Archive for January, 2013

Where is Science going?

Tuesday, January 29th, 2013

Science is changing fast and it is not hypothesis-driven anymore like it used to be. Any kind of research now faces increasing amounts of information and data to deal with. Fields such as astronomy, genomics, physics, drug discovery in biomedicine, and several others have been using Information Technology (IT) to analyze lots of data and make sense of it. We’ve got to a point that hypotheses are generated after you get the data from experiments. Then, high-throughput computer technology together with mathematical algorithms are used to answer questions. In other words, instead of generating data based on a specific hypothesis, you generate huge amounts of data and then ask the questions, thus formulating a hypothesis – it is backwards! This could be explained by the overlap we have been noticing of Information Technology with any kind of research. Well, we have always used computers for specific tasks, especially in research. But now computers are fast, the internet is even faster and we are creating an enormous gap. Science and young scientists (and I mean generally) are not prepared for this information overload named “Big Data”. One example is genomics, mainly because DNA sequencing machines are evolving in a pace that is leaving Moore’s Law in the dust (for more information see the article “Big Data in Genomics: challenges and solutions”). We are generating more data in the last years than we have produced in our entire existence. And the Big Data revolution is definitely impacting scientific and biomedical research. A specific example is the ENCODE project that is trying to map all functional regions in a person’s DNA (check the article “ENCODE: Big Data to deal with human complexity” for more information). As I mentioned before, science is facing an increasing deficit in people to not only handle big data, but more importantly to have the knowledge and skills to generate value from this data. How to aggregate and filter data, how to present the data, how to analyze it and gain insights, how to use the insights to aid in decision-making, and then how to integrate all the information is important for the future of science and scientific progress. The problem is that researchers need a toolbox of techniques, skills, processes and abilities to construct new solutions based on this accumulation of information. And they need the ability to create a user interface that turns their abstract findings into something others can understand. Scientists also need the skills to create elegant ways to transform raw data into information, and then investigate it. A Wired article put it all in a very nice essay: we are forgetting scientific theory and philosophy because of the Big Data. We are now giving lots of credit to computational power and are forgetting the main scientific ingredient that are human curiosity and instinct; and computers do not have these two ingredients. We’ve reached a point where supercomputers are fast enough to crunch data just as easily as anything else. This could be good or bad, depends on how we use this power. Time will tell, but for now, let me go back to my “hypothesis-generator”, or should I say my computer…Scientists have to work! (Image Credits: Nature Magazine)