This tutorial will teach you how to take time-series data from many field sites and create a shareable online map, where clicking on a field location brings you to a page with interactive graph(s).

The tutorial can be completed with a sample dataset (provided via a Google Drive link within the document) or with your own time-series data from multiple field sites.

Part 1 covers how to make interactive graphs in Google Data Studio and Part 2 covers how to link data pages to an interactive map with ArcGIS Online. The tutorial will take 1-2 hours to complete.

An example interactive map and data portal can be found at: https://temple.maps.arcgis.com/apps/View/index.html?appid=a259e4ec88c94ddfbf3528dc8a5d77e8

Preprocessed & clustered subsample of the teaseq dataset.

This video series presents 11 lessons and introduction to data literacy organized by the Open Development Cambodia Organization (ODC) to provide video tutorials on data literacy and the use of data in data storytelling. There are 12 videos which illustrate following sessions: * Introduction to the data literacy course * Lesson 1: Understanding data * Lesson 2: Explore data tables and data products * Lesson 3: Advanced Google Search * Lesson 4: Navigating data portals and validating data * Lesson 5: Common data format * Lesson 6: Data standard * Lesson 7: Data cleaning with Google Sheets * Lesson 8: Basic statistic * Lesson 9: Basic Data analysis using Google Sheets * Lesson 10: Data visualization * Lesson 11: Data Visualization with Flourish

Context

COVID-19, India This tutorial help in understanding basics of data visualization and mapping using Python.

Content

Data sets contain State wise confirmed cases, death toll, and cured cases till date.

Acknowledgements

I owe my thanks to the data sets provider.

Inspiration

Data visualization helps in creating trends, patterns, interactive graphs and maps. This will help policy and decision makers to understand,discuss and visualize the data.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Data required for completing the Galaxy tutorial entitled "Visualization with Circos"

A user experience book can forever change the way you experience and interact with your physical environment, open your eyes to the desirability of bad design and the desirability of good design, and raise your expectations of how design should be done.

If you're looking to take a UX course, nothing can help you like buying a UX book. These books act like a user experience class and provide you with the content you need. The style and tone of the user experience training book is such that it encourages the reader to learn the content and continue reading the book. This issue makes the reader have a deeper understanding of the content.

User experience tutorials define, identify, and analyze UX practices for XR environments, and explore techniques and tools for prototyping and designing XR user interactions. By reading the user experience book and using UX key performance indicators, you will get closer to individual perceptions of the system.

User experience textbooks also focus on case studies and UX design principles to illustrate the relationship between UX design and the growth of immersive technologies. Practical examples in these books show how to apply UX design principles. By reading the user experience pdf book, you will even be able to research user-friendly components so that you can create attractive and effective designs.

The best way to start designing software, website or to get more information in this field is to download the design experience book. Fortunately, today, more than hundreds of user experience training books have been written by expert authors in this field, which can make your steps in this direction more solid.

We have compiled a list of the best selling and best user experience books at Kitabarah to help you learn quickly. Buying user experience book pdf is not for beginners. Managers, marketers, programmers, and even salespeople who want to increase their knowledge in the field of UX can use these resources. Just start reading the user experience book to become a professional designer step by step.

Online experiment about the influence of the availability of a video tutorial on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.

NOTE: An updated Introduction to ArcGIS GeoEvent Server Tutorial is available here. It is recommended you use the new tutorial for getting started with GeoEvent Server. The old Introduction Tutorial available on this page is relevant for 10.8.x and earlier and will not be updated.The Introduction to GeoEvent Server Tutorial (10.8.x and earlier) introduces you to the Real-Time Visualization and Analytic capabilities of ArcGIS GeoEvent Server. GeoEvent Server allows you to:

Incorporate real-time data feeds in your existing GIS data and IT infrastructure. Perform continuous processing and analysis on streaming data, as it is received. Produce new streams of data that can be leveraged across the ArcGIS system.

Once you have completed the exercises in this tutorial you should be able to:

Use ArcGIS GeoEvent Manager to monitor and perform administrative tasks. Create and maintain GeoEvent Service elements such as inputs, outputs, and processors. Use GeoEvent Simulator to simulate event data into GeoEvent Server. Configure GeoEvent Services to append and update features in a published feature service. Work with processors and filters to enhance and direct GeoEvents from event data.

The knowledge gained from this tutorial will prepare you for other GeoEvent Server tutorials available in the ArcGIS GeoEvent Server Gallery.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 7 - March 30, 2018 - Compatible with ArcGIS GeoEvent Server 10.6 and later.

Previous

Release 6 - January 12, 2018 - Compatible with ArcGIS GeoEvent Server 10.5 thru 10.8.

Release 5 - July 30, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 4 - July 30, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x.

Release 3 - April 24, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x. Not available.

Release 2 - January 22, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x. Not available.

Release 1 - April 11, 2014 - Compatible with ArcGIS GeoEvent Server 10.2.x.

Nowadays, there is a growing tendency to use Python and R in the analytics world for physical/statistical modeling and data visualization. As scientists, analysts, or statisticians, we oftentimes choose the tool that allows us to perform the task in the quickest and most accurate way possible. For some, that means Python. For others, that means R. For many, that means a combination of the two. However, it may take considerable time to switch between these two languages, passing data and models through .csv files or database systems. There's a solution that allows researchers to quickly and easily interface R and Python together in one single Jupyter Notebook. Here we provide a Jupyter Notebook that serves as a tutorial showing how to interface R and Python together in a Jupyter Notebook on CUAHSI JupyterHub. This tutorial walks you through the installation of rpy2 library and shows simple examples illustrating this interface.

Here, we make available the supplemental material regarding data collection from the publicaiton "Research Data Curation in Visualization : Position Paper". The dataset represents an aggregated collection of the data policies of selected publication venues in the areas of visualization, computer graphics, software, HCI, and Virtual Reality with inclusions from multimedia, collaboration, and network visualization, for the years 2021-2022. Based on a derived index, long-term preservation and data sharing are evaluated for each venue. The index ranges from No policy to Required sharing and preservation. Additionally the verbatim statements (or the lack thereof) used to reach the concluded score are also provided. Abstract: Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher’s compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards.

I created this dataset while learning web scraping with the YouTube API in Jupyter Notebook. As someone who loves data and works in the Power BI and analytics space, I was curious to explore YouTube channels that share tutorials, tips, dashboards, and case studies related to data.

So I thought why not build a real-world dataset that could help others too?

Whether you’re:

Learning data visualization

Building a portfolio project

Practicing web scraping or APIs

This dataset can give you a good starting point.

Resources for Advanced Data Analysis and VisualizationResearchers who have access to the latest analysis and visualization tools are able to use large amounts of complex data to find efficiencies in projects, designs, and resources. The Data Analysis and Assessment Center (DAAC) at ERDC's Information Technology Laboratory (ITL) provides visualization and analysis tools and support services to enable the analysis of an ever-increasing volume of data.Simplify Data Analysis and Visualization ResearchThe resources provided by the DAAC enable any user to conduct important data analysis and visualization that provides valuable insight into projects and designs and helps to find ways to save resources. The DAAC provides new tools like ezVIZ, and services such as the DAAC website, a rich resource of news about the DAAC, training materials, a community forum and tutorials on how to use data analysis and other issues.The DAAC can perform collaborative work when users prefer to do the work themselves but need help in choosing which visualization program and/or technique and using the visualization tools. The DAAC also carries out custom projects to produce high-quality animations of data, such as movies, which allow researchers to communicate their results to others.Communicate Research in ContextDAAC provides leading animation and modeling software which allows scientists and researchers may communicate all aspects of their research by setting their results in context through conceptual visualization and data analysis.Success StoriesWave Breaking and Associated Droplet and Bubble FormationWave breaking and associated droplet and bubble formation are among the most challenging problems in the field of free-surface hydrodynamics. The method of computational fluid dynamics (CFD) was used to solve this problem numerically for flow about naval vessels. The researchers wanted to animate the time-varying three-dimensional data sets using isosurfaces, but transferring the data back to the local site was a problem because the data sets were large. The DAAC visualization team solved the problem by using EnSight and ezVIZ to generate the isosurfaces, and photorealistic rendering software to produce the images for the animation.Explosive Structure Interaction Effects in Urban TerrainKnown as the Breaching Project, this research studied the effects of high-explosive (HE) charges on brick or reinforced concrete walls. The results of this research will enable the war fighter to breach a wall to enter a building where enemy forces are conducting operations against U.S. interests. Images produced show computed damaged caused by an HE charge on the outer and inner sides of a reinforced concrete wall. The ability to quickly and meaningfully analyze large simulation data sets helps guide further development of new HE package designs and better ways to deploy the HE packages. A large number of designs can be simulated and analyzed to find the best at breaching the wall. The project saves money in greatly reduced field test costs by testing only the designs which were identified in analysis as the best performers.SpecificationsAmethyst, the seven-node Linux visualization cluster housed at the DAAC, is supported by ParaView, EnSight, and ezViz visualization tools and configured as follows:Six computer nodes, each with the following specifications:CPU: 8 dual-core 2.4 Ghz, 64-bit AMD Opteron Processors (16 effective cores)Memory: 128-G RAMVideo: NVidia Quadro 5500 1-GB memoryNetwork: Infiniband Interconnect between nodes, and Gigabit Ethernet to Defense Research and Engineering Network (DREN)One storage node:Disk Space: 20-TB TerraGrid file system, mounted on all nodes as /viz and /work

Open access database for all cognitive data collected from touchscreen related tasks. Performs data comparison and interactive data visualization for any data uploaded onto the site. There are also guidelines and video tutorials available.

The repository contains example data used in tutorial of HEIG v1.2.0. Note, this data is not compatible with v1.0.0, and some options are supported in v1.1.0. HEIG is a statistical framework for efficiently conducting joint analysis for large-scale imaging and genetic data.

The dataset has the following file structures:

• input
  • images1: 500 simulated images
  • images2: 500 simulated images
  • genotype: genotype data in PLINK bfile
  • misc: miscellaneous files
  • ldr_sumstats: 19 LDR GWAS summary statistic files of the superior fronto-occipital fasciculus
  • ld_regu8580: LD matrices with regularization {85%,80%}, including ~460,000 genotype array SNPs
  • ld_regu7570: LD matrices with regularization {75%,70%}, including ~460,000 genotype array SNPs
  • visualization: example files for visualization
• output
  • fpca: results of functional PCA
  • genotype: hail.MatrixTable of genotype data
  • gwas: LDR GWAS results generated by HEIG
  • herigc: results of heritability and (cross-trait) genetic correlation analysis
  • images: preprocessed 1000 images in H5DF file
  • ldr: LDRs constructed from 1000 images
  • sumstats: preprocessed 19 LDR GWAS summary statistic files of the superior fronto-occipital fasciculus
  • visualization: visualization results
  • voxelgwas: voxel-level GWAS results

The results in ouput are produced by data in input, which can verify if the user runs the code correctly.

Overview of command-line tools (CLTs) shipped with mdciao. These tools are one-shot tools that take users from basic input to production-ready figures and tables.

Crystal structures underpin many aspects of nanoscience and technology, from the arrangements of atoms in nanoscale materials to the ways in which nanoscale materials form and grow to the structures formed when nanoscale materials interact with each other and assemble. The impacts of crystal structures and their relationships to one another in nanoscale materials systems are vast. This Tutorial provides nanoscience researchers with highlights of many crystal structures that are commonly observed in nanoscale materials systems, as well as an overview of the tools and concepts that help to derive, describe, visualize, and rationalize key structural features. The scope of materials focuses on the elements and their compounds that are most frequently encountered as nanoscale materials, including both close-packed and nonclose-packed structures. Examples include three-dimensionally and two-dimensionally bonded compounds related to the rocksalt, nickel arsenide, fluorite, zincblende, wurtzite, cesium chloride, and perovskite structures, as well as layered perovskites, intergrowth compounds, MXenes, transition metal dichalcogenides, and other layered materials. Ordered versus disordered structures, high entropy materials, and instructive examples of more complex structures, including copper sulfides, are also discussed to demonstrate how structural visualization tools can be applied. The overall emphasis of this Tutorial is on the ways in which complex structures are derived from simpler building blocks, as well as the similarities and interrelationships among certain classes of structures that, at first glance, may be interpreted as being very different. Identifying and appreciating these structural relationships is useful to nanoscience researchers, as it allows them to deconstruct complex structures into simpler components, which is important for designing, understanding, and using nanoscale materials.

Context

Every day a new dataset is uploaded on kaggle. In order to make different from other datasets I worked on it and finally, I got a crazy idea which made me create this dataset.

I create a dataset on kaggle datasets (For now most voted dataset's) sounds interesting right?

The dataset consists of all the attributes which are projected on kaggle dataset page. I am excited to share the data. https://image.ibb.co/j9Ybwz/Screenshot_from_2018_10_05_19_47_35.png" alt="enter image description here">

Content

Dataset consists of 1960 rows and 15 columns. All the attributes which are on kaggle are in the dataset.

Columns details are : Votes - int64 Image- object Link - object Title - object Sub-title - object Uploader - object Updated - object Version - int64 Tags - object FileType - object FileSize - object License - object Kernels - object Discussions - float64 Views - object

Acknowledgements

Its hard to create this dataset. The main motto is to share the knowledge and create tutorials and we learned.

AboutRecent research shows that visualizing linguistic media bias mitigates its negative effects. However, reliable automatic detection methods to generate such visualizations require costly, knowledge-intensive training data. To facilitate data collection for media bias datasets, we present News Ninja, a game employing data-collecting game mechanics to generate a crowdsourced dataset. Before annotating sentences, players are educated on media bias via a tutorial. Our findings show that datasets gathered with crowdsourced workers trained on News Ninja can reach significantly higher inter-annotator agreements than expert and crowdsourced datasets. As News Ninja encourages continuous play, it allows datasets to adapt to the reception and contextualization of news over time, presenting a promising strategy to reduce data collection expenses, educate players, and promote long-term bias mitigation.

GeneralThis dataset was created through player annotations in the News Ninja Game made by ANON. Its goal is to improve the detection of linguistic media bias. Support came from ANON. None of the funders played any role in the dataset creation process or publication-related decisions.

The dataset includes sentences with binary bias labels (processed, biased or not biased) as well as the annotations of single players used for the majority vote. It includes all game-collected data. All data is completely anonymous. The dataset does not identify sub-populations or can be considered sensitive to them, nor is it possible to identify individuals.

Some sentences might be offensive or triggering as they were taken from biased or more extreme news sources. The dataset contains topics such as violence, abortion, and hate against specific races, genders, religions, or sexual orientations.

Description of the Data FilesThis repository contains the datasets for the anonymous News Ninja submission. The tables contain the following data:

ExportNewsNinja.csv: Contains 370 BABE sentences and 150 new sentences with their text (sentence), words labeled as biased (words), BABE ground truth (ground_Truth), and the sentence bias label from the player annotations (majority_vote). The first 370 sentences are re-annotated BABE sentences, and the following 150 sentences are new sentences.

AnalysisNewsNinja.xlsx: Contains 370 BABE sentences and 150 new sentences. The first 370 sentences are re-annotated BABE sentences, and the following 150 sentences are new sentences. The table includes the full sentence (Sentence), the sentence bias label from player annotations (isBiased Game), the new expert label (isBiased Expert), if the game label and expert label match (Game VS Expert), if differing labels are a false positives or false negatives (false negative, false positive), the ground truth label from BABE (isBiasedBABE), if Expert and BABE labels match (Expert VS BABE), and if the game label and BABE label match (Game VS BABE). It also includes the analysis of the agreement between the three rater categories (Game, Expert, BABE).

demographics.csv: Contains demographic information of News Ninja players, including gender, age, education, English proficiency, political orientation, news consumption, and consumed outlets.

Collection ProcessData was collected through interactions with the NewsNinja game. All participants went through a tutorial before annotating 2x10 BABE sentences and 2x10 new sentences. For this first test, players were recruited using Prolific. The game was hosted on a costume-built responsive website. The collection period was from 20.02.2023 to 28.02.2023. Before starting the game, players were informed about the goal and the data processing. After consenting, they could proceed to the tutorial.

The dataset will be open source. A link with all details and contact information will be provided upon acceptance. No third parties are involved.

The dataset will not be maintained as it captures the first test of NewsNinja at a specific point in time. However, new datasets will arise from further iterations. Those will be linked in the repository. Please cite the NewsNinja paper if you use the dataset and contact us if you're interested in more information or joining the project.