Does empty rhetoric
over data sharing slow science
? 🏥 Editorial by Nature arguing why “governments, funders and scientific communities must move beyond lip-service and commit to data-sharing practices and platforms
”. 🙌 The article references a recent paper on open data and digital morphology
, which proposes techniques for the “creation, storage, publication and dissemination of large 3D data sets” – importantly, research data should be made accessible “at the time of article publication” in order to prevent slowing science.
The messier the data, the better?
write up discussing the work of computational immunologist
Purvesh Khatri. Rather than turning to meta-analysis, Khatri scours “public repositories for data collected at different hospitals on different populations with different methods”. 📈 Then, “if a signal sticks around despite the heterogeneity of the samples, you can bet you’ve actually found something
Khatri and colleagues have uncovered signature genes that could allow clinicians to detect life-threatening infections that cause sepsis, classify infections as bacterial or viral, and tell if someone has a specific disease such as tuberculosis, dengue or malaria. In short, they’re deciphering the host immune response and turning key genes into diagnostics.
Even in the digital age, patients find their info cannot be easily shared between doctors, especially among different hospitals or clinics. This information tends to still live in PDF files attached to emails or delivered by fax machine. Those who do have access through so-called “patient portals” sometimes find that the user experience is poor and the information is limited.
Interesting engineering blog post by Airbnb
on how they have worked towards democratising data
and an insight into the company’s self-service data culture. 💯 Side note
– developers who use spaces make more money
than those who use tabs. 💪
Does your company know what to do with all its data?
👀 Article by Thomas C. Redman for Harvard Business Review. In short, Redman suggests the following seven methods to put data to work
- Make better decisions
- Innovate products, services, and processes
- Informationalise products, services and processes
- Improve quality, eliminate costs, and build trust
- Provide content
- Exploit asymmetries