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Constructing Data

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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]. 

What and How to Collect

Roopika Risam (on reproducing colonial knowledge and also on archive that contextualize colonial knowledge- think of Vanishing Race)
Participatory and Community Based archive- example Lesbian Herstory Archives pg 140 Intersectional Feminism
Posner radical new digital humanities (shifts focus from mainstream to marginalized but also develops ways of representing people's lives in data "as they have been experienced, not as they have been captured and advanced by businesses and governments".


How to Encode Information

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 digital collection with these functions, you can either build a relational database or use a digital publishing platform that functions as a relational database, such as Wordpress, Omeka, and CollectiveAccess [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, for our purposes we will be primarily concerned with its descriptive function [4]. A metadata schema ensures that collection items are described in a consistent fashion, ultimately facilitating human and computational analysis. 

When developing a metadata schema, it is generally advisable to build upon established ontologies and/or controlled vocabularies.  When discussing ONTOLOGIES go into LOC subjects and lack of black.

"Rather than flattening data into binaries to fit existing data models and international standards,  we strive tp represent uncertainty, historical contingency,  conflict, variation, instability, and multidimensionality. The stakes are high: in an age when we recognize the right to be forgotten, we must also weigh the danger...of hiding our history" [5].

Surveillance, safety, access (Mukurtu). hierarchy (flat ontology scalar) 

 

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  1. Data for Humanists Andrea Davis

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