Google Dataset Search out of beta

Google's Dataset Search (https://datasetsearch.research.google.com/) is now out of beta, with some new features added.

Across the web, there are millions of datasets about nearly any subject that interests you. If you’re looking to buy a puppy, you could  find datasets compiling complaints of puppy buyers or studies on puppy cognition. Or if you like skiing, you could find data on revenue of ski resorts or injury rates and participation numbers. Dataset Search has indexed almost 25 million of these datasets, giving you a single place to search for datasets and find links to where the data is. Over the past year, people have tried it out and provided feedback, and now Dataset Search is officially out of beta.

https://blog.google/products/search/discovering-millions-datasets-web/

Data management for data intensive research (i.e. eResearch) in a nutshell

Eleven tips for working with large data sets

“It’s a mindset,” says Teal, “treating data as a first-class citizen.

https://www.nature.com/articles/d41586-020-00062-z

IEEE Spectrum: Documenting algorithm designs for machine learning

From IEEE Spectrum:

Hey, Data Scientists: Show Your Machine-Learning Work
Documenting software development is standard practice—the same should hold for algorithm design

In the last two years, the U.S. Food and Drug Administration has approved several machine-learning models to accomplish tasks such as classifying skin cancer and detecting pulmonary embolisms. But for the companies who built those models, what happens if the data scientist who wrote the algorithms leaves the organization?
In many businesses, an individual or a small group of data scientists is responsible for building essential machine-learning models. Historically, they have developed these models on their own laptops through trial and error, and pass it along for production when it works. But in that transfer, the data scientist might not think to pass along all the information about the model’s development. And if the data scientist leaves, that information is lost for good.
That potential loss of information is why experts in data science are calling for machine learning to become a formal, documented process overseen by more people inside an organization.

Read the rest of the article at:
https://spectrum.ieee.org/computing/software/hey-data-scientists-show-your-machinelearning-work

Nature: Google health-data scandal spooks researchers

"Academics seeking health data for research rather than commercial purposes must typically get approval from an ethical-review committee before they can start a project. The researchers also often strip identifying information from the records they work with. Commercial uses of personal data don’t necessarily undergo the same review"

https://www.nature.com/articles/d41586-019-03574-5