Silk

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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Silk
 

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.

PRICE / FEE

Free


SOCIAL FEEDS:


LATEST BLOG POSTS FROM Silk

  • Announcing Silk's new and improved visualizations
    Posted 20 October 2014 | 12:49 pm
    We’re proud to announce Silk’s renewed visualizations today! We believe Silk is now one of the best ways to create clear and embeddable visualizations on the web. The updated charts and graphs are easier on the eyes, convey more information, and perform better. We also added a scatter plot, stacked bar chart, and donut chart visualization to the mix. Of course, the new visualizations look great as an embed: Data from world.silk.co New additions to the lineup are the scatter plot and stacked barchart. The scatter plot chart is an excellent way to show the correlation between two tags, something that wasn’t possible before. Below is a scatter plot from world.silk.co, which shows the correlation between the total median age and fertility rate of countries. You can clearly see that countries with a high median age usually have a lower fertility rate, and vice versa. Data from world.silk.co The stacked bar chart is very helpful if you want to get a feel for the combined effect of multiple tags. Here is a stacked bar chart showing roadways, railways and waterways of different countries: Data from world.silk.co We also added a donut chart, which is basically just a piechart with a hole in the middle. To learn more about how to use and create these visualizations, check out the new general visualization tutorial, and the support article about charts. See the new visualizations in action on the many example Silk’s on our home page, or see how they look on your own Silk site. Last but not least: this new visualization engine gives us much more power and freedom, so be on the lookout for new and improved visualizations soon!
    > Read more


  • Announcing the all new spreadsheet importer
    Posted 14 October 2014 | 1:06 pm
    As of today, everyone can use the new spreadsheet importer! The spreadsheet (or CSV) importer is an important tool for many users, so we gave it some much needed love. The end-result is a faster and super stable importer, with added features which give you more control over how your pages will end up looking on your Silk site. We think this upgrade caters to power- and casual users alike. It’s easier to quickly create a few pages from a simple spreadsheet, and we have added more advanced features under the hood. As you can see in the video above, the importer displays an exact preview of how your Silk pages will end up like. The gear icons let you fine-tune several things, like splitting values separated by commas. New features are the ability to create custom links, to disambiguate duplicate page names, and more. Head over to the updated tutorial on using the spreadsheet importer to learn about all options, or go to your dashboard to try it out right away.
    > Read more


  • 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 open-salary-and-equity-for-buffer.silk.co 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.
    > Read more


  • 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 us-military-gear.silk.co 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.
    > Read more



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