This dataset contains the results of a survey of quality assurance practices within the field of web archiving and its practitioners. To understand current QA practices, the authors surveyed institutions engaged in web archiving, which included national libraries, colleges and universities, and museums and art libraries. The survey was administered online. It includes the completed responses of 54 participants. The data has been anonymized for privacy reasons. This dataset was used in the "Current Quality Assurance Practices in Web Archiving" paper, available from the UNT Digital Library.
Date: December 2014
Creator: Reyes Ayala, Brenda; Phillips, Mark Edward & Ko, Lauren
This dataset contains information extracted from the UNT Libraries' Coda Digital Repository. It contains information related to number of files, size, and ingest date of digital objects added to that system. It can be used for analysis and investigation of the growth and makeup of digital repositories.
This dataset contains data samples from metadata records extracted from the UNT Libraries' Digital Collections. It contains one sample per metadata record version in the system with aggregate counts of fields and also hash values of an element as well. Data was collected in March 2014 with dates from May 19, 2004 to February 4, 2014.
This dataset contains Twitter JSON data for several Twitter search queries that were collected around the #YesAllWomen Twitter "conversation" between May 25, 2014 and June 8, 2014 using the twarc (https://github.com/edsu/twarc) package that makes use of Twitter's search API. A total of 2,805,763 Tweets and 34,532 images make up the combined dataset.
This dataset contains the descriptive metadata harvested from the Texas Digital Newspaper Program collection on The Portal to Texas History and is accompanied by a dataset derived from the harvested metadata. This dataset was used for an IFLA Newspaper Section and Rootstech presentation.
Datasets used in the presentation, "Towards Building a Collection of Web Archiving Research Articles." The files included here were used to conduct several Machine Learning classification experiments that result in a corpus of scholarly research articles on the topic of web archiving.
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