… Geospatial information is like the lifeblood of data, it creates the opportunity to bring information alive. By mapping data you can turn seemingly arbitrary statistics into a rich, interactive and highly personalised experience, particularly with the onset of Augmented Reality and other Web Squared tools hitting the mainstream market. There is a huge variety of geospatial innovation that you are all leading the charge on, and for that I thank you and commend your efforts.

Similarly FOSS is like a living blueprint – a map if you will – for trust, sustainability and interoperability in the implementation of Gov 2.0 principles. FOSS principles and methodologies are the best case studies you can find for tried and proven online collaboration with constructive outcomes. FOSS applications provide reference implementations for pretty much anything, which government can build upon and customise. We have a lot to learn from FOSS methods and practices, and I would ask all of you to assist us, in government, to understand…

Vía Open …

… Even though a clear divide is necessary, it doesn’t mean that Information Visualization and Information Artcannot coexist. I would even argue they should, since they can learn a lot from each other and cross-pollinate ideas, methods and techniques. In most cases the same dataset can originate two parallel projects, respectively in Information Visualization and Information Art. However, it’s important to bear in mind that thecontextaudience and goals of each resulting project are intrinsically distinct.

In order for the aspirations of Information Visualization to prevail, here are my 10 directions for any project in this realm: …

Rodrigo Orihuela en Amphibia las traduce y sintetiza:

1) La forma está sujeta a la función

2) Debe comenzarse con un interrogante

3) La interactivadad es clave

4) Debe citarse la fuente

5) El poder de la narrativa (los proyectos deben contener una narrativa cautivante)

6) No debe glorificarse la estética

7) Hay que buscar relevancia en los datos

8) La centralidad del tiempo

9) Hay que aspirar al conocimiento

10) Evitar visualizaciones injustificadas

DataSF is a central clearinghouse for datasets published by the City & County of San Francisco. The site allows you to find datasets in several ways: general search, tags/keywords, categories, and rating. The goal is to improve access to city data through open machine-readable formats. While the number and quality of datasets is increasing, we recognize there is much more that we can do. You can help by rating and commenting on existing datasets or by telling us what datasets we should make available to the public.

While there is plenty of room for improvement, our goal in releasing this site is:

(1) improve access to data 
(2) help our community create innovative apps 
(3) understand what datasets you’d like to see 
(4) get feedback on the quality of our datasets.

Moving from a Web of documents to a Web of data (or of Linked Open Data) is an oft-cited goal in the sciences. The Web of data would allow us to link together disparate information from unrelated disciplines, run powerful queries, and get precise answers to complex, data-driven questions. It’s an undoubtedly desirable extension of the way that the existing networks increase the value of documents and computers through connectivity - Metcalfe’s Law applied to more complex information and systems.

However, making the Web of data turns out to be a deeply complex endeavor. Data - here, a catchall word covering databases and datasets and generally meaning here information that is gathered in the sciences as a result of either experimental work or environmental observation - require a much more robust and complete set of standards to achieve the same “web” capabilities we take for granted in commerce and culture.

Unlike documents, the ultimate intended reader of most data is a machine. Some classic examples include search engines, analytic software, database back ends, and more. There is simply too much data in production to place people on the front lines of analysis. When data scales easily into the petabytes, we just can’t keep up using the existing systems.

There are three interlocking dimensions to interoperability in data: legal, technical, and semantic. By legal, we mean the contractual and intellectual property rights associated with the data; by technical, the standard systems (especially the computer languages) in which the data is published; and by semantic, the actual meaning of the data itself - what it describes, and how it relates to the broader world.

Each of these dimensions is complex on its own. Taken together, the three represent unsolvable complexity. The semantic layer alone requires an almost miraculous level of agreement on “what things mean,” and anyone who has witnessed argument among scientists, be they economists of physicists, knows that even apparently simple topics turn contentious over matters as basic as definitions. Consensus on the technical layer is somewhat easier - the existence of the Web and the Semantic Web “stack” of standard technologies has begun to take a leadership position in data networking - but still difficult, long, and open to argument. One of the only opportunities we have is in the legal layer, where we can look to a broad set of successes in legal interoperability through the use of a simple, flat standard: the public domain.

The public domain is a very simple concept - no rights are reserved to owners, and all rights are granted to users. The public domain exists as a counterweight to copyright in the creative space, but in some countries - especially the United States - as a first option for data that is not considered “creative.”

The public domain option currently underpins a wide variety of linked data that is already well on its way to achieving Web scale. From the International Virtual Observatory, whose members build an international data net on norms of “acknowledgment” rather than contracts of “attribution”, to the world of genomics, where entire genomes and related data are harmonized nightly across multiple countries, the public domain creates complete interoperability at the legal layer of the data network, and serves as a foundation for the next layer of technical interoperability.

Interestingly we have yet to observe similar network effects emerging in cases where the underlying data is treated in a more conservative “intellectual property” context by using copyright licenses or database licenses inspired by copyright. Indeed, in the case of the international consortium mapping human genomic variation, the implementation of a “click through” license was found in practice to impede integration of that mapped variation with other public domain data, limiting the value of the map. The license was removed, thepublic domain option instated, and the database was immediately technically integrated with the rest of the international web of gene data.

We have seen the public domain option work, again and again, across the scientific disciplines. Implementing the public domain as the interoperability standard for the legal dimension of the web of data holds the greatest promise for scalability and long-term achievement of the network effect for data, as it permits the widest range of experimentation and development at the technical and semantic layers.

… The list of environmental issues is long and in desperate need of powerful, functional and rich visualizations. Besides our moral duty, many of these topics present problems of organized complexity, where hundreds of variables are interconnected and interdependent, posing astoundingly compelling visualization challenges for developers, artists, designers and programmers. If you want to join this effort, here are some links of resources where you can get environmental data: