trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R

Research is an incremental, iterative process, with new results relying and building upon previous ones. Scientists need to find, retrieve, understand, and verify results in order to confidently extend them, even when the results are their own. We present the trackr framework for organizing, automatically annotating, discovering, and retrieving results. We identify sources of automatically extractable metadata for computational results, and we define an extensible system for organizing, annotating, and searching for results based on these and other metadata. We present an open-source implementation of these concepts for plots, computational artifacts, and woven dynamic reports generated in the R statistical computing language

Leveraging Citation Networks to Visualize Scholarly Influence Over Time

Assessing the influence of a scholar's work is an important task for funding organizations, academic departments, and researchers. Common methods, such as measures of citation counts, can ignore much of the nuance and multidimensionality of scholarly influence. We present an approach for generating dynamic visualizations of scholars' careers. This approach uses an animated node-link diagram showing the citation network accumulated around the researcher over the course of the career in concert with key indicators, highlighting influence both within and across fields. We developed our design in collaboration with one funding organization---the Pew Biomedical Scholars program---but the methods are generalizable to visualizations of scholarly influence. We applied the design method to the Microsoft Academic Graph, which includes more than 120 million publications. We validate our abstractions throughout the process through collaboration with the Pew Biomedical Scholars program officers and summative evaluations with their scholars

Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data

Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the user’s ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data. We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive

Looking at a digital research data archive - Visual interfaces to EASY

In this paper we explore visually the structure of the collection of a digital research data archive in terms of metadata for deposited datasets. We look into the distribution of datasets over different scientific fields; the role of main depositors (persons and institutions) in different fields, and main access choices for the deposited datasets. We argue that visual analytics of metadata of collections can be used in multiple ways: to inform the archive about structure and growth of its collection; to foster collections strategies; and to check metadata consistency. We combine visual analytics and visual enhanced browsing introducing a set of web-based, interactive visual interfaces to the archive's collection. We discuss how text based search combined with visual enhanced browsing enhances data access, navigation, and reuse.Comment: Submitted to the TPDL 201

Exploring scholarly data with Rexplore.

Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors ‘semantically’ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. ‘ordinary’ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves

RPYS i/o: A web-based tool for the historiography and visualization of citation classics, sleeping beauties, and research fronts

Reference Publication Year Spectroscopy (RPYS) and Multi-RPYS provide algorithmic approaches to reconstructing the intellectual histories of scientific fields. With this brief communication, we describe a technical advancement for developing research historiographies by introducing RPYS i/o, an online tool for performing standard RPYS and Multi-RPYS analyses interactively (at http://comins.leydesdorff.net/). The tool enables users to explore seminal works underlying a research field and to plot the influence of these seminal works over time. This suite of visualizations offers the potential to analyze and visualize the myriad of temporal dynamics of scientific influence, such as citation classics, sleeping beauties, and the dynamics of research fronts. We demonstrate the features of the tool by analyzing--as an example--the references in documents published in the journal Philosophy of Science