Communicating Digital Library
Services to Scientific Communities
Ying Liu
Computing Science
Department, University of Aberdeen
Kings College, Scotland,
UK
Anita S. Coleman
School of Information
Resources and Library Science
University of Arizona at
Tucson, USA
Abstract.
To date, systematic approaches to the provision of and
infrastructure of digital library knowledge services have not been able to
model accurately the communication, protocols, and discourse practices in
science disciplines. On one hand,
there is a growing need for collocation and retrieval of scientific works
online and not merely for services based on known-item identification. On the other, infrastructures for collection, such as the
web service, ontologies, or the semantic web, need specialized knowledge. We argue that these infrastructures are not
only limited to the web; they have
also introduced new systematic contextures constraining communication in
scientific communities. To assist the
process of developing a better contexture for such types of knowledge, we
report the lessons learned from the Alexandria Digital Library project and use
water resources management as a given domain of geo-referenced knowledge to
understand and develop library-user scenarios.
This research is a pre-requisite for the development of knowledge
services in a problem-solving environment.
1 Introduction
Recent advances
in digital technologies, storages, and Web-driven infrastructures enable us to
digitise, archive, and visualise informational materials on the Internet. These
technologies have created many new opportunities as well as problems in
organising digitized materials through gateways and repositories (all of which
we are concerned with here in terms of digital libraries).[1] Many researchers have conceded that the
emphasis on digital library technologies and tools is out of step with the
prevalent modes of user-communities for the processes of capturing, sharing,
determining, and utilising information.[2] According to a recent report (Lyman et al.
2002), the world currently produces between one and two billion-billion 8-bit
bytes of information each year. Most of
this information is in the form of images, sound, and numeric data. Digital textual documents account for only
0.003% of the total.
Indeed, for a
scientific community of users, little of the information seems to be made
available through digital library collections as most digital items are
file-based artefactsartifacts, i.e., packages of numerical
models, large datasets, geo-referenced images, statistical data tables,
etc. This has recently led some
information scientists to shift the research focus to a new theme, i.e., to
catalogue and index scientific items by treating scientific models as works,
and, in turn, to classify and index the works as entities for knowledge
retrieval.[3] Historically
there have been intensive studies in the construction of catalogs with the
focus on known-item identification and retrieval. Little attention has been paid to the collocation and retrieval
of like-items, related materials called works. A focus on works will help to advance
understanding of the role works play
in facilitating knowledge construction and in the importance of the work entity
as key to the construction of bibliographic and other databases or internet
search engines. New prototypes of
digital library knowledge services are under research and development, e.g.,
the THREDDS (the THematic Real-time Environmental Distributed Data Services)
in Benedict et al. (2003), or DLSI (Digital Library Service Integration)
projects (DLSI homepage, Chen et al. 2003).
For this line of research, we need to know how to collect materials in
support of a user community, why the materials are collected, and how people
actually use those materials.
This paper intends to show some contexts that critically affect digital
libraries with services intended to facilitate the communication of scientific
knowledge. We take water resources
management as a given domain of geo-referenced knowledge for potential digital
library users. The notion of “contexture” usually means “a weaving together of
parts; structural character of a thing; system; constitution; texture” (Online
Dictionary). Because
the contexts in question are systematically inter-related, the notion now has its
coherent place in internet-based infrastructures communicating to a scientific
community in a given problem-solving environment.[4] See Fig. 1 indicating our research
methodology. The rest of this paper is
organised as follows.
We first share the experiences and lessons learned from Alexandria
Digital Library (ADL). ADL is a
well-known geo-referenced digital library project funded by NSF, DARPA, and
NASA since 1994. We show how an infrastructural contexture constrains
the scalability of web-based information services and why ADL has been rapidly
updating itself with new infrastructural technologies but reporting little for
scientific usability of its large cartographic collection - now totalling more
than 4 million individual items according to Chen (1998, pp 197). In designing several user scenarios for
water resources management, we further discuss how a knowledge contexture determines
the system's knowledge classification through different knowledge
sub-domains in the users’ disciplinary communication and information flow
systems. Such a knowledge contexture
can be seen in discussion about the hydrological system, one of the most vital
elements in water resources management (Obasi 1996). Finally, we briefly consider the relationships between the
revealed infrastructural and knowledge contextures and draw some conclusions.
Fig. 1. Digital Libraries and Communities
2 Infrastructural Contextures: Lessons Learned from Alexandria Digital
Library
In considering the flow of information and knowledge
(Dretske 1981), the goals of developing a digital library (Chen
1998) are not different from the purposes of having a traditional
library (Chen op.
cit., Dretske
1981, Kemp
1976):
a) both provide information to users by
collecting items;
b) both serve as
disciplinary and general human-communication systems by collecting and
organizing the items of knowledge;
c) both depend for their
existence on the users’ needs and uses for the communication, construction, and
production of knowledge that take place in a social or other community setting.
The two, however, are clearly distinguished by
completely different technological origins - one consists foremost of printed
materials originating from print technology; the other is comprised foremost of
items existing only in digital form, thereby originating from computer
technology. This has determined two disparate sets of contextures: the printing
items move around social communities as Figure 2 indicates; the digital items
move through life cycles in internet computing environments where Web services,[5]
Web Ontology,[6] or Metadata[7]
are based, to name but a few (see Figure 1).
Such a Web system has become an increasingly integral element of our
social infrastructure. Thus, the
complexity of a digital library service stems not only from it’s infrastructure
but also from the contexture from which its’ infrastructure augments, where we
classify digital items we intend to collect and use. But before we illustrate this by ADL examples, it is necessary to
define and clarify the new meanings that go beyond the ordinary notion of
infrastructure.
Fig. 2. Traditional
Libraries and Communities
2.1 The Notion
of Infrastructure
Star (2002) notes that infrastructure, often referred to as
a list of technical specifications, black boxes, places, wires, plugs, roads,
bridges, stations, etc., appears to be singularly boring as an object for
scientists to study. Infrastructuring
is usually seen as engineering work, the establishment of public services and
utilities for societies and communities.
Roads, railways, bridges, pipelines, and electricity are all instances
of public, social infrastructures. Now
people also use the term infrastructure to refer to any substructure or
underlying system: most notably the information superhighway - the global
information and communication infrastructure that includes the Internet, WWW,
telephone networks, cable or satellite communication networks.
As we know, the Web is a collection of interlinked
electronic items including documents, texts, images, music files, video, etc.
hosted on servers all over the world, mostly on HTTP (Hypertext Transfer
Protocol) servers (Schatz et al. 1994).
The Web lives on the Internet by a set of protocols running over the
net. Although the Web is a part of the
net, the net is much larger than the Web.
The net also hosts e-mail, FTP (file transfer protocol), peer-to-peer,
VPNs (Virtual Private Network), telephony, automated sensors, wireless
networks, and more.
In software engineering, the Web has driven
Java-enabled technologies. In turn,
large scale meta-computing such as the Grid[8]
embraces the net-centric technologies by adding more tiers on top of Java-based
systems. Java can indeed “write it
once, and run everywhere.” But without
an infrastructure of Web-tiers and net-layers, it may run all over the place
for nothing or nowhere at all. This infrastructural impact has fundamentally
influenced software life cycles[9]:
specification, design, coding, debugging, operation, and maintenance.
An electronic infrastructure is a set of Web-based
software components and data network elements that are inter-operated and work
together to support online events of data/information exchange. Typical infrastructural components are Web
servers, application servers, APIs (Application Programming Interfaces), protocols,
portals, repositories, databases, application-ware, middle-ware, PCs, work
stations, electronic appliances, and programming languages. On top of these layers, a digital library
infrastructure may include (Chen, op. cit. 1998):
a) systems and
components for discovering, distributing, indexing, cataloguing, storing,
retrieving, etc.,
b)
social and technical communication, and
c)
tiers of distributed software architectures that realise
these functions.
2.2 Infrastructures and Tag Contextures in ADL
ADL is a prototype geo-referenced digital library intended
primarily to serve a research audience (ADL homepage). It is a well-known system, a product of one
of the six initial digital library projects funded by NSF, DARPA, and NASA. Its collection and services focus on
geographical information: maps, images, geo-referenced data sets with text, and
other information sources with links to geographic locations. It is a large collection of geo-referenced
items that are organized and maintained in digital formats within complex
Web-based infrastructures so that an end-user can access and explore the items.
ADL has had three successive versions:
a)
a rapid prototype system comprised of a relational database
of map and imagery metadata accessed through a desktop GIS (geographic
information system, Frew et al. 1995);
b)
a Web prototype
system that replaced the stand-alone
GIS with an HTTP server, generating an HTML forms-based user interface
accessible via the Web (Frew et al. 1997);
c)
the ADL integrated
system that extended the HTTP server into full-fledged middleware, supporting
HTTP interfaces to multiple clients and collections to multiple HTTP catalog
databases (Frew et al. 1999).
In spite of these experiments with changing infrastructures,
the research outcomes have a questionable impact on their research methodology and
conceptual frameworks driving the design. According to the chief
architect (Janėe 1999 & 2003) as well as others, the development of
the ADL and its services require
substantial fundamental, infrastructural modifications (Boxall 2002). The problems are summarized in Tables 1
& 2. The two categories of problems
are not only inter-related but also related externally to a service that is
summarized in Table 3. Currently, ADL
has evolved to become ADEPT (Alexandria Digital Earth Prototype) in the second
phase of the development (from 1999 to 2004).
ADEPT expands the use of ADL-features into new fields, e.g., classroom
based geo-referencing e-learning applications.
The focus of ADEPT has thereby broadened from a geospatial library to an
integrated environment for managing, querying, and presenting geospatial
information. This can be seen as a list
in Table 4. ADEPT is involved in
collaboration with new research partners for the development of new
applications. For example, Kent State University is developing a structured
database of scientific concepts for organizing, accessing, and using learning
materials that ADEPT will provide (Coleman, et al 2001, Smith et al. 2002). ADEPT will be able to “plug-in” Iscapes
(information landscapes) that the University of Georgia is developing, in which
ontological environments are based on the Earth metaphor lscapes (Isdis
homepage). ADL, however, has proved to
be very costly in terms of finance, human resources, and technology
investment. More important, there are
few references available to show how digital library research may tackle the
fundamental problems inherent in tag-infrastructural contextures of digital
libraries.
Table 1. Service Layer
Problems
|
S-Category: a service layer is completely missing
(S) |
|
|
S-Tags-Problem |
ADL services are too simplistic. Indeed, a search
is one “shot” followed by a stream of results back. Because of the next problem, a query is limited to the capacity
of what one XML protocol tags. |
|
S-Tags-Semantics-Problem |
The semantics of all the metadata are both unknown
and unknowable to ADL system. |
Table 2. System
Operational Problems
|
O-Category: no inter-operations between content
holding resources (O) |
|
|
O-Metadata-Content-Problem |
Metadata are formulated after the contents are identified by URLs;
thus, (see below) |
|
S-Metadata-Content-O-Finding-Problem |
Servicing the content behind those URLs is out of the question. |
Table 3. Integration
Problems
|
I-Category: no integration with other tools and services (I) |
|
|
I-External-S-Plug-in-O-Problem |
ADL’ s single, monolithic components support no inter-operation with
other tools and services. There is no new metadata standard adaptation, no
service layer, and therefore no component to be plugged either in or out. |
Table 4. More New
Components
|
Solutions (S: service layer, O: operation, I:
external integration, SYS: internal integration) |
|
|
S-O-Repository-Component |
collection registry, content repository |
|
O-Metadata-To-Metadata-Component |
metadata mapper, harvest loader, item tracker |
|
SYS-S-O-Component |
collection aggregator |
3 User Scenarios
The tag-infrastructural contextures that constrain
scalabilities of knowledge-retrieval services have to be understood in the
knowledge contexture in which digital items are created, edited, described,
indexed, or used. Furthermore, a knowledge contexture is not only interwoven
within community-based communication among creators and other owners, but it is
also embedded among individual or institutional users. (The following river authority board is an
example that attempts to describe this).
In this section, we outline a few user scenarios in order to explain the
nature of knowledge that is sought or that underlines the use of information
resources. In the next section, we
further define and discuss the knowledge contexture.
What follows
is a set of four related processes (described as user scenarios) enabling a
final decision to be made in water resources management. The user scenario is designed to “identify
the functions that the system should deliver, how these may be displayed to
users, what parameters of the human-computer equation should be satisfied, and
so on” (Crabtree et al. 1997).
Scenario I
Mark is a project engineer working for a local council. The council plans to build a huge holiday
park near a river. Among many other
environmental impacts to consider, Mark needs to initiate a set of conditions
defining the amount of water the central park needs each year, including the
amount, source, and times of discharge.
Based on this information and prior to granting Mark a consent of water
supply, the River Authority assesses if the requested discharge will have a
significant detrimental effect on the river network downstream of the discharge
point. To initiate this process, Mark
logs into his laptop and gets connected to his computer station at his
work. He opens the plan of the holiday
park and begins to review the estimated figures and browse the maps.
Scenario II
Mark finally finishes his report and drags it to the River
Authority’s working folder. The
Authority opens his report, browses the maps Mark has attached, and begins to
retrieve the water-related objectives.
These have been archived in a digital library. The Authority puts the information together and calls for a
meeting with Mark and Kelly, another environmental engineer.
Scenario III
In the meeting, Kelly reviews the references provided by the
Authority, logs into the digital library, and obtains an ecological model of
the watercourse and downstream abstractions that have been used before in that
region. She explains to Mark that an
abstraction license may not be granted because the downstream area will likely
be affected in summer seasons in the years to come.
Scenarios IV
Eventually the Authority, Mark, and Kelly agree that the
plan has no potential breaches of legal restrictions on water quantity and
quality and that all water-related objectives can be achieved. The Authority grants a consent to the city
council and registers this case in the digital library.
4 Knowledge Contexture in Communication
The above scenarios describe a typical problem-solving
process in a scientific community in which
a) online archiving
of scientific works is highly desirable;
b)
digital libraries have to cope with online raw data which
are semi-structured and file based;
c)
components of scientific works
are often formatted datasets, file-based, and operated instrumentally;
d)
components of scientific works
are usually acquired or systematically derived from a scientific simulation,
modeling environment, or data instruments; and
e)
a considerable degree of infrastructural transparency
between computing-system components and their integration with users’
workplaces is required.
Examining the communication patterns in the scenarios above,
we find that the layers of communications are based on levels of knowledge in
terms of natural or modelled systems in
which water resources management is primarily related to spatial water bodies,
an atmospheric water cycle such as snow or rainfall, and the dynamic processes
of human and social uses of water (Obasi et al. 1996).
The management involves a system called a relatively
closed hydrological system (C).
Hydrology is the science that deals with processes governing the
depletion and replenishment of water resources of the land areas of the
earth. The long journey of events
marking the process of a particle of water from the atmosphere to the land
masses and oceans and its return to the atmosphere is termed a hydrological
cycle. If the cycle is solar powered
and energy but not matter (water) is exchanged with the outside environment,
then the cycle is called a relatively closed hydrological cycle. If the cycle involves the exchange of
neither energy nor matter with the outside environment, it is called a closed
hydrological cycle.
If we define the geologic formation of water storage
as W and the set of the natural environmental elements and its effects as
system M, the level of the knowledge about the cycle can be denoted by the
interaction Io between W and N as C: {W, M}.
Management involves a water resource system (WR). The
natural setting of the cycle is not simple.
It is a result of interaction between man and the hydrologic
environment: few river systems are unregulated by surface-storage facilities;
atmospheric scientists are actively pursuing the goal of augmenting and
controlling precipitation and other objectives. In short, considering only the natural hydrologic phenomena is no
longer suitable. If we define the set
of the artificial environmental elements and its effects as a system AE,
another level of the knowledge about water resources system can be defined by
the interaction I1 between C and AE as WR: {C, AE}.
From a knowledge engineering perspective, WRM is
concerned with discrepancies that occur when the information related to the
major components of the system are compared with established criteria in order
to predict and understand the impacts of any action taken to control, manage,
and use water. We can then define the
interaction (I2) between WR and the criteria system (CR). CR consists of its subsystems: the system
that deals with human demands for water for survival and the effects on social
systems; the system that deals with environmental demands, such as living
plants and living organisms needing water, and the effects on environmental
systems; the system that deals with fast-developing economic needs, such as
industry and agriculture, demanding water, and the effects on economic systems,
etc. Now we have the third level of
knowledge about water resources system as WMR: {WR, CR}.
The most important point here is that the different
levels of knowledge form varying contextures as Table 5 shows.
Table 5. Instances of
Knowledge Contextures
|
C contexture (I0) |
The knowledge that relates to the results of interaction
between the geologic formation of water storage (W) system and the set of
natural environmental elements (N). |
|
WR contexture (I1) |
The knowledge that relates to the
results of interactions between C system and a set of artificial
environmental elements (AE). |
|
WM contexture (I2): |
The knowledge that relates to the results of interaction
between WR system and CR system. |
Formal scientific communication is
made up of the documentation and dissemination of concepts through works such
as scientific models (Coleman 2002, Papazoglou et al. 1999).
Models are well-known intellectual entities critical in the creation and
transmission of scientific knowledge (if not all human knowledge). Communication of scientific knowledge is
comprised of up to three levels of reasoning (Morrissey 2002):
a)
data and empirical observations
(phenomenology);
b)
information, formulas, trends, and
predictions generalized from data or derived from hypothesis and theory
(description); and,
c)
paradigmatic axioms, models,
metaphors (explanation).
5 Some Open Issues
The importance of a coherent
infrastructure-knowledge contexture in digital libraries relies on the
representation of knowledge organized as works. Appendix III shows how this may be partially accomplished through
metadata in which the levels of infrastructure detail are integrated in an
online knowledge service. Similarly, cross-browsing and cross-searching are
operated through subject gateways based on meta-data (Dublin Core homepage,
Z39.50 Document, Koch 2000, Hydrological metadata).
If, however, we consider the
scenarios presented in section 3, it is more likely for the planner to find a
real-world entity (i.e., body of water) by matching the same concept in other
related works. For this kind of
integration, metadata (even with the use of controlled vocabularies) may be too
broad. Metadata and controlled
vocabularies such as thesauri are inadequate for representing and
computationally enforcing explicit formalizations of the mental concepts that
people have about the real world. For
example, a body of water can be a lake that serves as recreation or as a
habitat for specific species. Therefore,
a special concept or name must refer to the lake. People perform such mental operations
based on associations, roles, and relationships all the time.
Web ontology represents
entities in hierarchies rather differently (see Appendix II). The choice of hierarchies as the
representation of ontology, however, leaves us with new problems. How can
ontology relate to a level of infrastructure as well as to knowledge
contexture? For example, a lake can be
an object at all the three levels described in Table 5: it can be an element in
the water-storage category or it can be an element in a criteria system
because, for the Parks and Recreation Department, the same entity is a
lake. Thus, in a problem-solving
environment, geo-referenced ontology must be designed with different views for
the same geographic phenomenon. This is
similar to an object that has an identity and can play different roles (Hornsby 1999, Pernici 1990, Albano et al. 1993, Wong et al. 1997, Fonseca et al. 2002).
6 Conclusion
Digital library knowledge service is not just a description
of the content of a digital item by a single unadorned URL suggesting “click
here.” There
is a growing need for collocation and retrieval of scientific works in a problem-solving environment, not merely
for the services on known-item identification based on subjects of disciplines.
Consequently, an internet-based
infrastructure is not only about creating a new computing platform but is also
about augmenting a systematic contexture for communication in a problem-solving
environment. For scientific users,
online archiving of referenced scientific data is highly desirable. Digital libraries, therefore, have to cope
with online raw data that are semi-structured or file based. Referenced electronic data are formatted
datasets and operated instrumentally.
These datasets are usually acquired or systematically derived from a
scientific simulation or modelling environment. Thus, a considerable degree of infrastructural transparency
between computing system components and their integration with users’
workplaces is required. Within these contextures,
retrieval of a work on the Web requires digital
library services to be able to cross levels of domain knowledge and to include
observation or experiment abstractions and measurements within a given
problem-solving environment.
Appendix I Examples
of Web Services
Web services are the latest software components and
technologies designed to bring us online for “what we need, when we need it via
any device we choose and access.” Using
a Web service, one can run or interact with an application without the
application’s being present on the user’s machine. This calls for an increase in infrastructural effort. Indeed, in providing Web services for
geo-referenced digital libraries, since we not only have more sophisticated
end-users, we need a set of more dedicated Web services. The two web service cases described below
indicate how an infrastructure determines the scalability of a given
application. In the IBM case, the
classic client-server-DB approach is suitable to well-structured data services;
the Sun situation, however, calls for standards and open services such as more
platform portability versus front-end and back-end couplings, more
inter-operations between applications versus databases mediation, and more
resource transformation engines versus data-bits transaction servers.
IBM Phone Book Web Service
|
Service Definition |
To bring the phone book service to the Web, IBM organizes materials in
terms of service categories. Data-centric services are request-driven
wrappers for the relational databases. This type of service would typically
be implemented as a servlet and/or Enterprise JavaBean (EJB) in a J2EE
environment, which maps names to phone numbers and provides get and set
operations for accessing the data. Process-oriented
services
perform complex operations that are network-requests spanning or existing
outside of individual user requests. Value-added services range from retrieving
the phone number to searching for extraterrestrial information, e.g., through
a phone number of a local restaurant to all the other entertaining programs
around. |
|
Main Infrastructural
Components |
Web servers, clients, relational databases, JavaScripts, API-clients,
JavaScripted Objects, TCP/IP, HTTP, XML, SOAP. |
|
Infrastructural
Dependencies |
Client-Server model,
general purpose databases, hosting stores, additional owner stores,
analytical engines, application logics. |
Sun Microsystems Cost-effective Web
Service
|
Service
Definition |
Services
on demand. The Brazilian National Healthcare System has developed a java-based
solution to 20,000 clinics serving over 12 million patients. These
applications run E10K servers using sophisticated Java-based security
implementation. Information is fed through a hierarchy of regional servers
from hospitals, clinics, and pharmacies. Every patient-clinician interaction
is recorded in the system, including X-rays, EKGs, endoscopic video exams,
and prescription information. Physicians have access to all patient
information via patient medical ID card that will be replaced by JavaCard. Coordinated-Negotiated
services.
Services that cross preserve data providers, long term solution evolution,
and equipment lines. Much data is streamed directly from instruments. |
|
Main
Infrastructural Components |
Business systems, Web servers, application servers,
browsers, portals, browser clients, workstations, laptops, cell phones, PDAs,
repositories, XML/SOAP, RDF, J2EE, servers( XML, JAX-RPC, JAXM, JAXB, JAXP),
JDBC, JMS, JAXR, DB servers. JAX-RPC Programming with SOAP JAXM Message oriented middleware, using
JMS JAXP Process the low-level contents of
an XML JAXR Access the UDI or ebXML registry to
advertise or discover a service JDBS Java databse access APIs |
|
Infrastructural
Dependencies |
Packaged
Service Creation Tools With Standard Protocols: JavaCard, Jini, JXTA, other
Grid Third Parties |
Appendix II: Web Ontology
Ontology has a long history in philosophy, in
which ontology is the study of a systematic account of beings, or existence of
things, or to being in the abstract as a “reality.” Science has shown a reality that is structured all the way
down. At bottom it consists not of four
types of gunk—earth, water, air, and fire—but rather of a finite number of
definite particles, lawfully related one to the other (Zimmerman 1996). Thus it is believed that we might build up
substantial information about our world from the elementary information we find
at the bottom of reality. So, ontology, in terms of philosophy, is a theory of
arguing and explaining.
The term ”ontology” has been used for a
number of years by the artificial intelligence and knowledge representation
community. It is now becoming a part of
the standard terminology of a much wider community that includes object
modelling and XML (Nambiar et al. 2002).
The key ingredients that make up ontologies are vocabularies of basic
terms and a precise specification of what those terms mean so that they can be
enforced using first order logic. Numerous researchers believe that the ontological
approach can help develop useful tools (Fensel 2001):
a) Ontology
is more than a controlled vocabulary.
It provides a set of well-founded constructs that can be leveraged to
build meaningful higher-level knowledge.
The terms in ontology are selected with great care, ensuring that the
most basic (abstract) foundational concepts and distinctions are defined and
specified. The terms chosen form a
complete set, whose relationship one to another is defined using formal
techniques. It is these formally
defined relationships that provide the semantic basis for the terminology
chosen.
b) Ontology is more
than a taxonomy or classification of terms.
Although taxonomy contributes to the semantics of a term in a
vocabulary, an ontology includes richer relationships between terms. It is these rich relationships that enable
the expression of domain-specific knowledge without the need to include
domain-specific terms.
Appendix III: Example of Metadata about
Identification Information
|
Identification_Information: |
Citation: |
Citation_Information: |
Originator: Publication_Date: Title: Geospatial_Data_Presentation_Form: Publication_Information: Publication_Place: Publisher: Other_Citation_Details: Online_Linkage: |
|
Description: |
Abstract: Purpose: Supplemental_Information: Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: Ending_Date: Currentness_Reference: Status: |
||
|
Supplemental_Information: Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: Ending_Date: Currentness_Reference: Status: Maintenance_and_Update_Frequency: Spatial_Domain: Bounding_Coordinates: West_Bounding_Coordinate: East_Bounding_Coordinate: North_Bounding_Coordinate: South_Bounding_Coordinate: Keywords: Theme: Theme_Keyword_Thesaurus: Theme_Keyword: |
|||
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