In this paper we describe the design and trial use of Cardinal, novel software that overcomes the limitations of existing research tools used in personal information management (PIM) studies focusing on file management (FM) behavior. Cardinal facilitates large-scale collection of FM behavior data along an extensive list of file system properties and additional relevant dimensions (e.g., demographic, software and hardware, etc). It enables anonymous, remote, and asynchronous participation across the 3 major operating systems, uses a simple interface, and provides value to participants by presenting a summary of their file and folder collections. In a 15-day trial implementation, Cardinal examined over 2.3 million files across 46 unsupervised participants. To test its adaptability we extended it to also collect psychological questionnaire responses and technological data from each participant. Participation sessions took an average of just over 10 minutes to complete, and participants reported positive impressions of their interactions. Following the pilot, we revised Cardinal to further decrease participation time and improve the user interface. Our tests suggest that Cardinal is a viable tool for FM research, and so we have made its source freely available to the PIM community.
Most current evaluation tools are focused solely on benchmarking and comparative evaluations thus only provide aggregated statistics such as precision, recall and F1‐measure to assess overall system performance. They do not offer comprehensive analyses up to the level of individual annotations. This paper introduces Orbis, an extendable evaluation pipeline framework developed to allow visual drill‐down analyses of individual entities, computed by annotation services, in the context of the text they appear in, in reference to the entities specified in the gold standard.
In recent years, the storage of qualitative data has been a challenge to data archives using repositories that are based on relational databases, as large files cannot really be represented well in these structures. Most of the time, two or more structures have to be in place e.g. a fileserver that includes versioning for large files and a relational database for the tabular information. These structures necessitate the handling of multiple systems at the same time. With the arrival of Hadoop and other big data technologies, qualitative data and quantitative data can now be stored as mixed mode data in the same structures. This paper will discuss our findings in developing an early prototype version of MMRepo at the University of Applied Sciences Eastern Switzerland HTW Chur. Our prototype of MMRepo is a combination of the Invenio portal solution from CERN with a Hadoop 2.0 cluster using the DDI 3.3 beta metadata scheme for data documentation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.