During the past few months I have conducted a literature review and interviewed other museum professionals working in digital media at the Metropolitan Museum of Art in New York, the Science Museum Group in the UK, and the Dallas Museum of Art to gain an understanding of the broader context in which the move towards digital analytics has taken place and how other museums are using digital analytics in their work.

Why Digital Analytics?

The first step was to contextualize and situate my assignment to a larger context. The increasing use of Google Analytics and other forms of digital analytics is linked to a wider organizational change of navigating towards data-driven decision-making. The vast digitization of our society has enabled the continuous production of enormous amounts of data, which obviously facilitates the process of becoming data-driven. In the past few years data has become a buzzword, especially in the business world, and data-savviness has been attributed to be one of the success factors that separates great businesses from good ones.

At the core of the data-driven decision-making approach lies the notion that any decision an organization executes ought to be justifiable with data. In other words, data should be an integral part of any decision-making process at any level. A defining characteristic of data-driven organizations is that they predominantly base their decisions in the rigorous analysis of data rather than are guided by the human mind and instinct solely. In fact, Erik Brynjolfsson (2011) and his team found that firms that adopt a data-driven decision-making approach have output and productivity that is 5–6% higher than what would be expected given their other investments and information technology usage. This finding has increased organizations’ interest towards data, which is often approached if not as a panacea to an organization’s problems, at least as a starting point for operational analysis and further betterment.

Furthermore, as an increasing number of an organization’s activities take place in the digital realm, organizations are faced with the challenge of developing effective ways to define, measure and evaluate the success of their online activities and performance. This might sound obvious to you, but it is important to understand how going digital on one hand enables data collection and measurement, but at the same time creates a new space for an organization’s activities, and the performance there ought to be measured too.

Digital Data in the Museum Context

Now that I had contextualized the project, the next step was to understand how an organization becomes data-driven. There is a whole discourse around this matter, but an essential requirement is to install and maintain an analytic culture. Certainly, this is not an overnight change and requires support from both senior management and staff at all levels as well as a shared understanding of the importance of data for decision-making.

During the past few years an increasing number of museums have turned their attention to the possibilities digital analytics and data offer them. At first glance, it could seem a little odd to think of museums engaging with such practices, but actually data and museums maintain a symbiotic relationship. Museums already collect a lot of data about the objects in their collections and as more museums release these data sets to the public, questions that were once difficult to answer are now easier to answer.

Furthermore, digital analytics also helps with many other museum activities. First, data allows museums to justify their organizational decisions to their funders and supporters. Obviously, in some countries this is mandated, but even when not, it is increasingly expected. Second, digital analytics can provide museums with additional ways to demonstrate their economic and social impact. Third, museums also operate in the digital realm, and digital data gives them the means to evaluate the success of their online activities. For example, out-of-the-box Google Analytics can help museums have a basic understanding of how their website is performing and get to know their online audience better.

How to encourage Digital Analytics in the Museum Realm

Now that the reasons why museums are interested in various forms of digital analytics have been briefly looked at, it is time to get into the more exciting stuff: the practicalities. Part of my job was to come up with a best practices plan for MoMA concerning the use of digital analytics in general and Google Analytics in particular, and now I am excited to share some bits of it with you. The following list contains some of the things I discovered during my research. It should not be thought of as a step-by-step approach; rather, the process is multifaceted and most of the actions ought to be taken simultaneously.

Enlist your colleagues to the data cause

An essential requirement for an organization to become at least partially data-driven is to create an organizational culture in which data is saluted and celebrated rather than disregarded.

Establish profound cross-departmental communication concerning the use of data in your decision-making. Museum professionals who are further along their data journeys underline that the first step is to install an analytics culture and adopt various analytics practices. On an organizational level, this transformation often begins with specific resources dedicated to the analytics, for example a data analyst is hired. Hopefully, the senior management in your museum is interested in and supportive of data-driven decision-making. However, do not be discouraged if your museum’s senior management is not on board immediately. A small group of engaged colleagues is a great start. Once you’ve started incorporating data into your decisions, your results can be used to enlist senior management’s support. The overall transformation into a data-driven organization eventually requires support from the management because it is depends on creating and maintaining an analytic culture that celebrates data. Show your colleagues how well data can guide you with your decisions and a change is likely to take place.

Be strategic: do the most important thing, not everything.

Elena Villaespesa and Tijana Tasich underline in their paper Making Sense of Numbers: A Journey of Spreading the Analytics Culture at Tate (2012) that museums cannot stumble blindly into the world of analytics, but they must approach digital analytics and metrics strategically and formulate a data strategy to serve their particular needs. For any organizational and cultural shift to happen, there ought to be a plan to guide you through the transformation. A strategic plan enables you to communicate the role and goals of analytics, key performance indicators (KPIs) that will be used to evaluate the success of digital presence and the plan to get there. I recommend checking out the Digital Strategy of the Science Museum Group and John Stack’s previous work for Tate here and here.

As you are formulating your strategic plan, it is crucial to understand that a strategic approach to analytics starts not with collecting data, but rather you first need to decide which stories you want to tell — or what your goals for your data analysis are— before you can tell them. It is also important to recognize that not all the stories are useful. Don’t try to do everything.

In order to not do everything, you need to have a cross-departmental dialogue about the specific metrics and goals that would be useful to your museum and related to your mission statement. You might want to consider how to measure reach, engagement, loyalty and impact through your choice of digital analytics. The goals of digital analytics can be, for example, defined in your museum’s digital strategy. These goals, then, ought to translate into specific everyday measurements and metrics. This, most likely, will present an interesting challenge: how to translate the KPIs into more everyday analytics that are measurable on a daily basis.

Increase data literacy and create dashboards

This action is an integral component of a data-driven organizational culture. There are two important points to this action: spark an interest towards data within your museum and make sure people share the same understanding of the terminology and significance of the specific metrics. In our interview, John Stack of the Science Museum Group underlined that when it comes to the Google Analytics terminology, there ought to be a shared understanding of the terms, because otherwise you really cannot have meaningful conversations about the data.

In the process of sparking an interest towards data and increasing the overall level of data literacy, dashboards come in handy as Elena Villaespesa has noted here and here. Internal dashboards are a useful means to communicate your museum’s strategy, align people working towards strategic objectives and report results within an organization. Dashboards are also effective in evaluating how your various digital activities are performing.

Concerning the use of Google Analytics in particular, consider creating specific dashboards inside the program. I created a number of new dashboards that are focused on the particular sections of MoMA.org. It is quite fascinating to learn who are the most visited artists, which objects gain most attention, and which exhibitions pages are most popular.

You might want to consider establishing an external dashboards too. There are a number of museums which currently make available dashboards, including the Dallas Museum of Art (DMA), The Cleveland Museum of Art and UCL Museums. The pioneer in the field was the Indianapolis Museum of Art (IMA), who launched its dashboard in late 2007, but it has since been taken offline. Rob Stein, the former Deputy Director of Research, Technology, and Engagement at IMA and the current Deputy Director of the DMA, argued that an external dashboard was helpful in the process of increasing transparency concerning the museum’s various operations and performances. Indeed, as your museum is becoming more data-detailed, you might want to consider establishing an external dashboard as well and become more transparent about your museum’s various activities. An external dashboard also enables you to foster a culture of metrics and data.

Without analysis, it’s all worthless

Creating dashboards is all good and fine, but it is a waste of time if you don’t actually use your dashboards. It is absolutely crucial to properly analyze and interpret your data. During my interviews with John Stack of the Science Museum Group and Shyam Oberoi of Dallas Museum of Art it became evident that proper analysis is hugely important in order to maximize the full potential data brings to a museum. Think of it this way: digital analytics will enable you to have all this data, but analysis and interpretation will help you understand why. And once you understand why, you’ll be able to do something about it.

Concluding thoughts

This project could be thought of as a prelude for MoMA concerning the use of digital analytics in general and Google Analytics in particular. The digital analytics realm contains great potential for the museum sector but will definitely also require special organizational efforts. There is a great variety of museums ranging on a continuum from iconoclastic art institutions to organizations committed to the presentation of one idea or theme, but all of them are eager to further improve their operations whether in the digital or physical domains. Hopefully in the future an increasing number of museums welcome the possibilities of data and continue to embark on this rather fascinating data journey. When data is used wisely and thoughtfully, it can help museums to thrive during these challenging, changing times.

What Does Data Have to Do with It? was originally published in Digital @ MoMA on Medium, where people are continuing the conversation by highlighting and responding to this story.

Over the course of my first six months at MoMA, we held three “innovation labs” looking at digital culture across online collections, fundraising, and events in the museum context. What’s an innovation lab, you ask? For us, it was a one-day workshop to explore an issue or opportunity that was too broad or dynamic to be framed as a discrete project or product. The findings of the labs were then prototyped by another team within the museum, and eventually integrated into workflows and products.

Here’s my how-to:

Before the Lab

Step 1: Get the right people in the room

The participants: For our labs, we invited internal staff, selecting a cross-departmental group of no more than 10 people. I would recommend having no more than 12 people in the room, including facilitators. The number is small enough that everyone is forced to be a full participant, but also big enough that people can work in small groups.

Outside facilitator: We considered running the labs ourselves (and even did run one by ourselves), but I recommend having an outside facilitator, for two main reasons. The first is simply that it is easier to deal with logistics like welcoming guest speakers and receiving catering deliveries when there is an outside facilitator as well as an internal lead. This is not a huge deal, but it does make the day go more smoothly. The second is that at some point, you’ll need someone to say, “What do you mean? Can you give me an example?” This is potentially a bit of a head-butting moment, so it’s useful to have an outside person doing that.

For our labs, we worked with Lisa Lindström and Phi-Hong Ha from Doberman, and Bryan Boyer of Dash Marshall.

Graphic facilitator: This was a suggestion from Bryan Boyer, who enlisted Craighton Berman as a graphic facilitator for the second lab. (We then asked Rachel Abrams of Turnstone Consulting to graphic facilitate the third lab.) A graphic facilitator essentially captures the day through illustrations. Although we documented each lab with a plethora of Post-It notes and pictures, we found that because the graphic facilitator needed to represent what was being said in a visual way, they often were able to politely push participants for more detail. This was another way to help participants make the transition from abstract to concrete ideas and helped flesh out what one iteration of an idea might look like. (The graphic facilitators also made everything look much better.)

Guest speaker: For each lab, we invited in an external speaker to talk to the group over lunch. We found it helpful to get an outside perspective halfway through the workshop to inject a few new ideas, but this may be unnecessary if you have external participants.

The Champions: For each lab, we selected two “champions,” who were either senior curators or senior deputy directors. Their primary purpose was to review the prototypes that came out of the labs, and help “champion” the implementation of the best ones into the Museum’s workflows. They often attended the beginning of the day, but did not stay the entire time.

Step 2: Find a room.

Once we had a date, we could book a room for the workshop. For two of the labs, we were fortunate enough to be able to use an empty gallery at MoMA PS1. This was an ideal space for an innovation lab: it was informal, with lots of natural light, and there was a wall in the middle of the gallery which divided it into two spaces.

Step 3: Do your homework, then assign it.

We circulated a written brief at least two weeks beforehand. At a minimum, it was a collection of readings and summaries, but ideally, it was a statement paper. The briefing helped establish a baseline for the group, as well as set clear expectations for the day, which was helpful since most participants had never attended an “Innovation Lab” before. For one lab, we asked participants to go on a “learning safari” beforehand that would become the basis for their “lightning talk” (see below).

The Innovation Lab

Although each lab was slightly different (a/b test everything!), we followed a similar format for each one.

1. Lightning Talks

We started the day off with five-minute lightning talks by all participants over breakfast. This provided everyone with the opportunity to speak their mind and unload their ideas, ones that had usually been percolating for a while. A bonus was that these talks were really enjoyable; it was a display of the depth and breadth of our colleagues’ knowledge. We learned a lot!

2. Core themes

During the lightning talks, we asked participants to note down things that stood out to them, and things they had questions about. Then, as a group, we pulled out core themes from the talks, establishing commonalities. This involved a lot of discussion about the ideas that people brought in, as the participants dug into them a bit further. I would recommend budgeting more time than you think is necessary for this stage.

3. Guest speaker

Over lunch, we brought in a guest speaker, who spoke about their work and served to inject new ideas after the initial core themes were laid out.

4. Challenges

After lunch, we divided the participants up into small groups to work on a series of “challenges” or “questions” (usually three of them). We gave them personas to help channel their ideas into concrete actions. After all the challenges, we again pulled out the core themes or concepts from the afternoon, and compared them to our initial group of ideas.

Any idea session started with people working individually, and then sharing their best ideas with the group. It was important to us that everyone shared something.

5. Prioritization exercise (“heat mapping”)

At the end of the day, we used “heat maps” to prioritize the ideas of the day and decide what to prototype. First, we placed each idea on a matrix that looked at impact, effectiveness, and difficulty (both technical and cultural).

Then, each participant marked their favorite and least favorite ideas with five blue and three red stickers. We required everyone to give out every sticker, particularly the reds, because we find people are generally very positive and we wanted to force the issue a bit. It was interesting to see which ideas were the “purples” — ideas that received both red and blue stickers.

The heat-mapping served as a cost-benefit analysis, helping us discern what could be an easy win and what might be more trouble than it’s worth.

After the Lab: Prototyping

From the workshop participants, we selected two prototyping leads who took the ideas prioritized by the group and worked with a separate team to refine and develop those ideas. The goal was quick comps that were just enough to make a concept believable. (This often ended up being a deck with visualizations.)

Prototyping was actually the most difficult part of the labs. We found that internal staff struggled to prioritize the project over the course of two weeks when competing with their other work, often leading to delays in the delivery of the prototypes. And although we specifically encouraged the teams not to be held back by existing policies or practices, they tended to get bogged down in our internal culture once they left the workshop setting.

Challenges

Of course, there were logistical challenges — it is difficult to get a group of ten staff members for an entire day, which is why we scheduled the labs offsite but still in New York City. (The Rockefeller Foundation’s social innovation labs take a different approach, sending participants to faraway locations, turning the labs into an experience similar to a conference or retreat.)

We made it clear we would provide food (including fresh fruit) and caffeine throughout the day, and scheduled breaks where participants could check email to limit distraction during the actual sessions. We also had candy in the afternoon to inject some sugar into people. Nevertheless, there were participants who could not stay the entire day for each of the labs. This was most problematic when they had to arrive late, as they missed the lightning talks.

The single biggest challenge was that our participants were often most comfortable thinking abstractly. This meant that they struggled to articulate a concrete example of their ideas, and always seemed dissatisfied with the translation of their ideas into something specific. Using personas helped them make that connection between an abstract idea and a concrete one, but it was not always easy. That’s where the facilitators were vital, stepping in to provide suggestions or find new ways to help participants think through how to translate an idea into something actionable.

What would I change if I were to do it again?

• Ideally, the lab would take place over two days, starting with a dinner on the first day to allow the group to get to know each other in a more casual setting
• Include a mix of internal and external participants, from a variety of industries (not just museum folks)
• End with a crit, with internal and external judges (the internal ones could be the “champions”)
• Commission prototypes for the ideas that come out of the Lab externally, then review the prototypes with the lab participants and “champions”

Have you held an innovation lab or similar workshop? We’d love to hear about your experiences!

How to run an innovation lab was originally published in Digital @ MoMA on Medium, where people are continuing the conversation by highlighting and responding to this story.

Interning with MoMA’s Digital Media department is great. Building a dashboard to track metrics across the museum is really complicated. Both of these statements are true, which is the kind of authoritative certainty I strive for in data analytics. This summer my job has been to create a dynamic dashboard that pulls in data from sources all over MoMA, that ideally updates automatically.

On its face, building a dashboard might sound like a straightforward task, composed of a) collecting the data, and b) visualizing it. But in practice it’s been much more complicated, requiring a significant amount of both human and technical collaboration. Figuring out where various metrics are coming from, if they are what they should be, and how to wrangle them into a dashboard requires answering a lot of questions with often imperfect solutions.

Part 1: Data audit — “What’s out there?”

Being new and legitimately having no idea how MoMA kept track of anything, there was no reasonable way to start this project without first talking to the people who knew their data best. MoMA had already identified the types of metrics it wanted to track, but the nitty gritty of how to generate them accurately and efficiently was something I needed to sort out. I began by sitting down with individuals from different departments and asking about what metrics they were interested in, what services they used, what their workflow looked like, what things worked well, and what things they’d like to do differently.

Having these conversations with people from retail, social media, email marketing, membership, management information, IT, collections technology, and others, was vital to the project for two primary reasons:

1. There would be no technical way to create the metrics I needed without knowing where they had to come from.
2. Without these conversations I wouldn’t be able to even start to answer the sorts of questions that have come up constantly throughout this project, and I’d be sending a dozen emails a day asking how numbers were calculated.

What those early conversations allowed me to do was be more efficient when I did have to bother people. They also meant that I had a better idea of what was possible to track, and critically at what point in the data-flow I should try to track from.

By the way, when you think you’re done you’re with this you’re not: something else will always come up.

Phase 2: Get the data — or SaaS (Spreadsheets as a Solution)

Of course the obvious question for all of this is how do you get data into the dashboard in the first place? How do you marshal it all together in a consistent and interoperable enough form where that’s possible? It’s a lot easier said than done when you start reaching dozens of separate services, and feel your brain start to melt thinking about how to combine your Instagram, Tumblr, Twitter, Facebook, YouTube, Periscope, and email subscriber engagements segmented by month, post, impression, or number of followers.

“Data” is great, but the practice of getting it cleanly, consistently, and quickly for multiple people can be a real headache when it’s spread so widely. This is part of the impetus for MoMA to build a unified data-dashboard. To create a single place where KPIs and other important metrics can be observed by multiple people, over time. Where there is enough information to be useful at a high level even if it isn’t necessarily the absolute truth, and the reasons for any inaccuracies are clearly documented.

It’s important to point out that this has been by far the longest step in the process. The state of data on the web in 2016 is one of APIs and a gauntlet of new vendor-specific dashboards you need to login to once a month, remind yourself where they hid the export button and date selection, and dump the data to a spreadsheet. For a place like MoMA this presents a kind of sweet spot of organizational difficulty: enough data and the savvy to utilize it within its channel, but enough distinct data sources that it becomes a monumental task to assemble everything into a bigger aggregated picture of what’s going on.

It’s great that (in a vacuum) so many of the museum’s data sources provide access to their content or analytics online. But as the number of services MoMA and other similar institutions use continues to creep upwards, these sorts of silo’ed check-each-service solutions start becoming really unwieldy.

I think it can be tempting, having reached this point, to do one of the following:

1. Buy a service that sells itself on integrating all your other services. This will inevitably pose its own complications, and will require yet another service in the future to better integrate the integrating services.
2. Develop something in-house that will require highly technical (and expensive) maintenance for the duration of its existence.
3. Say screw it to the whole thing and stick with your current inefficient workflow.

What I’ve tried to do with MoMA is split the difference between those choices, hopefully taking some of the benefits of each.

In my mind the core problem in sharing data across any like-minded medium-sized institution is making the data available at a level that is accessible from a range of technical perspectives. And I mean that both with respect to the technologies themselves and the expertise of the people utilizing them. It might be great to break your social media data out in a SQL database, or to say hey, here’s the API keys, have at it. But that doesn’t work for everyone’s level of technical know-how, even if it might work with your Tableau or Qlik integration. Building something that will last requires building something that can evolve alongside needs and expectations. So rather than focus on creating the perfect dashboard, I’ve worked to get MoMA’s data feeding into spreadsheets.

That may sound a little antiquated, but the reality is that spreadsheets are already in just about everyone’s workflow who deals with data. Getting data into Google Sheets means that nearly anyone can do something with it, from charting within the sheet, to pivot tabling it somewhere else, to importing it into just about any other data-dealing software imaginable (including in MoMA’s case, Tableau). However, what makes Google Sheets in particular an appropriate tool for building consistency across sources, is Google Apps Script.

Google Apps Script allows you with minimal fuss to write JavaScript that interacts with a corresponding Google Sheet. In turn that allows for the possibility of connecting to the APIs of the various data sources you’re trying to do something with. For MoMA that means things like our ticketing system, email service, social media channels, and our app downloads tracking service among others can all be setup such that each night a Google Apps Script goes out to each API, requests data from the previous day, and then inserts that back into a Google Sheet. And with Google Apps Script triggers, that happens entirely automatically with no human input. From there it can be brought in to visualization software like Tableau or Google Data Studio, or simply shared to someone else and they can do whatever they in turn want to do with it.

Admittedly, “here’s the solution, just write some JavaScript and couple it all to a dashboard in Tableau” is itself fairly technical, but on the scale of what it could be I think this is reasonable. Google provides sample code for a variety of services that without too much trouble that can be massaged into connections to others. And on a personal level, I think the reality is that as more of the systems we interact with shift to RESTful APIs this kind of institutional knowledge will have to grow.

What this sort of middle ground of technical automation and integration allows for is an easy source of consistent data. With proper documentation, this opens up metrics to more people without having to deal with access to the specific services themselves, and allows for them to answer more of their own data questions. The technical cost is in setting up the API connections in Google Apps Script, but once that’s done there should be much less upkeep necessary than for a more end-to-end from-source-to-viz solution.

TL;DR

The vast majority of the work in building a dashboard for MoMA’s digital properties has been in figuring out why the numbers are what they are, and how to get them into a consistent and useable format. Updating them automatically on a schedule has been icing on the cake. This is a project that has taken me across the museum and a good chunk of the internet. And it’s become abundantly clear that there is no substitute for personal conversations.

I’ll end with a few top tips for anyone undertaking a similar journey through data:

1. Talk to people.
2. Don’t assume you’ll remember what someone patiently explained to you; please write it down.
3. Accept imperfection. Lots of it.

And if I could give those tips a second time, I would do that too.

“Hey could you give me the numbers on that again?” was originally published in Digital @ MoMA on Medium, where people are continuing the conversation by highlighting and responding to this story.

Most recently the Audience Advocates, a cross-departmental team including representatives from Digital Media, Education, Visitor Services, Membership, and Marketing, which leads user testing and offers insights about visitor behaviors, conducted an open card-sort activity to help inform the information architecture of moma.org.

Over the course of two weeks, eight participants (of various ages, genders, interests, and connectedness to MoMA) took part in this card sort. Each session took between 60 and 90 minutes, followed by additional time to document and analyze the data. The card sort had three basic steps. Participants were asked to sort 67 cards (pulled from the top two tiers of moma.org navigation) into categories that made sense to them; label/title the categories; and arrange the categories as they would want to see them displayed on the website (keeping accessibility of content in mind). Throughout the process participants were encouraged to ask questions and share their thoughts and comments.

Perhaps not surprisingly, some of the categories that most users created were for MoMA PS1, visiting MoMA (e.g. tickets, hours and admission, etc.), learning (research resources, classes), shopping, and an “about MoMA” section (Museum history, a letter from the Director, etc.). Many of these categories already exist in the top-level navigation of the current site, but user testing confirmed these are areas that all users expect to see on a museum website.

There were also sub-headings which users felt needed to stand out, without being placed within a category, such as the Inside/Out blog, contact information, an FAQ, and an “about the site” section. Users reasoned that others might not want to go digging for that information and that it needed to be readily available. Many users pointed out that if we wanted traffic to our blog it needed to be prominently displayed.

Perhaps some of the most interesting insights came when users attempted to place MoMA’s collection, artists, and exhibitions within a hierarchy. Some users felt these needed to be stand-alone categories, while others placed them under categories related to learning, the calendar, and a general category about MoMA. Since the collection is so central to the Museum’s mission, we were surprised at how users seemed to have difficulty placing it. (This seems related to onsite visitor research we have conducted, in which visitors don’t see the distinction between “collection” and “exhibitions”).

Another eye-opening aspect of this card sort was the confusion users expressed over some of the sub-heading titles; there were several titles that proved unclear to the majority of participants. Uncertainties over what specific titles meant had an impact on what content users expected to find and where they expected to find it. Examples of titles that users found unclear included Mobile, Neighborhood, At the Museum, International Program, and MoMA R&D. Granted, in the card-sort scenario, these sub-headings were out of their usual context, but the feedback we received suggests that rethinking some of these titles might be helpful to future users.

Although we have a lot more feedback to gather around specific questions, and more work needs to be done related to the information architecture of the site, this card-sort activity was a good starting point, and it provided some useful feedback for next steps.

One other thing worth pointing out, especially if you are interested in trying this approach at your own museum, is that users enjoyed participating in this activity, and those of us who helped facilitate the sessions had fun as well. All of the users expressed how much they enjoyed themselves — and that’s always welcome feedback.

Sorting through moma.org was originally published in Digital @ MoMA on Medium, where people are continuing the conversation by highlighting and responding to this story.

Summary

There has been a resurgence in newsletters because — unlike the stream — they are finite and you can read them on your own schedule. Readers are looking for email that is super relevant to their interests and authored, with a strong point of view. The most successful emails feature content that has already played well on the website and social media.

Readings

For Email Newsletters, a Death Greatly Exaggerated by David Carr for The New York Times:

Carr contends that readers have grown tired of the endless stream of information on the Internet, and are returning to their inbox for “finite and recognizable” newsletters.

“With an email, there is a presumption of connection, of something personal, that makes it a good platform for publishers.”

2013: The Year ‘the Stream’ Crested by Alex C Madrigal for The Atlantic:

“It is easier to read Ulysses than it is to read the Internet. Because at least Ulysses has an end, an edge. Ulysses can be finished. The Internet is never finished… My newsletter is finite (always less than 600 words) and it comes once a day. It has edges. You can finish it.”

The Economist’s Tom Standage on digital strategy and the limits of a model based on advertising by Joseph Lichterman for NiemanLab:

“What we actually sell is what I like to call the feeling of being informed when you get to the very end. So we sell the antidote to information overload — we sell a finite, finishable, very tightly curated bundle of content.” (Tom Standage)

The Economist Espresso is a daily news digest of five articles no longer than 140 words (and without links), delivered via email or a dedicated app. It has been downloaded more than 600,000 times.

News roundups can be part of a smart media diet by Jihii Jolly for Coumbia Journalism Review:

“Each one is slightly different, but part of a growing trend: to tell readers “everything” they need to know, and to do it with bite-sized, voicey analysis.”

How BuzzFeed uses email marketing to drive their phenomenal growth by Campaign Monitor:

Email is consistently one of the top 5 or 6 referrers of traffic to Buzzfeed each month, up with Twitter and Pinterest. It’s also one of their highest growth channels. In 2014, traffic from email grew by 20% on a monthly basis. Their content strategy is all about relevance:

• Dedicated landing pages for every type of email they send
• Subscribe boxes in the sidebar of relevant sections of the site

“They Said Email Was Dying”: How BuzzFeed Revived the Email Newsletter by Ginny Soskey on HubSpot Blogs:

“I try to take note of the stuff that’s really getting big, getting some viral traffic. I try to pick the stuff that people are already sharing. If people are already sharing it a lot, it’s a good indication that we should be sharing it with our email audience too.” (Dan Oshinky)

How TinyLetter is Making Us Fall in Love with Email Again by Rebecca Greenfield for Fast Company:

“Email sits in your inbox until you do something with it. You don’t have to look at it right away. It just kind of waits for you.” (Kiefer Lee)

Tiny Letters to the Web We Miss by Joanne McNeil on Medium:

“There’s another reason why people are turning to newsletters to publish content now: it is a not-quite public and not-quite private way to share information… We subscribe to newsletters because we like someone and take interest in their unique points-of-view… But newsletters aren’t discussion lists. It is one-way communication. No one sees the replies but the sender.”

How a free email newsletter turned a computer programmer into a Newsweek columnist by Caroline O’Donovan for NiemanLab:

O’Donovan argues that the acquisition of Today in Tabs, an email newsletter powered by TinyLetter, was designed to reestablish Newsweek as an influential newsmaker for “snarky Manhattanites”.

Recommended TinyLetters

We concluded our background reading with an invited talk from Dan Oshinky of BuzzFeed. Oshinky positioned email as “the new living room”, urging MoMA to “be useful and delightful.”

Rethinking our newsletters: background reading was originally published in Digital @ MoMA on Medium, where people are continuing the conversation by highlighting and responding to this story.

In July 2015, MoMA released metadata for our full collection on GitHub. Fiona Romeo has documented some of the ways people have used that data since we released it, but when Laura Norén from NYU approached us about hosting an art datathon, we thought it would be a great way to really dig into the data.

Hold up: what exactly is a “datathon”?

Most people have heard of “hackathons” — events where people gather to engage in intensive and collaborative computer programming. A datathon is similar in that people gather to work together, but differs in the outcome: instead of an app or other software output, the results are answers to questions posed to the data. Datathons challenge participants to come up with research designs that can utilize specific data — in this case, collection data from museums — to create models, figures, maps, and other presentations of findings.

Untitled: Art Datathon was a two-day workshop in which multidisciplinary teams explored what could be learned from the data about museums, their collections, and art. Laura did a great job matching up teammates so that teams would be balanced in terms of technical skills and “domain expertise” — in this case, art historical knowledge.

Many of our participants did not have data science or programming backgrounds, so all of the data was available in its most basic form — as CSV files.

So how’d it go?

Day 1 started off with an R tutorial by Adriana Crespo-Tenorio, Lead Researcher at Facebook, to ensure a baseline knowledge among the group. After lunch, participants (and organizers) were treated to a guest presentation by Lev Manovich, who had used MoMA’s collection dataset with his students and gave the example of Helen Wall’s in-depth visualization of MoMA’s collection dataset. Lev also spoke about his project SelfieCity, which compares self-portraits (“selfies”) taken in five cities around the world.

Then, the real work started. Teams had just under 24 hours to come up with an interesting question to ask the data and then figure out the answer. In addition to MoMA’s collection data, datasets were available from the Cooper Hewitt, Tate, Carnegie Museum of Art, and SFMoMA, who provided us with a CSV file, and The Frick Collection’s Montias Database of 17th-century Dutch art inventories. MoMA also contributed two datasets not yet released to the public: one of our exhibitions from 1929–1990, and a second of all artists in our collection.

On the second day, each team presented their findings to judges Matt Lincoln, Alise Tifentale, Ramona Bronkar Bannayan, and Mark Hansen, who selected two teams as the winners.

Team 4, composed of Joan Beaudoin (Wayne State University, Detroit), Michael Fehrenbach (MoMA), Shira Feldman (NYU), Juliet Fong (BBDO), and Aiyi Zhang (NYU), won for their project, “Creators and Concepts: A Computational Analysis of Curatorial Approaches,” which examined curatorial approaches through language analysis of exhibition titles. The background of their presentation featured Helen Frankenthaler’s Mountains and Sea.

Team 6, composed of Woojin Kim (Columbia), Marily Konstantinopoulou (MoMA), Nomaduma Masilela (Museum Research Consortium Fellow), A’Nisa Megginson (NYU), and Manuel Rueda (Columbia), won for their study of the artists exhibited most often at MoMA over time.

They found that between 1929–1990, Pablo Picasso was on view in an exhibition at MoMA for 16,869 days — that’s three-quarters of the time MoMA had been open! While we know we have had a lot of Picasso shows, the hard data really did reinforce that our devotion to Pablo is steadfast.

What else did the teams find out?

Color and gender were popular themes that were examined by multiple teams. Team 1 looked at the use of color in works in MoMA’s collection over the course of the 20th century by gender, while Team 2 explored the relationship between dominant colors in paintings and geographical background of the artists. Team 3 compared the gender of artists in MoMA and SFMoMA’s collections, examined the age of MoMA’s collection over time, and engaged in a semantic analysis of works in the collection. Team 5 asked, “How contemporary is MoMA’s collection” by looking at the age of artworks when they entered the collection, while Team 7 looked at the breakdown of artists in MoMA’s collection by gender and nationality.

The most popular dataset was MoMA’s exhibition history. (The only other museum to have released exhibition data is the Cooper Hewitt.) Many teams dug into MoMA’s history to try to better understand the data, using press releases, images, and even MoMA’s inaugural director, Alfred H. Barr, Jr.’s torpedo diagram in their presentations to contextualize the questions they were asking the data.

Overall, what was most impressive was the excitement and enthusiasm of all the participants. In fact, many of them have continued to work with the data! We’re looking forward to seeing their questions develop even further.

What’s next?

We’ve now made our Artists dataset is available on GitHub, and based on the participants’ experience, we’re updating both the Artworks and Artists datasets to parse out nationality, birth year and death year, dimensions (height, width, depth), and add the URLs for thumbnails where available.

Helen Frankenthaler, Picasso, and Barr’s Torpedo, oh my! was originally published in Digital @ MoMA on Medium, where people are continuing the conversation by highlighting and responding to this story.

Our redesign of moma.org started in the spring of 2015 with five insights from Google Analytics. Our audience is younger than we realized: More than half of visitors to moma.org are aged 18–34. They are increasingly mobile: Almost 30% of our visitors access the website from a mobile or tablet device. Our online audience is more local than international, which is the inverse of what we see at our 53rd Street building. Our collection is our core asset, both on- and off-line: It attracts almost 30% of engaged views and it’s what most visitors have searched for. Every piece of content is the homepage: With the dominance of search, and rise of social media, the MoMA homepage is less critical to discovery and navigation. Today, only a quarter of our visitors arrive at our website via the homepage.

But we didn’t have a more detailed picture than that. The only audience research available to us outside of Google Analytics was a biannual marketing survey of visitors to MoMA (called the “360° Survey”). The questions asked about the website in this survey were institutionally-focused, asking about individual website sections, content types, or departmental initiatives. By contrast, museums like the IMA and Walker Art Center were exploring “entrance narratives” by asking one simple question: “What did you come to our website today to do?” This approach looks beyond demographics to visitors’ motivations for accessing a website on a particular day. Culture 24 applied this approach to 22 cultural organizations in the UK as part of an action research project, Let’s Get Real. They presented five motivations:

• Plan a visit to the museum
• Find specific information for research or professional purposes
• Find specific information for personal interest
• Engage in casual browsing without looking for something specific
• Book an exhibition or event, or buy something

The Tate went further, developing a segmentation of the different types of website visits based primarily on the motivations that drive users to the site, but also taking into account variables such as knowledge of art, vocational connection, and online behavior. Elena Villaespesa and John Stack wrote about this segmentation in detail in a conference paper, Finding the motivation behind a click: Definition and implementation of a website audience segmentation.

Art world start-up Artsy also organized its segmentation along a vector of art knowledge (plus one of commercial interest), identifying its main audience segments as scholars, art world professionals, students, teachers, and art enthusiasts.

Our research method

For MoMA, we adopted the one-question survey approach, with a modified list of motivations:

• I am searching for exhibitions and events
• I am preparing for my first visit
• I am planning to visit again
• I enjoy looking at art
• I am an academic looking to conduct research
• I want to learn about modern and contemporary art
• I am an artist or creative professional looking for inspiration
• I like to stay current with art news
• I want to find other ways to engage (membership, jobs)
• I plan to buy something from the store

We are running the survey quarterly across all sections of the website. It’s important to limit the survey to one question in order to get a large and representative sample of responses. We run the survey for two weeks at a time and get 25–40,000 responses each time. However, we also wanted to enrich this data with some demographic detail and understand our visitors’ level of art knowledge and involvement with MoMA. We did this by inserting the same question about website motivations into the 360°. This allowed us to characterize each of our motivation categories by demographics, level of art knowledge, and involvement with MoMA.

Demographic: gender, age, education, income, race, and region.

Level of art knowledge: knowledge of art in general, frequency of visits to art museums, other art activity participation, and profession (e.g. creative professionals).

Involvement with MoMA: membership status, participation in Free Friday Nights, MoMA PS1, retail, education, and PopRally.

Top motivations

There is some seasonal variation in the order but the top three motivations for moma.org as a whole are consistently: I enjoy looking at art; I am an academic looking to conduct research; and I am searching for exhibitions and events at MoMA.

Each time we run the survey, about 10% of our audience is made up of “artists or creative professionals looking for inspiration”, and another 10% “want to learn about modern and contemporary art”.

The motivations obviously change depending on the section of the website. Learning is a much more significant motivation for visitors to the collection than elsewhere, closely following “research” (1) and “looking at art” (2). Collection visitors are also more likely to be an “artist or creative professional looking for inspiration” (4) and are the only visitors that have an interest in “staying current with art news” (5).

People using the calendar are visit-focused but tend to be looking for a specific event or exhibition (1), or planning a repeat visit (2). They “enjoy looking at art” (3) and are seeking “inspiration” (5).

People looking at the homepage are visit-focused, with a greater likelihood to be first-time visitors. They are still interested in specific event and exhibition information (1), and “enjoy looking at art” (4). They have a much higher propensity to shop than visitors to other sections (5), particularly during the holiday season.

Linking the motivations to our 360° yielded a richer portrait of our web visitors. For example, those with aesthetic motivations (“enjoy looking at art”) tend to work in the arts, visual arts, architecture, and engineering professions, and report a medium level of art knowledge. Practitioners “looking for inspiration” report a lower level of art knowledge. People preparing for their first visit tend to be international and younger, and also report a low level of art knowledge. By contrast, people searching for exhibitions and events, or those interested in art news, report a high level of art knowledge and are much more likely than others to visit MoMA PS1.

Conclusion

In summary, visitors to our collection come with research, learning, and aesthetic motivations. They report medium to high art knowledge and are likely to be professionally engaged with our subjects. They are frequent visitors to MoMA, engaged across a spectrum of activities. They’re the most likely to be interested in art news.

Visitors to our calendar are predominantly NYC-based and likely to be members. They are engaged in the creative industries and report having a medium level of art knowledge.

As you’d expect, our homepage sees more first-time visitors than any other section. Google Analytics suggests that there are twice as many new visitors as returning. This audience is younger and more international than the rest of the site, with a lower level of art knowledge. They’re the most likely to shop, particularly during the holiday season.

What motivates a visit to MoMA’s website? was originally published in Digital @ MoMA on Medium, where people are continuing the conversation by highlighting and responding to this story.

One of the biggest barriers for museums in particular is that Wikipedia does not publish original research or consider the expertise of its contributors. Anecdotally, I’ve found that experts in their field who are new to Wikipedia frequently find their first edits reverted because they didn’t understand and follow Wikipedia’s particular rules around “verifiable accuracy”. Instead of deferring to professionally-recognized expertise, Wikipedia values transparency, verifiability, and many eyes: “Every past version of a page is saved, so any mistakes can be easily corrected.” And it mostly works. In their visualization of Wikipedia edits, History Flow (2003), artists Fernanda Bertini Viégas and Martin Wattenberg showed that certain types of bad edits to Wikipedia articles are corrected in just minutes.

Artist James Bridle produced a twelve-volume set of all changes to the Wikipedia article on the Iraq War: The Iraq War: A History of Wikipedia Changelogs (2010). Bridle produced this physical object to explore the historiography facilitated by Wikipedia:

Wikipedia is… not only a resource for collating all human knowledge, but a framework for understanding how that knowledge came to be and to be understood; what was allowed to stand and what was not; what we agree on, and what we cannot… This is historiography. This is what culture actually looks like: a process of argument, of dissenting and accreting opinion, of gradual and not always correct codification. And for the first time in history, we’re building a system that… is capable of recording every single one of those infinitely valuable pieces of information.

“Wiki loves GLAM”

The Wikipedia community has actively courted galleries, libraries, museums and archives (GLAM). MoMA itself took part in the 2009 wiki scavenger hunt and photography contest Wikipedia Loves Art. Richer collaborations followed in 2010, with the Brooklyn Museum using Wikipedia pages as extended labels within the Seductive Subversion exhibition (WikiPop) and the British Museum hosting the first “Wikipedian in residence”. The redesigned collection websites of the Walker Art Center and Cooper Hewitt Design Museum, launched in 2011 and 2013 respectively, include excerpted Wikipedia content.

Wikipedia’s multilingual, collaboratively-edited encyclopedia is one of the top-10 most visited websites in the world. The English version alone contains almost 5 million articles and benefits from the edits of more than 100,000 contributors each month. By contrast, MoMA is a top 25,000 site globally (top 10,000 in the US). If MoMA wants people to understand and enjoy the art of our time — as our mission says we do — then we should contribute to Wikipedia, which is, for most people, the content website of first resort.

Many of the people learning about art on Wikipedia use it as a starting point, not the start and end of their enquiry. We know this because Wikipedia is already a major referrer of traffic to moma.org, alongside Google and Facebook. When MoMA released its collection data into the public domain, Wikipedians made Wikidata properties P2174 and P2014, creating links from Wikipedia to more than 3,500 artists and 2,ooo artworks.

MoMA has embarked on a sustained and critical engagement with Wikipedia: proactively sharing MoMA information on Wikipedia; excerpting Wikipedia on our collection website, with an invitation to “view or edit”; and hosting regular edit-a-thons targeting underrepresented artists.

Wikipedia content excerpted on artist pages

MoMA’s web analytics show that artists are the entry point to our collection. Of the top 500 search terms on moma.org in 2014, 62% are artist names. (Just 8% are art terms and 6% are specific artworks.) People come to art through artists. And yet most of MoMA’s content development has been centered around the object, not the artist. Before the recent redesign of our collection website, most of our 10,000 artist pages simply included the artist’s name, brief bio (“Australian, born 1973”), and a list of the artist’s work in the collection.

In 2013, MoMA’s interpretation and research team conducted an audit of Wikipedia entries for a sample of artists in MoMA’s collection. This audit found that Wikipedia offered a good representation of MoMA’s collection artists. Wikipedia covered the full range of mediums of interest to MoMA, including design and film, with more entries on contemporary and emerging artists than art historical resources such as Grove Art Online. The individual entries were also more up-to-date. For example, the Wikipedia entry on Marina Abramović included extensive information on the 2010 MoMA exhibition The Artist is Present, which the Grove entry, written before the retrospective, lacked. Finally, the tone and content of Wikipedia articles were thought to be accessible and relevant to a general audience, with citations leading to further resources.

Knowing that our artist pages were an important entry point to the collection website, and having established that Wikipedia provided “good enough” entries for thousands of those artists, we decided to dynamically excerpt the lead paragraph of relevant Wikipedia articles. In this way, the information on MoMA’s artists pages will be continuously updated. The introductory sections in particular are monitored for accuracy and appropriateness by dedicated Wikipedia staff and volunteers.

However, Wikipedia does contain errors and omissions and has its own biases. Wikipedia is a “good enough” source but it’s not the only source we’re looking to. We excerpt information from both Wikipedia and Getty’s Union List of Artist Names and will soon introduce further links to carefully selected external resources, for example, SFMOMA’s Robert Rauschenberg catalog, and the artist conversations in the BOMB magazine archive. Every link to Wikipedia from moma.org is also a call to action: “View or edit the full Wikipedia entry.”

Wikipedia edit-a-thons hosted at MoMA

Wikipedia represents the best intentions of the web but it sometimes falls short. We can all contribute to making it better. MoMA hosts regular edit-a-thons targeting underrepresented artists. The edit-a-thons started organically but have become an important foundation of our sustained and critical engagement with Wikipedia.

In October 2011, MoMA hosted its first edit-a-thon in support of Paola Antonelli’s design exhibition Talk to Me. After a tour of the exhibition, the 30 edit-a-thon participants — including experienced Wikipedians and students from NYU and Staten Island CUNY — broke into groups and worked on editing and writing new Wikipedia articles related to artists and objects from Talk to Me. Improvements included links back to MoMA’s online resources.

More recently, in March 2015, MoMA and POWarts collaborated with Art+Feminism on an edit-a-thon addressing the representation of women artists, feminism, gender studies and LGBTQ issues. As of 2013, only 13% of Wikipedia contributors were women and there is ongoing controversy about the treatment of women contributors in the “Wikipedia boys club”.

The Art+Feminism Edit-a-thon at MoMA drew over 200 participants and there were 1,500 contributors across 75 satellite locations, editing in many languages. In MoMA’s evaluation of its own event:

73% of respondents said they had never edited a Wikipedia article before, and 76% attended one of the training sessions offered at MoMA during the event.

35% expressed how much it meant for them to participate in a communal event dedicated to making a difference.

52% of survey respondents stayed for several hours, and 100% said they would be interested in participating in future edit-a-thons.

Since then, MoMA has worked with Wikimedia on hosting almost monthly edit-a-thons that address the intersection between art and what Wikimedia terms “missing pieces” — constituents that are underrepresented on Wikipedia. Edit-a-thons this year have included Wiki Loves Pride, The Black Lunch Table (BLT), WikiAPA Asia Pacific Americans, Arte y Cultura Latinoamericana Latin American Art, and Basquiat Still Fly @55.

Now that we use Wikipedia as a resource within our own website, we have a direct — as well as indirect — stake in broadening and improving Wikipedia’s coverage of art and artists, in order to “bring [art] knowledge to everyone who seeks it.”

See also: Museums Open Up to Power of Wiki, by Robin Cembalest in The Wall Street Journal.

Reserve your place at the next Art+Feminism Edit-a-thon on Saturday March 5, 2016. We will provide tutorials for beginner Wikipedians, reference materials, childcare, and refreshments.

Bringing [art] knowledge to everyone who seeks it was originally published in Digital @ MoMA on Medium, where people are continuing the conversation by highlighting and responding to this story.

This phrase, which I’ve heard in several different circumstances over the past few years, continues to resonate almost daily. Doing for suggests a hierarchy, a top down beneficence, and doing with evokes more of a respectful opportunity to exchange and learn with each other. I’ve heard this from a formerly homeless individual describing how he felt treated as a recipient and perhaps a pawn for political gain. I’ve heard artists speak sensitively about carefully learning with and from the native intelligence, resources and experience within communities, establishing what each brings to the table, rather than the community being perceived as at a deficit. It’s a subtle shift of words, but one that speaks volumes about a way of operating in the world. Givers and takers? Or learners and acknowledgers of the merits, richness and knowledge of others whose circumstances are different.

I was reminded of this phrase while reading Dale Russakoff’s recent book, The Prize: Who’s in Charge of America’s Schools?, which documents the political machinations, complexities and ultimate failure of well-intentioned philanthropic initiatives to reform the failing Newark public school system. This well researched book underscores the problem of doing for rather than with the communities of parents, teachers and local activists. I highly recommend reading this book, which details one of America’s most significant problems of inequity which is and has been impacting generations of children and families. Beyond the specifics of Newark, this book provides a metaphor for the systemic connections between politics, public education, poverty, access to healthcare, housing, childcare and employment, and the justice system, which incarcerates high proportions of black and hispanic youth.

At the core…

Our ethos within MoMA’s education department continues to be “working with” people of all ages and abilities through experiences with art and artists. We consider ourselves as learners, and our work to be about continued learning with people, onsite at the museum, in schools, communities and online around the globe. In order to be relevant, we focus on continually learning and maintaining dialogue and an exchange of ideas with others artists, participants and colleagues, inside and outside the museum. In addition to working with communities across all five NYC boroughs in sustained partnerships, here are a few highlights.

A Tale of Three Cities: London, Chicago, and New York’s Art Scenes

Three huge cities, three major art museums, and three groups of young people collaborated to create our newest free online art teen course. Rather than adults creating content for teens, our strategy has been to create content with teens taking the lead.

For the first time ever, Tate, the Art Institute of Chicago, and The Museum of Modern Art worked together on a single educational and social experience, bringing the very best of each of our cities’ vibrant cultures together into one incredible four-week online course. This free teen-led initiative invites teens around the globe to visit international artists in their studios and learn about underground art spaces and emerging art scenes, and to explore the politics, communities, and everyday challenges that shape the contemporary art world today. The teens investigate issues surrounding fashion, gender, artistic value, curating, aesthetics, inequality, art making, and more. One of the great opportunities is that they gain free access to exclusive content available only to course participants, and meet other young, creative people from around the globe. I’m looking forward to seeing what participants can learn from each other through experiences with art and also what we can learn from a global community of teens. Be sure to check out the trailer for the course, available here:

To-date almost 400 young people are enrolled. In addition to areas all across the United States, over 70 of the young participants come from countries including Poland, Estonia, Saudi Arabia, Iceland, Philippines, Brazil, Guatemala, Netherlands, Belgium, Argentina, UK, Sweden, Singapore, Panama, France, Mexico, Israel, Malaysia, El Salvador, New Zealand, Russia, Australia, Ecuador, China, and Sri Lanka.

In addition, MoMA is unique in offering monthly programming for people of all ages who have developmental and learning disabilities and we look forward to continuing to offer a Create Ability program for teens in the upcoming year.

A few weeks ago we received feedback from a mother who brought her adult son to the program said, “I took my son, to the Create Ability program for the first time this past weekend and we both loved it. I have spent the past couple of days telling people about how great it was and trying to explain just exactly why it was so wonderful. The simple answer is that you talked to my son in an appropriate manner…..you spoke to him as if he had a brain. Sadly, that is so unusual for him. My son is non-verbal, but spells using a letter board. He understands everything and is so smart, but his body doesn’t listen to his brain. I can’t thank you enough for just talking to him as if he matters. It meant the world to him and to me. We are so excited for the November class.”

Collective Intelligence for the Common Good: Wikipedia-a-thons at MoMA

In the effort to bring more knowledge about our collection to the world, our colleagues in Interpretation, Research and Digital Learning in collaboration with Digital Media and the MoMA’s Library and Archives have hosted all-day Wikipedia events to foster more breadth of content on art & feminism, focus on LGBTQ arts and culture, and Arte y Cultura Latinoamericana (“WikiArte”), a communal day of creating, updating, improving, and translating Wikipedia articles about Latin American art and culture.

The recent Arte y Cultura Latinoamericana event was a collaboration between MoMA and the Colección Patricia Phelps de Cisneros and Wikimedia NYC. There were over 50 attendees at this event, and the impact is exponentially greater through their collective work.

Please be sure to read The Wall Street Journal article Museums Open Up To Power of Wiki, which further explores museums’ recent engagement with Wikipedia. The next will be held at MoMA’s Library on the evening of Tuesday, November 10th, focusing on the artist Jean-Michel Basquiat, Basquiat: Still Fly @55, organized with with AfroCrowd and Black Lunch Table (BLT). The BLT artist project is “an ongoing collaboration between artists Jina Valentine and Heather Hart that has sought to fill holes in the documentation of contemporary art history. BLT’s aim is the production of discursive sites at literal and metaphorical lunch tables, wherein people of color can engage in critical dialogue on topics directly affecting our communities.” On March 5, 2016 MoMA will host the third annual Art + Feminism edit-a-thon.

Again, working with communities of people, some experts and some simply interested in learning about art and creating good content to share allows us to achieve so much more and share our resources to support greater engagement with art, artists and ideas.

And over the past four years, we’ve invited artists into our process of thinking about ways to challenge traditional notions of education and knowledge through the Artists Experiment initiative. They’ve continued to help us question established hierarchical models, formats, and notions about knowledge and power within institutionalized education and experiment with new ways of engaging people with and through art. One of many examples include an upcoming event titled Tea.

Tea, Wednesday, November 11, 2015 at 6:30pm
This Veterans Day, artist Michael Rakowitz hosts Tea, a ceremony and discussion, led by Iraq War veteran and artist Aaron Hughes, that explores memories of war, detention, dehumanization, and love. When someone sits, sips, and reflects over a cup of tea there is space to ask questions about one’s relationship to the world — a world that’s filled with dehumanization, war, and destruction; a world that’s filled with moments of beauty, love, and humanity. Please join us to sit, reflect, share, and sip tea together. This program is presented as part of Artists Experiment, in collaboration with Michael Rakowitz.

“Doing with, rather than doing for…” was originally published in Digital @ MoMA on Medium, where people are continuing the conversation by highlighting and responding to this story.

MoMA accessioned the Creative Commons License Symbol into its collection in March 2015 and it’s now on display in our design galleries as part of the exhibition This Is for Everyone: Design Experiments for the Common Good. According to curator Paola Antonelli (@curiousoctopus), Creative Commons allows those who create content to “think beyond the default position of All Rights Reserved.” It therefore feels important that we just flipped our own default and shared data for more than 125,000 works from MoMA’s collection on GitHub using Creative Commons Zero (CC0).

This data release includes all of the works that have been both accessioned into MoMA’s collection and cataloged in our database. It includes basic data for each work, including title, artist, date made, medium, dimensions, and date acquired by the Museum. The data will be updated periodically with new acquisitions and research.

MoMA’s open data is primarily intended to be useful to scholars, so it was important to make each version citable. Arfon Smith (@arfon), co-founder of the Zooniverse and a former collaborator of mine, is now leading GitHub’s engagement with the academic community. He shared this useful guide to producing citable code on GitHub using Zenodo. A Digital Object Identifier (DOI) is automatically created for every MoMA data release, and the data is also archived to the cloud infrastructure used by CERN’s Large Hadron Collider.

While releasing this data with “No Rights Reserved” was a significant milestone for MoMA, a bigger cultural shift lies behind the records that are marked “not curator approved.” More than half of the records included in this data release have incomplete information and may contain errors. There is established evidence that researchers want online access to collection records as quickly as possible, “whatever the perceived imperfections or gaps in the records.” We therefore decided that we would share this work in progress in order to provide a more comprehensive view of MoMA’s collection.

The scope of the data release was informed, in part, by individual research requests received by MoMA’s Library and Archives. It was also influenced by the Artists Experiment residency of The Office for Creative Research (@the_o_c_r), a multidisciplinary group exploring new modes of engagement with data. Previous projects by OCR include installations at The New York Times, Public Theater, and London Science Museum, and work on the algorithm for the 9/11 Memorial. OCR was tenacious in pursuing data for their residency, explaining the importance to their practice of seeing everything, rather than simply working with the 55,000 “good” collection records that were already available to the public on MoMA’s website.

OCR eventually used a version of the collection data that we’re now releasing to everyone to create a live performance in the galleries, A Sort of Joy (Thousands of Exhausted Things).

A Sort of Joy was performed with Elevator Repair Service (@erstheater) in the Scenes for a New Heritage exhibition, as part of MoMA’s education program.

OCR has documented the project in a blog post where they also announce that they’ll be releasing the collection interface used to generate the script for the performance. According to OCR, “[MoMA’s] data can be and should be terrain for exploration, forum for interrogation, and substrate for creation. There is prose and poetry and performance to be made from these rows and columns.”

We’re really excited to see what you make of — and with — MoMA’s collection data.

Thanks to the Cooper-Hewitt and Tate for paving the way by releasing their own collection data on GitHub using CC0. Thanks also to George Oates (@goodformand) for reassuring us that a CSV is not just the easiest way to start but probably the most accessible format for a broad audience of researchers, artists, and designers.

Thousands of Exhausted Things, or why we dedicated MoMA’s collection data to the public domain was originally published in Digital @ MoMA on Medium, where people are continuing the conversation by highlighting and responding to this story.