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

In 2012, GreyNet published a page on its website and made accessible the first edition of IDGL, International Directory of Organizations in Grey Literature . The latest update of this PDF publication was in August 2016, providing a list of some 280 organizations in 40 countries worldwide that have contact with the Grey Literature Network Service. The listing appears by country followed by the names of the organizations in alphabetical order, which are then linked to a URL. This year GreyNet International marks its Twenty Fifth Anniversary and seeks to more fully showcase organizations, whose involvement in grey literature is in one or more ways linked to GreyNet.org. Examples of which include: members, partners, conference hosts, sponsors, authors, service providers, committee members, associate editors, etc. This revised and updated edition of IDGL will benefit from the use of visualization software mapping the cities in which GreyNet’s contacts are located. Behind each point of contact are a number of fields that can be grouped and cross-tabulated for further data analysis. Such fields include the source, name of organization, acronym, affiliate’s job title, sector of information, subject/discipline, city, state, country, ISO code, continent, and URL. Eight of the twelve fields require input, while the other four fields do not. The population of the study was derived by extracting records from GreyNet’s in-house, administrative file. Only recipients on GreyNet’s Distribution List as of February 2017 were included. The records were then further filtered and only those that allowed for completion of the required fields remained. This set of records was then converted to Excel format, duplications were removed, and further normalization of field entries took place. In fine, 510 records form the corpus of this study. In the coming months, an in-depth analysis of the data will be carried out - the results of which will be recorded and made visually accessible. The expected outcome of the project will not only produce a revised, expanded, and updated publication of IDGL, but will also provide a visual overview of GreyNet as an international organization serving diverse communities with shared interests in grey literature. It will be a demonstration of GreyNet’s commitment to research, publication, open access, education, and public awareness in this field of library and information science. Finally, this study will serve to pinpoint geographic and subject based areas currently within as well as outside of GreyNet’s catchment.

This is a E-commerce website logs data created for helping the data analysts to practice exploratory data analysis and data visualization. The dataset has data on when the website was accessed, IP address of the source, Country, language in which website was accessed, amount of sales made by that IP address.

Included columns:

Time and duration of of accessing the website
Country, Language & Platform in which it was accessed
No. of bytes used & IP address of the person accessing website
Sales or return amount of that person

The global big data and business analytics (BDA) market was valued at ***** billion U.S. dollars in 2018 and is forecast to grow to ***** billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around ** billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate **** ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around **** billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

This dataset was compiled from a financial security study conducted in 2013 by Department of Consumer and Worker Protection's Office of Financial Empowerment, in partnership with The Urban Institute. The dataset is published on the DCWP website and used for a web-based data visualization tool. More information about the data, the study, and the online web-application can be found on the DCWP website:

https://www1.nyc.gov/assets/dca/CitywideFinancialServicesStudy/index.html

Source: Ratcliffe, Caroline, Signe-Mary McKernan, Emma Kalish, and Steven Martin. 2015. “Where are the Unbanked and Underbanked in New York City?” Washington, DC: Urban Institute.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Post-processed flux data and visualization tools from FLUXNET2015 sites using the flux-data-qaqc Python package

This dataset is a post-processed version of the FLUXNET2015 dataset and it includes in situ measurements from 195 eddy covariance flux towers worldwide (Pastorello et al., 2020). The dataset includes daily and monthly aggregated ET, energy balance metrics, and micrometeorological data. Original half-hourly flux data was retrieved from FLUXNET2015 on December 7th 2023. The dataset is oriented towards ET and includes both ET that has been corrected for energy balance closure error as well as the uncorrected values. The dataset has many potential uses including evaluation of regional hydrologic and atmospheric models, energy balance analysis, and others.

Data processing methods

Automated post-processing of half-hourly eddy covariance data that were downloaded was performed using the flux-data-qaqc Python package Volk et al. (2021) using the same methodologies as described in Volk et al. (2023). Those steps are briefly described here. Energy balance components (latent and sensible heat flux, soil heat flux, and net radiation) were subject to limited gap-filling and the FLUXNET2015 quality control (QC) flags were applied to half-hourly data before temporal aggregation to daily timesteps. We filtered out poor quality half-hourly data by using the FLUXNET2015 QC values associated with each variable, specifically those where the values were greater than or equal to one. Energy balance closure corrections were applied using the daily energy balance ratio method described by Pastorello et al. (2020), with minor adjustments used in the OpenET benchmark ET dataset (Volk et al., 2024; Volk et al., 2023; Melton et al., 2022). Other meteorological measurements such as air temperature, precipitation, humidity, etc. are included for most stations depending on availability, and some additional variables were calculated. Interactive graphics of most post-processed data are also included. Station metadata for the flux stations, including instrument height (for wind speed measurements), canopy height, and land cover information, were collected from BADM (Biological, Ancillary, Disturbance, and Metadata) files available on the FLUXNET2015 website.

In addition to station metadata from FLUXNET BADM, metadata used in the study of Andrade et al. (2025) are included in the metadata file. The “Monthly sample size” columns present the amount of monthly evaporation data available, the number of months with sufficient data for application of the complementary relationship models, and the number of months remaining after applying the filtering and correction steps described in Table 3 of Andrade et al. 2025. The “Main manuscript” column indicates whether the data was included in the main manuscript results or presented only in the Supplementary Material.

Description of the data and file structure

The dataset is in a compressed (zipped) archive titled "flux_ET_dataset", so first it needs to be downloaded and extracted. Once extracted there are four major components within:

1. A collection of time series files with daily aggregated data (one for each station), these are in the directory named "daily_data_files" and are in CSV format.

2. A similar collection of time series files for monthly aggregated data in "monthly_data_files".

3. Interactive graphic files (HTML format) for each station which are in the "graphical_files" directory.

4. Two additional tables in the root directory, including a metadata file named "station_metadata.xlsx" with site information such as site ID, coordinates, DOI principal investigator information, etc. The other table named "variable_explanation.xlsx" lists all variables that were post-processed in the flux dataset and gives a short description of each as well as their units.

Each data and plot file starts with the station's ID or site ID which are listed in the station_metadata.xlsx file.

Here is a visual of the file structure:

flux_ET_dataset
│ README.PDF
│ variable_explanation.xlsx
│ station_metadata.xlsx
│
└───daily_data_files
│ │ [site ID]_daily_data.csv
│ │ ...
│ │
└───monthly_data_files
│ │ [site ID]_monthly_data.csv
│ │ ...
│ │
└───graphical_files
│ │ [site ID]_plots.html
│ │ ...

The variable names in the daily and monthly data files as well as the graphics all follow the same naming scheme which are defined in the variable_explanation.xlsx file. For example, LE stands for latent energy flux and is in units of W/m².

Sharing/access information

Contact information for each station as well as DOIs and data are included in the "station_metadata.xlsx" file. Data sharing policies follow the same attribution requirements of the original FLUXNET2015, and credit to the original data producers should be acknowledged (e.g., reference FLUXNET2015 DOIs) in addition to citing this dataset.

Code/Software

All files that comprise this dataset were generated using the "flux-data-qaqc" open-source Python package version 0.2.2. The package is hosted on GitHub and PyPI, it also has online documentation including an in-depth user tutorial.

References

Andrade, B. C. de, Huntington, J. L., Volk, J. M., Morton, C., Pearson, C., & Albano, C. M. (2025). Multi-model intercomparison of the complementary relationship of evaporation across global environmental settings. Wiley. https://doi.org/10.22541/essoar.173655402.29591853/v1

Melton, F. S., Huntington, J., Grimm, R., Herring, J., Hall, M., Rollison, D., Erickson, T., Allen, R., Anderson, M., Fisher, J. B., Kilic, A., Senay, G. B., Volk, J., Hain, C., Johnson, L., Ruhoff, A., Blankenau, P., Bromley, M., Carrara, W., ... Anderson, R. G. (2022). OpenET: Filling a critical data gap in water management for the western United States. JAWRA Journal of the American Water Resources Association, 58(6), 971–994. https://doi.org/10.1111/1752-1688.12956

Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., ... Papale, D. (2020). The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Scientific Data, 7(1), 225. https://doi.org/10.1038/s41597-020-0534-3

Volk, J., Huntington, J., Allen, R., Melton, F., Anderson, M., & Kilic, A. (2021). flux-data-qaqc: A Python package for energy balance closure and post-processing of eddy flux data. Journal of Open Source Software, 6(66), 3418. https://doi.org/10.21105/joss.03418

Volk, J. M., Huntington, J., Melton, F. S., Allen, R., Anderson, M. C., Fisher, J. B., Kilic, A., Senay, G., Halverson, G., Knipper, K., Minor, B., Pearson, C., Wang, T., Yang, Y., Evett, S., French, A. N., Jasoni, R., & Kustas, W. (2023). Development of a benchmark eddy flux evapotranspiration dataset for evaluation of satellite-driven evapotranspiration models over the CONUS. Agricultural and Forest Meteorology, 331, 109307. https://doi.org/10.1016/j.agrformet.2023.109307

Volk, J. M., Huntington, J. L., Melton, F. S., Allen, R., Anderson, M., Fisher, J. B., Kilic, A., Ruhoff, A., Senay, G. B., Minor, B., Morton, C., Ott, T., Johnson, L., Comini de Andrade, B., Carrara, W., Doherty, C. T., Dunkerly, C., Friedrichs, M., Guzman, A., ... Yang, Y. (2024). Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications. Nature Water, 2(2), 193–205. https://doi.org/10.1038/s44221-023-00181-7

The Tableau View extension for CKAN enables the display of Tableau Public visualizations directly within CKAN datasets. By providing a view plugin, this extension allows users to embed interactive Tableau vizzes, enhancing data presentation and exploration capabilities within the CKAN platform. This offers a seamless integration path for organizations already utilizing Tableau Public to share insights drawn from their data. Key Features: Tableau Public Viz Integration: Embed Tableau Public visualizations within CKAN resources through a dedicated view plugin. This plugin allows for the display of interactive Tableau dashboards alongside the underlying data. Simple Configuration: The extension primarily requires enabling the tableau_view plugin within the CKAN configuration file. Further configuration details and display examples may be available on the extension's wiki page (if any wiki pages exist). Streamlined Data Visualization: Provides a direct method to visually represent data managed in CKAN, improving user engagement and comprehension. Use Cases: Open Data Portals: Governments and organizations can use this extension to embed publicly available Tableau visualizations in their open data portals, enhancing the accessibility and understandability of data. Internal Data Dashboards: Organizations using CKAN for internal data management can use the extension to embed Tableau dashboards providing data summaries, trends, and performance metrics. Technical Integration: The extension integrates into CKAN as a view plugin. Once the tableau_view plugin is enabled in the CKAN configuration file (ckan.plugins), it becomes available as a view option for resources that support it. The readme suggests referring to a wiki page for additional configuration details, which, if available, is crucial for proper setup and usage. Benefits & Impact: The Tableau View extension streamlines data visualization for CKAN users. By embedding interactive Tableau Public visualizations, it becomes easier for users to explore, analyze, and understand the data managed by CKAN. This can lead to improved data literacy, more informed decision-making, and broader engagement with open data initiatives.

4D STEM dataset recorded with DECTRIS ARINA detector.

Sample is a monocrystalline domain of SmB6 oriented along the <110> zone axis, prepared with FIB by Elisabeth Mueller at PSI.

Data collection was with a probe-corrected 200kV TEM microscope, supported by Mingjian Wu at FAU.

Further experimental parameters are listed with the included txt file.

Data visualization and processing can be done with NOVENA software, freely available at DECTRIS website.

Alternatively, the files can be opened using a HDF5 file reader.

Additional file 1 Source code and sample data for SEQing. The Python code and samples of Arabidopsis thaliana iCLIP (GSE99427) and RNA-seq (GSE99615) data used to start the sample dataset dashboard.

A collection of industry-focused KPI dashboard examples with key metrics for business performance.

This resource contains a SQLite database of temperature values recorded at 15-minute intervals between 2014 and 2018 at the iUTAH GAMUT sites in the Logan River Watershed. Additionally contained with in this resource is a Jupyter Notebook that:

1) defines a function for querying the SQLite database and extracting the data for each site 2) gets temperature time series from database using function 3) creates time series for each year of data at each site 4) re samples the complete time series at each site to get the mean daily temperature 5) creates a figure showing (a) a plot of the mean daily temperature at each site between 2014 and 2018 and (b) a plot for each year of data showing a box plot of the temperature data recorded that year for each site.

AbstractThe dataset provided here contains the efforts of independent data aggregation, quality control, and visualization of the University of Arizona (UofA) COVID-19 testing programs for the 2019 novel Coronavirus pandemic. The dataset is provided in the form of machine-readable tables in comma-separated value (.csv) and Microsoft Excel (.xlsx) formats.Additional InformationAs part of the UofA response to the 2019-20 Coronavirus pandemic, testing was conducted on students, staff, and faculty prior to start of the academic year and throughout the school year. These testings were done at the UofA Campus Health Center and through their instance program called "Test All Test Smart" (TATS). These tests identify active cases of SARS-nCoV-2 infections using the reverse transcription polymerase chain reaction (RT-PCR) test and the Antigen test. Because the Antigen test provided more rapid diagnosis, it was greatly used three weeks prior to the start of the Fall semester and throughout the academic year.As these tests were occurring, results were provided on the COVID-19 websites. First, beginning in early March, the Campus Health Alerts website reported the total number of positive cases. Later, numbers were provided for the total number of tests (March 12 and thereafter). According to the website, these numbers were updated daily for positive cases and weekly for total tests. These numbers were reported until early September where they were then included in the reporting for the TATS program.For the TATS program, numbers were provided through the UofA COVID-19 Update website. Initially on August 21, the numbers provided were the total number (July 31 and thereafter) of tests and positive cases. Later (August 25), additional information was provided where both PCR and Antigen testings were available. Here, the daily numbers were also included. On September 3, this website then provided both the Campus Health and TATS data. Here, PCR and Antigen were combined and referred to as "Total", and daily and cumulative numbers were provided.At this time, no official data dashboard was available until September 16, and aside from the information provided on these websites, the full dataset was not made publicly available. As such, the authors of this dataset independently aggregated data from multiple sources. These data were made publicly available through a Google Sheet with graphical illustration provided through the spreadsheet and on social media. The goal of providing the data and illustrations publicly was to provide factual information and to understand the infection rate of SARS-nCoV-2 in the UofA community.Because of differences in reported data between Campus Health and the TATS program, the dataset provides Campus Health numbers on September 3 and thereafter. TATS numbers are provided beginning on August 14, 2020.Description of Dataset ContentThe following terms are used in describing the dataset.1. "Report Date" is the date and time in which the website was updated to reflect the new numbers2. "Test Date" is to the date of testing/sample collection3. "Total" is the combination of Campus Health and TATS numbers4. "Daily" is to the new data associated with the Test Date5. "To Date (07/31--)" provides the cumulative numbers from 07/31 and thereafter6. "Sources" provides the source of information. The number prior to the colon refers to the number of sources. Here, "UACU" refers to the UA COVID-19 Update page, and "UARB" refers to the UA Weekly Re-Entry Briefing. "SS" and "WBM" refers to screenshot (manually acquired) and "Wayback Machine" (see Reference section for links) with initials provided to indicate which author recorded the values. These screenshots are available in the records.zip file.The dataset is distinguished where available by the testing program and the methods of testing. Where data are not available, calculations are made to fill in missing data (e.g., extrapolating backwards on the total number of tests based on daily numbers that are deemed reliable). Where errors are found (by comparing to previous numbers), those are reported on the above Google Sheet with specifics noted.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

As large-scale cross-linking data becomes available, new software tools for data processing and visualization are required to replace manual data analysis. XLink-DB serves as a data storage site and visualization tool for cross-linking results. XLink-DB accepts data generated with any cross-linker and stores them in a relational database. Cross-linked sites are automatically mapped onto PDB structures if available, and results are compared to existing protein interaction databases. A protein interaction network is also automatically generated for the entire data set. The XLink-DB server, including examples, and a help page are available for noncommercial use at http://brucelab.gs.washington.edu/crosslinkdbv1/. The source code can be viewed and downloaded at https://sourceforge.net/projects/crosslinkdb/?source=directory.

Snapshot img

Web Analytics Market Size 2025-2029

The web analytics market size is forecast to increase by USD 3.63 billion, at a CAGR of 15.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the rising preference for online shopping and the increasing adoption of cloud-based solutions. The shift towards e-commerce is fueling the demand for advanced web analytics tools that enable businesses to gain insights into customer behavior and optimize their digital strategies. Furthermore, cloud deployment models offer flexibility, scalability, and cost savings, making them an attractive option for businesses of all sizes. However, the market also faces challenges associated with compliance to data privacy and regulations. With the increasing amount of data being generated and collected, ensuring data security and privacy is becoming a major concern for businesses.
Regulatory compliance, such as GDPR and CCPA, adds complexity to the implementation and management of web analytics solutions. Companies must navigate these challenges effectively to maintain customer trust and avoid potential legal issues. To capitalize on market opportunities and address these challenges, businesses should invest in robust web analytics solutions that prioritize data security and privacy while providing actionable insights to inform strategic decision-making and enhance customer experiences.

What will be the Size of the Web Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Entities such as reporting dashboards, schema markup, conversion optimization, session duration, organic traffic, attribution modeling, conversion rate optimization, call to action, content calendar, SEO audits, website performance optimization, link building, page load speed, user behavior tracking, and more, play integral roles in this ever-changing landscape. Data visualization tools like Google Analytics and Adobe Analytics provide valuable insights into user engagement metrics, helping businesses optimize their content strategy, website design, and technical SEO. Goal tracking and keyword research enable marketers to measure the return on investment of their efforts and refine their content marketing and social media marketing strategies.

Mobile optimization, form optimization, and landing page optimization are crucial aspects of website performance optimization, ensuring a seamless user experience across devices and improving customer acquisition cost. Search console and page speed insights offer valuable insights into website traffic analysis and help businesses address technical issues that may impact user behavior. Continuous optimization efforts, such as multivariate testing, data segmentation, and data filtering, allow businesses to fine-tune their customer journey mapping and cohort analysis. Search engine optimization, both on-page and off-page, remains a critical component of digital marketing, with backlink analysis and page authority playing key roles in improving domain authority and organic traffic.

The ongoing integration of user behavior tracking, click-through rate, and bounce rate into marketing strategies enables businesses to gain a deeper understanding of their audience and optimize their customer experience accordingly. As market dynamics continue to evolve, the integration of these tools and techniques into comprehensive digital marketing strategies will remain essential for businesses looking to stay competitive in the digital landscape.

How is this Web Analytics Industry segmented?

The web analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises


Application

  Social media management
  Targeting and behavioral analysis
  Display advertising optimization
  Multichannel campaign analysis
  Online marketing


Component

  Solutions
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

.

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

In today's digital landscape, web analytics plays a pivotal role in driving business growth and optimizing online performance. Cloud-based deployment of web analytics is a game-changer, enabling on-demand access to computing resources for data analysis. This model streamlines business intelligence processes by collecting,

The Describable Textures Dataset (DTD) is an evolving collection of textural images in the wild, annotated with a series of human-centric attributes, inspired by the perceptual properties of textures. This data is made available to the computer vision community for research purposes.

The "label" of each example is its "key attribute" (see the official website). The official release of the dataset defines a 10-fold cross-validation partition. Our TRAIN/TEST/VALIDATION splits are those of the first fold.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('dtd', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/dtd-3.0.1.png" alt="Visualization" width="500px">

Recent advances in sequencing technologies have enabled the production of massive amounts of data on somatic mutations from cancer genomes. These data have led to the detection of characteristic patterns of somatic mutations or “mutation signatures” at an unprecedented resolution, with the potential for new insights into the causes and mechanisms of tumorigenesis. Here we present new methods for modelling, identifying and visualizing such mutation signatures. Our methods greatly simplify mutation signature models compared with existing approaches, reducing the number of parameters by orders of magnitude even while increasing the contextual factors (e.g. the number of flanking bases) that are accounted for. This improves both sensitivity and robustness of inferred signatures. We also provide a new intuitive way to visualize the signatures, analogous to the use of sequence logos to visualize transcription factor binding sites. We illustrate our new method on somatic mutation data from urothelial carcinoma of the upper urinary tract, and a larger dataset from 30 diverse cancer types. The results illustrate several important features of our methods, including the ability of our new visualization tool to clearly highlight the key features of each signature, the improved robustness of signature inferences from small sample sizes, and more detailed inference of signature characteristics such as strand biases and sequence context effects at the base two positions 5′ to the mutated site. The overall framework of our work is based on probabilistic models that are closely connected with “mixed-membership models” which are widely used in population genetic admixture analysis, and in machine learning for document clustering. We argue that recognizing these relationships should help improve understanding of mutation signature extraction problems, and suggests ways to further improve the statistical methods. Our methods are implemented in an R package pmsignature (https://github.com/friend1ws/pmsignature) and a web application available at https://friend1ws.shinyapps.io/pmsignature_shiny/.

Snapshot img

Web Development Market Size 2025-2029

The web development market size is forecast to increase by USD 40.98 billion at a CAGR of 10.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing digital transformation across industries and the integration of artificial intelligence (AI) into web applications. This trend is fueled by the need for businesses to enhance user experience, streamline operations, and gain a competitive edge in the market. Furthermore, the rapid evolution of technologies such as Progressive Web Apps (PWAs), serverless architecture, and the Internet of Things (IoT) is creating new opportunities for innovation and expansion. However, this market is not without challenges. The ever-changing technological landscape requires web developers to continuously update their skills and knowledge. Additionally, ensuring web applications are secure and compliant with data protection regulations is becoming increasingly complex.
Companies seeking to capitalize on market opportunities and navigate challenges effectively should focus on building a team of skilled developers, investing in continuous learning and development, and prioritizing security and compliance in their web development projects. By staying abreast of the latest trends and technologies, and adapting quickly to market shifts, organizations can successfully navigate the dynamic the market and drive business growth.

What will be the Size of the Web Development Market during the forecast period?

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The market continues to evolve at an unprecedented pace, driven by advancements in technology and shifting consumer preferences. Key trends include the adoption of Agile methodologies, DevOps tools, and version control systems for streamlined project management. JavaScript frameworks, such as React and Angular, dominate front-end development, while Magento, Shopify, and WordPress lead in content management and e-commerce. Back-end development sees a rise in Python, PHP, and Ruby on Rails frameworks, enabling faster development and more efficient scalability. Interaction design, user-centered design, and mobile-first design prioritize user experience, while security audits, penetration testing, and disaster recovery solutions ensure website safety.
Marketing automation, email marketing platforms, and CRM systems enhance digital marketing efforts, while social media analytics and Google Analytics provide valuable insights for data-driven decision-making. Progressive enhancement, headless CMS, and cloud migration further expand the market's potential. Overall, the market remains a dynamic, innovative space, with continuous growth fueled by evolving business needs and technological advancements.

How is this Web Development Industry segmented?

The web development industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Retail and e-commerce
  BFSI
  IT and telecom
  Healthcare
  Others


Business Segment

  SMEs
  Large enterprise


Service Type

  Front-End Development
  Back-End Development
  Full-Stack Development
  E-Commerce Development


Deployment Type

  Cloud-Based
  On-Premises


Technology Specificity

  JavaScript
  Python
  PHP
  Ruby


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Spain
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The retail and e-commerce segment is estimated to witness significant growth during the forecast period. The market is experiencing significant growth due to the digital transformation sweeping various industries. E-commerce and retail sectors lead the market, driven by the increasing preference for online shopping and improved Internet penetration. To cater to this trend, businesses demand user-engaging web applications with smooth navigation, secure payment gateways, and seamless product search and purchase features. Mobile shopping's rise necessitates mobile app development and mobile-optimized websites. Agile development, microservices architecture, and UI/UX design are essential elements in creating engaging and efficient web solutions. Furthermore, AI, machine learning, and data analytics enable data-driven decision making, customer loyalty, and business intelligence.

Web hosting, cloud computing, API integration, and growth hacking are other critical components. Ensuring web accessibility, data security, and e-commerce development is also crucial for businesses in the digital age. Online advertising, email marketing, content strategy, brand building, and data visualization are essential aspects of digital marketing. Serverless computing, u

The Channel Islands Marine Sanctuary (CINMS) comprises 1,470 square miles surrounding the Northern Channel Islands: Anacapa, Santa Cruz, Santa Rosa, San Miguel, and Santa Barbara, protecting various species and habitats. However, these sensitive habitats are highly susceptible to climate-driven ‘shock’ events which are associated with extreme values of temperature, pH, or ocean nutrient levels. A particularly devastating example was seen in 2014-16, when extreme temperatures and changes in nutrient conditions off the California coast led to large-scale die-offs of marine organisms. Global climate models are the best tool available to predict how these shocks may respond to climate change. To better understand the drivers and statistics of climate-driven ecosystem shocks, a ‘large ensemble’ of simulations run with multiple climate models will be used. The objective of this project is to develop a Python-based web application to visualize ecologically significant climate variables near th..., Data was accessed through AWS and then after subsetted to the point of interest, a netcdf file was downloaded for the purposes of the web application. More information can be found on the GitHub repository here: https://github.com/Channelislanders/toolkit It should be noted that all data found here is just for the purpose for the web application., , # GENERAL INFORMATION

This dataset is the files that accompany the website created for this project. A subsetted version of the CESM 1 dataset was downloaded to instantly update the website.

1. Title of the Project

Constructing Visualization Tools and Training Resources to Assess Climate Impacts on the Channel Islands National Marine Sanctuary

2. Author Information

Graduate Students at the Bren School for Environmental Science & Management in the Masters of Environmental Data Science program 2023-2024.

A. Principal Investigators Contact Information

Names: Olivia Holt, Diana Navarro, and Patty Park

Institution: Bren School at the University of California, Santa Barbara

Address: Bren Hall, 2400 University of California, Santa Barbara, CA 93117

Emails: olholt@bren.ucsb.edu, dmnavarro@bren.ucsb.edu, p_park@bren.ucsb.edu

B. Associate or Co-investigator Contact Informat...

This data set has been generated using data from the Gapminder website, which focuses on gathering and sharing statistics and other information about social, economic and environmental development at local, national and global levels.

This particular data set describes the values of several parameters (see the list below) between 1998 and 2018 for a total of 175 countries, having a total of 3675 rows. The parameters included in the data set and the column name of the dataframe are as follows:

• Country (country). Describes the country name
• Continent (continent). Describes the continent to which the country belongs
• Year (year). Describes the year to which the data belongs
• Life expectancy (life_exp). Describes the life expectancy for a given country in a given year
• Human Development Index (hdi_index). Describes the HDI index value for a given country in a given year
• CO2 emissions per person(co2_consump). Describes the CO2 emissions in tonnes per person for a given country in a given year
• Gross Domestic Product per capita (gdp). Describes the GDP per capita in dollars for a given country in a given year
• % Service workers (services). Describes the the % of service workers for a given country in a given year

The rainbow color map is scientifically incorrect and hinders people with color vision deficiency to view visualizations in a correct way. Due to perceptual non-uniform color gradients within the rainbow color map the data representation is distorted what can lead to misinterpretation of results and flaws in science communication. Here we present the data of a paper survey of 797 scientific publication in the journal Hydrology and Earth System Sciences. With in the survey all papers were classified according to color issues. Find details about the data below.

• year = year of publication (YYYY)
• date = date (YYYY-MM-DD) of publication
• title = full paper title from journal website
• authors = list of authors comma-separated
• n_authors = number of authors (integer between 1 and 27)
• col_code = color-issue classification (see below)
• volume = Journal volume
• start_page = first page of paper (consecutive)
• end_page = last page of paper (consecutive)
• base_url = base url to access the PDF of the paper with /volume/start_page/year/
• filename = specific file name of the paper PDF (e.g. hess-9-111-2005.pdf)

Color classification is stored in the col_code variable with:

• 0 = chromatic and issue-free,
• 1 = red-green issues,
• 2= rainbow issues and
• bw= black and white paper.

See more details (e.g., sample code to analyse the survey data) on https://github.com/modche/rainbow_hydrology

Paper: Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549–4565, https://doi.org/10.5194/hess-25-4549-2021, 2021.