soldiers died from preventable diseases (in blue) rather than from battle wounds (in red).

The Rose Diagram was designed to show that improving sanitation in hospitals could save lives. It ultimately led to cleaner hospitals, where more lives were saved.

The Beautiful Science exhibit runs from 20th February to 26th May 2014 with free admission to the Folio Gallery.

Describing Data

Drowning by numbers by Jorge Franganillo

Drowning by numbers.  Picture by Jorge Franganillo, Flickr Creative Commons

Data is basically information – a set of quantitative or qualitative values.

As I said in my introduction, the term is used as a mass noun i.e. “the data shows…” (although “the data show…” is also correct).

An individual data point or value represents a piece of information.

Data is usually collected by measurement and visualised by images such as charts or graphs.

Raw data refers to unprocessed information in the form in which it was originally collected. This can be from scientific experiment (based on observation under laboratory conditions) or simply from the field.

However it is collected and in whatever form, it is first necessary to recognise exactly what type of data you are dealing with. The diagram below should give the reader a general idea of the different data types. It is just one way to look at data, and I hope it is clear.

Data types


Initially you can make two broad distinctions: whether the data is continuous or discrete.

Continuous data is always quantitative or numerical. It has a numerical value that may be an integer, ratio, or interval. This means it can be a whole number (1,2,3…etc.), or any number from zero to infinity with all decimal values in-between.

Discrete data is also called categorical data as it refers to data arranged in categories. Categorical data can be ordered or ranked such as first, second, third etc or mild, moderate, severe – this is therefore ordinal data.

Alternatively, categorical data may (often) be unranked such as colours of cars. This is nominal data. Both nominal and ordinal data do not have any numerical value – this makes them non-parametric. This is important when it comes to statistics, the subject that gives the data meaning and value.

There you have it – all the types of data. Although journalists probably don’t need to get bogged down in the details, it will always be handy to recognise exactly what you’re dealing with. This is especially true for science journalists I feel.

In case I don’t manage an easy-to-understand statistics post, it is worth me mentioning how we can handle data (as journalists or scientists). There are three broad stages…

  1. Collection: may be from surveys or (scientific) studies; for journalists, collection is usually from a source
  1. Presentation: usually in graphic format, with measurement of certain markers e.g. maximums, minimums, averages.
  1. Interpretation: using statistics is a major part of results analysis, although journalists perhaps rightly look to the expert discussion of the results too.

My aim is to understand the process better. I hope yours is too.