Collections as Data: Part to Whole (2019-2021)
With support from the Andrew W. Mellon Foundation, Collections as Data: Part to Whole aims to foster the development of broadly viable models that support implementation and use of collections as data. Over a period of three years, Part to Whole will fund and programmatically support two cohorts. Cohorts will be comprised of project teams jointly led by librarians and disciplinary scholars. Project teams will develop models that support collections as data implementation and holistic reconceptualization of services and roles that support scholarly use. Collections as data produced by project activity will exhibit high research value, demonstrate the capacity to serve underrepresented communities, represent a diversity of content types, languages, and descriptive practices, and arise from a range of institutional contexts.
Responsible Operations: Data Science, Machine Learning, and AI in Libraries (2019)
Responsible Operations is intended to help chart library community engagement with data science, machine learning, and artificial intelligence (AI). The community research agenda was developed in partnership with an advisory group and a landscape group comprised of more than 70 librarians and professionals from universities, libraries, museums, archives, and other organizations. The research agenda presents an interdependent set of technical, organizational, and social challenges to be addressed en route to library operationalization of data science, machine learning, and AI.
Always Already Computational: Collections as Data (2016-2018)
With support from the Institute of Museum and Library Services, Always Already Computational: Collections as Data will foster a strategic approach to developing, describing, providing access to, and encouraging reuse of collections that support computationally-driven research and teaching in areas including but not limited to Digital Humanities, Public History, Digital History, data driven Journalism, Digital Social Science, and Digital Art History.
Data Praxis: Resources for Working with Data
the sourcecaster helps you use the command line to work through common challenges that come up when working with digital primary sources.
Humanities Data Curation Record
A data curation record supports reuse of data and reproducibility of claims by documenting data source(s), data types and formats, data quality, as well as methods and tools used to subset, transform, augment, and derive insight from data.
Data praxis highlights a range of perspectives on the practice of digitally inflected research, pedagogy, curation, and collection building and augmentation. Topics span methods and tools in the context of research questions and/or exploratory trajectories, and extend to consider reflections on data definition, access, curation, sharing, and reuse.
Tutorials
Getting Starting with OpenRefine
This guide is a companion to the Data Preparation for Digital Humanities Research workshop. It is designed to help you begin using OpenRefine to: ☞ facet data ☞ filter data ☞ cluster data ☞ transform data.
Introduction to Network Analysis
This guide will help you: ☞ Visualize network data ☞ Measure network data ☞ Describe features of network data