Virtual Organizations

Wayne G. Lutters and Susan J. Winter

The intellectual challenges and institutional conditions of 21st century science and

engineering necessitate collaboration. Increasingly, scholars are confronted with challenges of a

scale and complexity that defy the boundaries of traditional academic fields as well as the limits

of individual capacity. Scientific inquiry increasingly focuses on system-level phenomena, such

as climate change, that demand the expertise of multi-disciplinary teams. Thus, there has been a

growing shift away from traditions of individual, narrowly focused, discipline-based science

toward more collaborative models requiring more diversified and systematized participation

among teams of researchers sharing common resources.

These teams are increasingly distributed geographically. A host of factors are driving this

trend. First and foremost is the globalization of all aspects of society (Friedman 2005).

Individuals are increasingly engaging problems and accessing resources outside of their local

environment. Secondly, intellectual resources are becoming more evenly distributed around the

world. For the past century, Europe and North America have been magnets for scientific talent,

with the United States driving much scientific innovation. Improved access to quality education

has increasingly developed talent in India, China, Southeast Asia and the Middle East. Proactive

government intervention has fostered rapidly growing native scientific and industrial programs

that have not only halted the "brain drain," but now attract scientific talent from around the

world. Successful scientific teams take advantage of this more distributed talent pool. Lastly,

science is more readily engaging the amateur "citizen scientist". In the 20th century, only

credentialed, career scientists were taken seriously. The return to importance of amateur

scientists has already transformed the distributed science of fields such as astronomy and

ecology.

In line with these trends, science is increasingly dependent on virtual organizations to

manage globally distributed teams. A virtual organization can be viewed as a collection of

individuals whose resources are dispersed across time and space, yet who function as a coherent

entity through the use of information and communication technologies (ICT) (Cummings et al.

2008). These virtual scientific organizations (VSO) are enabled by transformational advances in

networked information infrastructure such as the Internet. As this infrastructure matures,

computer-mediated human interaction becomes increasingly seamless. Using myriad ICT tools

allows for the robust, real-time interaction required for distributed science.

With the help of virtual organizations scientific inquiry has moved from simple problems

such as the identification of blood types to significant large scale and complex challenges

previously considered unattainable such as mapping the human genome. These same ICT-based

tools continue to push forward the boundaries of science allowing scientific teams to address

such complicated issues as identifying the interactions between environmental conditions and

gene expression, improving hurricane prediction, and mitigating global climate change.

The purpose of this chapter is to answer the question – How can leaders of science and

engineering efforts better promote and support this new form of collaborative science? As a

starting place, the discussion will be grounded in a concrete, contextual example – a brief case

synopsis of the Human Genome Project. Leveraging examples from this, the core of the chapter

will unpack the general principles of virtual organizing for scientific and engineering efforts

through the theoretic lens of sociotechnical systems. It concludes with an analysis of future

trends and recommended actions.

Science Transformed: the case of the Human Genome Project

The field of biology provides a good example of the transformation of science by these

forces. Until the 1980s, most biology focused on testing relatively simple hypotheses like:

"Tadpoles exude a chemical that inhibits metamorphosis of other tadpoles in the same pond."

Each biologist or small team of two or three biologists would develop a hypothesis, work to test

it in their lab, and publish the results in the academic literature. In the mid-1980s, one area of

biology began to undergo a transformation driven by a desire to map the human genome, a

challenge that was completed in 2006 when the last of the human chromosomes was sequenced.

Instead of allowing each biologist working in the lab to identify and test a genome-related

hypothesis, mapping the human genome was a coordinated, collaborative 20-year international

effort by researchers in 6 countries at 20 large centers and a cost of over $4 billion.

There are two stories behind this transformation of biology. The first is a social and

organizational story. Mapping the human genome required a transgressive reorganization of the

norms of the biological research community in that it 1) represented shared resource and

technology development rather than hypothesis-driven research; 2) required large-scale

coordinated efforts rather than single investigator endeavors; and 3) could jeopardize other

biology research due to its astronomical projected cost. The second is a technical story. Mapping

the human genome was enabled by significant improvements in computational power,

sophisticated base-calling software, sequencing assemblers, data handling techniques, modeling

and visualization. In combination, these social and technical changes enabled an enormous

improvement in the rate of gene sequencing. In 1985 when mapping the human genome was

considered absurd and impossible, only 1000 base pairs could be sequenced a day. By 2000, the

project was sequencing 1000 base pairs a second (Collins, Morgan, and Patrinos 2003).

The reorganization of the field of biology that was required to map the human genome has also

had an enormous and wide-ranging impact on the biological sciences. It has enabled entire fields

such as bioinformatics, proteomics, epigenetics, and biological models of gene function to arise.

Our improved understanding of the human genome has opened up such areas of research as

evolutionary biology, forensics, environmental factors in gene expression, and population

genetics with its promise of personalized medicine.

How did these social, organizational and technical changes interact over time to yield a

completed map of the human genome and to transform the field of biology?

Social/Organizational Changes

The idea of mapping the human genome was first seriously discussed at a small meeting

of top genetics researchers convened in 1985 as a potential biology challenge that would be

equivalent in size to physics and astronomy projects such as building the largest telescope or a

linear accelerator. A couple of the attendees were captivated with the idea, though they

recognized that it would require cooperation among thousands of scientists guided by some form

of centralized control. These biologists began building support for the idea within the

Department of Energy (DOE), an institution that had a long history of supporting and guiding

this form of scientific work in high energy physics through its network of national labs. The

DOE championed the project as a means of tracking mutations caused by radiation. By 1988,

sufficient support had been developed that a National Research Council panel endorsed the

project and the National Institutes of Health (NIH) became the lead agency through a memo of

understanding (MOU) with DOE. With NIH involvement, the project began to capture the public

imagination with its promise of identifying the genetic basis of diseases and new therapeutic

treatments. To ease the field of biology through the transition from an isolated single scientific

study form of inquiry to a collaborative, coordinated form and to allow development of the

complementary social and technical capabilities, the project took a phased approach that started

with maps of chromosomes and studies of simpler organisms.

In 1990, NIH (which had articulated a compelling rationale for the effort) and DOE

(which had experience in creating and managing VSOs) created a 5-year plan to map the human

genome, which was revised in 1993 and again in 1998 due to the unanticipated speed with which

the work was progressing. Five major research centers were chosen and organized to engage in

big science, while coordinating with multiple additional partner research centers. Bottom-up

decision making processes involving peer-review, advisory councils, and topic-specific

workshops were put into place. Project management and quality control measures were

instituted. Communication norms were developed including periodic face-to-face meetings of all

20 centers and weekly conference calls between the 5 largest centers to share advances in a "lab

meeting" format. The next year, norms for release of data and materials within 6 months of their

creation were established and a formal data release policy established in 1996 required

availability within 24 hours of discovery. The private sector started pursuing the right to patent

human genes, a right that was ultimately denied by the courts.

Technical Changes

In 1991, a data repository for human chromosome mapping was established. In that same

year, a new private sector start-up called Celera Genomic began using a newly developed whole-

genome shotgun sequencing technique that relied, in part, on advances in supercomputer

capabilities. Sophisticated base-calling software, sequencing assemblers, data handling

techniques, modeling and visualization were developed and improved over time. These and

related advances in technologies enabled faster progress than originally believed possible.

Results

The first human chromosome was completely sequenced in 1999. NIH and Celera

released a rough draft of the human genome with simultaneous publication in 2001, and the

project was declared complete in 2003. Current large-scale scientific endeavors in the field of

biology include the $50 million NSF-funded iPlant grand challenge project to map the tree of life

for all green plants and to relate genotype and phenotype, the $115 million NIH-funded

ENCODE functional genomics project, the 11-organization public-private structural biology

consortium, and the $138 million six-nation haplotype map of genetic variation, and DOE's

genomes to life project focusing on microbes.

Social/Organizational and Technical Underpinnings of Science

This transformation of biology is not unique. Many other fields of science and

engineering have undergone similar transformations and many more are poised to do so. To

support these new models of science, it is important to understand both the social/organizational

and technical underpinnings of science. Science and engineering are human endeavors predicated

on social conventions and requiring enabling technologies. For example, competence in a field of

science is certified through attainment of appropriate university degrees based on accredited

curricula. Traditionally, biological scientists had their competence certified by earning an

advanced degree in biology at an accredited university with up-to-date lab facilities. Scientific

findings and credit for one's work is communicated through publications in scientific journals

which were severely limited in the number of pages they could print in a year. Articles had to be

succinct and relatively few papers could be published each year so only the best papers got

published in the top journal in each field.

The social conventions and technologies adopted by a field interact with one another.

Changes in one affect the other and changing the field requires changes in both. For example,

ICT has led to an increase in online and distance education with attendant questions about

accreditation of remote programs and access to lab facilities. Is a graduate with an online degree

in biology as competent as one with a traditional degree? If so, what is the educational value of

residential campuses and physical classroom interaction? Does the value of these traditional

experiences vary depending on the nature of the student or the nature of the program? Similarly,

many states have regulated their universities to avoid direct competition between campuses.

Distance education allows universities to compete worldwide. How can a small, under-resourced

state school successfully compete for the best students when these students could enroll in a

distance program offered by a top school located in another state?

Similarly, the availability of electronic publication media is challenging the need for

paper-based journals, changing the role of academic libraries, and potentially altering the value

of traditional journal publication. Without the strict page limits of paper-based journals, many

more articles can be published. Will this reduce the quality of the journal or increase their

relevance? On-line articles can include links to the data that were collected. Will the authors then

lose ownership of the data that they worked so hard to collect and received reduced credit for

their intellectual contributions to the field? Do research teams that contributed data to the shared

human genome database receive credit for their intellectual contributions that is equivalent to a

journal publication? Online articles can also include enhanced graphics and interactive

simulations. To be successful, will biologists have to add proficiency in programming

simulations? Will this distract them from their scientific work?

Since science and engineering are supported by social conventions and enabling

technologies, to create effective virtual scientific organizations one must make changes in both

how scientists and engineers work and in the technologies they use in performing that work.

Because the social and technical elements of scientific organizations are dynamic, one must

continually make informed and reflective choices in designing, redesigning, and managing these

VSOs over time.

Thus, there is great utility in viewing VSOs as sociotechnical systems. This perspective

draws upon the principles of Social Informatics (Sawyer and Tapia 2007) to understand

technologically mediated social action as a dynamic system that operates within a complex

context, which itself forms a web of constraints. It acknowledges that there are distinctly social

components (e.g., university incentives for scientific staff) that are mediated by enabling

technologies (e.g., open access journals). Each of these components has its own, reasonably well

studied properties. However, the sociotechnical systems approach highlights the complex

interplay among these components as they constantly co-evolve (O'Day, Bowrow and Shirley

1996).

General Principles of Virtual Organizing

As noted in the discussion of virtual scientific organizations as sociotechnical systems

above, these are dynamic, emergent social orderings. Leaders of these endeavors will need to

make informed and reflective choices about a host of fundamental issues and how these play out

in their specific contexts. A complete treatment of the relevant design issues and contextual

factors could fill a series of books, and readers interested in further information on these are

referred to the list of additional resources. In this small chapter, a brief description of a few of

the most essential issues is provided with accompanying pointers toward the larger literature.

First, three underlying issues to which all VSOs must attend are highlighted. Then eight common

dimensions of contextual differences are addressed and their possible impact on VSOs are

explored.

Design and Management

Every organization has to make choices about the assets they will own or control.

Leaders of VSOs have to thoughtfully assess all of their resources. This includes things that are

usually thought of as technical assets, such as scientific and engineering equipment, databases,

and specialized software. However, assets also include social and organizational elements such

as team members, their knowledge and abilities, their work routines, and the procedures that

enable an organization to accomplish its tasks and achieve its mission.

Asset management is particularly challenging for VSOs as many of their assets are

geographically distributed or virtual or both. In addition, the alliance of stakeholders controlling

the requisite assets may be unstable. For example, a particular scientific group may partner with

the VSO only for a certain task or a particular project. Thus, VSO leaders must track, maintain

and strategically leverage an ever shifting landscape of human resources, instrumentation, and

knowledge bases.

Often, complex intellectual property ownership issues arise around VSOs. When there

are multiple, simultaneous "owners" of resources, usually with conflicting goals, property rights

must be explicitly negotiated. Some ideas, data, or equipment may be shared freely while others

are more tightly controlled. Provenance, the formal, temporal trace of data, analyses, and

findings, becomes critical for attribution of credit.

Many VSOs will have a lifespan longer than a single project. At this point these asset

assessment challenges become magnified. Active management is required to develop requisite

assets, ensure their sustainability, and help form a bridge between projects.

Leaders in VSOs make choices about who makes what kinds of decisions and about what

incentives will be provided to encourage various types of behaviors. This is an area commonly

referred to as governance. It includes decisions about an organization's structure such as how

many divisions, departments, and levels of hierarchy will be created. Within this structure, VSO

leaders have to determine the types of roles and jobs that will be required. Then they must

determine the authorities and responsibilities associated with each. These governance decisions

can be codified into the technologies used by the VSO, for example, setting access privileges for

equipment and decision privileges in workflow management software.

With many VSOs operating as flexible, networked organizations combining academic

and industry elements, boundary management becomes complicated. External constraints can

have a greater impact in distributed scientific collaborations, than on traditional laboratories.

VSO leaders must work within multiple community norms, navigate across various professional

societies, and operate within a network of often conflicting international, federal, state, and local

governmental regulations.

A VSO's human resources are often more diverse and may vary along multiple key

dimensions. Managers must be sensitive to inter-cultural communication, differences in

professional training, and diverse team member roles. In addition, VSO participation is often

voluntary and dynamic. The combination of a flat, matrixed VSO with shifting reporting

structures and a relatively large percentage of amateurs makes it difficult to enforce the

governance norms that do become established. Many bottom-up, grassroots VSOs, such as those

involved in free and open source software development or shared wiki resources, have struggled

to identify and enforce appropriate policies for behavior.

A third issue that leaders of VSOs must engage is who tells who what information and

when. This is an area called knowledge flow. It includes decisions about what data are collected

by and about the organization, how these data are combined and analyzed, which standard

periodic reports are generated, how often they are generated, and to whom they are sent. These

decisions should be made considering the timing of knowledge flows, the need for verification of

the knowledge and its trustworthiness.

At its core this is about designing and managing the communication pathways within and

without the organization; with VSOs this usually involves computer-mediated communication

(CMC). This CMC infrastructure has often been represented as a two-by-two matrix with time

(synchronous vs. asynchronous) and distance (collocated vs. distal) forming the two dimensions.

Various technologies can be used to support each of these four kinds of communication. For

VSOs, the systems that support collaboration at a distance are the most critical. Those that

support same-time remote interaction include chat and instant messaging as well as the

ubiquitous teleconferencing and videoconferencing systems. Telepresence systems go beyond

communication to virtual embodiment and action (Hollan and Stornetta 1992). These may

involve the teleoperation of scientific equipment or avatar-based interactions in virtual worlds.

Systems that support different-time, different-place interactions preserve the history of

interactions in repositories, blogs, and wikis.

Recall that VSOs are sociotechnical systems and that choices made about organizational

structure impact the adoption of technologies and vice versa. Much research has been conducted

on the mutual impact of these choices (Sproull and Kiesler 1992). Different types of

communication tools also fit different communication needs. The theory of media richness (Daft

and Lengle 1986) provides guidance to VSO leaders on the best communication method for each

type of information being communicated. Oral methods are generally best when there is a lot of

ambiguity or equivocality that needs to be reduced. For example, figuring out what needs to be

done and who will do it, tasks that are particularly important at the start and end of projects, are

often best done face-to-face. When communication of the information will likely evoke strong

emotional reactions, oral methods are often preferred; so communicating personnel actions or

performance reviews are often best done face-to-face. Written methods of communication are

able to handle more complex information and provide a permanent record that can be referred to

later to guide future action. For example, agreements between organizations such as the one

between NIH and DOE regarding their roles in mapping the human genome are often negotiated

face-to-face then confirmed with a MOU or contract.

In scientific organizations the physical processes of scientific inquiry are often

standardized into procedures and protocols with organizational structures mirroring these

processes. For example, an environmental hydrology lab may involve field staff to collect

specimens, taxonomists to identify insects related to water quality, data managers to codify and

systematize these data, analysts to extract key properties for each site and scientists to identify

trends across sites. This becomes complicated when the resources, tasks and actors are

distributed, as in VSOs. There has been significant progress in structuring the communication

pathways of VSOs using workflow management tools to optimize knowledge flow. Ensuring,

for example, that analysts are notified when the data are complete and analysis can begin.

Impact of Contextual Factors

The issues faced and the solutions that emerge for each virtual organization will vary, but

all VSO leaders will have to engage and manage assets, governance, and knowledge flows.

Optimal choices among these will depend on the specific context and situation, which can vary

along at least eight different dimensions: the virtual organization's lifecycle, problem

boundedness, scale and scope, task interdependence, actor interdependence, degree of shared

context among its members, regulatory environments, and technological readiness. While these

factors may shift during the lifetime of a particular VSO, the impacts of each dimension are

somewhat predictable. Thus, optimizing a VSO requires not only reflective engagement of

contextual factors at the time of design, but continuous monitoring of its operation and

refinement of its design in a dynamic environment.

Not all VSOs are created from scratch by government agencies as in the Human Genome

case, and may differ in lifecycle patterns. They may develop to support scientific projects at

multiples stages of their lifespans. Some are critical at the onset for creating a new scientific sub-

discipline (e.g., land change science), others are focused on rapid production, (e.g., HIV/AIDS

vaccine research), while still others are more essential at later stages of dissemination and

integration of results into the broader society (e.g., nanoHUB).

The longevity of VSOs can also vary from temporary through recurring to permanent.

Temporary VSOs often are formed in response to a crisis or opportunity, such as an oil spill or

the emergence of SARS, and may face additional time pressures. They need to figure out how to

organize to respond quickly and then will likely disband once the crisis is resolved or the

opportunity passes. In a short period of time, these VSOs need to quickly determine what assets

they have, who makes decisions and who needs to talk to whom. Other virtual organizations

address routinely recurring events like solar eclipses, designing and building the next collider for

the high energy physics field, or awarding scientific prizes. One of their main challenges is

development of an enduring archive or organizational memory that can be activated years later to

inform future endeavors. Many VSOs are intended to be permanent and have to build enduring

and flexible assets, governance structures, and knowledge sharing procedures that can be

maintained, renewed, and adapted to changing circumstances. It is not uncommon for VSOs

developed at one stage, for example in response to a natural disaster, to mature into a more

enduring and generalized organization. At all stages of the VSO lifecycle, leaders must engage in

thoughtful planning regarding sustainability (e.g., member engagement, funding) and obsolesce

(e.g., when a VSO fulfills its purpose).

VSOs may form to address relatively bounded problems that can be solved, like

identifying the structure of DNA. Some of these VSOs then disband, but others change focus and

adapt to address new challenges like identifying the mechanisms that link genotype to

phenotype. Other VSOs may be created to develop enduring infrastructure intended to be shared

and used by a large sector of the field like building the International Space Station. Mapping the

human genome was essentially an infrastructure project and the resulting data are freely

available. VSOs that develop enduring infrastructure also need to develop policies and

procedures for enabling long-term access to these assets and make plans to allow these assets to

be maintained and updated over time.

Optimal choices in assets, governance, and knowledge flow will also vary by the scale

and scope of the endeavor. VSOs may involve two scientists in adjacent offices or 2,000

scientists distributed around the globe. Large, distributed organizations often require distributed

and replicated assets for ease of access, more formalized and elaborated governance, and

development of both formal and informal knowledge flows. This added overhead of managing

large VSOs can become burdensome, consuming disproportionately large resources (Cummings

and Kiesler 2007). However, these challenges can also spark innovations that can revolutionize

society. The underpinnings of today's web, HTML and the Mosaic web browser, were invented

to ease the flow of knowledge among the thousands of high energy physicists relying on the

CERN experimental facility.

Successful leadership of a VSO will also vary by the degree of task independence of

functions being performed within the virtual organization. Some kinds of work are divisible and

independent. Each person or team can work on their own section of the task and are not affected

by the speed or accuracy of other people or teams. For example, each astronomer can observe a

separate portion of the sky looking for comets. The likelihood that one astronomer will see a

comet does not depend on whether or not another astronomer sees one. Access to shared

equipment can be scheduled in any order, governance can be relatively decentralized, and

knowledge flows can be slower and more centralized.

Other kinds of work are serially dependent. Completion of some tasks depends on others

having already been completed. For example, much scientific software is divided into modules

that are written independently then assembled to form a complete program. Modules cannot be

assembled until they have been completed, so assembly is serially dependent upon module

completion. Upstream tasks need to be scheduled to access to shared equipment before

downstream tasks, governance may need to be more centralized, and knowledge flows may need

to be more frequent, particularly between those performing upstream and downstream tasks.

Some kinds of work must be co-created among an interacting group of people. NASA's Mission

Control must work together at the launch of a space asset. Access to shared equipment may need

to be simultaneous, governance may need to be pre-determined and relatively inflexible, and

knowledge flows may need to be fast and decentralized.

As noted earlier, in a VSO the ownership of resources is more fluid. There is often

intense competition for scarce resources and it is important to establish a system of justice and to

proactively manage clear power relationships. Who has what say over dependencies? How are

these negotiated? How are conflicts equitably resolved?

The degree of interdependence among actors in VSOs varies significantly. Many VSOs

are tightly-coupled groups of professionals, where the product of one team is the clear input to

another. In these organizations the social network is often densely connected, the governance

strategies are often role-based, and knowledge flows mirror a clear production path. However,

not all VSOs are so tightly integrated. Some are more loosely coupled federations of interested

parties who may all share a common professional interest and culture, akin to a community-of-

practice (Wenger 1998). Amateur astronomers or ornithologists are a classic example. Here the

asset maps, governance structures and knowledge flows are more diffuse. Their more porous

organizational boundaries support legitimate peripheral participation (Lave and Wenger 1991)

which is essential for integrating newcomers into the scientific inquiry.

Virtual scientific organizations also vary in the degree to which their members have a

shared context or common understanding. If a VSO's members are in different academic

disciplines, departments, organizations, countries, native languages, and cultures, they likely

share relatively little common context and will have a more difficult time communicating and

cooperating with one another. The more context that is shared, the easier the collaboration will

be. Traditionally, a variety of solutions have been used to bridge diverse contexts. The well-

known and socially agreed upon structure and language of an academic journal article acts as a

form of shared, agreed upon context that enables communication between different scientists. To

help develop shared context the following have been adopted: a common language of science

(which used to be Latin, but now is English), a common curricula shared by all members of an

academic field, the structured lab meeting communication format, exchanges of scientists among

labs, and attendance at academic conferences.

Common ground is best established in face-to-face interaction. Much research has

emphasized the importance of a physical meeting of all team members before starting distance

collaboration. Scientists still routinely travel around the world for the relationship-building, trust-

establishing, oxytocin-exchanging opportunities afforded by workshops, conferences, symposia.

This is no different or less important for VSOs. Recent advances in ICT have allowed the

development of additional methods for sustaining shared context including weekly video or

webconference calls, shared databases with an agreed upon ontology and the development of

shared, standardized meta-data.

All science is conducted in a larger societal context with differing expectations s about

benefits and dangers to society at large and requisite oversight by non-scientific bodies. Thus,

there is a great diversity in the regulatory environment faced by VSOs. Domains such as

pharmaceutical research or high-energy physics are highly regulated, while others such as

oceanography or entomology are largely unsupervised. While traditionally viewed as

intervention by national and local governments, regulation can stem from multiple sources

including universities, industry, and professional societies. Operating within this web of

expectations and responsibilities, it is critical for leaders of VSOs to understand the variable

importance of each. Valuable questions to engage are how externalized are the standards and

norms for the VSO? What degrees of freedom are there in manipulating these?

In order to succeed, VSOs require technological readiness, which means they need to

have developed both the requisite technical and human infrastructure (Lee, Dourish and Mark

2006). The technologies to support asset management, governance, and knowledge flow must be

appropriate to the individual characteristics of the VSO. The tools may be professionally

developed custom solutions (e.g., a workflow management system) or adapted from general

commercial-off-the-shelf solutions (e.g., VBA modifications to a Microsoft Excel spreadsheet)

or end user developed (e.g., a PERL script or statistical technique in R). Regardless of their

origin they are likely to be operating in a highly heterogeneous environment, running across

multiple platforms including mobile devices, spanning myriad networks, and often ill-fitted to

the tasks at hand. Technical difficulties should be anticipated as the norm, not the exception and

resources prioritized accordingly. All too often VSOs underestimate both the complexity and

importance of their technical infrastructure, only to be frustrated later when spending more time

debugging software than doing science.

Technological readiness includes human resources as well. There are often large gaps in

computer literacy on teams, especially distributed, international, teams. Without the guidance of

formal information technology organization, most development system and support is ad-hoc.

Future Directions

Simply put, distributed science is the future of the scientific enterprise. Scientific inquiry

will continue to become more interdisciplinary, multidisciplinary and transdisciplinary as it

investigates more complex phenomena. This trend toward more system-level science will require

integration activities at all levels, from data collection through theory validation. As it becomes

more dependent on shared resources such as expensive equipment, concatenated databases, or

vast heterogeneous sensor networks to conduct this science, diverse communities must become

more closely integrated.

The virtual scientific organization will be the leading sociotechnical mechanism enabling

this new collaborative, distributed team science (Wulf 1993; Atkins 2003). The kind of radical

multi-national efforts now common in the European Union will become the new norm globally,

with increasing leadership from Asia. The management of teams and scientific processes will

become more tightly integrated as there is greater reliance on mutual awareness and workflow

management systems. The quest for building social cohesion among such far-flung, culturally

diverse teams will lead to improved "always on" teleconferencing facilities. Virtual worlds,

where the common ground can be engineered and the object of study can be equally accessed by

all, will become more commonplace (Rhoten and Lutters 2010). Indeed, there will be an

increasing number of "born virtual" collaborations, where the scientific teams have no physical

interaction and are not supported by any "real world" organization.

The emergent nature of these ad-hoc scientific collaborations surfaces the final important

trend – the increased participation of the non-professional (Shirky 2008). In many disciplines the

barriers between amateur and professional will weaken and more participatory, citizen science

efforts will gain credibility. This is necessitated by the continued decline in global production of

advanced science and engineering professionals and enabled by the development of inexpensive

and sophisticated scientific tools. There will likely be even more use of radical forms of

collaboration such as human sensor networks, distributed hybrid computation, and

crowdsourcing (Surowiecki 2004; Tapscott 2006).

Of course all of these pollyanna prognostications do not come without warning labels.

While there is a democratizing force to VSOs, the increasing dependence on networked

technology will exacerbate the difference between the digital infrastructure haves and have-nots.

Notions of intellectual property, attribution, and publication venues will shift as current

monolithic authority structures (e.g., government labs, universities) reinvent themselves to

accommodate. There is also healthy concern that greater reliance on digital data and simulation

will develop a generation of scientific methods, and scientists, too rarified from the physical

realities of their inquiry. As illustrated in the case, biology is the canary in the coal mine here,

having moved from a "wet" individual, lab-based science to a "dry" distributed, computational

science in one generation.

Conclusion

Given this future for science and engineering, how does one best prepare to lead virtual

scientific organizations? This chapter has provided a framing case and raised a fundamental set

of design and contextual issues to consider. This transition to collaborative, distributed

interdisciplinary, data-driven team science will not be easy. These are hard and persistent

organizational problems and the issues presented here are just the tip of a very big iceberg. In

addition, society and technology are dynamic so creating and managing effective VSOs requires

us to hit a continuously moving target.

Ultimately, the social, organizational and technical arrangements by which science and

engineering progresses will co-evolve to match one another more closely. The real question is

how quickly leaders can align the various elements to optimize their progress? Creating,

managing and participating in effective VSOs is possible if those involved know why they are

invested in these endeavors and remain steadfast in their commitment. Co-evolution of

sociotechnical elements occurs over relatively long timeframes. Mapping the human genome

took over 20 years and the ensuing transformation of biology is continuing to unfold. Those who

wish to contribute to this and similar transformations through successful VSOs must leverage

complementary human, technical, organizational and social assets that are often sparsely

distributed and poorly organized. This can be done by identifying and partnering with others

sharing the same vision, assembling the right assets, establishing effective governance structures,

fostering efficient and effective knowledge flows, and then continually revisiting these as the

conditions change to maintain an optimal alignment.

Effective VSO leadership recognizes that issue play out over long periods of time, not

individual projects, embraces the iterative processes of doing and learning, and is self-reflective

scanning for patterns across, not just within, projects as they evolve. Ultimately, science is

moving in the direction of distributed, interdisciplinary collaboration because the important

questions demand it and the technology enables it. Virtual scientific organizations are the

sociotechnical systems that will support this most human endeavor.

References and Further Readings

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Birnholtz, Jeremy P., and Daniel B. Horn D. B., 2007. "Shake, Rattle and Roles: Lessons from

Experimental Earthquake Engineering for Incorporating Remote Users in Large-scale E-

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Cummings, Jonathon, Thomas Finholt, Ian Foster, Carl Kesselman, Katherine Lawrence and

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