Blustein is a fully responsive portfolio theme coded with HTML5 and CSS3 features, it uses also Bootstrap 2.1. In 1955, Isaac Asimov published a short story titled "Franchise", about a system that decides who should be elected president (in 2008) by picking a single voter to represent the whole population. If a single voter is regularly selected at random then, over time, a larger, more representative sample of the population will build up. Distributed systems say "after a certain amount of time, enough votes will have been cast to be sure enough of a consensus".
Each voter must be selected at random, but if this selection is performed by a central machine, that machine must be trusted.
The system will, generally, consume energy up to the value of the reward for casting each vote. To save energy, votes can instead be given to those who have purchased the most shares (stake) in the system (i.e.
Another alternative, valid for small populations, is to collect the sample in a single poll: invite all members to participate, and generate the consensus after a certain amount of time has passed. I didn’t know Aaron, personally, but I’d been reading his blog as he wrote it for 10 years.
Philip Greenspun, founder of ArsDigita, had written extensively about the school system, and Aaron felt similarly, documenting his frustrations with school, leaving formal education and teaching himself. In 2000, Aaron entered the competition for the ArsDigita Prize and won, with his entry The Info Network — a public-editable database of information about topics. Aaron’s friends and family added information on their specialist subjects to the wiki, but Aaron knew that a centralised resource could lead to censorship (he created zpedia, for alternative views that would not survive on Wikipedia).
In order to pull information in from other people’s databases, you needed a standard way of subscribing to a source, and a standard way of representing information.
RSS feeds (with Aaron’s help) became a standard for subscribing to information, and RDF (with Aaron’s help) became a standard for describing objects. I find — and have noticed others saying the same — that to thoroughly understand a topic requires access to the whole range of items that can be part of that topic — to see their commonalities, variances and range.
He found that it was difficult to make political change when politicians were highly funded by interested parties, so he tried to do something about that. To return to information, though: having a single page for every resource allows you to make statements about those resources, referring to each resource by its URL. Aaron had read Tim Berners-Lee’s Weaving The Web, and said that Tim was the only other person who understood that, by themselves, the nodes and edges of a “semantic web” had no meaning.
To be able to understand this information, a reader would need to know which information was correct and reliable (using a trust network?).
He wanted people to be able to understand scientific research, and to base their decisions on reliable information, so he founded Science That Matters to report on scientific findings.
He had the same motivations as many LessWrong participants: a) trying to do as little harm as possible, and b) ensuring that information is available, correct, and in the right hands, for the sake of a “good AI”.
As Alan Turing said (even though Aaron spotted that the “Turing test” is a red herring), machines can think, and machines will think based on the information they’re given.
As much as individual, composable objects are interesting, the real understanding comes when a collection of items is analysed as a whole (or a part, if filtered).
There’s more to a collection of items than is immediately obvious - it’s not just a [1, 2, 3] list, with "array" methods for filtering and iteration: the Collection itself is an object with its own set of observable properties - many of which are summaries, in some way, of the properties in the items in the collection. These summaries describe some aggregate quality of the collection, and - ideally - an indication of the variance, or confidence intervals, for that value within the collection.
If you look around, you’ll see trees with different coloured leaves, depending on their genotype and phenotype. So: observed properties of a collection can vary over time, or over space, depending on the conditions in which they’re found and the conditions of observation. The observed colour of a tree - or a collection of trees - is a function with many inputs and one output: the wavelength(s) of light that leave the tree and enter your eye (or some other detector).
For any collection of items, a function can be written that describes one of their properties under certain conditions. For example, the value(s) that this function outputs might be the mean (average) and standard deviation of a series of measurements over time, or it may group those values into buckets (the sort of data that might be displayed as a bar chart). If you’re working with JSON or HTML (which is probably the case), these interface names make no sense. As is apparently the way with all DOM APIs, XMLHttpRequest wasn’t designed to be used directly. When an action (get, put, delete) is performed on a Resource, a Request is made to the URL of the resource. Instead of sending hundreds of requests to the same domain at once, send them one at a time: each Request is added to a per-domain Queue. Google Plus was formed around one observation: most of the people on the web don't have URLs.
For example, to show you which restaurants people you trust* have recommended in an area you’re visiting, a recommendation system needs to have a latitude + longitude for the area, a URL for each restaurant (solved by Google Places) and a URL for each person (solved, ostensibly, by Google Plus).
People might be leaving reviews in TripAdvisor, or Yelp, and there’s no obvious way to tie all those people together into any kind of coherent social graph. Google Plus has an extremely clever way of linking together all those accounts, which involves starting with one trusted URL (Google Plus account), linking to another URL (GitHub, say), then linking back from that URL to your Google Plus account to prove that you own the GitHub account and can write to it. The problem is (and the question “why” is an interesting one), even after people had their Google Plus account, they didn’t use it to post reviews. When Google tried to connect YouTube accounts to Google Plus accounts, and failed, it was because people felt that those personas were distinct, and wanted the freedom to do certain things on YouTube without having it show up on their “personal record” in Google Plus. This also perhaps explains why people are wary of using Google Plus authentication to sign in to an untrusted site - they’re not so much worried about Google knowing where their accounts are, but also that the untrusted site might create a public profile for them without asking, and link it to their Google Plus profile. Anyway, Google Plus is going away as a social network, and maybe even as a public profile, but the data’s still going to be connected together behind the scenes - perhaps using fuzzier, less explicit connections as a basis for recommendations and decision-making. You might notice that the published property is represented as a String, when it would be easier to use as a Date object. From this definition, you can see that the publishedDate property has a dependency on the published property: any computed properties should be updated when any of its dependencies are updated. This is fine when the dependencies are all stored locally, but it’s also possible to imagine data that’s stored elsewhere.
The Resource object used above is a Web Resource, part of a library I built to make it easier to fetch and parse remote resources.
In either of those cases, the data is being fetched asynchronously, and a Promise is returned. I talked about this kind of thing at XTech in 2008, illustrating the object as a Katamari Damacy-style of “ball of stuff”, being passed around various different services and accumulating properties as it goes. Talis’ data platform had a similar feature, where results from a SPARQL query could be augmented by passing each result through another data store, matching on identifiers and adding selected properties each time. The SERVICE feature of Wikidata’s SPARQL endpoint is also similar: it takes an object in each result and passes it to a specific service, assigning the resulting data to a specified property. In OpenRefine, remote data can be fetched from web services and added to each item in the background. The web is no longer a desktop publishing platform, it’s most often a networked medium for machine-machine communication.
All the old “features” that came part and parcel with printed documents are relics of an age where information was fixed in stone (well, wood pulp). Emscripten comes with its own SDK, which bundles the specific versions of clang and node that it needs. I’ve made a fork of xml.js which a) allows all the command-line arguments to be specified, so can be used for validating against a DTD rather than an XML schema, and b) allows a list of files to be specified, which are imported into the pseudo-filespace so that xmllint can access them.
For third-party libraries, you can either download production-ready code manually to a lib folder and include them, or install with Bower to a bower_components folder and include them directly from there. The benefit of this approach is that you can edit the source files through GitHub’s web interface, and the site will update without needing to do any local building or deployment.
Keep the config files in the root folder, but move the app’s source files into an app folder. Use Gulp to build the Bower-managed third-party libraries alongside the app’s own styles and scripts.
While keeping the source files in the master branch, use Gulp to deploy the built app in a separate gh-pages branch. The actual app source files (index.html, app styles, app-specific elements) are in the app folder. Earlier this week I attended a “Big Data Investigation Workshop” run by British Library Labs as part of the International Digital Curation Conference. The workshop was an introduction to working with tools for cleaning, analysing and visualising collections of data: OpenRefine (which is great but showing its age), Tableau (which is ridiculously impressive) and Gephi (which has fast graph layout but lacks usability).
As the workshop was co-organised by the Internation Crime Fiction Research Group, the theme of the data was “Crime Fiction”.
Although the news story didn’t link to any source data, it almost certainly came from the Electoral Commision’s register of donations to political parties.
Running a basic search of the Electoral Commision’s register, with no filters, produced a CSV file containing all registered donations since 2001, which we then loaded into Tableau Public (Tableau’s limited, free desktop application for data visualisation). The first visualisation was a simple bar chart of the total donations to each party, including only “political party” recipients, coloured according to the type of donation. The next visualisation was a summary of the donations from the individuals named in the news story.
Getting Tableau to recognise UK postcodes is a bit tricky, as it doesn’t recognise the full postcode - we had to write a function to separate out only the first part of the postcode. I’d been making graphs of Spotify’s “Related Artists” network, but was finding that pieces of the graph often remained disconnected. To connect these disparate parts of the network, I queried last.fm for the top tags that had been attached to each artist, and added those to the graph. This brought the network together nicely, so I applied it to a larger data set: all the unique artists that had ever been played on a particular BBC 6 Music radio show.


The full graph of artists and their tags was interesting, but to get a clearer overview of the show’s musical themes, the artist nodes were hidden after the graph had been laid out (using Gephi's "Force Layout 2" algorithm). This left just the tags, laid out in two dimensions, where the most similar tags are closest together and the most frequently used are largest.
As some of the labels were overlapping, I used Gephi’s "Label Adjust" layout algorithm to shift their positions enough that most of the overlapping was avoided.
One problem was that when several artists shared the same name, irrelevant tags would be attached to an artist.
In a sense, the artists are the “dark matter” of the graph: they pull the tags together and organise their macroscopic structure, but remain invisible in the final, visible map. It may be that a highly-concentrated cluster of artists (as well as one or two very loosely-connected artists) pushed some tags further apart than they deserved to be.
Process those two CSV files into a list of pairs of connected identifiers suitable for import into Gephi. Switch to the Preview window and adjust the colour and opacity of the edges and labels appropriately.
It would probably be possible to automate this whole sequence - perhaps in a Jupyter Notebook. Among CartoDB’s many useful features is the ability to merge tables together, via an interface which lets you choose which column from each to use as the shared key, and which columns to import to the final merged table. CartoDB can also merge tables using location columns, counting items from one table (with latitude and longitude, or addresses) that are positioned within the areas defined in another table (with polygons). I've found that UK parliamentary constituencies are useful for visualising data, as they have a similar population number in each constituency and they have at least two identifiers in published ontologies which can be used to merge data from other sources*.
Once the parliamentary constituency shapefile has been imported to a base table, any CSV table that contains either of those identifiers can easily be merged with the base table to create a new, merged table and associated visualisation.
So, the task is to find other data sets that contain either the OS “unit id” or the ONS “GSS code”.
Given an index of CSV files, like those in CKAN-based stores such as data.gov.uk, how can we identify those which contain either unit IDs or GSS codes?
As Thomas Levine's commasearch project demonstrated at csvconf last year, if you have a list of all (or even just some) of the known members of a collection of typed entities (e.g.
In a General Election, the residents of each UK parliamentary constituency elect one Member of Parliament to represent them in the House of Commons. Each party can nominate a maximum of one candidate per constituency, often chosen from a shortlist of potential candidates in a selection contest. Candidates who wish to stand for election must submit their nomination papers within one week after the notice of election has been published (i.e.
However, candidates usually start their campaigning several months earlier, and their intention to stand for election will often be announced in a local newspaper. AndyJS’ spreadsheet and a derived list of candidates by constituency, via the Vote UK Forum.
Dods People, a commercial monitoring service, used as the data source for the MHP General Election Campaign Outlook (GECO). As well as prospective parliamentary candidates, some MPs will be contesting their seats again, and some will be standing down. Every 5 years, the Boundary Commissions for England, Scotland, Wales and Northern Ireland review the UK parliamentary constituency boundaries.
The last completed Boundary Review recommended 650 constituencies, and took effect at the General Election in 2010.
The Office for National Statistics (ONS) has produced a guide to parliamentary constituencies and a map of the current constituencies. The Office for National Statistics publishes a CSV file listing the names and codes for each parliamentary constituency (650 in total), under the Open Government License. The parliamentary constituencies of England are named in The Parliamentary Constituencies (England) Order 2007. The Ordnance Survey produces the Boundary-Line data, which includes an ESRI Shapefile for the boundary of each parliamentary constituency. The Ordnance Survey’s administrative geography and civil voting area ontology includes a “hasUnitID” property, which provides a unique ID for each region, and a “GSS” property that is the ONS’ code for each region. The Boundary-Line Shapefile includes the Unit ID (OS) and GSS (ONS) code for each constituency, so they can easily be used to merge the boundary polygons with other data sources in CartoDB. If using CartoDB’s free plan, it is necessary to use a version of the Boundary-Line Shapefile with simplified polygons, to reduce the size of the data.
Following the next Boundary Review, the number of constituencies will be reduced from 650 to 600 by the Parliamentary Voting System and Constituencies Act, introduced by the current coalition government. Via Nautilus’ excellent Three Sentence Science, I was interested to read Nature’s list of “10 scientists who mattered this year”. One of them, Sjors Scheres, has written software - RELION - that creates three-dimensional images of protein structures from cryo-electron microscopy images. I was interested in finding out more about this software: how it had been created, and how the developer(s) had been able to make such a significant improvement in protein imaging.
I was hoping for a link to GitHub, but at least the source code is available (though the “for free” is worrying, signifying that the default is “not for free”). On the RELION Wiki, the introduction states that RELION “is developed in the group of Sjors Scheres” (slightly problematic, as this implies that outsiders are excluded, and that development of the software is not an open process).
The file is downloaded over HTTP, with no hash provided that would allow verification of the authenticity or correctness of the downloaded file. There’s an AUTHORS file, but it doesn’t really list the contributors in a way that would be useful for citation.
Original disclaimers in the code of these external packages have been maintained as much as possible. The source code for RELION should be in a public version control system such as GitHub, with tagged releases. The CHANGELOG should be maintained, so that users can see what has changed between releases.
There should be a CITATION file that includes full details of the authors who contributed to (and should be credited for) development of the software, the name and current version of the software, and any other appropriate citation details. Each public release of the software should be archived in a repository such as figshare, and assigned a DOI. There should be a way for users to submit visible reports of any issues that are found with the software.
The parts of the software derived from third-party code should be clearly identified, and removed if their license is not compatible with the GPL.
For more discussion of what is needed to publish citable, re-usable scientific software, see the issues list of Mozilla Science Lab's "Code as a Research Object" project. I used a PHP client to connect to Twitter’s streaming API as I was interested in seeing how it handled the connection (the client needs to watch the connection and reconnect if no data is received in a certain time frame).
The streaming API uses OAuth 1.0 for authentication, so you have to register a Twitter application to get an OAuth consumer key and secret, then generate another access token and secret for your account. The dat server that was started earlier with dat listen is listening on port 6461 for clients, and is able to emit each incoming tweet as a Server-Sent Event, which can then be consumed in JavaScript using the EventSource API. Big companies (Google, IBM, Wolfram) are positioning themselves to be the repository where sensors store their data.
Other companies are building platforms for applications to make use of that data in real-time. There’s a piece missing: it should be possible to query those data stores to build up a snapshot of information, then document and publish the collection of data (and the harvesting process) for others to read and explore. Firstly, seed-harvester imports an initial collection of items (which may be as simple as a list of identifiers or URLs) from CSV, JSON, or a JavaScript function that fetches the initial data set.
Secondly, leaf-builder provides an interface for adding leaves (properties; computed or otherwise) to each item of the data set.
Thirdly, vege-table itself extends HTML tables to present the collection of items, generating a row for each item and a column for each leaf. Once all the leaves have been added, the data collection can be published by exporting the table description and data files, placing them in the same folder as the main index.html file, and switching off the database. In one day, two separate authors demonstrated that they’ve solved the problem of “how to publish your research on the web”.
Dominic Tarr analysed the performance of different JavaScript cryptographic libraries, and Jure Triglav collected tweets mentioning sunny weather and correlated them to actual weather reports.
The reports are online for anyone to read, and the code and data are in version-controlled repositories, with instructions for anyone to reproduce them.
The README file describes the purpose of the project, the dependencies, what was tested, how to reproduce the experiments, and what license the project is released under. The process for generating the data (a Bash script that calls node commands) is present, and its usage is documented in the human-readable README.
All the machine-readable metadata needed for the project, including the list of dependencies, is present in package.json.
The results are written up as a paper in Markdown (including figure images directly from the output folder). The data is continuously updated in the background, and the figures and text are updated in real time. Note that neither of the reports have “references” sections at the end, for the simple reason that they don’t need to: if they need to refer to anything, they just need to link to it in the (hyper)text.
The Microdata DOM API allows JavaScript programs to read and write data embedded in HTML as Microdata. As specified by the W3C Working Group, document.getItems(itemtype) returns a collection of all the elements with an itemscope attribute in the current document that have the given itemtype attribute. Each itemscope element has a properties object that provides access to all of the element's itemprop descendants (either contained directly or referenced elsewhere in the document using the itemref attribute). These methods and properties allow the program to access all the Microdata nodes and values in the document.
The HTML is very simple: a single container for the whole card, with two sections inside - one for the front and one for the back.


If you can't tell why a technology would be useful to you, it's not for people, it's for the robots. Google Glass provides machines with vision and access to a network of institutional knowledge.
Bitcoin allows machine-machine transactions to be processed without needing any evaluation of trust.
Stephanie Haustein and colleagues recently described the lack of correlation between tweets about an article (using Altmetric data from July 2011 - December 2012) and formal citations of the article.
I decided to look at the data for smaller sets of articles, published in specific journals. Import a CSV file, with columns "doi" and "citations", to a new project named "citations_scopus".
One limitation of the data used here is that the dates of each tweet and citation are not known; it might be interesting to correlate tweets and citations during specific windows of time after article publication.
A very useful psd trifold mock-up template with a top view front and back cover to present your brochure project with ease.
To avoid this, everyone in the system is given a task that is guaranteed to give each participant an equal chance of completing first - a chance which is increased only by how much work they do. When it turned out that he wasn’t going to be writing any more, I spent some time trying to work out why.
Also, some people might add high-quality information, but others might not know what they’re talking about. To teach yourself about a topic, you need to be a collector, which means you need access to the objects.
It could contain metadata for each item (allowable up to a point - Aaron was good at pushing the limits of what information was actually copyrightable), but some books remained in copyright.
He also saw that this would require politicians being open about their dealings (but became sceptical about the possibility of making everything open by choice; he did, however, create a secure drop-box for people to send information anonymously to reporters). Each resource and property was only defined in terms of other nodes and properties, like a dictionary defines words in terms of other words. If an AI is given misleading information it could make wrong decisions, and if an AI is not given access to the information it needs it could also make wrong decisions, and either of those could be calamitous.
Your eye analyses the light arriving from the tree, and your brain tries to summarise the wavelengths that it’s seeing.
To be able to understand the shared properties of items in a group, and differences from items in a different group, is to begin to understand them. They’ve read Tim Berners-Lee’s books, and understand that there are Resources out there, with URLs that can be used to fetch them.
And that’s before you get into the jQuery.ajax option names (data for the query parameters, dataType for the response type, etc). It also doesn’t return a Promise, though there’s an onload event that gets called when the request finishes. Even with Gmail, there's no way to say that the person you email is the same person who's left a review, unless they have a URL (i.e. Now that both of those URLs are trusted, either of them can be used as the basis of a new trusted connection: linking from the trusted GitHub URL to a Flickr URL, and then from the Flickr URL to the trusted Google Plus URL (or any other trusted profile URL), is enough to prove that you also own the Flickr account and can write to it.
In this case, when the published property is updated, the publishedDate property is also updated.
The intense focus is on performance of Blink as a platform for mobile applications, and not at all on document rendering features. Hardly anyone writes English (though a lot of people, and some machines, can read it to some extent). This makes running xmllint in the browser much more like running xmllint on the command line.
We added a filter on the donor name, searched for their surname and selected those names which matched (there were several variations on each donor’s name in the database), then used Tableau’s grouping to group together the name variations. Once this was done, Tableau easily mapped the location of each donor, to produce the final visualisation: a map of each donation to a political party, coloured according to the recipient party and sized according to the value of the donation. To avoid this, only the artists that had been given MusicBrainz IDs in the BBC data were included, and these MBIDs were used to query last.fm for tags. I'd like to be able to do the same thing in D3, as Gephi is quite awkward to use, and has cropped the node labels when exporting the above images (it seems to only take the nodes into account when cropping the output, and not their labels). Fusion Tables creates a virtual merged table, allowing updates to the source tables to be replicated to the final merged table as they occur. The UK parliamentary constituency shapefiles published by the Ordnance Survey as part of the Boundary-Line dataset contain polygons, names and two identifiers for each area: one is the Ordnance Survey’s own “unit id” and one is the Office for National Statistics’ “GSS code”. Although there’s usually a property name in the first row, there’s rarely a datapackage.json file defining a basic data type (number, string, date, etc), and practically never a JSON-LD context file to map those names to URLs.
For example: country names (a list of names that changes slowly), members of parliament (a list of names that changes regularly), years (a range of numbers that grows gradually), gene identifiers (a list of strings that grows over time), postcodes (a list of known values, or values matching a regular expression). The Boundary-Line data is published under the OS OpenData license, which incorporates the Open Government License. On that page is a link to “Download RELION for free from here”, which leads to a form, asking for name, organisation and email address (which aren’t validated, so can be anything - the aim is to allow the owners to email users if a critical bug is found, but this shouldn’t really be a requirement before being allowed to download the software).
They are difficult to find: trying to download XMIPP hits another registration form, and BSOFT has no visible license. Apart from the folder name, the only way to find out which version of the code is present is to look in the configure script, which contains PACKAGE_VERSION=‘1.3’. The data table is paginated, sortable, filterable, and includes footer rows that summarise columns using facets where appropriate. This is most likely what people will see first, so it links to the code repository for all the information needed to repeat the experiments.
In theory this is good, but as it’s published in a system that doesn’t yet have version control, there’s no ability to compare past versions. However, browsers never fully supported the API, and are dropping any native support that did exist. However, in order to provide this flexibility, the DOM API can be quite long-winded when reading the value of a single property, which is most often what's needed.
A5), divided into two equal halves (front and back), produced using only HTML and CSS (and a PDF conversion). After writing a few scripts to fetch and parse data to CSV from various web services, using the DOI as the key for each row, I realised that it would be easier to gather the data in OpenRefine by incrementally adding columns. It is associated with natural and healthy food a€“ that is a good advertising for a restaurant.
We thrive on bringing you the best of the best in each of our beautifully crafted resources. I didn’t find out why the writing had stopped, exactly, but I did get some insight into why it might have started.
If everyone had their own wiki, and you could choose which trusted sources to subscribe to, you’d be able to collect just the information that you trusted, augment it yourself, and then broadcast it back out to others.
The colours might cycle over time, as day and night pass, and they might cycle over longer periods, as seasons pass. The further away you look, the greater likelihood that the colour of a tree will be more different from the closest trees - the variance within the collection will increase.
If this property was bound to the original table, you would see the new values being filled in as the data arrives! If this was available it would be ideal, as then the bower_components folder could be left out of the built app. In particular, we looked at a recent news story in The Independent, which stated that “three senior figures at scandal-hit [HSBC] bank donated ?875,000" to the Conservative Party in recent years. Pleasingly the totals almost exactly matched those given in the news story, for the three named donors.
There’s no way to know what has changed from the previous version, as the previous versions are not available anywhere (this also means that it’s impossible to reproduce results generated using older versions of the software). Happily, this is the format in which Twitter’s streaming API provides information, so it's ideal for piping into dat.
Once a leaf has been attached to an item, the data added can then be used to build further leaves. That’s ok though, as long as it’s saved in the Internet Archive whenever someone refers to it. I've written a jQuery plugin that provides equivalent functions and makes them easier to use. Easy on the eye, this design features everything you need to promote your place to the visitors. Share the love around, enjoy it at will, and be sure to give us your feedback to make pixeden your favorite place to hang out.
Each of my online profiles on different sites is literally a different “profile”, and I only choose to link some of them together.
Authorship is immaterial (jk, partly), and when is anything ever authored by a single person, anyway? Slider in the top works up an appetite with the high resolution images that look so appealing, neat typography in the headers and tiny buttons in coral red focus attention on the blocks of information. Join now and follow us to receive exclusive freebies and get to know our premium plans and services from $6 a month. Tasty looking a€?best choicea€? section invites the viewers to try some of the delicious dishes. You should try this Related Freebies Here is a free UI kit made of 55+ elements for designing clean and modern websites. Easy navigation and overall pleasant look make this theme a perfect fit for online restaurant presence.``ScreenshotsIf you like this theme, feel free to get it and say a warm welcome to your clients online.



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