|
The dividing line between this glossary and
Algorithms
& data analysis
is very fuzzy. In general this one focuses primarily
on unstructured data
(or a combination of structured and unstructured), while
Algorithms
centers on structured data Finding guide to terms in these glossaries
Informatics term
index Informatics includes Bioinformatics Clinical
informatics Drug
discovery informatics
IT
infrastructure
Ontologies & Taxonomies
are subsets of, and critical tools for Information management &
interpretation Technologies
Microarrays & protein chips
Sequencing
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:
Data science & machine learning
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.
collaborative filtering:
Drawing
upon the collective navigation and purchasing behavior of users creates a highly
distributed, adaptive solution. Amazon is the reigning champion, featuring People
who bought this item also bought and Purchase
Circles. Other examples
include Microsoft’s Top
Downloads and mp3.com’s Weekly
Bottom 40.
Peter Morville
Innovation Architecture
http://semanticstudios.com/innovation_architecture/
Wikipedia
https://en.wikipedia.org/wiki/Collaborative_filtering
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
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,
https://aclweb.org/aclwiki/Frequently_asked_questions_about_Computational_Linguistics
Linguistics, natural language, and
computational linguistics Meta- Index, Stanford Univ. US
http://www-nlp.stanford.edu/links/linguistics.html
data cleaning, data integration:
Algorithms
& data analysis
data
management methods:
Algorithms & data analysis
has automated methods, methods in this glossary generally
combine human and automated methods.
data mapping:
Wikipedia
http://en.wikipedia.org/wiki/Data_mapping
data management:
Each new generation of DNA sequencers, mass spectrometers, microscopes,
and other lab equipment produces a richer, more detailed set of data.
We’re already way beyond gigabytes (GB): a single next-generation
sequencing experiment can produce terabytes (TB) of data in a single run.
As a result, any organization running hundreds of routine experiments a
month or year, or trying to handle the output of next-generation sequence
instruments, quickly finds itself with a massive data management problem.
Data Management: The Next Generation, Salvatore Salamone, BioIT World, Oct
2007
http://www.bio-itworld.com/issues/2007/oct/cover-story-data-management
data mining:
Nontrivial extraction of implicit, previously unknown and potentially
useful information from data, or the search for relationships and global
patterns that exist in databases. W. Frawley and G. Giatetsky-Schapiro
and C. Matheus, “Knowledge Discovery in Databases: An Overview.” AI
Magazine, 213- 228, Fall 1992 Exploration and analysis, by automatic or
semi- automatic means, of large quantities of data in order to discover
meaningful patterns or rules. Berry, MJA, Data Mining Techniques for
Marketing, Sales and Customer Support John Wiley & Sons, New York 1997
cited in Nature Genetics 21(15): 51-55 ref 11, 1999
data
quality: A vital consideration for
data analysis and interpretation. While people are still reeling from the
vast amount of data becoming available, they need to brace themselves to both
discard low quality data and handle much more at the same time.
Data quality glossary, Graham Rind, GRC Data Intelligence,
http://www.dqglossary.com/
6,700 terms.
data science:
Data science & machine learning
Data
Visualization & Exploration Tools: Omics,
Drug Discovery, and Clinical Development 2020 April 21-23 Boston MA
With a sharp increase in the volume
and complexity of big data sets for research and drug discovery labs, data
visualization is needed to clearly express the complex patterns. It is
more important than ever to develop data visualization and exploration
tools alongside the rest of the analytics, as opposed to later in the
game. The Data
Visualization & Exploration Tools track
will address ways to not only develop, design, and implement visualization
tools in genomics, drug discovery, clinical development, and translational
research, but also address real-world case studies where these tools have
been successfully used. https://www.bio-itworldexpo.com/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 Definitions and
Rationale for Visualisation, D. Scott
Brown, SIGGRAPH, 1999 http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal2.htm
Related term: information visualization;
Broader term: visualization
databases:
Bioinformatics;
Databases & software directory
deep web:
http://en.wikipedia.org/wiki/Deep_Web
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/
digitization,[1][2] less commonly digitalization,[3][4][5] is the process of converting
information into a digital (i.e.
computer-readable) format, in which the information is organized into bits.[1][2] The
result is the representation of an object, image, sound, document or signal (usually an analog
signal) by generating a series of numbers that describe a
discrete set of its points or samples. The result is called digital representation or, more specifically, a digital image, for the object, and digital
form, for the signal. In modern practice, the digitized data is in the
form of binary numbers, which facilitate computer
processing and other operations, but, strictly speaking, digitizing simply
means the conversion of analog source material into a numerical format;
the decimal or
any other number system that can be used instead.
Digitization is of crucial importance to data processing, storage and
transmission, because it "allows information of all kinds in all formats
to be carried with the same efficiency and also intermingled".[6] Unlike
analog data, which typically suffers some loss of quality each time it is
copied or transmitted, digital data can, in theory, be propagated
indefinitely with absolutely no degradation. This is why it is a favored
way of preserving information for many organisations around the world.
Wikipedia accessed 2019 July 8
https://en.wikipedia.org/wiki/Digitization
disambiguate:
Make less ambiguous, clarify,
elucidate.
domain expertise: Wikipedia
http://en.wikipedia.org/wiki/Domain_expert
http://en.wikipedia.org/wiki/Domain_knowledge
Dublin Core:
The Dublin Core
Metadata Initiative, or "DCMI", is an open organization supporting innovation in
metadata design and best practices across the metadata ecology. DCMI's
activities include work
on architecture and modeling,
discussions and collaborative work in DCMI
Communities and DCMI Task Groups,
global conferences,
meetings and workshops,
and educational efforts to promote widespread acceptance of metadata standards
and best practices.
http://dublincore.org/about/
evolvability:
Tim Berners Lee defines
http://www.w3.org/Talks/1998/0415-Evolvability/slide3-1.htm
Wikipedia
http://en.wikipedia.org/wiki/Evolvability
See
also under interoperability
federated databases:
An integrated repository data from of multiple, possibly heterogeneous, data sources presented with consistent and
coherent semantics. They do not usually contain any summary data, and all of the data resides only at the data source (i.e. no local storage).
Lawrence Berkeley Lab "Advanced Computational
Structural Genomics" Glossary
federated information systems.
Their main characteristic is that they
are constructed as an integrating layer over existing
legacy applications and
databases. They can be broadly classified in three dimensions: the degree of
autonomy they allow in integrated components, the degree of heterogeneity
between components they can cope with, and whether or not they support
distribution. Whereas the communication and interoperation problem has come into
a stage of applicable solutions over the past decade, semantic data integration
has not become similarly clear. Susanne Busse et. al "Federated
Information Systems: Concepts, Terminology and Architecture"
Computergestützte Informations Systeme CIS, Berlin, Germany
https://en.wikipedia.org/wiki/Federated_architecture
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 This
article was originally entitled "The Fractal nature of the web". Since then, i
have been assured that while many people seem to use fractal to
refer to a Zipf (1/f) distribution, it should really only be used in spaces of
finite dimension, like the two-dimensional planes of MandelBrot sets. The
correct term for the Web, then, is scale-free.
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
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 http://foldoc.org/grain
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
Wikipedia
http://en.wikipedia.org/wiki/Granularity
Level of detail seems to be the essence of granularity.
informatics:
a branch of
information engineering. It involves the practice of information
processing and
the engineering of information
systems, and as
an academic
field it is an applied form
of information
science. The
field considers the interaction between humans and information alongside the
construction of interfaces, organisations, technologies and systems. As such,
the field of informatics has great breadth and encompasses many subspecialties,
including disciplines of computer
science, information
systems, information
technology and statistics.
Since the advent of computers, individuals and organizations increasingly
process information digitally. This has led to the study of informatics with
computational, mathematical, biological, cognitive and social aspects, including
study of the social impact of information technologies.
https://en.wikipedia.org/wiki/Informatics
Narrower terms:
bioinformatics;
cheminformatics; clinical
informatics: molecular informatics,
Biomaterials:
matinformatics
research informatics;
Drug
discovery & development: life sciences informatics,
Intellectual
property & legal; patinformatics;
Molecular
imaging: image informatics;
pharmacoinformatics,
Protein informatics
information architecture:
The art and science of organizing
information to help people effectively fulfill their information needs.
Information architecture involves investigation, analysis, design and
implementation. Top-down and bottom-up are the two main approaches to developing
information architectures; these approaches inform each other and are often
developed simultaneously. See also: bottom-up information architecture,
top-down information architecture, user investigation.
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
information extraction:
Automated ways of extracting unstructured or partially structured information from
machine readable files. Compare with
information retrieval. Related
terms: natural language
processing, term extraction
information harvesting: See under
Knowledge Discovery in Databases KDD
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/
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
Molecular Biomedicine in the Era of Teraflop Computing - DDDAS.org
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
Where's
my stuff? Ways to help with information overload, Mary Chitty, SLA
presentation June 10, 2002, Los Angeles CA
Wikipedia
http://en.wikipedia.org/wiki/Information_overload
information
retrieval:
Wikipedia
http://en.wikipedia.org/wiki/Information_retrieval
information visualization:
Wikipedia
http://en.wikipedia.org/wiki/Information_visualization
Related term: data visualization; Broader
term: visualization
invisible web:
Unlike pages on the
visible Web (that is, the Web that you can access from search engines and
directories), information in databases is generally inaccessible to the software spiders
and crawlers that create search engine indexes. Users are able to access most of
this information, but only through specific searches that unlock where this
information lives.
https://www.lifewire.com/invisible-web-how-to-find-it-3482490
Invisible web medical information
https://www.lifewire.com/medical-information-on-invisible-web-3482493
2017
Related
terms: deep web, semantic web
knowledge integration:
Wikipedia
http://en.wikipedia.org/wiki/Knowledge_integration
Related terms: ontologies,
semantics
knowledge management:
Systematic approach to acquiring, analyzing, storing, and disseminating
information related to products, manufacturing processes, and components ICH Q10
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm073517.pdf Related terms: ontologies, paraphrase problem, taxonomies
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.
Wikipedia
http://en.wikipedia.org/wiki/Knowledge_management
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.
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
machine-understandable: See under
metadata
markup languages:
Computers
& computing
mash-up
http://en.wikipedia.org/wiki/Mashup_(web_application_hybrid)
metadata:
Ontologies & Taxonomies
NISO Journal
Article Tag Suite (JATS) project
at NCBI is a continuation of the work done here to create and support the
"Archiving and Interchange Tag Suite" or the "NLM DTDs". The current
version (NISO JATS 1.0) reflects the changes made to v0.4 based on public
comments and is fully backward compatible with NLM version 3.0.
https://dtd.nlm.nih.gov/
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).
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. Related terms: ontologies, taxonomies
recall:
The percentage of applicable material retrieved by a specific query or search statement. Compare precision. Related term:
Genetic testing sensitivity
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:
Bradley Rhodes,
http://alumni.media.mit.edu/~rhodes/Papers/remembrance.html
The Remembrance Agent (RA) is a program which augments human memory by
displaying a list of documents which might be relevant to the user's current
context. Unlike most information retrieval systems, the RA runs continuously
without user intervention. Its unobtrusive interface allows a user to pursue or
ignore the RA's suggestions as desired.
Related
terms: collaborative filtering, just in time information
schema:
(databases) A
formal description of the structure of a database:
the names of the tables,
the names of the columns of
each table, and the data
type and other attributes of each
column. (markup languages) A formal description of data, data
types, and data file structures, such
as XML
schemas for XML files.
Wiktionary
https://en.wiktionary.org/wiki/schema
Schema may refer to:
SCHEMA (bioinformatics),
an algorithm used in protein engineering
Schema (genetic algorithms),
a set of programs or bit strings that have some genotypic similarity
Wikipedia disambiguation
https://en.wikipedia.org/wiki/Schema
accessed 2018 Oct 27
semantic:
Ontologies &
taxonomies
social
informatics: Wikipedia
https://en.wikipedia.org/wiki/Social_informatics
soft
computing:
The principal aim of soft computing is to exploit the
tolerance for imprecision and uncertainty to achieve tractability, robustness
and low solution cost. At this juncture, the principal constituents of soft
computing (SC) are fuzzy logic (FL), neural network theory (NN) and
probabilistic reasoning (PR), with the latter subsuming genetic algorithms,
belief networks, chaotic systems, and parts of learning theory. In the
triumvirate of SC, FL is concerned in the main with imprecision, NN with
learning and PR with uncertainty. In large measure, FL, NN and PR are
complementary rather than competitive. It is becoming increasingly clear that in
many cases it is advantageous to employ FL, NN and PR in combination rather than
exclusively.; Zadeh L.A. (1993) The role of fuzzy logic and soft computing in
the conception and design of intelligent systems. In: Klement E.P., Slany W.
(eds) Fuzzy Logic in Artificial Intelligence. FLAI 1993. Lecture Notes in
Computer Science (Lecture Notes in Artificial Intelligence), vol 695. Springer,
Berlin, Heidelberg https://link.springer.com/chapter/10.1007/3-540-56920-0_1
SOAP Simple Object Access Protocol:
https://www.w3.org/TR/soap12/
Software
Applications & Services: Tools
that Best Utilize Data to Drive Scientific Decision Making April 21-23,
2020 Boston MA
As data generation increases, there is a need
for workflows that are reproducible across infrastructures, able to
empower scientists and researchers to apply cutting-edge analysis methods.
A main challenge is scientific data is not centralized or standardized and
is fragmented – from instrumentation to clinical research to legacy
software. Explores how biopharma companies are utilizing software tools to
leverage data platforms to advance data strategies. Case studies will
focus on data analytics approaches, data methods and standards,
approaches, transparency, efficiency, security, and cost-effective
solutions.
https://www.bio-itworldexpo.com/software
syntactic,
syntax:
Ontologies & taxonomies
term extraction:
Robert Futrelle, Northeastern Univ., 2001
http://www.ccs.neu.edu/home/futrelle/bionlp/psb2001/psb01-tutorial-bib1.htm
See related information extraction
term mining:
Term Mining in Biomedicine, Sophia Ananiadou - University of Manchester,
2007
http://talks.cam.ac.uk/talk/index/6769
text
categorisation: See
Algorithms
& data analysis under support vector machines
text mining:
Text mining is the process of analyzing
collections of textual materials in order to capture key concepts and themes and
uncover hidden relationships and trends without requiring that you know the
precise words or terms that authors have used to express those concepts.
Although they are quite different, text mining is sometimes confused with
information retrieval. While the accurate retrieval and storage of information
is an enormous challenge, the extraction and management of quality content,
terminology, and relationships contained within the information are crucial and
critical processes.
h ttps://www.ibm.com/support/knowledgecenter/en/SS3RA7_15.0.0/com.ibm.spss.ta.help/tm_intro_tm_defined.htm
Using
data mining on
unstructured data, such as the
biomedical literature. Related terms: natural language processing;
Algorithms
& data analysis: support vector machines
Text Mining
Glossary, ComputerWorld, 2004
http://www.computerworld.com/s/article/93967/Sidebar_Text_Mining_Glossary
Includes Categorization, clustering, extraction, keyword search, natural
language processing, taxonomy, and visualization.
unstructured data:
Generally free text, natural language.
Related term: natural language
processing. Compare structured.
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;
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
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 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
http://www.w3.org/2002/ws/
Web services
glossary,
W3C,
http://www.w3.org/TR/ws-gloss/
web services 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
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.
workflows:
A collaborative environment where scientists can safely publish their workflows
and experiment plans, share them with groups and find those of others.
Workflows, other digital objects and collections (called
Packs) can now
be swapped, sorted and searched like photos and videos on the Web. ...
myExperiment makes it really easy for the next generation of scientists to
contribute to a pool of scientific workflows, build communities and form
relationships. It enables scientists to share, reuse and repurpose workflows and
reduce time-to-experiment, share expertise and avoid reinvention. myExperiment
http://www.myexperiment.org/
Information Management Resources
Jinfo blog
https://web.jinfo.com/go/blog/
W3C Glossary and
Dictionary
http://www.w3.org/2003/glossary/
Webopedia
http://www.webopedia.com/
whatis.com Information Technology encyclopedia. About 3,000 + definitions.
http://whatis.techtarget.com/
IUPAC definitions are reprinted with the permission of
the International Union of Pure and Applied Chemistry.
How
to look for other unfamiliar terms
|
