The Tetherless World Constellation at RPI is pleased to announce that TWC and the SciVerse team at Elsevier are planning a Health and Life Sciences-themed, 24-hour hackathon to be held 27-28 June 2011. The event is sponsored by Elsevier and held at Pat’s Barn, on the campus of the Rensselaer Technology Park.

After a short tutorial period by TWC RPI staff and distinguished guests, participants will compete with each other to develop Semantic Web mashups using linked data from TWC and other sources, web APIs from Elsevier SciVerse, and visualization and other resources from around the Web.

Prizes
The contest will encompass building apps utilizing the SciVerse API and other resources in multiple categories, including Health and Life Sciences and Open classes. Overall, there will be three winners:

• First place: $1500
• Second place: $1000
• Third place: $500

Judging
A distinguished panel of judges has assembled that includes domain experts, faculty and senior representatives from Elsevier:

• Paolo Ciccarese (Scientist and Senior Software Engineer, Mass General Hospital; Faculty, Harvard Medical School)
• Chris Baker (Research Chair, Innovatia)
• Bob Powers (Semantics Engineer, Consultant at Predictive Medicine)
• M. Scott Marshall (Department of Medical Statistics and Bioinformatics, Leiden University Medical Center)
• Ora Lassila (Principal Technologist, Nokia; co-author of the W3C RDF specification)
• Elizabeth Brooks (Head of Computing & IT, UHI, Scotland)
• Hajo Oltmanns (Elsevier: SVP Health Sciences Strategy)
• Scott Virkler (Elsevier: SVP e-Products Global Medical Research)
• Helen Moran (Elsevier: VP Smart Content Strategy)

Refreshments
All attendees will be provided lunch, dinner, and midnight snack on 27 June and breakfast and lunch on 28 June.

Travel Assistance
A small amount of travel assistance will be made available for students and non-profits on a competitive basis. Please see our Travel Assistance page or contact us for further details.

Travel and Lodging Information
See the Elsevier/Tetherless World Health and Life Sciences Hackathon web site for specific information about transportation and lodging near the venue. Please note that the Hackathon runs for 24 hours, so it is unlikely that participants will want lodging on the night of 27 June…

Contacts
Please browse to the Contacts area of the Elsevier/Tetherless World Health and Life Sciences Hackathon web site or follow the EventBright event organizer link if you have questions.

Follow us on Twitter!
The hash for this event is #TWCHack11

On June 17 I will be joining other researchers at a Patent Data Workshop jointly hosted by the USPTO and NSF at the U.S. Patent & Trademark Office in Alexandria, VA. This workshop, supported by the USPTO Office of Chief Economist and the Science of Science and Innovation Policy Program (SciSIP) at the NSF, will bring researchers together to share their ideas on how to facilitate the more efficient use of patent and trademark data, and ultimately to improve both the quantity and caliber of innovation policy scholarship.

The stated goals of this workshop include:

1. Creating an information exchange infrastructure for both the production and informed evaluation of transparent, high-quality research into innovation;
2. Promoting an intellectual environment particularly hospitable to high-impact quantitative studies;
3. Creating a distinct community with well-developed research norms and cumulative influence; and
4. Championing the development of a platform to support a robust body of empirical research into the economic and social consequences of innovation.

Each participant planning to attend this workshop has been asked to prepare a blog post that outlines (a) our understanding of the most significant theoretical or empirical challenges in this space, and/or (b) where the frontier of knowledge is, what innovative things are being done at the frontier — or within reach of being done to solve the set of problems — and where targeted funding could yield the highest payoffs in getting to solutions. The purpose of this post is to offer some of my thoughts based on progress made by linked open government data initiatives in the US and around the world.

Background: The Tetherless World and Linked Open Government Data
Since early 2010 the Tetherless World Constellation (TWC) at Rensselaer Polytechnic Institute has collaborated with the White House Data.gov team to make thousands of open government datasets more accessible for consumption by web-based applications and services, including mashups leveraging Semantic Web technologies. TWC has created an infrastructure, embodied by the TWC LOGD Portal, for automatically converting to RDF and enhancing government data published in tabular (e.g. CSV) format; publishing these converted datasets as downloadable “dump files” and through SPARQL endpoints; demonstrating highly effective methodologies for using such linked open government data assets as the basis for the agile creation of lightweight, powerful visualizations and other mashups. In addition to providing a searchable interface to thousands of converted Data.gov datasets, the TWC LOGD Portal publishes a growing set of demos and tutorials for use by the LOGD community.

The Data.gov/TWC LOGD partnership and similar international LOGD efforts, especially the UK’s Data.gov.uk initiative, have demonstrated the value and potential for innovation achieved by exposing government data using linked data principles. Indeed, the effective application of the linked data approach to a multitude of data sharing and integration challenges in commerce, industry and eScience has shown its promise as a basis for a more efficient, agile research information exchange infrastructure.

Recommendation: Create a “DBPedia” for Patent Data
The Linked Open Data Cloud diagram famously illustrates the growing number of providers of linked open data around the world. Careful examination of the LOD Cloud shows that most sources are sparsely linked, and a very few — most notably, DBPedia.org, are extremely heavily linked. The reason is that the Web of Data has increasingly adopted DBPedia as a reliable source or hub for canonical entity URIs. This means that as providers put their datasets online, they enhance their datasets by providing sameAs links to DBPedia URIs for named entities within these datasets. This enables their datasets to be easily linked to other datasets and increases their utility and value as the basis for visualizations and linked data mashups.

Providers embrace DBPedia’s URI conventions as “canonical” in order to make their datasets more easily adopted. Our objective with patent and trademark reference data and research information in general must be to break down barriers to its widespread use, recognizing that we may have no idea how it may be used. Linked data principles and the Web of Data emerging from them have re-written what it means to make data integration easy. Whereas even a few short years ago it was useful to simply provide a searchable patent database through a proprietary UI, next-generation innovation infrastructures will be based on globally interlinked graphs drive by concept and descriptive metadata extracted from patent records, research publications, business publications and indeed data from social networks. Scholars of innovation will traverse these graphs and mash them with other graphs in ways we cannot anticipate, and thus make serendipitous discoveries about the process of innovation we cannot predict today.

My DBPedia reference comes from the idea of identifying concepts and specific manifestations of innovation in the patent corpus. Consider an arbitrary patent disclosure; it can be represented as a graph of concepts and related manifestations. The infrastructure I’m proposing will enable the interlinking of URI-named concepts, not only with other patent records but also scientific literature, the financial and news media, social networks, etc. From a research standpoint, this will enable the study of the emergence, spread and influence on innovation in many dimensions.

Conclusions
The USPTO has already made great strides in improving access to and understanding of patent and trademark data; an excellent example is the Data Visualization Center and specific data visualization tools such as the Patent Dashboard which provides graphic summaries of USPTO activities. These are “canned apps,” however; the next generation of open government will require finer grained access to this data, presented as enhanced linked data and using open licensing principles. As USPTO datasets are presented in this way, researchers will be able to interlink this data with datasets from other sources, resulting in a more effective study of the causes of innovation and indeed the outcomes of government programs intended to stimulate innovation.

References

1. NSF Patent Data Workshop. NSF Award Abstract #1102468 (31 Jan 2011).
2. Julia Lane, The Science of Science and Innovation Policy (SciSIP) Program at the US National Science Foundation. OST Bridges vol. 22 (July 2009)

Set totally outrageous goals!

Well, one of the more “outrageous” goals for the Linking Open Government Data project at the Tetherless World Constellation at RPI this term is to create the most comprehensive and useful catalog of open government datasets in the world.

To this end, I am challenging our students — and indeed everyone within earshot — to participate in what I’ve dubbed the TWC LOGD Million Dataset Challenge: I’m challenging you to help us create a master catalog of more than 1 million open government datasets from around the world! In return, we’ll make the catalog publicly available through our TWC LOGD Portal, as RDF dumps and via a SPARQL endpoint.

To get this thing started and to make it as easy as possible, I’ve created a Google Form-based interface. Follow the link, add metadata, move on…

I’ve structured the form to accept both catalog and individual dataset entries. Just chose the right options in the form…

To submit a dataset: http://bit.ly/g0d3tY

To view the current status (spreadsheet): http://bit.ly/eANqSg Total (18 Feb 2011): More than 331,345 datasets

A few resources to get started:

1. Worldwide Search CKAN: The Data Hub Prime source!
2. Guardian.co.uk’s catalog of over 12K world government datasets Prime source!
3. Where can I get large datasets open to the public? (Quora) Prime source!
4. DataMarket.com’s International Dataset Search page Prime source!
5. USA Data.gov raw data catalog Prime source!
6. USA Data.gov geodata catalog Prime source!
7. UK Data.gov.uk project Prime source!
8. Africa Africover datasets download (multiple countries) Prime source!
9. Blog listing many catalogs Prime source!
10. EU Official National Data Catalogs Prime source!
11. ePSI Public Sector Information (PSI) Data Catalogues (by governments) Prime source!
12. Open Data Euskadi International Catalog (Based on eOSI list) Prime source!
13. Open Knowledge List of European Open Data Catalogues Prime source!
14. Civic Commons List of Open Data Initiatives Prime source!
15. Univ. of Colorado (Boulder) Libraries Foreign Information by Country Prime source!
16. Factual.com’s “comprehensive” repository of government data
17. New York State GIS Clearinghouse
18. New York State Dept. of Health Statistics
19. California Dept of Education data collections
20. OpenGovernmentData.org catalogs
21. GovLoop.com’s List of Open Government Plans (US federal agencies)
22. Socrata open datasets
23. Danish data catalog
24. Finnish data catalog
25. Australian data catalog
26. spaghettiopendata.org Citizen-driven Italian Open Data site
27. Istat.it Italian government statistical data
28. Datasets from the Piedmont region of Italy
29. data.CNR.it The Italian National Research Council site
30. Public Datasets on Amazon Web Services
31. Alaska State GeoSpatial Data Clearinghouse (Alaska DNR)
32. Asian Development Bank Statistical Database Service

Note: As the dataset grows we’ll improve both the data entry and catalog interface. Our immediate goal is to grow the list…

Note: Watch for answers to this Quora question: What is the most comprehensive list of international open government datasets?

There comes a time in life, when you walk away from all the drama and people who create it. You surround yourself with people who make you laugh, forget the bad, and focus on the good. So, love the people who treat you right. Pray for the ones who don’t. Life is too short to be anything but happy. Falling down is part of LIFE…Getting back up is LIVING………Re-post if you agree; I just did

I’ve been trying to trace the origins of this meme using Google and focusing on the quote, Falling down is part of LIFE…Getting back up is LIVING; it seems to have been active on the Web for about a year, especially in “mommy blogs” and on Facebook.

Update:

• My journey has taken me to KnowYourMeme, a site dedicated to tracking trends in Internet culture.
• An oft-cited story on the tracking of political memes, Study Measures the Chatter of the News Cycle described the 2009 paper by Jure Leskovecy, Lars Backstrom and Jon Kleinberg, Meme-tracking and the Dynamics of the News Cycle (PDF).

Patrick West, my fellow TWC undergrad research coordinator and I asked the students to blog about their work throughout the semester; with the end of term, we asked them to post summary descriptions of their work and their thoughts about the fledgling TWC undergrad research program itself. We’ve provided short summaries and links to those blogs below…

• Cameron Helm began the term coming up to speed on SPARQL and RDF, experimented with several of the public TWC endpoints, and then worked with Philip on basic visualizations. He then slashed his way through the tutorials on TWC’s LOGD Portal, eventually creating impressive visualizations such as this earthquake map. Cameron is very interested in the subject of data visualization and looks to do more work in this area in the future.
• After a short TWC learning period, Dan Souza began helping doctoral candidate Evan Patton create an Android version of the Mobile Wine Agent application, with all the amazing visualization and data integration required, including Twitter and Facebook integration. Mid-semester Dan also responded to the call to help with the crash” development of the Android/iPhone TalkTracker app, in time for ISWC 2010 in early November. Dan continues to work with Evan and others for early 2011 releases of Android, iPhone/iPad Touch and iPad versions of the Mobile Wine Agent.
• David Molik reports that he learned web coding skills, ontology creation, server installation and administration. David contributed to the development and operation of a test site for the new, semantic web savvy website for the Biological and Chemical Oceanography Data Management Office BCO-DMO of the Woods Hole Oceanographic Institute.
• Jay Chamberlin spent much of his time working on the OPeNDAP Project, an open source server to distribute scientific data that is stored in various formats. His involvement included everything from learning his way around the OPeNAP server, to working with infrastructure such as TWC’s LDAP services, to helping migrate documentation from the previous Wiki to the new Drupal site, to actually implementing required changes to the OPeNDAP code base.
• Philip Ng worked on a wide variety of projects this fall, starting with basic visualizations, helping with ISWC applications, and including iPad development for the Mobile Wine Agent. Philip’s blog is fascinating to read as he works his way through the challenges of creating applications, including his multi-part series on implementing the social media features.
• Alexei Bulazel began working with Dominic DiFranzo on a health-related mashup using Data.gov datasets and is now working on a research paper with David on “human flesh search engine” techniques, a topic that top thinkers including Tetherless World Senior Constellation Professor Jim Hendler have explored in recent talks. Note: For more background on this phenomena, see e.g. China’s Cyberposse, NY Times (03 Mar 2010)

Many of these students will be continuing on with these or other projects at TWC in 2011; we also expect several new students to be joining the group. The entire team at the Tetherless World Constellation thanks them for their efforts and many important contributions this fall, and looks forward to being amazed by their continued great work in the coming year!

John S. Erickson, Ph.D.

Data.gov Dataset Search (Profile View)

The US Government Dataset Search application is an easy way for SciVerse users and developers to search from among over 300,000 available US government datasets at http://data.gov to automatically find matches to their queries. Based on the user’s SciVerse Hub query, searches are simultaneously made against all datasets published through Data.gov as well as the RDF-converted data and related demos at the Linking Open Government Data (LOGD) portal, created by the Tetherless World Constellation (TWC) at Rensselaer Polytechnnic Institute (RPI).

Any user with the ability to search SciVerse Hub can use the US Government Dataset Search application. The application and the government data it exposes are made available free of charge. The US Government Dataset Search application is targeted at both SciVerse end users (researchers) and application developers interested in applying government datasets to their applications. Researchers utilizing SciVerse Hub are able to discover and access contextually relevant data from the US Government. Developers may utilize SciVerse Hub to identify RDF-converted data sets based on the US Government data and access this data in their applications through SPARQL endpoints or retrieve the datasets themselves.

How the US Government Dataset Search application works: For each SciVerse query the user makes, a keyword search across all current Data.gov datasets is made via a SPARQL endpoint at the TWC LOGD portal. A summary of these results is presented on the Hub search results page. Detailed results are presented in tabular form in the ‘Canvas’ (larger) view by clicking on any link. On the canvas view links are provided directly to the Data.gov dataset description pages as well as RDF-converted versions of these datasets at the TWC LOGD portal. Note that faceted search is not available with the application and only the original query in Hub willbe submitted.

All queries are made against the LOGD SPARQL endpoint at http://logd.tw.rpi.edu/sparql The application also makes use of the Google Visualization toolkit.

This application is optimized for Firefox, Chrome and Internet Explorer 8.

For more information about creating mashups using Data.gov datasets, please check out RPI’s Linking Open Goverment Data (LOGD) Portal at http://logd.tw.rpi.edu

About the TWC Linking Open Government Data project: The TWC LOGD team investigates opening and linking government data using Semantic Web technologies. TWC LOGD actively develops tools for the large-scale translation of government-related datasets into RDF, linking them into the ‘Web of Data’ and providing demos and tutorials on various means for consuming linked government data, including creating mashups, applications and data visualizations. The TWC LOGD Portal was awarded second place (open division) at the 2010 Semantic Web Challenge, held during the 2010 International Semantic Web ConferenceISWC2010.

About the Tetherless World Constellation at RPI: The Tetherless World Constellation addresses the emerging area of Web Science, focusing on the World Wide Web and its future use. Faculty in the constellation lead explorations into the principles that underlie the Web; enhance the Web’s reach beyond the desktop and laptop computer; and develop new technologies and languages that expand the capabilities of the Web. TWC researchers use powerful scientific and mathematical techniques from many disciplines to explore the modeling of the Web from network- and information- centric views. TWC’s objectives include making the next generation web natural to use while being responsive to the growing variety of policy and social needs, whether in the area of privacy, intellectual property, general compliance, or provenance. The Tetherless World Constellation is designing new techniques to explore social, scientific, and legal impacts of the evolving technologies deployed on the Web.

News about the TWC/Elsevier US Government Dataset Search Application

1. Featured in Looking Back at 2010 at Rensselar RPI News & Events (20 Dec 2010)
2. SciVerse Hub Application Connects Researchers with U.S. Government Datasets Information Today (20 Dec 2010)
3. U.S. Government Dataset Search Opens Data.gov to Scientists Data.gov website (14 Dec 2010)
4. New Application Allows Scientists Easy Access to Important Government Data RPI News & Events (10 Dec 2010)
5. New Application Allows Scientists Easy Access to Important Government Data Lab Manager Magazine (13 Dec 2010)
6. New Application Allows Scientists Easy Access to Important Government Data EurekAlert (10 Dec 2010)
7. New Application Allows Scientists Easy Access to Important Government Data Physorg.com (10 Dec 2010)
8. New Application Allows Scientists Easy Access to Important Government Data FirstScience.com (10 Dec 2010)
9. New Application Allows Scientists Easy Access to Important Government Data NewsoDrone.com (10 Dec 2010)

UPDATE: I’m currently developing an iGoogle Gadget version of the SciVerse app, based on the same core queries. A screen shot of the “profile” view of that app appears below. In addition to enabling me to monitor the health of our systems from my desktop, it also enables me to test out possible features for the SciVerse app itself.

iGoogle Gadget version of the US Government Dataset Search app

This version has been edited slightly to make it better suited for a blog…

As an “old guy” who has interviewed his share of CS, CE and EE’s over the years (and hire and/or managed more than a few of them), here are my thoughts from an “outcomes” perspective…

UPDATE (29 Oct 2010): Nature recently published this interesting article, Computational science: …Error …why scientific programming does not compute, (13 Oct 2010) on the increasing need for scientists to have hard-core software engineering skills to do their science.

There’s an increasing variety of data available as Linked Data coming from a range of different sources. I’m wondering what indicators we might use to judge the “quality” of a dataset…Clearly quality is a subjective thing, but I’d be interested to know what factors people might use to indicate whether a dataset was trustworthy, well modelled, sustainable, etc.

For starters, I think we can all agree at the highest level that the measure of data quality is subjective and that “beauty is in the eye of the beholder”: the quality of a dataset is measured by its fitness for use in specific applications. This question of determining and disseminating “fitness” scores is the rub!

In his answer to Leigh’s question, Tim Finin proposes adopting a PageRank-like mechanism, “LODrank” based on measured usage

We could define LODrank as a PageRank-like measure that was a function of the number of links to/from other LOD datasets weighted by their LODrank. Alternatively, it might divided by the number of linkable instances in the collection, so that large datasets did not have an advantage…

This approach scores data quality based on observed fitness as evidenced by discovered use and has the advantage of automation.

My replies went in a different direction, focusing instead on the subjective nature of data quality and the need to aggregate consumer-space rankings of datasets across a set of dimensions. In his 2005 white paper Principles of Data Quality [1] Arthur D. Chapman writes,

Data quality is multidimensional, and involves data management, modelling and analysis, quality control and assurance, storage and presentation. As independently stated by Chrisman [2] and Strong et al. [3], data quality is related to use and cannot be assessed independently of the user. In a database, the data have no actual quality or value [4]; they only have potential value that is realized only when someone uses the data to do something useful. Information quality relates to its ability to satisfy its customers and to meet customers’ needs [5]

Chapman goes on to enumerate a set of factors that contribute to fitness-for-use, citing Redman [6]:

• Accessibility
• Accuracy
• Timeliness
• Completeness
• Consistency with other sources
• Relevance
• Comprehensiveness
• Providing a proper level of detail
• Easy to “read”
• Easy to “interpret”

Each of these factors is fundamentally subjective, even if mechanisms exist within particular domains to take their measure “objectively.” Indeed, in some domains such ratings might only be done by humans, either through voting mechanisms or by individual reviewers.

I believe the greater linked data community needs to develop vocabulary terms for expressing metrics for data quality — consider the ten points above — and then within individual communities develop agreed-upon means to determine those values. Arguably this is a “Dublin Core” approach to the problem, in the sense that terms like completeness or consistency would be reused across domains with inherently different domain-specific meanings, but such reuse would facilitate consumers from other communities choosing datasets from outside their expertise. A non-physicist might then say, “The physics community says this dataset is accurate, by their measures.”

Some of these factors are even more deeply subjective and must be evaluated dynamically, based on the consumer’s immediate context. An example of this is relevance, which could be interpreted as equivalent to a recommendation.

If you have thoughts on data quality as it applies to linked data, consider answering Leigh’s question at SemanticOverflow!

References: (as cited by Chapman)

1. Chapman, A. D. 2005. Principles of Data Quality, version 1.0. Report for the Global Biodiversity Information Facility, Copenhagen.
2. Chrisman, N.R., 1991. The Error Component in Spatial Data. pp. 165-174 in: Maguire D.J., Goodchild M.F. and Rhind D.W. (eds)
3. Geographical Information Systems Vol. 1, Principals: Longman Scientific and Technical.
4. Strong, D.M., Lee, Y.W.and Wang, R.W. 1997. Data quality in context. Communications of ACM 40(5): 103-110.
5. Dalcin, E.C. 2004. Data Quality Concepts and Techniques Applied to Taxonomic Databases. Thesis for the degree of Doctor of Philosophy,
6. School of Biological Sciences, Faculty of Medicine, Health and Life Sciences, University of Southampton. November 2004. 266 pp.
7. English, L.P. 1999. Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits. New York: John Wiley & Sons, Inc. 518pp.
8. Redman, T.C. 2001. Data Quality: The Field Guide. Boston, MA: Digital Press.

Regarding the Singularity Movement, I think economic arguments such as that presented by Robin Hanson in IEEE Spectrum (2008) carry more weight than the gushing futurist predictions from the likes of Ray Kurzweil. In the Spectrum article Hanson cites two previous singularities — the agricultural and industrial revolutions — and suggests that a revolution in machine intelligence is leading to a third that will take shape over the next half-century.

I tend to take most of what futurists say with a grain of salt, because they rely on a belief/assumption/confidence that the introduction of disruptive technologies into a society yields predictable results — for good or bad — which never happens. The combination of factors including technologies being human constructions, the fact that we as humans never make completely rational decisions, and the fact that all of this takes place within a fundamentally chaotic, only approximately predictable context, means that we simply cannot know what will happen in the future!

Here’s what I know: We humans are wired to build and use tools and, to the extent possible, adapt to the environments we build — or die trying. Google, while amazing, is still a tool; an engineered system that (given enough time) I can explain to you. Ironically enough, the reason Google works so well is because it’s actually based on simpler, but more fundamental principals than the systems which preceded it, closer to how naturally-occurring networks emerge and function. But the way Google has been adopted and applied in the “ecosystem,” while making sense in hindsight, could not have been predicted.

I’m currently reading Jonah Lehrer’s How We Decide, a wonderful exploration of the biochemistry of how we make decisions. Any such discussion naturally much touch on how various imbalances (e.g. dopamine, etc) effect that process, and how well-intentioned efforts by doctors to counteract certain imbalances leads to very unexpected and usually undesired results.

Lehrer’s book makes it profoundly clear that we never know for certain what will happen when we diddle with the decision-making processes in our brain, whether it involves extending the lower levels of the nervous system (the sensory level) or the higher level processes. Researchers do know that we seem to adapt well to lower-level, e.g. neural prosthetics, but each higher-level process involves a synaptic algorithm that we don’t completely understand — mostly because our brain is a distributed system, not a single “algorithm,” whose “result” is emergent.

That ultimately is my point: our brains are distributed systems that exhibit adaptive and unpredictable behaviors, and we can’t begin to understand what will happen when we explore higher-level prosthetics based on “intelligent machines.” Something will happen, but there is no reason to believe it will lead to either a Utopian or Dystopian existence any more than the agricultural or industrial revolutions resulted in one or the other. Indeed, the introduction of those practices to certain natural and economic ecosystems led to both regional successes and catastrophes.

For Further Information:

• The Singularity University
• Merely Human? That’s So Yesterday. New York Times (11 June 2010).
• Economics Of The Singularity IEEE Spectrum (May 2008)
• Singularity articles at http://www.kurzweilai.net
• On Intelligence, the companion site to Jeff Hawkin’s provocative book by the same name. The book introduces the concept of Hierarchical Temporal Memory (HTM) based on a layered hierarchical model of how the neocortex functions.

Since Jill and I moved to Norwich some 18 years ago, I’ve been troubled by what seems like a lack of support for sustainable economic development within our town. I’m proud that Norwich has a high-quality global company “like” King Arthur based here, a company that is employ-owned, successful and growing. At the same time I’m embarrassed that Norwich isn’t doing more to sustain the economic well being of the Upper Valley.

15 years ago this month partners and I began the process of launching a company called NetRghts. Loving Norwich and Vermont, I had a vision of starting a sustainable high-tech company that would be based here and would create local jobs. The inevitable question of where to base our company arose; being the Vermonter in the mix and drinking from the KoolAid of iconic successes like Green Mountain Gringo, I argued for us to set up offices in Norwich, Wilder or WRJ. My co-founders thought this was ludicrous; not only did they envision the (obvious to them) negative tax implications, but they also perceived no end of difficulty with infrastructure, etc. Since they had been successful with a previous Lebanon-based software startup, I went along for the ride and we set up shop in downtown Lebanon.

But I wouldn’t give up that easily. At one point Vermont eTV — remember them? — had a call-in with Gov Dean’s youthful, energetic director of economic development. Vermont had recently provided incentives for ETI’s expansion, and my direct question to “Slick” was: what can Vermont do to keep companies like ours in Vermont? Or, were my co-founders right, there (weren’t) any incentives to lure us to Vermont. His answer: regrettably, yes, my co-founders were right. If we needed money for bricks-n-mortar expansion to grow a widget-building business, yes, but since we were “knowledge-based,” nothing. Frankly, I was shocked, since this was during the same period that Gov Dean (who I’m a fan of!) was roaming the state advocating green high-tech businesses in cabins on mountaintops…

I’ve bored you with this ancient history in order to provide some context as to why I believe the citizens of Norwich should greet initiatives such as King Arthur’s with the question, what can we as neighbors do to help? Their opening proposal may or may not be ideal — I’m not saying “Roll over, little Norwich!” — but I do believe it is our responsibility to do what we can to foster economic development in this town, and this includes hearing their plans with an open mind.

I’m tired of Norwich not merely depending on, but assuming that other towns in the region will feed our hungry, host our homeless, pay our salaries, sell us our auto parts. Instead, we should be asking how we can help those among us with the initiative to bring it on home to Norwich…

Disclaimer: I am not affiliated with King Arthur Flour, but I do confess to loving their products and have been known to roam their jobs portal from time to time…