There are some fundamental obstacles that STSCI's MAST and NIH's research networks both face. First is the scale of the data. At STSCI, the big data is petabytes of imagery and sensor data from NASA's space telescopes and other astronomy missions, as well as data from ground-based astronomy. At NIH, the "big data" that is most often in play is genomic data—a single individual's genome is three billion base pairs of data. Hundreds of thousands of genomes are being analyzed at a time by researchers hoping to find patterns in genes related to cancer or other illnesses. With all that data comes a big—and growing—demand for access. MAST currently serves up, on average, between 14 and 18 terabytes worth of downloads per month to scientists through its various applications. Much of this needs to be transformed from raw data into processed imagery before it's delivered, based on calibration data for the telescopes that collected it. (About half of that is from the Hubble Space Telescope.) Similarly, the demands at NIH aren't necessarily for raw genomic information, but for analysis data derived from it. This requires access to high-performance computing resources that researchers themselves may not have. This is a challenge to the openness of research using these massive data stores. Most of the work served by both STSCI and NIH is done by networks of researchers who are just as likely to be across the street from the institutions as they are on the other side of the globe. They not only need access to data and computing power, but these individuals need ways to collaborate around projects. It has led the institutions toward providing an increasing number of collaborative tools on top of their mission-specific applications.
