A specification is a formal method for describing objects (physical objects such as nuts and bolts or
symbolic objects, such as numbers, or concepts expressed as text). In general, specifications do not require
the inclusion of specific items of information (ie, they do not impose restrictions on the content that is included in or excluded
from documents), and specifications do not impose any order of appearance of the data contained in the document
(ie, you can mix up and rearrange specified objects, if you like). Specifications are not generally certified
by a standards organization. They are generally produced by special interest organizations, and the
legitimacy of a specification depends on its popularity. Examples of specifications are RDF (Resource Description
Framework) produced by the W3C (World Wide Web Consortium), and TCP/IP (Transfer Control
Protocol/Internet Protocol), maintained by the Internet Engineering Task Force. The most widely implemented
specifications are simple and easily implemented.
Data standards, in general, tell you what must be included in a conforming document,
and, in most cases, dictate the format of the final document. In many instances, standards bar inclusion
of any data that is not included in the standard (eg, you should not include astronomical data in a standard clinical
X-ray report). Specifications simply provide a formal way for describing the data that you choose to include
in your document. XML and RDF, a semantic dialect of XML, are examples of specifications. They both tell
you how data should be represented, but neither tell you what data to include, or how your document or data
set should appear. Files that comply with a standard are rigidly organized and can be easily parsed and manipulated
by software specifically designed to adhere to the standard. Files that comply with a specification are typically
self-describing documents that contain within themselves all the information necessary for a human or a computer
to derive meaning from the file contents. In theory, files that comply with a specification can be parsed and
manipulated by generalized software designed to parse the markup language of the specification (eg,XML, RDF)
and to organize the data into data structures defined within the file.
- Jules Berman (copyrighted material)
key words: standard, xml, rdf, resource description framework, jules j berman
Showing posts with label specification. Show all posts
Showing posts with label specification. Show all posts
Sunday, January 17, 2016
Wednesday, September 15, 2010
Naming rocks, minerals, and gems
Rocks, minerals, and gems have a rich vocabulary. There seems to be only one naming rule: no uppercase letters. This might be intended to simplify the nomenclature, but it can be confusing when you encounter a name such as "childrenite." You assume that name was inspired by a child, but the name comes from an adult; J.G. Children. The practice of using lowercase for eponyms is different from anatomic nomenclatures, wherein capitalization is preserved (e.g., Eustachian tube, not eustachian tube, after Bartolomeo Eustachio).
Similarly, you would think that "greenockite" must be a green rock (it's most often yellow, not green). It gets its name from Lord Greenock.
Likewise, biotite does not derive from a biologic precursor. It's named after Jean Biot.
Contrarily, rocks are sometimes named for lowercase (common) nouns.
Sepiolite is named for the cuttlefish bone, sepia.
Serpentine is named after the snake.
Where rocks get names from people, it's usually the surname. But not always.
Torbernite is named for Torbern Olaf Bergmann (the given name).
But don't get carried away. Bruceite was not named after someone whose given name was Bruce. It was named for a surname (Archibald Bruce). Likewise, Vivianite was not named after a woman named Vivian. It also came from the surname: J.G. Vivian
Perhaps the final solution for naming rocks after mineralogists was solved with the naming of frankhawthorneite after Professor Frank Hawthorne, of the University of Manitoba.
Here are a few more surprises:
If a gem is given a name, you'd think that it must be distinguishable from other gems with different names. No. Sapphire and ruby are the same gem, with different colorations (due to impurities in the stone). They're color-variants of corundum. Similarly, amethyst is just a color variant (purple) of common quartz.
Color in a mineral's name can be highly misleading. Glaucodot (greek for "blue"), is a gray to white mineral; never blue. Glaucodot is, however, used in the manufacture of blue glass, but you'd never know that by looking at the mineral.
Some rocks are named after the place where it was discovered or mined.
For example bytownite is named for Bytown, the former name for what is now called Ottawa.
This can be confusing, as franklinite is not named for Ben Franklin. It's named for Franklin, New Jersey. The city was named for Ben Franklin, but not the rock.
Consider Trona, California, where trona (sodium bicarbonate, and variously called tron) is mined. The mineral was not named for the city. The city was named for the mineral, which took it's name from tron, a shortened form of natron, the Arabic word for sodium.
Some rocks are named for their taste:
Calomel (probably from ancient Greek, meaning honey-taste)
or odor:
Scorodite (garlic-like, in Greek)
Some rocks are named after their included elements.
Bismuthinite contains bismuth, as you would expect. Zincote (a zinc dispersion) contains zinc, as does zincite (a true mineral).
But
Zinkenite contains no zinc (named after JKL Zinken, a German mineralogist).
Similarly, selenite, a clear crystal form of gypsum, contains no selenium.
If you're interested in the names of rocks, you must really know your Latin. Septarian concretions have complex internal structures, with multiple branches. You might think that the term comes from the latin septem (seven) referring to the number of branches. You'd be wrong. The name comes from the latin septum (partition).
It's also good to know your mythology. Pollucite was named for Pollux, the twin of Castor. The name has a certain inevitability. Pollucite is often found alongside petalite, previously known as castorite.
Some rocks are named for their geometry. There's tetrahedrite (tetrahedral crystals), triplite, clinoclase, microcline, and anorthoclase. But you can never generalize in mineralogy. Anglesite is not named for the angles in the crystal. It's named for Anglesey, Wales, where it is mined.
Sometimes, the relationship between a rock and it's name can be the opposite of what you might imagine. Fluorite is a rock that fluoresces. You might imagine that it was named because it had the property of fluorescence. Wrong. Fluorite was named for fluorine, from which it is composed (CaF2). The mineral fluorite was found to change color under UV light. The phenomenon was called fluorescence, after the first mineral shown to produce the effect. Today, every mineral that changes it's emission color under UV light is said to be fluorescent, whether it contains fluorine or not.
Sometimes you're sure a rock's name has been misspelled. Surely goethite should be geothite. Alas, no. Goethite is named for the polymath Wolfgang van Goethe.
In summary, if you're interested in the semiotics of rocks, you (unlike the rocks) will need to be flexible.
- © 2010 Jules Berman tags: nomenclature, specification, geology, rocks and minerals, hobbyists, semiotics, logophiles
Science is not a collection of facts. Science is what facts teach us; what we can learn about our universe, and ourselves, by deductive thinking. From observations of the night sky, made without the aid of telescopes, we can deduce that the universe is expanding, that the universe is not infinitely old, and why black holes exist. Without resorting to experimentation or mathematical analysis, we can deduce that gravity is a curvature in space-time, that the particles that compose light have no mass, that there is a theoretical limit to the number of different elements in the universe, and that the earth is billions of years old. Likewise, simple observations on animals tell us much about the migration of continents, the evolutionary relationships among classes of animals, why the nuclei of cells contain our genetic material, why certain animals are long-lived, why the gestation period of humans is 9 months, and why some diseases are rare and other diseases are common. In “Armchair Science”, the reader is confronted with 129 scientific mysteries, in cosmology, particle physics, chemistry, biology, and medicine. Beginning with simple observations, step-by-step analyses guide the reader toward solutions that are sometimes startling, and always entertaining. “Armchair Science” is written for general readers who are curious about science, and who want to sharpen their deductive skills.
Similarly, you would think that "greenockite" must be a green rock (it's most often yellow, not green). It gets its name from Lord Greenock.
Likewise, biotite does not derive from a biologic precursor. It's named after Jean Biot.
Contrarily, rocks are sometimes named for lowercase (common) nouns.
Sepiolite is named for the cuttlefish bone, sepia.
Serpentine is named after the snake.
Where rocks get names from people, it's usually the surname. But not always.
Torbernite is named for Torbern Olaf Bergmann (the given name).
But don't get carried away. Bruceite was not named after someone whose given name was Bruce. It was named for a surname (Archibald Bruce). Likewise, Vivianite was not named after a woman named Vivian. It also came from the surname: J.G. Vivian
Perhaps the final solution for naming rocks after mineralogists was solved with the naming of frankhawthorneite after Professor Frank Hawthorne, of the University of Manitoba.
Here are a few more surprises:
If a gem is given a name, you'd think that it must be distinguishable from other gems with different names. No. Sapphire and ruby are the same gem, with different colorations (due to impurities in the stone). They're color-variants of corundum. Similarly, amethyst is just a color variant (purple) of common quartz.
Color in a mineral's name can be highly misleading. Glaucodot (greek for "blue"), is a gray to white mineral; never blue. Glaucodot is, however, used in the manufacture of blue glass, but you'd never know that by looking at the mineral.
Some rocks are named after the place where it was discovered or mined.
For example bytownite is named for Bytown, the former name for what is now called Ottawa.
This can be confusing, as franklinite is not named for Ben Franklin. It's named for Franklin, New Jersey. The city was named for Ben Franklin, but not the rock.
Consider Trona, California, where trona (sodium bicarbonate, and variously called tron) is mined. The mineral was not named for the city. The city was named for the mineral, which took it's name from tron, a shortened form of natron, the Arabic word for sodium.
Some rocks are named for their taste:
Calomel (probably from ancient Greek, meaning honey-taste)
or odor:
Scorodite (garlic-like, in Greek)
Some rocks are named after their included elements.
Bismuthinite contains bismuth, as you would expect. Zincote (a zinc dispersion) contains zinc, as does zincite (a true mineral).
But
Zinkenite contains no zinc (named after JKL Zinken, a German mineralogist).
Similarly, selenite, a clear crystal form of gypsum, contains no selenium.
If you're interested in the names of rocks, you must really know your Latin. Septarian concretions have complex internal structures, with multiple branches. You might think that the term comes from the latin septem (seven) referring to the number of branches. You'd be wrong. The name comes from the latin septum (partition).
It's also good to know your mythology. Pollucite was named for Pollux, the twin of Castor. The name has a certain inevitability. Pollucite is often found alongside petalite, previously known as castorite.
Some rocks are named for their geometry. There's tetrahedrite (tetrahedral crystals), triplite, clinoclase, microcline, and anorthoclase. But you can never generalize in mineralogy. Anglesite is not named for the angles in the crystal. It's named for Anglesey, Wales, where it is mined.
Sometimes, the relationship between a rock and it's name can be the opposite of what you might imagine. Fluorite is a rock that fluoresces. You might imagine that it was named because it had the property of fluorescence. Wrong. Fluorite was named for fluorine, from which it is composed (CaF2). The mineral fluorite was found to change color under UV light. The phenomenon was called fluorescence, after the first mineral shown to produce the effect. Today, every mineral that changes it's emission color under UV light is said to be fluorescent, whether it contains fluorine or not.
Sometimes you're sure a rock's name has been misspelled. Surely goethite should be geothite. Alas, no. Goethite is named for the polymath Wolfgang van Goethe.
In summary, if you're interested in the semiotics of rocks, you (unlike the rocks) will need to be flexible.
- © 2010 Jules Berman tags: nomenclature, specification, geology, rocks and minerals, hobbyists, semiotics, logophiles
Science is not a collection of facts. Science is what facts teach us; what we can learn about our universe, and ourselves, by deductive thinking. From observations of the night sky, made without the aid of telescopes, we can deduce that the universe is expanding, that the universe is not infinitely old, and why black holes exist. Without resorting to experimentation or mathematical analysis, we can deduce that gravity is a curvature in space-time, that the particles that compose light have no mass, that there is a theoretical limit to the number of different elements in the universe, and that the earth is billions of years old. Likewise, simple observations on animals tell us much about the migration of continents, the evolutionary relationships among classes of animals, why the nuclei of cells contain our genetic material, why certain animals are long-lived, why the gestation period of humans is 9 months, and why some diseases are rare and other diseases are common. In “Armchair Science”, the reader is confronted with 129 scientific mysteries, in cosmology, particle physics, chemistry, biology, and medicine. Beginning with simple observations, step-by-step analyses guide the reader toward solutions that are sometimes startling, and always entertaining. “Armchair Science” is written for general readers who are curious about science, and who want to sharpen their deductive skills.
Friday, April 6, 2007
Tissue Microarray Data Exchange Specification
The Tissue Microarray Data Exchange Specification (TMA Specification) was developed by the Association for Pathology Informatics (API) and is available as an open access document, published in 2003.
TMAs, first introduced in 1998, are collections of hundreds of tissue cores arrayed into a single paraffin histology block (see Figure).
Each TMA block can be sectioned and mounted onto glass slides, producing hundreds of nearly-identical slides. TMAs permit investigators to use a single slide to conduct controlled studies on large cohorts of tissues, using a small amount of reagent. The source of tissue is only restricted by its availability in paraffin and ranges from cores of embedded cultured cells to tissues from any higher organism. In a typical TMA study, every TMA core is associated with a rich variety of data elements (image, tissue diagnosis, patient demographics or other biomaterial description, quantified experimental results). Under ideal circumstances, a single paraffin TMA block can be sectioned into nearly identical glass slides dispensed to many different laboratories. These laboratories may use different experimental protocols. They may capture data using different instruments, different databases, different data architectures, different data elements and immensely different formats. These laboratories would vastly increase the value of their experimental findings if they merged their findings with those of the other laboratories that used the same TMA block. Unfortunately, the practice of merging TMA data sets obtained at different laboratories using different information systems was infrequently practiced. A key barrier to this process was the incompatibility of the individual data sets. There simply was no community-based specification for exchanging TMA data. Without such a specification, TMA data files could not be effectively shared or merged among laboratories using different TMA applications.
Since publication of the TMA Specification week, the TMA Specification was downloaded from the BioMed Central site 7,424 times (April 2007).
At Google Scholar, the TMA specification paper was listed as being cited by 36 papers.
The TMA Specification has been adopted into several TMA database applications.
The common data elements for the specification are freely available.
An implemenation of the specification by an NCI-funded research consortium is also available.
-Jules Berman
My book, Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information was published in 2013 by Morgan Kaufmann.
I urge you to explore my book. Google books has prepared a generous preview of the book contents.
tags: big data, metadata, data preparation, data analytics, data repurposing, datamining, data mining
TMAs, first introduced in 1998, are collections of hundreds of tissue cores arrayed into a single paraffin histology block (see Figure).
Each TMA block can be sectioned and mounted onto glass slides, producing hundreds of nearly-identical slides. TMAs permit investigators to use a single slide to conduct controlled studies on large cohorts of tissues, using a small amount of reagent. The source of tissue is only restricted by its availability in paraffin and ranges from cores of embedded cultured cells to tissues from any higher organism. In a typical TMA study, every TMA core is associated with a rich variety of data elements (image, tissue diagnosis, patient demographics or other biomaterial description, quantified experimental results). Under ideal circumstances, a single paraffin TMA block can be sectioned into nearly identical glass slides dispensed to many different laboratories. These laboratories may use different experimental protocols. They may capture data using different instruments, different databases, different data architectures, different data elements and immensely different formats. These laboratories would vastly increase the value of their experimental findings if they merged their findings with those of the other laboratories that used the same TMA block. Unfortunately, the practice of merging TMA data sets obtained at different laboratories using different information systems was infrequently practiced. A key barrier to this process was the incompatibility of the individual data sets. There simply was no community-based specification for exchanging TMA data. Without such a specification, TMA data files could not be effectively shared or merged among laboratories using different TMA applications.
Since publication of the TMA Specification week, the TMA Specification was downloaded from the BioMed Central site 7,424 times (April 2007).
At Google Scholar, the TMA specification paper was listed as being cited by 36 papers.
The TMA Specification has been adopted into several TMA database applications.
The common data elements for the specification are freely available.
An implemenation of the specification by an NCI-funded research consortium is also available.
-Jules Berman
My book, Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information was published in 2013 by Morgan Kaufmann.
I urge you to explore my book. Google books has prepared a generous preview of the book contents.
tags: big data, metadata, data preparation, data analytics, data repurposing, datamining, data mining
Saturday, March 3, 2007
Patent farming in medical standards
In a prior blog, I listed some of the IP issues involving medical standards. One of the biggest problems is the inclusion of patented methods within standards. Hidden patents within standards can be asserted against anyone who implements the standard.
Bruce Perens, an open source Guru, has written extensively on this problem. He uses the term "patent farming" to describe the act of planting the seed, a patented method, into the standard while the standard is being developed. Once the standard is approved and implemented by the user community, the patent farmer reaps his crop by asserting the hidden patent. Perens provides several specific examples where patent farming has benefited standards committee members to the detriment of the users of the standard.
You might think that it would be a simple matter to require the participants in a standards effort to disclose any of their owned patents that might be included in the standard. In the Rambus case described by Perens, such a disclosure agreement was in evidence, but the patent disclosure policy of the standards organization proved unenforceable in court.
Even if an SDO (Standards Development Organization) had an enforceable disclosure agreement with its members, it would seem that a committee member could sell strategic patents to a patent holding company, striking a deal with the holding company that would yield IP earnings on an asserted patent positioned within a standard. By working with a holding company, the original patent holder divests the patent and would not need to disclose the existence of the patent to the standards committee.
Patent holding companies (called patent trolls by their detractors) buy portfolios of patents to assert IP rights in strategically chosen business sectors. Recently, hospital-based technologies (including radiology imaging) have been the target of at least one major patent holding company.
In the next blog, I will discuss measures that SDOs may take to reduce their vulnerability to patent farmers. I will also discuss some defensive strategies that are often suggested but which really do not work.
This blog will also appear on Bruce Friedman's Labsoft
site
- Jules Berman
My book, Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information was published in 2013 by Morgan Kaufmann.
I urge you to explore my book. Google books has prepared a generous preview of the book contents.
tags: big data, metadata, data preparation, data analytics, data repurposing, datamining, data mining, hidden patent, intellectual property, ip, medical standards, patent holding company, patent troll, specification, viral patent
Bruce Perens, an open source Guru, has written extensively on this problem. He uses the term "patent farming" to describe the act of planting the seed, a patented method, into the standard while the standard is being developed. Once the standard is approved and implemented by the user community, the patent farmer reaps his crop by asserting the hidden patent. Perens provides several specific examples where patent farming has benefited standards committee members to the detriment of the users of the standard.
You might think that it would be a simple matter to require the participants in a standards effort to disclose any of their owned patents that might be included in the standard. In the Rambus case described by Perens, such a disclosure agreement was in evidence, but the patent disclosure policy of the standards organization proved unenforceable in court.
Even if an SDO (Standards Development Organization) had an enforceable disclosure agreement with its members, it would seem that a committee member could sell strategic patents to a patent holding company, striking a deal with the holding company that would yield IP earnings on an asserted patent positioned within a standard. By working with a holding company, the original patent holder divests the patent and would not need to disclose the existence of the patent to the standards committee.
Patent holding companies (called patent trolls by their detractors) buy portfolios of patents to assert IP rights in strategically chosen business sectors. Recently, hospital-based technologies (including radiology imaging) have been the target of at least one major patent holding company.
In the next blog, I will discuss measures that SDOs may take to reduce their vulnerability to patent farmers. I will also discuss some defensive strategies that are often suggested but which really do not work.
This blog will also appear on Bruce Friedman's Labsoft
site
- Jules Berman
My book, Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information was published in 2013 by Morgan Kaufmann.
I urge you to explore my book. Google books has prepared a generous preview of the book contents.
tags: big data, metadata, data preparation, data analytics, data repurposing, datamining, data mining, hidden patent, intellectual property, ip, medical standards, patent holding company, patent troll, specification, viral patent
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