User-defined Metadata Research Articles

Documentation and Visualisation of Workflows for Effective Communication, Collaboration and Publication @ Source

Workflows processing data from research activities and driving in silico experiments are becoming an increasingly important method for conducting scientific research. Workflows have the advantage that not only can they be automated and used to process data repeatedly, but they can also be reused – in part or whole – enabling them to be evolved for use in new experiments. A number of studies have investigated strategies for storing and sharing workflows for the benefit of reuse. These have revealed that simply storing workflows in repositories without additional context does not enable workflows to be successfully reused. These studies have investigated what additional resources are needed to facilitate users of workflows and in particular to add provenance traces and to make workflows and their resources machine-readable. These additions also include adding metadata for curation, annotations for comprehension, and including data sets to provide additional context to the workflow. Ultimately though, these mechanisms still rely on researchers having access to the software to view and run the workflows. We argue that there are situations where researchers may want to understand a workflow that goes beyond what provenance traces provide and without having to run the workflow directly; there are many situations in which it can be difficult or impossible to run the original workflow. To that end, we have investigated the creation of an interactive workflow visualization that captures the flow chart element of the workflow with additional context including annotations, descriptions, parameters, metadata and input, intermediate, and results data that can be added to the record of a workflow experiment to enhance both curation and add value to enable reuse. We have created interactive workflow visualisations for the popular workflow creation tool KNIME, which does not provide users with an in-built function to extract provenance information that can otherwise only be viewed through the tool itself. Making use of the strengths of KNIME for adding documentation and user-defined metadata we can extract and create a visualisation and curation package that encourages and enhances curation@source, facilitating effective communication, collaboration, and reuse of workflows.

User-Defined Metadata: Using Cues and Changing Perspectives

User-defined metadata is useful for curating and helping to provide context for experiment records, but our previous investigations have demonstrated that simply providing the facility to add metadata is not enough to ensure that metadata is added, let alone to ensure that the metadata is of high quality. For metadata to be useful it first has to be present, but enforcing metadata generation is of no benefit if it is low quality, inconsistent, or irrelevant. Researchers need support. One strategy to encourage more effective metadata creation is to design user interfaces that invite users to add metadata by asking them questions. If we ask users specific questions about their experiments and other activities then we could capture more relevant or useful metadata, although there is a risk that asking the wrong questions may lead to loss of valuable metadata terms or the creation of irrelevant material. In this paper we report on a study to investigate how different questions could be used to generate metadata by eliciting information in three different conditions: free recall, changing perspective by thinking about search terms to help someone else, and providing cues by using a set of topic-based questions. We also investigate how responses varied with different information types. The results of the study show that different terms are created under the different conditions, as expected. The use of cues generates the highest numbers of terms and the most diverse range, including elements that are not captured in other conditions. However, important themes generated in other conditions are not produced because the cues to create them are missing. The study also generated a number of unexpected findings, including responses describing information that is not in the original material: personal opinions and experiences, and comments about the information text itself. These unexpected responses have both positive and negative consequences for the generation of metadata and the curation of scientific records. The results of studies using these techniques to capture metadata for chemistry experiments are also discussed.

USER-DEFINED CLASSIFICATION ON THE ONLINE PHOTO SHARING SITE FLICKR…OR, HOW I LEARNED TO STOP WORRYING AND LOVE THE MILLION TYPING MONKEYS

As evidenced by the growing popularity of sites which provide tagging and annotation functionality, like del.icio.us (http://del.icio.us), Flickr (http://www.flickr.com/), technorati (http://www.technorati.com/), and CiteULike (http://www.citeulike.org/), which already have combined user bases in the several millions, collaborative cataloging, or tagging, provides a workable solution for content organization, use, and exploration for many Internet users. This explosion in user-defined metadata has the potential to improve the way information is organized, navigated, and experienced on the web, and offers the Library and Information Science community the opportunity to augment and refine our existing classification methods and schemes to be more user-friendly, to allow for higher levels of precision and recall, and to enhance the human information interaction experience.

Scale and performance in semantic storage management of data grids

Data grids are middleware systems that offer secure shared storage of massive scientific datasets over wide area networks. The main challenge in their design is to provide reliable storage, search, and transfer of numerous or large files over geographically dispersed heterogeneous platforms. The Storage Resource Broker (SRB) is an example of a system that provides these services and that has been deployed in multiple high-performance scientific projects during the past few years. In this paper, we take a detailed look at several of its functional features and examine its scalability using synthetic and trace-based workloads. Unlike traditional file systems, SRB uses a commodity database to manage both system- and user-defined metadata. We quantitatively evaluate this decision and draw insightful conclusions about its implications to the system architecture and performance characteristics. We find that the bulk transfer facilities of SRB demonstrate good scalability properties, and we identify the bottleneck resources across different data search and transfer tasks. We examine the sensitivity to several configuration parameters and provide details about how different internal operations contribute to the overall performance.