EU Wants All Scientific Papers to be Open Access by 2020


The European Union recently decided that enough was enough and will be granting open access to all European scientific papers to the public by 2020.

From a legal perspective, this mandate can only be enforced on publically funded research, but they are hoping that privately held research firms will soon follow suit. Scientific journals are ultimately not very happy about this decision, as the previous subscription-based models would effectively be eliminated. In the current state, scientific journals can also selectively release the content that they want to the media, given them control over the knowledge that gets spread publically.

This decision was the result of a meeting between the Competitiveness Council, which included leaders in the scientific and technological communities.  All parties were in agreement with the goal to make scientific papers open access, according to Futurism, and the goal is to have it completed by 2020.

Making all of this knowledge open access would mean that the entire world would have access to millions of papers and scientific research that usually only paid subscribers and other higher up members of the scientific community would have.

The deadline is actually a fairly close one, and the council has provided no information on how the progress will be overseen. Making sure that every paper is open to the public will require a lot of work and oversight, but plans are beginning to be formulated on how to accomplish this task. Hopefully, having open access to all of this research will allow generations to become more science literate and increase the overall state of knowledge and education.

Original source

Not A Data Scientist? You Can Still Be Data Savvy


Christian Bonilla says he has been amused for a while by the tone of articles he’s read that marvel at the rise of the data scientist role, while not every article went so far as to declare that data scientists would have the “sexiest job of the 21st Century” as Harvard Business Review did, most of the posts seen lately echoed the we-have-seen-the-future tone.
Christian doesn’t think they’re necessarily wrong (although this short Fortune article is a good reminder that the laws of supply and demand apply to data scientists too) but he doesn’t see what is surprising or a new about this trend. If The Onion were covering this story, he’d expect a headline like: “New study reveals that people who are good at math and programming are employed, affluent.”

Where’s the news here? People with math and programming chops have been getting rich on Wall Street since the seventies. As more companies generate big data, the need for these skills has expanded to new industries, to say nothing of the demand for these skills in the tech sector, but it’s all part of a long-term upward trend of the value of quantitative skills.

At many companies, the mandate of the “customer insights” team is to serve as a shared resource for other departments when they need someone who can understand the damn data and answer their questions. And why is this?

The systems companies have in place are partly to blame. Many enterprises, particularly ones that grew by acquisition and inherited multiple IT departments as a result, store their data in systems that are difficult for non-technical employees to use. That alone discourages the vast majority of people from ever touching their company’s raw data. But the larger obstacle is simply that even if decent tools are available, it takes know-how and patience most people don’t have to analyze data that’s in a relational database as opposed to in a dashboard or an Excel file. It’s not just learning SQL, either. Understanding a company’s data model and how it stores data well enough that you can query it accurately takes patience and a lot of trial and error. There’s a big difference between the data you work with in business school and what you often see in the real world in terms of data reliability and quality. This is why the vast majority of people rely on aggregated reports and cleansed data they get from their IT departments; they can trust the data without thinking twice about it.

The problem with relying on dashboards and pre-built reports to do your analysis is that it’s hard to do work that sets you apart when you’re looking at the same small sliver of the facts as everyone else.

Data quality is important, and companies emphasize having a single version of the truth for good reason, but it can seriously constrain your creativity. That’s the kind of analysis that makes your boss lean in and listen to what you’re saying. Being able to do it on your own is ten times better than having to ask someone else to do it for you.

Best of all, you don’t need more than junior-high math to answer that question. All you need is an inquisitive mind and the right data.

Christian Borilla: It’s said that smart people ask hard questions while really smart people ask simple ones. Indeed, many of the most important questions you can ask about your company are the simplest. Why do people choose our products over our competitors’? Why do customers leave us when they do? Should we offer discounts to boost sales? When you’re up to your neck in being a good do-er it’s easy to lose sight of these fundamental questions because people don’t ask you to answer them when you’re still green. But oh, the liberation when you can! This is how you can begin to understand and contribute to solving some of themost important challenges facing your business today.

Learning SQL and how to interrogate a company’s raw operations data to answer fundamental questions about its business was probably the most useful business skill I acquired in the early years of my career. As it turned out, I was a natural at asking good questions and just needed the tools to be able to answer them. But more than that, a marvelous thing happens inside the business person’s mind as a result of analyzing a business through its internal databases: the discipline of querying databases teaches you to ask better questions. More specifically, it teaches you how to structure big questions in such a way that they can actually be answered with precision. It forces you to clean up lazy thinking, because computers don’t allow vague questions. It teaches you to think in sets, an incredibly valuable mindset, without even realizing it. In short, it makes you a better business person by allowing you to more fully capitalize on your domain expertise. I know it changed my career tremendously for the better.

Original Source (Smart Like How)