Think of science like an incurable intellectual disease (Part 3)

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ESIP welcomes first-time meeting goers

Part 3: Platforms and Norms: There’s a commons in your science future

Science is broken: Who’s got the duct tape and WD40?

So, here we are, Act III.

Act I was all about how personal science is. Scientists are individually infected with their own science quest. Act II was about how social science is. Why else would they take a hundred-thousand airline flights a year to gather in workshops and solve problems together (well, apart from the miles)? Act III needs to be about culture and technology. But not so much about the content of culture and the features of technology. Rather, about the doing of culture and the uses of technology.

Yes, the sciences are broken. Some part of this rupture was built-in (Merton, who outlined scientific norms in the 1940s, also outlined the integral tensions that disrupted these—i.e., the Matthew effect). But much of the damage has come from the displacement of the academy within society that has warped the culture of science.

Yochai Benker generally describes the tensions of this warping as “three dimensions of power”. These power dimensions (hierarchy, intellectual property, and the neoliberal need to always show more returns) work against science as a mode of peer production that self-commits to shared norms. Science needs to find alternative means to fight hierarchy, share its goods, and own its own returns.

The sciences are stuck and fractured, in need of both WD40 and duct tape—culture change and technological support. Scientists need to operationalize open sharing and collective learning. For this, they must discard the institutions that enable the above dimensions of power in favor of new communities and clubs (in Neylon’s sense of the term) that can house cultures of commoning, and activate global peer production.

At a recent workshop where the topic of the “scholarly commons” was the theme, I was again impressed by descriptions of how these dimensions of power are locally applied in academic institutions across the planet. The workshop was designed to arrive at a consensus on a universal statement, a short list of principles, such as a restatement of Merton’s norms. Instead, the organizers were reminded that these so-called universal principles could only be accepted as suggestions. These would need to be locally reexamined, reconfigured, reauthorized and only then applied as needed against the institutional cultural situation at hand. Here is another look at the dynamics of that workshop. 

Earlier in the Summer, I attended a breakout session at the ESIP Meeting where a long discussion about building an Earth science data commons concluded that ESIP was either already one, or ready to be one. A second determination was that ESIP was about the right size for this task, that multiple data commons could be built across the academy on the model of ESIP, but with their own sui generis culture and logic of practice, geared to local conditions and particular science needs.

The real question is not how to create the scholarly commons, but rather how to rescue (or re-place) current academic institutions using commons-based economies, and using the various norms of commoning as a baseline for the shared cultural practice of open science. The real task is then how to help move this process forward.

If commoning is the WD40 to release science for the sclerotic hold of its 19th Century institutions (Side note: Michelle Brook is assembling a list of learned societies in the UK. This list is already has  more than 800 entries), technology is the duct tape needed to help these hundreds and thousands of commons communities work in concert across the globe. The internet—which science needs to find out how to use as a lateral-learning tool at least as well as the global skateboarding community already does—holds the future of science. Shared community platforms, such as Trellis, now under construction at the AAAS, or the Open Science Framework, from the Center for Open Science can help solve the problems created by a thousand science communities supporting hundreds of thousands of clusters (collectives) needing to discover each others’ work in real time.

For commoning to gain traction in the academy, we must first explore this as a generative practice for open science. But as each commons spins up its own variety of commoning, we need to avoid prescribing universal norms for them. Instead, the most productive next step might be to unleash a more profound understanding of the circumstances of scholarly commoning by building a set of design patterns that will be localized and applied as needed to yank local institutions away from hierarchy, intellectual property wrongs, and the pull of the margins that preempt ethical decisions and norms.

Next summer, the ESIP Federation is hoping to host a two-day charrette at its Summer Meeting in Bloomington Indiana to begin the process of building scholarly commons patterns. A pattern lexicon for scholarly commoning will potentially help hundreds of science communities self-govern their own open resources and commoners.

Lessons learned (Parts 1-3):

  1. Science is intensely personal. Scientists are already engaged in their own struggle with the unknowns they hope to defeat. Their intellectual disease is fortunately incurable.
  2. Science is already social. Just in the US, several thousand workshops a year evidence the scientific need/desire to build collective knowledge.
  3. Science is cultural. Self-governed science communities can use intentional cultural practices to help scientists prepare to work together in virtual organizations with shared norms and resources.
  4. Community opens up arenas for online collaboration. Instant collectives, such as ESIP clusters, can replace expensive workshops and enable scientists to share knowledge and solve problems.
  5. These communities need to consider themselves as commons to replace institutions that have been twisted by the three dimensions of power (hierarchy, intellectual property, and neoliberal economics).
  6. Each commons needs to work locally, attuned to its local situation within science domains and academic institutions.
  7. The academy needs to harness the internet and technology platforms to knit together localized science/data commons into a global web of open shared resources and collective intelligence.

Think of science like an incurable intellectual disease (Part 2 of 3)

Or, why you’re funding the right thing—the wrong way.

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Ideas aren’t the only things having fun at ESIP

Part Two: The NSF and NIH spent a billion dollars funding science workshops last year*, and all I got was a lousy white-paper.

Link to Part ONE

A little recap. In Part One we discovered that the most engaged groups online were not communities as much as they were collectives. Their engagement was already built-in because these groups were formed by individuals who shared life-threatening, or life-style challenging medical diagnoses. I then made an analogy to science, suggesting that we treat science like a life-style challenging intellectual diagnosis. The idea is that scientists who go online to do science are likely to want to create collectives rather than join online communities. I also mentioned that we still need community.

There is a larger story about science becoming hyper-competitive, and about the fear of being scooped if you share your data, and the whole neoliberal warping of the norms of science. I’m not going to delve into nor dispute this story here. Instead I am going to point out that significant scientific funding and scientist participation in collectives can already be evidenced in the activity of hosting scientific workshops to address important, shared issues. Science workshops are a major current expression of the value and need for science collectives. Workshops are where scientists gather in place to collectively respond to challenges they face in their research.

Like many of you reading this, I have travelled to and participated in several workshops over the past decade. I’ve met a lot of really smart people. Shared gallons of really bad coffee. Had more than a few beers after long, long days of somewhat-facilitated work. And I have spent considerable time helping write reports and white-papers. Most of these papers I never saw again. A few got published. Some workshops are more successful. Some are a shambles. I am currently planning a workshop (charrette) for next summer.

As a mode of collective science, there are times when a workshop makes perfect sense, and maybe always will. What I will propose below, however, is that there is a way to make the great majority of workshops unnecessary, by funding and building science communities instead.

Just as digital journal articles have acquired their granularity and an arbitrary scarcity based on the history of printed journals, workshops have acquired their own granularity and scarcity. Here are some of their limits:

  • Workshops need to have enough “work” to do to fill 1-1/2 to 2 days of effort (to justify 2 days of travel). You can’t do a half-day or, say, a twenty-day workshop;
  • Workshops need to support say 16-34 participants, and these scientists must be available at the same time;
  • Workshops get funded to explore science research topics “important” enough to justify their $40k budget.  Other collective issues and needs are not currently very fundable.
  • Workshops need to have a topic that is still an issue months after the proposal submission.
  • Workshops require moving people around in airplanes.
  • Some fraction of workshop proposals don’t get funded at all.

Workshops are a product of Twentieth Century science. Science before the internet. Science before someone figured out how to let scientists create their own collectives online at no cost. That’s right NSF and NIH funders; there is a way you can support thousands of self-organized online workshops with a net marginal cost of zero. Well… zero, that is, after investing about 20% of the current outlay for workshops to support several dozen self-managed science communities.

We can explore a working model for this Twenty-First Century strategy. Real lessons already learned and ready to be copied across other research domains. A model that already supports better, more effective, and more nimble collectives than the current workshop model.

One example we can explore today is ESIP

The working model here is the Federation of Earth Science Information Partners (ESIP). ESIP runs two community meetings a year, with funding from NASA, NOAA, and USGS. These meetings are based on member-submitted sessions, and offer ample time for informal networking. The meetings are intentionally held in places surrounded by restaurants, coffee-shops, and taverns. These occasions of physical co-presence are highly valuable. They are where ESIP builds its culture.

The semi-annual meetings offer enough face-time for community members to build the personal connections and interpersonal trust that can sustain hundreds of productive online interactions. Some members go to every meeting, some once a year, some every couple years. While a great amount of work is exhibited and done at these meetings—several workshops (from 1/2 day to 2 day) are also held at these meetings—they are also social gatherings of the self-governed community. Spaces of conversation. Places where, as Matt Ridley says, “ideas go to have sex.” The real work of ESIP happens when members decide to run their own workshop-like online groups called “clusters.”

Clusters are a model for the future of online science collectives. They have the virtues of being free, instant, active, and nimble (See: Appendix). They can merge with one another or diverge from their original intent as desired. They have no requirements for a deliverable, except that they reward the services of the volunteer time they spend. And so they are motivated to get real work done. Being surrounded by the much larger community that spawns them, they can grow to whatever collective size they need. And when their work is finished they disappear, leaving their findings in a discoverable location on the community wiki, and/or published in science journals.

The key to ESIP clusters is that they are grounded by a community that supports a shared vision and shared norms. This fosters teamwork that can better avoid becoming dysfunctional.  Not all clusters will accomplish what they originally intended. Some will accomplish much more than that.  ESIP has two dozen clusters running at this time. (Note to NASA and NOAA: that’s like running 24 workshops, which would cost funders about a million dollars to do independently.)  ESIP could support a hundred clusters without adding additional infrastructure. Note: the use of clusters as a form of science collective is a practice that is still open to innovation.

A while back I wrote a list of the returns on investment for funding community growth in virtual science organizations. I need to add this return to the list: fund and grow community and it will generate any number of science collectives that can accelerate understanding and innovation within that science arena.

In a pre-internet world, funding several thousand physical workshops a year helped fill some of the need for science collectives. In the future, internet-enabled science could be based on scientist-led communities that each spawn hundreds of active online collectives as these are needed. Imagine a couple hundred ESIP-style communities, funded at a million dollars a year each, and every community supports a hundred clusters. For a couple hundred million dollars, agency funders can get an equivalent ROI of their current billion dollar funding. The question is this: will new modes of internet-enabled science collectives (clusters) drive a change in the funding model?

Six more lessons learned:

  1. Cluster-like groups can become an important mode of online collective work across the sciences, with huge savings in time, money, and effort.
  2. When funders support travel to community-run meetings that grow a culture of sharing and trust, they enable these communities to host their own online collectives. Funders will save hundreds of millions of dollars by NOT directly funding workshops.
  3. Each additional cluster can be started with a zero marginal cost (based on existing support for backbone community organizational tools and services).
  4. Funders and community staff coordinate among these clusters to amplify the impacts of their results.
  5. Funders encourage cross-community online clusters for trans-disciplinary science.
  6. Funders can target some travel and other support to improve diversity at the community level. Staff work to nudge diversity at the cluster level.

Coming Soon: Part Three: Platforms and Norms: There’s a commons in your science future

Preview: Science is broken: Who’s got the duct tape and WD40?

*I’m just estimating here. I found about 5000 active independent NSF funded workshops listed on the website, and popped in an average of $40k each. I then doubled this to account for workshops organized inside funded projects, synthesis centers and networks. The NIH budgets for workshops are not so easy to pin down, but I’m guessing they are slightly higher than the NSF, since the overall budget is significantly higher. It would be great if I could get real numbers for all these. Not even counting NASA, NOAA, DOE, etc..

Appendix: Comparing Clusters to Workshop RFPs

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Think of science like an incurable intellectual disease

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Or, why your online science community engagement plans are probably wrong.

Part one (of three): It’s a collective, not a community, and that’s OK

Nearly a decade ago I was on a team that was exploring a new online network platform for ocean scientists—one of those “Facebook for X” forays that never took off. During the research phase I learned that online groups exhibited a wide range of “stickiness,” a description for member engagement. In general, engagement could be plotted on the usual power law curve; a handful of really engaged members on one side, and hundreds or thousands of mostly un-engaged members in the “long-tail” end of the curve.

One genre of online groups completely broke this curve. These were the most engaged groups online, and by a long ways. Their entire membership regularly contributed content. The problem—for them most of all, and for any online community manager trying to emulate their engagement on the open web—was that these groups were made of individuals who had been diagnosed with terminal or incurable chronic physical diseases.

These online groups, numbering in the hundreds, shared personal stories about symptoms and medication advice, uploaded and argued over new medical findings, and identified sources of emotional support for members and their families. They sought answers beyond the ken of their individual medical advisors, and they collectively shouldered the news when one of their members inevitably passed on.

The feeling of “community” was evident in their mutual concern, but this feeling was not central in these groups. “Belonging” was not the goal; it was their circumstance, their fate, their bad luck. Nobody was trying to get into these groups. Yes, they grew to care for and about one another. But they didn’t join for that purpose. Members joined because the circumstances of their lives brought them to this sad place: a space of collective struggle against a common and specific foe: their diagnosis.

Let’s explore the dynamics of these groups. Each online group focuses on a single disease or condition, from ADHD to Zika. Each member shows up already fully engaged in their own private struggle. What they need and find is an online collective, a place to share what would remain private in any other circumstance. A space of mutual learning. Douglas Thomas and John Seely Brown have described these spaces in their book A New Culture of Learning.  “Collectives are made up of people who generally share values and beliefs about the world and their place in it, who value participation over belonging, and who engage in a set of shared practices. Thus collectives are plural and multiple. They also both  form and disappear regularly around different ideas, events, or moments” (Pp 56-57). For more than a decade, the most engaged groups on the internet have been collectives, not communities.

The global internet has two virtues: it scales pretty will up to billions of users (e.g., Facebook); and it can host a hundred million independent groups. Online communities generally (and always when these are commercial in intent) love to grow bigger. Group size is a key metric. Belonging builds the brand. No company wants to say, “sorry, we don’t need any more customers at this point.”

On the very other hand, online collectives only need to grow to the size that optimizes the group’s collective intelligence and variety of knowledge. In fact, you know you’re in a collective when you try to join and somebody asks you what you bring to the conversation. Collectives have no long tail of non-participants. The collective may be very sensitive to an internal “signal-to-noise” ratio. The quality of participation is a feature.

To use another analogy (getting away from disease for a moment): if you joined a church congregation, you’re a part of that community, even if you only attend twice a year, and toss in a bit of coin now and then. But if you also join the choir, you enter a collective. Everyone in the choir is supposed to—you guessed it—sing. If you just stand there with your mouth shut, people will notice. If you don’t show up at all, someone will call you and ask where you are. There is no “long-tail” majority of choir members standing up in the choir loft not singing. The choir has zero need for a “choir engagement manager” to encourage choir members to actually sing. Singing is why members join. And if you happen suck at it, others in the collective might encourage you to leave.

This leads me (finally) to science (including data science) and to the online engagement of scientists in social networks. From a series of cases and anecdotes collected from other community managers who have attempted to “engage” scientists online, it is clear that science effects its “victims” (scientists) much like an incurable (intellectual) disease. Scientists commonly spend sixty or more hours a week chasing unknowns in their labs, gathering field data, or tracking down software bugs. They share a fever for knowledge and their own common foe: the specific unknown that stands between the state-of-the-science in their specialty and a better understanding of the object of their study; the peculiar intellectual challenge (disease) they have chosen as their quest and their foe.

Scientists don’t need and don’t want to join online communities to do science. I am sorry, but if that’s all your new platform or service provides, your dance floor will remain empty. What scientists need are online collectives that can amplify and accelerate their own research, and reward their contributions to new knowledge in their chosen specialty.

Six Lessons so far:

  1. The most engaged online groups (at least in 2008) are collectives, not communities.
  2. Collectives don’t follow the power-law curve.
  3. Collectives form around specific issues, and common foes. They house a hunger for collective intelligence in the face of inadequate information. The driver here is a collective need to know.
  4. Unlike online communities, membership growth is not a desired metric within collectives. Small can be beautiful.
  5. In terms of engagement, science acts like an intellectual disease, a diagnosis of a specific lack of understanding about some object of study that drives the scientist to devote her life to discovery.
  6. Scientists will already be engaged if they join an online collective, and will already be disengaged if they are asked to join an online community.

Coming soon: Part Two: what the internet can really do for science.

Preview: The internet can provide is the capability of enabling millions of scientific collectives, linking these into a web of knowledge across the planet. It just hasn’t done this yet. We can fix that. Oh, yes. And why we still need community.

What are scholarly commons?

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I’ve just returned from the Summer ESIP Federation meeting, where we held a powerful discussion about the need for data commons (plural). This discussion got hung up a bit by a lack of clarity on the definitions of the terminology (including the word “commons”) and also a general lack of knowledge about the current literature on the commons (the group were mostly Earth data scientists).

So here I want to offer some short and very basic definitions (my own) and bring up some ideas and questions that might be of value to these discussions in the future. [I will also come back to this text  in the future and link to a bibliography that is just now being created by the Force11 team.]

Scholarly commons are…

Intentional communities (plural) formed around the shared use of open scholarly resources (a type of common-pool resource). Commoners work together as a community to optimize the use of the open resources they share. Scholarly commons are resource-near communities. They have an immediate and professional stake in the open resources they want to use. The whole community assumes a stewardship role toward these resources. These groups are self-defining and self-governing, each with their own emergent rules. Since scholarly commons are built upon open public resources, anybody on the planet can access them. When these are digital resources, they are not diminished by overuse. However, these resources cannot be sustained without the commons, or some other economy. These commons represent the social/cultural destination for any number of open-science efforts. (Note: Principles that can help all scholarly commons work together at the social level and as technical infrastructure are being considered at this moment in Force11.)

Scholarly commoners are…

Members of these intentional communities, with the freedoms and responsibilities that their communities provide and demand. Commoners work for the benefit of the whole community and for the sustainability of its open, shared scholarly resources. An individual commoner may belong to several commons. It is the role and the goal of commoners to help these open, shared resources flourish.

Scholarly commoning is…

The practice (and an attitude) that commoners bring to the scholarly commons. It begins with a logic of abundance, and depends on an active culture of sharing. Commoning is the activity to build and sustain the commons through shared practice (thanks to Cameron Neylon for this wording). Scholarly commoning is also imbued with an ethos of scholarship/science (however defined). Scholarly commoning informs how science can be accomplished through the use of open, shared resources (open ideas, open data, open software, open workflows, open-access publishing with open review, etc.) inside commons, instead of through other types of economies.

Other ideas/questions:

Can a single object in one open repository be claimed as a resource by more than one commons?

Scholarship needs to be fearless. One role of academic tenure was to protect this condition. In the face of the neoliberal market, tenure has failed in this role. Can the commons provide this protection?

Someone noted that many data objects are “uncommon” objects that require knowledge and knowhow to use and share. Scholarly commons also maintain knowledge and knowhow.

Someone said that the data commons might just be a thousand ESIPs, each one stewarding its own collections, optimizing their value, and creating APIs to share them. Sounds pretty good to me!  What does everybody think?

Using Patterns to Design the Scholarly Commons

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Force11 is looking to build an alternative academy based on a scholarly commons that supports the entire research to publication effort.

I just published a blog on the AGU blogspace.  Take a look here, or keep reading to get the gist.

Several groups (e.g., Force11 and theEuropean Commission) are calling for an integrative scholarly commons, where open-science objects—from ideas to published results—can be grown, shared, curated, and mined for new knowledge.

Building a commons is more complex than simply opening up objects to the public. The activity of commoning is what separates a commons from other examples of publicly shared resources. Research into the various commons found across the globe reveals that every successful commons is also an intentional cultural activity. And so, when open-science organizations talk about building a commons, they also need to consider growing a community of commoners.

How do we attain an intentional and reflexive cultural purview of commoning for science? One promising idea is to borrow from the open-access software community’s reliance on design patterns. Software design patterns reveal solution spaces and offer a shared vocabulary for software design.

A lexicon of design patterns could play the same role for the scholarly commons (See also: Patterns of Commoning). Since every commons requires a different set of practices suited to its peculiar circumstances, various commons within the academy will need to grow their own ways of commoning. The pattern lexicon would be expanded and improved as these scholarly commons emerge and grow.

Developing a pattern lexicon for the scholarly commons is an important and timely step in the move to an open-science future. Design patterns for a scholarly commons can reveal some promising solution spaces for this challenge, helping the academy make a transition from archaic print- and market-based models to commons models based on open network platforms.

Acknowledgements: Thanks to David Bollier for his contributions to this post.

Don’t like Open Science…?

…How about Ryanair’s Laws of Motion?

Science Laws to have new official names

For immediate distribution:

The International Council of Learned Societies has finalized new naming rules for scientific laws, based on negotiations with funders. These new names will be in effect for a twenty year period, starting January 1, 2020, after which a new competition will be made.

All schools, textbooks, lectures, articles, books, blogs, facebook mentions, and tweets are instructed to adhere to these new names. All digital files in any repository will be updated automatically.

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OF COURSE… this has not happened… yet.

As Yochai Benkler warns us, science needs to step away from the enveloping need to compete in the market. “The Tyranny of the Margin – the need to compete in the market, to increase economic margins. A context where you have to compete and survive and deliver returns on investment. This postpones the ethical commitment. Entreprenuers with an ethical commitment vs investors raising money.” From: Notes on Benkler’s talk at OuiShare Festival.

Thoughts on Governance for your New Big Data VO

A well cared for volunteer community is like a great South Berkeley garden!

A well cared for volunteer community is like a great South Berkeley garden!

NOTE: too long for a blog (sorry), but I did want this to be available.
The West Big Data Innovation Hub held its first all-hands-meeting in Berkeley last Thursday. What follows is a short talk I gave to the newly-formed Governance Working Group.
The Hub seeks to become a community-led, volunteer-run organization that can bring together the academy and industry… and that other academy (the one with the statues), and regional and metro government organizations into a forum where new knowledge will be born to build the practices and the technologies for big data use in the western US.
To become this organization it will need to spin up governance. An initial task for the governance working group is to draft a preliminary governance document that outlines the shape of the Hub’s decision space, and the desired processes to enable those HUB activities needed to realize the mission of the organization.
Virtual organization governance is hard. And the knowledge of how to succeed is not well understood.  We do know that the opportunities for failure are numerous. Funders will need to exercise patience and forbearance during the spin-up process. 
I don’t know of any NSF-funded community-led, volunteer-run organization that can be a model for this governance. I would be very happy to hear about one.  It would be great if this Hub becomes that successful organization.    
I have three suggestions (with the usual caveats) to help frame the work of this working group.

NUMBER ONE: Your community does not yet exist.  

There is a quote attributed to either Abraham Lincoln or Darryl Royal (depending if you’re from Texas or not)… “If you have five minutes to cut down a tree, spend the first three sharpening your axe.” 
Community building activities is the hub sharpening its axe.
Right now, when someone talks about the “big data community” that’s just another word for a bunch of people whose jobs or research involve big data. That’s a cohort, not a community. If you want community—and you do want community—you have to build it first.  That’s why you need to spend resources getting more people into the process and give them every reason to stay involved.
The first real job of the hub is to build your member community. 
Part of building your community is to give your members a stage for their vision of the future.  Challenge your members to envision the destination that marks the optimal big-data future for a wide range of stakeholders, then build a model for this destination inside the Hub.  
To meld vision with action and purpose and forge something that is new and useful, that’s a great goal: think of the Hub as the Trader Joes of big data. The place people know to go to… in order  to get what they need.
NOTE: Why do you actually need community? There’s a whole other talk there….  Community is the platform for supporting trustful teamwork… without it, you will not get things done. Without it emails will not get answered, telecons will not be attended, ideas and problems will not surface in conversations… and meetings will be tedious.

NUMBER TWO: Engagement is central. 

ANOTHER QUOTE: Terry Pratchett, the philosopher poet, once wrote: “Give a man a fire and he’s warm for a day. Ah, but set a man on fire and he’s warm for the rest of his life…” 
You governance effort should be centered on maximizing member engagement by giving the greatest number of members opportunities to do what they believe is most important for them to do RIGHT NOW. Invite new members to join and then ask them what the hub can do for them. This is not a Kennedy moment.
Your members want pizza… it’s your job to build them a kitchen and let them cook.
Your steering committee (or whatever this is called) needs to be 90% listening post and 10% command center. It needs to listen and respond to members who want to use the Hub to do what they think the hub should do. It needs to coordinate activities and look for gaps. It needs to remind members of the vision, the values, and the mission goals of the organization, and then remind them that this vision, these values, and the mission belong to them and are open to all members to reconfigure and improve.
The Hub needs to be a learning organization with multiple coordinated communication channels… Members need to know their ideas have currency in the organization.  
Do not be afraid of your members, but do be wary of members that seem to want to lead without first attracting any followers. Spread leadership around. Look for leadership on the edges and grow it.
Engagement will lead to expertise.   Over time, the members will learn to become better members.  The organization should improve over time. It will not start out amazing.  It can become amazing if you let it.
Each member needs to get more than they give to the organization. If they don’t, then you’re probably doing it wrong. This will be difficult at first, so the shared vision will need to carry people through that initial phase.
Creating a bunch of committees and a list of tasks that need to be finished on a deadline is NOT the way to engage members. If you think that’s engagement, you are probably doing it wrong.  YES, some things need to be done soon to get the ball rolling. But remember that volunteers have other, full time jobs.

NUMBER THREE:  There can be a great ROI for the NSF

The Hub’s success will provide the NSF with a return on its investment that is likely to be largely different than what it expects today, but also hugely significant and valuable.
Final quote here: Brandon Sanderson, the novelist wrote: “Expectations are like fine pottery. The harder you hold them, the more likely they are to break.”
The hub is NOT an NSF-funded facility, or a facsimile of a facility…
Unlike a facility, the NSF will not need to fund a large building somewhere and maintain state-of-the-art equipment. The NSF already funds these facilities for its big data effort.  The Hub is not funded to be a facility and will not act like a facility. 
The hub is also not just another funded project… 
Unlike a fully funded project, the NSF will not be paying every member to accomplish work in a managed effort with timelines and deliverables. 
Volunteers are not employees. They cannot and should not be tasked to do employee-style work. They have other jobs.  The backbone coordination projects for the hubs and spokes are paid to enable their volunteer members to do the work of volunteers. The Hub is not a giant funded project. It will not work like a giant funded project. It cannot be managed. It must be governed.  This means it needs to govern itself. 
Self governance is the biggest risk of failure for the hub. That’s why the work you do in this working group is crucial.
Self governance is also the only pathway to success. So, there is a possible downside and potentially a really big upside…
Remember that process is always more important than product.  You may need to remind your NSF program managers of this from time to time.
The Hub needs to take full advantage of the opportunities and structural capacities it inherits as a community-led, volunteer-run organization. It’s goal is to be the best darn community-led, volunteer-run organization it can be.  Not a facility and not a big, clumsy funded project.
Here are Seven Things the NSF can get only by NOT funding them directly, but through supporting the HUB as a community-led virtual organization of big-data scientists/technologists:
1. The NSF gets to query and mine a durable, expandable level of collective intelligence and a requisite variety of knowledge within the HUB;
2. The NSF can depend on an increased level of adoption to standards and shared practices that emerge from the HUB;
3. The NSF will gain an ability to use the HUB’s community network to create new teams capable of tackling important big-data issues (also it can expect better proposals led by hub member teams);
4. The NSF can use the HUB’s community to evaluate high-level decisions before these are implemented (=higher quality feedback than simple RFIs);
5. Social media becomes even more social inside the HUB big-data community, with lateral linkages across the entire internet. This can amplify the NSF’s social media impact;
6. The Hub’s diverse stakeholders will be able to self-manage a broad array of goals and strategies tuned to a central vision and mission and with minimal NSF funding; and,
7. The NSF and the Hub will be able to identify emergent leadership for additional efforts.
Bottom Line: Sponsoring a community-led, volunteer-run big data Hub offers a great ROI for the NSF. There are whole arenas of valuable work to be done, but only if nobody funds this work directly, but instead funds the backbone organization that supports a community of volunteers. This is the promise of a community-led organization.
And it all starts with self-governance…
To operationalize your community-building effort you will be spinning up the first iteration of governance.  If you can keep this first effort nimble, direct, as open to membership participation as you can, and easy to modify, all will be good.  Do not sweat the details at this point.  Right now you are building just the backbone for the organization. Just enough to enable and legitimate the first round of decisions.
Make sure that this document is not set in concrete… it will need to change several times in the next 3-5 years. In the beginning, create a simple process and a low threshold for changes (not a super majority). TIP: Keep all the governance documents on GitHub or something like that. Stay away from Google Docs! Shun Word and PDFs!   

Postscript:

Hallmark moments in the future of this Hub if it is successful:
At some point 90% of the work being done through the Hub will be by people not in this room today. The point is to grow and get more diverse. With proper engagement new people will be finding productive activities in the hub. [with growth and new leadership from the community] 
At some point none of the people on the steering committee will be funded by the NSF for this project…  [this is a community-led org… yes?]… 
At a future AHM meeting more than 50% of the attendees will be attending for the first time.