OONI: Open Observatory of Network Interference

Are you ready to join us in the fight against censorship?

Join us now!

Open Methodologies

By having openly known and peer reviewed methodologies, we able to produce data that is of scientific value. We believe in the importance of reproducible research, meaning that any researcher should be able to obtain the same results independently. This can only be achieved by detailing the methods we use in our experiments in plain English as well as in code. We plan to set the standards for best practices in developing network surveillance and filtering detection tests. Focusing on the creation and utilization of tests based on open methods will allow us to build a taxonomy of surveillance and censorship. This will expand the ability of the larger community to independently implement tests, perform experiments, and reference specifics.

Read the methodology »

Free Software

Making the tool available as Free Libre Open Source Software allows researchers to fully grasp the inner workings of the software, and it allows the building of a strong skillful community around OONI. People interested in expanding it will be able to do so freely, which will help sustain it as a basis for future work. A FL/OSS tool has the most likely chance of being studied and improved by the research community.

View the source »

Open Data

It is very important that all of the data collected by OONI is released to the public under an Open license such as the Creative Commons by Attribution license. This allows anybody to freely use and republish this information as they wish without restriction. The publishing of scientific data is in line with the Open Science data movement – the rate of discovery and scientific progress is accelerated by better access to data. While other projects have attempted to share data, we assert that high level summaries are simply not enough. We wish to ensure that open access to data submitted is available to all and that techniques for analysis will be equally available. This will allow for factual assertions based on the data collected rather than on organizational reputation alone. While we think that this model may have risks, no project has resolved such risks by refusing to publish their datasets. At best, hiding data may stop casual attackers, and, at worst, it creates a very valuable target for attack.

See the reports »


l>