One of the things you can never escape from in modern science is the sheer volume of data that needs to parsed, processed and presented. One of the things I particularly love about many of our smart clients is that they specifically request “no data dumps” preferring instead to receive relevant strategic insights. Of course, these take longer to generate, more experience in the subject area and more brain power to produce, but ultimately they also have more value to customers.
This morning, I was pleased to see that Science magazine are running a very timely overview of data in science in their current issue. It’s free (with registration to non-subscribers) for those interested in a broad look across multiple science disciplines.
Included in the edition, is an overview of data in climate change, ecology, neuroscience, social science, stem cells and other topics.
Oddly, they haven’t included an article on cancer specifically, although bioinformatics in this area would be particularly fascinating since it is probably far advanced compared to many other life science disciplines in data processing.
Still, there is one on genomics and next generation sequencing that many of you may be interested in, since the article raises some interesting questions, such as:
“The availability of deep (and large) genomic data sets raises concerns over information access, data security, and subject/patient privacy that must be addressed for the field to continue its rapid advances.”
Personally, I love reading Science magazine, and its sister publication, Science and Translational Medicine, as they are usually both chock full of good articles to read on a weekly basis to pass the spare time on trains and planes while travelling to conferences. I often Instapaper the PDFs for later reading on my iPhone, but you can also read them in the paper magazine or online, depending upon your preference.
Check out the current Science Special Edition on Data and see what you think for yourselves. By chance, it is virtually a year, almost to that day, that Phil Baumann and a bunch of us Pharma types on Twitter were debating the value of content and process that led to Phil’s excellent summary of the topic on his blog. Check it out, it’s well worth a read.
Kahn, S. (2011). On the Future of Genomic Data Science, 331 (6018), 728-729 DOI: 10.1126/science.1197891