ENVIRONMENTAL INFORMATICS 1

1
ENVIRONMENTAL INFORMATICS (1)

•  Draft outline of a discipline devoted to the
  study of environmental information
• creation, storage, access, organization,
  dissemination, integration, presentation and
  usage
• Rudolf B. Husar
• Center for Air Pollution Impact and Trend
  Analysis (CAPITA)
• Washington UniversitySt. Louis, MO 63130
• September 1992

2
ENVIRONMENTAL INFORMATICSApplication of
Information Science, Engineering and Technology
to Environmental Problems

• Rudolf B. Husar
• Director, Center for Air Pollution Impact and
  Trend Analysis
• Washington University, St. Louis, MO
•  
• Environmental information is becoming
  unmanageable by traditional methods.
• There is a need to develop effective methods to
  store, organize, access, dessimilate, filter,
  combine and deliver this peculiar resource.
• Information science is to explain information as
  a resource and the manner in which it is created,
  transformed and used.
• Information engineering deals with the design of
  information systems, while information technology
  deals with the actual processes of storage
  transformation and delivery
• Presented topics will include information as a
  resource user driven data model value-added
  processes application of database, geographic
  information systems, hypertext, multimedia,
  expent system technologiesand the integration of
  these technologies into information systems.
• The principles of Environmental Informatics will
  be discussed in the context of Global Change
  databases organized by ORNL-CDIAS, NASA and by
  Washington University.
• The talk will be augmented a live demonstration
  of the Voyager 1 Data Delivery System that
  combines database, GIS, hypertext, direct
  manipulation and multimedia technologies.

3
THE PROBLEM

•  
• The researcher cannot get access to data
• if he can, he can not read them
• if he can read them,
• he does not know how good they are
• and if he finds them good
• he can not merge them with other data
•  
•  
•  
• Form
•  
• Information Technology and the Conduct of
  Research
• The Users View.
• National Academy Press, Washington, D.C. 1989

4
 DATA PATHWAY

•  DATA PATHWAY
• Monitoring Site
• Principal Investigator
• Information Centers

5
INFORMATICS - THE SCIENCE

• Systems exists that organize, store, manipulate,
  retrieve, analyze, evaluate, and provide
  information in various chunks to a variety of
  people.
• The practice of informatics has evolved from
  professional know-how and technology, not as a
  product of 'basic' research.
• Informatics is in a prescientific stage of
  naming, taxonomy, descriptions and definitions.
• First we need to understand how existing
  information systems work.
• Next we need to formulate a model of these
  practices components, activities, values added,
  clients served and the problems solved by the IS.
• Finally, we have to apply the newly gained
  insights (science) to the design of better IS.
• Note The steam engine was used in practice well
  before the Carnot cycle theory was invented.
• SCIENCE
•  
• The field is in pre-scientific stage. Mostly
  taxonomy of working systems.
•  
• Goals Understanding the forms of environmental
  knowledge
• Usages of environmental knowledge
• Processes of new knowledge creation
• Informatics is in a pre-scientific stage of
  naming, taxonomy, descriptions and definitions.
  However, information systems exists that
  organize, store, manipulate, retrieve, analyze,
  evaluate, and provide information to a variety of
  people.
• Practical information management has evolved from
  professional know-how and technology, not as a
  product of 'basic' research. In order to develop
  the science and engineering
• First we need to understand how existing
  information systems work.

6
 INFORMATICS - THE ENGINEERING

•  
• Information systems exists that organize, store,
  manipulate, retrieve, analyze, evaluate, and
  provide information in various chunks to a
  variaty of people.
• Design of information storage and flow systems.
  Emphasis on user driven design to complement
  technology and content driven info flows.
•  
• Goals Augment human decision and learning
  processes.
• Unite data and metadata
• Reduce resistances to info flow
• The activities of information engineering
  include
• Matching the information need of the user to the
  information sources, using available technology.
• Develop methodologies for the organization,
  transformations and delivery of environmental
  data/information/knowledge.
• Identify the key information values and the
  processes that will enhance those values.
• Seek out a set of universal values that can be
  added to information, that are independent of the
  user environment ( i.e. accessibility, common
  coding, and documentation).
• Develop new tools that will enhance and augment
  the human mind in dealing with environmental
  information, e.g. to minimize the 'info-glut'.

7
 INFORMATICS - THE TECHNOLOGY

• The information revolution is driven by the
  confluence of comuter hardware, software and
  communications technologies.
• Hardware Computers, communications,
  microelctronics.
• Software Database, hypertext, geographic
  information systems (GIS), hypertext, multimedia,
  object orientation.
• Communications Wide area (Internet) and local
  networks bulletin boards, CD ROM.
• Intellectual Technologies Indexing,
  classification/organisation, searchning,
  presenting.
• These technologies provide the hope to overcome
  the information/data glut.
• Develop knowledge and data storage, delivery and
  processing systems.
• Goals Merge database, hypertext, numerical
  modeling technologies
• User programmable, socially well behaving info
  systems
• Ultimately interoperable with the universe
• Information systems are implemented using
  suitable technologies. The information revolution
  is driven by the confluence of computer hardware,
  software and communications technologies.
• Hardware Computers, communications,
  microelectronics.
• Software Database, hypertext, geographic info
  systems (GIS), multimedia, object orientation.
• Communications Wide area (Internet) and local
  networks bulletin boards, CD ROM.
• Intellectual Technologies Indexing,
  classification/organization, searching,
  presenting.
• These technologies along with developments in
  information engineering and science provide the
  hope to overcome the information/data glut.

8
USER-DRIVEN INFORMATION PROCESSING

•   Action
• matching goals
• compromising DECISION
• bargaining PROCESSES
• choosing
• Productive Knownledge
• presenting
• options JUDGMENTAL
• advantages PROCESSES
• disadvantages
• Informing Knowledge
• separating
• evaluating
• validating ANALYZING
• interpreting PROCESSES
• synthesizing
• Information
• grouping
• classifying ORGANIZING

9

• VALUE ADDED PROCESSES
• Metaphors are useful in describing new,
  unfamiliar topics. Environmental Information
  systems can be viewed as refineries that
  transform low-value data into information and
  knowledge through a series of value-adding
  processes.
• Data constitute the raw input from which
  productive knowledge, used for decision making is
  derived.
• Data refers to numbers, files and the associated
  labeling that describes it. Data are turned into
  information when one establishes relationships
  among data, e.g. relational database. Informing
  knowledge educates while productive knowledge is
  used for decision making.
• In fact, one of the practical definitions of
  knowledge is 'whatever is used for
  decision-making'.

10
USES OF ENVIRONMENTAL DATA

• Environmental data/information is used to
•  
• Provide Historical Record
• Identify Deviation from Expected Trend
• Anticipate Future Environmental Problems
• Provide Legal/Regulatory Record
• Support Research
• Support Education
• Support Communication
•  
• The main uses are in science, education and to
  support regulations.

11
CONTENT, TECHNOLOGY AND USER DRIVEN DATA FLOWS

• Most agencies are disseminating information
  relevant to their own domain of activity. Such
  data flow is content driven.
• New technologies such as the papyrus, printed
  book, CD-ROM and computer networks provide bursts
  of information flow resulting in
  technology-driven information flows.
• However, in scientific, educational, and
  regulatory use of environmental data, there is a
  need for compatible information from various
  domains, requiring data merging and synthesis.
  Such data flow is user driven since the user
  dictates the form, content and flow of the data.
• Content and technology-driven data flows are fine
  but they are inadequate to handle modern
  information needs. The challenge is to develop
  the user-driven model and to reconcile and
  integrate it with the other models.

12
ENVIRONMENTAL INFORMATICS

•  
• The study of environmental information and its
  use in environmental management, science and
  education.
• More than the study of computers in
  environmental information. It's focus is on the
  environmental field, rather then on computers and
  the technology.
• Pressedent Medical Informatics, a mature field
  with a goal, domain, textbooks, college courses,
  research groups and funding agencies.
• ENVIRONMENTAL INFORMATICS, EI
• A tentative definition of EI is
• The study of environmental information and its
  use in decision making, education and science.
• EI focuses on the environmental field, rather
  then on computers and the technology. It's
  approach is to systematically study environmental
  information, as branches of science, engineering
  and technology.
• Much of the presentation below is a synthesis of
  ideas 'borrowed' and adopted to the environmental
  field.
• There is precedent Medical Informatics, a mature
  field with a goal, domain, textbooks, college
  courses, research groups and funding agencies.
• Other relevant fields include library sciences,
  management sciences, information engineering.

13
INFORMATION AS A RESOURCE

• Environmental information and information in
  general has several unique characteristics. In
  the post-industrial era, material goods were
  replaced by information as the commodity of
  transactions. It became a resource in itself.
• As other resources, information needs to be
  acquired, organized and distributed i.e. managed.
  However, it is a remarkable resource
• It can not be depleted by use.
• In fact, it expands and gets better with use.
• Information is not scarce it is in chronic
  surplus.
• Scarcity is in time to process it into
  knowledge.
• The processing costs are borne by the info
  user.
• Info can be owned by many at the same time.
• It is shared, not exchanged in transactions.
•  
• Therefore, one must develop different tools from
  those that proved useful for natural, capital,
  human and technological resource management.

14
 DATA FLOW IMPEDIMENTS

•  DATA FLOW IMPEDIMENTS

15
 ASSUMPTIONS AND RATIONALE

• For the foreseeable future, environmental
  information will grow in quantity and quality.
• Individual agencies are collecting, organizing,
  and disseminating information relevant to their
  own domain of activity.
• There is not enough manpower and time to digest,
  analyze, integrate, and ultimately make use of
  the accumulated environmental information.
• Therefore, there is a need for a systematic
  effort to develop suitable data organization,
  manipulation, integration, and delivery system.
• A possible mechanism for accomplishing these
  task is to form a consortium of
  informatics-minded institutions - the EI Group.
• For the foreseeable future, environmental
  information will grow in quantity.
• There is not enough manpower and time to
  analyze, integrate, and use of all the data
• The problem is not so much the quantity of data,
  but rather the form in which is delivered, e.g.
  the automobile windshield delivers lots of data
  but we can still process it with ease.
• What is needed is a faster way to metabolize the
  expanding environmental data sets.
• Therefore, there is a need for a systematic
  effort to better understand environmental
  information its characteristics, use and
  management.

16
USER DRIVEN FLOW OF ENVIRONMENTAL INFORMATION

• USER DRIVEN FLOW OF ENVIRONMENTAL INFORMATION
• In scientific, educational, and regulatory use of
  environmental data, there is a need for
  multiplicity of compatible data sets and
  knowledge from various domains.
• There is a set of universal values that can be
  added to the data such as accessibility, common
  coding, and documentation. These values in
  conjunction with a set of software tools could
  minimize the "info-glut".
• Use of data for science, education, regulation
  and policy requires
• Specification of the information need by the
  user
• An information system (model, educational
  software or a decision support system) that is
  capable of delivering the needed information.
• Domain data supplied by the producer or brokers.

17

• POSSIBLE ACTIVITIES OF EI GROUP
• EI Science Define the domain of EI
  environmental information as a resource seek
  general laws of EI info uses driving forces.
• EI Engineering Study the components of EI
  systems creation value-added processes
  data/information/knowledge structures for storage
  and transmission design of EI systems.
• Education Develop educational materials on EI
  conduct workshops, training sessions.
• Work closely with others on
• Data Integration Collect, reconcile,
  integrate, document data/information/knowledge
  bases.
• Data Exchange Foster exchange through
  depositories, data catalogs, transfer mechanisms,
  and nomenclature standards.
• Tools Development Evaluate and develop
  software tools for the access, manipulation, and
  presentation of environmental information.

18

• REQUIREMENTS FOR THE EI GROUP
• The EI GROUP has to have a solid understanding
  of environmental data needs for science,
  education, and policy development, regulations,
  and other uses.
• Know how to translate the information needs to
  information systems and to design a data flow and
  transformation systems (information engineering).
• The EI GROUP has to be well versed in modern
  information science and technology as applicable
  to environmental informatics. Where necessary,
  the Group has to develop new concepts and
  technologies.
• It has to interface with the users of the
  environmental information, to assure the
  usefulness of the effort.
• Interface with, and utilize the existing
  governmental and private data sources, building
  on and enhance not competing with those effort.

19

• OUTPUT OF THE EI GROUP
• Technology Adopt and apply evolving technologies
  for DBMS, GIS, Hypertext, Expert Systems, User
  Interfaces, Multimedia, Object Orientation
• Public Databases Prepare relevant, high quality,
  well documented, compatible, integrated, raw, and
  aggregated environmental databases to be usable
  for science, education, enforcement, and other
  purposes. Make such high quality-high value data
  environmental information available to many
  users.
• Software Tools Provide "smart" data
  display/manipulation tools that will help turning
  data into knowledge. e.g. GIS, Voyager, Movie,
  Hypertext, Video/Sound.
• Federal agencies have recognized these needs
  and formed the Interagency Working Group on Data
  Management for Global Change IWGDMGC
• The federal effort could be augmented by
  companion academic efforts, possibly through a
  consortium of informatics-minded institutions -
  the EI Group.

20
POSSIBLE ACTIVITIES OF THE EI GROUP

• Data Integration Collect, reconcile,
  integrate, document information bases.
• Data Exchange Foster exchange of environmental
  data through depositories, data catalogs,
  transfer mechanisms, and nomenclature standards.
• Tools Development Evaluate and develop
  software tools for the access, manipulation, and
  presentation of environmental information.
• End-Use Projects Conduct specific research and
  development projects for science, education, and
  regulations.
• Education Conduct workshops, training
  sessions, and prepare educational material for
  environmental informatics.

21
REQUIREMENTS FOR THE EI GROUP

•   The EI GROUP has to have a solid understanding
  of environmental data needs for science,
  education, and policy development, regulations,
  and other uses.
• Know how to translate the information needs to
  information systems and to design a data flow and
  transformation systems (information engineering).
• The EI GROUP has to be well versed in modern
  information science and technology as applicable
  to environmental informatics. Where necessary,
  the Group has to develop new concepts and
  technologies.
• Interface with, and utilize the existing
  governmental and private data sources, building
  on and enhance not competeing with those effort.
• It has to interface with the users of the
  environmental information, to assure the
  usefulness of the effort.

22
EI GROUP OUTPUT

• New Developments Environmental Informatics
• Science Define the domain of EI. Develop new
  methods to classify, organize, and create
  environmental knowledge.
•  
• Engineering Create an infrastructure and
  methodology for the organization, transformation,
  and delivery of environmental information.
•  
• Technology Examine the evolving technologies for
  Database Management Systems (DBMS), Geographic
  Information System (GIS), Hypertext, Expert
  Systems, User Interface, Multimedia, Object
  Orientation. Apply and adopt these technologies
  to environmental information.
• Provide High Grade Environmental Databases for
  Public Use
• Prepare relevant, high quality, well documented,
  compatible, integrated, raw, and aggregated
  environmental databases to be usable for science,
  education, enforcement, and other purposes. Make
  such high quality-high value data environmental
  information available to many users.
• Provide Software Tools
• Provide "smart" data manipulation tools that will
  help turning data into knowledge.
• Provide tools for data access, manipulation, and
  presentation (e.g. GIS, Voyager, Movie,
  Hypertext, Video/Sound).

23
Funding
24
Information and Decision Making (1)Arno Penzias
Ideas and Information

• An instrument operator, traffic controller,
  economist .... all process information. A common
  thread among these activities is that is decision
  making. A decision may be simple such as
  selecting ....replacing a . or as complex as
  developing a new clean air legislation. Decisions
  are followed by actions and actions generally in
  new information . This rather circular behavior
  keeps the decision process going until some goal
  is met, the task is finished , or the project is
  set aside for a time. Healthy flow of information
  separates winning organizations from losers. (
  More on the flow concept here)
•  
•  
• For quality information, today's consistently
  successful decision makers rely on a combination
  of man and mashine. Getting the best combination
  requires understanding how the two fit together
  and the roles each may play. It also requires
  having an information strategy that is suitable
  for both he decision-maker's preferences and the
  problem at hand.
•  
• Knowledge is whatever information is used to make
  decision.
• "Deciding" is acting on information.
• Managers are transformers of information
• pp125

25
Information and Decision Making (2)Arno Penzias
Ideas and Information

• Information Flow and Decision Making
• An instrument operator, traffic controller,
  economist .... all process information. A common
  thread among these activities is that is decision
  making. A decision may be simple such as
  selecting ....replacing a . or as complex as
  developing a new clean air legislation. Decisions
  are followed by actions and actions generally
  reswult in new information . This rather circular
  behavior keeps the decision process going until
  some goal is met, the task is finished , or the
  project is set aside for a time.
•  
• Barring blind luck, the quality of decision can
  not be any better than the quality of the
  information behind it.
• Healthy flow of information separates winning
  organizations from losers. ( More on the flow
  concept here)
•  
• Knowledge is whatever information is used to make
  decision.
• "Deciding" is acting on information.
• Managers are transformers of information
• pp125
• Despite the explosive growth in computing, we
  have yet to feel the full impact of the
  information-processing resource that
  microprocessors offer. The computing power will
  immensity the challenge of developing ever more
  powerful methods of telling mashines to do what
  we whish them to do. This requires the solution
  of "the software problem". -
•  
• Solving the "the software problem" includes
  producing software more quickly, with fewer bugs
  at lower cost- software that is easier to to
  understand, modify and reuse different
  applications. Give user to customize a system by
  modifying.

26
Information and Decision Making (3)Arno Penzias
Ideas and Information

• UNIX - Social behavior
• Most applications use different formats to move
  information between them. UNIX programs
  communicate with each other in a specific way.
  This arrangement allows the programmer to plug
  programs together like Lego sets, without
  worrying about the details of interfacing. UNIX's
  modularity permits users to build customized
  application programs out of modular pars from
  libraries and programs borrowed from friends.
  Convenient "User programmability" has the
  potential to unleash the creative powers of many
  users instead of relying on the program creator
  for all the insights needed to create well suited
  applications
• What next? Search of nonprocedural programming
  that frees users from worrying about how a given
  task is to be accomplished and allow them to
  merely state what they want.

27
Information and Decision Making (4)Arno Penzias
Ideas and Information

• Networking
• To benefit from information created for different
  purposes under different conditions and at
  different location, users need convenient
  interfaces to the systems providing the data.
  Ultimately, the intervening networking technology
  that provides the interface should be flexible
  enough to accept information in whatever format
  the data source provides it and translate it to
  the needed format most suitable for human
  perception.
• Human pattern recognition skills, tactile
  sensitivities and similar interfaces to the
  external world attest to the massive processing
  power that the brain dedicates to such functions.
• Evidently, the experience of evolution has
  demonstrated the need for a variety of sensitive
  interfaces, . The greatest subtlety of our own
  human interfaces appears to be in the way we
  effortlessly integrate disparate sensory inputs.
  It is the single good feeling you get in a
  theater or sports arena from words, music,
  spectacle, and someone sitting next to you- all
  at the same time. In contrast, most of our
  present technology tends to deal with each input
  the words the visual input etc. as a separate
  entity.
• User preferences and productivity needs are the
  driving forces behind the call for better
  interface between people and mashines.
• Much of the additional computer processing power
  will be devoted to providing better interfaces
  between people and mashine.

28
Information and Decision Making (5)Arno Penzias
Ideas and Information

• Computers and human information processing
•  
• While computers afford humans much valuable help
  in processing massive amounts of data. However,
  mashines are best at manipulate numbers or
  symbols people connect them to meaning.
•  
• Machines offer little serious competiion in areas
  of creativity, integration of disparate
  information, and flexible adaptation to
  unforeseen circumstances. Here the human mind
  functions best. Computing systems lack a key
  attribute of human intelligence the ability to
  move from one context to another.
•  Just-in-time Information processing symbiotinc
  co-evolution
•  Computers and communiation systems can speed up
  the Connectivity can spped
•  
• Today, access to on-line data reduction schemes
  enables us to think of the results as we get
  them. These better tools can profoundly change
  the way we work. Today, we can ask questions in
  time to get answers, make decisions and create
  more powerful ideas. Generate knowledge faster
• While ideas flow from human minds, computers can
  help shaping much of the information that leads
  to those ideas. By providing needed information
  in timely way and in digestable form, electronic
  data processing and delivery system can someone
  make informed decisions,
• Tools of the mind , mind ampliing. Same way as
  steam enfine amplies humans physical power, the
  computer/communication technologies can amlify
  its mental powers.
• In this sence, the goal of the information
  techloogy promoted here is not so much to
  intruduce artificail intelligence, but tho
  amplify the actual intelligence of humans to
  perfom increasingly complex taks.
•  

29
Information and Decision Making (6)Arno Penzias
Ideas and Information

• The Software Problem
•  
• Despite the explosive growth in computing, we
  have yet to feel the full impact of the
  information-processing resource that
  microprocessors offer. The computing power will
  immensity the challenge of developing ever more
  powerful methods of telling machines to do what
  we whish them to do. This requires the solution
  of "the software problem". -
•  
• Solving the "the software problem" includes
  producing software more quickly, with fewer bugs
  at lower cost- software that is easier to to
  understand, modify and reuse different
  applications. Give user to customize a system by
  modifying.
•  
• Most applications use different formats to move
  information between them. UNIX programs
  communicate with each other in a specific way.
  This arrangement allows the programmer to plug
  programs together like Lego sets, without
  worrying about the details of interfacing. UNIX's
  modularity permits users to build customized
  application programs out of modular pars from
  libraries and programs borrowed from friends.
  Convenient "User programmability" has the
  potential to unleash the creative powers of many
  users instead of relying on the program creator
  for all the insights needed to create well suited
  applications
• What next? Search of nonprocedural programming
  that frees users from worrying about how a given
  task is to be accomplished and allow them to
  merely state what they want.

30
Information and Decision Making (7)Arno Penzias
Ideas and Information

• Data Access
•  
• To benefit from information created for different
  purposes under different conditions and at
  different location, users need convenient
  interfaces to the systems providing the data.
  Ultimately, the intervening networking technology
  that provides the interface should be flexible
  enough to accept information in whatever format
  the data source provides it and translate it to
  the needed format most suitable for human
  perception.
• Human pattern recognition skills, tactile
  sensitivities and similar interfaces to the
  external world attest to the massive processing
  power that the brain dedicates to such functions.
•  
• Evidently, the experience of evolution has
  demonstrated the need for a variety of sensitive
  interfaces, . The greatest subtlety of our own
  human interfaces appears to be in the way we
  effortlessly integrate disparate sensory inputs.
  It is the single good feeling you get in a
  theater or sports arena from words, music,
  spectacle, and someone sitting next to you- all
  at the same time. In contrast, most of our
  present technology tends to deal with each input
  the words the visual input etc. as a separate
  entity.
• User preferences and productivity needs are the
  driving forces behind the call for better
  interface between people and machines.
• Much of the additional computer processing power
  will be devoted to providing better interfaces
  between people and machine.
•  
•  
•  

31
Spatial Time Series Analysis-Forecasting -
ControlBennett, R.J. Pion Limited, London 1979

• Description (Characterization)
• In order to understand the functioning of
  organisms, one has to understand
• 1. individual holons (downward face)
• 2. the relationship between the holons (upward)
  Koestlers holarchy
•  
• Involves summarizing the response characteristics
  of the system by purely descriptive measures.
• Description is accomplished by monitoring,
  followed by descriptive statistics.
• Explanation
• Associate and explain events that occur in
  space-time. Build assotiative, causal
  relationships, build model. Analysis stages (p.
  20)
• Stage 1. Prior hypothesis of systems structure
• Stage 2. System identification and specification
• Stage 3. Parameter estimation
• Stage 4. Check of model fit
• Stage 5. System explanation, forecasting,
  control

32
Moors Law

• The single most important thing to know about the
  evolution of technology is Moore's Law. Most
  readers will already be familiar with this "law."
  However, it is still true today that the best of
  industry executives, engineers, and scientists
  fail to account for the enormous implications of
  this central concept.
• Gordon Moore, a founder of Intel Corporation,
  observed in 1965 that the trend in the
  fabrication of solid state devices was for the
  dimensions of transistors to shrink by a factor
  of two every 18 months. Put simply, electronics
  doubles its power for a given cost every year and
  a half.
• In the three decades since Moore made his
  observation the industry has followed his
  prediction almost exactly. Many learned papers
  have been written during that period predicting
  the forthcoming end of this trend, but it
  continues unabated today. Papers projecting the
  end are still being written, accompanied with
  impressive physical, mathematical, and economic
  reasons why this rate of progress cannot
  continue. Yet it does.
• Moore's Law is not a "law" of the physical world.
  It is merely an observation of industry behavior.
  It says that things in electronics get better,
  that they get better exponentially, and that this
  happens very fast. Some, even Gordon Moore
  himself, have conjectured that this is simply a
  self-fulfilling prophecy. Since every corporation
  knows that progress must happen at a certain
  rate, they maintain that rate for fear of being
  left behind.
• It is also possible that Moore's Law is much
  broader than it appears. Possibly it applies to
  all of technology, and has applied for centuries
  while we were unaware of its consequences or
  mechanisms. Perhaps it was only possible to be
  explicit about technological change in 1965
  because the size of transistors gave us for the
  first time a quantitative measure of progress. If
  this is so, then we are embedded in an expanding
  universe of technology, where the dimensions of
  the world about us are forever changing in an
  exponential fashion.
• The notion of exponential change is deceptively
  hard to understand intuitively. All of us are
  accustomed to linear projection. We seem to view
  the world through linear glasses -- if something
  grows by a certain amount this year, it will grow
  an equal amount the next year. But according to
  Moore's Law, electronics that is twice as
  effective in a year and a half will be sixteen
  times as effective in 6 years and over a thousand
  times as effective in 15 years. This implies
  periodic overthrows of everything we know. An
  executive in the telecommunications industry
  recently said that the problem he confronted was
  that the "mean time between decisions exceeded
  the mean time between surprises." Moore's Law
  guarantees the frequency of surprises.

33
Metcalfe's Law -- Network Externalities

• There is another "law" that affects the
  introduction of new technology -- this time in an
  inhibiting fashion. Metcalfe's Law, also known to
  economists generally as the principle of network
  externalities, applies when the value of a new
  communications service depends on how many other
  users have adopted this service. If this is the
  case, then the early adopters of a given service
  or product are disincented, since the value they
  would obtain is very small in the absence of
  other users. In this situation innovation is
  often throttled.
• Metcalfe's law often applies to communications
  services. A classic example, of course, is the
  videotelephone. There is no value in having the
  first videotelephone, and it only acquires value
  slowly as the population of users increases. If
  there are n users at a given time, then there are
  n(n-1) possible one-way connections. Thus the
  value grows as the square of the number of users.
  The value starts slowly, then reaches some point
  where it begins to rise rapidly. It seems as if
  there needs to be a critical mass for takeoff,
  and that there is no way to achieve that critical
  mass, given the burden on initial subscribers.
• Metcalfe's Law has defeated many technological
  possibilities, left stillborn at the starting
  gate of market penetration. Nonetheless, there
  are important examples of breakthroughs. For
  example, facsimile became a market success, but
  only after decades of technological viability.
  Even so, facsimile is a complex story, involving
  the evolution of standards, the inevitable
  progress of electronics, the equally-inevitable
  progress in the efficiency of signal-processing
  algorithms, and the rise of the business need for
  messaging services.
• Moore's and Metcalfe's laws make an interesting
  pair. In the communications field Moore's law
  guarantees the rise of capabilities, while
  Metcalfe's law inhibits them from happening.
  Devices that appear to have little intrinsic
  value without the existence of a large networked
  community continue to diminish in cost themselves
  until they reach the point where the value and
  cost are commensurate. Thus Moore's Law in time
  can overcome Metcalfe's Law.

34
Metcalfe's Law -- Network Externalities(2)

• Economists know it as the law of increasing
  returns, of network externalities, but the idea
  is that the more people that are connected to a
  network the more valuable it is.  Specifically,
  the value of a network grows by the square of the
  number of users.  The value is measured by how
  many people I can communicate with out there, so
  the total value of the network grows as the
  square of the number of users.  Now, what this
  means is that a small network has almost no
  value, and a large network has a huge value. 
  What it gives you is the lock-in phenomenon of
  winner takes all.  You want to have the same
  thing as everybody else.  The idea is that you
  dont want to be the first person on your block
  to get the plague.  But when all your friends get
  it, you think about getting it.  The more people
  have it, the more youre likely to get it and
  suddenly there is this capture effect where
  everybody has it.  This law of network
  externality governs so much of the business and
  is at the heart of the Microsoft trial.  Why does
  Microsoft have a monopoly?  Is this a natural
  phenomenon that has to do with networks?
• David Reed coined another lawReeds Lawthat
  says theres something beyond Metcalfes Law. 
  There are three kinds of networks. 
• First, theres broadcast like radio and TV, which
  well call a Sarnoff network.  The value of that
  network is proportional to the number of people
  receiving the broadcast.  Amazon would be this
  type of network, because people shop there but
  dont interact with each other. 
• Then theres the Metcalfes Law-type network
  where people talk to each other, for example,
  classified ads.  Reed said that the important
  thing about the Internet is neither of those. 
• The Internet exhibits a third kind of lawwhere
  communities with special interests can form.  The
  thing about communities is there are 2n of them,
  so in a large network the value of having so many
  possible communities and subnetworks is the
  dominant factor.  He predicts a scaling of
  networks, starting with small networks having
  only the Sarnoff linear factor, larger networks
  dominated by the square factor, and giant
  networks dominated by the 2n factor of the
  formation of communities.
• Napster is another example of whats going on in
  information technology.  First, its an example
  of the kind of network where winner takes all. 
  Napster is where all the songs are, so thats
  where everybody else is.  If Napster goes under,
  when they go under, then all the little sites
  wont be able to replace it because people wont
  find what they want there.  Napster also brings
  up one of the other properties of information,
  which is troublesome and is going to shape our
  society in the coming yearsthe idea that
  information can be copied perfectly at zero
  cost.  That flies in the face of so much of what
  we believe about commerce.  As my friend Douglas
  Adams said to me, we protect our intellectual
  property by the fact that its stuck onto atoms,
  but when its no longer stuck onto atoms, there
  is really no way to protect it.  He would like to
  sell his books at half a cent a page, the idea
  being that for every page you read, you pay him
  half a cent.  If you get into the book 20 pages
  and you say, This book is really bad, you dont
  pay anymore.  That would eliminate the copying
  of information at zero cost issue that he
  experiences as an author.  He says people come up
  to him in the street and say, Ive read your
  book 10 times, and he says, Yes, but you didnt
  pay 10 times.
• So these are some of the things that trouble me
  about the future of information technology.  What
  are its limits?  Will the laws of network effects
  doom us all to a shared mediocrity?  What will
  happen to intellectual property and its effect on
  creativity?  Is it like the railroads, or is this
  something fundamentally different that will last
  through the next century?

35
The Evolution of the World Wide Web

• The most important case study in communications
  technology is the emergence of the World Wide
  Web. This revolutionary concept seemed to spring
  from nothingness into global ubiquity within the
  span of only two years. Yet its development was
  completely unforeseen in the industry an
  industry that had pursued successive long and
  fruitless visions of videotelephony, home
  information systems, and video-on-demand, and had
  spent decades in the development of ISDN with no
  apparent application. It now seems incredible
  that no one had foreseen the emergence of the
  Web, but except for intimations in William
  Gibsons science fiction novel Neuromancer, there
  is no mention in either scientific literature or
  in popular fiction of this idea prior to its
  meteoric rise to popularity.
• There is a popular notion that all technologies
  take 25 years from ideation to ubiquity. This has
  been true of radio, television, telephony, and
  many other technologies prevalent in everyday
  life. How, then, did the Web achieve such
  ubiquity in only a few years? Well, the
  historians argue, the Web relied on the Internet,
  which in turn was enabled by the widespread
  adoption of personal computers. Surely this took
  25 years. We might even carry this further. The
  personal computer would not have been possible
  without the microprocessor, which depended on the
  integrated circuit evolution, which itself
  evolved from the invention of the transistor, and
  so forth. By such arguments nearly every
  development, it seems, could be traced back to
  antiquity.
• Although the argument about the origin and length
  of gestation seems an exercise in futility, the
  important point is that many revolutions are
  enabled by a confluence of events. The seed of
  the revolution may not seem to lie in any
  individual trend, but in the timely meeting of
  two or more seemingly-unrelated trends. In the
  case of the World Wide Web the prevalence of PCs
  and the growing ubiquity of the Internet formed
  an explosive mixture ready to ignite. Perhaps no
  invention was really even required. The world was
  ready -- it was time for the Web. While this
  physical infrastructure was forming in the
  worlds networks and on the desktops of users,
  there was a parallel evolution of standards for
  the display and transmission of graphical
  information. HTML, the hypertext markup language,
  and HTTP, hypertext transmission protocol, were
  unknown acronyms to the majority of technical
  people, let alone the lay public. But the
  definition of these standards that would enable
  the computers and networks to exchange rich
  mixtures of text and pictures was taking shape in
  Switzerland at the physics laboratory CERN, where
  Tim Berners-Lee was the principle champion.
• The role of standards in todays information
  environment is critical, but often unpredictable.
  What is really important is that many users agree
  on doing something exactly the same way, so that
  everyone achieves the benefits of
  interoperability with everyone else. It is
  exactly the same concept of network externalities
  that is at work in Metcalfes law. An
  international standard can stimulate the market
  adoption of a particular approach, but it can
  also be ignored by the market. Unless users adopt
  a standard it is like the proverbial tree falling
  in the forest without a sound. Standards are, for
  the most part, advisory. User coalitions or
  powerful corporations can force their own
  standards in a fascinating and ever-changing
  multi-player game. Moreover, de facto standards
  often emerge from the marketplace itself.
• So in the middle 1980s there was a prevalent
  physical infrastructure with latent capabilities
  and an abstract agreement on standards for
  graphics. One more development and two brilliant
  marketing ideas were required to jumpstart the
  Web. The development was that of Mosaic at the
  National Center for Supercomputing Applications
  at the University of Illinois. Mosaic was the
  first browser, a type of program now known
  throughout the world for providing a simple
  point-and-click user interface to distributed
  information. Following the initial versions of
  Mosaic from NCSA, commercial browsers were
  popularized by Netscape and Microsoft.
• The revolutionary marketing ideas needed for the
  Web now seem obvious and ordinary. A decade ago,
  however, they were not at all obvious. One idea
  was to enable individual users to provide the
  content for the Web. The other idea was to give
  browsers free to everyone. Between these ideas,
  Metcalfe's Law was overcome. Even though browsers
  initially had almost no value, since there were
  no pages to browse, they could be obtained
  electronically at no cost. The price was directly
  related to the value. Thus browsers spread
  rapidly, just as their value began to build with
  the accumulation of web pages.
• Allowing the users to provide content was counter
  to every idea that had been held by industry. The
  telecommunications and computer industries had
  tried for a decade to develop and market remote
  access to information and entertainment held in
  centralized databases. This was the cornerstone
  of what were called "home information systems"
  that were given trials in many cities during the
  1970s and 1980s. Later, the vision pursued by the
  industry was that of video-ondemand -- the dream
  of providing access to every movie and television
  show
• ever made, like a giant video rental store, over
  a cable or telephone line.
• Virtually every large telecommunications company
  had trials and plans for videoon-demand, and the
  central multi-media servers required for content
  storage were being developed by Microsoft,
  Oracle, and others. The Web exemplifies some
  powerful current trends -- the empowerment of
  users, geographically-distributed content,
  distributed intelligence, and intelligence and
  control at the periphery of the network. Another
  principle is that of open, standard interfaces
  that allow users and third parties to build new
  applications and capabilities upon a standardized
  infrastructure.
• It is hard to criticize industry for pursuing the
  centralized approach. Imagine proposing the Web
  to a corporate board in 1985, and describing how
  browsers would be given away free, and how
  industry would depend upon the users to provide
  whatever content might appear. Even today many
  corporations wonder and worry about the business
  model for the Web, and few are making any profits
  at all.

36
Information Technology and the Conduct of
Research The Users ViewNational Academy Press,
Washington, D.C. 1989

• Committee rationale There are serious
  impediments to the wider and more effective use
  of information technology. Committee members were
  active researchers are outside the field of
  "information technology". In the absence of
  considerable knowledge about the field, the panel
  was approaching it by asking the researchers
  about their experiences.
• p 1. Information technology - the set of computer
  and communications technologies - has changed the
  conduct of scientific, engineering and clinical
  research. New technologies offer the prospect of
  new ways of finding, understanding, storing, and
  communicating information and should increase the
  capabilities and productivity of researchers.
  Among these new technologies are simulations, new
  methods of presenting observational and
  computational results as visual images, the use
  of knowledge-based systems as "intelligent
  assistants" and more flexible and intuitive ways
  for people to interact with and control
  computers.
• The conduct of research The everyday work of
  researcher involves writing proposals, developing
  theoretical models, designing experiments,
  collecting data, analyzing data, communicating
  with colleagues, studying research literature,
  reviewing colleagues work, and writing articles.
  They look at three particular aspect of research
  data collection and analysis, communication and
  collaboration, and information storage and
  retrieval.

37
Information Technology and the Conduct of
Research The Users ViewNational Academy Press,
Washington, D.C. 1989

• DATA COLLECTION AND ANALYSIS
• It is one of the most widespread use of
  information technology in research. Trends
• Increased use of computers
• Dramatic increase of data storage and processing
  capacity
• Creation of new computer controlled instruments
  that produce more data
• Increase communication among researchers using
  networks.
• Availability of software packages for standard
  research (e.g. statistical)
•  
• Difficulties
• 1. Uneven distribution of computing resources,
  the has and have-nots
• 2. Finding the right software. Commercial
  software is often unsuitable for specialized
  needs. Most researchers, although they are not
  skilled software creators, develop their own
  software with the help of graduate students. Such
  software is designed for one purpose and it is
  difficult to understand, to maintain or transport
  to other computing environments.
• 3. Transmitting data over networks at high speed.
  
• COMMUNICATION AND COLLABORATION
• Routine word processing and electronic mail are
  the most pervasive form of computer use.
  Electronic publishing and data communication-coord
  ination is becoming increasingly used. Trends
• Information can be shared more quickly
• New collaborative arrangements
• Difficulties
• Incompatibility of technologies
• Networks are anarchic.

38
Information Technology and the Conduct of
Research The Users ViewNational Academy Press,
Washington, D.C. 1989

• IFORMATION STORAGE AND RETRIEVAL
• How it is stored determines how accessible it is.
  Scientific text is stored on print ( hard copy)
  and accessible though indices, catalogs of a
  library. Data and databases are stored mostly on
  computers disks.
• A database along with the procedures for
  indexing, cataloging and searching makes up an
  information management system.
• Difficulties
• The researcher cannot get access to data if he
  can, he can not read them if he can read them,
  he does not know how good they are and if he
  finds them good he can not merge them with other
  data .
• Difficulty accessing data stored by other
  researchers. Such access permits reanalysis and
  replication, both essential elements of
  scientific process. At present data storage is
  largely an individual researcher's concern, in
  line with the tradition that researchers have
  first right to their data. The result has bee a
  proliferation of idiosyncratic methods for
  storing, organizing, and indexing data, with the
  researchers data essentially inaccessible to all
  other researchers.
• Formats in data files vary from researcher to
  researcher, even within a discipline. These
  problems prohibit a researcher from merging
  someone else's data in his own database. Hence,
  considerable effort must be dedicated to
  converting data formats. not enough metadata.
• Finally when a researcher reads another database,
  he has no notion as to the quality of the data it
  contains. The data sets do not have enough QC
  information and descriptive metadata. There is a
  need for evaluated high quality databases.
• Given a high quality well described database, a
  major difficulty exists in conducting searches.
  Most info searches are incomplete, cumbersome,
  inefficient, expensive, and executable only by
  specialists. Searches are incomplete because the
  databases themselves are incomplete. Updating is
  expensive because data are stored in more than
  one database. Cumbersome and inefficient because
  different databases are organized according to
  different principles. ( data models)
• Another difficulty in storing data information is
  private ownership. By tradition, researchers hold
  their data privately. In general, they neither
  submit their data to a central archive nor make
  their data available via computer. Increasingly,
  however, in disciplines such as meteorology and
  biomedical sciences, submission of primary data
  into databanks is has become accepted as a duty.
  In some fields, the supporting agencies require
  that the data be archived in machine readable
  format and that any professional article be
  accompanied by a disk describing the underlying
  data. Also, a comprehensive reference service for
  computer-readable data should be developed.
  Master directory
• In addition, peer review of articles and
  proposals has been constrained by the difficulty
  of gaining access to the data used for the
  analysis. If writer were required to make their
  primary data available, reviewers could repeat at
  least part of their analysis reported. Such a
  review would be more stringent, would demand more
  effort from reviewers and raises some operational
  questions that need to be resolved. but
  arguably lead to more careful checking of
  published results.
• Underlying difficulties in information storage
  and retrieval are problems in the institutional
  management of resources. . Who is to mange,
  maintain, and update info services.? Who is to
  create and enforce standards? At present, the
  research community has tree alternatives federal
  government which manages resources as MEDLINElt
  and GenBank professional societies such as the
  American Chemical Society which manages the
  Chemical Abstracts Service and non-profit
  organizations such as Institute for Scientific
  Information.

39
Information Technology and the Conduct of
Research The Users ViewNational Academy Press,
Washington, D.C. 1989

• Recommendations
• Institutions supporting researchers must develop
  support policies,services standards for better
  use if info technology. The institutions are
  Universities, University Departments, Funding
  Agencies, Scientific Associations, Network
  Administrators, Info Service providers, Software
  vendors and professional groups
• The Federal Government should support software
  development for scientific research. The software
  should meet standards of compatibility,
  reliability, documentation and should be made
  available to other researchers.
• Data collected with government support rightfully
  belong to the public domain. with reasonable time
  for first publication should be respected.
• There is a pressing need for more compact form of
  storage
• Tool building for non-defense software should be
  encouraged
• The Federal Government should fund pilot projects
  to on information storage and dissemination
  concepts in selected disciplines and implement
  software markets with emphasis on the development
  of generic tools useful for multiple disciplines.
• The institutions lead by the federal government
  should develop an information technology network
  for use by all qualified researchers.

40
Measuring for Environmental Resultsby William K.
Reilly, EPA Journal, May-June 1989

• A key element in any effort to measure
  environmental success is information--information
  on where we've been with respect to environmental
  quality, where we are now, and where we want to
  go. Since its beginning, EPA has devoted a great
  deal of time, attention, and money to gathering
  data. We are spending more than half a billion
  dollars a year on collecting,, processing, and
  storing environmental data. Vast amounts of data
  are sitting in computers at EPA Headquarters, at
  Research Triangle Park, North Carolina, and at
  other EPA facilities across the country.
• But having all this information--about air and
  water quality, about production levels and health
  effects of various chemicals, about test results
  and pollution discharges and wildlife
  habitats--doesn't necessarily mean that we do
  anything with it. The unhappy truth is that we
  have been much better at gathering raw data than
  at analyzing and using data to identify or
  anticipate environmental problems and make
  decisions on how to prevent or solve them. As
  John Naisbitt put it in his book Megatrends "We
  are drowning in information but starved for
  knowledge."
• Our various data systems, and we have hundreds of
  them, are mostly separate and distinct, each with
  its own language, structure, and purpose.
  Information in one system is rarely transferable
  to another system. I suspect that few EPA
  employees have even the faintest idea of how much
  data are available within this Agency, let alone
  how to gain access to it. And if that is true of
  our own employees, how must the public feel when
  they ponder the wealth of information lurking,
  just out of reach, in EPA's huge and seemingly
  impenetrable data bases?
• The strategic information effort I have
  described, however, will require a new attitude
  on the part of every EPA program manager--a
  willingness to break out of the traditional
  constraints of media-specific and
  category-specific thinking.
• Just as important, we must find ways to share our
  data more effectively with the people who paid
  for it in the first place the American public.
  Eventually, as EPA makes progress in
  standardizing and integrating its information
  systems, the information in those systems--apart
  from trade secrets--should be as accessible as
  possible. Such information could be made
  available through on-line computer
  telecommunications, through powerful new compact
  disc (CD-ROM) technologies, and perhaps a
  comprehensive annual report on environmental
  trends.
• Sharing information with the public is an
  important step toward establishing a common base
  of understanding with the American people on
  questions of environmental risk. As the recent
  furor over residues of the chemical Alar on apple
  products shows, there can be a wide gap between
  public perceptions of risk and the degree of risk
  indicated by the best available scientific data.
• EPA must share and explain our information about
  the hazards of life in our complex industrial
  society with others--with other nations, with
  state and local governments, with academia, with
  industry, with public-interest groups, and with
  citizens. We need to raise the level of debate
  on environmental issues and to insure the
  informed participation of all segments of our
  society in achieving our common goal a cleaner,
  ,healthier environment.
• Environmental data, collected and used within the
  strategic framework I have described, can and
  will make a significant contribution to
  accomplishing our major environmental objectives
  over the next few years. Strategic data will
  help us
•         Create incentives and track our progress
  in finding ways to prevent pollution before it
  is generated.
•         Improve our understanding of the complex
  environmental interactions that contribute to
  international problems like acid rain,
  stratospheric ozone depletion and global
• warming.
•         Identify threats to our nation's ecology
  and natural systems--our wetlands, our marine
• and wildlife resources--and find ways to reduce
  those threats.
•         Manage our programs and target our
  enforcement efforts to achieve the greatest
• environmental results.

41
USES OF ENVIRONMETAL DATA

• Environmental data are used for many purposes.
  They may be to support environmental management
  or to the good of the society by by deriving more
  general environmental knowledge
• Provide Historical Record
• Identify Deviation from Expected Trend
• Anticipate Future Environmental Problems
• Provide Legal Record
• Support Environmental Research
• Support Environmental Education
• Support Communication
• Record Monitoring and Control Procedures

42
Taylor Model

• Taylor Model
• One of the specific tools employed by the staff
  of University Library was the Taylor Model.5
  Taylor's model is a theoretical model and is not
  predictive. The University Library adapted it as
  a working tool and, in turn, adopted the concepts
  of "value-adding" and the importance of
  Information Use Environments as critical guiding
  principles in the constr