Astronomical data artifacts and publications exist in disjointed repositories. The conceptual relationship that links data and publications is rarely made explicit. One of the Seamless Astronomy group's primary projects is providing critical input to the development of new ADSlabs features, including integration with Dataverse and ADS All Sky Survey. The overarching goal is a platform that allows data and literature to be seamlessly integrated, interlinked, mutually discoverable.
Astronomers use, peruse and produce vast amounts of scientific data. Making these data publicly available is important because it supports the reproducibility of results, and ensures their long term preservation and reuse.
Glue is a Python library built to explore and visualize relationships within and amongst data sets. Its main features include linked statistical graphics, flexible linking across high-dimensional data sets, and full scripting capability.
Astronomers, and more broadly, the scientific community, are increasingly using blogging, micro-blogging, and other social media for both discovering and disseminating scientific knowledge. We are exploring several avenues for studying the impact of Twitter and other social networking sites on scientific readership.
Established in 2011, the Viz-e-Lab was founded as a testing ground for new software efforts in visualization and e-Science at the CfA. Seamless Astrononmy projects are piloted and tested on users in this space, located on the third floor of the 160 Concord Avenue building of the CfA. The lab is used to test new hardware--primarily input devices--as well as new software. At present, two main focii are the development of sophisticated tools "linked view" visualization of high-dimensional data (see "Glue", below) and the integration of WorldWide Telescope into research and teaching paradigms.
The ADS is not a data repository per se, but implicitly contains valuable holdings of astronomical data in the form of images, tables, and object or observation references contained within the articles. We place these sources of data in to two categories: 1) data-literature connections, which are made possible from the curatorial efforts of teams around the world, such as the object-literature connections from our SIMBAD collaborators; 2) images, figures, tables, and other scientifically relevant matter embedded in the article but otherwise not extracted anywhere else.