Merge pull request #1053 from StephenAbbott/patch-1

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@@ -15,28 +15,28 @@ From this question, other important questions emerge, such as:
 
 In this year’s edition, we also experimented and measured aspects of “practical openness” like data findability. These are also acknowledged by the [International Open Data Charter Principles two to four](http://opendatacharter.net/principles/). The information we gained from this assessment is displayed in the results and will be available to download. It will also inform internal research which can be tracked on [GitHub](https://github.com/okfn/opendatasurvey)
 
-### What the Index does NOT cover
-GODI intentionally limits its inquiry to the publication of national government data. It does not look at other aspect of the common open data assessment framework such as context, use or impact. This narrow focus enables it to provide a standardized, robust, comparable assessment of open data around the world. While we are only looking at publication, we are yet to cover data quality which is a significant barrier to reuse. We hope we will be able to do this in the future.
+### What GODI does NOT cover?
+GODI intentionally limits its inquiry to the publication of national government data. It does not look at other aspects of the common open data assessment framework such as context, use or impact. This narrow focus enables it to provide a standardized, robust, comparable assessment of open data around the world. While we are only looking at publication, we are yet to cover data quality which is a significant barrier to reuse. We hope we will be able to do this in the future.
 
 ## Research assumptions
 This section presents the key assumptions that were taken into consideration while collecting and assessing the data.
 
-#### Assumption 1: Open Data is defined by the Open Definition.
+#### Assumption 1: Open data is defined by the Open Definition.
 We define open data according to the [‘Open Definition’](http://opendefinition.org/). The Open Definition is a set of principles that define openness of data and content. It is also simple and easy to operationalise.
 We note one small deviation from the current v2.1 of the Open Definition. The only part of our methodology that is not aligned with the Open Definition is our assessment of ‘open machine-readable’ formats. **We give a full score to machine-readable formats even if their source code is not open.** Instead, formats must be usable with at least one free and open source software. Thereby the Index gives preference to practical openness over the actual openness of a format.
 
 #### Assumption 2: The role of government in publishing data.
 In the past, there have been questions about the role government should play to ensure the publication of open data (INSERT LINK TO DISCUSS). Government services may be privatised, which means the data can be owned and produced by a company and not the state. We assume that for the key data categories we survey, the government has a responsibility to ensure their publication, even if it is held and managed by a third-party.
 #### Assumption 3: The Global Open Data Index is a ‘national’ indicator.
-We acknowledge that not all countries have the same political structure. It is possible that not all of the sub-national governments produce the same data as they are potentially subject to different laws and/or procedures. The Global Open Data Index therefore does not only assess data publication of national government, but data publication at the national level. “National” publication of open data can take three forms:
-* The data describes national government processes or procedures ( government entities operating on the highest administrative level).
+We acknowledge that not all countries have the same political structure. It is possible that not all of the sub-national governments produce the same data as they are potentially subject to different laws and/or procedures. GODI therefore does not only assess data publication of national government, but data publication at the national level. “National” publication of open data can take three forms:
+* The data describes national government processes or procedures (government entities operating on the highest administrative level).
 * The data is collected or produced by national government or a national government agency (on highest administrative level).
 * The data describes national parameters and public services for the entire national territory, but is collected by sub-national actors.
-For example we check if budgets are available for the national government of a federal state, or if air quality data exists for all country regions. **Only in cases where we see legal and administrative autonomy from a higher government, the Index will look into sub-national territories individually** (see assumption 4).
+For example we check if budgets are available for the national government of a federal state, or if air quality data exists for all country regions. **Only in cases where we see legal and administrative autonomy from a higher government, GODI will look into sub-national territories individually** (see assumption 4).
 
-#### Assumption 4: The Global Open Data Index assesses ‘places’ instead of ‘countries’.
-The Index seeks to be a **meaningful and actionable indicator for government**. Therefore, the Global Open Data Index 2016 ranks ‘Places’ and not ‘Countries’.
-For years the Index struggles to assess countries with devolved power. In some cases, such as Northern Ireland, sub-national governments mainly operate autonomously from higher national government, and are granted administrative and legislative autonomy.  In order to be a relevant indicator, we experimented how to better assess data on a sub-national level in a comparable way. As a test case, the Index assesses Northern Ireland separately from Great Britain this year. By separating Northern Ireland, we seek to address those government bodies that are actually responsible for publishing open data, and open up the debate how to understand open data on a subnational level. A short explanation why we regard Northern Ireland separately, can be found [here](https://docs.google.com/document/d/1bP5nZdEgtgfM36ShmE-r9RWsnWTyAx6c-d_eDKk_zuU/edit#). We would love to hear your feedback in the discuss.okfn.org forum.
+#### Assumption 4: GODI assesses ‘places’ instead of ‘countries’.
+GODI seeks to be a **meaningful and actionable indicator for government**. Therefore, GODI 2016 ranks ‘Places’ and not ‘Countries’.
+For years GODI struggled to assess countries with devolved power. In some cases, such as Northern Ireland, sub-national governments mainly operate autonomously from higher national government, and are granted administrative and legislative autonomy.  In order to be a relevant indicator, we experimented how to better assess data on a sub-national level in a comparable way. As a test case, the Index assesses Northern Ireland separately from Great Britain this year. By separating Northern Ireland, we seek to address those government bodies that are actually responsible for publishing open data, and open up the debate how to understand open data on a subnational level. A short explanation why we regard Northern Ireland separately, can be found [here](https://docs.google.com/document/d/1bP5nZdEgtgfM36ShmE-r9RWsnWTyAx6c-d_eDKk_zuU/edit#). We would love to hear your feedback in the discuss.okfn.org forum.
 Furthermore, the British Crown Dependencies (Isle of Man, Jersey, Guernsey) are regarded individually because they are not part of the UK government and operate largely autonomously. In other cases, we receive submissions for places that are not officially recognised as independent countries (such as Kosovo).
 
 ## What data does the Index look at?
@@ -48,12 +48,12 @@ Each data category contains the following information:
 For two categories - water quality and draft legislation we have lowered the bar by making some characteristics optional. This is because we are trying to understand better what data is out there and to improve definitions for these datasets in the future.
 * **Aggregation level:** Some data is available in different levels of aggregation. For example, water quality data can exist for each individual water source, or it can be presented as total annual pollution for regions or the country. *In most cases GODI assesses detailed, disaggregated data.*
 Detailed data increases the use cases and broadens the insights people can draw from it. [The International Open Data Charter](http://opendatacharter.net/) also emphasizes that the data should be published in its raw, original format as disaggregated data. Being clear about the aggregation level helps to guide our researchers looking for the correct dataset.
-* **Time Intervals:** Different datasets are updated in different time intervals. Our survey includes the question “This data should be updated every [TIME INTERVAL]. Is it up-to-date?” to assess whether data is up-to-date. Data that is not up-to-date often is less useful.
+* **Time intervals:** Different datasets are updated in different time intervals. Our survey includes the question “This data should be updated every [TIME INTERVAL]. Is it up-to-date?” to assess whether data is up-to-date. Data that is not up-to-date often is less useful.
 
-Government often publishes data on multiple websites, and in many files and formats. To make an informed and consistent decision which data to pick, reviewers followed two approaches:
+Government often publishes data on multiple websites, and in many files and formats. To make an informed and consistent decision about which data to pick, reviewers followed two approaches:
 
 1. **Choosing one reference dataset:** Reviewers find one reference dataset or file that contains all relevant characteristics. They answer the survey using this dataset. This can be a CSV file, a shapefile, or data presented on a website. If reviewers have to choose between two or more similar datasets, they should choose the one that scores highest and document their choice in a comment.
-2. **Referencing multiple datasets (if one reference file is not available):** Reviewers could not find a reference dataset, because the data is split across many files, formats and places. In this case, they refer the survey to different files. It is important that the sum of these files contains all required data characteristics. Example: If one dataset displays votes on bills and is in a machine-readable format, but another one contains bill texts and is not machine-readable, then the data is not considered to be machine-readable.
+2. **Referencing multiple datasets (if one reference file is not available):** Reviewers could not find a reference dataset, because the data is split across many files, formats and places. In this case, they refer the survey to different files. It is important that the sum of these files contains all required data characteristics. Example: if one dataset displays votes on bills and is in a machine-readable format, but another one contains bill texts and is not machine-readable, then the data is not considered to be machine-readable.
 
 ## The list of data categories
 Our data categories reflect key data that is relevant for civil society at large. The categories have been developed in partnership with domain experts, including organisations championing open data in their respective fields. In some cases we base our definition on international data production and reporting standards used by governments around the world. Each year we refine our definitions to reflect learnings from these experts.