Digital Memory
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Digital Memory
About tools and methods to remember your (digital) path
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the Data Liberation Front

This site is a central location for information on how to move your data in and out of Google products. Welcome. Click on any product link on the left hand side for more information on how to liberate your data from (or to!) that product.

In June of 2011, the Data Liberation Front announced their first revolutionary product. Read more on our Google Takeout page.

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Spark: Visualizing Physical Activity Using Abstract Ambient Art | Quantified Self

Spark: Visualizing Physical Activity Using Abstract Ambient Art | Quantified Self | Digital Memory | Scoop.it

Chloe Fan has been self-tracking since she was 14 years old and saw the first Harry Potter movie in theaters. She is currently a Ph.D. student at Carnegie Mellon’s Human Computer Interaction Institute. After finding her passion for data visualization and information design for self-tracking tools, she has decided to take a year off grad school to pursue her dreams at full speed in the Bay Area. She is available for consulting or full time positions!

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Google Accounts

Google Accounts | Digital Memory | Scoop.it

What is Me-trics?
Me-trics is a set of tools that helps you improve your life by tracking and learning from your own behavior, and the behaviors of others.

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Why should I use Me-trics?
Tracking your behavior allows you to find patterns and correlations that are difficult to identify on a day by day basis. You might smoke more when your stress level is higher. You might be happier on days that you spend less time online. Or, you might discover that you really haven't been trying to be healthier... at least on the days you drink... you know, all of them.

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How do I use Me-trics?
The short answer is:
Get a Google Account and sign in. Pick some things you'd like to track. Track said things by answering the questions (repeat this step regularly… forever). Open the "Analyzer" and look for patterns and correlations.

A more detailed screen-cast of this process will be posted soon.

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What is a survey?
A survey is just a collection of things you're tracking. Many users only have one, and are darn happy to have it! However, the real data fiends find it easy to have questions broken down into sets, which we named "surveys". You might have a before work survey, during work survey, and evening survey, each with questions you usually answer at different times of the day.

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How much does it cost?
Comparable data tracking sites might charge $50, even $100 dollars per minute!! Me-trics is only three easy payments of $0! That's right, all the fun of answering questions and looking at data for free!!!

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Will you sell my personally identifiable data?
Nope.

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Then how does Me-trics make money?
Volume!

Actually, Me-trics headquarters is in the great state of Michigan, where we've learned to live without money.

We may introduce new features in the future that might require a paid subscription. However, Me-trics is currently a labor of love (and perhaps a bit of narcissistic obsession with personal activity). Enjoy.

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I have a question / suggestion / complaint / bag of flaming excrement – how do I contact you?
We use Get Satisfaction – ask away. If you would like to contact us directly, send your missives to info@me-trics.com

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DATAMI

The DATAMI project/application intends to support users in managing their interactions with content on the Web. More and more information is being exchanged between individual users and a large variety of online organizations. From the point of view of the Web user, these exchanges are happening in a fragmented, un-managed way, which makes it harder for them to take full benefit from this content, to obtain an overview of their online activities and make efficient reuse of these activities. We will build on the Stanbol Services to produce a semantic personal Web history for users, enriched with information about the various types of entities (websites, people, organisations, places) they encounter. The need for such types of application relate in particular to scenarios where individuals rely on information found on the Web, but without necessarily having at their disposal the tools, time or capacity to organise the online content they interact with. In other terms, what DATAMI intends to do is to provide ways to automatically, without affecting the user’s normal activities, monitor and organise the resources he/she is interacting with, so that it can be later explored, re-used and retrieved.
Objectives

In summary, DATAMI will produce the following outcome:
A method and a tool to locally track a user’s activities online, through monitoring the Web traffic generated by the user
Back-end processes using IKS Stanbol services to semantically analyse the online resources involved in the user’s Web interactions to extract common, important entities in these resources, and keep track of their relation to the resources and user’s interactions
A front-end interface showing an entity cloud based on information extracted by the processes described above. This interface (similar to a tag cloud) will emphasise entities based on their importance and allow the user to explore their semantic representation, as well as their connections with the user’s Web interactions

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Stephen Wolfram Blog : The Personal Analytics of My Life

Stephen Wolfram Blog : The Personal Analytics of My Life | Digital Memory | Scoop.it
Utilizing data analysis capabilities in Wolfram|Alpha Pro, Stephen Wolfram looks at his quantified self based on his large collection of lifelogging data.
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The Personal Analytics System — DASHbay

The Personal Analytics System — DASHbay | Digital Memory | Scoop.it

Corporations aren’t the only ones who can benefit from better data collection and analysis methods. Personal activity trackers now give the power of automated data collection and analysis to consumers. The websites even follow the Metrics Driven Management technique of a dashboard that displays all your pertinent metrics with the ability to drill-down for additional details. Data is now everywhere, even in your every day activities. Companies are now using data collection techniques and business intelligence technology to bring analysis to all aspects of our lives.

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Hacking Knowledge: 77 Ways to Learn Faster, Deeper, and Better | OEDb

Hacking Knowledge: 77 Ways to Learn Faster, Deeper, and Better | OEDb | Digital Memory | Scoop.it
Tips on how to learn better than ever before.
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Tagging Play - Pew Research Center

This survey is a little bit too old...

"Taggers look like classic early adopters of technology. They are more likely to be under age 40, and have higher levels of education and income."

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Jenn's PLE Conceptual Framework « Jenn's Studious Life

Jenn's PLE Conceptual Framework « Jenn's Studious Life | Digital Memory | Scoop.it
It took me many months and many iteration to come up with this PLE conceptual framework to encourage learner autonomy. *Those highlighted in grey are the focus of my research. Regardless of the learning environments, ...
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Doc Searls Weblog · The absent market for personal data

Not so sure I fully agree with this:

"Selling one’s personal data amounts to marketing exposure of one’s self. It’s like stripping, only less sexy. And for a lot less money".

 

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The Seven Needs of Real-Time Curators — Scobleizer

1. Real-time curators need to bundle. 
2. Real-time curators need to reorder things. 
3. Real-time curators need to distribute bundles. 
4. Real-time curators need to editorialize. 
5. Real-time curators need to update their bundles. 
6. Real-time curators need to add participation widgets. 
7. Real-time curators need to track their audience. 

 

Why are companies ignoring our needs? In talking with CEOs at companies in the real-time space I’ve identified a few reasons:
1. Building-cross-platform tools is difficult. 
2. Fear of platform vendors. No one builds these kinds of features because they are scared that Facebook or Google will build these kinds of APIs and kill their businesses.
3. Assumption that these features are only going to be used by “weirdos or professionals or both.”

 

VERY INTERESTING (BUT MANY OUTDATED) COMMENTS

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Quantified Self | Self Knowledge Through Numbers

Quantified Self | Self Knowledge Through Numbers | Digital Memory | Scoop.it

There has been an exponential rise in the number of people talking and writing about Quantified Self. Some call it a movement, some call it “the next big thing.” In most, if not all cases, there is a an overwhelming emphasis on the role of technology. Be it new sensor systems, applications, or analytical tools, there is an interesting need to equate Quantified Self with technology. It should come as no surprise then that when people start asking me about Quantified Self one of the first questions I hear is, “What device should I buy?” or “What is the killer app/tool/service for QS?” Maybe this is something you’re asking yourself so let’s talk a little bit about how tools fit into the Quantified Self experience.

Think about the last home improvement project you started. Whether it was fixing a leaky faucet or replacing your carpet you most likely went about your work in a simple step-wise fashion: 1) Identify the problem, 2) Examine possible solutions, 3) Identify the most appropriate solution, 4) Gather the right tools to implement the solution, and then 5) Fix the problem. Tools don’t come in to equation until late in the game. I think the same can be said for your self-tracking or self-experiment. The tool is not the piece that defines what you should be tracking or what experiment you should run. It is merely there to help you gather information that is necessary to produce a new piece of knowledge. And that is the point of this whole endeavor – creating new knowledge. Unfortunately, this is often overlooked because in most cases knowledge isn’t as sexy as a new shiny wireless device.

So if tools are not the end-game here, what is? Let’s take a quick look at the Three Prime Questions:

What did you do?
How did you do it?
What did you learn?
Those three simple questions are great guiding principle for Quantified Self and your own personal self-experimentation. You’ll notice that technology isn’t mentioned in our methodology (what some consider to be a simplified scientific method). In fact, the most important aspect of this methodology, and where we recommend you start your self-experimentation journey, is the last question: What did you learn? Perhaps it is better to phrase it this way, “What do you want to learn?” What is the question that has been nagging you lately. What lifehack, productivity secret, or health tip have you come across and wondered. “Is that true?” or “Will that work for me?” This is where all good experiments start. Whether it is a million dollar experiment in a renowned university lab or a personal experiment that starts in your kitchen, the production of new knowledge starts with a good question.

Only after you’ve identified and refined your question should you begin to look into tools that will help you produce the information that helps you develop the understanding that may lead to an answer. You may even want to develop a methodology or experimental plan before identifying what tools works best for you. In any case, keep in mind that the goal of self-experimentation, of Quantified Self, is to produce and share new knowledge.

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UCIAD

UCIAD | Digital Memory | Scoop.it

Generally, UCIAD II is looking at two complementary aspects of the general idea of user-centric activity data (i.e., giving back to users the data about their own activity):

What are the concrete scenarios in which users can benefit from having access to, understanding of and control over their own activity data?
What are the changes in terms of organisations’ policies on data access, data protection, data licensing and privacy that are made necessary by such approaches.
The goal of UCIAD 2 is therefore to provide early-stage answers to these questions through a study, realised with a group of users (students and staff) of the Open University websites. In a nutshell, we will give these users early dedicated access to the evolving set of tools prototyped in UCIAD, populated with their activity data on the Open Universities websites. We will record and reflect with them on their usage of these tools and their reactions to them, in order for them to act as a ‘focus group’ to establish what the access to their own activity data could enable, and what can be judged acceptable in terms of the organisation’s policies on managing these data.

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Fluxtream

Fluxtream | Digital Memory | Scoop.it

Fluxtream is now open source software. We also just published a first draft export API. Fluxtream provides a fantastic work bench to experiment with personal data and imagine new quantified-self experiences. It is simple to operate within a user community and works with commonplace technologies like MySQL, jQuery and JSP. The next version of the frontend will offer a Twitter Bootstrap-based framework that makes it easy to create new widgets, and it works on tablets and laptops or desktops alike. Today, Fluxtream is already cross-pollinating with another great quantified self open source project called BodyTrack (video).

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“Little Data” – Personal Analytics Goes Mainstream

What Comes Next

The future of these personal analytics is very similar to the future of “Big Data” in general. As more and more data is created, creative ways of cross-referencing the information, and allowing people to gain insight from the data will make this kind of data collection ever more useful. As more and better sensors are created, we discover innovative ways of combining their data with existing technology like the smartphones we all carry around with us, the number and value of these analytics will only increase.

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Personal analytics: 7 years of personal email activity

Personal analytics: 7 years of personal email activity | Digital Memory | Scoop.it
Last month, I posted some analytics on my behavior over the course of 12 years of work emails that I've sent. At the time, I had data for just my work email and just for sent messages.
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Facebook Election Is a Bust: 0.00038% of Users Voted on Privacy Change

Facebook Election Is a Bust: 0.00038% of Users Voted on Privacy Change | Digital Memory | Scoop.it
The online voting poll determining which Facebook policies will be put into place closed this morning with a serious lack of voter interest.
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How to Get Your Kindle Highlights into Evernote

How to Get Your Kindle Highlights into Evernote | Digital Memory | Scoop.it
What if you could get all your Kindle notes and highlights into Evernote where they were instantly available? Now you can by following these eight simple steps.
Except they are not so simple.
It's so pathetic! Actually people payed money for those books and they own the notes, but Amazon would not allow copy from text and export of notes such as people "won't steal".
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DAI-Labor > Competence Centers > CC IRML > Ongoing Projects > PIA

DAI-Labor > Competence Centers > CC IRML > Ongoing Projects > PIA | Digital Memory | Scoop.it

Besides a typical web search engine interface, the PIA system allows users to define and save searches which are then continually monitored by search agents for any new developments. The architecture of the PIA system is designed to allow information sources to be flexibly integrated into the system. Information is analyzed and filtered using advanced filtering methods, e.g. content-based or collaborative filtering techniques. The use of multiple filtering techniques, which are guided by integrating user feedback from a learning and user modelling component, guarantees a high accuracy of search results.

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News Discovery Tool Positions Itself as Personal Research Assistant: Squirro

Giuseppe Mauriello reports: "Squirro creates a living collection of content that’s updated continuously and automatically in a simple, elegant digest that can be shared with colleagues and friends.

 

More specific than search and broader than feeds, Squirro scans multiple sources to find the most relevant information on topics of interest and includes tools to clip, save and share."

 

From the official website: "Squirro is your personal digital research assistant. Squirro curates the most relevant content from multiple sources and delivers it in a living collection you can really harvest and share. Squirro filters out the noise to give you the content that matters most."

 

Right now Squirro is open only to invitations.

 

Check out the "Introducing Squirro" video: http://vimeo.com/42116416

 

Request an invite here: http://squirro.com

 


Via Giuseppe Mauriello, Robin Good
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Mo Hall's comment, May 29, 2012 5:10 PM
Just signed up for the beta invite, looks interesting, hope it don't take 2 months to get it (LOL). Let us know what you think.
digitalassetman's comment, May 30, 2012 12:27 PM
Looks like a nice idea ... However, signing up for the beta with an email address is fine. Then they want you to spam your contacts before you even get a chance to see how it works.

Then an email follow up which states the following "The more of your friends sign up, the higher you jump in the waiting list queue"

#fail ... but thanks for sharing
Robin Good's comment, May 30, 2012 12:42 PM
Digitalassetman I fully share your frustration. :-) Ridicolous ways of marketing a service.
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Artificial Intelligence Versus Collective Intelligence

Artificial Intelligence Versus Collective Intelligence | Digital Memory | Scoop.it

A short slideshow about the difference between artificial and augmented intelligence and the "glacial pace of AI" compared to the web. Also to consider: AI + CI (Google's PageRank, for example) -- Howard

 

 


Via Howard Rheingold
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Morals and the machine

Morals and the machine | Digital Memory | Scoop.it

 Three laws for the laws of robotics

 

First, laws are needed to determine whether the designer, the programmer, the manufacturer or the operator is at fault if an autonomous drone strike goes wrong or a driverless car has an accident. In order to allocate responsibility, autonomous systems must keep detailed logs so that they can explain the reasoning behind their decisions when necessary.

 

This has implications for system design: it may, for instance, rule out the use of artificial neural networks, decision-making systems that learn from example rather than obeying predefined rules.

 

Second, where ethical systems are embedded into robots, the judgments they make need to be ones that seem right to most people. The techniques of experimental philosophy, which studies how people respond to ethical dilemmas, should be able to help.

 

Last, and most important, more collaboration is required between engineers, ethicists, lawyers and policymakers, all of whom would draw up very different types of rules if they were left to their own devices. Both ethicists and engineers stand to benefit from working together: ethicists may gain a greater understanding of their field by trying to teach ethics to machines, and engineers need to reassure society that they are not taking any ethical short-cuts.

Technology has driven mankind’s progress, but each new advance has posed troubling new questions. Autonomous machines are no different. The sooner the questions of moral agency they raise are answered, the easier it will be for mankind to enjoy the benefits that they will undoubtedly bring.

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