Constructing Data
When you call something data, you imply that it exists in discrete fungible units; that it is computationally tractable; that its meaningful qualities can be enumerated in a finite list; that someone else performing the same operations on the same data will come up with the same results. This is not how humanists think of the material they work with.
Despite discomfort with the term, humanists today engage with data on a regular basis. The data that shapes our professional lives can be defined as "a digital, selectively constructed, machine-actionable abstraction representing some aspects of a given object of humanistic inquiry" (Schöch, 2013). As this definition suggests, the state of our data - and its utility for research - depends on the construction process. For analogue objects, the process begins with digitization. From there, both digitized and born-digital objects need to be curated, structured and/or annotated to facilitate human and computational analysis.
To create your metadata schema, it is generally advisable to build upon established ontologies and/or controlled vocabularies. Not only will this help you structure your data, but, ultimately, it will increase access and allow you to integrate your collection into Linked Data initiatives. For the purposes of this workshop, you will build a schema based on Dublin Core (DC), which is one of the most popular ontologies for describing digital resources. Dublin Core consists of fifteen elemental terms (contributor, coverage, creator, date, description, format, identifier, language, publisher, relation, rights, source, subject, title, and type) and a series of qualified terms that extend or refine the original fifteen elements. Many DC terms, in turn, can be linked to controlled vocabularies, such as those developed by the Library of Congress and the Getty Institute, to increase access and provide additional structural support.
Before building a metadata schema based on Dublin Core, it is worth drawing attention to DC alternatives and critiques. While Dublin Core is explicit in its cross-disciplinary aim to transcend the "boundaries of information silos on the Web and within intranets," you may have reasons to use a more specialized ontology, such as the VRA Core for visual culture, the PBCore for audiovisual content, and the IPTC for media resources [7]. Alternatively, you may have reasons to eschew established ontologies and vocabularies altogether; be it to 'decolonize knowledge' or "represent uncertainty, historical contingency, conflict, variation, instability, and multidimensionality" [8]. The stakes of eschewal in today's networked knowledge society, however, are high. As Michelle Schwartz and Constance Crompton write about their decision to reject "existing data models and international standards" in Lesbian and Gay Liberation in Canada (an interactive digital resource for the study of lesbian, gay, bisexual, and transgender history in Canada from 1964 to 1981) "in an age where we recognize the right to be forgotten, we must also weigh the danger... of hiding our history" [9].
Encoding Activity
In this activity you will develop a metadata schema for your collection based on Dublin Core, and follow your schema to encode each of your imported items. As the previous discussion should make clear, the objective of this activity is to practice using metadata to structure your collection, not to convince you that all collections should be encoded with Dublin Core.To create your schema: consult the Dublin Core Elements and Qualifiers manuals; note that each term is optional and can be repeated; and remember that many DC terms can be paired with controlled vocabularies. Your schema should include directions and examples for titling, describing, and adding DC metadata to your collection items (along the lines of the example below).
dcterms: Spatial
Direction: Provide GPS coordinates of the place where the original analogue item was created. You can search for coordinates using this website. Put the DD coordinates (separated by a ",") in Scalar's dcterms: spatial field. (Note that GPS coordinates must be formatted in this manner to create maps in Scalar).
Example: 41.65606, 0.87734
Annotation #4
Assignment #4 (Adapted from Jentery Sayers)
Contributor, Coverage, Creator, Date, Description, Format, Identifier, Language, Publisher, Relation, Rights, Source, Subject, Title, Type.
As you add these elements, review the guidelines and examples provided in “Using Dublin Core – The Elements.” (Note: the title, description, and content type for your actual Scalar page may differ from the DC title, description, and type for the resources you import. Also note: not all elements may necessarily apply to your resource. See the guidelines above to determine what elements are relevant.) Once you have imported both resources and added the appropriate DC metadata, use the Scalar annotation tool to add interpretive information to your resources (for direction on how to annotate images, and audio/video with Scalar go here). In the workbook, address the following questions alongside your resources, which should be displayed with your metadata captions and annotations (per the example above): When assigning metadata to your resources, what issues did you encounter? For instance, what decisions were difficult? Generally speaking, what have you learned about DC metadata and the practice of assigning it? How (if any) of that learning relates to our discussion of the assigned readings? How is annotating similar to and/or different from assigning DC metadata? Title your entry "Student's Name + Assignment #4," and follow the instructions on the "Assignment" page of our workbook to make sure that it shows up in the contents of your personal page and the "Assignment #4" page.
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- Assignments Andrea Davis