|
The dividing line between this glossary and Algorithms
& data analysis is very fuzzy. In general this one focuses on unstructured data
(or a combination of structured and unstructured), while Algorithms
centers on structured data Finding guide to terms in these glossaries Informatics
Map Site
Map
Informatics includes Bioinformatics
Computers & computing In silico & Molecular Modeling
Ontologies & Taxonomies
are subsets of, and critical tools for Information management &
interpretation
Technologies Microarrays & protein chips
Sequencing
Advances
in biology and new high-throughput technologies are generating massive amounts
of data that overwhelm the current information technology infrastructure. The
challenge is to build a common capability that enables a more efficient
translation of data into knowledge that leads to new and effective treatments.
caBigTM and Molecular Medicine, NCI, NIH http://cabig.cancer.gov/molecular/overview.asp
Google = "data analysis" about
1,420,000 as of July 23, 2002; about 4,480,000 as of Sept. 23,
2004; about 16,000,000 Nov 18, 2009
"data interpretation" about 58, 200 July 23, 2002;
about 147,000 as of Sept. 23, 2004; about 928,000 Nov 18, 2009
3D technologies:
Visual
communications are pervasive in information technology and are a key enabler of
most new emerging media. In this context, the NRC Institute for Information
Technology (NRC-IIT) performs research, development and technology transfer
activities to enable access to 3D information of the real world. Research in the 3D Technologies program focuses on three main areas: Virtualizing Reality
and Visualization, Collaborative Virtual Environments, 3D
Data Mining and Management [Institute for Information Technology, National
Research Council, Canada, 3D Technologies] artificial intelligence: Algorithms
& data analysis
Google = about 1,120,000 July 19,
2002; about 3, 040,000 Oct. 22, 2004
BIRN Biomedical Informatics
Research Network: http://www.nbirn.net/
bias:
One of the two components of
measurement error (the other one being variance). Bias is a systematic
error that causes the measurement to differ from the correct value. Since bias
is systematic, it affects all experiment replicas the same way.
bibliomining:
The combination of data mining, bibliometrics, statistics, and reporting tools
used to extract patterns of behavior- based artifacts from library systems.
Scott Nicholson, Bibliomining: Data Mining for Libraries, Syracuse Univ. US http://www.bibliomining.com/
bioinformatics
visualization: BIoinformatics
biomedical computing: Computers
& computing
Google = about 11,800 July 19, 2002;
about 20,900 Oct. 22, 2004
biomedical
informatics: caBIG®
stands for the cancer Biomedical Informatics
Grid®. caBIG® is an information network
enabling all constituencies in the cancer community – researchers, physicians,
and patients – to share data and knowledge. The components of caBIG®
are widely applicable beyond cancer as well. The mission of
caBIG® is to develop a truly collaborative information network that
accelerates the discovery of new approaches for the detection, diagnosis,
treatment, and prevention of cancer, ultimately improving patient outcomes.
National Cancer Institute, NIH, US About caBig, 2008 https://cabig.nci.nih.gov/overview/
Google
about 66,600 Oct. 22, 2004; about 388,000 Nov 18, 2009
BIONLP.org: Bioinformatics
biopharmaceutical informatics:
Drug
companies go through a very arduous and regulated discovery, applied research,
and development process- typically spanning five years of laboratory research and
ten years of clinical studies .. multinational clinical studies, which need to
be done with tremendous precision over a very long period of time. The study
parameters must be identical for every patient (many times numbering 10,000
patients, followed for five or more years), and all the participating hospitals
essentially have to behave in exactly the same way for the trial to be valid. ..
The
life science industry is conservative by nature, and therefore it is a late-
adopting industry. It is very sensitive to standards because of the legacy
according to which these companies have to maintain data and information. Major
pharmaceutical companies typically adopt a 100-year minimum document retention
policy, ...each of the industry's four industrial sectors - the pharmaceutical,
the biotech, the medical device, and the diagnostics sector - has a different
set of needs and desires, as well as its own requirements for unique IT
solutions. ...
Life science companies are dealing with very large computational data sets. Some
are now approaching half terabyte sizes and upward Life science companies also
immensely concern themselves with security, because their data represent their
crown jewels. Other major concerns expressed by this industry include the
stability, scalability, and security of an operating environment. Life science
companies and regulatory bodies such as the FDA are more concerned than ever
with operating environments that decay with use: When under computational
stress, these fragile operating systems have a habit of crashing, and when these
systems crash, they tend to corrupt data. ...
Post-genomic,
proteomic, chemical information, and other data sets have created a major
appetite for solutions to deal with this tremendous amount of data. Scientists
are now asking their IT professionals for the ability to better conceptualize
and interpret the meaning of this vast information. To do this, scientists need
tools for 3D visualization with a tremendous degree of high definition and
accuracy. The next step is to take disparate data sets, render them into 3D
values, see the DNA and RNA interface, watch protein folds, and then put a
therapeutic small molecule in there and see how it relates within a virus that
environmentally influences a different process. Scientists Are
Demanding Solutions for Dealing with the Post-Genomic, Proteomic, and Chemical
Data Deluge: An Interview with Howard Asher, Director, Global Life Sciences
Group, Sun Microsystems, CHI GenomeLink 30 http://www.healthtech.com/newsarticles/issue30_1.asp
Biosemantics
Group:
http://www.biosemantics.org/
Addresses concept identification and disambiguation algorithms, meta-analysis
and visualization techniques, and biological applications [interconnect genes
and proteins, semi-automated annotations of protein functions.] Medical
Informatics department of the Erasmus MC
University Medical Center of Rotterdam and the Center
for Human and Clinical Genetics of the Leiden
University Medical Center.
blog:
Wikipedia http://en.wikipedia.org/wiki/Blog
Related
terms: blogging, blogosphere, microcontent, nanopublishing, weblog
blogging:
In
the beginning - say 1994 - the phenomenon now called blogging was little more
than the sometimes nutty, sometimes inspired writing of online diaries. These
days, there are tech blogs and sex blogs and drug blogs and onanistic teenage
blogs. But there are also news blogs and commentary blogs, sites packed with
links and quips and ideas and arguments that only months ago were the near-
monopoly
of established news outlets. Poised between media, blogs can be as nuanced and
well-
sourced
as traditional journalism, but they have the immediacy of talk radio.
Andrew Sullivan, "The blogging revolution" Wired Magazine, May 2002 http://www.wired.com/wired/archive/10.05/mustread.html?pg=2
CML Chemical Markup Language:
Chemoinformatics
classification:
Involves the development and use of a scheme for the systematic organization of knowledge. (Taylor p 576) Arlene Taylor identified three approaches to
classification: enumerative, hierarchical, and analytico- synthetic. Enumerative classification attempts to assign headings for every subject and
alphabetically enumerates them. Hierarchical classification uses a more philosophical approach based on the inherent organization of the
subject being classified, and establishes logical rules for dividing topics into classes, divisions, and subdivisions.
Analytico- synthetic classification assigns terms to individual concepts and provides rules for the local cataloger to use in constructing headings for composite
subjects. Traditional classification systems in this country are basically enumerative, though many contain some elements of hierarchy and
faceting. (Taylor pp 319- 321) Amanda Maple, "FACETED ACCESS: A REVIEW OF THE LITERATURE"
Working Group on Faceted Access to Music, Music Library Association Annual Meeting, 10 February 1995 http://theme.music.indiana.edu/tech_s/mla/facacc.rev
Indexing in the
library and information management sense. See also classification, classifiers
classification:
Can
be done manually by human experts or automatically by software of many different
types. However, the term as used in the microarray
field has a more specific meaning: It always refers to automatic methods, and
usually means automatic methods in which the classifier is built by adjusting
parameters of a general model. These methods are sometimes called supervised
computer- learning methods, in contrast to unsupervised methods, such as
clustering.
classifier:
A decision procedure that categorizes data into two or more predefined groups.
Classifiers are also called predictors. Classifiers usually emit a score that
can be interpreted as the likelihood that the data fall into a certain category,
rather than just a binary yes/ no answer. In many applications it is necessary
to convert this likelihood into a yes/ no answer, or perhaps a yes/ no/ maybe
answer, typically through a simple thresholding scheme.
collaborative filtering:
Tools that leverage user preferences, patterns, and purchasing behavior to customize organization and navigation systems. [Peter Morville "Software for Information Architects" Argus Center for Information Architecture, 2000]
http://argus-acia.com/strange_connections/current_article.html
Amazon's recommendations based on what other buyers of a specific title are
buying is a familiar example of collaborative filtering.
Google = about 21,600
July 19, 2002; about 49,300 Oct. 22, 2004
collaborative
metadata:
A robust increase in both the amount and
quality of metadata is integral to realizing the Semantic Web. The research
reported on in this article addresses this topic of inquiry by investigating the
most effective means for harnessing resource authors' and metadata experts'
knowledge and skills for generating metadata. Jane Greenberg, W. Davenport
Robertson, Semantic web construction: An Inquiry of Authors' Views on
Collaborative Metadata Generation, International Conference DC 2002, Metadata
for e-Communities, Oct. 13- 17, 2003, Florence Italy http://www.bncf.net/dc2002/program/ft/paper5.pdf
Google = about 116
Apr. 24, 2003; about 377 Oct. 22, 2004
communications standards: Pharmacogenomics
communities of practice:
Alliances
competitive
intelligence: Business of
biopharmaceuticals
computational linguistics:
Computational Linguistics, or Natural Language Processing (NLP), is not a new field. As early as 1946, attempts have been undertaken to use
computers to process natural language. These attempts concentrated mainly on Machine Translation
... the limited performance of these systems made it clear that the underlying
theoretical difficulties of the task had been grossly underestimated, and in the following years and decades much effort was spent on basic
research in formal linguistics. Today, a number of Machine Translation systems are available commercially although there still is no system that
produces fully automatic high- quality translations (and probably there will not be for some time). Human intervention in the form of pre-
and/ or
post-editing is still required in all cases. Another application that has become
commercially viable in the last years is the analysis and synthesis of spoken language, i.e. speech
understanding and speech generation. ... An application that will become at least as important as those already mentioned is the creation, administration, and presentation of texts by
computer. Even reliable access to written texts is a major bottleneck in science and commerce. The amount of textual information is enormous
(and growing incessantly), and the traditional, word- based, information retrieval methods are getting increasingly insufficient as either precision
or recall is always low (i.e. you get either a large number of irrelevant documents together with the relevant ones, or else you fail to get a large
number of the relevant ones in the collection). Linguistically based retrieval methods, taking into account the meaning of sentences as encoded
in the syntactic structure of natural language, promise to be a way out of this quandary.
[Computational Linguistics FAQ, Univ. of Zurich, Switzerland, 2001] http://www.ifi.unizh.ch/groups/CL/CL_FAQ.html
Google = about 97,100 July 19,
2002, about 283,000 Oct. 22, 2004
Linguistics, natural language, and
computational linguistics Meta- Index, Stanford Univ. US
http://www-nlp.stanford.edu/links/linguistics.html
configurable:
Many out-of-the-box solutions claim to be easy to
"customize," when in fact they are referring to configuration options,
not true customizability. Manufacturers have distinct challenges, some
which can be addressed out of the box, but many of which cannot. Manufacturers
also need the ability to capitalize on changing dynamics in the marketplace
before their competitors do. That's why it's imperative to understand the
differences between configuration and customization and the value of selecting a
CRM system that offers the flexibility to adapt and model specific manufacturing
business processes. Why you need to know the difference between
Customizable and Configurable CRM, CDC Software podcast, Intelligent
Enterprise, 2006 http://www.blogmanno.com/?q=node/33
Customized
implies programming and expense. Configurable gives users the chance to
modify options, without expensive programming.
contextual
data: While proteomic studies
initially focused largely on expression and protein identification, progress in
these areas drove the demand for more detailed types of proteomic data. Now
researchers want information about where specific proteins are expressed, both
in terms of tissues and localization within the cell. Information relating
proteins to function require additional details of post- translational
modification, and studies of protein interactions have moved beyond just looking
at binary interactions to studies of protein complexes.
For both genomics and proteomics, this
shift can be characterized as an interest in more contextual data. Enhanced
insight into biological context is essential for obtaining a better
understanding of how biology actually works, and thus there is now an emphasis
to move from genomic and proteomic snapshots to time series data of expression.
Such context is of particular value if biological studies are to be translated
into medical advances, because of the importance of being able to predict the
impact of potential treatments. The integration of genomic and proteomic data
with medical conditions, treatment and outcomes becomes another critical type of
contextual information. Christina Lingham, Beyond Genome: Thinking Globally,
Cambridge Healthtech http://www.beyondgenome.com/download/editorial.pdf
controlled
vocabularies:
Robin Cover's XML Cover Pages is described as "a collection of references on matters of Subject Classification, Taxonomies, Ontologies, Indexing, Metadata, Metadata Registries, Controlled Vocabularies, Terminology, Thesauri, Business Semantics",
2003 http://xml.coverpages.org/classification.html
A limited number of words or phrases used in an indexing system (subject headings) or database, to ensure reliable, consistent retrieval. Long used to enhance retrievability and consistency, ontologies and/ or taxonomies certainly sound sexier than "controlled vocabularies" but continue to have a good deal in common.
Taxonomies add hierarchies, while ontologies make information "machine- understandable" as well as
machine- readable.
Google = about 39,700 July
19, 2002; about 85,300 Oct. 22, 2004; about 496,000 Nov 18, 2009
Controlled
vocabularies Standards, NISO
ANSI/NISO Z39.19-2005 http://www.niso.org/kst/reports/standards/kfile_download?id%3Austring%3Aiso-8859-1=Z39-19-2005.pdf&pt=RkGKiXzW643YeUaYUqZ1BFwDhIG4-24RJbcZBWg8uE4vWdpZsJDs....
Broader terms:
ontology, taxonomy Related terms: RDF, semantic web
customizable:
Quite labor intensive and can be very expensive. Compare configurable.
DAML DARPA Agent Markup Language:
The goal of the DAML effort is to develop a language and tools to facilitate the concept of the semantic web.
http://www.daml.org/
Related term: OIL
DAML + OIL http://www.w3.org/TR/daml+oil-walkthru/
data cleaning, data integration: Algorithms
& data analysis
Google = "data cleaning"
about 12,200; about 22,500 July 3, 2003
"data integration" about 175,000 July 19,
2002; about 306, 000 July 3, 2003; about 817,000 Mar. 22, 2004; about 2,940,000
June 22, 2007
data
conversion: Originally
data conversion was primarily a matter of moving text and database files from
one medium to another, one hardware platform to another, one operating system
environment to another. But as text and database representations became more
sophisticated it became apparent that application interoperability was going to
be the overriding issue of concern. Company History, Data Conversion Lab http://www.dclab.com/company_history.asp
Glossary,
DCL Labs http://www.dclab.com/glossary.asp
30+ definitions
data
management methods: Algorithms & data analysis
has automated methods, methods in this glossary generally
combine human and automated methods.
data
management vocabulary: Ontologies
& taxonomies
data mart, data mining, data pipelining,
data reduction methods, data warehouse: Algorithms
& data analysis
data visualization: The
classical definition of visualization is as follows: the formation of mental
visual images, the act or process of interpreting in visual terms or of putting
into visual form. A new definition is a tool or method for interpreting image
data fed into a computer and for generating images from complex
multi-dimensional data sets (1987). Definitions and
Rationale for Visualisation, D. Scott
Brown, SIGGRAPH, 1999 http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal2.htm
includes information on data visualization.
Related term: information visualization;
Broader term: visualization
databases: Bioinformatics; Databases & software directory
deep web:
Most of the Web's information is buried far down on dynamically generated sites,
and standard search engines never find it. The deep Web is qualitatively different from the surface
Web. Deep Web sources store their content in searchable databases that only
produce results dynamically in response to a direct request. But a direct query
is a "one at a time" laborious way to search. [Michael K.
Bergman "The deep web: surfacing hidden value" White Paper,
BrightPlanet, 2000-2002] http://www.brightplanet.com/deepcontent/tutorials/DeepWeb/index.asp
Another version at http://www.press.umich.edu/jep/07-01/bergman.html
Google = about 10,200 Aug. 17, 2002;
about 42,900 Oct. 22, 2004
Related term: invisible web
description logic:
Has
existed as a field for a few decades yet only somewhat recently has appeared to
transform from an area of academic interest to an area of broad interest. This
paper provides a brief historical perspective of description logic developments
that have impacted DL usability to include communities beyond universities and
research labs. Deborah L.
McGuinness. ``Description Logics Emerge from Ivory Towers''. Stanford
Knowledge Systems Laboratory Technical Report KSL-01-08 2001. In the Proceedings
of the International Workshop on Description Logics. Stanford, CA, August 2001.http://www.ksl.stanford.edu/people/dlm/papers/dls-emerge-abstract.html
The main effort of the research in knowledge
representation is providing theories and systems for expressing structured
knowledge and for accessing and reasoning with it in a principled way. Description
Logics are considered the most important knowledge representation formalism
unifying and giving a logical basis to the well known traditions of Frame- based
systems, Semantic Networks and KL- ONE-like languages, Object- Oriented
representations, Semantic data models, and Type systems. [Description Logic
Knowledge Representation] http://dl.kr.org/
Description
Logics Home Page,
Patrick Lambrix,
Linkoping Univ. Sweden http://www.ida.liu.se/labs/iislab/people/patla/DL/index.html
digital libraries:
International digital libraries research is intended to contribute to the fundamental knowledge required to create information systems that can operate in multiple languages, formats, media, and social and organizational contexts. International collaborative research can bring complementary approaches, resources and perspectives to bear on common needs and information technology research challenges. International digital libraries applications testbeds are intended to build operational prototypes for globally distributed, internet- based resources, and to implement these in a variety of applications contexts. The testbeds are expected to advance technologies across the digital libraries lifecycle, focus collective work on organizing domain- specific content, and engage researchers, scholars, students and teachers in enhancing research and knowledge resources in a variety of subject domains. [National Science Foundation, International Digital Libraries Collaborative Research & Applications Testbeds program solicitation, 2002]
http://www.nsf.gov/pubs/2002/nsf02085/nsf02085.html
Google = about 197,000 July
19, 2002; about 1,480,000 Oct. 22, 2004
Directed Acyclic Graph
DAG: Ontologies & Taxonomies
disambiguate:
Make less ambiguous, clarify,
elucidate.
Google = about 33,100 July 19,
2002; about 65,300 Oct. 22, 2004, about 340,000 Nov 18, 2009
domain expertise: Wikipedia
http://en.wikipedia.org/wiki/Domain_expert
http://en.wikipedia.org/wiki/Domain_knowledge
Google = about 25,500 Dec. 18, 2002;
about 68,500 Oct. 22, 2004; about 785,000 June 22, 2007; about 1, 120,000 Nov
18, 2009
drug
discovery informatics:
Dublin Core Metadata Initiative:
An open forum engaged in the development of interoperable online
metadata standards that support a broad range of purposes and business models. The original workshop for the Initiative was held in Dublin, Ohio [OCLC] in 1995.
http://dublincore.org/
evolvability:
Tim Berners Lee defines http://www.w3.org/Talks/1998/0415-Evolvability/slide3-1.htm
Wikipedia http://en.wikipedia.org/wiki/Evolvability
Google = evolvability about 8,210
July 19, 2002; about 21,400 Oct. 22, 2004; about 51,000 Nov 18, 2009
See
also under interoperability
facet,
faceted: Ontologies & taxonomies
fractal nature of the web: http://www.w3.org/DesignIssues/Fractal.html
Tim Berners- Lee, Commentary on architecture, Fractal nature of the web, first
draft
Society
has to be fractal - people want to be involved on a lot of different levels. The
need for things that are local and special will create enclaves. And those will
give us the diversity of ideas we need to survive. Tim Berners Lee, in "The
father of the web", Evan Schwartz, Wired Mar. 1997 http://www.wired.com/wired/archive/5.03/ff_father_pr.html
GIS Geographic Information Systems:
Maps have traditionally been used to explore the Earth and to exploit its
resources. GIS technology is an expansion of cartographic science. Geographic
information systems (GIS) technology can be used for scientific investigations,
resource management, and development planning. It has enhanced the efficiency
and analytic power of traditional mapping. GIS technology is becoming an
essential tool in the effort to understand the process of global change.
[Is GIS in your future? Boston Chapter, Special Libraries Association
meeting, Mar. 12. 2002] http://www.sla.org/chapter/cbos/meetings/fy02/sci_tech.htm
Good
Informatics Practices Guidance
Document (GIP): A newly drafted comprehensive body of information of
regulatory requirements in the form of existing (GLP, GMP, GCP and Part 11) and
currently used standards compiled in one reference guide for an IT system of a
life science or healthcare environment. http://www.lsit.org/initiatives/gip.php
GUI Graphical User Interface: Computers
& computing
granularity:
Wikipedia http://en.wikipedia.org/wiki/Granularity
<jargon, parallel> The size of the units of code under consideration in some
context The term generally refers to the level of detail at which code is considered, e.g. "You can specify the granularity for this profiling tool". The most common computing use is in parallelism where "fine grain parallelism" means individual tasks are relatively small in terms of code size and execution time, "coarse grain" is the opposite. You talk about the "granularity" of the parallelism. The smaller the granularity, the greater the potential for parallelism and hence speed- up but the greater the overheads of synchronisation and communication.
FOLDOC 1997 http://www.swif.uniba.it/lei/foldop/foldoc.cgi?granularity
The extent to which a system contains separate components (like granules). The more components in a system - or the greater the granularity - the more flexible it is. [Webopedia]
http://www.webopedia.com/TERM/g/granularity.html
Choosing different levels of granularity, i.e., imposing different quality criteria on models built by homology from representative, experimentally determined [protein] structures, leads to different numbers of family representatives as targets.
NIGMS Structural Genomics Targets Workshop February 11-12,
1999 http://www.nigms.nih.gov/news/meetings/structural_genomics_targets.html
Level of detail seems to be the essence of granularity.
Google = about 250,000 July 19, 2002;
about 454,000 Oct. 22, 2004; about 2,170,000 Nov 18, 2009
health information data: Includes
Clinical data captured during the process of diagnosis and treatment.
Epidemiological databases , that aggregate data about a population. Demographic
data used to identify and communicate with and about an individual. Financial
data derived from the care process or aggregated for an organization or
population. Research data gathered as a part of care and used for research or
gathered for specific research purposes in clinical trials. Reference data that
interacts with the care of the individual or with the healthcare deliver
systems, like a formulary, protocol, care plan, clinical alerts or reminders,
etc. Coded data that is translated into a standard nomenclature or
classification so that it may be aggregated, analyzed, and compared. [Health
Information Management; Professional definitions, Committees on Professional
Development, American Health Information Management Association, 1999, 2000] http://www.ahima.org/infocenter/definitions/HIMprofessionaldefinition.htm
health information management:
Health
information management improves the quality of healthcare by insuring that the
best information is available to make any healthcare decision. Health
information management professionals manage healthcare data and information
resources. The profession encompasses services in planning, collecting,
aggregating, analyzing, and disseminating individual patient and aggregate
clinical data. It serves the healthcare industry including: patient care
organizations, payers, research and policy agencies, and other healthcare-
related industries. [Health Information Management; Professional
definitions, Committees on Professional Development, American Health Information
Management Association, 1999, 2000] http://www.ahima.org/infocenter/definitions/HIMprofessionaldefinition.htm
Google = about 56,700 Jan. 2, 2003;
about 145,000 Oct. 22, 2004; about 980,000 Nov 18, 2009
heterogeneous data:
informatics:
The study of the application of computer
and statistical techniques to the management of information. In genome
projects, informatics includes the development of methods to search databases
quickly, to analyse DNA
sequence information, and to predict protein
sequence and structure
from DNA sequence data. ORD Office of Rare Diseases, NIH glossary http://ord.aspensys.com/asp/resources/glossary_a-e.asp#A
Narrower terms: bioinformatics;
cheminformatics;
Computers &
computing clinical
informatics, molecular informatics, Biomaterials
matinformatics research
informatics; Drug
discovery & development life sciences informatics, Intellectual
property & legal; patinformatics; Molecular
imaging image informatics; pharmacoinformatics,
pharmainformatics Proteomics
protein informatics
information -- how
much? How Much Information 2003,
School of Information Science and Systems, Univ. of California, Berkeley, 2003 http://www.sims.berkeley.edu/research/projects/how-much-info-2003/index.htm
information architecture: "Involves the design of organization, labeling, navigation, and searching systems to help people find and manage information more successfully."
Lou Rosenfeld, Peter Morville interview quoted in Mark Hurst "About
Information Architecture, Apr. 3, 2000] http://www.goodexperience.com/columns/040300infoarch.html
Google = about 132,000 July 19, 2002;
about 258,000 July 3, 2003; about 622,000 Oct. 22, 2004; about 5,760,000 Nov 18,
2009
Information architecture glossary,
Kat Hagedorn, Argus Associates, 2000, 60 + definitions http://argus-acia.com/white_papers/iaglossary.html
information ecology: Wikipedia
http://en.wikipedia.org/wiki/Information_ecology
The Information Ecology group (formerly
the Physical Language Workshop) explores ways to connect our physical
environments with information resources. Through the use of low-cost, ubiquitous
technologies, we are creating seamless and pervasive ways to interact with our
information—and with each other. We focus on projects that harness the ecology
of consumer electronics and sensor devices—present and future—to more
smoothly mediate the boundaries between the physical and information worlds we
inhabit. MIT Media Lab Design
Ecology/Information Ecology 2009 http://eco.media.mit.edu/
Google =
about 11,100 Oct. 22, 2004; about 70,200 Nov 18, 2009
information extraction:
Automated ways of extracting unstructured or partially structured information from
machine readable files. Compare with information retrieval.
Google = about 43,100 July 19, 2002;
about 590,000 Nov 18, 2009
Related
terms: natural language
processing, term extraction
information harvesting: See under
Knowledge Discovery in Databases KDD
Google = about 871 July 19, 2002;
about 1,230 July 3, 2003; about 1,730 Oct. 22, 2004; about 1,140,000 June 22,
2007
information
integration: Our research group is developing
intelligent techniques to enable rapid and efficient information integration.
The focus of our research has been on the technologies required for constructing
distributed, integrated applications from online sources. This research
includes: Information
Extraction: Machine learning techniques for extracting information from
online sources; Source
Modeling: Constructing a semantic model of wrapped sources so that they can
be automatically integrated with other sources; Record
Linkage: Learning how to align records across sources; Data
Integration: Generating plans to automatically integrate data across
sources; Plan Execution:
Representing, defining, and efficiently executing integration plans in the Web
environment; Constraint-based
Integration Interactive constraint-based planning and integration for
the Web environment. Information Integration Research Group, Intelligent Systems
Division, Information Sciences Institute (ISI), University of Southern
California http://www.isi.edu/integration/
Google =
about 4,430,000 July 3, 2003; about 1,080,000 June 22, 2007; about 1, 160,000
Nov 18,m 2009
information management:
Information services of various kinds are fundamental to the discovery,
development and use of medicines. Within the pharmaceutical industry, often
regarded as the epitome of the 'information intensive' industry, research
information units provide both external and internal information provision and
management to discovery and development programmes, while medical information
units provide in- depth information on the company's products to external
doctors, pharmacists, etc., and commercial information units handle information
on competitors, marketing data, etc. Additionally, information personnel are
involved in activities such as records management and archiving, regulatory
affairs, data administration, IT support, and many more. Within the NHS
[National Health Service, UK] , Drug Information Pharmacists provide information
services on effective use of medicines to all healthcare professions, and are
also involved in databases compilation, records management, current awareness
etc. The move towards evidence- based medicine, with consequent need for
evaluation and presentation of information, is of obvious importance to this
group. Other sectors with a heavy reliance on the handling pharmaceutical
information and knowledge include publishing, database production, software
services, and consultancy of varied kinds. [MSc in Pharmaceutical
Information Management, City Univ. London, UK, Dept of Information
Science, Introduction, 2002 ]http://www.soi.city.ac.uk/organisation/is/teaching/pim/
Narrower term: health information
management
Google = about 1,470,000 Jan. 2, 2003;
about 4,200,000 Oct. 22, 2004; about 19,300,000 Nov 18,m 2009
information overload:
Biomedicine is in the middle of revolutionary advances. Genome projects, microassay methods like DNA chips, advanced radiation sources for crystallography and other instrumentation, as well as new imaging methods, have exceeded all expectations, and in the process have generated a dramatic information overload that requires new resources for handling, analyzing and interpreting data. Delays in the exploitation of the discoveries will be costly in terms of health benefits for individuals and will adversely affect the economic edge of the country. [Opportunities in Molecular Biomedicine in the Era of Teraflop Computing: March 3 & 4, 1999, Rockville, MD, NIH Resource for Macromolecular Modeling and Bioinformatics Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana- Champaign]
http://www.ks.uiuc.edu/Publications/Reports/teraflop/node4.html
Many
of today's problems stem from information overload and there is a desperate need
for innovative software that can wade through the morass of information and
present visually what we know. The development of such tools will depend
critically on further interactions between the computer scientists and the
biologists so that the tools address the right questions, but are designed in a
flexible and computationally efficient manner. It is my hope that we will
see these solutions published in the biological or computational literature.
Richard J. Roberts, The early days of bioinformatics publishing, Bioinformatics
16 (1): 2-4, 2000
"Information overload" is not an overstatement these days. One of the biggest challenges is to deal with the tidal wave of data, filter out extraneous
noise and poor quality data, and assimilate and integrate information on a
previously unimagined scale
Wikipedia http://en.wikipedia.org/wiki/Information_overload
Google = about 118,000
July 19, 2002; about 249,000 Oct. 22, 2004; about 1,480,000 Nov 18, 2009
Where's
my stuff? Ways to help with information overload, Mary Chitty, SLA
presentation June 10, 2002, Los Angeles CA
information
retrieval: Wikipedia http://en.wikipedia.org/wiki/Information_retrieval
information theory: Algorithms
& data analysis
information visualization: The direct
visualization of a representation of selected features or elements of complex
multi- dimensional data. Data that can be used to create a visualization
includes text, image data, sound, voice, video - and of course, all kinds of
numerical data. Our visual analysis systems also provide the tools to interact
with the data that has been visualized so that users can explore, discover and
learn. Users do not look at static images, but can subset the data, run queries,
do time sequence studies and create categories and correlations of data type. [Pacific
Northwest National Lab, About Visualization at PNNL, 1999] http://www.pnl.gov/infoviz/
Wikipedia http://en.wikipedia.org/wiki/Information_visualization
Google = about 28,100 July 19, 2002;
about 94,200 Oct. 22, 2004; about 1,330, 000 Nov 18, 2009
Information visualization resources on
the web, 2002 http://graphics.stanford.edu/courses/cs348c-96-fall/resources.html
Related term: data visualization; Broader
term: visualization
informational repositories:
A new strategy that allows universities to apply serious,
systematic leverage to accelerate changes taking place in scholarship and
scholarly communication, both moving beyond their historic relatively passive
role of supporting established publishers in modernizing scholarly publishing
through the licensing of digital content, and also scaling up beyond ad-hoc
alliances, partnerships, and support arrangements with a few select faculty
pioneers exploring more transformative new uses of the digital medium. Clifford
Lynch, Institutional Repositories: Essential Infrastructure for Scholarship in
the Digital Age, ARL Bimonthly Report 226, Feb. 2003 http://www.arl.org/newsltr/226/ir.html
DSpace,
MIT http://www.dspace.org/
integration: Bioinformatics
interoperability: Ontologies &
taxonomies
invisible web:
For this study, we have avoided the term "invisible Web" because it
is inaccurate. The only thing "invisible" about searchable databases
is that they are not indexable nor able to be queried by conventional search
engines. http://www.brightplanet.com/deepcontent/tutorials/DeepWeb/index.asp
Those parts of the web which are inaccessible to current search engines. A straightforward example
was PubMed/ Medline
(until Google started indexing it.) You still can't usually access proprietary (fee- based) databases
such as Thomson Dialog or Lexis- Nexis. except directly. Until fairly recently PDF documents and PowerPoint slides were inaccessible to search engines.
Google = about 17,300 July 19, 2002;
about 278,000 Oct. 22, 2004; about 802,000 Nov 18, 2009
Invisible or Deep Web: What it is,
How to find it, and Its inherent ambiguity http://www.lib.berkeley.edu/TeachingLib/Guides/Internet/InvisibleWeb.html
Related terms: deep web, semantic web
just in time information:
90,200 websites were found with this phrase by Google on
May 23, 2007. An increasing need as we are deluged with information and data -- and still need time to reflect, discuss and think about
what all these mean.
Google = about
2,900 March 14, 2002, about 3,400 July 19, 2002; about 51,600 Feb. 21, 2006; about 88,400 May 7, 2007;
about 781,000 Nov 18, 2009
Just-In-Time Information Retrieval. Bradley J. Rhodes. Ph.D. Dissertation, MIT Media Lab, May 2000. Just in time retrieval agents Bradley J. Rhodes
http://www.research.ibm.com/journal/sj/393/part2/rhodes.html
Related terms: information overload, remembrance agents;
Bioinformatics modularity
Knowledge Discovery in Databases (KDD): Algorithms
& data analysis
knowledge integration:
Wikipedia http://en.wikipedia.org/wiki/Knowledge_integration
Related terms: ontologies,
semantics
knowledge management:
An organization's collective knowledge - and the ability to access it - comprises a key corporate asset. Smart organizations know that to maintain competitive advantage, they need to manage their data, information, and knowledge effectively and systematically. Knowledge management involves much more than compiling data and retrieving information. It should be seen as an overarching concept that combines a management philosophy with
data warehousing, workflow strategies, database management, and knowledge distribution in a network computing environment. [William A. Woods "Knowledge Management Needs Effective Search Technology" Sun Journal]
http://www.sun.com/dot-com/sunjournal/V2N1/03_feat2a.html
Wikipedia
http://en.wikipedia.org/wiki/Knowledge_management
Google = about 826,000 July 19, 2002;
about 3,520,000 Oct. 22, 2004; about 11,000,000 Nov 18, 2009
Knowledge
Management, FDA, 2004 http://www.fda.gov/cdrh/strategic/km.html
Virtual Library: Knowledge Management, May 2000
http://www.brint.com/km/ Definition, articles, white papers, interviews, business and technology library, periodicals and publications, “out of box thinking”, “movers and shakers”, “think tank”, calendar of events, emerging topics.
Knowledge Management definitions,
Charlie Matthews, VisualInterconnections, 2002 http://www.visualinterconnections.com/CEM/definitions.htm
KM
Glossary, GOTCHA, Univ. of California
Berkeley, 1999 About 50 terms. http://sims.berkeley.edu/courses/is213/s99/Projects/P9/web_site/glossary.htm
Related terms: ontologies, paraphrase problem, taxonomies
knowledge risk: Business
of biopharmaceuticals
laboratory
informatics: A relatively new field that aims to
expedite the exchange of laboratory data via electronic data exchange.
Laboratory informatics specialists design standards and systems to support the
acquisition, retrieval and communication of test results and other laboratory
data. Information systems are as critical to public health laboratories as
instrumentation and reagents. Association of Public Health Laboratories,
2008 http://www.aphl.org/aphlprograms/informatics/Pages/defofinformatics.aspx
Related term: Drug
discovery & development LIMS
Google = about
1,250 Dec.
31, 2002; about 3,000 Oct. 22, 2004; about 31,900 Nov 18, 2009
lexical
semantics:
http://en.wikipedia.org/wiki/Lexical_semantics
lexicon:
A
machine- readable dictionary that may contain a good deal of additional
information about the properties of the words, notated in a form that parsers
can utilize. [Bob Futrelle, A brief introduction to NLP, BIONLP.org, , Computer Science,
Northeastern Univ., US, 2002] http://www.ccs.neu.edu/home/futrelle/bionlp/intro.html
A linguistics term (words and their definitions), an
artificial intelligence term. Sometimes a synonym for glossary or dictionary.
Google = about 768,000 July 19, 2002;
about 1,960,000 Oct. 22, 2004
life sciences informatics:
Informatics are
essential at every step of genomics- based drug discovery and development. The
commercial landscape of life sciences information technology has changed
dramatically in the last few years. Bioinformatics,
in particular, has gone through a dramatic boom/bust. While IT companies are
looking to the drug discovery and development arena as a new market opportunity,
pharmaceutical companies are faced with rising pressure to reduce (or at
least control) costs, and have a growing need for new informatics tools to help
manage the influx of data from genomics, and turn that data into tomorrow's
drugs. Key IT tools, such as high- performance computing, Web services, and
grids, are being used to improve the speed and efficiency of drug discovery and
development. True breakthroughs are still lacking, particularly in key areas
such as gene prediction, data mining, protein structure modeling and prediction,
and modeling of complex biological systems. However, most experts agree that IT
and bioinformatics are essential to reaching the improved productivity the
pharmaceutical industry craves.
linked
data:
Linked
Data is about using the Web to connect related data that wasn’t previously
linked, or using the Web to lower the barriers to linking data currently linked
using other methods. http://linkeddata.org/
Linked data glossary http://linkeddata.org/glossary
machine-readable: See under
metadata
Google= about 303,000 July 19, 2002;
about 535,000 Oct. 22, 2004
machine-understandable: See under
metadata
Google= about
3,730 July 19, 2002; about 8,950 July 14, 2004
markup languages: Computers
& computing
Google = about 639,000 Aug. 9, 2002;
about 170,000 Oct. 22, 2004
mash-up
http://en.wikipedia.org/wiki/Mashup_(web_application_hybrid)
Google
= about 22,100,000 Oct. 27, 2006; about 20,400,000 Nov 18, 2009
Medbiquitous
Consortium: Technology standards
based on XML and web services. http://www.medbiq.org/index.html
medical informatics:
The field of information science concerned with the analysis and dissemination of medical data through the application of computers to various aspects of health care and medicine.
MeSH, 1987
Medical
informatics has to do with all aspects of understanding and promoting the
effective organization, analysis, management, and use of information in health
care. While the field of medical informatics shares the general scope of these
interests with some other health care specialties and disciplines, medical
informatics has developed its own areas of emphasis and approaches that have set
it apart from other disciplines and specialties. For one, a common thread
through medical informatics has been the emphasis on technology as an integral
tool to help organize, analyze, manage, and use information. In addition, as
professionals involved at the intersection of information and technology and
health care, those in medical informatics have historically tended to be engaged
in the research, development, and evaluation side of things, and in studying and
teaching the theoretical and methodological underpinnings of data applications
in health care. However, today medical informatics also counts among its
profession many whose activities are focused on dimensions that include the
administration and everyday collection and use of information in health care.
What is Medical Informatics? History of Medical Informatics, AMIA American
MEdical Informatics Association http://www.amia.org/history/what.html
Google = about 163,000 July 19, 2002;
about 479,000 Oct. 22, 2004, about 696, 000 Oct. 3, 2005; about 1,690,000 Nov
18, 2009
metadata:
Could elevate the status of the web from machine- readable to something we might call machine- understandable. Metadata is "data about data" or specifically in our current context "data describing web resources." The distinction between "data" and "metadata" is not an absolute one; it is a distinction created primarily by a particular application ("one application's metadata is another application's data"). [W3C, "Introduction to RDF Metadata" 1997]
http://www.w3.org/TR/NOTE-rdf-simple-intro
Metadata is machine understandable
information for the web. The W3C
Metadata Activity addressed the combined needs of several groups for a
common framework to express assertions about information on the Web, and was
superceded by the W3C Semantic Web Activity.
[W3C, Metadata and Resource Description, W3C Technology and Society Domain,
2001]http://www.w3.org/Metadata/
Information about data that enables intelligent, efficient access and management of data. … metadata is always less than the data. [Robyne M. Sumpter “Whitepaper on Data Management” Lawrence Livermore National Laboratory, February 10, 1994]
http://www.llnl.gov/liv_comp/metadata/papers/whitepaper-draft.html
Google = about 1,640,000 July 19, 2002;
about 4,850,000 Oct. 22, 2004; about 25,600,000 May 9, 2005; about
62,700,000 May 7, 2007
Narrower
terms: Dublin Core Metadata Initiative, faceted metadata Related terms: interoperability, RDF, semantic web
micro-theories:
An ontology about a specific domain, that fits within, and for the most part
is consistent with, an ontology with a broader scope. For example, structural biology fits within the larger context of biology. Structural biology will have its own terminology and specific algorithms that apply within the specific domain, but may not be useful or identical to, for example, the genome community. [Lawrence Berkeley Lab "Advanced Computational Structural Genomics" Glossary]
Google = about 953 July 19, 2002;
about 8,670 Oct. 22, 2004
modularity:
Bioinformatics
molecular informatics: Molecular
Informatics presents methodological innovations that
will lead to a deeper understanding of ligand-receptor interactions,
macromolecular complexes, molecular networks, design concepts and processes that
demonstrate how ideas and design concepts lead to molecules with a desired
structure or function, preferably including experimental validation.
The journal's scope
includes but is not limited to the fields of drug discovery and chemical
biology, protein and nucleic acid engineering and design, the design of
nanomolecular structures, strategies for modeling of macromolecular assemblies,
molecular networks and systems, pharmaco- and chemogenomics, computer-assisted
screening strategies, as well as novel technologies for the de novo design of
biologically active molecules. Molecular Informatics, Wiley 2010 forward,
was QSAR & Combinatorial Science http://www.wiley-vch.de/publish/en/journals/alphabeticIndex/7777/?jURL=http://www.wiley-vch.de:80/vch/journals/2022/molinf/index.html
Google = about 2,580 July 19, 2002;
about 4,410 Oct. 22, 2004; about 342,000 Nov 18, 2009
molecular information theory: Algorithms
& data analysis
nanopublishing:
The word nanopublishing was coined by Jeff
Jarvis, creative director of the US company Advance
Publications Inc. Jarvis first used the term after being shown Gawker,
a New York media gossip weblog launched by Nick Denton in December 2002.
MacMillan Word of the Week Archive 2005 http://www.macmillandictionaries.com/wordoftheweek/archive/050207-nanopublishing.htm
START
Natural Language Question Answering System,
InfoLab Group, Computer Science and Artificial Intelligence Lab, MIT http://www.ai.mit.edu/projects/infolab/start-system.html
OIL Ontology Inference Layer:
A
proposal for a web- based representation and inference layer for ontologies,
which combines the widely used modelling primitives from frame- based languages
with the formal semantics and reasoning services provided by description logics.
It is compatible with RDF Schema
(RDFS), and includes a precise semantics
for describing term meanings (and thus also for describing implied information).
http://www.ontoknowledge.org/oil/
object based ontologies:
Ontologies & taxonomies
Office of the National
Coordinator for Health Information Technology (ONC):
Provides leadership for the development and nationwide implementation of an
interoperable health information technology infrastructure to improve the
quality and efficiency of health care and the ability of consumers to manage
their care and safety. http://www.hhs.gov/healthit/
ontology,
ontologies: The word "ontology" seems to generate a lot of controversy in discussions about AI
[artificial intelligence]. It has a long history in philosophy, in which it refers to the subject of existence. ... In the context of knowledge sharing, I use the term ontology to mean a specification of a conceptualization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set- of- concept- definitions, but more general. And it is certainly a different sense of the word than its use in philosophy. What is important is what an ontology is for. My colleagues and I have been designing ontologies for the purpose of enabling knowledge sharing and reuse. In that context, an ontology is a specification used for making ontological commitments. ... Notes: 1) Ontologies are often equated with taxonomic hierarchies of classes, but class definitions, and the subsumption relation, but ontologies need not be limited to these forms.
Tom Gruber, Stanford Univ. "What is an ontology?", 2001
http://www-ksl.stanford.edu/kst/what-is-an-ontology.html
more in Ontologies * Taxonomies
Narrower terms: bottom-
up ontologies, biomedical ontologies, common ontology, descriptive ontology, domain ontology, dynamic ontology, heavyweight ontologies, lightweight ontologies, logic based ontologies, micro-
theories, middle ontologies, mixed ontologies, taxonomies, natural language ontologies,
navigational ontology, object based
ontologies, orthogonal ontologies, pure ontologies, reusable ontologies, shared
ontologies, simple ontologies, structured ontology, top- down ontology, upper ontologies;
Functional genomics Gene
OntologyTM GO;
Related terms: interoperability, metadata,
OIL Ontology Inference Layer, ontological commitment, ontology annotation tools, ontology editors,
ontology evolution, ontology interoperability, RDF, semantic web, web ontology
language; Microarrays Ontology Working
Group
organizational
informatics: A field which studies the development and
use of computerized information systems and communication systems in
organizations. It includes social studies of their conception, design, effective
implementation within organizations, maintenance, use, organizational value,
conditions that foster risks of failures, and their effects for people and an
organization's clients. It is an intellectually rich and practical research area.
"Social Informatics" Indiana Univ, School of Library & Information
Science http://www.slis.indiana.edu/SI/oi1.html
Narrower
term: social informatics
Google = about 153 July 19, 2002;
about 211 Oct. 22, 2004
Related term:
knowledge management
pattern,
pattern language: Patterns, discussion FAQ http://g.oswego.edu/dl/pd-FAQ/pd-FAQ.html
portal:
An entry or starting point on the web, with a mixture of content and services, usually capable of personalization.
Narrower term: web portal
precision:
Percentage of unrelated material excluded by a specific query or search statement.
Related
terms: Genetic testing
analytical specificity, clinical specificity
Compare recall
query contraction:
Needed when a
search engine retrieves thousands of citations. May consist of additional
(Boolean AND terms) or different (Boolean OR).
Google = about 26 July 19, 2002;
about 130 Oct. 22, 2004
query expansion:
Adding new and/ or
different terms to a search statement (particularly when a search engine or
database retrieve no hits). Often uses Boolean OR.
Google = about 7,500 July 19, 2002;
about 21,300 Oct. 22, 2004
Related terms: ontologies, taxonomies
RDF Resource Description Framework:
Integrates a variety of web- based
metadata activities including sitemaps, content ratings, stream channel definitions, search engine data collection (web crawling), digital library collections, and distributed authoring, using
XML as an interchange syntax. The RDF specifications provide a lightweight ontology system to support the exchange of knowledge on the Web. [W3C, Semantic Web Activity: Resource Description Framework (RDF) Mar. 2001]
http://www.w3.org/RDF/
Related term: knowledge management
RSS
[Really Simply Syndication] feeds: A Web content syndication format based on
XML. Cathleen Moore, Search engines target weblogs, InfoWorld, Mar. 17, 2003 http://www.infoworld.com/article/03/03/17/HNblogs_1.html
Newsreaders
http://directory.google.com/Top/Reference/Libraries/Library_and_Informat...e
RSS
2.0 specifications, Dave Winer http://blogs.law.harvard.edu/tech/rss/
recall:
The percentage of applicable material retrieved by a specific query or search statement.
Compare precision. Related term:
Genetic testing sensitivity
regulated
information systems: Drug approvals
relevance:
Percentage of truly related material retrieved by a specific query or search statement.
Related terms: precision
Genetic testing &
diagnostics analytical specificity, clinical specificity. Compare recall
remembrance agents:
A set of applications that watch over a user's shoulder and suggest information relevant to the current situation. While query- based memory aids help with direct
recall, remembrance agents are an augmented associative memory. [Bradley Rhodes, Remembrance Agents Because serendipity is too important to be left to chance..., 2001]
http://rhodes.www.media.mit.edu/people/rhodes/RA/
Google = about 673 July 19, 2002;
about 549 Oct. 22, 2004
Related
terms: collaborative filtering, just in time information
research informatics:
Research
resourceome:
-Omes & -Omics
Rosetta:
A systems- level design
language developed to address requirements specification for systems- on- chip
designs. Rosetta specifically addresses problems associated with heterogeneity
and complexity in current systems. Specifically, Rosetta allows designers to
develop and integrate specifications written in multiple semantic models to
provide language and semantic support for concurrent engineering of electronic
systems. Accellera Rosetta Standards Committee Homepage, EDA Industry
Working Groups, 2002
http://www.eda.org/slds-rosetta/
SOAP Simple Object Access Protocol:
A
lightweight protocol for exchange of information in a decentralized, distributed
environment. [SOAP, W3C 1.1, work in progress] http://www.w3.org/TR/SOAP/
semantic:
Ontologies &
taxonomies
social
informatics: Social Informatics (SI) refers to the body of research
and study that examines social aspects of computerization, including the roles
of information technology in social and organizational change, the uses of
information technologies in social contexts, and the ways that the social
organization of information technologies is influenced by social forces and
social practices. http://rkcsi.indiana.edu/
The term "Social Informatics"
emerged from a series of lively conversations in February and March 1996 among
scholars with an interest in advancing critical scholarship about the social
aspects of computerization, including Phil Agre, Jacques Berleur, Brenda Dervin,
Andrew Dillon, Rob Kling, Mark Poster, Karen Ruhleder, Ben Shneiderman, Leigh
Star and Barry Wellman. As the conversation developed, it became clear that
labels that could energize scholars in one sub- community could readily turn off
participants in other communities. Various participants preferred different
labels; a sufficient consensus emerged around "Social Informatics"
that it can serve as a working label. ["Conceptions of social
informatics" Indiana Univ., School of Library and Information Science,
2002] http://www.slis.indiana.edu/SI/concepts.html
A serviceable working conception of "social informatics" is that it identifies a body of research that examines the social aspects of computerization. A more formal definition is "the interdisciplinary study of the design, uses and consequences of information technologies that takes into account their interaction with institutional and cultural contexts."
... Social informatics has been a subject of systematic analytical and critical research for the last 25 years. Unfortunately, social informatics studies are scattered in the journals of several different fields, including computer science, information systems, information science and some social sciences. Each of these fields uses somewhat different nomenclature. This diversity of communication outlets and specialized terminologies makes it hard for many
non- specialists (and even specialists) to locate important studies. [Rob Kling,
What is social informatics and why does it matter? D-Lib 5(1): Jan. 1999] http://www.dlib.org/dlib/january99/kling/01kling.html
Social informatics HomePage http://www.slis.indiana.edu/SI/
Red Rock Eater News Service, Phil Agre, UCLA,
US http://polaris.gseis.ucla.edu/pagre/rre.html
structure:
In a biological or
anatomical context, the term structure is associated with two distinct concepts
(meanings): 1. a material object generated as a result of coordinated gene
expression, which necessarily consists of parts (e.g., hemoglobin molecule,
cell, heart, human body); and 2. the manner of organization or interrelation of
the parts that constitute a structure specified by the first definition (i.e.,
the structure of a structure). Both definitions emphasize the critical need for
declaring the principles according to which units of organization can be defined
in order to be able to state what is 'whole' and what is 'part'. Specifying the
manner in which parts interrelate must satisfy two requirements: 1. to determine
the kinds of parts of which various structures may be constituted; and 2. to
state the manner of spatial organization of parts by describing their
boundaries, continuities and attachments, as well as their location, orientation
and spatial adjacencies in terms of qualitative coordinates (in addition to the
quantitative geometric coordinates, which are embedded in the Visible Human data
sets). [Cornelius Rosse, et. al., Visible Human, Know Thyself: The Digital
Anatomist Dynamic Structural Abstraction, National Library of Medicine, US] http://www.nlm.nih.gov/research/visible/vhpconf2000/AUTHORS/ROSSE/TEXTINDX.HTM
Related terms: Cell
biology, Expression Compare
unstructured.
subsumption:
http://ai.eecs.umich.edu/cogarch0/subsump/
Google = about 30,800 July 19, 2002;
about 80,500 Oct. 22, 2004; about 159,000 May 2, 2005
syntactic,
syntax:
Ontologies & taxonomies
taxonomies,
taxonomy: Frustrations with search engines and information retrieval (and information overload) have led to increased interest in specialized taxonomies. A form of controlled vocabulary, with hierarchical relationships (broader terms, narrower terms) which provide additional suggestions for browsing, as do lateral relationships (related terms) and preferred terms. While there is theoretical interest in
natural language processing, a very small percentage of web search engine queries actually use natural language processing successfully.
more in Ontologies * Taxonomies
Narrower terms:
bottom-up taxonomies, controlled vocabularies, descriptive taxonomies, domain
taxonomies, dynamic taxonomies, integrated taxonomy, lightweight taxonomies, morphological taxonomies, navigational taxonomies, orthogonal
taxonomies, shared taxonomies, top- down taxonomy; Cancer
genomics , diagnostics & Therapeutics molecular taxonomies Phylogenomics
molecular taxonomy, phylogenetic taxonomy;
Related terms: classifiers, query expansion; Broader/narrower? term: ontologies
See also FAQ
question # 4 which has more about taxonomies.
term extraction:
Robert Futrelle, Northeastern Univ., 2001 http://www.ccs.neu.edu/home/futrelle/bionlp/psb2001/psb01-tutorial-bib1.htm
Google
- about 49,900 Nov 18, 2009
See related information extraction
term mining:
Term Mining in Biomedicine, Sophia Ananiadou - University of Manchester,
2007 http://talks.cam.ac.uk/talk/index/6769
Google = about 1,990
June 16, 2003; about 2,980 Oct. 22, 2004; about 40,100 June 22, 2007
text
categorisation: See Algorithms
& data analysis under support vector machines
Google = about 902 "text
categorization" 9,220 July 19, 2002 about 27,100 Oct. 22, 2004
text mining:
Usually
data mining technologies mine knowledge from data with well-formed schemes such
as relational tables. But, text data don't have such scheme, and information is
described freely in the documents. Therefore, we focus on Natural Language
Processing (NLP) technologies to extract such information. Using NLP
technologies, documents are transformed into a collection of concepts, described
using terms discovered in the text.
Usually, "text
mining" is used to indicate a text search technique. But, we think of text
mining as having more functions. Text mining technologies extract more
information than just picking up keywords from texts: facts, author's
intentions, their expectations, and their claims. Tokyo Research Lab, IBM,
Text Mining http://www.trl.ibm.com/projects/textmining/index_e.htm
Using data mining on unstructured data, such as the
biomedical literature.
Text Mining
Glossary, ComputerWorld, 2004 http://www.computerworld.com/databasetopics/businessintelligence/story/0,10801,93967,00.html
Includes Categorization, clustering, extraction, keyword search, natural
language processing, taxonomy, and visualization.
Related terms: natural language processing; Algorithms
& data analysis: support vector machines
Google = about 20,600 July 19, 2002
about 39,300 July 3, 2003; about 113,000 Oct. 22, 2004; about 1,110,000 June
22, 2007
thesaurus, thesauri: See under controlled vocabulary
Google = thesaurus about
2,760,000 thesauri about 448,000 July 19, 2002; thesaurus about
6,270,000 Oct. 22, 2004
NISO Z39.19 Standard for Structure and
Organization of Information Retrieval Thesauri http://www.niso.org/standards/resources/Z39-19.html
UDDI:
Business of biopharmaceuticals
UMLS Unified Medical Language System
In 1986, the National Library of Medicine (NLM), began a
long term research and development project to build a Unified Medical Language
System ® (UMLS ® ).
The purpose of the UMLS is to aid the development of systems that help health
professionals and researchers retrieve and integrate electronic biomedical
information from a variety of sources and to make it easy for users to link
disparate information systems, including computer- based patient records,
bibliographic databases, factual databases, and expert systems. The UMLS project
develops "Knowledge Sources" that can be used by a wide variety of
applications programs to overcome retrieval problems caused by differences in
terminology and the scattering of relevant information across many databases.
[UMLS FactSheet, National Library of Medicine, NIH, US, 2002] http://www.nlm.nih.gov/pubs/factsheets/umls.html
unstructured data:
Today, transforming
unstructured data into a structured form is primarily a manual process; it is
time consuming and costly. However, all leading software applications must
leverage structured data to be effective. [About Mohomine] http://www.mohomine.com/about/index.asp
Generally free text, natural language.
Related term: natural language
processing. Compare structured.
Google = about 21,200 July 19, 2002
variance: One of the two components of
measurement error (the other one being bias). Variance results from
uncontrolled (or uncontrollable) variation that occurs in biological samples,
experimental procedures, and arrays themselves;
versioning:
visualization:
A method of computing by which the enormous bandwidth and
processing power of the human visual (eye- brain) system becomes an integral
part of extracting knowledge from complex data. It utilizes graphics and
imaging techniques as well as knowledge of both data management and the human
visual system. [Lloyd Trenish, Visualization for Deep Thunder, IBM
Research, 2002] http://www.research.ibm.com/weather/vis/w_vis.htm
Use of computer-
generated graphics to make the
information more accessible and interactive. Related term data mining Narrower terms:
data
visualization, information visualization; Algorithms
& data analysis dendogram, heat map, profile chart
visualisation:
As
the quantity of data produced by simulations grows, so does the difficulty of
extracting useful information. It is now clear that in many applications visual
methods are the only practical way of extracting information from the data.
Computer graphics and scientific visualisation techniques have become more
important in the last few years with the increased availability of computing
resource and of visualisation tools. Visualisation is becoming one of the
key tools for problem solving both in traditional areas such as visualisation of
complex flow and in new applications areas like the planning of surgical
operations using 3-D recontruction of anatomical sites using diagnostic images
or the development of highly-realistic aeroplane simulators for pilot
training. DIRECT Development of an Interdisciplinary Roundtable for
Emerging Computer Technologies, Edinburgh University, Scotland http://www.epcc.ed.ac.uk/DIRECT/vect.html
Definitions and
Rationale for Visualisation, D. Scott
Brown, SIGGRAPH, 1999 http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal2.htm
W3C World Wide Web Consortium: Develops
interoperable technologies (specifications, guidelines, software, and tools) to
lead the Web to its full potential. W3C is a forum for information, commerce,
communication, and collective understanding. http://www.w3.org/
web:
The genome community was an early adopter of the Web, finding in it a way to publish
its vast accumulation of data, and to express the rich interconnectedness of biological information. The Web is the home of primary data, of
genome maps, of expression data, of DNA and
protein sequences, of X-ray crystallographic structures, and of the genome project's huge outpouring of publications. ... However the Web is much more than a static repository of information. The Web is increasingly being used as a front end for sophisticated analytic software. Sequence similarity search engines, protein structural motif finders, exon identifiers, and even mapping programs have all been integrated into the Web. Java applets are adding rapidly to Web browsers' capabilities, enabling pages to be far more interactive than the original click- fetch- click interface. [Lincoln D. Stein "Introduction to Human Genome Computing via the World Wide Web", Cold Spring Harbor Lab, 1998]
Related terms: fractal nature of the
web, weblike Narrower terms: semantic web, web portals, web
services
web harvesting: A Web site is usually viewed as a collection of individual pages interconnected by a simple URL links. This is the common
basis for Web harvesting engines, where these pages are harvested, indexed, and the search results made available to
end- users. As Web sites become increasingly large and sophisticated, it is worthwhile to see how prevalent simple linking is, or
if other Web page navigation techniques are replacing the simple linking model.
[Web Characterization Project, OCLC, 2001] http://wcp.oclc.org/pubs/rn2-navigation.html
Google = about 536 July 19, 2002;
about 3,000 Oct. 22, 2004
weblogs:
Wikipedia http://en.wikipedia.org/wiki/Weblogs
A
history and a perspective http://www.rebeccablood.net/essays/weblog_history.html
Bob's
Weblog Backgrounder,
Bob Stepno http://radio.weblogs.com/0106327/stories/2002/12/14/bobsWeblogBackgrounder.html
Related
terms: blog, blogging, blogosphere, microcontent, nanopublishing
web portals: 2.1 Web
Portals, W3C, Requirements for a web ontology
language, work in progress http://www.w3.org/TR/webont-req/#usecase-portal
Google = about 74,600 ("web portal" about
738,000) July 19, 2002
Web search
glossary, Google http://www.google.com/support/bin/answer.py?answer=50187
60 definitions
web service interoperability: Web services
technology has the promise to provide a new level of interoperability between
software applications. It should be no wonder then that there is a rush by
platform providers, software developers, and utility providers to enable their
software with SOAP, WSDL, and UDDI capabilities. http://www-106.ibm.com/developerworks/webservices/library/ws-inter.html
Google = "web service
interoperability" about 412 "web services interoperability"
about 9,620 July 19, 2002; about 283,000 Nov 17, 2006
web services: The goal of the Web Services Activity
is to develop a set of technologies in order to bring Web services to their full
potential. W3C "Web Services Activity 2002 http://www.w3.org/2002/ws/
Google = about 2,110,000 July 19, 2002;
about 122,000,000 Nov 17, 2006
Web services
glossary, W3C, http://www.w3.org/TR/ws-gloss/
webizing: "Webizing Existing
Systems" Tim Berners-Lee, last updated 2001 http://www.w3.org/DesignIssues/Webize
weblike:
[Tim Berners- Lee, Ralph
Swick, Semantic web Amsterdam, 2000 May 16] http://www.w3.org/2000/Talks/0516-sweb-tbl/slide3-1.html
Tim
Berners- Lee writes in his account of coming up with the idea of the web
Weaving the Web about "learning to think in a weblike way". I don't know that I can claim to approach this yet, but the more that I write and research this glossary on and for the web, the more insight I'm getting into what he might mean. Metaphors
like "shooting at a moving target" and like Wayne Gretzky
"skating to where the puck is going to be" are helpful images.
Google = about 3,020
July 19, 2002; about 5,510 Oct. 22, 2004; about 75,700 Nov 17, 2006
"web like" about 788,000,000 Nov 17, 2006
Wiki
collaborative software:
Allows users to post and edit content remotely. An
exciting (and free) way to build and manage content. Wiki Web sites allow
all users to add and edit content. While it might sound like a free-for-all, the
authors suggest such Web sites have been used successfully in research,
business, and education to document project designs, for brainstorming, and for
otherwise creating content in a collaborative fashion. Bo Leuf, Ward
Cunningham, The
Wiki Way: Collaboration and sharing on the internet, 2001
wild
cards and Google
http://www.google.com/support/bin/answer.py?answer=3178&ctx=sibling
Yes you can.
XML: Computers
& computing
Bibliography
Barnes, Ken et. al, Microsoft Lexicon or Microspeak made easier,
1995- 1998, 150 +
terms. http://www.cinepad.com/mslex.htm
FOLDOC Free On-line Dictionary of Computing, Denis Howe, 2007.
14,400+ terms. http://foldoc.org/
Glossary of Ontology Terms, Stanford Univ., 2001, 24 terms.
http://www-ksl-svc.stanford.edu:5915/doc/frame-editor/glossary-of-terms.html
Information Resource Management
Glossary, Government of British Columbia, Canada, 2001 http://www.cio.gov.bc.ca/other/daf/IRM_Glossary.htm
Lycos Tech
Glossary 2002 http://webopedia.lycos.com/
Barnes,
Ken et. al, Microsoft Lexicon or Microspeak made easier, 1995- 1998, 150 +
terms. http://www.cinepad.com/mslex.htm
Schneider, Tom and
Karen Lewis, Glossary for Molecular Information Theory and the Delila System, Lab of Computational and Experimental Biology, NCI
Frederick, US, 2004. 100+ definitions. http://www.lecb.ncifcrf.gov/~toms/glossary.html
W3C Glossary and
Dictionary http://www.w3.org/2003/glossary/
Web search glossary, Google http://www.google.com/support/bin/answer.py?answer=50187
60 definitions
Web services
glossary, W3C, http://www.w3.org/TR/ws-gloss/
Webopedia http://www.webopedia.com/
whatis.com Information Technology encyclopedia. About 3,000 + definitions.
http://whatis.techtarget.com/
XML
Glossary http://www.softwareag.com/xml/about/glossary.htm
Alpha
glossary index
IUPAC definitions are reprinted with the permission of
the International Union of Pure and Applied Chemistry.
How
to look for other unfamiliar terms
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