VisAhoi: Towards a Library to Generate and Integrate Visualization Onboarding Using High-level Visualization Grammars

Visualization onboarding supports users in reading, interpreting, and extracting information from visual data representations. General-purpose onboarding tools and libraries are applicable for explaining a wide range of graphical user interfaces but cannot handle specific visualization requirements. This paper describes a first step towards developing an onboarding library called VisAhoi, which is easy to integrate, extend, semi-automate, reuse, and customize. VisAhoi supports the creation of onboarding elements for different visualization types and datasets. We demonstrate how to extract and describe onboarding instructions using three well-known high-level descriptive visualization grammars - Vega-Lite, Plotly.js, and ECharts. We show the applicability of our library by performing two usage scenarios that describe the integration of VisAhoi into a VA tool for the analysis of high-throughput screening (HTS) data and, second, into a Flourish template to provide an authoring tool for data journalists for a treemap visualization. We provide a supplementary website that demonstrates the applicability of VisAhoi to various visualizations, including a bar chart, a horizon graph, a change matrix or heatmap, a scatterplot, and a treemap visualization

The 13th Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-IV Survey Mapping Nearby Galaxies at Apache Point Observatory

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) began observations in July 2014. It pursues three core programs: APOGEE-2,MaNGA, and eBOSS. In addition, eBOSS contains two major subprograms: TDSS and SPIDERS. This paper describes the first data release from SDSS-IV, Data Release 13 (DR13), which contains new data, reanalysis of existing data sets and, like all SDSS data releases, is inclusive of previously released data. DR13 makes publicly available 1390 spatially resolved integral field unit observations of nearby galaxies from MaNGA,the first data released from this survey. It includes new observations from eBOSS, completing SEQUELS. In addition to targeting galaxies and quasars, SEQUELS also targeted variability-selected objects from TDSS and X-ray selected objects from SPIDERS. DR13 includes new reductions ofthe SDSS-III BOSS data, improving the spectrophotometric calibration and redshift classification. DR13 releases new reductions of the APOGEE-1data from SDSS-III, with abundances of elements not previously included and improved stellar parameters for dwarf stars and cooler stars. For the SDSS imaging data, DR13 provides new, more robust and precise photometric calibrations. Several value-added catalogs are being released in tandem with DR13, in particular target catalogs relevant for eBOSS, TDSS, and SPIDERS, and an updated red-clump catalog for APOGEE.This paper describes the location and format of the data now publicly available, as well as providing references to the important technical papers that describe the targeting, observing, and data reduction. The SDSS website, http://www.sdss.org, provides links to the data, tutorials and examples of data access, and extensive documentation of the reduction and analysis procedures. DR13 is the first of a scheduled set that will contain new data and analyses from the planned ~6-year operations of SDSS-IV.PostprintPeer reviewe

Performance prediction, pacing profile and running pattern of elite 1-h track running events

Purpose: This study aimed at comparing the predictive accuracy of the power law (PL), 2-parameter hyperbolic (HYP) and linear (LIN) models on elite 1-h track running performance, and evaluating pacing profile and running pattern of the men’s best two 1-h track running performances of all times. Methods: The individual running speed–distance profile was obtained for nine male elite runners using the three models. Different combinations of personal bests times (3000 m-marathon) were used to predict performance. The level of absolute agreement between predicted and actual performance was evaluated using intraclass correlation coefficient (ICC), paired t test and Bland–Altman analysis. A video analysis was performed to assess pacing profile and running pattern. Results: Regardless of the predictors used, no significant differences (p > 0.05) between predicted and actual performances were observed for the PL model. A good agreement was found for the HYP and LIN models only when the half-marathon was the longest event predictor used (ICC = 0.718–0.737, p < 0.05). Critical speed (CS) was highly dependent on the predictors used. Unlike CS, PLV20 (i.e., the running speed corresponding to a 20-min performance estimated using the PL model) was associated with 1-h track running performances (r = 0.722–0.807, p < 0.05). An even pacing profile with minimal changes of step length and frequency was observed. Conclusions: The PL model may offer the more realistic 1-h track running performance prediction among the models investigated. An even pacing might be the best strategy for succeeding in such running events

Design of Visualization Onboarding Concepts for a 2D Scatterplot in a Biomedical Visual Analytics Tool

Biomedical research is highly data-driven. Domain experts need to learn how to interpret complex data visualizations to gain insights. They often need help interpreting data visualizations as they are not part of their training. Integrating visualization onboarding concepts into visual analytics (VA) tools can support users in interpreting, reading, and extracting information from visual presentations. In this paper, we present the design of the onboarding concept for an interactive VA tool to analyze large-scale biological data, particularly high-throughput screening (HTS) data. We evaluated our onboarding design by conducting a cognitive walkthrough and interviews with thinking aloud. We also collected data on domain experts’ visualization literacy. The results of the cognitive walkthrough showed that domain experts positively commented on the onboarding design and proposed adjusting smaller aspects. The interviews showed that domain experts are well-trained in interpreting basic visualizations (e.g., scatterplot, bar chart, line chart). However, they need support correctly interpreting the data visualized in the scatterplot, as they are new to them. Another important insight was fitting the onboarding messages into the domain’s language

Design of Visualization Onboarding Concepts for a 2D Scatterplot in a Biomedical Visual Analytics Tool

Biomedical research is highly data-driven. Domain experts need to learn how to interpret complex data visualizations to gain insights. They often need help interpreting data visualizations as they are not part of their training. Integrating visualization onboarding concepts into visual analytics (VA) tools can support users in interpreting, reading, and extracting information from visual presentations. In this paper, we present the design of the onboarding concept for an in- teractive VA tool to analyze large scaled biological data, particularly high-throughput screening (HTS) data. We evaluated our onboard- ing design by conducting a cognitive walkthrough and interviews with thinking aloud. We also collected data on domain experts’ visu- alization literacy. The results of the cognitive walkthrough showed that domain experts positively commented on the onboarding design and proposed adjusting smaller aspects. The interviews showed that domain experts are well-trained in interpreting basic visualiza- tions (e.g., scatterplot, bar chart, line chart). However, they need support correctly interpreting the data visualized in the scatterplot, as they are new to them. Another important insight was fitting the onboarding messages into the domain’s language

VisAhoi: Towards a library to generate and integrate visualization onboarding using high-level visualization grammars

Visualization onboarding supports users in reading, interpreting, and extracting information from visual data representations. General-purpose onboarding tools and libraries are applicable for explaining a wide range of graphical user interfaces but cannot handle specific visualization requirements. This paper describes a first step towards developing an onboarding library called VisAhoi, which is easy to integrate, extend, semi-automate, reuse, and customize. VisAhoi supports the creation of onboarding elements for different visualization types and datasets. We demonstrate how to extract and describe onboarding instructions using three well-known high-level descriptive visualization grammars — Vega-Lite, Plotly.js, and ECharts. We show the applicability of our library by performing two usage scenarios that describe the integration of VisAhoi into a VA tool for the analysis of high-throughput screening (HTS) data and, second, into a Flourish template to provide an authoring tool for data journalists for a treemap visualization. We provide a supplementary website (https://datavisyn.github.io/visAhoi/) that demonstrates the applicability of VisAhoi to various visualizations, including a bar chart, a horizon graph, a change matrix/heatmap, a scatterplot, and a treemap visualization