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  1. page home edited ... This wiki contains papers dealing with applications of information technologies in research in…
    This wiki contains papers dealing with applications of information technologies in research in the Humanities and Social Sciences, including bibliographic tools. There is no guarantee that this site will continue indefinitely or for any specific period of time, but the intent is that it will be a permanent home for papers that illustrate the range of possibilities that information technologies have provided to disciplines in the Humanities and Social Sciences.
    Papers are accessible via
    alphabetical list ("Technology Applications"("A list of articles on this wiki" on the
    descriptors (tags);
    a tag cloud (below); and
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  2. page ArtStor edited ArtStor Jill Straub The 1990s witnessed the gradual emergence of digital technologies, datab…

    Jill Straub
    The 1990s witnessed the gradual emergence of digital technologies, databases that store and deliver information in a digital format. The Library of Congress’s American Memory is a prime example of a digitalized technology, which strives to digitalize images of the LOC’s primary resources. Users can use American Memory to search photographs, newspaper clippings, sound recordings, maps, letters, and various other formats of publications. While American Memory digitalized a wide array of primary resource materials, other digitalized technologies specialized in more discipline specific information. Among these digital technology databases, JSTOR was launched under the creation and control of the Arthur W. Mellon Foundation. JSTOR specialized in digitalizing scholarly journals. Because JSTOR delivered immediate access to full-text scholarly journal articles, it became it a quick success to its subscribers. Subscribers, which were mostly libraries, were better able to assist patrons in acquiring reliable information in a speedy format, sometimes reducing the reliance on a somewhat cumbersome interlibrary loan service.
    During this time, the visual arts began struggling with trying to digitalize its artifacts. They, too, begin questioning how they could transfer their images from an analog catalog into a digital image collection. JSTOR provided the inspiration. Also, funded by the Andrew W. Mellon Foundation, ArtStor, a non-profit organization, was launched and ready to begin serving the public, most notably the teachers and students of higher education. To ensure that users enjoy a high-quality and meaningful experience with ArtStor, the Mellon Foundation designed its mission to “use digital technology to enhance scholarship, teaching, and learning in the arts and associated fields” (“Mission and History,” para. 1). Carrying out this mission, ArtStor developed three specific goals that set the framework of the organization. The three primary goals are:
    1. To assemble image collections from across many time periods and cultures that will, in the aggregate, have sufficient depth, breadth, and coherence to support a wide range of educational and scholarly activities;
    2. To create an organized, central, and reliable digital resource that supports noncommercial use of images for research, teaching, and learning; and
    3. To work with the arts and educational communities to develop collective solutions to the complex challenges that are an inescapable part of working in a changing digital environment (“Mission and History” para. 6-8).
    ArtStor’s initial project illustrated how these goals would be carried out. Collaborating with the U.S., China, France, and the U.K., ArtStor built the digital collection, the Mellon International Dunhuang Archive. The Dunhuang Archive consisted of 40 cave grottoes in the Gobi Desert. These grottoes comprised one of the world’s largest Buddhist art sites, and to accompany the grottoes were silk banners and manuscripts. Images were taken of the 40 cave grottoes, silk banners, and manuscripts, and digitalized to form ArtStor’s first image repository. This project was a far-reaching effort, which extended beyond cultural and geographical divides. While the 40 cave grottoes resided in China, the silk banners and manuscripts were brought to France and England at the turn of the 20th century. Hence, piecing together this initial project required the effort from various countries. Yet, once completed and digitalized into ArtStor, it became a collection that could be utilized in one physical setting and
    by people from around the globe (“Mission and History,” para. 11).
    The success of the Dunhuang collection soon lead a burgeoning palette of digital collections. Among the collections were included “190,000 old master drawings originally photographed at over 100 different repositories, 20 years of contemporary New York City gallery shows, archives of Islamic textiles, the restored Ghiberti ‘Gates of Paradise,’ African masks, medieval manuscripts, images of all exhibitions shown at MoMA, and many others” (“Mission and History,” para. 12).
    Since the initial creation of ArtStor, the art repository database has grown to include 22 subject guides, which were distinct, discipline-specific collections. The subject guides included: African-American--American Studies; American Studies; Anthropology; Architecture and the Built Environment; Asian Studies; Classical Studies; Design; Decorative Arts; Fashion and Costume; History of Medicine and Natural Science; Languages and Literature; Latin American Studies; Maps and Geography; Medieval Studies; Middle Eastern Studies; Music History; Native American Studies; Photography; Religious Studies; Renaissance Studies; Theatre and Dance; and Women’s Studies (“Subject Guides,” para. 1-22).
    ArtStor has ardently kept up with the quick pace of evolving technology to meet the needs of the educational community. James Shuman and Neil Rudenstine, President and Chairman of the Board, state that over 1,375 educational institutions use ArtStor for some sort of academic or curatorial purposes. Because of this, ArtStor designed more tools to make ArtStor more versatile for teaching purposes. These tools include PowerPoint presentations and digital flashcards accessible on an iPad or iPhone.
    In addition to these features, ArtStor recently developed “Shared Shelf,” a new cataloguing and image management system that educational art institutions can use to better manage their own local collections. Another additional key tool to better serve ArtStor’s cliental, is ArtStor’s “Images for Publication” feature. Started by the Metropolitan Museum in 2007, “Images for Publication” collaborates with Bryn Mawr College, Yale University Art Gallery, the Indianapolis Museum of Art and other art educational institutions, to allow scholars to use images on ArtStor for publication use at no extra charge. This is particularly advantageous to scholars needing to use images as a focal point of their research, and subsequently, publications (“Letters from the Chairman,” para. 3).
    The heart of ArtStor, however, is in its functionality as a database. A user can utilize ArtStor in a variety of ways. Images in ArtStor can be browsed by collection, classification or geography, and searched for keyword and advanced terms, which include options such as time, date, geography, field, classification, or collection. Users can search through keywords or exact phrases, using wildcard symbols, such as quotation marks, question marks, asterisk, or dollar signs. Once images are retrieved, ArtStor offers the capability to organize them. Once the users begins collecting images, the images can be organized into groups, which can then be placed into folders. Instructors are granted the added benefit of creating multiple folders of the images that students can access.
    ArtStor allows instructors to add images from a personal collection or from an institutional collection. When instructors add images from a personal collection, the images initially can only be viewed in the instructor’s personal collection; however, once adding personal images, the user can make the added images accessible to users within the same user institution who have access to the same folders. Institutions subscribed to ArtStor can also upload and post institutional digital images with ArtStor’s digital images. The institution then can allow other subscribed institutions access to the digital images if so chosen to do so.
    Because ArtStor is primarily a teaching tool, it offers two formats to present images: Offline Image Viewer (OIV) or a presentation tool of the instructor’s personal choice. With OIV, users can use digital images up to 3200 pixels to present in a classroom. Over one million images, including an instructor’s personal images, possess the capacity to be shown using the OIV. With this feature, users can view images side-by-side, zooming in on the images, panning the images, and comparing and contrasting them. Yet, the instructor can also choose to use a different presentation software, such as PowerPoint or Keynote. Equally, slides from a PowerPoint or Keynote presentation can be imported into the OIV presentation (“Presenting Images,” para. 1, 2).
    As long as images are shared with other ArtStor subscribers, three formats exist to share the images: individual, image groups, and through an OIV presentation. Valid URL addresses can be integrated into emails, Word or Page documents, syllabi, and course websites. Users can click on the URL, where, if properly subscribed to ArtStor, they are taken to the image to scan, pan, and zoom the image. Similar to sharing individual images, image groups have the capability to be incorporated into email, word processing software, syllabi, and course websites. Image groups open with the first image, where users can proceed to the next images in the group.
    The OIV also serves as a format to integrate images into. Saving the OIV presentation as “read-only,” students can have access to the OIV presentation through a course website, email, Word, or Page document (“Integrating with Courseware & Local Systems,” para. 2-7).
    A large part of what makes ArtStor the success it is today is the searching functionality behind the scenes. ArtStor uses ArtStor XML Gateway. The XML Gateway additionally is supported by the Search and Retrieval Url Service (URL). The Search and Retrieval System, which is a Web-based product, searches keywords and retrieves results. Further enhancing this service is the Common Query Language (CQL), another keyword search. Designed to submit search queries, CQL is a versatile service, which can search both both simple and complex queries, as well as to assist in decoding human writing (“Metasearch into ArtStor,” para. 1-3).
    In an early review of ArtStor, “As the Image Library Turns One Year Old, It is Finding an Expanding Audience Across Disciplines,” written by Barbara Rockenbach and Max Marmor, one of the database’s toughest challenges was noted--balancing the needs of the content users while respecting the rights of the content owners. While the United States has developed “fair use” exemptions to copyright laws for educational purposes, it is significant to remember that copyright laws do not exist in most countries. Hence, it is tougher to enforce “appropriate” use of content on the users while still respecting the varying cultural perceptions of artists on an international scale. Rockenbach and Marmor attest that this challenge requires compromise, both from the content users as well as from the content owners. Content users must respect that downloading and interoperating images in other software is prohibited, while content owners should understand and focus on ArtStor’s reason for existence--to build and encourage a thriving educational community, where owners can share their work and users can gain the educational benefits of of observing, discussing, and interacting with valued pieces of art (para. 14-15).
    I would add another challenge to ArtStor’s mission: to gain the actual understanding and use of this powerful database across the disciplines, particularly the sciences and to an extent, the social sciences. As a former Humanities instructor, many of us in the department used ArtStor as a teaching tool. The depth and breadth of ArtStor makes it an excellent resource when educating our students about the artistic contributions of a specific culture in a specific time period. The visual abilities to scan and zoom in close enough to see a paintbrush stroke help the bring the piece of an art as close to the viewer that he/she may ever get. Yet, at least in my institution, ArtStor is not widely used across the disciplines. While instructors of Art History and the Humanities might make smart use of it, it is not a resource used in a variety of disciplines. Yet, the richness of ArtStor’s vast collections span a multitude of disciplines, including the natural sciences, theatre, geography, and literature. Without the full recognition of ArtStor’s power as a resource that can benefit an amazingly wide audience, it may remain an undiscovered treasure.
    “Integrating with Courseware & Local Systems,” ArtStor. ARTstor Inc. n.d. Web. 22 Oct. 2011.
    “Letters from the Chairman and President.” ArtStor. ARTstor Inc. n.d. Web. 14 Oct. 2011.
    “Metasearch into ArtStor,” ArtStor. ARTstor Inc. n.d. Web. 23 Oct. 2011.
    “Mission and History.” ArtStor. ARTstor Inc. n.d. Web. 14 Oct. 2011.
    “Presenting Images.” ArtStor. ARTstor Inc. n.d. Web. 22 Oct. 2011.
    Rockenbath, Barbara and Max Marmor. “As the Image Library Turns One Year Old, It is Finding an Expanding Audience Across Disciplines.” Library Journal. 15 July 2005. Web. 14 Oct. 2011.
    Roncevic, Mirela. “Using ArtStor.”Library Journal. 15 July 2005. Web. 14 Oct. 2011.
    “Subject Guides.” ArtStor. ARTstor Inc. n.d. Web. 23 Oct. 2011.

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  3. page ArtStor edited ArtStor
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  7. page Musipedia edited Musipedia William Ziegler Music information retrieval (MIR) is the interdisciplinary science o…

    William Ziegler
    Music information retrieval (MIR) is the interdisciplinary science of retrieving information relating to music. Music information retrieval systems help manage collections of music material and provide access to music collections. Although the first published work on music retrieval appeared a long time ago, only until recently has a significant amount of research been done on music retrieval (Chowdhury, 1998)
    Music information primarily consists of seven facets. These facets include pitch, tempo, harmony, timbre, editing, text, and bibliography (Chowdhury, 1998)). Traditionally, most music information retrieval has involved text-based metadata tags to describe identifying features. These identifying features generally included the title of the song, the composer, and the performer. However, in terms of music retrieval, these text-based metadata did not accommodate to the needs of all information seekers. Searchers may not know the metadata info mentioned. For instance, information seekers often know the melody or rhythm of a song, but they do not know the title or performer of the song. Recently, query by humming (QbH) retrieval software has been developed in an attempt to solve these types of problems relating to music information retrieval.
    Query by humming (QbH) is a music retrieval system that works differently than the original classification and retrieval systems that focus primarily on the title, artist, composer, and genre. Instead, QbH primarily applies to music with a distinct single theme or melody. The system involves taking a user-hummed melody, which is the input query, and it compares the hummed melody to an existing database. The system then returns a ranked list of music closest to the input query.
    Musipedia is an example of a QbH system, but Musipedia uses a variety of input methods. Not only does it use input methods such as humming or whistling, but it also involves tapping a rhythm on the keyboard , using the computer keyboard as a piano keyboard to search melodies, and it includes a contour search, using Parsons Code to encode the music pieces.
    Rainer Typke started Musipedia in 1997, and he has been developing the program ever since. He first called the music search engine Melodyhound and Tuneserver. But after adding the Wikipedia-like collaboration features, such as users editing and deleting existing entries, he decided to change the name to Musipedia. As the name suggests, the site is inspired by Wikipedia, and the site refers to itself as the “Open Music Encyclopedia.” It is a searchable, editable, and expandable collection of tunes, melodies, and musical themes. Also, since 2006, the Musipedia search engine can also be used for searching the entire internet for MIDI files. Musipedia locates the MIDI files that go into its search index by using the Alexa Web Search service, which has been available since December 2005.
    Since Musipedia is a collaborative music encyclopedia, any user can modify the collection of melodies and enter MIDI files, bitmaps with sheet music, lyrics or some text about the piece, or the melodic contours as Parsons Code. Musipedia has different language options, which is can be beneficial to some users that do not speak English. The different languages that a user can choose for the site are French, German, English, and a Chinese language.
    Some MIR systems only check for exact matches or cases where the search string is a substring of database entries. For such tasks, standard string searching algorithms are used. For example, Themefinder searches the database for entries matching regular expressions. In this case, there is still no notion of distance, but different strings can match the same regular expression. One of the most notable challenges regarding MIR is the fact that information seekers can have poor skills in reproducing music for their queries. This is why approximate matching can be beneficial in MIR, and with approximate matching it can be useful to compute an editing distance. Musipedia is an example of a system that computes an editing distance (Typke et al, 2005).
    For example, the melodic contour search uses an editing distance, and because of this, the search engine finds not only entries with exactly the contour that is entered as a query, but also the most similar ones among the contours that are not identical. Similarity is measured by determining the editing steps (inserting, deleting, or replacing a character) that are needed for converting the query contour into that of the search result. Since only the melodic contour is relevant, one can find melodies even if the key, rhythm, or the exact intervals are unknown.
    The pitch and onset time-based search takes more properties of the melody into account. This search method, which is the default method, is still transposition-invariant and tempo-invariant, but it takes rhythm and intervals into account. The melody can be entered in various ways, but the primary way is by clicking on a virtual keyboard on the screen. The search engine then segments the query, converts each segment into a set of points in the two-dimensional space of onset time and pitch, and it then compares each point set to pre-computed point sets representing segments of melodies from the database. As with the contour search, little alterations of the query will lead to correspondingly small changes in the results, which helps when there is a user error when the user submits a query (Wikipedia, 2011).
    The query by tapping method only takes the rhythm into account, and the user taps in the rhythm with any key on the keyboard. The tapping method uses the same algorithm as the pitch method, but assumes all pitches to be the same.
    For more technical information as to how the melody search tool works, a user can view citations of various articles written by Rainer Typke. This can be found by clicking on a link at the bottom of Musipedia’s “About” page. After clicking on the link, a user can view published works by Typke from 2001 to 2011. Most of the content of the articles are notably technical and can be difficult to understand for those without considerable computer programming knowledge.
    Musipedia does not always seem to be accurate, and the collection of music that Musipedia pulls from can often seem limited. However, since people usually do not memorize everything about a song the first time they hear it, and since information such as title, composer and performer are often learned at a much later stage of a person’s relationship with a song, Musipedia and other QbH systems seem to be information retrieval tools that are leading the way to more practical, and more encompassing music retrieval systems.
    Chowdhury, G. (2010). Introduction to modern information retrieval. (3 ed., pp. 348-349). London, England: Facet Publishing.
    Musipedia. Retrieved from
    Typke, R., F. Weiring, & R. Veltkamp (2005). Survey of music information retrieval systems. ISMIR, 153-160.
    Wikipedia: article on Musipedia. (2011) Retrieved from

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