Get a free Silk site to share reports and other data, and create interactive visualizations

Silk is a platform where you can make websites with structured data and visualizations.

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Full Description

A Silk site consists of normal web pages that have a special place to enter structured data. This data can be used for interactive graphs, maps and tables. Ideal for transparent sharing of NGO reports or data analysis. Create a site from scratch, or use the spreadsheet importer. The convenience of a database powered site, without technical experience required.





  • Silk + Buffer = Radical Transparency as Data Visualizations
    Posted 29 September 2014 | 12:55 pm
    Last Thursday we won the Startup DemoDay competition at WeWork, the co-working space where we have our San Francisco office. That was awesome. We got to demo Silk for hundreds of people. We won the “People’s Choice” Award with twice as many votes as the nearest participant (although all of the demoing companies were impressive). Our favorite part of the evening, though, came after the awards at the end of the night. We got the chance to build a useful, insightful Silk for a data-savvy customer. Live and in real-time. In 15 minutes. No lie. Data from That customer was three senior folks from the Buffer Happiness Team (Åsa Nystrom, Carolyn Kopprasch, and Mary Jantsch, a trio of talented ladies. We knew them virtually quite well. We had interacted with each other often over the past year. Buffer is our primary social media management and scheduling tool. We like it a lot. When we were demoing Silk for them, they got it right away. And then they said something interesting: “Why don’t you build a Silk of our salary and equity data? It’s posted online.” Buffer is an unusual company in that the CEO Joel Gascoigne has a policy of radical transparency. There is a specific formula for employee salaries and equity grants. Everyone knows exactly what everyone else is earning because it’s publicly posted. It’s one thing to see this data in a spreadsheet. But it’s entirely another thing to see it visualized before your eyes. The Buffer team pointed us to the spreadsheet and I gave it a quick look. The data needed a tiny bit of scrubbing, and this gave me a chance to give a few pointers on good data hygiene for spreadsheet-to-Silk conversions. We deleted two rows that had horizontally and vertically merged cells (you need a perfectly flat spreadsheet for good conversions). We downloaded that spreadsheet as a CSV and quickly uploaded it as a Silk. Then I built a nice pie-chart visualization (see above) with inline filters showing the salary breakdown of Buffer. I copied the visualization to my Silk clipboard, went to the homepage of Open Salary and Equity Buffer Silk, and pasted it onto the home page. We also dragged a picture of Joel and Leo talking to make the home page prettier. For good measure, we added a nice column chart of the equity distribution. No surprise - Joel and co-founder Leo have the biggest stakes but others on the team also have significant stakes. Voila! The data was published and online, and with beautiful SEO and easy to read on iPads, phones and laptops. You could filter data by role, seniority and any other parameter that was a column header on the spreadsheet. Jaws dropped. High-fives all around and big smiles. We thought Joel might like it so we tweeted him. His response is below. Thanks, Joel. We think Silk is very cool, too. Note: If you are a Radical Transparency company, please contact me about putting your data into a Silk. We’d like to create a comparative Silk with data from multiple Radical Transparency companies. Thanks for reading.
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  • Open Source Journalism: Data and the New News
    Posted 25 September 2014 | 7:42 am
    When images of camouflage-clad cops sitting atop armored personnel carriers in Ferguson, Missouri lit up the Internet, New York Times visualizations editor Tom Giratikanon wondered what data might be relevant to tell the story of the militarization of U.S. police forces. The answer? An obscure U.S. Department of Defense program called the Excess Property program (1033D). This program transferred surplus military gear such as armored personnel carriers, assault rifles and grenade launchers to local police forces at subsidized rates or for free. AP Photo/Jeff Roberson/Captions by Paul Szoldra/Business Insider The 1033D program was not a new story. Washington Post reporter Radley Balko had covered the topic extensively in his book, Rise of the Warrior Cop. Regardless, scant comprehensive data existed on the full scope of the military materials transfers to local police departments, many of them in smaller U.S. communities where violent crime was virtually non-existent and terrorist targets not in evidence. So he sought to obtain all the transfer data from the Department of Defense. This meant a data file with hundreds of thousands of entries to clean up. Many of the transfer items were non-lethal items such as towels and boots. Giratikanon had to sift out the assault rifles and Mine-Resistant Ambush Protected vehicles to properly tell the tale. He did and published this amazing visualization. It went viral on the Internet, garnering millions of pageviews. And then Giratikanon did an interesting thing. He put the data that he had painstakingly cleaned up on GitHub for anyone to use. In effect, he open sourced the value of his laborious work. From there, an analytics startup Mode Analytics pulled in the data and built their own county-by-county visualization. Our talented data journalist Alice Corona also took the NYT data, reshaped it to fit best into Silk and generated her own visualizations - along with some big questions. (Such as, why did Alabama and Florida end up with so much of the military surplus gear?). She posted her additional data sources here. At each step of the journey, additional insights and ways to see the data surfaced. Below you can see a graph from the Silk showing the total acquisition cost of per state in dollars per inhabitant. Data from We are entering an era of Open Source Journalism and its rather exciting. Publish an infographic, table, map or other visualization is no longer sufficient. The expectation is growing that the data sources and files data must be made publicly available, particularly if it results from a Freedom of Information Act (FOIA) request through an organization like FOIA Machine or MuckRock. The Guardian publishes data it maintains on its Data blog. The startup content site Five-Thirty-Eight, which focuses on covering the news through the lens of statistical analysis, also maintains a GitHub repository for its data. The Los Angeles Times, the BBC, and BuzzFeed are other organizations doing data-driven journalism and regularly pointing to the sources of their data. Open Source Journalism levels the playing field. Every neighborhood blogger in California or New York or London can now post visualization using the very same data that the biggest news organizations in the world have use. And the blogger can focus that data down on the local impact. Open Source Journalism also makes the news process more transparent so errors can more easily be spotted and the news organization’s work can be replicated using the original data source, much like the standard for scientific research. We’ve all been journalists for some time now, since the pajama bloggers broken into the top ranks of media. In the very near future, we’ll all be data journalists, too. It’s going to be great fun.
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  • Baseball Analysis With Silk: MoneyBall and Big Money
    Posted 23 September 2014 | 5:07 am
    One of the most vibrant areas of Data Journalism is sports journalism. Driven by statistics, sports junkies revel in the arcana of ratios and comparisons. Major League Baseball is probably the most highly-examined professional sport in terms of statistical minutiae. So we thought we could kick in our two cents (and our SF team is all baseball fans) with a Silk analyzing which teams got the best bargain from their players this season in terms of wins per dollars spent. The column chart below reflects our analysis. Data from Analysis: As the MLB season enters the final weeks, we have a good idea of which teams are going to win and go to the playoffs. In baseball, clubs seek to get as many wins as possible for each dollar spent. But a cadre of teams have sought to use statistical analysis to spend less per player and extract more wins from their roster. We put aggregate team salaries based on this AP article and matched those to the number of wins per team. We then calculated ratios of cost-per-win and salary efficiency (the team that spent the least amount of money per win. Then we asked the question - for the 2014, did MoneyBall beat Big Money — teams that spent lavishly such as the Los Angeles Dodgers and the New York Yankees? The answer appears to be that Big Money beat MoneyBall. The chart below of the 10 most winning teams in the MLB as of September 20 shows this clearly. Teams with a low “win efficiency rank” (meaning, higher numbers) have taller red bars. Teams with a low “salary cost rank” have taller blue bars. As you can see, only three of the 10 winningest teams have clearly taller red bars than blue bars. The opposite is true for 7 of the teams. And of those three teams, only the Baltimore Orioles are in First Place in their division. You can do your own analysis by filtering by Division or League or build your own visualizations in the “Explore” mode.
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  • Introducing the Official Ig® Nobel Prizes Database
    Posted 18 September 2014 | 10:42 am
    At Silk we do love science. We like weird and improbable science even more. So it is our distinct pleasure to announce that we have been helping the Annals of Improbable Research put the Ig® Nobel Prizes into a proper online data format. For those who don’t know about the Ig® Nobels, they are annual awards for hilarious but thought-provoking scientific research. Data from At a ceremony in Cambridge, Massachusetts on the campus of Harvard University, each year the 10 Ig® Nobels are awarded by actual Nobel Prize winners from years past hand. This is no Stockholm fest. There are no cash prizes. The Ig® Nobel winners fly in at their own expense, from all over the world. But great fun is had by all. The research, too, is very real and often does hold some useful insights for both practical and theoretical purposes. Such as the research into whether the Ultrasonic Velocity in Cheddar Cheese is Affected by Temperature. As befits the madcap science mantra of the Ig® Nobel awards, their data was organized in, shall we say, a strange formatting system that rendered it difficult to search, filter and share. That’s music to our ears and our crack data journalism team (with Alice Corona on this project) whipped up a fully structured searchable interactive database going back to the inception of the first Ig® Nobel ceremony. We also built some nice visualizations like categories that get the most awards and a visible grouping of award winners by category. (Pro Tip: If you want to embed a copy of the Ig® Nobels on your website, you can easily do so. This works great on and Tumblr blogs, too). All awards are listed and indexed, as well as a brief description of the research. We have only included the awards through 2013 and will add the 2014 edition after the ceremony on September 18th. In the meantime, we invite you to peruse the Ig® Nobels, find out about improbable research, and enjoy the fruits of our labor. And if you have other suggestions for improbable Silk databases we can build for your organization or any other, do drop us a line on Twitter or via email.
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