Better article PDF

When you publish an article, you want it to be discoverable by other researchers. This requires that it be indexed. Indexing systems need metadata, which they usually extract from the PDF files in digital libraries or other document repositories.

Ideally, a manuscript submission system collects the necessary metadata from the corresponding author when the final manuscript is submitted. Since author names are not unique, a good system will require each author to log into the system using their ORCID credentials, which also ensures that all coauthors know they are such.

Some metadata is not known by the submitter, for example, the DOI, volume and issue number, page, and publication date. Such metadata is added by the managing editor. In some journals, the managing editor creates all metadata, but it is safer when the publication system generates algorithmically the metadata from that provided by the corresponding author.

Some publishers omit the verified ORCID collection from the authors. In that case, you should put each author's identifier in the author declaration. When you create your manuscript using LaTeX, you can use the package orcidlink. In the preamble add

\usepackage{orcidlink}

and in the author declaration add your ORCID

\author{John Doe\,\orcidlink{nnnn-nnnn-nnnn-nnnn}}

Some publishers just publish in their digital libraries the final PDF they receive. If you just submit the default format file you generated, it will not have any metadata and your article will not be discoverable. It is safer, to submit the article in the PDF/A archival format and with your metadata included as XMP so it can be extracted by the web crawlers of the indexing organizations.

You could accomplish this using the full version of Acrobat, but as I wrote above, manual operations are not recommended and you should let the LaTeX typesetter do it. Fortunately, River Valley Technologies has contributed a package called pdfx to automate this step. You already have this package with the standard LaTeX installation.

After importing this package, you also import hyperref. Then you write an xmpdata file declaring the metadata. You can find all the information in the exhaustive help file. The import order is important, for example in the preamble you could declare

\usepackage[a-1b]{pdfx}

\usepackage{hyperref}

If you are an editor and maintain a template for your journal, you can also embed the xmpdata file at the top of the preamble. The help file explains how to do that.

You can check the PDF format and the metadata with the free Acrobat Reader. I have generated the following two documents using the method described above:

article

slides

Industrial Research

Previous related posts: Career Networking, Scholarly Publications.

With the industrial revolution, large companies introduced central research laboratories to accelerate the invention of new products. After the Sputnik crisis, in the USA these laboratories became very prestigious, under the influence of people like Joseph Licklider. The labs flourished, receiving government jobs paid under the cost-plus model, where the paid price was the cost of producing the technology plus a margin for the company's profit.

After receiving their Ph.D., young researchers would join learned societies in their field and attend their annual meetings to stay current in the field and to network. They would join a prestigious central lab with the plan to work there until retirement. With the pressure for excellence, there were always personal frictions, but they were offset by the companionship and respect researchers had for each other.

In research, the hierarchies are relatively flat. The organizations were very flexible, and researchers moved around in the lab and formed robust networks. They would attend the annual meetings of their societies and present their progress: their network was not only dense but also vast. By subscribing to the same journals, there was a shared knowledge of the state of the art.

At the end of the Cold War, the central labs rapidly disappeared. The government no longer had the need to outbrain the Russians, companies put more emphasis on quarterly results instead of long term success, therefore executives had less understanding for research. The universities adapted and started programs to teach students to become entrepreneurs. All this contributed to the central labs to disappear in a very short time.

At first, one would think that industrial research has completely disappeared. However, this is not the case, and today there are more researchers than during the Cold War. But the research infrastructure has radically changed. For example, professors no longer spend occasional time in industrial central labs as visiting scientists, but have more secure part-time positions in large companies, with job titles like Fellow.

Typically, a larger technology company has a VP of research with numerous researchers. The latter no longer sit in a central location but are dispersed throughout the engineering divisions. On one side, this allows them to glean important hard problems with which the engineers are grappling and get inspired for new technologies. On the other side, when engineers get stuck with a problem for which there is no clear solution on Stack Overflow, they can informally ask the local researcher for a lead.

The researchers are not embedded in the development organizations. They report to a remote manager, and they have long term goals instead of the daily Jira tasks. They do not have short term deadlines, but at the end of the day they cannot turn off their brains until the next morning. Today's researchers are much more lonely than the researchers in the central labs of yore.

Today's researchers are less dependent on learned societies and tend to network using LinkedIn. The annual meetings have disappeared and have been replaced by topical meetings. Instead of a steady shower of scientific articles in journals, researchers today do searches on Google Scholar for the knowledge they need at the moment. One of the corollaries is that today papers should no longer have memorable titles, but the titles have to have the important terms at the beginning, so they show up at the top in searches.

This requires learned societies to adapt. Researchers are isolated and change employers more often. Flexible regular meetups are maybe more important than rigid conferences. When in the past societies could solicit sponsorships from central labs, today the researchers are decentralized and there is no longer a budged item for sponsorships. Despite this, anecdotally there is more money for essential expenses and while in the past page charges were a barrier, today they are no longer important and researchers publish in journals with high impact factor, regardless of cost.

There is another important factor in the lives of researchers. Since they are now dispersed, it is more difficult to advance in the career. The Anglo-Saxon countries always had the concept of mentorship instead of the more formal master-apprentice system of other western countries. Other cultures are now copying the mentorship system to help researchers to succeed in life. This is a new role to which learned societies have to pay attention.

As I mentioned, researchers do searches for related art on the web instead of staying current by subscribing to journals. Search engines are based on n-grams and do not know the history of science. Therefore, it is easy to get the related art in the introduction wrong, and especially the references are often wrong. For example, the CIELAB color model operator was not introduced at the INTERACT-2010 conference, but much earlier and no later than 1976.

Thus, editors and reviewers have a much harder job verifying the introduction and the references of a manuscript. This and the current career path of researchers (see post on Career Networking) prompted, for example, the SPIE about 15 years ago to limit the terms of the editors in their journals. Learned societies must give high consideration to mentorship for their journals and conferences. Conference chairs and editors must have a good number of young researchers who work closely with the old hands to learn the ropes. Fortunately this is easy because, as noted earlier, today's researchers are lonely and long for mentors.

Scholarly Publications

The evolution of approaches to employment was discussed here.

Fifty years ago, a professor in the mathematics department at the Swiss Federal Institute of Technology (ETH) was expected to publish a substantial paper every two years, at least one every 4 years. The paper was top quality and presented a true advance in the field. Once a year, the professor would also present at a conference. When the professor had come up with a lecture presenting a new field or a novel approach to an existing field, their Ph.D. students would sit in the front row of the auditorium and take extensive notes, which would be the basis for a new book.

Students were not expected to write papers. They would write reports and give seminar presentations to get the required credits.

The experimental physics departments was quite different: the American publish-or-perish way of life had taken over. By twenty years ago, also the mathematicians were living by the publish-or-perish paradigm. However, something else changed: the students would submit their reports to scholarly journals.

The flood of submissions required the editorial boards to change their criteria for reviewing manuscripts. Moreover, with the introduction thirty years ago of the World Wide Web by the European Organization for Nuclear Research (CERN) in Geneva, a huge number of publications became easily available, making the editor's job of separating the wheat from the chaff more urgent, otherwise researchers just waste their time reading useless articles.

Certain measures were easy to implement, like eliminating unintelligible manuscripts, plagiarisms, and nothing-wrong-papers (papers well written but not advancing the field). More editorial work was required for papers where the authors did some valid research, but did not understand it well themselves—in principle, author's supervisor would be responsible, but for the past twenty years they have been increasingly remiss of this duty. The other editorial task is to identify salame papers and reject them; salame papers refers to when the result of a project is sliced up and submitted as a series of papers. While this is OK for conference papers, it is not for journal papers.

A form of plagiarism was also to submit a paper to multiple journals using variations of the author's names. This was solved by requiring a persistent digital identifier (ORCID iD) and using a Digital Object Identifier (DOI) for every reference.

When fifty years ago papers were well written, today they tend to be sloppy. When an editor accepts a manuscript for publication, the authors tend to ignore the orthography and grammar errors pointed out by the reviewers, even when good authoring tools are available. This sloppiness increases the publication cost because a copy editor has to rework the manuscript. When a sweatshop is used, producing a 12-page article typically costs about $1,500 while using professional copy editors doubles or triples the cost. Societies usually slightly increase the publishing fee to allow for discounts for members and to subsidize financially challenged authors.

Fifty years ago, a scholarly journal covered its production costs by charging a small page charge and with subscriptions by institutional libraries. With the flood of articles in the past twenty years, the number of journals has increased and with the higher required production costs, libraries have an issue affording journals. In the long term, scholarly journals are only viable with the open access approach, where the authors pay the full publication costs.

For a large institution, the publication costs are significant especially when the copy editing cost go up with the increasing sloppiness. This leads to a hybrid solution where institutions pay a fixed yearly price and get a certain number of submissions and downloads.

Usually institutions are not too sensitive to the publication costs, as long as the journal has a good impact factor. A journal builds its impact factor not with the work of the copy editors, but with the work of the editors. For scholarly journals, these are usually researchers who volunteer their work. The question is how does one find a good team of editors-in-chief and associate editors? For this we have to look at how research evolved in the last decades.

Before, let me point out that editors have to ensure that relevant references to articles in their own journal should not be omitted and the journal must be indexed. Last but not least, the most important words of the articles should be at the beginning of the title, otherwise citing authors will miss it in a Google Scholar search.

Career Networking

In the US, the multigenerational workforce is divided into five age groups, which have quite different approaches to employment.

The traditionalists (or silent generation, born 1925–1945), have these stereotypical characteristics: striving for financial security; "waste not, want not"; nobility of sacrifice for the common good; focus on quality and simplicity; loyal to employers and expect loyalty in return; believe promotions, raises and recognition should come from job tenure; work ethic focused on timeliness and productivity; conformity and following authority.

The baby boomers (born 1946–1964), have these stereotypical characteristics: the importance of hard-work (instilled by parents); loyalty to an employer would lead to reward and seniority; willingness to take on additional responsibilities; conscientious and dependable; service-oriented; ambitious; dutiful.

The generation X (born 1965–1981) have these stereotypical characteristics: the importance of education; shaping one's own career path; work-life balance and autonomy; innovation and entrepreneurialism; comfortable with challenging conventional wisdom; outcome-oriented; collaborative decision making.

The millennials (or gen Y, born 1982–1997) have these stereotypical characteristics: need intellectual challenge; entrepreneurial; value continuous learning opportunities; achievement / results-oriented; innovative and open to new ideas; collaborative decision makers; like praise and recognition; value teamwork and equality; value independence / autonomy; seek meaningful work; value work-life balance and flexibility; value fun at work; technology-driven

The centennials (or iGen or gen Z, born 1998 and later) are just entering the workforce and the stereotypes have not yet been formed.

The traditionalists rely on local organizations like the Rotary or the golf club for networking, but also professional societies and conference attendance. The baby boomers participate actively in international societies and conferences, building a global network. The generation X still participates in conferences but is less active in professional societies and the organization of conferences. The millennials are on social media and use search engines to find information and attend local meet-ups for networking.

While in the past peoples managed contacts using a Rolodex, membership directories, etc., today colleagues are constantly on the move and everybody has to maintain their personal contact information on a professional network like Xing or LinkedIn, through which they connect to their professional contacts.

Professional network sites make money by selling your information to business intelligence and salespeople as well as recruiters looking for employees. The service is free for you, but you have to maintain your own information.

The sites are continuously improved, so you have to keep monitoring your profile for changes in the way your information is organized to be more valuable to paying customers. For example, the skills section is sorted by the number of endorsements you receive for each skill, which is not what you want. Edit this section by clicking on the pencil on the top right, then unpin the top three skills, reorder the skills by dragging the horizontal lines on the right, and pin your top three skills.

When LinkedIn bought SlideShare, your presentations appeared in the media section. However, the original site was abandoned and your media is in cold storage. To get acceptable access times, you have to upload your PDFs again directly into LinkedIn. Furthermore, videos are no longer supported, so you have to upload them to YouTube and then make them available in your LinkedIn profile as linked media.

If you apply to a job on a professional social network by clicking on the "apply" button, the probability that you will have that job in your profile is very low. Instead, you have to click on your best connection working there because jobs go mostly through internal referral.

Of course, you have to have a contact working there. The quality of the contact is important because this person has to be your advocate. You can easily increase your network by turning on Bluetooth in your mobile LinkedIn app and invite all people in your vicinity, but they will not be your advocates. Your network has to be dense.

The best way to create a dense network is to organize conferences because people will remember well your skills and leadership qualities. The second best is presenting at conferences and the easiest is to present at meet-ups. Even easier is to write a blog, but you should post at least once a week and advertise each post on LinkedIn and Twitter.

New Swiss State Secretary for Education, Research and Innovation

Today, the Swiss Federal Council appointed Martina Hirayama as the new State Secretary for Education, Research and Innovation at the request of the Federal Department of Economic Affairs, Education and Research EAER.

Martina Hirayama has been president of the Institute Council of METAS, the Federal Institute of Metrology, since 2012. She has also been vice president of the board of Innosuisse, Switzerland’s Innovation Promotion Agency (up to the end of 2017 the Commission for Technology and Innovation) since 2011 and a member of the Swiss National Science Foundation’s Foundation Council since 2016. Since 2011 Ms Hirayama has been dean of the ZHAW School of Engineering and is a member of the ZHAW’s Executive Board. Since 2014 she has also been Head of International Affairs.

Martina Hirayama studied chemistry at the University of Fribourg, at the ETH Zurich and at Imperial College London, obtaining a doctorate in technical sciences from the ETH. She later took a postgraduate degree in economics at the same institution. Following her doctorate she was group leader at the Institute of Polymers at the ETH Zurich, from 1995. During this time, Ms Hirayama co-founded a start-up in new coating technologies, and was CEO of the company until 2008. In 2003 she began lecturing in industrial chemistry at Zurich University of Applied Sciences Winterthur ZHW, where she developed and headed the field of polymer materials and obtained her professorship. From 2007 to 2010 she developed the Institute of Materials and Process Engineering. Ms Hirayama is a citizen of both Switzerland and Germany.

With such wide-ranging experience in research, teaching, entrepreneurship, management and administration, Ms Hirayama is very well equipped to head the State Secretariat for Education, Research and Innovation SERI. She has impressive expertise at the interface between science and business. The Federal Council has chosen a person with huge initiative and creativity, with a broad network in the field of education, research and innovation as well as politics, public administration and the private sector.

Ms Hirayama perfectly meets the exacting requirements of this position of State Secretary for Education, Research and Innovation. The important task of equipping Switzerland’s excellent ERI system for the digital future falls to the state secretariat she will now head. The Confederation, cantons, professional organisations and other players must work together to continue to strengthen both vocational and professional education and training and academic education, and to maintain Switzerland’s position as a world leader in research and innovation.

Creative Professions

The Silicon Valley has seen radical changes in how people work. By people, I mean mostly the creative professionals who conceive the products that made the valley famous. Today, these professionals are not as creative as in the past. We are transitioning to the gig economy, where professionals do not have a fixed job but use the internet to find small assignments. The pay is very low, there are no benefits, and the money is all made by the service website owners: all work is purely transaction oriented and in the case of software, when an app breaks, it is simply abandoned.

There are conventional jobs with an employment contract and benefits, but the setting is more that of factory workers doing piecework controlled by the company's GitHub site. The companies do not invest in their workers, which do not learn new technologies and hop to a new employer every couple of years.

The gig economy is different from consulting. Consultants earn approximately twice the salary of a regular employee and make a considerable investment to deepen their expertise.

In the past, an employee was a resource groomed by companies. The new trend is the reason the most creative products now come from outside the Silicon Valley. The new centers for innovation include (from west to east) London, Lausanne, Zurich, Berlin Beijing, Shanghai, Taipei, Seul, Tokyo, …

Before the transition to piecework, a paradigm popular in the Silicon Valley was that of the field dependence of cognitive styles, going back to Herman Witkin in 1962. This paradigm was used to give employees work in which they could excel, form powerful synergistic teams, and also to design user interfaces.

People with a field dependent cognitive style, are driven by an inner motor (god). They think in a global context and tend to think in parallel, making associations. Field-dependent employees often work well in teams, as they tend to be better at interpersonal relationships. When designing user interfaces, approaches that connect different parts of a topic are useful for field-dependent learners. For example, users can discuss what they know about a topic, predict content, or look at and read related material.

People driven by a field-independent cognitive style are driven by an outer motor, for example, the product's user. They are analytical, detail oriented, and tend to think sequentially, drawing inferences. Field-independent workers tend to rely less on managers or colleagues for support. In user interfaces, approaches such as extensive reading and writing, which users can carry out alone, are useful.

Research labs looked for employees that have both a field dependent and a field-independent cognitive style. Such people can envision new theories and can also reduce them to practice by implementing them. Such an activity is called speculative design.

This paradigm can be extended to the pieceworker of today, who is driven by greed (self). It is also useful to extend the idea to other activities, as shown in this diagram:

AAAS Statement on Scientific Freedom and Responsibility

Scientific freedom and scientific responsibility are essential to the advancement of human knowledge for the benefit of all. Scientific freedom is the freedom to engage in scientific inquiry, pursue and apply knowledge, and communicate openly. This freedom is inextricably linked to and must be exercised in accordance with scientific responsibility. Scientific responsibility is the duty to conduct and apply science with integrity, in the interest of humanity, in a spirit of stewardship for the environment, and with respect for human rights.

For more information: https://www.aaas.org/page/aaas-statement-scientific-freedom-responsibility

Where the sky and the Earth touch

Critical thinking in a changing world

Monday evening, Gioia Deucher, the new CEO of swissnex San Francisco on Pier 17, hosted a double event on critical thinking. The first event was only for ETH alumni and consisted of networking followed by a speech by ETH President Lino Guzzella and a general discussion. Prof. Guzzella noted that in recent years, students have changed and despite social media have become much nerdier and socially isolated. Consequently, the ETH has to change how it teaches.

As a professor of mechanical engineering, Guzzella does not expect any new breakthroughs in the physics for building mechanical equipment. What is more important for a mechanical engineer is to understand the context requiring a new machine and grasp the problem holistically and proposing a new approach.

The human genetic code has not changed over the ages and is still the same as for hunter gatherers. Critical thinking is essential, but it is hard to criticize oneself: we are dependent on a group that mutually criticizes and debates.

This autumn, the ETH is introducing significant changes. In teaching, the emphasis will be more on understanding and solving problems than on learning. Students will have the option for project-oriented study and more personal coaching with group study. In the study directions, the ETH is starting a new department of medicine, which will allow a proper medical study. Initially, the new department will only go until the bachelor level, after which students can transfer directly to a Swiss university with a medicine program or change to a more traditional ETH department like bioinformatics. As we live longer and longer, significant medical progress is necessary to maintain life quality into the old age.

When a question came about ETH's plans for massive open online courses (MOOC), Prof. Guzzella stated that they go counter the new direction to foster critical thinking and team work: students need physical proximity and a shared experience to become extraordinary people.

The public second event, which had unexpectedly high attendance, started with lightning talks and a panel discussion, followed by a discussion with the audience and finally a standing dinner with animated discussions and networking.

The speakers were Lino Guzzella, President of ETH Zurich and Professor for thermotronics; Gerd Folkers, Chair Science Studies and Critical Thinking Initiative at ETH and former Head of the Collegium Helveticum, a joint think-tank of ETH and University of Zurich; Hans Ulrich Gumbrecht, Professor in Literature in the Departments of Comparative Literature and of French & Italian at Stanford; Philippe Kahn, the CEO of Fullpower, the creative team behind the Sleeptracker IoT Smartbed technology platform and the MotionX Wearable Technology platform. The moderator was Chris Luebkeman, Arup Fellow and Global Director of Arup Foresight.

There was a consensus that to contribute to the wellness and progress of society, and it is indispensable to excel in critical thinking and bring about paradigm shifts. There is no point for a bright mind to just do repetitive intellectual tasks like at the Academy of Projectors. Critical thinking requires a fertile environment, therefore creating groups and projects is more important than promoting individual excellence.

Publications are a very bad metric. A paper needs the unpaid work of three reviewers and is expensive regarding social costs, yet 52% of publications are never cited and consequently have no value because they do not contribute to society.

Excellence in research requires freedom and money. Professors should not be told which research to conduct and should not waste time chasing grants. Science is for the good of society and society should fund research and tuition at universities (I never had to pay a penny of tuition for my diploma in mathematics and my doctorate in informatics). Critical thinking is what prevents the Lagado of Gulliver's third voyage: a habitat for scientists critically thinking in a changing world instead of an Academy of Projectors.

When Stanford wanted to introduce the option for STEM students to major or minor in literature, Prof. Gumbrecht was the strongest opponent. However, after the first year, he now realized that his best students had all come from STEM and he has become a strong advocate for the program.

Business backs the basics

The last third of the year has been very busy and I did not have a chance to stay current with my reading. Consequently I do not have anything to write.

Editors write editorials, which are rarely read. Indeed, editorials are useful mostly for editors because it forces them to structure their journal or conference. Unfortunately, they are usually written under time pressure and are not always well-rounded. Still, better than my writer's block, here is an editorial written by Subra Suresh and Robert A. Bradway for their CEOs and Leaders for Science retreat at Sunnylands in Rancho Mirage, as it was published in Science 14 Oct 2016: Vol. 354, Issue 6309, pp. 151 DOI: 10.1126/science.aal1580.

Earlier this year, a number of leaders from major U.S. corporations gathered at Sunnylands in California to discuss the critical importance of basic scientific research. For decades, the private sector has withdrawn from some areas of basic research, as accelerating market pressures, the speed of innovation, and the need to protect intellectual property in a global marketplace made a Bell Labs–style, in-house model of discovery and development hard to sustain. However, the leaders who gathered for the “CEOs and Leaders for Science” retreat (which we convened) agreed that basic research will make or break corporations in the long term. Why?

Long-term basic research, substantially funded by the U.S. government, underlies some of industry's most profitable innovations. Global positioning system technology, now a staple in every mobile phone, emerged from Cold War Defense Department research and decades of National Science Foundation explorations. As well, long-term public–private partnerships in basic research have driven U.S. leadership, from information technology to drug development and medical advancement. For example, the Human Genome Project combined $14.5 billion in federal investment with a private-sector initiative, generating nearly $1 trillion in jobs, personal wealth for entrepreneurs, and taxes by 2013. Such endeavors created a science ecosystem that in turn generated the talent pipeline upon which it depended.

Although for-profit corporations still invest in proprietary product development and expensive clinical trials, industry finds itself unable to invest in basic research the way it once did. The need for increased corporate secrecy, market force–driven short-term decision-making, and narrowing windows to monetize new technologies have whittled away industry's willingness and ability to conduct basic research. This change threatens U.S. preeminence in research. For instance, the nation may lose its ability to attract and retain the finest talent from around the world. A good fraction of the students who earn advanced degrees in science and technology in the United States come from abroad because of the nation's scientific excellence. For decades, American companies could attract and retain the finest talent from around the world. But if the U.S. loses its edge in research, it may also lose this vital resource of expertise and innovation.

Consequently, business leaders assembled at Sunnylands resolved to use their individual and collective credibility, and their stature as heads of enterprises that fuel the economy, to advocate for greater government support for basic scientific research to revitalize the science ecosystem. However, they will need to lift sagging public opinion because many Americans now see basic research as a luxury rather than a necessity. A 2015 Pew poll found that Americans who view publically funded basic research as “not worth it” rose from 18 to 24% between 2009 and 2014. At the same time, those who believe private investment is enough to ensure scientific progress also increased from 29 to 34%.

With that in mind, the CEOs will partner with academic leaders to educate the public about the importance of basic research. Together, they will advocate for this in meetings with federal officials, through various media channels, and by asking presidents in the Association of American Universities to identify corporate leaders in their respective communities to join the effort. The hope is that this concerted action positions basic research atop the next U.S. president's agenda.

History has shown that investments in basic research are the primary engine by which humanity has advanced, and major economic gains—often unanticipated when the research was initially funded—have been realized. In the United States, that will require a long-term commitment from the government, complementing the ongoing investment of risk capital and key industry sectors.

America's leadership role in scientific innovation is an inherited responsibility and an economic imperative. It must not be neglected.

Industrie 4.0: Entwicklungsfelder für den Mittelstand

In den 80er Jahren verschob sich die US-amerikanische industrielle Herstellung nach Fernost, vorwiegend China. Es war ein unaufhaltbares Ereignis, denn der Stand der industriellen Fertigung hatte sich seit Anfang Jahrhundert nicht mehr weiterentwickelt.

Nachdem ich in den 60er und 70er Jahre mehrere Werke in der Schweiz und Deutschland besucht hatte, alle sauber wie ein Spital und Qualitätskontrollen an jedem Schritt, und die Fachkräfte im angewandten Physik Institut kennenlernte, die jedes noch so verrückt schwierige Apparat-Teilchen mit unglaublich engen Toleranzen herstellen konnten, war mir der erste Besuch einer US-amerikanischen Fabrik ein grosser Schock, den ich wohl nie vergessen werde.

Es war die Burroughs B1700 Fabrik in Goleta bei Santa Barbara, ende 1977. Die Fabrikationshalle war laut, da die Arbeiterinnen jede ein Transistor Radio an hatte, jeder auf einem anderen Sender. Der Fussboden war schmutzig, die Arbeitsflächen voller Chips- und Kekskrümeln. Die Lötstrasse in der Mitte der Halle sah aus, als ob sie aus einem Charlie Chaplin Film herausgefallen war. Keine Arbeits-Station hatte eine Qualität-Kontrolle: am Ende der verknoteten Herstellungsstrasse war ein grosser Tisch an dem sich die fertigen Schaltkreis-Platinen stapelten. Eine Arbeiterin nahm eine nach der anderen die Platinen und klopfte sie mit einem Bleistift nach kalten Lötstellen ab, denn auf dem Ende des Bleistifts war ein Schreibmaschinen-Radiergummi aufgeschoben.

Nun wusste ich, weshalb wir die Computer in die Schweiz erst im Geschäft aufbauen mussten und dann die Defekte suchen mussten, bevor wir sie den Kunden liefern konnten. Praktisch jede Platine musste nachgelötet werden. Ein Jahrzehnt später, war die Fabrik bei Xerox in Rochester immer noch auf dem gleichen Niveau, nur wurde dort mehr mit dem Hammer gearbeitet um bei der manuellen Montage die fehlenden Toleranzen zu überwältigen.

Mit diesem Hintergrund, stand der Verlagerung nach Fernost mit den niedrigen Löhnen nichts im Wege. Ich glaubte, dass sich die industrielle Fertigung in den USA nie erholen würde. Dies ist ganz anders als in Europa oder Japan, wo das gesellschaftliche Schwergewicht auf Schulung der Arbeiter und Verbesserung der Herstellungs-Technologien liegt. Das Resultat ist, dass zum Beispiel der Zughersteller Stadler Rail einen Grossauftrag aus den USA erhalten konnte: Das Bahnunternehmen Caltrain hat bei Stadler 16 Doppelstocktriebzüge für 551 Millionen Dollar bestellt. Die Züge werden zwischen San Francisco und dem Silicon Valley fahren, wie der Tages Anzeiger vom 16. August berichtete.

Aber, es bewegt sich nun doch etwas im Silicon Valley. Praktisch jede Auto Firma hat jetzt eine Niederlassung im Silicon Valley, aber nicht nur die klassischen Firmen: die Chinesischen Firmen die die amerikanischen Produkte anfertigen, investieren hier in Forschungs- und Entwicklungs-Labors um die neuesten Technologien zu konzepieren. Während deutsche Hersteller immer noch 7 Jahre brauchen um ein neues elektronisches Auto zu entwickeln, kann man im Silicon Valley frischgebackene Doktoren von Stanford und UC Berkeley anstellen, die die gleiche Arbeit in ein paar Jahre machen, denn mit Industrie 4.0 haben wir eine Revolution.

Gerade letztes Wochenende hatte es im New York Times einen Artikel, wie die General Electric ein Labor mit 1400 Software-Entwicklern in San Ramon aufgestellt hat, um das System Predix zu schmieden, welches nich nur eine Grundlage für Industrie 4.0 sein soll, sondern auch es ermöglichen soll, die Wartung der Produkte im Einsatz vorherzusagen.

Das Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA baut nun das Applikationszentrum Industrie 4.0 auf. Jedes Unternehmen sollte die Studie Industrie 4.0: Entwicklungsfelder für den Mittelstand anfordern und sie sorgfältig lesen. Sie ist von Herrn Markus Bressner, markus.bressner(at)ipa.fraunhofer.de erhältlich, Telefon +49 711 970-1808.

A new open forum for scientists working on color

The American Association for the Advancement of Science (AAAS) is setting up a new platform for scientific collaboration called Trellis. A key feature is that you can upload papers you want to discuss and anybody in the group can read the paper online: the AAAS takes care of all the copyright issues with the journal publisher. Another feature is that you can control how much noise you get from Trellis. Messages can have up to 2500 characters, but if you have longer text you can create a PDF and upload it like a paper. The same holds for images and videos.

Many AAAS members are teachers: if you do crowd-sourced experiments, you can easily find subjects. The AAAS is also involved in policy making in Washington, in case you need help with that. You can also announce conferences and other scientific events, and use the shared calendar.

Trellis is currently in pilot phase for educators, policy makers, and Section T of AAAS (Information, Computing, and Communication). There are still a few rough edges that are being ironed out. That might be why you have not yet heard of Trellis.

I am setting up a group called Computational Color Science in Section T. However, we are trying out something new. We are making it a completely open group, i.e., anybody with the URL can sign up and participate, without having to be an AAAS member. To join, go to http://www.trelliscience.com/color/. You can invite anybody else you want by giving them the URL and they can sign up.

bottom-up vs. top-down

Last week I wrote about systems of autonomous components, also dubbed as bees vs. the bee hive. In management, there is the somewhat related concept of bottom-up vs. top-down management.

Bottom-up used to be popular in engineering companies. Engineers work in small groups that push beyond the bleeding edge of technology and invent new product concepts. As the product evolves, more people are recruited and the product is polished by UX experts, manufacturability experts, marketing and sales teams, etc.

In a top-down company, the leader of the company is an expert visionary who has a new technology idea. A team of second level executives is assembled, which repeats the process for the next level, etc., down to the worker bees who develop the product. This was the basis of the utopian concept described in Tommaso Campanella's Città del Sole.

The preference for one paradigm or the other oscillates with time. Both are viable. Things only go wrong when a mediocre person becomes the head honcho of a bottom-up company and transforms it to top-down. In a bottom-up company, the management's role is mostly to take out of the way the obstacles that slow down the engineers. Typically their nature is more that of a facilitating person than that of a leader.

When the polarity of a company is switched from bottom-up to top-down, the management layers typically fail. With a mediocre person at the top, the company is doomed. It can take decades, but in the end, there is no escape from the spiral of death.

Industrie 4.0

In our post on the Fourth Industrial Revolution last January, we showed a table from the World Economic Forum. It had 1784 as the date for the first industrial revolution, 1870 for the second, 1969 for the third, and a big question mark for the fourth.

Indeed, technology breakthroughs can rarely be pinned to a single date. In a post on a color scientist's role I had written that discoveries are in the air or ether. Discoveries happen when the time is ripe for them, and at that time, many people will have the same insight with a time interval of a few weeks or months. Research is very expensive, it is a high-risk investment. Timing is everything, otherwise, you lose your investment.

With that, I meant that technologies emerge over an interval of time. The widespread adoption of a revolutionary technology takes an order of magnitude longer than its emergence—giving a precise date is not possible.

When the industry was still artisanal, the force was supplied by waterwheels, donkeys, or indentured labor. Although this allowed mechanical production equipment, the force generation was unreliable and limited. In the first industrial revolution, steam allowed a steady supply of considerable mechanical force. It became possible to build factories with elaborate mechanical force distribution systems based on rods and belts. The layout of a factory floor was dictated by the force distribution system.

In the second industrial revolution, the electric motor made the design of the factory floor layout much easier, because what was distributed was the electricity, while the mechanical force was generated independently and locally at each workstation by electric motors. This allowed the distribution of labor, conveyor belts, and true mass production.

The third industrial revolution was the digitization of the entire design and production process through the use of CAD/CAM tools. Engineers could use interactive software to design new products. The design consisting of a software program meant that the program could be executed to generate simulations. It was not only possible to design smoother products with Bézier curves: the simulation could verify if a part was manufacturable by a CNC lathe and how long the process took. CAD/CAM programs were able to generate the control program for the CNC lathe, thus automating the entire manufacturing process.

If we have to put dates and names to the birthing of the fourth industrial revolution, it would be Kristen Nygaard (1957) and Ole-Johan Dahl (1962). When he was a visiting scientist at Xerox PARC, Nygaard told me how a newspaper trade union came to him for help with stressed out newspaper workers. The newspapers were introducing digital layout systems, which were supposed to be much more ergonomic that the old system of comps and rubylith. As a professor for operations research, he conducted research on planning, control, and data processing to sleuth the source of the stress.

The corollary was in 1962 Simula I, a superset of Algol 60, for simulating typesetting (discrete event networks). This was refined in Simula 67 with objects, classes, subclasses, virtual methods, co-routines, discrete event simulation, and garbage collection. In a first visit from Oslo to Palo Alto, this begat Smalltalk.

In 1973 came Hewitt & co-worker's extension to exploit massively parallel computers with the concurrent execution of objects. Communication was by message passing, decoupling the communication from the sender. The key new concept is that of an actor, a computational entity that, in response to a message it receives, can concurrently 1) send a finite number of messages to other actors, 2) create a finite number of new actors, and 3) designate the behavior to be used for the next message it receives.

In the actor model, messages are simply sent, there is no buffering, no synchronous handshaking, no ordering—FIFO requires explicit queue actor. Everything is local. These ideas were influenced by packet switched networks. The power comes also from the fact that a message can contain another actor, e.g., resumption, (a.k.a. continuation, stack frame) to which the recipient sends a response, enabling a variable topology.

This technology is also known as functional programming because the behavior is that of a mathematical function to express what an actor does when it processes a message.

To build the bridge to industry, the actor is a model for a machine or workstation. Variable topology means that each workstation can autonomously adapt the workflow depending on external factors (sensors). This requires refining the concept of modeling.

Modeling is the act of representing a system or subsystem formally. From a mathematical point of view, it is a set of assertions about properties of the system such as its functionality or physical dimensions. The constructive counterpart defines a computational procedure that mimics a set of properties of the system, also called executable mode or simulation.

With this, we can define design as the act of defining a system or subsystem. Usually, this involves defining one or more models of the system and refining the models until the desired functionality is obtained within a set of constraints.

Embedded software is software that resides in devices that are not first-and-foremost computers. A key feature of embedded software is that it engages the physical world, and hence has temporal constraints that desktop software does not share.

Executable models are constructed under a model of computation, which is the set of ``laws of physics'' that govern the interaction of components in the model. A set of rules that govern the interaction of components is called the semantics of the model of computation.

In the example of a printing plant, each actor can be a PDF transformer that performs the step of a workstation (e.g., imposition, folding, or trimming) by performing the corresponding operation on a PDF file. By combining the actors in a workflow, we can simulate the set of all print jobs in the printing plant and assert correctness. When timing information is collected, the simulation can find bottlenecks and deadlocks. Because rush jobs increase the profit, the simulation allows the workflow topology to reconfigure itself locally dynamically to maximize the profit function. Also, if there is a breakdown, e.g., a jam, the other workstations can reconfigure their use of queues.

An example of a work station is the raster image processor. We can change the algorithm for the black skeleton, gamut mapping, or halftoning. The actor can use simulation to select the most efficient algorithms. It considers the corresponding ICC profiles for the printer and can simulate giving the printer a virtual Farnsworth-Munsell 100-hue test, as shown in this diagram of the workflow in physical respectively simulation mode.

Returning to the concept of the fourth industrial revolution, in Germany, the brand name for this initiative is Industrie 4.0. They use slightly different terminology based on the concept of the twin. They would write:

Four aspects drive the future of manufacturing: modularity, connectivity, autonomy, digital twin. A digital twin of an autonomous system is a very realistic model of the current state of the process and its own behavior in interaction with its environment in the real world. It is a notion where the information created in each stage of the product lifecycle is seamlessly made available to subsequent stages.

The digital twin approach is the next wave in modeling, simulation and optimization technology. Simulation is extended to all life cycle phases as a core product or system functionality.

The concept of using “twins” is rather old. It dates back to NASA’s Apollo program, where at least two identical space vehicles were built to allow mirroring the conditions of the space vehicle during the mission. One vehicle remaining on earth was called the twin. NASA definition: A digital twin is an integrated multiphysics, multiscale simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin. At Airbus, a digital twin is called an iron bird.

The Talented Silicon Valley

The Silicon Valley is not an institution, which tend to be rigid. There have been several attempts to clone the Silicon Valley as an institution, for example, Sophia Antipolis in France and Tsukuba Science City in Japan, but they have not been successful, at least as compared to the impact on society that the Silicon Valley has.

The Silicon Valley is a biotope, which is relentlessly evolving. If you want an economic force like the Silicon Valley, you have to create a habitat for your own ecological system.

If we look at Silicon Valley's evolution, it started with first class educational institutions like Stanford University (est. 1891, motto "die Luft der Freiheit weht" freely following videtis illam spirare libertatis auram) and UC Berkeley (est. 1868, motto "fiat lux"), available capital, and intrapreneurial professors like Frederick Terman (1900–1982), who is credited (with William Shockley) with being the father of Silicon Valley. Terman's doctoral advisor was Vannevar Bush and his notable students included Russell and Sigurd Varian and the HP triad William Hewlett, David Packard and Bernard M. Oliver.

People are the living beings in the Silicon Valley biotope. The brightest minds are attracted and nurtured. Attraction is not accomplished with money, but the recognition and grooming of talent, where people are selected only on the basis of their ability to create insanely great products and are nurtured to fulfill their intellectual potential. In the Silicon Valley, people do not try to predict the future: they have the passion for building it.

Nurturing takes place through an intellectual climate where ideas can flow freely and people see each others as challenging colleagues rather than enemies, even when they are competitors. The open flow of ideas happens through myriad conferences, seminars, meet-ups, dojos and incubators, and even cafes. For example, adjacent to the Samsung R&D building is the famous Hacker Dojo, on the site of HP's first building (Redwood Building on 395 Page Mill Road) is the AOL incubator, and on University Avenue SAP has transformed the New Varsity Theater into Hana Haus.

For an individual, it might not be a tragedy when they are employed below their intellectual potential. The ability to accomplish tasks much faster than their co-workers will yield some freedom and allow for less supervision of their work. However, the intelligent people will be missing in important functions in a company. The society as a whole develops a problem when less intelligent people have to step into senior management positions. Fulfilled potential is called talent, and the Silicon Valley is good at developing talent through mentoring.

Last but not least, this open intellectual climate and talent development make any work very productive and efficient, because when you need to know something, you know whom to ask. You do not have to spend days googling the Internet for an answer that may be incorrect. You get your answer immediately— maybe when it is complex, at the cost of a coffee or a beer.

This is life in the biotope. A characteristic of the ecosystem has always been its rapid evolution. During the cold war and the quest to outbrain the Russians, high-risk research was possible because the government agencies paid cost plus and it was not necessary to worry about commercializing products for the consumer market. When world politics changed, institutions like SRI, IBM Almaden, Xerox PARC, SLAC, HP Labs and NASA Ames eclipsed, but the brains wandered down the road to new institutions, taking with them expired patents and deep knowledge. In the Silicon Valley, the talent is preserved.

While in Rochester the scientists who invented digital photography were lost to humanity when Eastman Kodak faltered and then faded, their colleagues at HP Labs just modified their commute from Palo Alto to Cupertino and are still working on the iPhone camera and imaging system. Few know that Siri was born at SRI and is now evolving at Nuance in the skilled hands of PARC alumni. Maybe, Google could start a self-driving car project due to the available engineers who built navigation systems for submarines.

In fact, the less than thousand scientists who have worked at PARC in its first 20 years, have created the largest pot of wealth in Silicon Valley, as documented by Henry Chesbrough, the executive director of the Center for Open Innovation at the Haas School of Business at UC Berkeley. A beautiful example of ecological brain recycling! The Silicon Valley is a biotope that promotes talent.

Update: a related article just appeared in the HBR: Renaissance Florence Was a Better Model for Innovation than Silicon Valley Is [paywall]

The Fourth Industrial Revolution

In the last couple of decades we have been ambulating in a buzzword fog, with terms that started from the erudite ubiquitous computing to the folksy data mining. Then the buzzwords became increasingly silly with cloud, virtualization, big data, data lakes, social networks, mobility, internet of things, and the like.

Something is going on, but in this buzzword fog it can be difficult to discern what is really happening. Starting tomorrow in Davos and Klosters, the World Economic Forum plans to shine some light on this cacophony and elucidate the current technological events.

First and foremost, a new term to replace the buzzword fog: The Fourth Industrial Revolution. The First Industrial Revolution used water and steam power to mechanize production. The Second used electric power to create mass production. The Third used electronics and information technology to automate production. Now a Fourth Industrial Revolution is building on the Third, the digital revolution that has been occurring since the middle of the last century. It is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres.

The inexorable shift from simple digitization (the Third Industrial Revolution) to innovation based on combinations of technologies (the Fourth Industrial Revolution) is forcing companies to reexamine the way they do business. The bottom line, however, is the same: everybody needs to understand their changing environment, challenge the assumptions of their operating teams, and relentlessly and continuously innovate.

The important here is not to be a Luddite and to keep learning. Like our great-grandparents had to learn manufacturing car engines instead of buggy whips, we have to learn aggregating information, services, and tools to produce more efficient tools for increasing the efficiency of society. As always, not everything is rosy—for example, at the upcoming Super Bowl event here in the Valley, the FBI is fearing a biohazard delivered by drone swarms flying over the Santa Clara stadium—but we have to stay focused and determined.

We must develop a comprehensive and globally shared view of how technology is affecting our lives and reshaping our economic, social, cultural, and human environments. The World Economic Forum annual meeting starting tomorrow will shine some light and let us see what we should learn and where we should go next.

International Year of Light

Here in Switzerland the weather tends to be bad and we have a Zwinglian/Calvinistic Leitkultur, which might explain our tendency towards pessimism and feeling more unlucky than lucky: it is customary to first look at the negative side of things and then to let us be surprised and feel lucky when things turn out to be positive. In this context, nobody is surprised when the newspapers announce the new year by listing negative anniversaries: 700 years Morgarten, 500 years Marignano, 70 years end of World War II.

Today's Zürich is home to many computer science labs and the city has as many nerds as gnomes (the equivalent persona in banking). They may see 2015 as the year of the palindrome, because 2015₁₀ = 11111011111₂. Or the many mathematicians in Zürich will see 2015 as a Japanese cube, because in the Japanese calendar it is 平成２７年 or Heisei 27 = 3³. For movie buffs, this year is MMXV.

For color scientists, 2015 is the International Year of Light and Light-based Technologies, a United Nation observance that aims to raise awareness of the achievements of light science and its applications, and its importance to humankind. The IYL 2015 will launch at the UNESCO headquarters in Paris on 19 January 2015, with the unveiling of 1001 Inventions and the World of Ibn Al-Haytham.

Indeed, 2015 marks the anniversaries of several events related to light, optics, and vision:

• 1015, a millennium ago, the Iraqi scientist Ibn Al-Haytham published his Book of Optics
• 1815 Augustin-Jean Fresnel proposed the notion of light as a wave
• 1865 James Clerk Maxwell proposed the electromagnetic theory of light propagation
• 1915 Albert Einstein embedded his 1905 theory of the photoelectric effect into cosmology through general relativity
• 1965 Arno Penzias and Robert Woodrow Wilson discovered the cosmic microwave background
• 1965 Charles Kao theorized and proposed to use glass fibers to implement optical broadband communication

In ancient Greece, there where two competing theories of vision. One theory was called the emission theory (Euclid, Ptolemy) and claimed that vision worked by little flame exiting the eye, traveling on rays, scanning the objects in the visual field, and traveling back to the eye reporting what they detected. In the intromission theory (Aristotle), when an object is looked at, it replicates itself and the replica travels along a ray into the viewer's eye, where it is seen.

For a millennium, there was a raging discussion of whether the emission theory or the intromission theory was the correct one. This discussion was based purely on theoretical considerations and heuristics. In his 1015 book, Ibn Al-Haytham introduced the modern concept of scientific research based on experimentation and controlled testing that we still use today: a hypothesis is formulated, an experiment is conducted varying the parameters, the results of the experiment are discussed, and the conclusions are drawn. Because of this, Ibn Al-Haytham is often referred to as the first scientist.

Using the scientific method, Ibn Al-Haytham developed the first plausible theory of vision. Among other contributions, he also explained the camera obscura and catoptrics. He has strongly influenced later scientists like Averroes, Leonardo da Vinci, Galileo Galilei, Christian Huygens, René Descartes, and Johannes Kepler.

Ibn Al-Haytham's full name was Abū ʿAlī al-Ḥasan ibn al-Ḥasan ibn al-Haytham. His Latinized name was originally Alhacen; since 1572, when Friedrich Risner misspelled his name, in the West he has been known as Alhazen. He was born and raised in Basra, where he initially worked. Later he worked in Baghdad and Cairo.

For more information on the International Year of Light see here.

These days the moon is made of cheese

From the remarks by President Obama at University of California-Irvine Commencement Ceremony, Angel Stadium Anaheim, California, June 14, 2014, 12:10 P.M. PDT.

Part of what’s unique about climate change, though, is the nature of some of the opposition to action. It’s pretty rare that you’ll encounter somebody who says the problem you’re trying to solve simply doesn’t exist. When President Kennedy set us on a course for the moon, there were a number of people who made a serious case that it wouldn’t be worth it; it was going to be too expensive, it was going to be too hard, it would take too long. But nobody ignored the science. I don’t remember anybody saying that the moon wasn’t there or that it was made of cheese.

Official transcript of the remarks: The White House Office of the Press Secretary

Friedrich Miescher

James Watson and Francis Crick may be the names most associated with DNA, but many people were involved significantly in the study of DNA. Most famously, British biophysicist and X-ray crystallographer Rosalind Elsie Franklin (25 July 1920 – 16 April 1958) took the X-ray diffraction images of DNA which led to the discovery of the DNA double helix. According to Francis Crick, her data was key to determining the structure to formulate Crick and Watson's 1953 model regarding the structure of DNA.

DNA was first isolated by the Swiss physician Johannes Friedrich Miescher (13 August 1844 – 26 August 1895) who, in 1869, discovered a microscopic substance in the pus of discarded surgical bandages. As it resided in the nuclei of cells, he called it "nuclein." He intuited that the molecule played a role in heredity, but did not believe that a single molecule could lead to a huge variety of individuals and species, says Ralf Dahm, of the Institute of Molecular Biology in Mainz, Germany, who has written about Miescher's work. He says Miescher has kept a low historical profile, in part due to his "introverted" and "insecure" personality.

Earlier this year, CureVac, a company based in Tübingen, Germany, that develops RNA-based vaccines and therapies, won a €2 million prize for innovation from the European Union and announced plans to use some of that money to restore the University of Tübingen lab where Miescher made his discovery. Together with the university, the firm wants to transform the lab, the former kitchen of the old town's medieval castle, into a public exhibition about his work and legacy. (The university now uses the space as a computer room.)

Traps in big data analysis

When I was a student, I had chosen mathematical statistics as one of my majors. At the time, the hot topics were robust statistics, non-parametric methods and optimal stopping times. Descriptive statistics was not part of the curriculum (PowerPoint did not yet exist and there was no need for meaningless 3-D pie charts).

In the student houses I lived, there were always medical students at the end of their studies who had to get a doctorate. Residencies were grueling and at that time the least effort thesis was to punch in some historical medical data. On their way home from the clinic, these students would spend part of the night in the empty punch card rooms, for about 6 months.

Thereafter, they would bring the punch cards to the data center and get 10 to 20 centimeters of SAS printout—and the desperation of not knowing how to get from hundreds of cryptic tables to a one hundred page thesis.

Many of them ended up knocking on my door with the printout and scratching their head. Because in the data center the students could not tell what analyses they needed—after all, there never was an experimental design—the data center people just ran all and every function available in SAS. Classical garbage-in garbage-out.

So, I had to tell the students to stare at the data and come up with a few hypotheses, then use the ANOVA routines to confirm them and the regression routines to do a few nice graphs.

Unfortunately, after all these years we are not much better off. Indeed, now we have to deal also with "big data hubris," the often implicit assumption that big data are a substitute for, rather than a supplement to, traditional data collection and analysis. Now we have tools like Google Correlate that allow us to correlate tons of apples with megatons of oranges.

A recent interesting paper by David Lazer et al. is a nice summary of how big data analysis allows us to create more statistical garbage: Lazer D, Kennedy R, King G, Vespignani A. Big data. The parable of Google Flu: traps in big data analysis. Science. 2014 Mar 14;343(6176):1203-5. doi: 10.1126/science.1248506. PubMed PMID: 24626916.

The authors conclude: "Big data offer enormous possibilities for understanding human interactions at a societal scale, with rich spatial and temporal dynamics, and for detecting complex interactions and nonlinearities among variables. We contend that these are the most exciting frontiers in studying human behavior. However, traditional 'small data' often offer information that is not contained (or containable) in big data, and the very factors that have enabled big data are enabling more traditional data collection. The Internet has opened the way for improving standard surveys, experiments, and health reporting. Instead of focusing on a 'big data revolution,' perhaps it is time we were focused on an 'all data revolution,' where we recognize that the critical change in the world has been innovative analytics, using data from all traditional and new sources, and providing a deeper, clearer understanding of our world."

Business Model Innovation

A cute video on business model innovation from the University of St.Gallen (HSG):