There has always been an emphasis on the generation of novel data in science. Being a scientist involves progressing from observation to hypothesis to experiment to output. In the past, a combination of scarce data to look at and low throughput machinery to make more has led to limited experimental outcomes.
Atma Ivancevic
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Why? It’s how science works today – even in academia. You can’t do it all on your own — you need to work with others who have expertise in different areas to identify the right research questions, to ensure that your experiments answer the questions properly and that your data are robust, to fully interpret results and understand their broader implications and ramifications (as well as potential commercial application in some cases).
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As I reported in a Nature feature published this week, I found more online courses that were being developed than were actually in place. Resources to help scientists do more robust research are set to expand quickly. For example, the National Institute of General Medical Sciences has a competitive program that awards funds to institutions to enhance graduate student training; of 15 such supplements awarded in 2015, a dozen involved data analysis, statistics, or experimental rigor. You can find more here, and that is only a fraction of what is available. Some courses are still being developed and piloted to select students; others are being offered only to those in a particular department or training grant. If you find one that interests you, it can’t hurt to ask.
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NSF on 26 September announced US$94 million to support four new Science and Technology Centers (STCs). Each awardee will receive up to $24 million over a 5-year period, with the possibility of a continuation for 5 more years. In addition to these latest awards, NSF supports eight active STCs across the United States. Each STC involves partnerships across universities, federal labs, industry and other organizations.
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On my first day as a new PhD student, freshly awarded molecular genetics degree in hand, I was sat down at a laptop with an unfamiliar operating system and was encouraged to explore some data using arr. What sounded like pirate speak turned out to be R, a statistical programming language. Yep – for my PhD I swapped pipettes for programming, dilutions for data and spectrophotometers for statistics. Others experienced the opposite, entering the world of biology from a computer science background.
Réka Nagy
One of the authors interviews survivors a few days after the September 2009 South Pacific tsunami in the rubble of their communities in Samoa, as part of the UNESCO post-tsunami survey team reporting into the Prime Minister and King of Samoa. “It was a hard day for all of us,” says Dale Dominey-Howes.
Earth is destabilizing rapidly. Terrorism, conflict, genocide, human displacement, socio-economic disruption, rapid global environmental change, slow emergencies and natural disasters are more common than at any point in history. Consequently, opportunities exist for researchers to investigate the causes, consequences and potential management solutions arising from this instability. For this to happen, we need a well-trained workforce equipped with the skills and capabilities to work with ‘traumatic’ research content, people and places. Continue reading