Welcome to Things we read this week, a weekly post featuring articles from around the internet recommended by BMJ Labs.
- Will James Gregg, Christopher Erdmann, Laura A D Paglione, Juliane Schneider, Clare Dean review the literature to identify the challenges, opportunities, and gaps in knowledge with regard to the use of metadata in scholarly communications. For us, the biggest challenge to improving our data is probably the information gap between the people that set metadata standards and how we, as publishers, are meant to implement them – what have we got to change in our JATS XML to create that specific output? More community-run QA tools would also be super helpful.
- TechDirt has a roundup of the spat between between Elsevier, Citationsy, Martin Paul Eve and ‘the site that must not be named’. (We’re been caught linking to this particular site too)
- In an interesting development Springer Nature and City, University of London have formed a new partnership to support researchers with research data management. Sometimes tools are the answer, sometimes people might be a better option.
- Is data the new oil? Adam Steventon explores the strengths and limitations of this analogy when thinking about how data is used to tackle health and care challenges.
- Rob Fitzpatrick has produced an awesome (and short!) book on how to talk to customers & learn if your business is a good idea. There’s also a free email course to help audit your customer conversations and spot the big mistakes. “The belief that any question is a good question and any data is good data is called the feedback fallacy. It’s simply not true. And if you’re collecting bad data, then 100% of the time you’ve spent on customer learning is worthless. Fortunately, the problem is easily fixed. By asking good questions and running a good process, you can avoid the bad data, collect the good data, and also save a ton of time. “
- Amy Webb on How to Do Strategic Planning Like a Futurist. “Deep uncertainty merits deep questions, and the answers aren’t necessarily tied to a fixed date in the future. Where do you want to have impact? What it will take to achieve success? How will the organization evolve to meet challenges on the horizon? These are the kinds of deep, foundational questions that are best addressed with long-term planning.”
- Strategyzer have developed a tool to assess the progress that innovation teams are making in their quest to find business models that work.
- Karen Hao at MIT Technology Review have put together some super helpful flowcharts to explain what is machine learning? and what is AI?.
- Matt Beane, assistant professor at the University of California, Santa Barbara, talks about how robot-assisted surgery is disrupting the traditional learning pathway of younger physicians on the HBR IdeaCast. He says this trend is emerging in many industries, from finance to law enforcement to education and shares lessons from trainees who are successfully working around these new barriers.
An awesome ‘edible abstract’ from Michele Melchior….
— Michele Melchior (@Mussel_Michele) August 5, 2019