Can I use your software to make my own visualizations?

Our interactive visualizations are made through the Our World in Data Grapher, developed by us. All our software is open-source and free for everyone to use, but the code will require a relatively experienced developer to implement. If you are looking only to publish one or a few interactive visualizations on the web we recommend https://www.datawrapper.de.

The Grapher is very helpful for publications looking to bring together many different datasets and publish hundreds of visualizations based on this data. You can read more about the Grapher here: https://ourworldindata.org/about/owid-grapher.

Is your work free?

Yes, all of our work is free for anyone in the world to access and use. Our goal is to present open and accessible information for everyone.

Our World in Data is created as a public good. All data is available for download. All visualisations are Creative Commons licensed. And all the tools we build are entirely open source.

Can I use your data and visualizations in my article, blog, book, presentations?

Absolutely. You don’t need to ask permission to use any of our work.

Visualizations and text are licensed under Creative Commons BY-SA and may be freely used for any purpose. The data in all interactive charts is available for download – you can find it under the Data tab at the bottom of the visualisation.

Can I use your work in teaching?

Yes, we love to see people using our work for teaching. We know – based on surveys from users – that many teachers do use our work. Surprisingly, this extends from primary school children through to postgraduate university students.

You can use and include any of the content you find on Our World in Data for this.

We also have a Teaching Hub where we provide resources for teaching and learning about global development. For specific topics you will find interactive teaching notes, presentation slides, charts and many other resources.

If you use our teaching already we’d love to hear from you and would be happy if you send us your slides or teaching material. And if something is missing for you or you have any ideas or suggestions for how to make or work more useful for teaching, please do get in touch at info@ourworldindata.org or through our Feedback page.

Where do you get your data from?

One of our key tasks in producing this publication is to bring together the most reliable and informative data sets on a particular topic.

Sources of data that we bring together are published by 3 different sources:

  1. Specialized institutes – like the Peace Research Institute Oslo (PRIO)
  2. Research articles – like Bourguignon & Morrison (2002) – ’Inequality Among World Citizens: 1820-1992’ in the American Economic Review.
  3. International institutions or statistical agencies – like the OECD, the World Bank, and UN institutions.

In every visualization we indicate clearly the source of the presented data. Where we have combined data sources or made changes to original datasets (such as regional aggregations), this is also indicated.

How do you decide what data sources to use?

We have six guidelines to decide which sources to accept and which data to present.

1) As far back into the past as possible – but up to today

The goal is to give a perspective on the long-term development and therefore we always aim to find time series data that reach back as far as possible. Unfortunately the availability of data is often itself an achievement of modern development and data is not available for the more distant past. A solution for this problem is data that has later been reconstructed and we aim to give a more complete picture by taking this data into account.

At the same time the idea is also to present a 'history of today' and we therefore also want to ensure that the data presented reaches until today. The limitation here is often that it takes up to several years for researchers and international institutions to publish important data for the most recent period.

2) As global as possible

A second objective is to give an account of each topic that includes as many societies, countries, and world regions as possible.

3) Present data in its entirety

Shorter sample periods may mask important trends and a recent reversal of a long-term trend could be falsely interpreted as the direction of the long-term trend. The merit of taking a historical perspective that studies long-term trends is that it shows the direction in which some aspect of our world is developing. Therefore we also always ensure to present the whole dataset and we do not want to cut off the original data.

4) Comparable through time and across societies

A third objective is to ensure that the data we present is comparable across time and across societies.

When data is not comparable across countries and through time we highlight this in the text accompanying the visualisation.

5) There is no other data - or we would include this data

An important promise is that we are not withholding any data that would give a different impression of the long-term development of some aspect. If two credible sources would publish statistics that contradict each other – indicating an open debate between researchers – then we would say so.

6) Reference the original source

To make the database useful for readers and credit the important work of those who construct the data presented here we aim to always reference the original source of the data.

We take great care to follow these guidelines. Unintentional mistakes or omissions, whilst hopefully rare, are of course possible. If you find any instance where we have not followed these guidelines, or you have any other complaints, please do get in touch at info@ourworldindata.org or through our Feedback page.

How do you decide which topics to cover?

We have a list of all the topics we want to cover and have been working through this list for several years now. Our goal here is to cover all quantifiable aspects that matter for our living conditions and the earth’s environment.

In deciding which topics to cover next we take several aspects into account:

  1. Do we have someone in the team or can we collaborate with someone who is an expert on the topic?
  2. We tend to give priority to topics that are not covered well in other publications. For instance we cover plastic pollution and air pollution extensively, but have less content on the consequences of climate change. This does not reflect our view of the relative importance of these topics, but rather of where we think our work can be the most useful in filling gaps in other publications.
  3. Another consideration is whether we have funding to work on a particular topic. Research grants that go to the University of Oxford where the researchers are based are grants to work on specific topics. We have a lot of freedom thankfully to work on those topics that are most important, but we cannot use funding we have to work on economic inequality to work on historical analysis of agriculture. Because we receive donations from readers this constraint is not limiting us currently so that aspects 1 and 2 are most important in practice.

We are interested in the state of the world and how it changed. Many of the trends that we discuss in our articles are positive; and since fewer people are aware of these positive developments, these trends often get considerable attention from our audience. But it’s not that we have an editorial agenda to only study positive trends. Indeed, in our publication you will also find very worrying trends: inequality is rising in many countries, obesity is rising in all world regions, CO2 emissions have increased for many decades while they need to fall urgently).

Additionally, we are convinced that covering positive trends is not in conflict with acknowledging just how awful the world continues to be in many ways, for many people. In fact the opposite is true: charting the progress of the past helps us see just how much better the world could be in the future, if we make this our goal.

Consider a concrete example. Every tenth person in the world today lives in extreme poverty. That statistic summarises a degree of suffering that is barely comprehensible. But the fact that, since 2000, there are a billion fewer people living beneath this very low poverty line shows us that ending extreme poverty is possible, if we choose to make it happen. This is the very reason we write about extreme poverty.

How/where is your work used?

You can read about our audience and some of the different ways people use our work here.

How are you funded?

We are funded through grants and reader donations.

Reader donations are essential to our work, providing us with the stability and independence we need, so we can focus on showing the data and evidence we think everyone needs to know.

You can learn more about our supporters here. And you can help us do more by donating today – it will make a real difference.

Can I contribute to Our World in Data?

  • If you are interested in writing and researching for Our World in Data, details on how you can apply are here.
  • If you are a web developer and you want to support us you find our code on GitHub here. And to get started it is going to be helpful if you get in touch with the developer team.
  • We are very grateful when our articles get translated (for example French, Japanese, Portuguese). If you translated an article we very much appreciate if you let us know – info@ourworldindata.org – and we will link to it from the original text.
  • And it is also very helpful if you can support us financially. You can make a donation here.

Why do you use log charts / why do you use linear charts?

Charts using a logarithmic axis are helpful to visualize variables that span several orders of magnitude, such as GDP per capita. Viewed on a linear scale it is very difficult to see differences among lower-income countries, which may be large relative to their own income, but very small relative to that of much richer countries.

GDP per capita in US since 1820 – viewable on a log or linear scale
Click to open interactive version

When you plot a metric that increases exponentially, on a logarithmic scale the increase is shown as a straight line where the slope of the increase corresponds to the growth rate. We can see in the chart above that the US has displayed a remarkably constant growth rate over the last two centuries.

Log charts also make it easier to visualize relationships where a variable moves proportionately in relation to another, for instance self-reported life satisfaction and GDP per capita.

GDP per capita vs self-reported life satisfaction – viewable on a log or linear scale
Click to open interactive version

On the other hand, using a log scale can visually give a misleading sense of the absolute differences between e.g. the richest and the poorest countries.

For this reason, with our interactive charts we often give users the option to switch between log and linear scales – as in both charts above. The default view we select will depend on what point the chart is being used to convey.

Do you have any job openings?

Any current job openings are listed on our Jobs page.

How do I stay updated with your latest work?

You can subscribe to our newsletter.

You can follow Our World in Data on Twitter.

And all of us have a Twitter profile too – here is the list.

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And you can subscribe to our RSS feed.

How big is your audience/reach?

Our website is currently (early 2019) accessed by more than one million people every month an average. More information about who and how people use our work is is available here.

Where are your users based (i.e. country or geographical breakdown)?

We are very happy about the fact that our readers come from every country in the world. More information about who and how people use our work is is available here.

What are you working on and what will be added later?

Our World in Data is always a work in progress. We will be working on this for many years. We have a list of all current and future data-entries that shows which topics we will cover in this publication. We will be writing 275 entries. For all entries we have started to collect material and this collection includes much more than ten thousand references to visualisations, data sources, and research papers.