I’m pleased to announce the Community Data Science Collective Dataverse. Our dataverse is an archival repository for datasets created by the Community Data Science Collective. The dataverse won’t replace work that collective members have been doing for years to document and distribute data from our research. What we hope it will do is get our data — like our published manuscripts — into the hands of folks in the “forever” business.
Over the past few years, the Community Data Science Collective has published several papers where an important part of the contribution is a dataset. These include:
In the case of each of the first groups of papers where the dataset was a part of the contribution, we uploaded code and data to a website we’ve created. Of course, even if we do a wonderful job of keeping these websites maintained over time, eventually, our research group will cease to exist. When that happens, the data will eventually disappear as well.
The text of our papers will be maintained long after we’re gone in the journal or conference proceedings’ publisher’s archival storage and in our universities’ institutional archives. But what about the data? Since the data is a core part — perhaps the core part — of the contribution of these papers, the data should be archived permanently as well.
Toward that end, our group has created a dataverse. Our dataverse is a repository within the Harvard Dataverse where we have been uploading archival copies of datasets over the last six months. All five of the papers described above are uploaded already. The Scratch dataset, due to access control restrictions, isn’t listed on the main page but it’s online on the site. Moving forward, we’ll be populating this new datasets we create as well as replication datasets for our future empirical papers. We’re currently preparing several more.
The primary point of the CDSC Dataverse is not to provide you with way to get our data although you’re certainly welcome to use it that way and it might help make some of it more discoverable. The websites we’ve created (like for the ones for redirects and for page protection) will continue to exist and be maintained. The Dataverse is insurance for if, and when, those websites go down to ensure that our data will still be accessible.
I recently finished a paper that presents a novel social computing system called the Wikipedia Adventure. The system was a gamified tutorial for new Wikipedia editors. Working with the tutorial creators, we conducted both a survey of its users and a randomized field experiment testing its effectiveness in encouraging subsequent contributions. We found that although users loved it, it did not affect subsequent participation rates.
Start screen for the Wikipedia Adventure.
A major concern that many online communities face is how to attract and retain new contributors. Despite it’s success, Wikipedia is no different. In fact, researchers have shown that after experiencing a massive initial surge in activity, the number of active editors on Wikipedia has been in slow decline since 2007.
Research has attributed a large part of this decline to the hostile environment that newcomers experience when begin contributing. New editors often attempt to make contributions which are subsequently reverted by more experienced editors for not following Wikipedia’s increasingly long list of rules and guidelines for effective participation.
This problem has led many researchers and Wikipedians to wonder how to more effectively onboard newcomers to the community. How do you ensure that new editors Wikipedia quickly gain the knowledge they need in order to make contributions that are in line with community norms?
To this end, Jake Orlowitz and Jonathan Morgan from the Wikimedia Foundation worked with a team of Wikipedians to create a structured, interactive tutorial called The Wikipedia Adventure. The idea behind this system was that new editors would be invited to use it shortly after creating a new account on Wikipedia, and it would provide a step-by-step overview of the basics of editing.
The Wikipedia Adventure was designed to address issues that new editors frequently encountered while learning how to contribute to Wikipedia. It is structured into different ‘missions’ that guide users through various aspects of participation on Wikipedia, including how to communicate with other editors, how to cite sources, and how to ensure that edits present a neutral point of view. The sequence of the missions gives newbies an overview of what they need to know instead of having to figure everything out themselves. Additionally, the theme and tone of the tutorial sought to engage new users, rather than just redirecting them to the troves of policy pages.
Those who play the tutorial receive automated badges on their user page for every mission they complete. This signals to veteran editors that the user is acting in good-faith by attempting to learn the norms of Wikipedia.
An example of a badge that a user receives after demonstrating the skills to communicate with other users on Wikipedia.
Once the system was built, we were interested in knowing whether people enjoyed using it and found it helpful. So we conducted a survey asking editors who played the Wikipedia Adventure a number of questions about its design and educational effectiveness. Overall, we found that users had a very favorable opinion of the system and found it useful.
Survey responses about how users felt about TWA.
Survey responses about what users learned through TWA.
We were heartened by these results. We’d sought to build an orientation system that was engaging and educational, and our survey responses suggested that we succeeded on that front. This led us to ask the question – could an intervention like the Wikipedia Adventure help reverse the trend of a declining editor base on Wikipedia? In particular, would exposing new editors to the Wikipedia Adventure lead them to make more contributions to the community?
To find out, we conducted a field experiment on a population of new editors on Wikipedia. We identified 1,967 newly created accounts that passed a basic test of making good-faith edits. We then randomly invited 1,751 of these users via their talk page to play the Wikipedia Adventure. The rest were sent no invitation. Out of those who were invited, 386 completed at least some portion of the tutorial.
We were interested in knowing whether those we invited to play the tutorial (our treatment group) and those we didn’t (our control group) contributed differently in the first six months after they created accounts on Wikipedia. Specifically, we wanted to know whether there was a difference in the total number of edits they made to Wikipedia, the number of edits they made to talk pages, and the average quality of their edits as measured by content persistence.
We conducted two kinds of analyses on our dataset. First, we estimated the effect of inviting users to play the Wikipedia Adventure on our three outcomes of interest. Second, we estimated the effect of playing the Wikipedia Adventure, conditional on having been invited to do so, on those same outcomes.
To our surprise, we found that in both cases there were no significant effects on any of the outcomes of interest. Being invited to play the Wikipedia Adventure therefore had no effect on new users’ volume of participation either on Wikipedia in general, or on talk pages specifically, nor did it have any effect on the average quality of edits made by the users in our study. Despite the very positive feedback that the system received in the survey evaluation stage, it did not produce a significant change in newcomer contribution behavior. We concluded that the system by itself could not reverse the trend of newcomer attrition on Wikipedia.
Why would a system that was received so positively ultimately produce no aggregate effect on newcomer participation? We’ve identified a few possible reasons. One is that perhaps a tutorial by itself would not be sufficient to counter hostile behavior that newcomers might experience from experienced editors. Indeed, the friendly, welcoming tone of the Wikipedia Adventure might contrast with strongly worded messages that new editors receive from veteran editors or bots. Another explanation might be that users enjoyed playing the Wikipedia Adventure, but did not enjoy editing Wikipedia. After all, the two activities draw on different kinds of motivations. Finally, the system required new users to choose to play the tutorial. Maybe people who chose to play would have gone on to edit in similar ways without the tutorial.
Ultimately, this work shows us the importance of testing systems outside of lab studies. The Wikipedia Adventure was built by community members to address known gaps in the onboarding process, and our survey showed that users responded well to its design.
While it would have been easy to declare victory at that stage, the field deployment study painted a different picture. Systems like the Wikipedia Adventure may inform the design of future orientation systems. That said, more profound changes to the interface or modes of interaction between editors might also be needed to increase contributions from newcomers.
In addition to papers, Aaron Shaw will also be chairing of the Critical Digital Labor and Algorithmic Studies session. Mon, May 29, 14:00 to 15:15, Hilton San Diego Bayfront, 2, Indigo 202A
We look forward to sharing research and socializing with you at ICA!
The Community Data Science Collective had an excellent week showing off our stuff at CHI 2017 in Denver last week. The collective presented three papers. If you didn’t make it Denver, or if just missed our presentations, blog post summaries of the papers — plus the papers themselves — are all online:
Additionally, Sayamindu Dasgupta’s “Scratch Community Blocks” paper — adapted from his dissertation work at MIT — received a best paper honorable mention award.
All three papers were published as open access so enjoy downloading and sharing the papers!
Last week, we presented a new paper that describes how children are thinking through some of the implications of new forms of data collection and analysis. The presentation was given at the ACM CHI conference in Denver last week and the paper is open access and online.
Over the last couple years, we’ve worked on a large project to support children in doing — and not just learning about — data science. We built a system, Scratch Community Blocks, that allows the 18 million users of the Scratch online community to write their own computer programs — in Scratch of course — to analyze data about their own learning and social interactions. An example of one of those programs to find how many of one’s follower in Scratch are not from the United States is shown below.
Last year, we deployed Scratch Community Blocks to 2,500 active Scratch users who, over a period of several months, used the system to create more than 1,600 projects.
As children used the system, Samantha Hautea, a student in UW’s Communication Leadership program, led a group of us in an online ethnography. We visited the projects children were creating and sharing. We followed the forums where users discussed the blocks. We read comment threads left on projects. We combined Samantha’s detailed field notes with the text of comments and forum posts, with ethnographic interviews of several users, and with notes from two in-person workshops. We used a technique called grounded theory to analyze these data.
What we found surprised us. We expected children to reflect on being challenged by — and hopefully overcoming — the technical parts of doing data science. Although we certainly saw this happen, what emerged much more strongly from our analysis was detailed discussion among children about the social implications of data collection and analysis.
In our analysis, we grouped children’s comments into five major themes that represented what we called “critical data literacies.” These literacies reflect things that children felt were important implications of social media data collection and analysis.
First, children reflected on the way that programmatic access to data — even data that was technically public — introduced privacy concerns. One user described the ability to analyze data as, “creepy”, but at the same time, “very cool.” Children expressed concern that programmatic access to data could lead to “stalking“ and suggested that the system should ask for permission.
Second, children recognized that data analysis requires skepticism and interpretation. For example, Scratch Community Blocks introduced a bug where the block that returned data about followers included users with disabled accounts. One user, in an interview described to us how he managed to figure out the inconsistency:
At one point the follower blocks, it said I have slightly more followers than I do. And, that was kind of confusing when I was trying to make the project. […] I pulled up a second [browser] tab and compared the [data from Scratch Community Blocks and the data in my profile].
Third, children discussed the hidden assumptions and decisions that drive the construction of metrics. For example, the number of views received for each project in Scratch is counted using an algorithm that tries to minimize the impact of gaming the system (similar to, for example, Youtube). As children started to build programs with data, they started to uncover and speculate about the decisions behind metrics. For example, they guessed that the view count might only include “unique” views and that view counts may include users who do not have accounts on the website.
Fourth, children building projects with Scratch Community Blocks realized that an algorithm driven by social data may cause certain users to be excluded. For example, a 13-year-old expressed concern that the system could be used to exclude users with few social connections saying:
I love these new Scratch Blocks! However I did notice that they could be used to exclude new Scratchers or Scratchers with not a lot of followers by using a code: like this:
when flag clicked
if then user’s followers < 300
stop all.
I do not think this a big problem as it would be easy to remove this code but I did just want to bring this to your attention in case this not what you would want the blocks to be used for.
Fifth, children were concerned about the possibility that measurement might distort the Scratch community’s values. While giving feedback on the new system, a user expressed concern that by making it easier to measure and compare followers, the system could elevate popularity over creativity, collaboration, and respect as a marker of success in Scratch.
I think this was a great idea! I am just a bit worried that people will make these projects and take it the wrong way, saying that followers are the most important thing in on Scratch.
Kids’ conversations around Scratch Community Blocks are good news for educators who are starting to think about how to engage young learners in thinking critically about the implications of data. Although no kid using Scratch Community Blocks discussed each of the five literacies described above, the themes reflect starting points for educators designing ways to engage kids in thinking critically about data.
Our work shows that if children are given opportunities to actively engage and build with social and behavioral data, they might not only learn how to do data analysis, but also reflect on its implications.
This blog-post and the work that it describes is a collaborative project by Samantha Hautea, Sayamindu Dasgupta, and Benjamin Mako Hill. We have also received support and feedback from members of the Scratch team at MIT (especially Mitch Resnick and Natalie Rusk), as well as from Hal Abelson from MIT CSAIL. Financial support came from the US National Science Foundation.
Attracting newcomers is among the most widely studied problems in online community research. However, with all the attention paid to challenge of getting new users, much less research has studied the flip side of that coin: large influxes of newcomers can pose major problems as well!
The most widely known example of problems caused by an influx of newcomers into an online community occurred in Usenet. Every September, new university students connecting to the Internet for the first time would wreak havoc in the Usenet discussion forums. When AOL connected its users to the Usenet in 1994, it disrupted the community for so long that it became widely known as “The September that never ended.”
Our study considered a similar influx in NoSleep—an online community within Reddit where writers share original horror stories and readers comment and vote on them. With strict rules requiring that all members of the community suspend disbelief, NoSleep thrives off the fact that readers experience an immersive storytelling environment. Breaking the rules is as easy as questioning the truth of someone’s story. Socializing newcomers represents a major challenge for NoSleep.
Number of subscribers and moderators on /r/NoSleep over time.
On May 7th, 2014, NoSleep became a “default subreddit”—i.e., every new user to Reddit automatically joined NoSleep. After gradually accumulating roughly 240,000 members from 2010 to 2014, the NoSleep community grew to over 2 million subscribers in a year. That said, NoSleep appeared to largely hold things together. This reflects the major question that motivated our study: How did NoSleep withstand such a massive influx of newcomers without enduring their own Eternal September?
To answer this question, we interviewed a number of NoSleep participants, writers, moderators, and admins. After transcribing, coding, and analyzing the results, we proposed that NoSleep survived because of three inter-connected systems that helped protect the community’s norms and overall immersive environment.
First, there was a strong and organized team of moderators who enforced the rules no matter what. They recruited new moderators knowing the community’s population was going to surge. They utilized a private subreddit for NoSleep’s staff. They were able to socialize and educate new moderators effectively. Although issuing sanctions against community members was often difficult, our interviewees explained that NoSleep’s moderators were deeply committed and largely uncompromising.
That commitment resonates within the second system that protected NoSleep: regulation by normal community members. From our interviews, we found that the participants felt a shared sense of community that motivated them both to socialize newcomers themselves as well as to report inappropriate comments and downvote people who violate the community’s norms.
Finally, we found that the technological systems protected the community as well. For instance, post-throttling was instituted to limit the frequency at which a writer could post their stories. Additionally, Reddit’s “Automoderator”, a programmable AI bot, was used to issue sanctions against obvious norm violators while running in the background. Participants also pointed to the tools available to them—the report feature and voting system in particular—to explain how easy it was for them to report and regulate the community’s disruptors.
In 2012, Washington State became one of the first two US states to legalize cannabis for non-medical use. Since then, sales tax revenues from the “green economy” have flooded state coffers. Washington’s academic institutions have been elevated by that rising tide. The University of Washington (one of our research group’s two institutional homes) is now home to pot-focused grants from UW’s Center for Cannabis Research and the UW Law School’s Cannabis Law and Policy Project.
Today, our research group — formerly known as the “Community Data Science Collective” — announces that we too will be raiding that pantry to satisfy our own munchies. Toward that end, we have changed our name to the Cannabis Data Science Collective. We’ll still be the CDSC, but we’re changing our logo to match our new focus.
The CDSC’s new logo!
Our research will leverage our existing expertise in studying the chronic challenges faced by online communities, peer production, and social computing. We plan to blaze ahead on this path to greener pastures.
Although we’re still in the early days of this new research focus, our group has started a work on series of projects related to cannabis, communication, and social computing. The preliminary titles below are a bit half-baked, but will give you a whiff of what’s to come:
Altered state: Mobile device usage on public university campuses before and after marijuana legalization
A tale of two edibles: Automated polysemy detection and the stevia/sativa controversy
Best buds: Online friendship formation and recreational drug use
Bing bong: The effect of legalization on Microsoft’s search results
Blunt truths: The effect of the joint probability distribution on community participation
Dank memes: The role of viral social media in marijuana legalization
Decision trees: The role of deliberation in governance of a marijuana sub-Reddit
The Effects of cannabis on word usage: An analysis of Wikipedia articles pre/post pot legalization
Fully baked: Evidence of the importance of completing institutionalized socialization practices from an online cannabis community
Ganja rep: A novel approach to managing identity on the World Weed Web
Hashtags: Bottom-up organization of the marijuana-focused Internet public sphere
Higher calling: Marijuana use and altruistic behavior online
Joint custody: Overcoming territoriality with shared ownership of work products in a collaborative cannabis community
Pass the piece: Hardware design and social exchange norms in synchronous marijuana-sharing communities
Pipe dreams: Fan fiction and the imagined futures of the marijuana legalization movement
Sticky icky: Keeping order with pinned messages on an online marijuana discussion board
Turn on, tune in, drop out: Wikipedia participation rates following marijuana legalization
Weed and Wikipedia: Marijuana legalization and public goods participation
World Weed Web: A look at the global conversation before and after half of the United States decriminalized
We planned to post this announcement about three weeks ago but our efforts were blunted by a series of events outside our control. We figured it was high time to make the announcement today!
Online communities have become ubiquitous, providing not only entertainment but wielding increasing cultural and political influence. While news organizations and researchers have focused a lot of attention on online communities after they become influential, very little is known about how or why they get started. Our survey of hundreds of Wikia.com founders shows that typical online communities are actually very different from the communities that are “in the news”. Online community founders have diverse motivations, but typically have modest goals which are focused on filling their own needs, and they don’t necessarily care if their projects ever get very big. Our research suggests that rather than being failures, small online communities are both intentional and common.
Most online communities are small —Our research is inspired by the skewed distribution of attention online. For example, these three graphs show the number of contributors to each subreddit, github project, and Wikipedia page. (Note the log scale – the reality is even more skewed than these plots make it appear). In every case, there is a “long tail” of projects with very few contributions or attention, while the most popular projects get the lion’s share. It is perhaps unsurprising, then, that they also garner the majority of scholarly attention. However, what these graphs also show is that most online communities are very small.
Even when scholars include smaller communities in their analysis, they typically treat longevity and size as measures of success. Using this metric, the vast majority of new projects fail. So why do people start new online communities? Are they simply naive, not realizing that large-scale success is so rare? Are community founders trying to win the attention lottery?
Our Survey —We worked with some great folks at Wikia to send a survey to community founders right after they started their community. We received partial or full responses from hundreds of founders.
Wikia homepage as it appeared during our data collection (via archive.org) with the invitation to found a new wiki highlighted. Twilight was really big in 2010.
In addition to demographic information, we asked a set of thirteen questions about the motivations of founders, based on the contributor motivation literature, and seven questions about their goals for their community. We also asked founders about their plans for their community, and whether they were planning to follow some of the best practices for building and running online communities.
Founders have diverse motivations and modest goals — We found that Wikia founders have diverse motivations. We used PCA to identify four main motivations for creating new wikis: spreading information and building a community, problems with existing wikis, for fun or learning, and creating and publicizing personal content. Spreading information and building a community was the most common motivation, but each of these was marked as a primary motivation by multiple respondents.
We also found that the barriers to starting a new community – both technological and cognitive – are very low. Only 32% of founders reported planning on starting their wiki for a few weeks or longer, while fully 46% of founders had only planned it for a few hours or a few minutes.
As with motivations, founders had diverse goals. The most common top goal was the creation of high-quality information, with nearly half of respondents selecting it. Community longevity/activity and growth were also common goals.
Finally, we looked at whether there was a relationship between motivations and goals, and between goals and plans for community building. We found that those whose top goal was information quality were less likely to be motivated by fun and learning, and that they were less likely to plan on recruiting contributors or encouraging contributions. In future research, we are looking at how a founder’s goals and plans relate to membership and contribution growth.
Distribution of founder motivations and plans, based on whether their top goal is community or information quality.
So what? —We believe that platform designers and researchers should focus more of their resources on understanding small and short-lived communities. Our research suggests that the attention paid to the more popular and long-lived online communities has perpetuated a false assumption that all communities seek to become large and powerful. Indeed, our respondents are typically not seeking or even hoping for large-scale “success”.
In addition, we believe that in many contexts, understanding online communities can be augmented by focusing on founders. Platform designers can study founders to understand how users would like to use a system and researchers can do more to understand the differences between founders and other contributors.
There is also a need to generalize this research – founders on other online platforms (Reddit, github, etc.) may have a different set of motivations and goals (although we suspect that they will be similarly modest in their ambitions). Overall, there is lots of room for additional research on how and why things get started online.
This post (and the paper) were written by Jeremy Foote, Aaron Shaw and Darren Gergle. The charts at the beginning of the post were created using data from the great public datasets at Big Query. Anonymized results of the survey are publicly available, and code is coming.
You may have heard of Change.org. It’s a popular online petitioning platform. You may have even noticed there can many online petitions about popular topics. For instance, it is easy to find dozens of petitions protesting the Lychee and Dog Meat Festival with varying levels of support.
Imagine you want to start an online petition. You might worry if your petition is very similar to other people’s petitions that already have signatures. These other petitions have a head start and will get all the attention. That said, if nobody has made any similar petitions, maybe that’s because the issue you are petitioning about doesn’t yet have a lot of popular support. You might also worry if your petition is unusual. Which of these two worries (making a duplicate petition and making a petition no one cares about) should concern you, dear petition creator? In my research, I set out to answer this question. The project is still in progress. I recently presented it as a poster at CSCW ’17.
Sociologists of organizational ecology considered similar questions about businesses and social movement organizations. They wanted to explain why organizations were more likely to die when an industry was young or old, but less likely to die in between. They argued that density, or the number of organizations in the population, was tied both to processes of legitimation and competition. There aren’t many firms in unproven industries because it’s not clear the industry will succeed, but when an industry is mature it becomes competitive. Everybody wants a piece of the pie, but you might not get enough pie to survive! This notion is called density dependence theory.
I think it is intuitive to apply this logic to online petitions and topics. If you make a petition about a low-density topic, chances for success should be lower because the petition is more likely to be unusual or illegitimate. However if you make a petition in a high-density topic, now you have to worry about competition with all the other petitions in the topic. You want your petition to be original, but not weird!
To collect data to test this theory, I downloaded a large set of petitions from Change.org, spam filtered them, and removed very short ones. Next I used LDA topic modeling to group petitions into topics. This makes it possible to assign petitions to points in a topic space. The more crowded this part of topic space, the denser the petition’s environment.
Finally, I used a regression model to predict petition signature counts. Since density dependence theory predicts that the relationship between density and signature count is shaped like an upside-down U, I included a quadratic term for density. The plot below shows that observed relationship between density in topic space and signature count is what the theory predicted. The darkness of the lines at the bottom of the plot show that most petitions are in less dense parts of topic space. So you, dear petition creator, should worry about competition and legitimacy, but worry about legitimacy first!
I’m excited by this result because it shows interesting similarities between efforts to organize coordinated activism online and traditional organizations like firms. I’m planning to apply this method to other forms of online coordination like wikis and online communities.
This blog-post and the work it describes is a collaborative project between Nate TeBlunthuis, Benjamin Mako Hill and Aaron Shaw. We are still at work writing this project up as a research article. The work has been supported by the US National Science Foundation
Front page of the Scratch online community (https://scratch.mit.edu) during the period covered by the dataset.
Since 2010, I havepublishedaseriesofpapers using quantitative data collected from the database behind the Scratch online community. As the source of data for many of my first quantitative and data scientific papers, it’s not a major exaggeration to say that I have built my academic career on the dataset.
I was able to do this work because I happened to be doing my masters in a research group that shared a physical space (“The Cube”) with LLK and because I was friends with Andrés Monroy-Hernández, who started in my masters cohort at the Media Lab. A year or so after we met, Andrés conceived of the Scratch online community and created the first version for his masters thesis project. Because I was at MIT and because I knew the right people, I was able to get added to the IRB protocols and jump through the hoops necessary to get access to the database.
Over the years, Andrés and I have heard over and over, in conversation and in reviews of our papers, that we were privileged to have access to such a rich dataset. More than three years ago, Andrés and I began trying to figure out how we might broaden this access. Andrés had the idea of taking advantage of the launch of Scratch 2.0 in 2013 to focus on trying to release the first five years of Scratch 1.x online community data (March 2007 through March 2012) — most of the period that the codebase he had written ran the site.
After more work than I have put into any single research paper or project, Andrés and I have published a data descriptor in Nature’s new journal Scientific Data. This means that the data is now accessible to other researchers. The data includes five years of detailed longitudinal data organized in 32 tables with information drawn from more than 1 million Scratch users, nearly 2 million Scratch projects, more than 10 million comments, more than 30 million visits to Scratch projects, and much more. The dataset includes metadata on user behavior as well the full source code for every project. Alongside the data is the source code for all of the software that ran the website and that users used to create the projects as well as the code used to produce the dataset we’ve released.
Releasing the dataset was a complicated process. First, we had navigate important ethical concerns about the the impact that a release of any data might have on Scratch’s users. Toward that end, we worked closely with the Scratch team and the the ethics board at MIT to design a protocol for the release that balanced these risks with the benefit of a release. The most important features of our approach in this regard is that the dataset we’re releasing is limited to only public data. Although the data is public, we understand that computational access to data is different in important ways to access via a browser or API. As a result, we’re requiring anybody interested in the data to tell us who they are and agree to a detailed usage agreement. The Scratch team will vet these applicants. Although we’re worried that this creates a barrier to access, we think this approach strikes a reasonable balance.
Beyond the the social and ethical issues, creating the dataset was an enormous task. Andrés and I spent Sunday afternoons over much of the last three years going column-by-column through the MySQL database that ran Scratch. We looked through the source code and the version control system to figure out how the data was created. We spent an enormous amount of time trying to figure out which columns and rows were public. Most of our work went into creating detailed codebooks and documentation that we hope makes the process of using this data much easier for others (the data descriptor is just a brief overview of what’s available). Serializing some of the larger tables took days of computer time.
In this process, we had a huge amount of help from many others including an enormous amount of time and support from Mitch Resnick, Natalie Rusk, Sayamindu Dasgupta, and Benjamin Berg at MIT as well as from many other on the Scratch Team. We also had an enormous amount of feedback from a group of a couple dozen researchers who tested the release as well as others who helped us work through through the technical, social, and ethical challenges. The National Science Foundation funded both my work on the project and the creation of Scratch itself.
Because access to data has been limited, there has been less research on Scratch than the importance of the system warrants. We hope our work will change this. We can imagine studies using the dataset by scholars in communication, computer science, education, sociology, network science, and beyond. We’re hoping that by opening up this dataset to others, scholars with different interests, different questions, and in different fields can benefit in the way that Andrés and I have. I suspect that there are other careers waiting to be made with this dataset and I’m excited by the prospect of watching those careers develop.
The paper and work this post describes is collaborative work with Andrés Monroy-Hernández. The paper is released as open access so anyone can read the entire paper here. This blog post was also posted on Benjamin Mako Hill’s blog.
A good chunk of the collective is heading to San Diego this week for the 2017 international communication association conference.
The 
