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A recipe for data: lessons from EUDAT

A recipe for data: lessons from EUDAT | Data & Informatics | Scoop.it
EUDAT understands what is important when it comes to common data services.
Stephen Dale's insight:

EUDAT provides an integrated solution for finding, sharing, storing, replicating, staging and performing computations with primary and secondary research data. EUDAT is currently rolling out its first set of data services, with more to come in 2014. Based on your data needs you can pick one or more:-

 

B2SHARE: a user-friendly, reliable and trustworthy way for researchers and communities to store and share small-scale research data coming from diverse contexts.

 

B2SAFE: a robust, safe and highly-available replication service allowing community and departmental repositories to replicate and preserve their research data. Different access and deployment options are offered which range from tailored solutions for Fedora and DSpace repository systems via simplified utilization options to a full integration of repositories with the network of EUDAT data nodes.

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Data & Informatics
The application and usage of data along with the interaction between people, organisations and technology
Curated by Stephen Dale
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Social Media Strategy: Why Insight and Evidence is So Important

Social Media Strategy: Why Insight and Evidence is So Important | Data & Informatics | Scoop.it

Via janlgordon
Stephen Dale's insight:

A timely call for a dispassionate, unbiassed and "agnostic" analysis of data to discover what it is really telling us, and then acting on this information. Sounds obvious? Then why are we so often misled through our ignorance of good and accurate data analysis? 

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Daniel Maina's comment, June 4, 2014 7:36 AM
Thanks for sharing:)
Daniel Maina's comment, June 4, 2014 7:36 AM
Thanks for sharing:)+
Daniel Maina's comment, June 4, 2014 7:37 AM
Thanks for sharing:)
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Human or Machine: The Most Important Question in Analytics

Human or Machine: The Most Important Question in Analytics | Data & Informatics | Scoop.it
Tom Davenport discusses two types of data scientist, two types of decision makers, and why the distinctions matter.
Stephen Dale's insight:
It’s not humans who are the recipients and decision makers of data and analysis, it’s machines. Machines are making all or most of the decisions in areas like programmatic advertising, search engine optimization, credit approval, insurance underwriting, Internet of Things applications, and many more. 

Machines are necessary for these jobs because there is a vast amount of data involved, and the results have to be so granular that many different models are involved. The decisions also need to be made in real time, and humans can’t react at that pace.
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Big data: Getting a better read on performance | McKinsey & Company

Big data: Getting a better read on performance | McKinsey & Company | Data & Informatics | Scoop.it
The benefits match those of earlier technology cycles, but companies must scale up their data-analytics skills to reap the gains.
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McKinsey survey results indicate that to produce significant returns, companies need to invest substantially in data-analytics talent and in big data IT capabilities. #bigdata

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An Introduction to Data Visualization

An Introduction to Data Visualization | Data & Informatics | Scoop.it
Guest blog post by Divya Parmar
After data science, which I discussed in an earlier post, data visualization is one of the most common buzzwords thrown around in the tech and business communities. To demonstrate how one can actually visualize data, I want to use one of the hottest tools in the market right now: Tableau. You can download Tableau Public for free here, and the “Cat vs. Dog” dataset can be found here. Let’s get started.

1. Play around with the data and find what looks interesting.
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One of the hottest tools in the market right now: Tableau. You can download Tableau Public for free at: https://public.tableau.com/s/

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6 lessons academic research tells us about making data visualizations | Poynter.

6 lessons academic research tells us about making data visualizations | Poynter. | Data & Informatics | Scoop.it
A Global Leader in Journalism | Journalism training, media news & how to's
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6 lessons that we can learn from academia on the effective use of data visualisation. The practice of data visualisation straddles the line between science and art with the science—empirical research on effectiveness—informing data users how best to implement the art.

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Has our faith in data been misplaced?

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It’s relatively easy to collect information, aggregate it and apply standard algorithms to derive insights, but much harder to understand where the data comes from, what type of analysis is being applied and what types of error are involved.  Insights, even when powered by impressive technology, never come easy.  To get full value from data, you must understand it. 

 

Data leads to information, and information without context can lead us down may false avenues. The need for critical thinking is essential when presented with "evidence" manufactured from algorithms we don't understand. Look out for:  

 

Harvard economists published a working paper which warned that US debt was approaching a critical level. As it turned out, they had made a simple Excel error. 

And...the item on data journalism, reporting about the Ukraine. 

All quite worrying. #data #analytics #evidence

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Stephen Dale's curator insight, October 5, 2015 5:44 AM

It’s relatively easy to collect information, aggregate it and apply standard algorithms to derive insights, but much harder to understand where the data comes from, what type of analysis is being applied and what types of error are involved.  Insights, even when powered by impressive technology, never come easy.  To get full value from data, you must understand it. 

 

Data leads to information, and information without context can lead us down may false avenues. The need for critical thinking is essential when presented with "evidence" manufactured from algorithms we don't understand. Look out for:  


Harvard economists published a working paper which warned that US debt was approaching a critical level. As it turned out, they had made a simple Excel error. 

And...the item on data journalism, reporting about the Ukraine. 

All quite worrying. #data #analytics #evidence

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How to Tell Stories with Numbers

How to Tell Stories with Numbers | Data & Informatics | Scoop.it
A guide to using the techniques of data mining and data journalism to create compelling, original content in the form of data visualizations.

Via Jim Signorelli,Story-Lab
Stephen Dale's insight:

Finding useful knowledge nuggets amongst the torrents of data is a skill in itself. Creating insightful stories that bring the data to life is an emergent skill practiced by data journalists.  An excellent article with lots of useful references for anyone who aspires to blend data analytics with storytelling.

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Carlos Silva's curator insight, September 16, 2015 10:40 AM

añada su visión ...

ManufacturingStories's curator insight, September 16, 2015 1:20 PM

#BigData #Numbers #Analytics #ContentMarketing

Sarah McElrath's curator insight, September 16, 2015 2:04 PM

Unless the data tells a story, no one cares.

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20/20 Foresight

20/20 Foresight | Data & Informatics | Scoop.it

Many business leaders need to improve their
perceptual acuity. Here’s how you can develop the ability to look around corners — and become a catalyst for change.

 


Via Kenneth Mikkelsen
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Kenneth Mikkelsen's curator insight, August 25, 2015 10:06 AM

You can cultivate perceptual acuity by watching catalysts and adopting the disciplined practice of looking over the horizon and searching for new ideas, events, technologies, or trends — things that an imaginative person could combine to meet an unmet need or create a totally new product. As your acuity sharpens, you’ll spot catalysts more easily and begin to see the world as they see it: as full of new possibilities and opportunities.


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Data storytelling skills take on key role in analytics process

Data storytelling skills take on key role in analytics process | Data & Informatics | Scoop.it
Data storytelling has become a vital part of the analytics process in some organizations, which are tapping workers with good communications skills to explain analytics results to execs.
Stephen Dale's insight:

The article advocates an extension to traditional (science-based) data analytics teams to include journalistic skills in order to support more effective communication of findings and insights through data storytelling techniques. It is argued that this will help communicate the team's analytical findings in a way that corporate executives and business managers can easily understand. 


Reading time: 5mins

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Jean-Pierre Blanger's curator insight, July 30, 2015 4:42 PM

The article advocates an extension to traditional (science-based) data analytics teams to include journalistic skills in order to support more effective communication of findings and insights through data storytelling techniques. It is argued that this will help communicate the team's analytical findings in a way that corporate executives and business managers can easily understand. 

 

Reading time: 5mins

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How people engage with data visualisations and why it matters | CILIP

How people engage with data visualisations and why it matters | CILIP | Data & Informatics | Scoop.it

The skills that we and our participants identified as integral to ‘visualisation literacy’ include:

Language skills, to be able to read the text within visualisations (not always easy for people for whom English is not their first language).A combination of mathematical or statistical skills (knowing how to read particular chart types or what the scales mean) and visual literacy skills (understanding meanings attached to the visual elements of datavis) – sometimes called ‘graphicacy’ skills.Computer skills, to know how to interact with a visualisation on screen, where to input text, and so on.Critical thinking skills, to be able to ask ourselves what has been left out of a visualisation, or what point of view is being prioritised.
Stephen Dale's insight:

The skills identified as integral to ‘visualisation literacy’ include:

Language skills, to be able to read the text within visualisations (not always easy for people for whom English is not their first language).A combination of mathematical or statistical skills (knowing how to read particular chart types or what the scales mean) and visual literacy skills (understanding meanings attached to the visual elements of datavis) – sometimes called ‘graphicacy’ skills.Computer skills, to know how to interact with a visualisation on screen, where to input text, and so on.Critical thinking skills, to be able to ask ourselves what has been left out of a visualisation, or what point of view is being prioritised.

 

Reading time: 7mins

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Big data: are we making a big mistake? - FT.com

Big data: are we making a big mistake? - FT.com | Data & Informatics | Scoop.it
Five years ago, a team of researchers from Google announced a remarkable achievement in one of the world’s top scientific journals, Nature. Without needing the results of a single medical check-up, they were nevertheless able to track the spread of
Stephen Dale's insight:

From the article:


"Cheerleaders for big data have made four exciting claims... that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what causes what, because statistical correlation tells us what we need to know; and that scientific or statistical models aren’t needed because, to quote “The End of Theory”, a provocative essay published in Wired in 2008, “with enough data, the numbers speak for themselves”.


Unfortunately, these four articles of faith are at best optimistic oversimplifications. At worst, according to David Spiegelhalter, Winton Professor of the Public Understanding of Risk at Cambridge university, they can be “complete bollocks. Absolute nonsense.”

 

A well-rounded and informative piece of journalism from the FT.

 

Reading time: 12mins

 
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Proportional Pie Chart of the World's Most Spoken Languages

Proportional Pie Chart of the World's Most Spoken Languages | Data & Informatics | Scoop.it

There are at least 7,102 known languages alive in the world today. 

The 23 languages make up the native tongue of 4.1 billion people.


Via Ivo Nový
Stephen Dale's insight:

Each language is within black borders with the numbers of native speakers (in millions) by country. The colour of these countries shows how languages have taken root in many different regions.

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SageRave of Get Custom Content's curator insight, June 30, 2015 6:25 PM

Ladies and Gentlemen, find your Chinese tutors!

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Internet of Things video series: the tipping point has come

Internet of Things video series: the tipping point has come | Data & Informatics | Scoop.it
Predictions about the amount of devices that will be connected to each other via Internet of Things systems vary, but the numbers are always impressive: 50 billion to 212 billon devices. And although Van Der Bel says that the landscape can seem "big, noisy and confusing”, he offers consolation.
Stephen Dale's insight:

Rolls Royce and Formula 1 have been connecting monitoring technology for a long time, so there are lessons to be learned from the past. Now, a “tipping point has come”,  thanks to cheaper components and connectivity; the power of cloud computing; and the advancement of tools that enable you to exploit the data.


Video: 9mins

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How Does Business Analytics Help In Value Addition

How Does Business Analytics Help In Value Addition | Data & Informatics | Scoop.it
How can business analytics & data validation add value to the data gathered? What aspects are important to consider while business analysis in order to maintain quality?
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Oxford Internet Institute - Webcasts - Fact Factories: How Wikipedia's Logics Determine What Facts Are Represented Online

Oxford Internet Institute - Webcasts - Fact Factories: How Wikipedia's Logics Determine What Facts Are Represented Online | Data & Informatics | Scoop.it
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Heather Ford discusses how Wikipedia's logics determine what facts are represented online, including crowd-sourcing (e.g. wisdom of crowds). 

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IoT’s about us: Emerging forms of innovation in the Internet of Things

IoT’s about us: Emerging forms of innovation in the Internet of Things | Data & Informatics | Scoop.it
The observation that the Internet of Things encompasses people holds a number of transformative business and societal implications. This form of IoT innovation can be aggregated and analyzed to create fundamentally new types of products and services.
Stephen Dale's insight:

The implication of the IoT including people is that issues of privacy, transparency, data stewardship, and data ownership are paramount. Many of us enjoy the benefits of smarter homes, cars, and transportation networks. But few relish the thought of Internet companies being able to track our every move and make highly personal inferences and predictions based on the digital breadcrumbs we leave behind as we go about our daily activities. A trade-off must be struck between the benefits, innovations, and analytic insights that IoT brings with the need to maintain societies that people want to live in.

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Data, The Speed Of Light And You

Data, The Speed Of Light And You | Data & Informatics | Scoop.it
We -- humanity, that is -- created 4.4 zettabytes of data last year. This is expected to rise to 44 zettabytes by 2020. And no, I didn’t make up the word..
Stephen Dale's insight:

Data at the speed of light? It's possible, but at huge cost, and somehow we have to cope with moving 44 zettabytes of data around by 2020.

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15 Stunning Data Visualizations (And What You Can Learn From Them)

15 Stunning Data Visualizations (And What You Can Learn From Them) | Data & Informatics | Scoop.it
15 examples of data visualizations that will give you a clearer understanding of what makes a good visualization--and what makes a bad one.
Stephen Dale's insight:

In the seismic shift awaiting us, referred by some as the Industrial Revolution of Data, we have to get better and more efficient at creating innovative data visualization that make the complex easy to understand. Some great examples here.

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IBM’s Watson Won’t Be Replacing Humans Any Time Soon

IBM’s Watson Won’t Be Replacing Humans Any Time Soon | Data & Informatics | Scoop.it
There is little doubt that IBM's Watson artificial intelligence system is an incredible piece of technology, It's capable of searching across vast..
Stephen Dale's insight:

BM's Watson Artificial Intelligence System is capable of searching across vast repositories of unstructured data and returning answers to natural language queries, but it won't replace humans. Instead, the system will augment humans and help us to make better decisions.  

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Big Data Doesn’t Exist

Big Data Doesn’t Exist | Data & Informatics | Scoop.it
My customers always lie to me. They don’t lie about what they can afford. They don’t lie about how much (or how little) customer service they’ll need...
Stephen Dale's insight:

Liked this article because it challenges some of the sales hype around "big data". Size doesn't matter - quality and relevance does. I've always believed that "big" is relative. For an SME or startup with 20 or so employees, a 500Mb spreadsheet is probably big, and with the right tools they can probably extract many of the same insights that large multinationals can with their multi-terrabyte data warehouses.

 

This quote just about sums it up:  "....big data isn’t big, but good data is even smaller". #bigdata

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Machine Learning And Human Bias: An Uneasy Pair

Machine Learning And Human Bias: An Uneasy Pair | Data & Informatics | Scoop.it
“We’re watching you.” This was the warning that the Chicago Police Department gave to more than 400 people on its “Heat List.” The list, an attempt..
Stephen Dale's insight:

Talking about bias is uncomfortable, but can we afford to ignore this conversation in the machine learning space? To avoid scaling stereotypes or infringing on personal rights, we have to talk about this as it applies to each machine learning algorithm that aims to identify and categorize people.

 

Transparency in the inputs to such algorithms and how their outputs are used is likely to be an important component of such efforts. Ethical considerations like these have recently been recognized as important problems by the academic community.

 

It’s easy to imagine how the Chicago PD Heat List could be used in a responsible way. It’s also easy to imagine worst-case scenarios: What if Senator Joe McCarthy had access to personal analytics during the communist witch hunts of the late 1940s and 50s? Today, what if countries with anti-gay and anti-transgender laws used this technology to identify and harm individuals?

 

Reading time: 7mins

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DEXSTR, a Translational Science Company's curator insight, August 3, 2015 8:42 AM

Talking about bias is uncomfortable, but can we afford to ignore this conversation in the machine learning space? To avoid scaling stereotypes or infringing on personal rights, we have to talk about this as it applies to each machine learning algorithm that aims to identify and categorize people.

 

Transparency in the inputs to such algorithms and how their outputs are used is likely to be an important component of such efforts. Ethical considerations like these have recently been recognized as important problems by the academic community.

 

It’s easy to imagine how the Chicago PD Heat List could be used in a responsible way. It’s also easy to imagine worst-case scenarios: What if Senator Joe McCarthy had access to personal analytics during the communist witch hunts of the late 1940s and 50s? Today, what if countries with anti-gay and anti-transgender laws used this technology to identify and harm individuals?

 

Reading time: 7mins

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Rise of the Data Visualization Competency Center

Rise of the Data Visualization Competency Center | Data & Informatics | Scoop.it
You re already familiar with the business intelligence competency center (BICC). Here s how data visualization takes the competency center model to its next level.
Stephen Dale's insight:

Advocating the role of the recently formed " Data Visualizaruion Competency Centre", which promotes successful data visualization using the right kind of graphicacy to correctly interpret and analyze data, as well as employing the right combination of design principles to curate a meaningful story.

 

Reading time 6mins

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A little known hack from Japan to get your notebook organized

A little known hack from Japan to get your notebook organized | Data & Informatics | Scoop.it

The back of your notebook will act like a tag list or index. Every time you create a new entry at the front of the book you’re going to “tag” it.

For example let’s imagine you’re keeping a notebook for recipes and you just wrote down a Chinese recipe on the first page.

Next you’d go to the last page and create the tag ‘Chinese’ by writing it on the first line right next to the papers left edge.

Now you’d go back to the first page where the recipe is and on the exact same line as the ‘Chinese’ label you just wrote you’d make a little mark on the right edge.

You’d make this mark so that even when the notepad was closed the mark would be visible. After repeating this for various recipes you’d now have various tags visible on the notebooks edge.


Via Howard Rheingold
Stephen Dale's insight:

I love the simplicity and elegance of this. "Analogue tagging"

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Chad Gorski's curator insight, July 10, 2015 9:55 AM

Self-explanatory but simple way to organize an old-school notebook using index markings on the edge of each page.

Deanna Mascle's curator insight, July 10, 2015 3:52 PM

Not little known among the #NWP teachers I know...

Dave Wee's curator insight, August 3, 2015 7:12 PM

Tagging is a great infotention tool. Why confine it to digital media? h/t @Bopuc

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Enterprises Don’t Have Big Data, They Just Have Bad Data

Enterprises Don’t Have Big Data, They Just Have Bad Data | Data & Informatics | Scoop.it
PayPal co-founder and venture capitalist Peter Thiel commonly harps on the tech community for overusing buzzwords like “cloud” and “big data.” He’s..
Stephen Dale's insight:

Enterprises have historically spent far too little time thinking about what data they should be collecting and how they should be collecting it. Instead of spear fishing, they’ve taken to trawling the data ocean, collecting untold amounts of junk without any forethought or structure. Deferring these hard decisions has resulted in data science teams in large enterprises spending the majority of their time cleaning, processing and structuring data with manual and semi-automated methods.


Reading time: 5mins

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An executive’s guide to machine learning | McKinsey & Company

An executive’s guide to machine learning | McKinsey & Company | Data & Informatics | Scoop.it
It’s no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix. A McKinsey Quarterly article.
Stephen Dale's insight:

"A frequent concern for the C-suite when it embarks on the prediction stage is the quality of the data. That concern often paralyzes executives. In our experience, though, the last decade’s IT investments have equipped most companies with sufficient information to obtain new insights even from incomplete, messy data sets, provided of course that those companies choose the right algorithm. Adding exotic new data sources may be of only marginal benefit compared with what can be mined from existing data warehouses. Confronting that challenge is the task of the “chief data scientist.”


But the article does not make clear how to "choose the right algorithm". Are we becoming over-dependent and too trusting of machines and algorithms that mots of us don't understand?

 

Reading time: 15mins

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June 2015 Newsletter: Fostering a Data Ecosystem

June 2015 Newsletter: Fostering a Data Ecosystem | Data & Informatics | Scoop.it
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A letter from the Development gateway CEO:


To foster a stronger data ecosystem, Development Gateway continues to engage with governments to support country ownership of data for better decision-making; with international organizations dedicated to improving data interoperability and uptake; and with innovative initiatives that work to close the feedback loop between governments and citizens.

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