Notes on open government data evaluation and assessment frameworks

The evaluation of open data initiatives has become an increasingly pressing concern for many. As open data initiatives have proliferated, there have been a number of attempts to develop assessment, monitoring and measurement frameworks that can inform policy, and that will support comparative assessment of different open data efforts, or that can guide the creation of new initiatives. In this post I look at a number of the frameworks that have been put forward, or are currently in development. This post is part of my thinking aloud in planning for some common research tools in the Exploring the Emerging Impacts of Open Data in Developing Countries project, and in putting together a methods section for my PhD.

My working notes for this post, with a short summary of each of the frameworks described can be found here.

What is being measured?

The frameworks I explored fall into three broad categories:

  • Readiness assessments – looking at whether the conditions exist for an open data initiative to be started or successful.This category includes the Web Foundation Open Government Data Feasibility Studies and World Bank Open Data Readiness Assessment.
  • Evaluating implementation – looking at whether existing initiatives, or organisations, meet some criteria for ‘good’ open data implementation.This was the largest group, including the Five Stars of Linked Open Data (Berners-Lee, 2010); The Open Data Census [LINK]; The Open Data Index (Farhan, D’Agostino, & Worthington, 2012); mOGD-I; MELODA (Garcia, 2011); The State of Open Data method (Braunschweig, Eberius, Thiele, & Lehner, 2012); the assessment of open budgetary data in Brazil (Craveiro, Santana, & Alburquerque, 2013); Grading Government’s Open Data Publication Practices (Harper, 2012); and the Data Openness Index and Government Data Openness Index (Murillo, 2012).
  • Impact assessment – none of the frameworks I looked at explicitly address impact (though there are a number of studies that have developed methods to try and quantify economic impacts of open data (Vickery, 2011)), but a few frameworks in development do seek to make connections between implementation and different kinds of potential open data impacts (Jetzek, Avital & Bjorn-andersen, 2012; Huber, 2012).

The frameworks I explored operate at a number of different levels. Readiness assessments tend to operate at the country level, although the World Bank suggest their Open Data Readiness Assessment can also be applied at sub-national levels.

Implementation assessments may target a variety of:

  • Individual datasets
  • Open data portals
  • Individual institutions
  • Open data initiatives
  • Whole countries

A number of frameworks generate aggregate assessments of initiatives, portals or institutions based on aggregating up numerical scores for the ‘openness’ of datasets belonging to that parent entity. For example, MELODA, and a recent implementation of the Five Stars of Open Data on assign scores to institutions based on an average of the scores assigned to their individually published datasets.

How does measurement take place?

There are a number of non-mutually exclusive approaches to measurement, including:

  • Survey of technical features – identifying a list of features that datasets or data portals should possess, and carrying out an automated, or manual, survey of whether these features are in place. These approaches are generally agnostic as to the subject of the data, but are interested in whether datasets are machine readable, openly licensed and well catalogued (Braunschweig et al., 2012; Garcia, 2011) and the 5 Stars of Linked Open Data.
  • Specific dataset checklist – these approaches determine a short list of particularly important datasets and ask about whether these are available, and then conduct a technical assessment of these particular datasets. The Open Data Index, and Open Data Census both adopt this approach.
  • Domain specific assessments – Harper’s grading of US departments dataset publication practices identifies ideal features of specific datasets, and evaluates them against these (Harper, 2012). For example, where a standard exists for representation of a particular kind of data, it would judge a department higher where it adopts this standard.
  • Added value features – The Open Data Index, and the proposed mOGD-I model include questions on whether applications have been built on top of data, or whether there are accompanying tools around datasets. The readiness assessments also consider the capacity of states to support and stimulate activities that might increase uptake and use of open data.
  • Features of the environment – the readiness assessments major on this, describing social, technical, legal, political, economic and organisational contexts for open data.
  • Expert surveys – most assessment frameworks draw to a degree on survey methods, even though some attempt to automate elements. In most cases a single informant is used.

Some frameworks look to generate a single number that can be used to rank the subject of analysis, as in the case of the Open Data Index, MELODA, or implementation of the 5-stars of open data model. Other frameworks present a multi-dimensional assessment of their subject, either omitting aggregation altogether, or providing aggregation along a number of dimensions such as legal, organisation, technical etc. 

What does all this mean for the ODDC project?

In the Exploring the Emerging Impacts of Open Government Data in Developing Countries research project there are a number of things we want to try and understand.

  1. How does the context that an open data initiative operates within affect the use of data in governance processes?
  2. How do the technical features of an open data initiative affect the use of data in governance processes?

The first question draws upon the sort of data that might feature in a readiness assessment. The second draws upon the sort of data gathered in an implementation assessment. Like Huber (2012), and Jetzek et. al. (2012) we are hypothesising that the way an open data initiative is implemented may be slanted towards particular kinds of data re-use and thus impacts. By trying to connect context, implementation and impacts, we will be looking to both draw upon, and inform the further development of, evaluation frameworks.

Within the project we need to be able to perform evaluation at two levels:

  • The macro level – as we build upon learning from the Web Index to refine methods of generating country-level indicators that can inform an assessment of the extent to which a country has capacity to benefit from open data, and the extent to which this is being realised.
  • The case level – as the individual qualitative cases in developing countries generate comparable descriptions of how open data has been used.

The development of the macro level framework will be an ongoing task over the next year, but with the individual cases kicking off very soon, there is some immediate work to be done to develop two resources: a simple contextual questionnaire for describing the environment in a country or city; and a dataset assessment tool that can be applied at the level of individual datasets, collections of datasets, or intermediary platforms.

Hopefully a further iteration of working through the frameworks listed in this post will inform the development of these. As I get started on this task I would welcome pointers to any resources I have missed.


Berners-Lee, T. (2010, July). Linked Data – Design Issues. Retrieved from

Braunschweig, K., Eberius, J., Thiele, M., & Lehner, W. (2012). The State of Open Data Limits of Current Open Data Platforms. WWW2012. Retrieved from

Craveiro, G. da S., Santana, M. T. De, & Alburquerque, J. P. de. (2013). Assessing Open Government Budgetary Data in Brazil. ICDS 2013.

Farhan, H., D’Agostino, D., & Worthington, H. (2012). Web Index 2012. Retrieved from

Garcia, A. A. (2011). Methodology for Releasing Free Data (MELODA) (pp. 1–15). Retrieved from

Harper, J. (2012). Grading the Government’s Data Publication Practices.

Huber, S. (2012). The fitness of OGD for the creation of public value. In P. Parycek, N. Edelmann, & M. Sachs (Eds.), CeDEM12 – Proceeding of the Conference for E-Democracy and Open Government. CeDEM.

Jetzek, T, Avital, M., & Bjorn-andersen, N. (2012). The Value of Open Government Data : A Strategic Analysis Framework. Orlando. Retrieved from

Murillo, M. J. (2012). Including all audiences in the government loop: From transparency to empowerment through open government data.

Vickery, G. (2011). Review of Recent Studies on PSI re-use and related market developments. PAris.


One Comment

  1. Amparo Ballivian

    Great blog Tim, your thesis touches on a very important subject, which is not sufficiently researched. The Bank is joining forces with ODI to further research on OGD impacts on growth, employment creation, poverty reduction, better public service delivery and other areas.

    Two comments: the Bank’s Open Data Readiness Assessment tool was developed with countries in mind, but it can easily be applied at the sub-national and city environments. We are currently thinking of possible adaptations of the tool to a sector-centric, multi-country context. And it is action-oriented rather than evaluation-oriented: it’s aim is to help countries arrive to an action plan so they can implement a good OGD initiative or improve their initial, on-going efforts.

    Secondly, on OGD metrics, there is work done also by the OECD Secretariat which you may want to check.

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Open Data in Developing Countries

The focus of my work is currently on the Exploring the Emerging Impacts of Open Data in Developing Countries (ODDC) project with the Web Foundation.

MSc – Open Data & Democracy

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