A glimpse of HTML 5.1
The release of the HTML5 standard about two years ago was a big deal in the web development community. Not only because it came packing an impressive list of new features, but also because it was the first major update to HTML since HTML 4.01 was released in 1999. You can still see some websites bragging about the use of the “modern” HTML5 standard today.
Fortunately, we didn’t have to wait quite that long for the next iteration of HTML. In October 2015, the W3C started working on the draft of HTML 5.1 with the goal of fixing some of the issues that were left open in HTML5. After many iterations, it reached the state of “Candidate Recommendation” in June 2016, “Proposed Recommendation” in September 2016 and finally a W3C Recommendation in November 2016. Those who followed this development probably noticed that it was a bumpy ride. A lot of initial HTML 5.1 features were dropped due to poor design or a lack of browser vendor support.
While HTML 5.1 was still in development, the W3C has already started working on a draft of HTML 5.2 which is expected to be released in late 2017. In the meantime, here’s an overview of some of the interesting new features and improvements introduced in 5.1. Browser support is still lacking for these features but we’ll refer you to at least some browsers which can be used to test each example.
The following SitePoint article covers the following:
Context Menus Using the
Details and Summary Elements
More input types —
Responsive Images ( The
srcsetImage Attribute, The
sizesImage Attribute, The
Validating Forms with
Allowfullscreen for Frames
Read the article here: SitePoint
Clinical trials were conducted with the help of paper based case report forms (CRFs) for
decades. This was – being -changed during the last 30 years. In this presentation I show
how the electronic CRFs (eCRFs) superseded the paper based CRFs gradually.
There was a very complex regulation and well established practice for managing of paper
CRFs by the 80’s. It turned out that this regulation and practice cannot be adequately applied
to eCRFs. The processes of randomisation, query handling or adverse event reporting
should have been reconsidered.
Safety concerns are always present with respect to a computer system. But if this system is
for collection of sensitive data in order to obtain a valuable pattern, these concerns are
increased. I give examples how the regulation – Title 21 CFR Part 11 – controls of data
It is also important to understand how other standard, like CDISC (Clinical Data Interchange
Standards Consortium) and their development influence the implementation of eCRFs.
In a study design the choice between a paper based and an electronic CRF is partly an
economical question. This aspect was already investigated in detail, and I will summarize
how costs of the two tools can be compared.
At the end I would like to give a view on the most probable future of the eCRF concept.
What is Data Visualization?
Data Visualization is a way of representing complex data and stats in a pleasing, visually-appealing way. Visual data may include components like pie and graph charts, maps or tables, and can be presented in different forms, such as infographics, videos, illustrations and interactive reports.
Why is it important? The answer is simple. Our brains absorb visual information better, faster, more easily.
Benefits of Data Visualization
The benefits of visualizing data include:
- providing clearer information for clients
- making it easier to view and analyze patterns and trends
- enabling interaction with the data
- allowing for more information to be absorbed, and more quickly
- better identify peaks and troughs.
Sitepoint’s arcticle is going to assess how a new tool, Google Data Studio, can help us build beautiful and interactive reports.
Google Data Studio
Google Data Studio (GDS) is a new tool by Google that makes it easy to create beautiful, engaging, responsive, branded and interactive reports. It does this by pulling metrics from Google’s properties, such as Google Analytics, Adwords and YouTube Analytics, as well as spreadsheets and SQL databases.
For the article, the author will be using Data Studio to create a visual report using Google Analytics data. To do this, you first need to have an active Google Analytics property that is properly integrated with the website.
The same applies to other reports. If you wish to pull the data from your Adwords or YouTube Analytics, make sure to sign in with an appropriate Google account that has that data.
Read the Setup Guide on Sitepoint: Here
OpenTrialsFDA works on making clinical trial data from the FDA (the US Food and Drug Administration) more easily accessible and searchable. Until now, this information has been hidden in the user-unfriendly Drug Approval Packages that the FDA publishes via its dataportal Drugs@FDA. These are often just images of pages, so you cannot even search for a text phrase in them. OpenTrialsFDA scrapes all the relevant data and documents from the FDA documents, runs Optical Character Recognition across all documents and links this information to other clinical trial data.
Explore the public beta version through a new user-friendly web interface at https://fda.opentrials.net.
Today’s challenge is a geographical one. Do you know which cities are the most populated cities in the world? Do you know where they are? China? USA? By way of contrast, do you know which cities are the smallest cities in the world?
Today we want to show you where you can find the largest and the smallest cities in the world by population on a map. While there is general agreement from trustworthy sources on the web about which are the most populated cities, agreement becomes sparser when looking for the smallest cities in the world. There is general agreement though about which ones are the smallest capitals in the world.
We collected data for the 125 world’s largest cities in a CSV text file and data for the 10 smallest capitals of equally small and beautiful countries in another CSV text file. Data includes city name, country, size in squared kilometers, population number, and population density. The challenge of today is to localize such cities on a world map. Technically this means:
- To blend the city data from the CSV file with the city geo-coordinates from the Google Geocoding API into KNIME Analytics Platform
- Then to blend the ETL and machine learning from KNIME Analytics Platform with the geographical visualization of Open Street Maps.