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• 1 media/1505283b821541de196157757c467ead.jpg 2019-01-04T13:05:44-08:00 Constructing Data 355 plain 2019-02-20T14:04:50-08:00 As a model of knowledge, datasets not only represent information for the purposes of study.  They also embody a range of ideas and practices related to the broader politics of representation, inclusion, and access. Therefore, as Trevor Owens of the Library of Congress explains, constructing a dataset requires careful "choices about what and how to collect and how to encode the information" [1]. Following Owens' lead,  this section of the workshop on constructing data will be divided into two parts: collecting and encoding.

  Collecting

  In her book New Digital Worlds Roopika Risam writes that "the opportunity to intervene in the digital cultural record—to tell new stories, shed light on counter-histories, and create spaces for communities to produce and share their own knowledges should they wish is the great promise of digital humanities" [2]. According to this vision of the digital humanities, collection building should not be a straightforward matter of digitizing the written and material records as preserved by governments, libraries, and museums. Rather, it should entail critical decisions about: what to collect and why; who participates in the collection process; and how collections are organized, managed, and shared. This type of critical inquiry has begun to coalesce in new digital collection methods and models ranging from participatory, performative, and post-custodial archives, to cultural protocols for indigenous archival materials (Native American, Aboriginal and Torres Strait Islander). 

  Collection Activity

  In the first activity of this section, you will select ten digital objects related to a personal teaching or research interest and develop a short statement describing your collection. 

  Encoding

  For digital collections, item information is frequently encoded in relational databases.  A relational database is a structured set of data that contains a series of formally described tables from which data can be queried or organized in different ways. This structure enhances searching, browsing, and exhibition functions by enabling users to access items and reassemble collections based on tabular information.
  To create a collection with these functions, you can either build a relational database or use a content management system (CMS) that functions as a relational database [2]. 
  
  Either way, before getting started you will want to develop a metadata schema for your collection. Metadata, or data about data, "provides a means of indexing, accessing, preserving, and discovering digital resources" [3]. While metadata can serve different functions related to these tasks, in this workshop we will be primarily concerned with its descriptive function [4]. Specifically, you will learn how to encode individual items with descriptive metadata and develop a schema to ensure that all of the items in your collection are described consistently 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 allow you to integrate your collection into Linked Data initiatives. For the purposes of this workshop, we will build our schemas on Dublin Core (DC), which is one of the most popular ontologies for describing digital resources. Dublin Core consists of fifteen elemental terms (in italics below) and a series of qualified terms that extend or refine the original fifteen elements. Many of these terms, in turn, can be linked to controlled vocabularies, such as those developed by the Library of Congress and the Getty Institute. 
  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 [5].  Alternatively, you may have reasons to revise or eschew established ontologies and vocabularies in efforts to 'decolonizing knowledge' or "represent uncertainty, historical contingency, conflict, variation, instability, and multidimensionality" [6]. The stakes of eschewal in today's networked knowledge society, however, can be 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" [7]. 

  Metadata Schema Activity

  In this activity you will develop a metadata schema for your collection based on Dublin Core. As the previous discussion should make clear, the objective of this activity is to practice using metadata to structure a 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 keep in mind that many DC terms can be paired with controlled vocabularies. Your schema should include your selected DC terms with collection-specific directions and examples for each (along the lines of the example below).