•	Title: Sowing the Seeds for More Usable Web Archives: A Usability Study of Archive-It
	•	Creator: Samantha Abrams (Ivy Plus Libraries), Alexis Antracoli (Princeton University), Rachel Appel (Temple University), Celia Caust-Ellenbogen (Swarthmore College), Sarah Denison (Delaware Public Archives), and Sumatra Duncan (The Frick Art Reference Library).
	•	Language: English
	•	Dates: June 2017-January 2019
	•	Project description:
	⁃	In 2017, seven members of the Archive-It Mid-Atlantic Users Group (AITMA) conducted a study of 14 subjects representative of their stakeholder populations to assess the usability of Archive-It, a web archiving subscription service of the Internet Archive. While Archive-It is the most widely-used tool for web archiving, little is known about how users interact with the service. This study intended to teach us what users expect from web archives, which exist as another form of archival material. End-user subjects were asked to execute four search tasks using the public Archive-It interface and the Wayback Machine to access archived information on websites from the facilitators’ own harvested collections and provide feedback about their experiences. The tasks were designed to have straightforward pass or fail outcomes, and the facilitators took notes on the subjects’ behavior and commentary during the sessions. Overall, participants reported mildly positive impressions of Archive-It public user interface based on their session. The study identified several key areas of improvement for the Archive-It service pertaining to metadata options, terminology display,  indexing of dates, and the site’s search box. 

		This submission includes the results summary, codebook, notes from testing sessions related 			to the study, and a full bibliography.
	
	•	Methodology: Data was generated through note taking whileobserving participants and through a pass/fail system for the tasks given. Through an iterative and collaborative process of qualitative analysis, we created 24 codes recognizing the most common behaviors or themes.

	•	Data Structure: The files include a high-level summary of our results, our codebook from qualitative analysis, the notes where we derived those codes, and a full bibliography based on works cited.