The good news this week was that on the most recent CT scan the doctors were unable to find any sign of the primary tumor at all. The chemo has apparently melted that tumor away, which they say happens in about 10-20% of cases. That will allow the radiation area to be smaller and more focused, which should help minimize the side effects.

Our friend Mauri is here from New York this week, so she’ll be keeping me company in the hospital while Michele is working and Mo is at school. And I’ll keep working remotely in between getting zapped.

We went to see the Descendants last night – a terrific, sad yet hopeful movie. Great acting! Now I’m arming myself by downloading TV episodes, podcasts, and music to take to the hospital. Along with the huge pile of books on my bedside table (not to mention all the work I’ve got on my plate), I should be able to occupy my time in the hospital and at home in the coming weeks.

Marketing the potential benefit of things people don’t understand yet.

Think more like researchers and entrepreneurs.

Value proposition – should differentiate you from your competitors. Give people confidence that you can meet their unmet needs. Are we presenting our value effectively?

What are the perceived vs. real unmet needs?
Are they confident that we offer the right things?
What is IT’s competition?

Perceived Value – actual value is very complex and we can’t expect people to understand it, so market by differentiation against competitors.

Perceived value
Actual value
Competitors
Differentiators

IT has more than one customer. They all see us differently.

Admin – actual value: cost savings, security and regulatory expertise, sells the campus
– competitors: outsourcing, ROI
Differentiators – IT is part of campus culture, higher quality than outsourcing because of our expertise,
The only cost center on campus whose returns increase year after year.
Learn the language of administration
Express cultural value and significance better.
Leverage data in better ways.

Faculty -
Actual value – streamlining and supporting necessary teaching and research tasks, pertness for innovation (faculty who you haven’t helped don’t know because faculty son’t talk to each other).
Competitors – “edupunk” (routing around the campus and not tell anyone), contagious griping and misinformation,
Differentiators – making meaningful connections with faculty, seeking opportunities to support/encourage learning and research, even if it’s one faculty at a time, network with department IT professionals, create faculty evangelists, establish trust and confidence by being practice as well as responsive.

Staff -
perceived value: new and expert at finding new ways to cause delays.
Actual value – problem solving. Need to understand what staff do.
Competitors: budget, ad-hoc solutions, loss of confidence (so they don’t even ask)
Differentiators: transparency – time and costs; providing expertise in processes and tools; partner to learn their challenges (not just tech); provide them with expertise, not just support;

Students
Perceived value – “they’re watching!”
actual value – 99.999%; access to tools and software; discounts and cost savings; enabling student-provided devices;
Competitors: hacker mentality; perceptions that IT is behind the times; edge-user behavior
Differentiators: savings – time and money; transparent efforts to understand usage needs/differentiators;
Don’t assume – ask. Leverage benefits they care about. Create evangelists

Start expressing your value
today:
- get to know one faculty member, one staff member, follow a faculty member on twitter
- act like a marketer: pay attention to conversations, take notes of trends and perceptions.

This month:
- ask for volunteers to become disciplinary experts and department partners.
- create focus groups or listening posts for student sentiment.

This semester/year:
- start teaching basic business acumen to all IT staff.
- brag about the value of IT’s contributions to all audiences

Starting with dogs! SNIF – social networking in fur. Device goes on dog’s collar. Digs exchange business card info when close. “social petworking”

Lovegety – little device programmed with food music and movies you like. Flashes in proximity of potential love interest. When asking undergrads who likes this technology, it’s the engineering students, regardless of gender.

These are examples of use of technology to find the right people to connect with. Types of tech we have now are made by engineers for engineers. Important for people in IT space to work closely with social science knowledge.

First use of tech is to substitute. Not enough to offset or justify investments. 2nd stage is enlargement – technology increases activity. Technology increases gap between haves and have-nots. Part of what we need to do is to think creatively about how to reduce that gap. Productivity paradox – investment in IT doesn’t necessarily show return. Why? Giving Pony Express riders cell phones to call ahead to ask for water. Need to achieve 3rd stage – reconfiguration.

Ascendance of teams -
More research being done in teams
Research in teams has a higher impact.
Those in different disciplines have higher impact yet
And those involving different disciplines across different campuses have the highest impact.

But another study found that interdisciplinary distributed research is less likely to succeed. So how do we build tools to enable successful collaboration?

Understanding team assembly is key, and this is a very good time to do that.

Why do we form teams? In past, teams were assigned. But increasingly teams are self-forming. Sometimes based on self-interest. Or it may be based on social exchange. Or mutual interest and collective action. Contagion (everybody wants to work with the popular person). Balance – friends of friends. Homophily (birds of a feather). Proximity – form links with people close by – if you look at your buddy lists, most are people close by. Using tech to facilitate proximate communication. Each of these motivations have a structural signature. If you know what drives these networks, you can understand how to make them better.

Multidimensional network – not all nodes are people. Also includes documents, datasets, etc. Linked Open Data – publicly connecting datasets.

Team assembly for interdisciplinary NSF. When assembling a team, want high productivity from diversity, but also want smooth coordination stemming from shared cognitive models. How do we assemble teams to do both? 1,103 grant proposals submitted to NSF in 2 interdisciplinary programs. Researchers not likely to randomly form collaboration with each other. Researchers from top tier institutions are less likely to collaborate. Those with higher tenure are most likelybto collaborate. Researchers with high H-index are less likely to collaborate. Researchers are more likelynto collaborate with those they’ve collaborated with before.

Women are more likely to collaborate on funded proposals.
Odds of funding are higher when you collaborate with someone you’ve previously co-authors with, but not cited.

Exemplar 2 – massively multiplayer games. Virtual world exploratorium. Need to work with different characters to be successful. Motivations for creating teams in this context – selectivity and transivity (friend of friend) exists. Homophily of age and experience is supported. Short distances are important. Gender matters.

Are more diverse groups more successful? Uses Blau’s index to measure. Is group cosmopolitan characteristics more successful? Found diversity helps groups achieve more. Being more cosmopolitan helps avoid losses.

Using this data to build “match-making” Systems for forming research teams. c-iknow1.northwestern.edu.

Initial relationships across disciplines are being encouraged by funding agencies and institutions. Need to keep those relationships fresh and not homogenize interests.

In multi-team systems the connections between the teams is more important than the connections within each team. ABC dimensions – Affective, Behavioral, Cognitive.

Different kinds of goals for teams – exploring, exploiting, mobilizing, bonding, swarming.

The idea of the workshop is to try to come up with a set of actionable best practice recommendations that can be used to move the state of the art forward. I’ll try to keep up with activities here on the blog, and you can also follow along by watching the #rdlmw tag on Twitter, or by watching the live video stream at The web page for the event is at:

Theme #1 – Old Guys (and Gals) Rule

• Robert Plant – Band of Joy

  I was never much of a Led Zep fan, primarily because Plant always sounded like a cat that was having its tail stepped on. I began to get interested when he collaborated with Allison Krause, mostly because it seemed like such an odd couple. But this new album floored me. Spooky, rootsy music with lots of atmosphere, but always retaining soul. Banjos and upright on some tunes, lots of electric distortion on others, and sometimes all of the above together.

• Mavis Staples – You Are Not Alone

  A joyous celebration by the legendary gospel and soul singer. Produced by Wilco’s Jeff Tweedy, who had the good sense to record Mavis with her superb hard working road band.

• Tom Petty and the Heartbreakers – Mojo

  To my ears the strongest Petty work since Damn The Torpedoes (that ought get a rise out of some folks). I read in Tape Op that before this Petty had been listening to lots of Muddy Waters, while Mike Campbell was spending time listening to old Zeppelin. Both influences show and work together beautifully. A rocking, bluesy, mature work from what is arguably the best band in the business.

• Los Lobos – Tin Can Trust

  Are these guys simply too good to be popular? This is their best effort in a while, showing off the incredible range and sonic versatility they’re capable of. The one-two guitar punch of Cesar Rosas and David Hidalgo just gets better and better, and the songwriting and singing are equally strong.

• Keith Jarret and Charlie Haden – Jasmine

  What can you say? This is another gem in a spread out series of Charlie Haden’s duets with marvelous pianists (previous efforts featured Hank Jones and Kenny Barron), and it’s wonderful. Charlie Haden, as always, makes every note count, and Jarrett plays right in sync with him. Gorgeous music.

• Bettye LaVette – Interpretations: The British Rock Songbook

  While it sounds like a concept that might fail miserably (veteran neglected soul singer takes on hoary British rock anthems from ’60s to the present), it mostly works quite well. I’m particularly fond of her takes on the Beatles’ The Word and Paul McCartney’s Maybe I’m Amazed. And I like her good taste in picking Salt of the Earth, though her version doesn’t really add anything to the Stones’ original.

Theme #2 – In which I discover some younger veterans that I had never listened to before

• Superchunk – Majesty Shredding

  I had never listened to their earlier stuff (this is their ninth album, and the first since 2001), but I really like this one. Great power pop, putting an equal emphasis on both words. Killer hooks, heavy guitar, terrific sound.

• Jenny and Johnny – I’m Having Fun Now

  Thoroughly modern girl group surf music that’s more sophisticated than it sounds on first listen. Jenny is Jenny Lewis, formerly of Rilo Kiley. Johnny is Jonathan Rice, who has worked with Elvis Costello. Big Wave is the catchiest tune yet written about the economic crisis. Pure pop for now people.

• Belle and Sebastian – Write About Love

  I’d tried little snippets of Belle and Sebastian before, but Bryn convinced me to give them a go again, and I’m glad she did! Perhaps a bit too earnest for some, but their tuneful, intelligent Scottish pop somehow soothed my hectic fall season as I started transitioning to a new job in a new city, and it’s stuck with me.

• Spoon – GaGaGa

  I know – everybody’s been into Spoon like forever – where have I been? I like this. Don’t make me a target!

Theme #3: Not all good jazz is from the US

• Moutin Reunion Quartet – Soul Dancers

  Francois Moutin is probably the number one on-call jazz bassist in Europe. His upright technique is awesome, but it never gets in the way of his musicianship. His own quartet, including his brother Louis on drums, really come into their own on this recording. All acoustic (well, some light electronic keys), but to me it seems heavily influenced by late quintet to early electric Miles, say Filles de Killimanjaro to In A Silent Way and early Weather Report, while still finding an original voice.

• Sunna Gunnlaugs – The Dream

  Sunna Gunnlaugs is an Icelandic jazz pianist. I came across her after somehow becoming Twitter mutual-followers with her drummer (and husband), Scott McLemore. This is a quartet effort, and it’s one of the few new jazz releases from this year that I keep coming back to. Accessible and melodic, but not insipid or wimpy – there’s a core of strength and adventure running underneath the beauty that seems characteristic somehow of Icelandic music. Worth seeking out – get it from her web site: http://www.sunnagunnlaugs.com/shop.htm

That’s my list for now – I’ve got a bunch of stuff I haven’t gotten to that is showing up on other people’s end of year lists, like Brandi Disterheft’s Second Side (she’s a Canadian bassist and singer – where do these monstrously good young jazz bassists keep coming from?), the Drive By Truckers’ Big To Do, The Head and the Heart (a good sounding new Seattle band), and lots of others.

5 crises
Computing tech
Data, provenance, and viz
Software
Organization for multidisciplinary science
Education

Science and scholarship are team sports
Collaboration/partnerships will change
- building dynamic coalitions in real time
Ownership if data plusnlow cost fuels growth and number of data systems
- federation ant interop become mire important

Innovation and discovery will be driven by analysis
Mobility and personal control will drive innovation and research communities. -
- eg using accelerometers foe earthquake detection
Gaming, virtual worlds, social networksm will transform the way we do science, research and education

All the layers have to work together for the system to function. Cyberinf. Ecosystem.

The goal of virtual proximity – you are one with your resources. Collapse the barrier of distance. All resources are virtually present, accessible, and secure.

NSF

Data enabled science
Community research networks
New computational infrastructure
Access and connection to facilities

Impacts on NSF
CI as enabling infrastructure for S&E
New role for data
Multi-disciplinary approaches essential
Education – embedded and integral
More coordinated post-award management

Examples

Transient & data-intensive astronomy
- seeing events as they occur
- complex interconnected earth systems

Four data challenges
Volume
Growth
Distribution
Data sharing

Sea of data
- data enabled sciences
– immediate and long term support of data
– focus on data life cycle issues
– development of data tools – mining, visualization, algorithms
– broad computation science education program
- advanced computational infrastructure
– software elements -> integration -> institutes
– sustained long-term investment in software
- data services – integration, preservation, access, analysis
– community access networks – building virtual communities
— collab tools, secure systems to link peplum etc
- access and connectivity
– connections ton facilities and instruments
— ooi, sensor networks, telescopes (desktop connectivity hasn’t improved)
– cybersecurity
– networking, end-to-end

- data sciences

Why 20 years? Predicting the future is hard – failures of imagination and failures of nerve (Arthur Clark). Long enough to see change, but not be science fiction. Going to invite anyone who wants to respond to an opening essay that Cliff will write. Will reach out to some specific people. Will package up a selection of the essays in the ebook plus a print on demand option by end of CY 2011. Provide an offset to the short term focus of the last couple of years. When you look at some of the abrupt changes, the have long-term ramifications that need to be thought through. 

It is a good time to think about the long term. Things that have happened in the last year worth mentioning. Some quite incredible things.

Cyberinfrastructure and e-science. Can already see big steps happening in changing of scholarship. Emergence of plan for next gen networks coming out of NLR an I2  - provisioning 10gb lambdas for researchers. Emergence of sensor networks – example of high speed trading, where the speed of light makes a difference. Area of greatest interest here -data curation. Big announce,net is NSF data plan requirement. Major step because it brings researchers face to face with questions about data. What’s important? Where can I get help? Good to get the conversation going across a wide range of disciplines. We’ll see other finders follow suit. Getting services in place for researchers is a non-trivial issue. Guidance for researchers is vague. Review panels could use some guidance. This is a great collective experiment. Would be good to have a database of successful data management plans, use that as a way to get a grip on what we should do going forward. We don’t have a good understanding of data life cycles. Not hearing words lime “forever” in is context. Hearing things like “a few years after the grant” That’s good – we’re good at keeping data for 5 or 6 years. 

Open data movement. The idea that  Data should be open and shared gaining inexorable traction in some areas. . Not paying enough attention yo software. Erosion of reproducibility makes it difficult. The idea from people like Ian Foster, where everybody should be able to inspect and run the model. Entering an age of simulations and models.seeing things like a proposal out of eth asking for a billion euros to build simulation of social data incorporating input from 70 databases. New kinds of simulation, multiple-input agents.

Getting to be strange world of artifacts. Digital preservation. Trying to get to a shared standard of what constitutes the historical record. Think of the change in news. Community journalism – a form of social network. If you look at how much time people spend I. Social media, you come to the conclusion that we should be preserving and archiving – LC getting the Twitter archive. Not only important retroactively, but turning out that some of the social media are predictive. A whole series of papers from folks like Hal Varian – things like twitter or search streams are good for predicting things like movie box office. Google has been working with CDC to predict disease archives by looking at queries about symptoms combined with geo-location. Interstingnhow difficult iti is to look at these in academic social science because of human subject issues.

Wikileaks – enormous dumps of data on the net that presumably have some historical value. Some libraries starting to amass data documenting human rights violations – the kind of puzzles we’ll be dealing with. The viciousness of responses is interesting. The network is getting to be a vicious place in ways that it didn’t used to be – e.g. The stutznet worm. A very complicated and sophisticated thing with some very specific targets. Lots of implications for what documentation of the social record looks like and our confidence in its integrity.

Rise of new scale phenomena – David Rosenthal has done some fine work. In a big enough system things are always broken, so you have to be able to design around that. The probability that you can read an entire disk is becoming a problem – need different ways of thinking. Resilient system design.

Mobile computing – not just about laptops or cell phones. Seeing devices in the middle, or image sensors, digital capture, overlays on the world. Old news – putting a camera in every pocket has had all sorts of social ramifications. Before the web, we used to have a zoo full of one-off apps, that wanted to be silos. Now we’re seeing that come back in the mobile world. Hundreds of apps, each talks to one specific info resource. Need to think hard about this as we think about integration of mobile. The potential to re-license content we already own is large.

Teaching and learning – seeing a maturation of LMS market. Being extended into collaboration suites. Also seeing a resurgence of other reads where computing gets involved – Intelligent tutoring, e.g. – actual teaching done with statistical models and machine intelligence. Long history of this that never gained traction in higher Ed, though it did in some niche markets commercially. This might be a direction for textbook evolution.

Been a lot of interest unleashing space – want to engage students at a deeper level, and have them take responsibility for their learning. Worry about saturation – engagement exhaustion. The problem is one of local optimization, at the level of the course. Need to think above the course – degree, certificate, etc. Will intersect with discussions of retention and time to degree.

We are busily building systems that collect data on students. Now want to exploit it – retention, student progress, etc. Need to use them wisely and transparently. If we’re not clear with students about data collection, we may lose the ability to make use of data streams. In consumer markets we are seeing the wheels of regulation move, which will complicate things while maybe not solving  them. 

Special collections entering a new golden age as they become digital. Fascinating things going on with individual personal collections. The public interest in private records is a frontier policy area.

Many services migrating out to the network level – software as a service. Lots we don’t u derstand. What do databases look like – linked data, trust, authoritative data, issues. Croppingnup in discussions of bibliographic control. We’ll also see it with names how does that connect to databases of things like grant proposals, biography, family history. The example of mathematical genealogy – your children are the people you advised on their thesis.

Will see lots of development with the relation between what campuses are doing and what’s going on nationally.

A couple of weeks ago I went to visit my friend Jim Fricke in Milwaukee, Wisconsin. Jim is the head curator for the Harley-Davidson Museum, and I spent an afternoon and the following morning browsing the museum and lending a bit of help to Jim and the crew who were installing the new Evel Knievel exhibit.

I’m not a motorcycle rider, and I didn’t really know very much about Harley’s history, so this was a voyage of discovery for me. The Museum is really good, loads of cool bikes and interesting exhibits and explanations. I loved the “engine room” where you could see why Harley’s engines are different than others, leading to the characteristic Harley sound. There’s a wall that shows the various engine variations over Harley’s history and you can listen to recordings of the different versions – how cool is that?

The story of how Harley was acquired by AMF and became part of a large corporate enterprise in the 1970s, with subsequent declines in quality and reputation, and then had a remarkable rebirth after a group of company executives repurchased the company in 1981 is legendary.

But what I was struck by as I went through the museum exhibits covering those events was the amazing foresight that those executives had in leveraging two overlapping social networks to resurrect the company’s quality and reputation. They realized that using the network of Harley dealers and through them rallying the owners of Harleys to help the company would be key to their effort. The creation of the Harley Owner’s Group (HOG) in 1983 is frequently cited as a stellar example of brand management, but I think it’s more interesting as a very successful pre-online example of using social networking to help inform and support corporate strategy.

Drivers & Incentives – eScience/eResearch. NSF requirement for data management plans. Compliance – e-discovery, FERPA, HIPAA, Sarbanes-Oxley. Institutional record retention regulations and policies. Storage services for libraries, archives, cultural heritage entities. Great efficiencies.

Expectations (not supported) – storage is cheap; storage is smart; stuff on the internet is persistent; digital safer than analog; storage provider – curators and preservation experts; repositories take care of preservation; metadata will take care of it; libraries will take care of it; the cloud will take care of it.

The reality – new roles, new responsibilities, new collaborations, practices, workflows; Intellectual capital requirements – digital preservation; clout antithetical to preservation?; increased management requirements; scaling issues with preservation requirements.

Standards/Technologies
iRODS – From SDSC, integrated rule-based data system. Second generation of SRB.
Content addressable storage – fixed content storage, retrieval based on content rather than location
eXtensible Access Method (XAM)

Initiatives -
NSF DataNet – Data Conservancy Project – JHU lead with 23 institutions.
Chronopolis – SDSC, UCSD, UMIACS, NCAR – federated data grid using SRB/IRODS
LOCKSS (Lots of Copies Keep Things Safe) – replication of licensed journals and other content
MetaArchive – a private LOCKSS archive
Internet Archive
National Digital Information Infrastructure & Preservation Program (NDIIP) – Library of Congress project.
California Digital Library
DuraSpace – DuraCloud project to implement a preservation-oriented cloud storage service
HaithiTrust – Repository and storage infrastructure initiated for CIC Google book project
Sun PReservation and Archiving SIG (PASIG)
Storage Networking Industry Association

Penn State activities – Content Stewardship PRogram – strategic collaboration between Libraries and ITS. Goal – a suite of services to support the lifecycle of the digital object – creation, discovery, access, storage, preservation, and archiving. Hired Digital Library Architect and Digital Collections Curator; worked on governance.

Sally Jackson says that the Library School at Illinois now has a program in digital curation.

Cliff – decisions on what to curate, and what to keep, are less binary in digital formats than in print. Eg, Portico for scholarly journals, vs. “digital archaeology” status. It’s about risk management and resource allocation. Some of what we’re trying to understand in bit-management is really about risk and cost. How many redundant copies do you need? Failure modes are not well understood. Very scary data from physics labs about undetected bit flip errors. What does that cost in a preserved object? If it’s encrypted in clever ways it can cost a lot!

File Sharing through cloud: Institutional sharing?
- Eliminated Xythos (done)
- Common contract with Dropbox?

Student and faculty portfolios?
- Alumni offerings

Bernard – in context of move to Google, thy’ve clarified policies around PHI, ITAR data, FERPA.

One institution reports that as far as their CISO is concerned, if it’s verifiably sufficiently encrypted, they’d regard it the same as shredded paper.