This week features news on the anticipated .ngo domain, some interesting examples of how open government data is being reused and some discussion on whether big data analytics spells the beginning of the end for the social science theorist.
NGO data An Alternative to .org? Say Hello to .ngo
In this Chronicle of Philanthropy article Derek Lieu explains what the introduction of a .ngo domain could mean for charities by discussing the challenges and opportunities for ngos and their stakeholders. The Public Interest Registry, which now manages the .org addresses, is up against dotNGO, to control the process of assigning names to nonprofit groups. In the article TechSoup’s Marnie Webb explains the need for quality vetting of the NGOs that receive the domain.
Data Visualization Seven things I learned about data visualization
Following the recent World Bank Data Team Visualized event, Tariq Khokhar highlights his key learnings from the event. They include, using competitions to also help build communities, and acting on the knowledge that those external to an organisation can often make more creative use of its data. In addition, he describes how making reusable visualisations avoids reinventing the wheel and very simple but also complex visualisation can have maximum impact.
Building Philly’s open data movement
In this interview Mark Headd, Philadelphia’s first chief data officer notes the top three datasets all cities should make available and useable and some challenges faced in improving public access to information. He also gives his view on the type of apps, platforms or projects that will increase in number and how citizens can help contribute to the open data movement.
Big Data Big data and the death of the theorist
This post in Wired UK discusses whether the use of big data analysis in disciplines that are not science and mathematics will change or eliminate the role of academics in humanities. Data in many disciplines already exists in a machine readable format and supercomputers can now easily and quickly crunch that data. Despite this it must not be forgotten that big data patterns still need someone to understand them and those that can should also be included for its analysis.