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The National Information Infrastructure and the Earth
Sciences: Possibilities and Challenges
Mark R. Abbott
Oregon State University
As with most areas of science, computer networks (especially
wide area networks) have become indispensable components of the
infrastructure required to conduct environmental research. From
transport of data to support for collaboration, to access to remote
information processing resources, the network (which I will use as
synonymous with the Internet) provides many essential services. In
this paper I discuss the various technical challenges in the use of
information networks for the Earth sciences. However, the technical
issues, though important, are not the essential point. The network
is, after all, only a collection of wires, switches, routers, and
other pieces of hardware and software. The most serious issue is
the content carried across these networks and how it engenders
changes in the way Earth scientists relate to data, to each other,
and to the public at large. These changes have impacts that are far
more profound than access to bandwidth or new network
protocols.
Most of the discussions of the national information
infrastructure (NII) have focused on technical details (such as
protocols) and implementation (e.g., provision of universal
access), with little discussion of the impacts of an NII on the
scientific process. Instead, discussions of the interactions
between technology and human activities focus almost exclusively on
the positive aspects of networks and social interactions. For
example, networks have been extolled as tools for an expanding
sense of community and participatory democracy. However, technology
does not have only positive effects; the impacts are instead far
more subtle and often more extensive than they first appear. They
may not appear for decades. In this paper I neither extol nor
condemn the impacts of computer networks on the conduct of science.
Rather, it is essential that we become aware of these impacts, both
positive and negative. I show that networks do far more than simply
move bits; they fundamentally alter the way we think about
science.
Earth Science and Networks
Data and Earth Science
Before exploring the role of networks in Earth science, I first
briefly discuss the role of data in environmental research. As my
background is in oceanography, my comments focus on the ocean
sciences, but these observations are generally applicable to the
Earth sciences.
Unlike experimental sciences such as chemistry or physics, most
Earth sciences cannot conduct controlled experiments to test
hypotheses. In some cases, though, manipulations of limited areas
such as lakes or small forest plots can be done. Other branches of
science that depend heavily on observations, such as astronomy, can
observe many independent samples to draw general conclusions. For
example, astronomers can measure the properties of dozens of blue
dwarf stars. However, Earth science (particularly those fields that
focus on large-scale or global-scale processes such as
oceanography) must rely on many observations collected under a
variety of conditions to develop ideas and models of broad
applicability. There is only one Earth.
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Earth science thus is driven strongly by developments in
observing technology. For example, the availability of satellite
remote sensing has transformed our view of upper ocean biology. The
spring bloom in the North Atlantic, the sudden ''flowering" of
phytoplankton (the microscopic plants of the ocean) that occurs
over a period of a few weeks, was thought to be primarily a local
phenomenon. However, satellite imagery of ocean color (which is
used to infer phytoplankton abundance) has shown that this event
covers the entire North Atlantic over a period of a few weeks.
Here, a new observing technique provided an improved understanding
of what was thought to be a well-known process.
There are instances where new observing systems have transformed
our understanding of the Earth. For over 40 years, the State of
California has supported regular sampling of its coastal waters to
understand the relationship between ocean circulation and fisheries
production. A sampling grid was designed based on our understanding
of ocean processes at the time. When satellite images of sea
surface temperature (SST) and phytoplankton abundance became
available in the early 1980s, they revealed a complex system of
"filaments" that were oriented perpendicular to the coast and
sometimes extended several hundred miles offshore. Further studies
showed that these filaments are the dominant feature of circulation
and productivity of the California Current, yet they were never
detected in the 40-year record. The original sampling grid had been
too widely spaced. This example shideas can sometimes lead us to
design observing systems that miss critical processes.
The interaction between ideas and observations occasionally
results in more subtle failures, which may be further obscured by
computing systems. A notable example occurred during the
1982–1983 El Niño/Southern Oscillation (ENSO) event.
ENSO events are characterized by a weakening of the trade winds in
the tropical Pacific, which results in a warming of the eastern
Pacific Ocean. This shift in ocean circulation has dramatic impacts
on atmospheric circulation, such as severe droughts in Australia
and the Pacific Northwest and floods in western South America and
southern California. The 1982–1983 ENSO was the most dramatic
event of this century, with ocean temperatures 5°–6°F
warmer than normal off southern California. This physical event
strongly influenced ocean biology as well. Lower than normal salmon
runs in the Pacific Northwest are associated with this major shift
in ocean circulation.
The National Oceanic and Atmospheric Administration (NOAA)
produces regular maps of SST based on satellite, buoy, and ship
observations. These SST maps can be used to detect ENSO warming
events. Because of the enormous volume of satellite data,
procedures to produce SST maps were automated. When SST values
produced by the satellites were higher than a fixed amount above
the long-term average SST for a region, the computer processing
system would ignore them and would use the long-term average value
instead (i.e., the processing system assumed that the satellite
measurements were in error). As there was no human intervention in
this automated system, the SST fields continued to show "normal"
SST values in the eastern tropical Pacific in 1982. However, when a
NOAA ship went to the area in late 1982 on a routine cruise, the
ocean was found to be significantly warmer than had ever been
observed. An alarm was raised, and the satellite data were
reprocessed with a revised error detection algorithm. The enormous
rise in SST over much of the eastern Pacific was revealed. The
largest ENSO event of the century had been hidden for several
months while it was confidently predicted that there would be no
ENSO in 1982.
This episode reveals that the relationship between data and
ideas has become more complex with the arrival of computers. The
increasing volume and complexity of the data available for Earth
science research have forced us to rely more heavily on automated
procedures. Although this capability allows us to cope with the
volume, it also relies on precise specification of various filters
and models that we use to sort data in the computer. These filters
may reflect our preconceived notions about what the data should
actually look like. Although computers and networks apparently
place more data into our hands more rapidly, the paradox is that
there is increasing distance between the scientist and the actual
physical process. This "hands-off" approach can lead to significant
failures in the overall observing system.
As noted by Theodor Roszak 1, raw
data are of little value without an underlying framework. That is,
ideas come before data. There must be a context for observations
before they can make sense. A simple stream of temperature readings
will not advance science unless their context is defined. Part of
this framework includes the ability to repeat the measurements or
experiment. Such repeatability strengthens the claim of the
scientist that the process under study is a general phenomenon with
broad applicability. This framework also includes a
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Representative terms from entire chapter:
network bandwidth
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historical context as well. Because of the strong observational
nature of Earth science, it depends on earlier field programs to
develop new theories and understanding.
With the availability of networks and computers, the task of
obtaining, managing, and distributing data has become critical. The
information system has become part of the observing system. The way
we collect, store, and retrieve our data becomes another set of
filters, just like the sampling strategy or measurement technique.
Often technical problems obscure the underlying science. That is,
the information science issues can dominate the Earth science
issues. I explore these technical issues in the next section.
Earth Science and Information
Systems
The network and computational requirements for Earth science
focus on the more obvious problems of bandwidth, accessibility,
ease of use, and so on. I argue that although these issues are
important, the profound shift in networks and computational systems
has exacerbated the fundamental conflicts between information
systems and the conduct of science while simultaneously obscuring
these conflicts. Analogous to the analysis of television by Mark
Crispin Miller 2, information
technology has both revealed these problems and hidden them within
the medium itself.
Technical Requirements
Earth science relies heavily on close interactions between many
researchers from many disciplines. A single scientist cannot
reserve an entire oceanographic research vessel for a cruise. Such
expeditions require the work of many scientists. The study of
problems such as the impacts of climate change on ocean
productivity require an understanding of the physical dynamics of
both the atmosphere and ocean, besides knowledge of ocean biology.
Earth scientists must therefore develop effective mechanisms for
sharing data.
Along with the need to share data and expertise among widely
dispersed investigators, the characteristics of the data sets
impose their own requirements. As Earth science moves toward an
integrated, global perspective, the volumes of data have increased
substantially. Although the dominant data sources continue to be
Earth-observing satellites, field observations have also grown
significantly. Sensors can now be deployed for longer time periods
and can sample more rapidly. The number of variables that can be
measured has increased as well. A decade ago, a researcher would
come back from a cruise with a few kilobytes of data; today, a
researcher will return with tens of gigabytes. However, these
numbers are dwarfed by the data volumes collected by satellites or
produced by numerical models. The Earth Observing System (EOS),
which is planned by the National Aeronautics and Space
Administration (NASA), will return over 1 terabyte per day of raw
data.
The demands for near-real-time access to data have also
appeared. Satellite-based communications to remote sampling sites
have opened up the possibilities of having rapid access to
observations, rather than waiting several months to recover
environmental sensors. With more capable database servers, data can
be loaded and made accessible over the network in hours to days
after collection. This is in contrast to an earlier era when data
were closely held by an individual investigator for months or even
years. Although real-time access does open new areas for
environmental monitoring and prediction, it does not necessarily
address the need to accumulate the long, consistent, high-quality
time series that are necessary for climate research. The pressures
to meet everyday demands for data often distract scientists from
the slower retrospective analyses of climate research.
As data become more accessible faster, public interest
increases. As with the comet impact on Jupiter in 1994, public
interest in science and the environment can often far exceed the
anticipated demand. The EOS Data and Information System (EOSDIS)
was originally designed to meet the needs of several thousand Earth
science researchers. Now it is being tasked with meeting the
undefined needs of the much larger general public 3. This presents many technical
challenges to an agency that has little experience dealing with a
potentially enormous number of inexperienced users.
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Technical Challenges
Against this backdrop of new technical requirements, Earth
science is facing a new set of technical challenges in addition to
the continuing challenge of network bandwidth. The structure of the
Internet is undergoing massive changes. With the departure of
National Science Foundation funding for the network backbone and
for the regional providers as well in the near future, there will
be increasing emphasis on commercial customers. Interoperability
and functionality of the network access points remain problematic.
The possibility of balkanization of the network is real and not
insignificant. The number of users has also expanded rapidly, and
with the appearance of new applications such as the World Wide Web
(WWW), the effective bandwidth has dropped dramatically.
As the science community becomes a smaller and smaller fraction
of the network user community, network providers focus less on
meeting scientific needs and more on meeting commercial needs. The
search for bandwidth has become more intense. Telecommunication
companies claim that new protocols (such as asynchronous transfer
mode [ATM]) and new hardware(fiber optics) will usher in a new era
of unlimited bandwidth. Some researchers claim that software
"agents" will reduce the need for bandwidth by relying on
intelligence at each node to eliminate the need for bulk transfers
of data. Besides the technical hurdles to bringing such
technologies to market, there are economic forces that work against
such developments. In a process that is well known to freeway
designers and transportation planners, bandwidth is always used to
capacity when the direct costs of the bandwidth are not borne by
the users. Funding for the network is hidden from the user so that
any increase in personal use of network capacity is spread over
every user. In reality, bandwidth is not free, though it is
essentially free to the individual user. Even in the case of a
totally commercial network, it is likely that the actual costs will
be amortized over a broad customer base so that an individual user
will have little incentive to use bandwidth efficiently. Although I
pay a certain amoun directly for the interstate highway system
through my gasoline bills, the actual costs are hidden in many
other fees and spread over a broad range of the population, many of
whom may never even use the highway system.
With the rise of commercial Internet providers such as America
Online and NetCom, will this situation change? Will users pay the
true costs of using the network as opposed to paying only a
marginal cost? I would argue that this is unlikely on several
grounds. First, many users currently have access to virtually free
Internet services through universities and other public
institutions; it will be difficult to break this habit. Second, the
government, through its emphasis on universal access, is unlikely
to completely deregulate the system so that rural users (who truly
are more expensive to service) will pay significantly more than
urban users. Third, and perhaps more compelling, network bandwidth
is no different from any other commodity. Every second that the
network is not used, revenue is lost. Thus it is in the network
providers' interest to establish a pricing structure that ensures
that at least some revenue is generated all the time, even if it is
at a loss. Some revenue is always greater than no revenue, and the
losses can be made up elsewhere in the system. This is a
well-established practice in the long-distance telephone industry
as well as in the airline industry. Off-peak prices are not lower
to shift traffic from peak periods to less congested periods; they
are designed to encourage usage during off-peak periods, not reduce
congestion during peak periods. They raise the "valleys" rather
than lowering the "peaks."
The final threat to network bandwidth is the proliferation of
new services. There is no doubt that as network bandwidth
increases, new services become available. Early networks were
suitable for electronic mail and other low-bandwidth activities.
This was followed by file transfers and remote logins. The WWW
dramatically increased the capabilities for data location and
transfer. Just as the interstate highway system fosters the
development of new industries (such as fast food franchises and
overnight package delivery systems), so also has the Internet. As
with highways, new services create new demand for bandwidth.
Although considerable effort is being devoted within the NII to
develop rational pricing strategies, it is more likely that the
search for bandwidth will continue. It appears to be a law of
networks that spare bandwidth leads to frivolous traffic.
As services and users proliferate, it has become more difficult
for users to locate the data of interest. Search tools are
extremely primitive, especially when compared with the tools
available in any well-run library. Various "web crawlers" will
locate far more irrelevant information than relevant information.
For a user who does not know exactly where to find a specific data
source, much time will be spent chasing down dead ends, or circling
back to the beginning of the search. Although the relative chaos of
the network allows anyone to easily
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set up a data server, this chaos confounds all but the most
determined user. There are no standard methods for defining the
contents of a server and without "truth in advertising" rules,
servers, that may superficially appear to be relevant may contain
totally irrelevant information. The only users who have time to
"surf" the network are those who have nothing else to do, as noted
by Negroponte 4. Once the
appropriate server has been located, there is no consistent method
for indexing and archiving data. Although data may be online, it
often still requires a phone call to locate and access the relevant
data sets.
With the increase in computer processor speeds and desktop mass
storage, it has become increasingly important to match network
speeds with these other components of the system. Following
Amdahl's Rule where 1 bit of input/output requires one instruction
cycle, this implies that a 1,000-MIPS (million instructions per
second) processor will require a 1-gigabit-per-second network
interface. Assuming that processor performance continues at a rate
of 50 to 100 percent per year, we will have 1,000-MIPS machines in
about 1 to 2 years. It is becoming commonplace for researchers to
have 10-to 20-gigabyte disk storage systems on their desktop; in 2
to 3 years it will not be unusual for scientists to have 100
gigabytes of disk subsystems. Network speeds are increasing at a
far slower rate, and the present Internet appears to many users to
be running slower than it did 3 to 4 years ago.
This imbalance between processor speed, disk storage, and
network throughput has accelerated a trend that began many years
ago: the decentralization of information. In an earlier era, a
researcher might copy only small subsets of data to his or her
local machine because of limited local capacity. Most data were
archived in central facilities. Now it is more efficient for the
individual scientist to develop private "libraries" of data, even
if they are rarely used. Although the decentralization process has
some advantages, it does increase the "confusion" level. Where do
other researchers go to acquire the most accurate version of the
data? How do researchers maintain consistent data sets? On top of
these issues, the benefits of the rapid decrease in
price/performance are more easily acquired by these small
facilities at the "fringes'' of the network. Large, central
facilities follow mainframe price/performance curves, and they are
generally constrained by strict bureaucratic rules for operation
and procurement. They are also chartered to meet the needs of every
user and usually focus on long-term maintenance of data sets. In
contrast, private holdings can evolve more rapidly and are not
required to service every user or maintain every data set. They
focus on their own particular research needs and interests, not on
the needs of long-term data continuity or access by the broader
community.
Since small, distributed systems appear to be more efficient and
provide more "niche" services than do central systems, it has
become harder to fund such centers adequately (thus further
reducing their services.) This situation is similar to the
"Commons" issue described by Hardin nearly 30 years ago 5. For the centralized archive, each
individual user realizes great personal benefit by establishing a
private library while the cost is spread evenly over all of the
users. It is therefore in the interest of the individual user to
maximize individual benefit at the cost of the common facility. Not
until the common facility collapses do we realize the total cost.
The situation is similar in the area of network bandwidth.
The final two technical challenges facing Earth science and
networks encroach into the social arena as well. The first is the
area of copyrights and intellectual property. Although scientists
are generally not paid for the use of their data sets or their
algorithms, there is a strong set of unwritten rules governing use
and acknowledgment of other scientists' data sets. With the network
and its rapid dissemination of information and more emphasis on the
development of Web browsers, this set of rules is being challenged.
Data are being moved rapidly from system to system with little
thought of asking permission or making an acknowledgment. Copying
machines have created a similar problem, and the publishing
industry has reacted vigorously. However, the rate at which a
copying machine can replicate information is far slower than that
for a computer network, and its reach is far smaller. As new data
servers appear on the network, data are rapidly extracted and
copied into the new systems. The user has no idea of what the
original source of the data is nor any information concerning the
data's integrity and quality.
Discussions over copyrights have become increasingly heated in
the past few years, but the fundamental issues are quite simple.
First, how do I as a scientist receive "payment" for my
contributions? In this case, payment can be as simple as an
acknowledgment. Second, how do I ensure that my contributions are
not used incorrectly? For example, will my work be used out of
context to support an argument that is false? With global, nearly
instantaneous dissemination of information, it is difficult to
prevent either nonpayment or improper use. After a few bad
experiences, what incentive is there for a scientist to provide
unfettered access?
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The last technical challenge involves the allocation of
resources. Network management is not for amateurs. The Internet
requires experts for its operation. Web browsers are not easy to
design, build, and maintain. Thus programming talent that was
originally hired to engage in scientific analysis is spending a
larger fraction of its time engaged in nonscientific activities.
Although developments in the commercial field are simplifying these
and associated tasks, they do cost money. With declining resources
for science, one can ask whether this is a reasonable use of scarce
resources. The pace of technological change is also increasing, so
that the intellectual and technical infrastructure that was
assembled 5 to 10 years ago is largely irrelevant. For example, it
is becoming harder to hire FORTRAN programmers. Experts on ATM have
not yet appeared.
The computer industry and Earth science researchers have
generally focused on these challenges from a technological point of
view. That is, bandwidth will increase through the provision of ATM
services. Data location will be enhanced through new WWW services.
Copyrights (if they are not considered absolutely irrelevant or
even malevolent) can be preserved through special identification
tags.
The technology optimists have decided that the problems of Earth
science (and science in general) can be solved through the
appropriate application of technology. That is, the fundamental
problems of science will be addressed by "better, faster, cheaper"
technical tools. On the science side of the issue, it appears that
the scientific community has largely accepted this argument. As
information systems approach commodity pricing, scientists acquire
the new technology more rapidly in an attempt to remain competitive
in an era of declining federal support. As put forth by Neil
Postman 6, this is the argument of
"technology." That is, the technology has become an end in itself.
The fundamental problem of science is understanding, not the more
rapid movement of data. Although we have seen that the link between
understanding and observations is perhaps more closely entwined in
the Earth sciences, we must be aware of the implications of our
information systems for how we conduct science. It is to this point
that I now turn.
The Hidden Impacts
A recent book by Clifford Stoll 7
provided a critical examination of the information infrastructure
and where we are headed as a society. Although he makes many valid
points, Stoll does not provide an analysis as to how we arrived at
this predicament. I draw on analyses of the media industries
(especially television) by Postman and Miller that show some
parallels between information systems and mass media. I do not
argue that we should return to some pristine, pre-computer era.
There is no doubt about the many positive aspects of information
technology in the Earth sciences. However, it is worth examining
all of its impacts, not just the positive ones.
Information Regulation
Postman postulates that culture can be described as a set of
"information control" processes 8.
That is, we have established mechanisms to separate important
knowledge from unimportant knowledge (such as a school curriculum)
and the sacred from the profane (such as religion). Even in the
world of art and literature, we are constantly making judgments as
to the value of a particular work of art. The judgments may change
over time, but the process remains.
We are constantly inundated with information about our world,
either from the natural world or by our creations. Somehow we must
develop systems to winnow this information down to its essential
elements. In the scientific world, we have established an elaborate
set of rules and editorial procedures, most of which are not
written down. For example, experiments that cannot be repeated are
viewed as less valuable than those that can be. Ideas that cannot
be traced to previous studies are viewed with more skepticism than
those that can be traced through earlier research. Occasionally, a
new idea will spring forth, but it, too, must go through a series
of tests to be evaluated by the scientific community. This
"editorial" process essentially adds value to the information.
The second point is that the scientific community believes that
there is an objective reality that is amenable to observation and
testing. Although clearly science is littered with ideas that
rested on an individual's biases and processes that were missed
because they did not fit our preconceived notions, we still believe
that the
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natural world can be measured and understood in a predictable
manner. Assumptions and biases can at least be described and
perhaps quantified. Scientific knowledge is more than a set of
opinions.
Through the scientific process, researchers add value to data.
Useful data are separated from useless data and interpreted within
a framework of ideas. Data are therefore placed in a structure that
in turn can be described. Raw information that is presented out of
context, without any sense of its historical origins, is of little
use. Thus Earth science is not limited by lack of observations;
rather, the correct types of observations are often missing (e.g.,
long, calibrated time series of temperature).
The process of adding value has arisen over the last several
centuries. Publishing in peer-reviewed journals is but one example
of how valid data are separated from invalid data, and how
observations are placed within a larger framework for
interpretation. Although data reports are often published, they are
perceived to be of less importance than journal articles. Even
"data" produced by numerical models (which are the products of our
assumptions) are viewed as less valuable than direct
measurements.
The Network and Science
There is no doubt that networks simplify many tasks for Earth
science. However, there are many obvious problems, such as the
separation of information from its underlying context, the
difficulty in locating information of interest, and the lack of
responsibility for the quality and value of a particular data set.
Much as with television, it has become difficult to separate
advertising from reality on the network. A recent discussion on the
future of research universities in Physics Today 9 highlighted some troublesome issues
associated with networks. Graduate students have become
increasingly unwilling to use the library. If reference materials
are not available online in digital form, then students deem them
to be irrelevant. Although electronic searches can be helpful, it
is clear that this attitude is misguided. Most scientific documents
will never be placed online because of the associated costs.
Second, digital storage is highly ephemeral and can never be
considered a true archive. There will always be a machine and
software between the reader and the data, and these tools are
always becoming obsolete. Third, digital searching techniques
follow rigid rules. Present search methods are quite sparse and
limited. Thus far, no one has shown the ability to find material
that truly matches what the reader wants although the search was
incorrectly specified. Such serendipitous discoveries are common in
libraries.
The network is becoming a place of advertising, with little true
content and little personal responsibility. Home pages are
proliferating that merely test what the network is capable of doing
instead of using the network to accomplish a useful task. We have
fixated on the technology, on the delivery system for information,
rather than on the "understanding system" 10. Speed, volume, and other aspects of
the technology have become the goals of the system. Although these
are useful, they do not necessarily solve the problems of
collaboration, data access, and so on. In fact, they can distract
us from the real problems. The issue of scientific collaboration
may require changes in the promotion and tenure process that are
far more difficult than a new software widget.
The emphasis on speed arises in part from the need to have short
"return on investment." In such an environment, market forces work
well in the development of flexible, responsive systems. For Earth
science, this is a useful ability for some aspects of research. For
example, development of new processing algorithms for satellite
sensors clearly benefits in such an environment. However, this
short-term focus is not sufficient. Long-term climate analysis,
where the data must be collected for decades (if not centuries) and
careful attention must be paid to calibration, will not show any
return on investment in the short run. These activities will "lose"
money for decades before one begins to see a return on the
investment. In a sense, long time series are "common facilities,"
much like network bandwidth and central archives. They are the
infrastructure of the science.
The Network and Television
The early days of television were filled with predictions about
increased access to cultural activities, improved "distance"
learning, and increased understanding between people. The global
village was predicted to be just around the corner. However, the
reality is quite different. Television may fill our screens
with
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information, but the nature of the medium has not increased our
understanding 11. Science
programming is useful as a public relations tool (and such contacts
with the public should be encouraged), but the medium is not
suitable for scientific research.
As discussed above, one of the key activities of science is the
structuring of information to sort valid from invalid. But computer
networks increasingly encourage disjointed, context-free searches
for information. Our metaphors resemble those of television and
advertising, rather than those of science and literature. Home
pages encourage rapid browsing through captivating graphics; long
pages of text are viewed as a hindrance.
In the early days of television, the message was in front of the
viewer, as discussed by Miller 12.
It was clear what product was being advertised. Today, both the
medium and its aesthetics are in front. The smooth graphics, the
rapidly shifting imagery, the compelling soundtrack are the primary
elements. The advertising and the product are in the background.
Feelings about products rather than rational evaluations are
transmitted to the viewer. This sense of the aesthetic has
permeated other media, including film, and to some extent,
newspapers. Presentation is more important than content.
The early days of computers were characterized by cumbersome,
isolated, intimidating machines that were safely locked away in
glass rooms. The computer was viewed as a tool whose role was
clearly understood. Today, computers are ubiquitous. Most Earth
scientists have at least one if not two computers in their offices,
plus a computer at home. This demystification of computers has been
accompanied by much emphasis on the interface. Computers are now
friendly, not intimidating. Their design now focuses on smoothness,
exuding an air of control and calm. As described in a biography of
Steve Jobs 13, design of the NeXT
workstation focused almost exclusively on the appearance of the
machine. Most of the advances in software technology have focused
on aesthetics, not on doing new tasks. These new software tools
require more technical capability (graphics, memory, and so on) to
support their sophisticated form. This emphasis on form (both
hardware and software) violates the Bauhaus principle of form
following function. The computer industry appears to focus more on
selling a concept of information processing as opposed to selling a
tool.
Postman 14 has described print as
emphasizing logic, sequence, history, and objectivity. In contrast,
television emphasizes imagery, narrative, presentation, and quick
response. The question is, Where do computer networks sit with
regard to print versus television? There is no doubt that networks
are beginning to resemble television more than print. The process
of surfing and grazing on information as though it were just
another commodity reduces the need for thoughtful argument and
analysis. Networks encourage the exchange of attitudes, not ideas.
The vast proliferation of data has become a veritable glut. Unlike
television, anyone can be a broadcaster; but to rise above the
background noise, one must advertise in a more compelling manner
than one's competitors.
Conclusions and Recommendations
Networks will continue to play an important role in the conduct
of Earth science. Their fundamental roles of data transport and
access cannot be denied. However, there are other effects as well
that are the result of a confluence of several streams. First, the
next-generation networks will be driven by commercial needs, not by
the needs of the research and education community. Second, the
sharp decrease in price/performance of most computer hardware and
the shortened product life cycles have required the science
community to acquire new equipment at a more rapid pace. Third,
expected decreases in federal funding for science have resulted in
greater emphasis on competitiveness. This confluence has caused
scientists to aim for rapid delivery of information over the
network. Without the regulations and impedance of traditional paper
publishing, scientists can now argue in near real time about the
meaning of particular results. "Flame" wars over electronic mail
are not far behind. The community now spends nearly as much time
arguing about the technical aspects of the information delivery
system as it does in carrying out scientific research.
Networks allow us to pull information out of context without
consideration of the framework used to collect and interpret the
data. Ever-increasing network speeds emphasize the delivery of
volume before content. If all information is equally accessible and
of apparently equal value, then all information is trivial. Science
is at risk of becoming another "consumable" on the network where
advertising and popularity are the main virtues.
Page 9
Long, thoughtful analyses and small, unpopular data sets are
often overwhelmed in such a system. Similar processes are at work
in television; the metaphors of the TV world are rapidly appearing
in the network world.
One can successfully argue that Earth science is currently
limited by the lack of data (or at least the correct data), but an
equally serious problem is the inability to synthesize large,
complex data sets. This is a problem without a technological
solution. While information systems can help, they will not
overcome this hurdle. Delivering more data at a faster rate to the
scientist will obscure this fundamental problem. Indeed, technology
may give the appearance of solving the problem when in reality it
exacerbates it. As stated by Jacob Bronowski,
This is the paradox of imagination in science,
that it has for its aim theimpoverishment ofimagination. By that
outrageous phrase, I mean that the highest flight ofscientific
imagination is toweed out the proliferation of new ideas. In
science, the grand view is amiserly view, and a rich modelof the
universe is one which is as poor as possible in hypotheses.
Networks are useful. But as scientists, we must be aware of the
fundamental changes that networks bring to the scientific process.
If our students rely only on networks to locate data as opposed to
making real-world observations, if they cannot use a library to
search for historical information, if they are not accountable for
information that appears on the network, if they cannot form
reasoned, logical arguments, then we have done them a great
disservice.
The balance between market forces with their emphasis on
short-term returns for individuals and infrastructure forces with
their emphasis on long-term returns for the common good must be
maintained. There is a role for both the private sector and the
public sector in this balance. At present, the balance appears to
be tilted toward the short term, and somehow we must restore a
dynamic equilibrium.
Notes
[1] Roszak, Theodore. 1994. The Cult of
Information: A Neo-Luddite Treatise on High-Tech, Artificial
Intelligence, and the True Art of Thinking. University of
California Press.
[2] Miller, Mark Crispin. 1988. Boxed
In: The Culture of TV. Northwestern University Press.
[3] U.S. Government Accounting Office.
1995. "Earth Observing System: Concentration on Near-term EOSDIS
Development May Jeopardize Long-term Success," Testimony before the
House Subcommittee on Space and Aeronautics, March 16.
[4] Negroponte, Nicholas. 1995. "000 000
111—Double Agents," Wired, March.
[5] Hardin, Garrett. 1968. "The Tragedy of
the Commons," Science 162:1243–1248.
[6] Postman, Neil. 1992. Technopoly:
The Surrender of Culture to Technology. Knopf, New York.
[7] Stoll, Clifford. 1995. Silicon
Snake Oil: Second Thoughts on the Information Superhighway.
Doubleday, New York.
[8] Postman, Technopoly, 1992.
[9] Physics Today. 1995. "Roundtable:
Whither Now Our Research Universities?" March, pp. 42–52.
[10] Roszak, The Cult of
Information, 1994.
[11] Postman, Technopoly, 1992.
[12] Miller, Boxed In, 1988.
[13] Stross, Randall. 1993. Steve Jobs
and the NeXT Big Thing. Atheneum, New York.
[14] Postman, Technopoly, 1992.
