IntroductionPublic health is not only threatened by diseases, pandemics, or epidemics. It is also challenged by deficits in the communication of health information. The current COVID-19 pandemic demonstrates that impressively. One way to deliver scientific data such as epidemiological findings and forecasts on disease spread are dashboards. Considering the current relevance of dashboards for public risk and crisis communication, this systematic review examines the state of research on dashboards in the context of public health risks and diseases.MethodNine electronic databases where searched for peer-reviewed journal articles and conference proceedings. Included articles (n = 65) were screened and assessed by three independent reviewers. Through a methodological informed differentiation between descriptive studies and user studies, the review also assessed the quality of included user studies (n = 18) by use of the Mixed Methods Appraisal Tool (MMAT).Results65 articles were assessed in regards to the public health issues addressed by the respective dashboards, as well as the data sources, functions and information visualizations employed by the different dashboards. Furthermore, the literature review sheds light on public health challenges and objectives and analyzes the extent to which user needs play a role in the development and evaluation of a dashboard. Overall, the literature review shows that studies that do not only describe the construction of a specific dashboard, but also evaluate its content in terms of different risk communication models or constructs (e.g., risk perception or health literacy) are comparatively rare. Furthermore, while some of the studies evaluate usability and corresponding metrics from the perspective of potential users, many of the studies are limited to a purely functionalistic evaluation of the dashboard by the respective development teams.ConclusionThe results suggest that applied research on public health intervention tools like dashboards would gain in complexity through a theory-based integration of user-specific risk information needs.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=200178, identifier: CRD42020200178.

Methods for 3D‐imaging of biological samples are experiencing unprecedented development, with tools such as X‐ray micro‐computed tomography (μCT) becoming more accessible to biologists. These techniques are inherently suited to small subjects and can simultaneously image both external and internal morphology, thus offering considerable benefits for invertebrate research. However, methods for visualising 3D‐data are trailing behind the development of tools for generating such data. Our aim in this article is to make the processing, visualisation and presentation of 3D‐data easier, thereby encouraging more researchers to utilise 3D‐imaging. Here, we present a comprehensive workflow for manipulating and visualising 3D‐data, including basic and advanced options for producing images, videos and interactive 3D‐PDFs, from both volume and surface‐mesh renderings. We discuss the importance of visualisation for quantitative analysis of invertebrate morphology from 3D‐data, and provide example figures illustrating the different options for generating 3D‐figures for publication. As more biology journals adopt 3D‐PDFs as a standard option, research on microscopic invertebrates and other organisms can be presented in high‐resolution 3D‐figures, enhancing the way we communicate science.

The purpose of this code is to produce a line graph visualization of COVID-19 data. This Jupyter notebook was built and run on Google Colab. This code will serve mostly as a guide and will need to be adapted where necessary to be run locally. The separate COVID-19 datasets uploaded to this Dataverse can be used with this code. This upload is made up of the IPYNB and PDF files of the code.

The vast majority of studies using functional magnetic resonance imaging (fMRI) are analyzed on the group level. Standard group-level analyses, however, come with severe drawbacks: First, they assume functional homogeneity within the group, building on the idea that we use our brains in similar ways. Second, group-level analyses require spatial warping and substantial smoothing to accommodate for anatomical variability across subjects. Such procedures massively distort the underlying fMRI data, which hampers the spatial specificity. Taken together, group statistics capture the effective overlap, rendering the modeling of individual deviations impossible – a major source of false positivity and negativity. The alternative analysis approach is to leave the data in the native subject space, but this makes comparison across individuals difficult. Here, we propose a new framework for visualizing group-level information, better preserving the information of individual subjects. Our proposal is to limit the use of invasive data procedures such as spatial smoothing and warping and rather extract regional information from the individuals. This information is then visualized for all subjects and brain areas at one glance – hence we term the method brainglance. Additionally, our method incorporates a means for clustering individuals to further identify common traits. We showcase our method on two publicly available data sets and discuss our findings.

Abstract:

Background: Performance bugs can lead to severe issues regarding computation efficiency, power consumption, and user experience. Locating these bugs is a difficult task because developers have to judge for every costly operation whether runtime is consumed necessarily or unnecessarily. Objective: We wanted to investigate how developers, when locating performance bugs, navigate through the code, understand the program, and communicate the detected issues.

Method: We performed a qualitative user study observing twelve developers trying to fix documented performance bugs in two open source projects. The developers worked with a profiling and analysis tool that visually depicts runtime information in a list representation and embedded into the source code view.

Results: We identified typical navigation strategies developers used for pinpointing the bug, for instance, following method calls based on runtime consumption. The integration of visualization and code helped developers to understand the bug. Sketches visualizing data structures and algorithms turned out to be valuable for externalizing and communicating the comprehension process for complex bugs.

Conclusion: Fixing a performance bug is a code comprehension and navigation problem. Flexible navigation features based on executed methods and a close integration of source code and performance information support the process.

Dataset:

1. Tutorial: We provide the slides (PDF) and the video (MP4) we used in the tutorial phase of our study.

2. Locating Bugs: We also provide supplementary material for each research question. We provide the advices we prepared for each bug in case a team got stuck (PDF); the questions we asked after each bug fixing session can be found on the introduction slides (PDF).

   • RQ1: Navigating and Understanding

     • RQ1.1: How was information from the profiling tool or other parts of the IDE used to locate the performance bug? Cross-case analysis (in German) (XLSX+ODS)

     • RQ1.2: Is the in-situ visualization of the profiling data beneficial compared to a traditional list representation? Cross-case analysis (in German) (XLSX+ODS)

     • RQ1.3: What navigation strategies do developers pursue to locate a specific performance bug? Interaction logs (TXT), Navigation visualizations (SVG), Screen recordings for Bug 3 (MP4, without audio because of confidentiality)

   • RQ2: Understanding and Communicating

     • RQ2.1: How do developers communicate with each other when locating a performance bug? Coding (XLSX+ODS), Sketches (PDF), Screen recordings for Bug 3 (MP4, without audio because of confidentiality)

     • RQ2.2: Could sketches help to understand and communicate a performance bug? Coding (XLSX+ODS), Sketches (PDF), Cross-case analysis (in German) (XLSX+ODS), Sketching videos for Bug 3 (MP4, without audio because of confidentiality)

3. Questionnaire: The questionnaire that the participants filled out at the end of the study can be found here (PDF).

Fossils, including those that occasionally preserve decay-prone soft-tissues, are mostly made of minerals. Accessing their chemical composition provides unique insight into their past biology and/or the mechanisms by which they preserve, leading to a series of developments in chemical and elemental imaging. However, the mineral composition of fossils, particularly where soft-tissues are preserved, is often only inferred indirectly from elemental data, while X-ray diffraction that specifically provides phase identification received little attention. Here, we show the use of synchrotron radiation to generate not only X-ray fluorescence elemental maps of a fossil, but also mineralogical maps in transmission geometry using a two-dimensional area detector placed behind the fossil. This innovative approach was applied to millimetre-thick cross-sections prepared through three-dimensionally preserved fossils, as well as to compressed fossils. It identifies and maps mineral phases and their distribution at the microscale over centimetre-sized areas, benefitting from the elemental information collected synchronously, and further informs on texture (preferential orientation), crystallites size and local strain. Probing such crystallographic information is instrumental in defining mineralization sequences, reconstructing the fossilization environment and constraining preservation biases. Similarly, this approach could potentially provide new knowledge on other (bio)mineralization processes in environmental sciences. We also illustrate that mineralogical contrasts between fossil tissues and/or the encasing sedimentary matrix can be used to visualize hidden anatomies in fossils.

Methods Data were collected at the DiffAbs beamline of the SOLEIL Synchrotron source (France). Synchronous synchrotron rapid scanning X-ray fluorescence and diffraction mapping (SRS-XRFD) was performed using an incident X-ray beam of 16.2 or 18 keV, monochromatised using a Si(111) double-crystal monochromator, with a beam size diameter reduced down to 50 or 100 µm using platinum pinholes, or focused down to ~10 µm using Kirkpatrick-Baez mirrors. XRF was collected using a 4-element silicon drift detector (SDD, Vortex ME4, Hitachi High-Technologies Science America, Inc., total active area: 170 mm2) oriented at 90° to the incident beam, in the horizontal plane. XRD was collected in transmission geometry using a 2D hybrid pixel detector (XPAD S140, 240×560 pixels of 130 µm each), placed behind the sample at a distance of typically 200–300 mm such to intercept diffraction rings over an angular range of ~7° in scattering angle (2θ). Two-dimensional scanning was done by moving laterally the fossils in a plane rotated around the vertical axis by 20° to the primary beam (i.e., incident angle), to limit X-ray beam footprint on the sample but also such that the sample exhibits its surface to the SDD detector (no shadowing of the reflected XRD signal, figure 1a). Mapping over the entire fossils at a 35–100 µm lateral resolution was performed on the fly using the FLYSCAN platform. A full XRF spectrum and one or several XRD images were collected at each pixel.

The present dataset includes 6 types of data:

(1) Synchrotron X-Ray Fluorescence elemental maps

Methods: All elemental distributions presented in the paper correspond to integrated intensities around emission lines of elements of interest (XRF peaks), represented using linear (expect figure 1b, logarithmic) grey or color scales that go from dark to light, respectively for low to high intensities.

Data: figure1b_AsPb-map_DATA_XRF.txt; figure1b_Mn-map_DATA_XRF.txt; figure1b_Zn-map_DATA_XRF.txt; figure1b_stackRGB_DATA_XRF.tif; figure2b_Ca-map_DATA_XRF.txt; figure2b_Fe-map_DATA_XRF.txt; figure2b_Y-map_DATA_XRF.txt; figure2b_stackRGB_DATA_XRF.tif; figure5f_Y-map_DATA_XRF.txt

(2) Synchrotron X-Ray Diffraction detector images

Methods: A few XPAD detector images are shown in the paper, either simply after flat correction (figure 1c) or after conversion to (2θ-Ѱ) coordinates (figure 2e). These images are represented using logarithmic color scales that go from dark to light, respectively for low to high intensities.

Data: figure1c_left_DATA_XRD.txt; figure1c_right_DATA_XRD.txt; figure2e_DATA_XRD.xlsx

(3) Diffractograms

Methods: XPAD detector images were processed (azimuthal data regrouping along y direction) to extract their respective diffractograms (Intensity vs. 2θ profiles). Phase identification and 2θ calibration were performed using powder XRD diffractograms obtained on fragments of the sedimentary matrix (and of the fossil when possible) using the Match! software (Crystal Impact) making use of the International Centre for Diffraction Data (ICDD)- PDF 2015 database. Additional peaks in the XRD maps could then be identified using Match/ICDD database, as well as from the elemental information provided by the XRF data.

Data: figure1d_DATA_XRD.xlsx; figure2f_DATA_XRD.xlsx

(4) Synchrotron X-Ray Diffraction mineral maps

Methods: During XPAD detector images processing 4D datasets (x, y, 2θ, intensity) were also generated, and then particular XRD contrast maps. Phase identification and 2θ calibration is discussed above. All phase distributions presented in the paper correspond to integrated intensities of XRD peaks of interest, represented using linear grey or color scales that go from dark to light, respectively for low to high intensities.

Data: figure1e_left_DATA_XRD.txt; figure1e_center_DATA_XRD.txt; figure1e_right_DATA_XRD.txt; figure2c_A211-map_DATA_XRD.txt; figure2c_C006-map_DATA_XRD.txt; figure2c_Q101-map_DATA_XRD.txt; figure2c_stackRGB_DATA_XRD.tif; figure2d_C012-map_DATA_XRD.txt; figure2d_C113-map_DATA_XRD.txt; figure2d_C202-map_DATA_XRD.txt; figure2d_stackRGB_DATA_XRD.tif; figure4a_17p97-map_DATA_XRD.txt; figure4a_25p09-map_DATA_XRD.txt; figure4a_26p01-map_DATA_XRD.txt; figure4a_stackRGB_DATA_XRD.tif; figure4b_22p51-map_DATA_XRD.txt; figure4b_26p97-map_DATA_XRD.txt; figure4b_27p63-map_DATA_XRD.txt; figure4b_stackRGB_DATA_XRD.tif; figure5b_FAp002-map-head_DATA_XRD.txt; figure5b_FAp211-map-head_DATA_XRD.txt; figure5b_phyll-map-head_DATA_XRD.txt; figure5b_stackRGB-head_DATA_XRD.tif; figure5b_FAp002-map-tail_DATA_XRD.txt; figure5b_FAp211-map-tail_DATA_XRD.txt; figure5b_phyll-map-tail_DATA_XRD.txt; figure5b_stackRGB-tail_DATA_XRD.tif; figure5c_FAp002-map-cropped_DATA_XRD.txt; figure5e_FAp002-map_DATA_XRD.txt

(5) Crystallite sizes

Methods: By Gaussian fitting the 2θ profile of XRD peaks attributed to different crystalline phases, corresponding crystallite sizes were extracted (for each pixel of the maps) by converting their full width at half maximum (FWHM) using Scherrer’s formula. It was assumed that only the crystallite size is contributing to the broadening, and an instrument resolution function measured as ~0.035° (amounting several 10 %, and up to 50 % of the measured peak FWHM) was also taken into account for FWHM deconvolution. Crystallite size distributions are represented in the paper using linear color scales that go from dark to light, respectively for low to high intensities.

Data: figure2g_A211-crystSize-map_DATA_XRD.txt; figure2g_C006-crystSize-map_DATA_XRD.txt; figure2g_Q101-crystSize-map_DATA_XRD:txt; figure2g_stackRGB_DATA_XRD.tif

(6) Local texture measurements

Methods: In order to confirm some microstructure results obtained using the local probe XRD approach, supplementary local texture measurements were performed. This was done by scanning it in azimuth (Φ, rotation around the sample surface normal) and elevation (Ѱ, rotation around the projection of the impinging X-ray beam on the sample surface), while recording, at each position, the X-ray scattered signal. The resulting intensity is represented in a map, in polar coordinates (azimuth angle and elevation, e.g. figures 3f–h). In this way, when one or several crystallites are oriented such that the Bragg law is fulfilled for the particular inter-reticular distance probed (or the particular Bragg angle 2θ), high signal is found in the particular corresponding regions of the polar map, allowing: i) to retrieve the particular orientation of the grains (j, y), and ii) to possibly quantify the volume ratio of that particular orientation, compared to other orientations on the map. Rapid texture measurements were performed using the XPAD area detector. The sample was illuminated by the impinging X-ray beam (of size ~ 150 × 150 µm2 in this case) and the azimuth (Φ) and elevation (Ѱ) angles were scanned, the first one continuously. An image was recorded in each point, then texture maps for various 2θ angles (i.e. volumes) were reconstructed. Then, a similar dataset was recorded at the next vertical position on the sample. Local texture measurements are represented in the paper using logarithmic color scales that go from dark to light, respectively for low to high intensities.

Data: Figure3b-d_DATA_XRD.xlsx; figure3f_left_DATA_XRD.txt; figure3f_right_DATA_XRD.txt; figure3g_left_DATA_XRD.txt; figure3g_right_DATA_XRD.txt; figure3h_left_DATA_XRD.txt; figure3h_right_DATA_XRD.txt

Synthetic populations for regions of the World (SPW) | JordanDataset informationA synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics). LicenseCC-BY-4.0 AcknowledgmentThis project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541). Contact informationHenning.Mortveit@virginia.edu Identifiers Region name Jordan Region ID jor Model coarse Version 0_9_0 Statistics Name Value Population 5723567.0 Average age 23.5 Households 1235755.0 Average household size 4.6 Residence locations 1235755.0 Activity locations 131978.0 Average number of activities 6.4 Average travel distance 44.5 Sources Description Name Version Url Activity template data World Bank 2021 https://data.worldbank.org Administrative boundaries ADCW 7.6 https://www.adci.com/adc-worldmap Curated POIs based on OSM SLIPO/OSM POIs http://slipo.eu/?p=1551 https://www.openstreetmap.org/ Household data DHS https://dhsprogram.com Population count with demographic attributes GPW v4.11 https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11 Files descriptionBase data files (jor_data_v_0_9.zip) Filename Description jor_person_v_0_9.csv Data for each person including attributes such as age, gender, and household ID. jor_household_v_0_9.csv Data at household level. jor_residence_locations_v_0_9.csv Data about residence locations jor_activity_locations_v_0_9.csv Data about activity locations, including what activity types are supported at these locations jor_activity_location_assignment_v_0_9.csv For each person and for each of their activities, this file specifies the location where the activity takes place Derived data files Filename Description jor_contact_matrix_v_0_9.csv A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model. Validation and measures files Filename Description jor_household_grouping_validation_v_0_9.pdf Validation plots for household construction jor_activity_durations_{adult,child}_v_0_9.pdf Comparison of time spent on generated activities with survey data jor_activity_patterns_{adult,child}_v_0_9.pdf Comparison of generated activity patterns by the time of day with survey data jor_location_construction_0_9.pdf Validation plots for location construction jor_location_assignement_0_9.pdf Validation plots for location assignment, including travel distribution plots jor_jor_ver_0_9_0_avg_travel_distance.pdf Choropleth map visualizing average travel distance jor_jor_ver_0_9_0_travel_distr_combined.pdf Travel distance distribution jor_jor_ver_0_9_0_num_activity_loc.pdf Choropleth map visualizing number of activity locations jor_jor_ver_0_9_0_avg_age.pdf Choropleth map visualizing average age jor_jor_ver_0_9_0_pop_density_per_sqkm.pdf Choropleth map visualizing population density jor_jor_ver_0_9_0_pop_size.pdf Choropleth map visualizing population size

Synthetic populations for regions of the World (SPW) | Sweden

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (swe_data_v_0_9.zip)

Derived data files

Validation and measures files

Learn how to visualize an E.coli outbreak by importing a spreadsheet of data. In this Learn GIS PDF lesson you will:Build a spreadsheet in the CSV format and import it into a map. Mark a location using a Map Note. Use a proximity tool to generate lines illustrating data and origin points linesThe lesson takes approximately 30 minutes to complete._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This is a network of 14 systematic reviews on the salt controversy and their included studies. Each edge in the network represents an inclusion from one systematic review to an article. Systematic reviews were collected from Trinquart (Trinquart, L., Johns, D. M., & Galea, S. (2016). Why do we think we know what we know? A metaknowledge analysis of the salt controversy. International Journal of Epidemiology, 45(1), 251–260. https://doi.org/10.1093/ije/dyv184 ). FILE FORMATS 1) Article_list.csv - Unicode CSV 2) Article_attr.csv - Unicode CSV 3) inclusion_net_edges.csv - Unicode CSV 4) potential_inclusion_link.csv - Unicode CSV 5) systematic_review_inclusion_criteria.csv - Unicode CSV 6) Supplementary Reference List.pdf - PDF ROW EXPLANATIONS 1) Article_list.csv - Each row describes a systematic review or included article. 2) Article_attr.csv - Each row is the attributes of a systematic review/included article. 3) inclusion_net_edges.csv - Each row represents an inclusion from a systematic review to an article. 4) potential_inclusion_link.csv - Each row shows the available evidence base of a systematic review. 5) systematic_review_inclusion_criteria.csv - Each row is the inclusion criteria of a systematic review. 6) Supplementary Reference List.pdf - Each item is a bibliographic record of a systematic review/included paper. COLUMN HEADER EXPLANATIONS 1) Article_list.csv: ID - Numeric ID of a paper paper assigned ID - ID of the paper from Trinquart et al. (2016) Type - Systematic review / primary study report Study Groupings - Groupings for related primary study reports from the same report, from Trinquart et al. (2016) (if applicable, otherwise blank) Title - Title of the paper year - Publication year of the paper Attitude - Scientific opinion about the salt controversy from Trinquart et al. (2016) Doi - DOIs of the paper. (if applicable, otherwise blank) Retracted (Y/N) - Whether the paper was retracted or withdrawn (Y). Blank if not retracted or withdrawn. 2) Article_attr.csv: ID - Numeric ID of a paper year - Publication year Attitude - Scientific opinion about the salt controversy from Trinquart et al. (2016) Type - Systematic review/ primary study report 3) inclusion_net_edges.csv: citing_ID - The numeric ID of a systematic review cited_ID - The numeric ID of the included articles 4) potential_inclusion_link.csv: This data was translated from the Sankey diagram given in Trinquart et al. (2016) as Web Figure 4. Each row indicates a systematic review and each column indicates a primary study. In the matrix, "p" indicates that a given primary study had been published as of the search date of a given systematic review. 5)systematic_review_inclusion_criteria.csv: ID - The numeric IDs of systematic reviews paper assigned ID - ID of the paper from Trinquart et al. (2016) attitude - Its scientific opinion about the salt controversy from Trinquart et al. (2016) No. of studies included - Number of articles included in the systematic review Study design - Study designs to include, per inclusion criteria population - Populations to include, per inclusion criteria Exposure/Intervention - Exposures/Interventions to include, per inclusion criteria outcome - Study outcomes required for inclusion, per inclusion criteria Language restriction - Report languages to include, per inclusion criteria follow-up period - Follow-up period required for inclusion, per inclusion criteria

The U.S. Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP), executed by the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX), collects and processes high resolution aerial imagery and lidar data that provide regional datasets to support assessments of coastal change. The NCMP surveying methods and data products are invaluable tools for coastal management and have the potential to produce rapid-response data following extreme storm impacts. Emergency post-storm deployment of this system was used following hurricanes Matthew and Irma, and will likely continue to be used following future hurricane impacts. This web application visualizes and provides access to multiple coastal change datasets, including both storm-response and general comparisons. The operational layers visible in this application include the individual Map Blocks, for each of which .pdf files visualizing the data are available. Dune Features contains dune crest and toe data. Elevation Difference Grids includes the difference grids generated by subtracting one elevation dataset from another, allowing for easy visualization of erosion and deposition patterns. The Analysis Sections shows volume change and shoreline change data quantified in bins alongshore. For more information on these layers and their sub-layers, see their respective descriptions.

These data correspond to the set of problems used for the evaluation of the proposal What Are You Gazing At? An Approach to Use Eye-tracking for Robotic Process Automation.

Each problem consists of a set of 10 screenshots with the same look and feel but different data values for those values that can be entered/modify by the user. Each problem has its associated gaze fixation data. In each of the problems there is a key UI element that primarily attracts the attention of the user.

The evaluation is based on a set of images which resemble realistic screenshots of activities in the administrative domain. More precisely, 5 different set of screenshots (S) are generated, each of them with a different level of complexity. Complexity is measured in terms of the number of UI elements per screenshot. The sets are:

S1 Mockup-based email view. Represents the activity of viewing an email to check if it contains an attachment. In this case, the key UI element that receives the attention is the attachment inside the email.

S2 Mockup-based CRM user details. Represents a user's detail viewing activity within a Client Relationship Management (CRM) platform. The key UI element is the checkbox that indicates if the user has all his invoices paid.

S3 Real screenshot email view. Analogous to S1 but with real screenshots. It represents the activity of viewing an e-mail to check if it contains an attachment. In this case, the key UI element to which attention is paid is the attachment contained in the e-mail.

S4 Real screenshot CRM user details. Analogous to S2 but with real screenshots. It represents a user's detail viewing activity within a CRM platform. The key UI element is the checkbox indicating whether the user has all their invoices paid.

S5 Real screenshot CRM user details. Represents the split-screen display of two applications. On the left side a pdf viewer, showing a covid vaccination certificate. And on the right side a human resources management system (basic recreation of real system for privacy reasons). In this one the detail of the employee to whom the certificate of the left side corresponds is visualized. These screenshots, having two applications, have two key UI elements. In the pdf viewer it is the name of the certificate holder and in the human resources management system it is the name of the employee whose detail view is being displayed. The activity being carried out is the verification that the covid certificate received corresponds to that of an employee.

Two types of filters based on the gaze fixation data are applied to these sets of screenshots: Pre-filtering and Post-filtering, corresponding to applying the filtering before and after detecting UI components in the screenshots, respectively. The structure of the data packages is divided in two folders input and output. The input folder is organized as follows:

input/

screenshots/: corresponds to the screenshots. The sets of screenshots are easily identifiable, they are named following the pattern: SX_screenshot_DDDD.jpeg. Where X indicates to which of the set of screenshots described in the previous list it belongs, and DDDD represents a unique identifier for each screenshot. Each group consists of 10 screenshots, being 50 in total.

fixation.json: It is a JSON file that contains a key associated with each of the screenshots. For each screenshot, it contains a "fixation_points" key where information about the fixations that have occurred on the screenshot is stored. Here's an example:

"S5_screenshot_0050.jpeg": {
  "fixation_points": {
    "334.25#497.166666666667": {
      "#events": 6,
      "start_index": 33224,
      "ms_start": 553962.1467,
      "ms_end": 554061.9899,
      "duration": 99.8432000001194,
      "imotions_dispersion": 0.300325967868111,
      "last_index": 33229,
      "dispersion": 14.044275227531914
    },
    "1258.80769230769#507.576923076923": {
      "#events": 13,
      "start_index": 33234,
      "ms_start": 554128.5427,
      "ms_end": 554345.3595,
      ...

The output folder is organized in three subfolders, the first one containing the information of the non-filtered screenshots (i.e. without having applied to them any filtering or processing), and the next two with the information resulting from pre-filtering and post-filtering.

output/

non-filter/

borders/: screenshots with highlighted borders of all UI components detected in it.

components_json/: a collection of JSON files with the same name as the screenshot, containing the "img_shape" key with a list of the screen resolution and the number of layers the image has: [1080, 1920, 3], and the "compos" key with a list of all UI components representing the Screen Object Model.

pre-filter/ and post-filter/

borders/: screenshots with the borders of the relevant UI components. In the case of prefiltering, the detection of components is only performed on the parts of the screenshot that have received attention. In postfiltering, the complete screenshot is shown, with only the borders of the relevant UI components highlighted.

components_json/: a collection of JSON files with the same name as the screenshot is included, containing the following keys:

"img_shape": A list representing the screen resolution and the number of layers in the image, e.g., [1080, 1920, 3].

"compos": A list of all UI components representing the Screen Object Model (SOM). During post-filtering, each UI component is augmented with an additional property called "relevant." If this property is set to true, it indicates that the respective UI component has received attention.

(pre)/(post)filter_attention_maps/: represent the attention maps. In the case of prefiltering, any surface of the screen that has not received attention will be shown in black. In the case of postfiltering, the areas of attention will be shown as red circles, and the UI components whose area intersects with the areas of attention by more than 25% will be shown in yellow.

In conclusion, the described data package consists of sets of screenshots, accompanied by prefiltering and postfiltering filters using gaze fixation data, enabling the identification of relevant UI components. The organized data packages include input and output folders, where the output folder offers processed screenshots, UI component information, and attention maps. This resource provides valuable insights into user attention and interaction with UI elements on different types of scenarios.

The U.S. Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP), executed by the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX), collects and processes high resolution aerial imagery and lidar data that provide regional datasets to support assessments of coastal change. The NCMP surveying methods and data products are invaluable tools for coastal management and have the potential to produce rapid-response data following extreme storm impacts. Emergency post-storm deployment of this system was used following hurricanes Matthew and Irma, and will likely continue to be used following future hurricane impacts. This web application visualizes and provides access to multiple coastal change datasets, including both storm-response and general comparisons. The operational layers visible in this application include the individual Map Blocks, for each of which .pdf files visualizing the data are available. Dune Features contains dune crest and toe data. Elevation Difference Grids includes the difference grids generated by subtracting one elevation dataset from another, allowing for easy visualization of erosion and deposition patterns. The Analysis Sections shows volume change and shoreline change data quantified in bins alongshore. For more information on these layers and their sub-layers, see their respective descriptions.

The U.S. Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP), executed by the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX), collects and processes high resolution aerial imagery and lidar data that provide regional datasets to support assessments of coastal change. The NCMP surveying methods and data products are invaluable tools for coastal management and have the potential to produce rapid-response data following extreme storm impacts. Emergency post-storm deployment of this system was used following hurricanes Matthew and Irma, and will likely continue to be used following future hurricane impacts. This web application visualizes and provides access to multiple coastal change datasets, including both storm-response and general comparisons. The operational layers visible in this application include the individual Map Blocks, for each of which .pdf files visualizing the data are available. Dune Features contains dune crest and toe data. Elevation Difference Grids includes the difference grids generated by subtracting one elevation dataset from another, allowing for easy visualization of erosion and deposition patterns. The Analysis Sections shows volume change and shoreline change data quantified in bins alongshore. For more information on these layers and their sub-layers, see their respective descriptions.

• The data can be utilized to visualize, simplify and compare the MNREGA data with the help of Charts, Graphs supported by dynamic search filters.
• Data can be filtered on the basis of various important parameters like Age, Sex, Race, District, State, etc.
• The data provides the option to download every chart image in PNG/JPG Format or pdf format created with visualization techniques and also a unique link for charts will be provided to share. **USE CASE: ** Government Officials can access the role-based admin panel to import, export, update, and delete the data that has to be shown to the users. An error management system would be provided to address the issues like Data Anomalies, grievances, etc.

The University of Trinidad and Tobago (UTT) has published its faculty and staff statistics by staff position, department/unit, category, campus, and gender using Tableau software. The publication is part of UTT's Institutional Data Profile (IDP) and allows the data visualisation to be downloaded in an image, PDF and PowerPoint format. The dashboard displays an interactive visualisation of all UTT campus locations in Trinidad and Tobago that can be filtered by faculty and staff statistics.

Synthetic populations for regions of the World (SPW) | Alabama

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (usa_140002904_data_v_0_9.zip)

Derived data files

Validation and measures files

The University of Trinidad and Tobago (UTT) has published its graduation statistics by academic field, graduation year, gender, enrolment status, academic areas, and programmes using Tableau software. The publication is part of UTT's Institutional Data Profile (IDP) and allows the data visualisation to be downloaded in an image, PDF and PowerPoint format. There are three (3) tabs for the visualisation: Graduation Statistics, Graduation Data by Programme, and Academic Areas and Programmes. You can also filter the dashboard as well as share the link.

GIS and PDF data set of the Urbanisation Documents of the Local Urban Planning Plan of LA GARDE ADHEMAR 26138 — PLU approved on 27/05/2013 — SUP update of 22/05/2018 — VALIDITE FIN: 25/08/2019 enforceability of the main proceedings approved on 08/07/2019

Synthetic populations for regions of the World (SPW) | New Mexico

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (usa_140002890_data_v_0_9.zip)

Derived data files

Validation and measures files