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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]. 

How and What 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 from scratch or use a digital publishing platform that functions as a relational database, such as Wordpress, Omeka, Scalar, and CollectiveAccess [2]. While building a custom database may allow for greater processing opportunities down the line, using a publishing platform can streamline the construction process. 

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 the aforementioned tasks, for the purposes of our digital collections we will be concerned, primarily, with its descriptive function [4]. 

"A researcher must make choices about how to describe an object within the taxonomical affordances of the available toolset" 

"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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