This article is an example of how theoretical frameworks about how people learn science were used in combination with computational techniques to develop authentic assessments and intelligent tutoring for science.
By Janice D. Gobert, April 2023
The XRDS blog highlights a range of topics from conference coverage, to security and privacy, to CS theory. Selected blog posts, edited for print, are featured in every issue. Please visit xrds.acm.org/blog to read each post in its entirety. If you are interested in joining as a student blogger, please contact us.
By Abdelrahman Hosny, June 2016
By Lara Zupan, Marinka Zitnik, November 2015
By William Cox, Mike Freenor, July 2015
Recent advances in genome typing and sequencing technologies have enabled quick generation of a vast amount of molecular data at very low cost. The mining and computational analysis of this type of data can help shape new diagnostic and therapeutic strategies in biomedicine.
By Marina Sirota, Bin Chen, July 2015
Suchi Saria of Johns Hopkins University shares how big data and machine learning can help improve the practice of healthcare, and how computing students can contribute.
By Narges Razavian, July 2015
How technology enables the data geek in life sciences and healthcare.
By Sarah Aerni, Hulya Farinas, Gautam Muralidhar, July 2015
By Marinka Zitnik, July 2015
An interview with Paul Wicks, Vice President of Innovation at PatientsLikeMe, a patient network and real-time research platform.
By Diana Lynn MacLean, December 2014
Intelligently leveraging data from millions of social media posts is a modern public health approach that has the potential to save many lives.
By Munmun De Choudhury, December 2014
Babbel's Director of Didactics, Miriam Plieninger, weighs in on how mobile apps are rapidly changing the way we approach language learning.
By Daniel Bauer, Billy Rathje, October 2014
The vast amounts of data that are now available provide new opportunities to social science researchers, but also raise huge privacy concerns for data subjects. Differential privacy offers a way to balance the needs of both parties. But how?
By Christine Task, September 2013
Distinguished Scientist at Microsoft Research, Dr. Cynthia Dwork, provides a first-hand look at the basics of differential privacy.
By Michael Zuba, September 2013
By Robert Aboukhalil, June 2013
The rate at which electronic information is generated in the world is exploding. In this article we explore techniques known as sketching and streaming for processing massive data both quickly and memory-efficiently.
By Jelani Nelson, September 2012
Approaches from computer science and statistical science for assessing and protecting privacy in large, public data sets.
By Ashwin Machanavajjhala, Jerome P. Reiter, September 2012
How Facebook is analyzing big data.
By Raghotham Murthy, Rajat Goel, September 2012
New user interfaces can transform how we work with big data, and raise exciting research problems that span human-computer interaction, machine learning, and distributed systems.
By Jeffrey Heer, Sean Kandel, September 2012
An invitation to the digital science of life.
By Cliburn Chan, September 2012
Why running a startup is a lot like building a research lab.
By Eldar Sadikov, Montse Medina, June 2012
Internet startup POPVOX connects constituents to Congress in a play to disrupt the world of advocacy.
By Joshua Tauberer, December 2011
Although public information is open, it is not always easily accessible.
By Harlan Yu, Stephen Schultze, December 2011
By Dmitry Batenkov, June 2011
Research in proteomics has created two significant needs: the need for an accurate public database of empirically derived mass spectrum information and the need for managing the I/O and organization of mass spectrometry data in the form of files and structures. Lack of an empirically derived database limits the ability of proteomic researchers to identify and study proteins. Managing the I/O and organization of mass spectrometry data is often time-consuming due to the many fields that need to be set and retrieved. As a result, incompatibilities and inefficiencies are created by each programmer handling this in his or her own way. Until recently, storage space and computing power has been the limiting factor in developing tools to handle the vast amount of mass spectrometry information. Now the resources are available to store, organize, and analyze mass spectrometry information.The Illinois Bio-Grid Mass Spectrometry Database is a database of empirically derived tandem mass spectra of peptides created to provide researchers with an organized and searchable database of curated spectrum information to allow more accurate protein identification. The Mass Spectrometry I/O Project creates a framework that handles mass spectrometry data I/O and data organization, allowing researchers to concentrate on data analysis rather than I/O. In addition, the Mass Spectrometry I/O Project leverages several cross-platform and portability-enhancing technologies, allowing it to be utilized on a variety of hardware and operating systems.
By Eric Puryear, Jennifer Van Puymbrouck, David Sigfredo Angulo, Kevin Drew, Lee Ann Hollenbeck, Dominic Battre, Alex Schilling, David Jabon, Gregor von Laszewski, December 2006
By Bryan Stroubeé, May 2006
By Ricardo Hoar, Joanne Penner, March 2003
By Suzie Allard, March 2001
By Kevin Henry, March 2001
By Jinsoo Park, October 1997
By Vineet Kapur, Douglas Troy, James Oris, October 1997
By Joseph Beck, Mia Stern, Erik Haugsjaa, September 1996
