The data is made available under a Creative Commons Attribution 4.0 License, and we encourage you to explore, re-use and remix the data.
Please cite any uses of the data as: World Wide Web Foundation, Open Data Barometer Global Report (Fourth Edition), and include a link to http://www.opendatabarometer.org
Historical and comparable quantitative consolidated data for all four editions have been prepared and is now available online and for download. These are the files used to drive the report, website and online data explorer.
You can also explore full quantitative and qualitative data of the Barometer at our public online dashboard. We continue to explore ways to improve the provision of qualitative and quantitative data alongside the Open Data Barometer.
Details of the questions addressed by researchers, the scoring thresholds applied during research and review, and information on the research process can also be found in the Open Data Barometer Research Handbook (PDF version).
4th Edition – Quantitative datasets
Data from the fourth edition of the Barometer includes:
- ODB-4thEdition-Rankings.csv contains the full Open Data Barometer calculated scores, as well as sub-index and sub-component values, country classifications and other contextual information.
- ODB-4thEdition-Scores.csv contains the raw scores given through the expert research for all readiness, implementation and impact questions.
- ODB-4thEdition-Datasets-Scored.csv contains a row for each dataset assessed during the technical survey, with the overall dataset score, and score values for each data openness checklist item.
Labels and details of each of the variables in the Rankings and Survey files are provided in:
4th Edition – Qualitative data
We are also sharing the main qualitative source information provided by researchers. This information was collected in order to justify and validate the quantitative scores given, and is not designed as a comprehensive review in response to each question
Notes on the datasets
The datasets survey employed conditional logic which meant that some justification fields were hidden depending on the state of the related question. However, if a question answer changed, the hidden data was not deleted, and so some data contained in these fields may not represent the final set of judgements made about a dataset.
The overall dataset scores are also based on a conditional logic, designed to score countries on the basis of existing machine-readable online data. Researchers were encouraged, however, when machine-readable data was not available, to still complete questions with respect to the best data they could locate. For this reason, simple summation of scores, or simple searching of fields on the assumption that their values represent properties of machine-readable data, is not possible. Instead, fields will need to be filtered on the basis of the values in ODB-4thEdition-Datasets-Scored.csv if looking to understand the justifications for a given dataset score.