by Andrea Payant, Utah State University – University Libraries Quality control checklist for Cataloging and Metadata Services personnel to use with digital collections in CONTENTdm Available through the Library Workflow Exchange: CONTENTdm Metadata Quality Control Checklist
OpenRefine Python Client Library by Paul Makepeace The OpenRefine Python Client Library provides an interface to communicating with an OpenRefine server. https://github.com/PaulMakepeace/refine-client-py
refinr by Chris Muir R package implementation of two algorithms from the open source software OpenRefine. These functions take a character vector as input, identify and cluster similar values, and then merge clusters together so their values become identical. The […]
by OpenRefine GitHub repository contributors You can use Refine to translate street addresses to lat/lng coordinates to plot data on maps. While there is no native geocoding command in Refine, you can still accomplish this task calling HTTP-based geocoding services […]
by OpenRefine GitHub repository contributors This page collects OpenRefine recipes, small workflows and code fragments that show you how to achieve specific things with OpenRefine. Available at this URL: https://github.com/OpenRefine/OpenRefine/wiki/Recipes Including: String Manipulation Here are some examples of possible types of […]
by Ruth Tillman When developing local collections, librarians and archivists often create detailed metadata which then gets stored in collection-specific silos. At times, the metadata could be used to augment other collections but the software does not provide native support […]
This tool consumes the clusters exported by OpenRefine and aims to provide an easy interface for stakeholders to participate in the process of deciding what the “canonical” representation of a data value should be. See the demo at: http://amiaopensource.github.io/TIDY A […]
By Lynn Whittenberger, Metadata Librarian, Raynor Memorial Library, Marquette University Abstract: Describes the steps taken to extract Author/Corporate names from our local catalog (III-Sierra), the initial data cleanup work done in Microsoft Excel, and the final data cleanup work and […]
Blog post by Noah Huffman, Duke University Libraries As part of a recent project to migrate data from EAD (Encoded Archival Description) toArchivesSpace, I needed to clean up about 27,000 name and subject headings spread across over 2,000 EAD records […]
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 http://thomaspadilla.org/dataprep/