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Measuring the Openness of Government Data in the Balkans

Blina Meta - May 24, 2017 in Global Open Data Index

Open Data Kosovo is a civic-tech organization that uses technology to contribute towards social good. The organization has created an exciting network of partners both local and international while working on projects related to visualizing procurement data, mapping satellite imagery for human rights violations, data collection and entry of 112 emergency calls, countering violent extremism online, providing digital solutions to public institutions, index measurement of the degree of openness of public institutions, visualizing election data, growth of the female coders community, and more. This portfolio made us a trustworthy candidate for the next task from Open Knowledge International, measuring the state of openness of government data for the countries in South Eastern Europe: Bulgaria, Macedonia, Serbia, Kosovo, Croatia, Albania, Slovenia, Bosnia and Herzegovina, Romania, Montenegro.   We agreed to the task, and thereby the journey of measuring the openness of the Southern Europe countries began. We had a two month period of submissions time, which at first glance looked like enough time but that’s always a tricky perspective. The first weeks went relatively calm: we dug up some old contacts in various countries and reached out to our partners and friends who would be interested in submitting to the index. We received positive replies by most of them and I felt calm and confident, but I also had an instinct that is only created by experience of crowdsourcing contributions, so obviously I had a plan B. We asked for help from Arianit Dobroshi, a longstanding friend of Open Data Kosovo who is excited about mapping, openness, and general digital goodness. His task was to help us with the submissions, fill out on whatever country-specific problems may there arise, and make sure tasks are completed. Time was passing and pressure was rising, and there were very few submissions on the index. It was the end of the year so I started to receive staff emails of planned vacations. This triggered an emergency alert on me: I panicked, and did what a modern woman does when they panic: I took a break and procrastinated even further for an hour or two. Then I pulled myself together and started contacting our friends from the region. First on the list was Zoran Luša, Senior IT Adviser, Ministry of Public Administration of the Republic of Croatia. Zoran immediately was up for the task and invited his colleague Anamarija Musa to join in the efforts. Croatia was never measured before so they needed to do it from scratch. Not an easy task, so we asked for some extra help just in case. We contacted Miroslav Schlossberg from CodeForCroatia, who promptly informed us that they were supposed to do a sprint to evaluate local cities so they included contributions to GODI 2016 in there. The mix was perfect: these people are serious in their digital contributions and the kind of people you want to work with. Croatia was covered. Parallel to the Global Open Data Index 2016, I was managing an EU-funded project that did a thorough index research for the openness of public institutions in the Western Balkan countries. This project is implemented with a regional network of organizations called ACTIONSEE. So I reached out to our friends from this network one by one.
  • In Serbia, we contacted our great friends from the local organization CRTA. We work with them in many exciting projects and they are always very thrilled to be part of initiatives that combine transparency and technology. Pavle Dimitrij was quick to jump on board and promised timely and accurate submissions for Serbia. Slobodan Marković reached out to us and was interested to participate, so we had two parties involved and a team at the office to make sure it goes smoothly: Serbia was covered.
  • When you think internet and government in Macedonia, you think of the Metamorphosis foundation. They are the leaders in their field, so of course we reached out to them. Tamara Resavska and Goran Rizaov rose to the challenge: Macedonia was covered.
  • Next, we contacted our friends in Albania, the organisation MJAFT. We discussed a couple of common national problems in sweet Albanian and agreed that this index submission is important. Ms. Xheni Lame promised to submit, and so she did.
  • Lastly, the Montenegro submission was agreed upon with our friends from CDT, where Milena Gvozdenovic memorably said “I find this Index very interesting and valuable. Therefore, we’ll complete the survey within the deadline.” The remaining countries were mostly filled out by the team at Open Data Kosovo: that’s how the index submission was completed, and how the community was wrangled.
The results are out today and I can’t help but feel sad for the low score of Kosovo, ranked #56 out of 94 countries with a score of 29%. Currently, we are living in a very bad environmental pollution situation, and the having open data related to the environment would surely be a good step towards advocating for improvement. Furthermore, Kosovo does have some budgetary information but they are presented in a low quality, and not in an open data format, which further decreased our score. In fact, all the Balkan countries seem to line up together at the bottom of the list sharing similar openness problems and challenges. It’s been a great experience working with Open Knowledge International and acting as Community Wrangler. I learned a lot about the state of open data in the region but I also established a network of like-minded individuals who care about having transparent countries, who are eager to see them rank higher, who thrill on seeing improvement and want to contribute towards it. I am looking forward to being part of it again next year!

Measuring the Openness of Government Data in the Balkans

Blina Meta - May 24, 2017 in Global Open Data Index

Open Data Kosovo is a civic-tech organization that uses technology to contribute towards social good. The organization has created an exciting network of partners both local and international while working on projects related to visualizing procurement data, mapping satellite imagery for human rights violations, data collection and entry of 112 emergency calls, countering violent extremism online, providing digital solutions to public institutions, index measurement of the degree of openness of public institutions, visualizing election data, growth of the female coders community, and more. This portfolio made us a trustworthy candidate for the next task from Open Knowledge International, measuring the state of openness of government data for the countries in South Eastern Europe: Bulgaria, Macedonia, Serbia, Kosovo, Croatia, Albania, Slovenia, Bosnia and Herzegovina, Romania, Montenegro.   We agreed to the task, and thereby the journey of measuring the openness of the Southern Europe countries began. We had a two month period of submissions time, which at first glance looked like enough time but that’s always a tricky perspective. The first weeks went relatively calm: we dug up some old contacts in various countries and reached out to our partners and friends who would be interested in submitting to the index. We received positive replies by most of them and I felt calm and confident, but I also had an instinct that is only created by experience of crowdsourcing contributions, so obviously I had a plan B. We asked for help from Arianit Dobroshi, a longstanding friend of Open Data Kosovo who is excited about mapping, openness, and general digital goodness. His task was to help us with the submissions, fill out on whatever country-specific problems may there arise, and make sure tasks are completed. Time was passing and pressure was rising, and there were very few submissions on the index. It was the end of the year so I started to receive staff emails of planned vacations. This triggered an emergency alert on me: I panicked, and did what a modern woman does when they panic: I took a break and procrastinated even further for an hour or two. Then I pulled myself together and started contacting our friends from the region. First on the list was Zoran Luša, Senior IT Adviser, Ministry of Public Administration of the Republic of Croatia. Zoran immediately was up for the task and invited his colleague Anamarija Musa to join in the efforts. Croatia was never measured before so they needed to do it from scratch. Not an easy task, so we asked for some extra help just in case. We contacted Miroslav Schlossberg from CodeForCroatia, who promptly informed us that they were supposed to do a sprint to evaluate local cities so they included contributions to GODI 2016 in there. The mix was perfect: these people are serious in their digital contributions and the kind of people you want to work with. Croatia was covered. Parallel to the Global Open Data Index 2016, I was managing an EU-funded project that did a thorough index research for the openness of public institutions in the Western Balkan countries. This project is implemented with a regional network of organizations called ACTIONSEE. So I reached out to our friends from this network one by one.
  • In Serbia, we contacted our great friends from the local organization CRTA. We work with them in many exciting projects and they are always very thrilled to be part of initiatives that combine transparency and technology. Pavle Dimitrij was quick to jump on board and promised timely and accurate submissions for Serbia. Slobodan Marković reached out to us and was interested to participate, so we had two parties involved and a team at the office to make sure it goes smoothly: Serbia was covered.
  • When you think internet and government in Macedonia, you think of the Metamorphosis foundation. They are the leaders in their field, so of course we reached out to them. Tamara Resavska and Goran Rizaov rose to the challenge: Macedonia was covered.
  • Next, we contacted our friends in Albania, the organisation MJAFT. We discussed a couple of common national problems in sweet Albanian and agreed that this index submission is important. Ms. Xheni Lame promised to submit, and so she did.
  • Lastly, the Montenegro submission was agreed upon with our friends from CDT, where Milena Gvozdenovic memorably said “I find this Index very interesting and valuable. Therefore, we’ll complete the survey within the deadline.” The remaining countries were mostly filled out by the team at Open Data Kosovo: that’s how the index submission was completed, and how the community was wrangled.
The results are out today and I can’t help but feel sad for the low score of Kosovo, ranked #56 out of 94 countries with a score of 29%. Currently, we are living in a very bad environmental pollution situation, and the having open data related to the environment would surely be a good step towards advocating for improvement. Furthermore, Kosovo does have some budgetary information but they are presented in a low quality, and not in an open data format, which further decreased our score. In fact, all the Balkan countries seem to line up together at the bottom of the list sharing similar openness problems and challenges. It’s been a great experience working with Open Knowledge International and acting as Community Wrangler. I learned a lot about the state of open data in the region but I also established a network of like-minded individuals who care about having transparent countries, who are eager to see them rank higher, who thrill on seeing improvement and want to contribute towards it. I am looking forward to being part of it again next year!

What is the difference between budget, spending and procurement data?

Danny Lämmerhirt - May 18, 2017 in Global Open Data Index, godi, OpenSpending

Fiscal data is a complex topic. It comes in all different kind of formats and languages, its’ availability cannot be taken for granted and complexity around fiscal data needs special skills and knowledge to unlock and fully understand it. The Global Open Data Index (GODI) assesses three fiscal areas of national government: budgets, spending, and procurement. Repeatedly our team receives questions why some countries rank low in budgets, public procurement or spending, even though fiscal data is openly communicated. The quick answer: often we find information that is related to this data but does not exactly describe it in accordance with the described GODI data requirements. It appears to us that a clarification is needed between different fiscal data. This blogpost is dedicated to shed light on some of these questions. As part of our public dialogue phase, we also want to address our experts in the community. How should we continue to measure the status of these three key datasets in the future? Your input counts! Should we set the bar lower for GODI and avoid measuring transactional spending data at all? Is our assessment of transactional spending useful for you? You can leave us your feedback or join the discussion on this topic in our forum.

The different types of fiscal data

A government budget year produces different fiscal data types. Budgeting is the process where a government body sets its priorities as to how it intends to spend an amount of money over a specific time period (usually annually or semi-annually). Throughout the budgeting cycle  (the process of defining the budget), an initial budget can undergo revisions to result in a revised budget. Spending is the process of giving away money. This mean, the money might be given as a subsidy, a contract, refundable tax credit, pension or salary. Procurement is the process of selecting services from a supplier who fits best the need. That might involve selecting vendors, establishing payment terms, some strategic tender or other vetting mechanism meant to prevent corruption. Not only are the processes linked to each other, the data describing these processes can be linked too (e.g. in cases where identifiers exist linking spending to government budgets and public contracts). For laypersons, it might be difficult to tell the difference when they are confronted with a spending or procurement dataset: Is the money I see in a dataset spending, or part of contracting? The following paragraphs explain the differences.

Budget

As mentioned above, budgeting is called the process where a government body decides how to spend money over a certain time period. The amount is broken into smaller amounts (budget items) which can be classified as follows:
  • Administrative (which government sub-unit gets the money)
  • Functional (what the money is going to be used for)
  • Economic (how the money is going to be used, e.g., procurement, subsidies, salaries etc.)
  • Financing source (where the money should come from).
After the budget period ends, we know how much money was actually spent on each item – in theory. The Global Open Data Index assesses budget information at the highest administrative level (e.g. national government, federal government), which is broken down in one of these classifications. Here is an example of some fully open budget data of Argentina’s national government.

Example of Argentina’s national government budget 2017 (table shortened and cleaned)

The image shows the government entity, and expenditures split into economic classification (how the money is used). At the far right, we can see a column describing the total amount of money effectively spent on a planned budget expenditure. It basically compares allocated and paid money. This column must not be mixed with spending information on a transactional level (which displays each single transaction from a government unit to a recipient).

Spending

The Spending Data Handbook describes spending as “data relating to the specific expenditure of funds from the government”. Money might be given as a subsidy, as payment for a provided service, a salary (although salaries will seldom be published on a transactional level), a pension fund payment, a contract or a loan, to name just a few.
GODI focusses on transactions of service payments (often resulting from a prior procurement process). Monetary transactions are our baseline for spending data. GODI assesses the following information:
  • The amount that was transferred
  • The recipient (an entity external to the government unit)
  • When the transaction took place
  • Government office paying the transaction
  • Data split into individual transactions
GODI exclusively looks for single payment transfers. The reason why we are looking at this type of data is that spending patterns can be detected, and fraud or corruption uncovered. Some of the questions one might be able to address include: Who received what amount of money? Could government get its services from a cheaper service provider? Is government contracting to a cluster of related companies (supporting cartels)? GODI’s definition of spending data, even though ambitious in scope, does not consider the entire spectrum of transactional spending data. Being produced by many agencies, spending data is scattered  across different places online. We usually pick samples of specific spending data such as government payments to external suppliers (e.g. the single payments through a procurement process). Other types of payment, such as grants, loans or subsidies are then left aside. Our assessment is also ‘generous’ because we accept spending data that is only published above a certain threshold. The British Cabinet Office, a forerunner in disclosing spending data, only publishes data above £25,000. GODI accepts this as valid, even though we are aware that spending data below this amount remains opaque. There are also many more ways to expand GODI’s definition of spending data. For instance, we could ask if each transaction can be linked to a budget item or procurement contract so that we understand the spending context better.

Example image of British Spending data (Cabinet Office spending over £25,000)

Above is an example image of Great Britain’s Cabinet Office spending. You can see the date and the amount paid by government entity. Using the supplier name, we can track how much money was paid to the supplier. However, in this data no contract ID or contract name is provided that could allow to fully understand as part of what contracts these payments have been made.

Procurement

When purchasing goods and services from an external source, government units require a certain process for choosing the supplier who fits best the need. This process is called procurement and includes planning, tendering, awarding, contracting and implementation. Goals are to enable a fair competition among service providers and to prevent corruption. Many data traces enable to shed light on each procurement stage. For example one might want to understand from which budget a service is gonna be paid, or what amount of money has been awarded (with some negotiation possible) or finally contracted to a supplier. This blogpost by the Open Contracting Partnership illustrates how each of the procurement stages can be understood through different data. GODI focuses on two essential stages, that are considered to be a good proxy to understand procurement. These however do not display all information. Tender phase
  • Tenders per government office
  • Tender name
  • Tender description
  • Tender status
Award phase
  • Awards per government office
  • Award title
  • Award description
  • Value of the award
  • Supplier’s name
Any payment resulting out of government contracts with external suppliers (sometimes only one, sometimes more) has to  be captured in government spending. For example, there might a construction contractor that is being paid by milestone, or an office supplies dealer which is chosen as a supplier. Then each spending transaction is for a specific item purchased through a procurement process. Below you can see a procurement database of Thailand. It displays procurement phases, but does not display individual transactions following from these. This particular database does not represent actual spending data (monetary transactions), but preceding stages of the contracting process. Despite this the platform is misleadingly called “Thailand Government Spending”.

Procurement database in Thailand

Another example is a procurement database indicating how much money has been spent on a contract:

Example for the procurement website ‘Cuánto y a quién se contrató’ (Colombia)

The road ahead – how to measure spending data in the future

Overall, there is slow but steady progress around the openness of fiscal data. Increasingly, budget and procurement data is provided in machine-readable formats or openly licensed, sometimes presented on interactive government portals or as raw data (more detail see for example in the most recent blogpost of the Open Contracting Partnership around open procurement data). Yet, there is a long way to go for transactional spending data. Governments do first laudable steps by creating budget or procurement websites which demonstrate how much money will or has been spent in total. These may be confusingly named ‘spending’ portals because in fact they are linked to other government processes such as budgeting (e.g. how much money should be spent) or procurement (how much money has been decided to pay for an external service). The actual spending in form of single monetary transactions is missing. And to date there is no coherent standard or specification that would facilitate to document transactional spending. We want to address our experts in the community. How should we continue to measure the status of these three key datasets in the future? Your input counts!  You can leave us your feedback and discuss this topic in our forum.   This blog was jointly written by Danny Lämmerhirt and Diana Krebs (Project Manager for Fiscal Projects at Open Knowledge International)

The Global Open Data Index as a national indicator – So why do we have Northern Ireland?

Open Knowledge International - May 9, 2017 in Global Open Data Index, Open Data Index, Open Data measurements, Open Knowledge

In May 2nd, 2017 we launched the Global Open Data Index (GODI). This blog post is part of a series that explore the main findings of GODI and the next challenges in open data measurement.   In the past, we were asked why the Global Open Data Index assesses ‘places’ and not countries. Why do we evaluate Hong Kong? Why the Crown Dependencies Isle of Man, Jersey and Guernsey? And why do we regard Northern Ireland separately from Great Britain in this year’s edition? To clarify our rationale, we first have to explain which data we are looking at. The Global Open Data Index assesses the publication of open data at the highest administrative level in a country. This can take three forms:
  • The data describes national government processes or procedures ( government bodies operating at the highest administrative level)
  • The data is collected or produced by national government or a national government agency
  • The data describes national parameters and public services for the entire national territory but is collected by sub-national agencies.
The Global Open Data Index looks at very different government data: from national budgets to water and air quality information. We acknowledge that not all countries have the same political structure. Data assessed through the index might not necessarily be produced by national government due to the devolution of power. Furthermore, it is possible that not all sub-national governments provide the same data as they are potentially subject to different laws and/or procedures. So why do we look at ‘places’ instead of countries?  The Index wants to be a meaningful and actionable indicator for government by assessing those government bodies that are responsible for data publication. We regard territories with legislative, executive, and administrative autonomy separately, including the Crown Dependencies (Isle of Man, Jersey, Guernsey) and Hong Kong. We keep the option open to include regions with a disputed status that are not officially recognised as independent countries.   Why does the Index include sub-national government this year? As described above, sub-national governments may act autonomously from the national government and collect/produce data individually. This has always been a challenge for the Index – sometimes open data was provided in one region but not in another. How to adequately assess these gaps? This year we experiment how more systematically measure data on a sub-national level in a comparable way. As a test case, we considered Northern Ireland separately from Great Britain. By doing so, we investigate how the responsibilities of open data publication are distributed across government. Thus we open up the debate how to understand open data on a subnational level. This experiment is part of a larger research effort to understand open data governance models (see our call for research). We did ask both of Northern Ireland and the UK government to comment on this decision, but due to the Purdah (Pre-elections period), we were unable to get a comment.

Πώς να διαβάσετε τα αποτελέσματα του Global Open Data Index

Χριστίνα Καρυπίδου - May 4, 2017 in Featured, Featured @en, Global Open Data Index, News, ανοικτά δεδομένα, ανοικτή διακυβέρνηση, Νέα

Από τον Danny Lämmerhirt Το  Global Open Data Index (GODI) είναι ένα εργαλείο για την εκπαίδευση της κοινωνίας των πολιτών και των κυβερνήσεων σχετικά με τη δημοσίευση ανοικτών κυβερνητικών δεδομένων. Αυτό γίνεται με την παρουσίαση διάφορων πληροφοριών, συμπεριλαμβανομένων των βαθμολογιών των θέσεων, της κατάταξης και των βαθμολογιών για κάθε κατηγορία δεδομένων ανά θέση, και των […]

Παρουσιάζοντας το 4ο Global Open Data Index – Προώθηση της κατάστασης των ανοικτών δεδομένων μέσω του διαλόγου

Χριστίνα Καρυπίδου - May 4, 2017 in Featured, Featured @en, Global Open Data Index, News, ανοικτά δεδομένα, ανοικτή διακυβέρνηση, Νέα

Από το Open Knowledge International Είμαστε στην ευχάριστη θέση να παρουσιάσουμε την 4η έκδοση του Global Open Data Index (GODI), μια παγκόσμια εκτίμηση της έκδοσης ανοικτών κυβερνητικών δεδομένων. Το GODI συγκρίνει την εθνική κυβέρνηση σε 94 μέρη στα 15 βασικά σύνολα δεδομένων που αξιολογήθηκαν από την κοινότητά μας ως τα πιο χρήσιμα για την επίλυση […]

Our country sample (and what it tells about our contributors)

Oscar Montiel - May 3, 2017 in Global Open Data Index

The Global Open Data Index has changed a lot this year, from our methodology to our software. This has been a community effort. This year we also collaborated with an amazing group of community leaders who helped gather the submissions in different regions as well as an extraordinary group of reviewers who helped make sure all the data we gathered are correct and accurate. At the same time, we aim for the GODI to be a useful tool to advocate for better and more available data. GODI’s success is based on the effort that our community makes each year. After four years of Index efforts, we want to share some insights into how our country sample changed this year.

Methodological note

To get to the results presented here we took two different approaches to analyzing data from the Index.
  1. We gathered all the final results of GODI from 2013 on. We removed all the countries that haven’t been assessed in the four years of the Index. We used only the datasets that have been assessed in the four years. We analyzed the URLs provided for each dataset, in order to see if there are changes in the domains and locations of the data. The results of changes presented below come from only analyzing the hostnames over time.
  2. We did a comparison of submissions from 2015 and 2016 to learn what has changed in the last year in terms of countries and regions that submitted.

Findings

About the sustainability of a volunteer-based Index

This year we came across an issue that we had noticed before but became more obvious with the development of this year’s Index.

Volunteer fatigue

Relying on volunteers from our Network to fulfill the task of submitting can prove challenging from certain regions. This was even more apparent in MENA (The Middle East and Northern Africa), Central Africa, parts of West Africa and Central Asia. For most of the regions where outreach might be complex, we worked with allied organizations who could ensure there were individuals to submit or knew the region enough to be able to find information about certain countries. For MENA, Central Asia and Central Africa finding an ally organization or an individual became a complex task and due to our schedule, we decided to look into it deeper in our next editions. In a similar thread, 2016 was the first time we didn’t have submissions from countries which had appeared in every previous edition of the Index. Even though there was a work of outreaching to partner organizations or members of the community, we still had a difficult time finding submitters for this year. This is an interesting issue for us, especially because all of the countries where we didn’t have submissions for the first time are or were part of our Network, whether as chapters or local groups.   We think that there might be several causes to this phenomena:
  1. Did groups burn out and do not have the capacity to participate in GODI?
  2. We changed the submission phase timeline this year from August – September to November-January. December is usually a month when participation is low due to the holidays.
  3. Political events, such as general elections, always take priority over the index, For example, the US elections were a big barrier in finding contributors to GODI from the US.
These changes make us ponder how we can keep the Index being a community effort and still ensure its sustainability and accuracy. We would like to have the community input on that matter. Please add your thoughts on this forum thread Ranking changes We wanted this year’s GODI to reflect, as accurately as possible, the state of open data in the places we assessed. For this, we tried to get submissions from people with extensive experience in the corresponding region or country. We see this both as a success for having a more realistic representation of our community network and a challenge for next year, as we will have to double our efforts to ensure we cover more places as we maintain accuracy.   This change in the sample affected the ranking as well. Usually, countries from the Middle East and North Africa (MENA) ranked lower. This has changed and now Caribbean countries are at the bottom. Similarly, since the country sample decreased, some countries could jump higher in the ranking. This increase can be connected to an actual increase in the quality of the data or to the changes in the methodology so we wouldn’t recommend making a comparison of last year’s ranking since the datasets and way of measuring them are different.  

Conclusion

Making GODI happen requires engagement from the submitters for each country. This engagement can become exhausting since the effort of finding data and submitting to GODI varies from dataset to dataset. After four years we have more questions than answers about how to make GODI sustainable through the community but we are trying to provide some possible answers to work on that. Community engagement depends on momentum and relevance in the political moment. For example, this year we started to accept submissions a few days after the US elections and this clearly affected the engagement of volunteers in that country. Something similar happened with South Korea, where the political agenda focused on something more crucial for the country. In other areas like MENA, Central Asia, and Central Africa we need to expand our network and identify possible partners not only for projects like GODI but to understand what open data means in that context. We also think that this reflects not only to the OKI network, but the greater open data movement, and shows gaps we need to address. If we consider having more countries is necessary, we need to start engagement earlier in the year and also expand the network of possible submitters to groups who wouldn’t traditionally work with open data, but that might benefit from having access to it. Also, we have found that some governments really care about the GODI and are open to discuss results and use GODI as a tool for internal advocacy to improve the quality of their data. Having civil society and government as partners in the implementation of GODI since the beginning might be a good experiment for the near future. In addition, having some countries be “champions” of openness in case they have used the GODIas measurement for improvements (e.g. Argentina) might work to get others on board.

How to Read the Global Open Data Index Results

Danny Lämmerhirt - May 2, 2017 in Global Open Data Index, godi, Open Government Data

The Global Open Data Index (GODI) is a tool to educate civil society and governments about open government data publication. We do so through presenting different information, including places scores, ranking, and scores for each data category per place, and comments by our submitters and reviewers. Even though we try to make this assessment coherent and transparent as possible, interpreting the results is not always straightforward. While open data has a very strict definition, scoring of any index is a discretional action. In real life, you can’t be partly open – either the data fit the criteria, or they do not.  This blog post will help GODI user to understand the following:  – What does the final score mean?  – How to interpret scores that vary between 0%, 40% or 70%?  – What does a score of 0% mean? For a more thorough explanation on how to read the results, go to index.okfn.org/interpretation/ 

What does the score mean?

Our scoring (ranging from 0% open to 100% open) does not necessarily show a gradual improvement towards open data. In fact, we assess very different degrees of data openness – which is why any score below 100 percent only indicates that a dataset is partially open. These levels of openness include public data, access-controlled data, as well as data gaps (See GODI methodology). To understand the differences we highly recommend reading each score together with our openness icon bar (see image below). For instance: a score of 70% can say that we found access-controlled, machine-readable data, that cannot be downloaded in bulk. Any score below 100% means “no access”, “closed access” or “public access”. Here we explain what each of them means, and how the data for each category look in practice.

Public Access Data

Data is publicly accessible if the public can see it online without any access controls. It does not imply that data can be downloaded, or that it is freely reusable. Often it means that data is presented in HTML on a website. The image above shows a search interface of a company register. It allows for targeted searches for individual companies but does not enable to retrieve all data at once. Individual search results (non-bulk) are displayed in HTML format and can then be downloaded as PDF (not machine-readable). Therefore the score is 70% and visualised as follow openness icon bar in our ranking:

Access-controlled data

Data is access-controlled if a provider regulates who, when, and how data can be accessed. Access control includes: * Registration/identification/authentication * Data request forms, data sharing agreement (stipulating use cases), * Ordering/purchasing data.   There are many reasons for establishing access controlled data including website traffic management, or to maintain control over how data is used. It is debatable whether some registration/authentication mechanisms reduce the openness of data (especially when registration is automated). Data request forms, on the other hand, are clearly not open data.   This image shows a data request form. The dataset is entirely hidden behind a “paywall”. This often prevents our research team from assessing the data at all. In this case, we could not verify in which format the data will be provided, and neither whether the data are actually weather forecast data (the particular category we look at). Therefore this access-controlled data gained 0 points and counts as 0% open. By contrast,  access-controlled data often score very high, up to 85% (because we subtract 15 out of 100 points for access-controls like registration requirements). 
 

How to read a score of 0%?

The are many reasons why datasets will score 0%. We tried to address the reasons in the reviewer or submitter’s comments as well. See here for the main reasons: Data gaps A data gap can mean that governments do not produce any data in a given category. Sometimes, if GODI shows a zero percent score, we see data gaps. For instance the case for Western African countries that lack air quality monitoring systems, or countries that have no established postcode system. Data gaps indicate that the government information systems are not ready to produce open data, sometimes because resources are missing, at times because it is not a priority of government.

Exist, but only to governmental use

Sometimes government has the data, but for many reasons choose not to open it to the public at all.  

Not granular

Since our criteria look for particular granularity, we considered all datasets that didn’t reach this granularity levels as not granular, and therefore they were regarded as not available. For example – Great Britain has published elections results, but not on poll station level, which is a crucial level to detect voter fraud. Therefore, while there is some data for UK elections, it is not at the right level and considered as non-existent.

 Do not fit our criteria

We are looking for particular datasets in GODI. When they don’t have all the characteristics we are looking for, we consider them as not available. For the full explanation on how to read the results see – index.okfn.org/interpretation/

Introducing the 4th Global Open Data Index – Advancing the State of Open Data Through Dialogue

Open Knowledge International - May 1, 2017 in Featured, Global Open Data Index, godi, Open Government Data

We are pleased to present the 4th edition of the Global Open Data Index (GODI), a global assessment of open government data publication. GODI compares national government in 94 places across the 15 key datasets that have been assessed by our community as the most useful for solving social challenges. For this edition, we received 1410 submitted of datasets, but only 10% of these are open according to the Open Definition. At Open Knowledge International (OKI), we believe it’s important to look further than just the numbers. GODI is not just a benchmark, it can be and should be used as a tool to improve open data publication and make data findable, useful and impactful. This is why we include a new phase, the dialogue phase for this edition. Measuring the publication of open government data is a constant challenge and while open data has a fixed definition, practices for data publishing that vary widely from one government to government, and even within each government department. The GODI community has faced a number of difficult questions that make assessment harder. ‘Which government agency publishes the data?’ or ‘Is the data published on a portal or a department web page?’ are just two examples. For this edition of the GODI, we gave attention to the development of the methodology to help us address some of these challenges in the assessment process. Not only did we incorporate feedback from the past GODIs, but we engaged in systematic and detailed in a consultation with the community. In addition, we had a more extensive review phase including an expanded quality assurance stage. We believe that these changes are necessary, and makes our assessment better than ever. In past years we published GODI as a snapshot in time and didn’t make any change to it after publication. Like any assessment tool, we are not perfect, and we did find ourselves publishing an errata section to accommodate errors. This year we want to be responsive to feedback from the community and government, and give both the opportunity to debate the results. We encourage every user to contest the results for a dataset by publishing a comment about it on our forum. We will be accepting feedback for the next 30 days, and on June 2nd we will re-evaluate the index, correct errors before closing it for submissions until the next edition. What are we looking to achieve from the dialogue stage?  
  1. Feedback from the full spectrum of stakeholders including government publishers to data users. This helps to improve future editions of the index.
  2. Help government to understand where the gaps are in their publication of data.
  3. Help citizens to find the data sets they are looking for.
  4. Help publishers of data to understand the difficulties of users accessing the data
Different challenges arise from open data publication today. Over the next couple of weeks, we will be publishing more of our insights and thoughts on these difficulties. Our main findings are:
  1. GODI helps to identify “data gaps.”
  2. Open data is not easy to find, and governments frequently don’t publish on their portals but across different government websites or split across many pages on one website.
  3. There is data online, but users find it difficult to access and work with
  4. Licenses remain an issue in open data publication
You can read the more about GODI findings on the insights page Learn how to read the results, and download the data for your own research Please take a look at the new edition of GODI and challenge us by letting us know what you think on the forum!  

Introducing the 4th Global Open Data Index – Advancing the State of Open Data Through Dialogue

Open Knowledge International - May 1, 2017 in Featured, Global Open Data Index, godi, Open Government Data

We are pleased to present the 4th edition of the Global Open Data Index (GODI), a global assessment of open government data publication. GODI compares national government in 94 places across the 15 key datasets that have been assessed by our community as the most useful for solving social challenges. For this edition, we received 1410 submitted of datasets, but only 10% of these are open according to the Open Definition. At Open Knowledge International (OKI), we believe it’s important to look further than just the numbers. GODI is not just a benchmark, it can be and should be used as a tool to improve open data publication and make data findable, useful and impactful. This is why we include a new phase, the dialogue phase for this edition. Measuring the publication of open government data is a constant challenge and while open data has a fixed definition, practices for data publishing that vary widely from one government to government, and even within each government department. The GODI community has faced a number of difficult questions that make assessment harder. ‘Which government agency publishes the data?’ or ‘Is the data published on a portal or a department web page?’ are just two examples. For this edition of the GODI, we gave attention to the development of the methodology to help us address some of these challenges in the assessment process. Not only did we incorporate feedback from the past GODIs, but we engaged in systematic and detailed in a consultation with the community. In addition, we had a more extensive review phase including an expanded quality assurance stage. We believe that these changes are necessary, and makes our assessment better than ever. In past years we published GODI as a snapshot in time and didn’t make any change to it after publication. Like any assessment tool, we are not perfect, and we did find ourselves publishing an errata section to accommodate errors. This year we want to be responsive to feedback from the community and government, and give both the opportunity to debate the results. We encourage every user to contest the results for a dataset by publishing a comment about it on our forum. We will be accepting feedback for the next 30 days, and on June 2nd we will re-evaluate the index, correct errors before closing it for submissions until the next edition. What are we looking to achieve from the dialogue stage?  
  1. Feedback from the full spectrum of stakeholders including government publishers to data users. This helps to improve future editions of the index.
  2. Help government to understand where the gaps are in their publication of data.
  3. Help citizens to find the data sets they are looking for.
  4. Help publishers of data to understand the difficulties of users accessing the data
Different challenges arise from open data publication today. Over the next couple of weeks, we will be publishing more of our insights and thoughts on these difficulties. Our main findings are:
  1. GODI helps to identify “data gaps.”
  2. Open data is not easy to find, and governments frequently don’t publish on their portals but across different government websites or split across many pages on one website.
  3. There is data online, but users find it difficult to access and work with
  4. Licenses remain an issue in open data publication
You can read the more about GODI findings on the insights page Learn how to read the results, and download the data for your own research Please take a look at the new edition of GODI and challenge us by letting us know what you think on the forum!