Opening up Course Builder data

Posted by John Cox and Pavel Simakov, Course Builder Team, Google Research

Course Builder is an experimental, open source platform for delivering massive online open courses. When you run Course Builder, you own everything from the production instance to the student data that builds up while your course is running.

Part of being open is making it easy for you to access and work with your data. Earlier this year we shipped a tool called ETL (short for extract-transform-load) that you can use to pull your data out of Course Builder, run arbitrary computations on it, and load it back. We wrote a post that goes into detail on how you can use ETL to get copies of your data in an open, easy-to-read format, as well as write custom jobs for processing that data offline.

Now we’ve taken the next step and added richer data processing tools to ETL. With them, you can build data processing pipelines that analyze large datasets with MapReduce. Inside Google we’ve used these tools to learn from the courses we’ve run. We provide example pipelines ranging from the simple to the complex, along with formatters to convert your data into open formats (CSV, JSON, plain text, and XML) that play nice with third-party data analysis tools.

We hope that adding robust data processing features to Course Builder will not only provide direct utility to organizations that need to process data to meet their internal business goals, but also make it easier for educators and researchers to gauge the efficacy of the massive online open courses run on the Course Builder platform.

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Applauding the White House Memorandum on Open Access

Posted by Alfred Spector, Vice President of Research and Special Initiatives

Last week the Obama Administration issued a Memorandum that could vastly increase the impact of federally funded research on innovation and the economy. Entrepreneurs, businesses, students, patients, researchers, and the public will soon have digital access to the wealth of research publications and data funded by Federal agencies. We're excited that this important work will be made more broadly accessible.

This memorandum directs federal agencies with annual research and development budgets of $100 million or more to open up access to the crucial results of publicly funded research (including both unclassified articles and data). These agencies will need to provide the public with free and unlimited online access to the results of that research after a guideline 12 month embargo period. Before last week only one agency, the National Institutes of Health, had a public research access policy.

The federal government funds tens of billions of dollars in research each year through agencies like the National Science Foundation, National Institutes of Health, and the Department of Energy. These investments are intended to advance science, accelerate innovation, grow our economy, and improve the lives of all Americans and members of the public. Opening this research up to the public will accelerate these goals.

Federal investment in research and development only pays off if it has an impact. Researchers, businesses, policymakers, entrepreneurs, and the public need to be able to access and use the knowledge contained in the articles and data generated by those funds. Making the results of scholarly research accessible and reusable in digital form is one important way to increase the impact of existing taxpayer investments.

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ReFr: A New Open-Source Framework for Building Reranking Models

Posted by Dan Bikel and Keith Hall, Research Scientists at Google

We are pleased to announce the release of an open source, general-purpose framework designed for reranking problems, ReFr (Reranker Framework), now available at: http://code.google.com/p/refr/.

Many types of systems capable of processing speech and human language text produce multiple hypothesized outputs for a given input, each with a score. In the case of machine translation systems, these hypotheses correspond to possible translations from some sentence in a source language to a target language. In the case of speech recognition, the hypotheses are possible word sequences of what was said derived from the input audio. The goal of such systems is usually to produce a single output for a given input, and so they almost always just pick the highest-scoring hypothesis.

A reranker is a system that uses a trained model to rerank these scored hypotheses, possibly inducing a different ranked order. The goal is that by employing a second model after the fact, one can make use of additional information not available to the original model, and produce better overall results. This approach has been shown to be useful for a wide variety of speech and natural language processing problems, and was the subject of one of the groups at the 2011 summer workshop at Johns Hopkins’ Center for Language and Speech Processing. At that workshop, led by Professor Brian Roark of Oregon Health & Science University, we began building a general-purpose framework for training and using reranking models. The result of all this work is ReFr.

From the outset, we designed ReFr with both speed and flexibility in mind. The core implementation is entirely in C++, with a flexible architecture allowing rich experimentation with both features and learning methods. The framework also employs a powerful runtime configuration mechanism to make experimentation even easier. Finally, ReFr leverages the parallel processing power of Hadoop to train and use large-scale reranking models in a distributed computing environment.

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Keeping an “OER mind” about shared resources for education

Posted by Maggie Johnson, Director of Education and University Relations

With ever-increasing demands being placed on our education system, including new skill sets that need to be taught to create a pipeline that can fill 21st century jobs, we must figure out how to make high-quality education more accessible to more people without overburdening our existing educational institutions. The Internet, and the platforms, tools and programs it enables, will surely be a part of the answer to this challenge.

Open Educational Resources (OER) are one piece of the solution. OER are teaching and learning resources that anyone can share, reuse and remix. As part of our ongoing commitment to increasing access to a cost-effective, high-quality education, we’re supporting the OpenCourseWare Consortium — a collaboration of higher education institutions and associated organizations from around the world creating OER — in organizing Open Education Week 2012, which begins today.

An example of OER in action is OpenStax, a recent non-profit initiative of Rice University and Connexions to offer students free, professional quality textbooks that meet scope and sequence requirements for several courses. They believe that these books could save students over $90 million in the next five years. Non-profit isn’t the only model for open education. Flat World Knowledge has built a business around OER by providing free online access to open textbooks, then selling print-on-demand copies and supplemental materials.

We’ll be acknowledging OER week through a panel event in Washington, DC, and over on our +Google in Education page, where we’ll be posting articles, sharing stories and interviews about the benefits of open education resources. Opening these resources to everyone can improve the quality of education while getting more out of our investments in educational resources. We hope you’ll join us in celebrating Open Education Week. Go to openeducationweek.org to learn more and get involved.

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Data and code open sourced from Google's Renewable Energy Cheaper than Coal project

Posted by Ross Koningstein, Engineer, Google RE<C team

Cross-posted with the Open Source at Google Blog

Google’s RE<C renewable energy research project has recently open sourced a new tool and a significant amount of data to support future CSP (concentrating solar power) heliostat development.

HOpS Open Source Site

HOpS, heliostat optical simulation, is an open source software tool for accurately and efficiently performing optical simulations of fields of heliostats, the actuated mirror assemblies that direct sunlight onto a target in CSP applications.

Google used this tool to help evaluate heliostat field layouts and calculate heat input into a CSP receiver for power production. HOpS works by passing "packets" of light between optical elements (the sun, heliostats, and elements of the target surface), tracking shadowing and blocking masks along the way. For our analysis goals, this approach gave our researchers more flexibility and accuracy than analytic tools (such as DELSOL or HFLCAL), and it was easier to set up for thousands of runs than using ray tracers. Output from the simulation includes heliostat efficiency, target irradiance, and more, while an included shell script facilitates plotting heat maps of the output data using gnuplot.


REC-CSP Open Source Site

The REC_CSP open source project contains data sets and software useful for designing cheaper heliostats.


Available on the project site are:

1. Thirty days of three-dimensional wind measurement data taken with ultrasonic anemometers (sampled at ~7 Hz), recorded at several near surface elevations.  The data is presented in the RE<C wind data collection document and is available for download on the open source site here.

2. A collection of heliostat aerodynamic load data obtained in a NASA wind tunnel and graphically represented in the appendix.  This data is available for download on the open source site here.

3. Matlab software for high-precision, on-target heliostat control with built-in simulation for testing. This is essentially the same software used in the RE<C heliostat control demonstrations and described in the accelerometer sensing and control system design documents.  The source code is available for download here.

Video: Demonstrating single and multiple heliostat control

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