#yourdataserver

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YDS #21: My data recommendation for innovation

Hello, how are you? If you’re looking for innovation using data process you’re in the right place!

Let’s start with a reference, what about “The framework”? Is the main one for innovation surveys. You can use it for measuring innovation outcomes based on the approach of many questions used.

There are results that show many report innovations about data, and is available to download.

This innovation is based on many countries with a database query tool, available to help understand the innovation and technology across various firms. So, I highly recommend The Framework!

[yourdataserver-blog]

YDS #16: My data recommendation for management

Hello. You heard about data management, I hope you did. There is a large stage of work on data that will ensure accessibility and longevity of our data.

However, there are many plans that you can follow, one of them is my favorite:

Start by complete your DMP so you’ll not sidelined when start collecting data.

When calculating the data management costs go by creation, analytics, storage, processing, preservation and sharing. There are many types of that and recommended/avoid formats but when naming those try to be consistent and descriptive.

[yourdataserver-blog]

YDS #15: Big data, big problems! I tell you what to do.

How are you? As you may know there is limits for everything even the amount of digital data available.

Before talk about the problems let’s say something good first, alright. The big data improved government policymaking, programs, scholarship and much more!

But even Google’s search engine is not immune. It doesn’t matter the enormous amount of data overseen by some of the world’s top data scientists, its results are susceptible to “search-engine optimization” and manipulation, such as “Google bombing,” “spamdexing,” and other methods serving parochial interests…

The good news is that the hazards of Big Data can be largely avoided. 

[yourdataserver-blog]

YDS #12: My data recommendation for science

Hi, welcome to another resumed, useful and unique advice. I bring to you my life science research that increasingly relies on large-scale computational analyses. You may ask, what’s Software packages? They are critical in data-driven studies. Do not use proprietary software that cannot be accessed by other researchers.

In my opinion, the best solution would be train scientists in data science as a element of a cultural revolution for data analysis. The young scientists in data science training. Maintain the lab Github repository and trainees can take advantage of online learning platforms that provide available training in tools and practices for data-driven research.