Also available in Bahasa
It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts. – Sherlock Holmes.
It's a game changer for those working on Indonesia's sub-national development issues. Comprehensive data at the sub-national level is now available to the public through INDO-DAPOER (Indonesia Data for Policy and Economics Research) at data.worldbank.org. DAPOER, which means ‘kitchen’ in Indonesian, is intended to be a ‘place’ where various data are blended, like spices, and cooked to produce analytical works, research papers, and policy notes.
INDO-DAPOER is the first World Bank sub-national database consisting of both province and district level data to be publicly accessible from anywhere in the world. The database provides access to around 200 indicators from almost 500 districts and 34 provinces in Indonesia, which in general go back to the early 1990s and even 1980s for some. The indicators are grouped into four main categories: fiscal, economic, social demographic, and infrastructure. Indicators range from sub-national government revenue and expenditure, sub-national GDP, to specific education, health, and infrastructure indicators such as net enrollment rate for junior secondary, immunization rate, and household access to safe sanitation.
Also available in Bahasa
Can open data lead to reduced energy consumption (and therefore slow down climate change)? Can open data help improve maternal health services (and thus improve facets of public delivery of services)? Can open data help farmers and crop insurers make better crop predictions (and thus lead to smarter investment decisions in agriculture)? Can open data empower citizens to fight back against police corruption (and thus help promote the rule of law)?
क्या ओपन डेटा (खुला/सर्वसुलभ डेटा) की वजह से ऊर्जा खपत में कमी आ सकती है (और इसलिए जलवायु परिवर्तन धीमा हो सकता है)? क्या ओपन डेटा से मातृत्व स्वास्थ्य सेवाएं बेहतर हो सकती हैं (और इस तरह सेवाओं के सार्वजनिक वितरण संबंधी पहलुओं को बेहतर किया जा सकता है)? क्या ओपन डेटा से किसानों और फसल बीमाकर्ताओं को फसल संबंधी पूर्वानुमान लगाने में मदद मिल सकती है (और इस तरह कृषि में निवेश के अधिक समझदारी वाले निर्णय लिये जा सकते हैं)? क्या पुलिस भ्रष्टाचार से लड़ने में ओपन डेटा नागरिकों को सशक्त कर सकता है (और इस तरह कानून के शासन को प्रोत्साहित करता है)?
If a child is born today in a country where the life expectancy is 75, they can expect to live until they are 75… right?
The statistic “Life expectancy at birth” actually refers to the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. In other words, it’s looking at the number of people of different ages dying that year, and provides a snapshot of these overall “mortality characteristics” that year for the population.
हमारी दुनिया आँकड़ों की बढ़ती हुई मात्रा से भरी हुई है, परन्तु संभावित दर्शकों के लिए ये आँकड़े कम उपयोगी होते हैं जिसका सबसे स्वाभाभिक कारण है - आँकड़े उनकी भाषा ही नहीं बोलते हैं।
Although I now live in Washington DC, I’m from Cambridge in England. I still like to keep track of my Member of Parliament (MP) back home, Julian Huppert, using a site first developed in 2004 by mySociety called TheyWorkForYou.
It presents an aggregation of data on MPs from sources including official transcripts of parliamentary discussions (Hansard), election results, the Register of Interests and Wikipedia entries. In short, it’s a “digital dossier” on all of the country’s parliamentarians.
But take another look at Jullian’s page and the “Numerology” section halfway down. Isn’t it reassuring to see that he: “Has used three-word alliterative phrases (e.g. "she sells seashells") 244 times in debates — average amongst MPs.”
Our world is awash with increasing amounts of data, but potential audiences for this data remain under-served for the most obvious of reasons - the data just doesn’t speak their language.
This has been true for the data on the World Bank Group Finances website which has only ‘spoken’ English since it was launched. Yes, we should have done this earlier but the website, and its associated open datasets, are now available in 5 new additional languages - Chinese, French, Hindi, Russian, and Spanish . The mobile app has been available for some time in 9 languages (Arabic, Chinese, English, French, Hindi, Indonesian Bahasa, Portuguese, Russian, and Spanish) and the new release of the website is in line with the program’s quest to include new audiences and communities in the use and dissemination of open financial data.
As the “Data Revolution” takes shape, Multilateral Development Banks, the UN, and IMF have already begun to improve their collaboration on development data.
This process was launched on April 19, 2013, when Heads of these institutions signed a Memorandum of Understanding (MOU) that sets out the basis for enhanced collaboration in statistical activities. In short, the MOU is part of the global response to the evidence focus of the post-2015 agenda, and the eagerness for policy makers to accelerate the quantity, quality, availability, and usability of data for development.
A revolution needs revolutionaries (and here are a couple of genuine data pioneers). But how can diverse groups around the world come together to help make development data better? The panel proposes a new “Global Partnership on Development Data”; this post proposes a first idea of how this might work and how it might differ from past efforts. I’d like to know what you think.
The open data community is chock-full of do-gooders.
There are "open" data-driven applications that track government legislation in the US, tools that help calculate taxi fares in Bogota, Colombia, applications that track how tax payer funds are spent in the UK, the state of school sanitation in Nepal, and many more.
It's clear that innovators are out there and full of terrific ideas about how to help their fellow citizens by harnessing public data. The question is, how can more of these projects follow examples like GovTrack and transition from hobby to successful, sustainable business models? And while there may be technical talent out there, what about entrepreneurial skills? How many data rockstars out there also have the "courage to create a business"?