Organization of Information

UNC School of Information and Library Science, INLS 520, Fall 2011

August 24
Introduction

First, we get to know each other a bit. Then, the basics: how the the class meetings will be run, how you’ll be evaluated, expectations regarding readings and assignments, and so on. Finally, a brief and high-level overview of the topics that will be covered in the course, and how they are related.

August 29
The Organizing System

This course is an introduction to the conceptual foundations of information organization and retrieval: identifying things, describing things, grouping things, relating things, and selecting things. Traditionally these things have been textual documents in the narrow sense: books, periodicals, letters, administrative records, etc.—the kinds of things organized by libraries and archives. But the principles that underlie organization in libraries and archives can be generalized and applied to organize documents and information more broadly, in a variety of contexts. To emphasize what these contexts have in common, rather than how they differ, we will use the abstract notion of an organizing system.

Explicitly or by default, an organizing system makes many interdependent decisions about the identities of things of interest and the ways they are represented as “information.” The organizing system defines how things will be named and described, how they can be grouped and related, and how people or software can create, transform, combine, compare and otherwise use these names, descriptions, groups and relations. When considering the how to make these decisions, we can ask five questions: What is being organized? Why it is being organized? How much is it being organized? When is it being organized? By whom (or by what computational processes) it is being organized?

To read before this class:

  1. Glushko, Robert J. “1. Foundations for Organizing Systems.” In The Discipline of Organizing, edited by Robert J. Glushko, 3rd ed. O’Reilly, 2015.

    Introduction to the concept of an organizing system and the five facets along which one can analyze organizing systems.

  2. Svenonius, Elaine. “Information Organization.” In The Intellectual Foundation of Information Organization, 1–14. Cambridge, Massachusetts: MIT Press, 2000. PDF.

    Defines the concepts of information and document and proposes a framework for thinking about what systems for organizing information are. Explains why we need a set of principles for designing these systems.

  3. Morville, Peter, and Louis Rosenfeld. “Organization Systems.” In Information Architecture for the World Wide Web, 53–81. 3rd ed. Sebastopol, California: O’Reilly, 2006. http://proquestcombo.safaribooksonline.com/book/web-development/0596527349/basic-principles-of-information-architecture/i86131__chapterstart__chapter_5.

    Broad overview of the ways organizing schemes and structures are deployed on Web sites.

August 31
XML Foundations

Assignment #1 Organization of Information in the News  due

You may already have some familiarity with XML, but perhaps mostly as a data format for applications or programming. In IO and IR it is essential to take a more abstract and intellectual view of XML and understand how it represents structured information models. XML encourages the separation of content from presentation, which is an important principle of information architecture. Encoding information in XML is an investment in information organization that pays off “downstream” in IR and language processing applications.

To read before this class:

  1. Glushko, Robert J. “XML Foundations.” In Document Engineering, 42-72. Cambridge, Massachusetts: MIT Press, 2005. http://people.ischool.berkeley.edu/~glushko/DocumentEngineeringBookDraft/DEBook/ch2_FINAL.pdf.

September 5
Labor Day

No class.

September 7
Identity & Identification

An organizing system reflects (or produces or enforces) a specific view of the world by defining what the things being organized are. This involves making decisions about when things are to be considered the same or different, i.e. how they are to be identified. Decisions about identity and identification define the basic units of organization, and these decisions have consequences for every other aspect of the organizing system.

To read before this class:

  1. Glushko, Robert J. “Design Patterns for Organizing Systems.” In The Discipline of Organizing, edited by Robert J Glushko, 2012. PDF.

    The great variety in what individuals, groups, and enterprises do is reflected in the huge breadth of organizing systems we encounter and the diversity of the resources that these systems organize. Even so, because every organizing system has a collection of resources at its foundation and shares some of the same general purposes and goals, organizing systems tend to follow patterns in how they organize resources, the interactions they support, and how they are implemented and operated.

  2. Glushko, Robert J., Daniel D. Turner, Kimra McPherson, and Jess Hemerly. “3. Resources in Organizing Systems.” In The Discipline of Organizing, edited by Robert J Glushko, 3rd ed. O’Reilly, 2015.

    An organizing system either explicitly creates, or assumes the existence of, a framework for identifying things.

  3. Kent, William. “Entities.” In Data and Reality, v–19. Amsterdam: North-Holland, 1978. PDF.

    Through its (explicit or implicit) framework of identity and identification, an organizing system defines a set of entities. These entities are a model, not of reality, but of how some people or organizations process information about reality.

September 12
Naming & Describing Resources

When we describe things to one another in everyday life, we choose words freely yet our choices depend on our particular experiences and social contexts. As a result, we often use different words for the same things and the same words for different things. (And of course, we may have made different decisions about when things are to be considered the the same or different.) Because these mismatches can have serious consequences for finding and understanding things, an organizing system usually tries to impose some control on the language used to create metadata. It might seem straightforward to control or standardize the language we use, and much technology exists for attacking the “vocabulary problem,” but technology alone is not a complete solution because language use constantly evolves, as does the world being described.

To read before this class:

  1. McPherson, Kimra. “Describing Resources.” In The Discipline of Organizing, edited by Robert J Glushko, 2012. PDF.

    Once we’ve decided what our basic units of organization are—our entities or “instances”—we need to decide how to describe them. What makes a “good” description?

  2. Branting, L. Karl. “Name Matching in Law Enforcement and Counter-Terrorism.” In ICAIL Workshop on Data Mining, Information Extraction, and Evidentiary Reasoning for Law Enforcement and Counter-Terrorism. Bologna, 2005. http://www.karlbranting.net/papers/icail2005.pdf.

    A brief discussion of the challenges of matching personal names and how to address them.

September 14
Document & Data Models

One kind of decision regarding the basic units of organization that is commonly encountered by system designers has to do with the distinction between “documents” and “data.” Some designers contrast the two and argue that they cannot or should not be organized using the same terminology, techniques, and tools. Yet in practice there is no clear boundary between the two. Different design decisions can make the things being organized more “document-like” or “data-like,” and there is a continuous spectrum of decisions that can be made and perspectives that can be taken. Often, however, decisions about whether a system is organizing documents or data depend on how things were done in the past (the history of the people and organizations making the decisions) and reflect unstated assumptions about the nature of the domain.

To read before this class:

  1. Thomale, Jason. “Interpreting MARC: Where’s the Bibliographic Data?” Code4Lib, no. 11 (2010). http://journal.code4lib.org/articles/3832.

    A metadata librarian discusses some of the problems that arise when trying to write computer programs to work with MARC (library cataloging) records. Don’t worry about following the details of MARC or the algorithm he describes; instead focus on his analysis of the broader issues involved regarding MARC’s original purpose vs. the needs of today’s library systems.

  2. Glushko, Robert J. “Modeling Methods and Artifacts for Crossing the Data/Document Divide.” In Proceedings of the 2005 IDEAlliance XML Conference. Amsterdam: IDEAlliance, 2005. http://people.ischool.berkeley.edu/~glushko/glushko_files/GlushkoXML2005.pdf.

    Discusses the differences between treating information as documents vs. treating it as data and argues that rather than a sharp distinction there is a spectrum of modeling choices between the two.

September 19
Metadata

Descriptions can take a number of forms, depending on who is describing some thing and why they are describing it. What a “good” description is cannot be decided outside of these specific contexts, but we can identify some commonly recurring patterns. An organizing system systematizes the process of description by deciding what aspects of a thing will be described and how descriptions will be recorded. This kind of systematized description is called “metadata.” The level and degree of systemization that a system imposes will depend on its context of use.

To read before this class:

  1. Greenberg, Ryan, Kimra McPherson, and Matthew Mayernik. “Metadata: Storing Descriptions.” In The Discipline of Organizing, edited by Robert J Glushko, 2010. PDF.

    The descriptions that we choose to store for an entity constitute metadata. Why do we store these descriptions? Where do we store them? How do we store them? What language do we use to codify the descriptions? Who decides which descriptions we store and which ones go unrecorded? Note that the latest draft of this chapter is not ready yet, so the file linked here is an older draft that has a different title (“Metadata: Storing Descriptions”).

  2. Doctorow, Cory. Metacrap: Putting the Torch to Seven Straw-Men of the Meta-Utopia, 2001. http://www.well.com/~doctorow/metacrap.htm.

    A savage critique of the standard approach to metadata, and a call for a different approach.

  3. Park, Jung-Ran. “Metadata Quality in Digital Repositories: A Survey of the Current State of the Art.” Cataloging & Classification Quarterly 47 (April 9, 2009): 213-228. http://www.tandfonline.com/doi/abs/10.1080/01639370902737240.

    A survey of research looking at metadata quality, measurement, and evaluation criteria and best practices for improving metadata quality.

September 21
Metadata for Visual Media

Assignment #2 Creating a Vocabulary & Descriptions  due

Metadata is particularly important for non-textual and multimedia documents, because it is difficult to index the content of these documents directly. Yet because the meaning of non-textual media is even less fixed than that of text, questions of what to describe when creating metadata become particularly thorny. Thesauri and other aids for professional “metadata makers” are invaluable but rarely used by ordinary people when they tag photos or videos. On the other hand, technology for creating multimedia can easily record contextual metadata at the time of creation, and systems for sharing multimedia can be designed so that document accumulate metadata over time.

To read before this class:

  1. Harpring, Patricia. “The Language of Images: Enhancing Access to Images by Applying Metadata Schemas and Structured Vocabularies.” In Introduction to Art Image Access: Issues, Tools, Standards, and Strategies, edited by Murtha Baca. Los Angeles: Getty Publications, 2002. http://www.getty.edu/research/publications/electronic_publications/intro_aia/harpring.pdf.

    How metadata schemas and controlled vocabularies are used to describe, catalogue, and index works of art and architecture, and images of them.

  2. Naaman, Mor, Susumu Harada, QianYing Wang, Hector Garcia-Molina, and Andreas Paepcke. “Context Data in Geo-Referenced Digital Photo Collections.” In Proceedings of the 12th annual ACM international conference on Multimedia - MULTIMEDIA  ’04, 196. New York, New York, USA: ACM Press, 2004. http://portal.acm.org/citation.cfm?doid=1027527.1027573.

    Given devices that can automatically capture time and location metadata for, e.g., digital photographs, it is possible to generate additional contextual metadata by querying data services.

  3. Shamma, David A, Ryan Shaw, Peter L Shafton, and Yiming Liu. “Watch What I Watch.” In Proceedings of the international workshop on Workshop on multimedia information retrieval - MIR  ’07, 275. New York, New York, USA: ACM Press, 2007. http://portal.acm.org/citation.cfm?doid=1290082.1290120.

    For certain kinds of media, “implicit” metadata about how the media is being used may be more useful than metadata that attempts to describe the content of the media.

September 26
Describing Kinds of Resources

We impose meaning on the world by “carving it up” into concepts and categories. The conceptual and category boundaries we impose treat some things or instances as equivalent and others as different. Sometimes we do this implicitly and sometimes we do it explicitly. We do this as members of a culture and language community, as individuals, and as members of organizations or institutions. Across these different contexts the mechanisms and outcomes of our categorization efforts differ. In most cases the resulting categories are messier than our information systems and applications can handle, and understanding why and what to do about it are essential skills for information professionals.

To read before this class:

  1. Glushko, Robert J., Rachelle Annechino, Jess Hemerly, and Longhao Wang. “6. Categorization: Describing Resource Classes and Types.” In The Discipline of Organizing, edited by Robert J. Glushko, 3rd ed. O’Reilly, 2015.

    What categories are, how they are used in information management, and how changes in the understanding of human cognitive processes have altered theories of categorization over the years.

  2. Glushko, Robert J, Paul P Maglio, Teenie Matlock, and Lawrence W Barsalou. “Categorization in the Wild.” Trends in Cognitive Sciences 12, no. 4 (April 2008): 129–35. http://dx.doi.org/10.1016/j.tics.2008.01.007.

    In studying categorization, cognitive science has focused primarily on cultural categorization, ignoring individual and institutional categorization. Because recent technological developments have made individual and institutional classification systems much more available and powerful, our understanding of the cognitive and social mechanisms that produce these systems is increasingly important.

September 28
Classification

A classification is a system of categories, ordered according to a pre-determined set of principles and used to organize a set of instances or entities. This doesn’t mean that the principles are always good or equitable or robust: every classification is biased in one way or another. Classifications are embodied in every information-intensive activity or application. Faceted or dimensional classification is especially useful in domains that don’t have a primary hierarchical structure.

To read before this class:

  1. Glushko, Robert J., Jess Hemerly, Vivien Petras, Michael Manoochehri, Longhao Wang, Jordan Shedlock, and Daniel Griffin. “7. Classification: Assigning Resources to Categories.” In The Discipline of Organizing, 3rd ed. O’Reilly, 2015.

    The terms “classification” and “categorization””are often used interchangeably, but they are not the same. Having a set of categories is not sufficient to create a classification. A classification must be principled so that we know where to place new items and entities in accordance with our system.

  2. Hearst, Marti. “UIs for Faceted Navigation: Recent Advances and Remaining Open Problems.” In Proceedings of the Workshop on Computer Interaction and Information Retrieval (HCIR 2008). Redmond, Washington, 2008. http://flamenco.berkeley.edu/papers/hcir08.pdf.

  3. Wright, Alex. “Our Sentiments, Exactly.” Communications of the ACM 52, no. 4 (April 2009): 14. http://portal.acm.org/citation.cfm?doid=1498765.1498772.

    Classification is increasingly done by algorithms. Algorithmic classification schemes are usually far more simple and crude than ones designed for human use, but they have the advantage of being able to scale to vast numbers of items. “Sentiment analysis” is an example of algorithmic classification used by companies to assess online opinion as manifested in millions of tweets, posts and updates.

October 3
Describing Relations among Resources

An ontology defines the concepts and terms used to describe and represent an area of knowledge and the relationships among them. A dictionary can be considered a simplistic ontology, and a thesaurus a slightly more rigorous one, but we usually reserve “ontology” for meaning expressed using more formal or structured language. Put another way, an ontology relies on a controlled vocabulary for describing the relationships among concepts and terms.

To read before this class:

  1. Glushko, Robert J., Matthew Mayernik, Alberto Pepe, and Murray Maloney. “5. Describing Relationships and Structures.” In The Discipline of Organizing, edited by Robert J. Glushko, 3rd ed. O’Reilly, 2015.

    Defines “relationship” and introduces five perspectives for analyzing relationships among resources: semantic, lexical, structural, architectural, and implementation.

  2. Pepper, Steve. The TAO of Topic Maps: Finding the Way in the Age of Infoglut, 2000. http://www.ontopia.net/topicmaps/materials/tao.html. PDF.

    Topic maps are an ISO standard for describing knowledge structures and associating them with information resources. Topic maps are grounded in a basic model consisting of Topics, Associations, and Occurrences (TAO).

    The ontopia.net site may be down, so don’t overlook the alternative PDF link above.

October 5
The Semantic Web

Assignment #3 Classifying  due

The “Semantic Web” vision imagines that all information resources and services have ontology-grounded metadata that enables their automated discovery and seamless integration or composition. Whether it is possible “to get there from here” with today’s mostly HTML-encoded Web, or whether “a little semantics goes a long way” are key issues for us to consider.

To read before this class:

  1. Ray, Kate. Web 3.0, 2010. http://vimeo.com/11529540.

    A video about the Semantic Web.

  2. Marshall, Catherine C, and Frank M Shipman. “Which Semantic Web?” In Proceedings of the fourteenth ACM conference on Hypertext and Hypermedia - HYPERTEXT  ’03, 57–66. New York: ACM Press, 2003. http://portal.acm.org/citation.cfm?doid=900051.900063.

    Examines three different perspectives on the Semantic Web from rhetorical, theoretical, and pragmatic viewpoints, with an eye toward possible outcomes.

  3. Web Ontology Working Group. OWL Web Ontology Language: Use Cases and Requirements. W3C, n.d. http://www.w3.org/TR/webont-req/.

    Specifies usage scenarios, goals and requirements for a web ontology language. An ontology formally defines a common set of terms that are used to describe and represent a domain. Ontologies can be used by automated tools to power advanced services such as more accurate web search, intelligent software agents and knowledge management.

  4. Weinberger, David. The Molecule of Data. Mp3. Library Lab, n.d. https://soundcloud.com/harvard/008-the-molecule-of-data.

    In this podcast Karen Coyle explains why libraries are keen on the idea of using Linked Data to produce more value from their cataloging efforts.

October 10
ASIS&T Annual Meeting

No class.

October 12
Catching Up

Due to the shortened class time, we will spend today catching up on material we didn’t have time to cover, and looking ahead to the “branches” portion of the course.

October 17
Comparing Descriptions

Despite the fact that they are typically treated as separate subjects, information organization is fundamentally intertwined with informational retrieval. The core problems of information retrieval are finding relevant resources and ordering the found resources according to relevance. The IR model explains how these problems are solved by (1) designing the representations of queries and resources in the collection being searched and (2) specifying the information used, and the calculations performed, that order the retrieved resources by relevance.

To read before this class:

  1. Buckland, Michael, and Christian Plaunt. “On the Construction of Selection Systems.” Library Hi Tech 12, no. 4 (1994): 15–28. PDF.

    An examination of the structure and components of information storage and retrieval systems and information filtering systems. Argues that all selection systems can be represented in terms of combinations of a set of basic components. The components are of only two types: representations of data objects and functions that operate on them.

  2. Rao, Ramana. “From IR to Search, and Beyond.” Queue 2, no. 3 (May 2004): 66. http://dl.acm.org/citation.cfm?doid=1005062.1005070.

    A brief history of information retrieval, beginning in the 1960s, to Xerox PARC in the 1980s, and then to mainstream uses of information currently on the Internet. Highlights the contrast between narrowly defined technological approaches and a broader understanding of the full problem set and the possible solutions.

October 19
Structural & Social Metadata

Assignment #4 Building a Taxonomy  due

Structure-based IR models combine representations of terms with information about structures within documents (i.e., hierarchical organization) and between documents (i.e. hypertext links and other explicit relationships). This structural information tells us what documents and parts of documents are most important and relevant, and provides additional justification for determining relevance and ordering a result set. The nature and pattern of links between documents has been studied for almost a century by “bibliometricians” who measured patterns of scientific citation to quantify the influence of specific documents or authors. The concepts and techniques of citation analysis seem applicable to the web since we can view it as a network of interlinked articles, and Google’s “page rank” algorithm is now the classic example. With the advent of “social media” there are now a wealth of new potential sources of structural metadata.

To read before this class:

  1. Diaz, Alejandro M. “Through the Google Goggles: Sociopolitical Bias in Search Engine Design”. Stanford University, 2005. http://epl.scu.edu/~stsvalues/readings/Diaz_thesis_final.pdf#page=55.

    The most famous and influential exploitation of “structural metadata” is PageRank, the secret sauce behind Google search (and now all other major search engines). While the idea behind PageRank is simple, its implications as a system for mediating access to information are not. Read only chapters 4 and 5.

  2. MacRoberts, M. H, and Barbara R MacRoberts. “Problems of Citation Analysis.” Scientometrics 36, no. 3 (July 1996): 435–444. http://www.springerlink.com/index/10.1007/BF02129604.

    As this examination of citation analysis shows, interpretations can vary widely as to what “links” in a given structure mean.

  3. Iskold, Alex. Social Graph: Concepts and Issues, 2007. http://www.readwriteweb.com/archives/social_graph_concepts_and_issues.php.

    With the success of Facebook, a new buzzword appeared: the “social graph.”

October 24
Standards I

Until now we’ve focused on developing a conceptual understanding of how to define and describe entities and types of entities when organizing information. However to progress further we must familiarize ourselves with some of the various (and constantly evolving) methods and standards for formally expressing these concepts in machine-readable ways, and for guiding information organization processes to ensure consistency and interoperability. Today we’ll look at two kinds of standards: standardized syntaxes for data interchange and standardized conceptual or structural models.

Standard syntaxes for data interchange

Syntax governs the arrangement of symbols to create properly formed (but not necessarily meaningful) messages.

The dominant syntax standard for encoding data so that it can be exchanged among different organization systems is the eXtensible Markup Language (XML). Review the XML Foundations reading from 8/31, and the XML tutorials at ZVON and W3Schools if you’ve forgotten what you learned about XML.

An increasingly popular alternative syntax standard is JavaScript Object Notation (JSON). Read JSON: The Fat-Free Alternative to XML.

Standard conceptual or structural models

Conceptual or structural models aim to standardize the way information is conceptualized. They can range from very abstract to very specific. Unlike syntax standards, they do not specify how symbols are arranged but instead specify basic concepts and how they are related to one another. However, conceptual or structural models often specify how their concepts should be represented in one or more syntaxes.

As we discussed in class two weeks ago, The Resource Description Framework (RDF) is the conceptual model at the foundation of the Semantic Web. It is a very abstract conceptual model because it aims to standardize concepts suitable for modeling any kind of data. Watch Jenn Riley’s RDF for Librarians presentation for a more detailed explanation of RDF.

A higher-level yet still rather abstract conceptual model is the Functional Requirements for Bibliographic Records (FRBR). Read What is FRBR?

The Atom Syndication Format is a model for describing the structure of blog feeds, or any kind of data that can be expressed as a list of time-stamped items. Atom is an example of a structural model that is relatively tightly tied to a specific syntax (XML).

Google recently released the Dataset Publishing Language (DSPL), a new conceptual model for describing quantitative datasets such as demographic statistics. Skim through the DSPL Tutorial.

Finally there are conceptual or structural models for relatively concrete, well-understood kinds of things such as contact information, calendar events, postal addresses, and recipes. Recently the three major search engines agreed on a set of conceptual models for these types of information and published them at schema.org. Skim the schema.org documentation and take a look at the model for structuring recipes.

October 26
Standards II

Today we’ll look at two more kinds of standards: standardized values or names and standardized processes.

Standard values or names: Controlled vocabularies & thesauri

Conceptual or structural models usually define the kinds of attributes that entities have, but may not specify the actual values that those attributes can take. This is the role of value standards, which are usually lists or hierarchies of names or identifiers that can be used as values for certain kinds of attributes.

A very simple example of a value standard is ISO 3166-1, which standardizes 2 and 3-letter codes for identifying countries.

More complex value standards resemble (or are) classifications, with faceted and/or hierarchical structure. Browse through the Art & Architecture Thesaurus, the AGROVOC agricultural vocabulary, and the Medical Subject Headings (MeSH).

Standard processes: Rules & best practices

Finally, rules or best practices seek to standardize the processes by which people organize information. Among other things, they may specify when and how the other kinds of standards should be used to describe and organize particular kinds of information.

Although not an official standard, the database guidelines at Discogs are a good example of what rules for cataloging look like. Read the Quick Start Guide and skim through some of the other database guidelines such as Genres/Styles and Master Release.

An example of a more official standard is Graphic Materials: Rules for Describing Original Items and Historical Collections, which provides rules for describing photographs, posters, cartoons, prints and drawings. Skim through the standard to get a sense of the variety of aspects of the description process that it attempts to standardize.

October 31
Algorithmic Description

Guest speaker: Jane Greenberg will discuss automatic metadata generation in the context of her HIVE system.

To read before this class:

  1. Greenberg, Jane. “Metadata Generation: Processes, People and Tools.” Bulletin of the American Society for Information Science and Technology 29, no. 2 (January 2005): 16–19. http://doi.wiley.com/10.1002/bult.269.

    This article sketches a framework for thinking about how human and automatic metadata generation can complement one another.

  2. Hlava, Marjorie M. “Automatic Indexing: A Matter of Degree.” Bulletin of the American Society for Information Science and Technology 29, no. 1 (January 2005): 12–15. http://doi.wiley.com/10.1002/bult.261.

    A basic overview of automatic text classification, indexing and categorization systems.

November 2
Standards Development & Governance

Assignment #5 Computationally Representing Text  due

Today we’ll consider the vocabulary problem as it manifests itself across organizational contexts. Within an organization, different information systems might use data models that are incomplete or incompatible with respect to each other, and between organizations these differences can be even greater. Structural, syntactic, and semantic mismatches cause problems when processes and services attempt to span these system and organizational boundaries (for example, to create a complete model of a “customer” or to conduct a business transaction). We’ll consider how technical standards and transformation techniques can help achieve integration and interoperability, but we’ll acknowledge that interoperability is not always possible and that non-technical factors play a huge role in determining the approach.

To read before this class:

  1. Cargill, Carl F. “Why Standardization Efforts Fail.” Journal of Electronic Publishing 14 (2011). http://dx.doi.org/10.3998/3336451.0014.103.

    The ostensible failure of a standard has to be examined not so much from the focus of whether the standard or specification was written or even implemented (the usual metric), but rather from the viewpoint of whether the participants achieved their goals from their participation in the standardization process.

  2. Mazzocchi, Stefano. “Interoperability by Friction.” Stefano’s Linotype, 2008. http://web.archive.org/web/20080521183013/http://www.betaversion.org/~stefano/linotype/news/143/.

    Stable standards are dead standards.

November 7
Midterm Review

November 9
Midterm

Assignment #6 Midterm Exam  due

November 14
Domains of Organizing Systems

Now that we’ve discussed the intellectual foundations for organizing systems - description, classification, vocabulary control, relations, and so on … we can apply them to a range of domains in which organizing systems are created. We’ll see the issues and principles that are shared by these domains, and those that distinguish or are characteristic of them. First, we’ll cover the “classical” or “core” domains of library and information science — libraries, archives, and museums — and then move into other domains to discuss organizing systems in scientific, and personal contexts.

To read before this class:

  1. Rayward, W. Boyd. “Electronic Information and the Functional Integration of Libraries, Museums, and Archives.” In History and Electronic Artefacts, edited by Edward Higgs, 207–225. Oxford: Oxford University Press, 1998. PDF.

    Differences in the organizational philosophies of libraries, archives and museums have arisen from differences in the formats and media they deal with. As digital and digitized information gains prominence in these institutions, might these differences disappear, leading to more integrated approaches to organization?

  2. Borgman, Christine L, Jillian C Wallis, and Noel Enyedy. “Little Science Confronts the Data Deluge: Habitat Ecology, Embedded Sensor Networks, and Digital Libraries.” International Journal on Digital Libraries 7, no. 1-2 (July 2007): 17–30. http://www.springerlink.com/index/10.1007/s00799-007-0022-9.

    While “big science” fields such as physics and astronomy have tools and repositories to handle massive amounts of data, “little science” areas dependent upon fieldwork lack the tools and infrastructure to manage the growing amounts of data generated by new forms of instrumentation.

  3. Karger, David R, and William Jones. “Data Unification in Personal Information Management.” Communications of the ACM 49, no. 1 (January 2006): 77. http://portal.acm.org/citation.cfm?doid=1107458.1107496.

    Users need ways to unify, simplify, and consolidate information too often fragmented by location, device, and software application.

November 16
Branching Kickoff

This will be the last day that we meet as a class. For the remainder of the semester you will work with your branch groups, checking in with me periodically as needed.

Archives Branch

Cataloging Branch

Note: The cataloging branch will meet with Professor Jane Greenberg on Monday, 11/21 at 12:30 in Manning 304.

Database Branch

Scholarly Communication Branch

December 9
Final Reports Due

Assignment #7 Final Report  due