This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

Monthly analytics reports for the Brisbane City Council website

Information regarding the sessions for Brisbane City Council website during the month including search terms used.

Monthly analytics reports for the Brisbane City Council website

Information regarding the sessions for Brisbane City Council website during the month including search terms used.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

You will find well-rounded ways to scout the competitors:

• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

In March 2024, search platform Google.com generated approximately 85.5 billion visits, down from 87 billion platform visits in October 2023. Google is a global search platform and one of the biggest online companies worldwide.

Web Analytics Market Valuation – 2024-2031

Web Analytics Market was valued at USD 6.16 Billion in 2024 and is projected to reach USD 13.6 Billion by 2031, growing at a CAGR of 18.58% from 2024 to 2031.

Web Analytics Market Drivers

Data-Driven Decision Making: Businesses increasingly rely on data-driven insights to optimize their online strategies. Web analytics provides valuable data on website traffic, user behavior, and conversion rates, enabling data-driven decision-making.

E-commerce Growth: The rapid growth of e-commerce has fueled the demand for web analytics tools to track online sales, customer behavior, and marketing campaign effectiveness.

Mobile Dominance: The increasing use of mobile devices for internet browsing has made mobile analytics a crucial aspect of web analytics. Businesses need to understand how users interact with their websites and apps on mobile devices.

Web Analytics Market Restraints

Data Privacy and Security Concerns: As data privacy regulations become stricter, businesses must ensure that they collect and process user data ethically and securely.

Complex Web Analytics Tools: Some web analytics tools can be complex to implement and use, requiring technical expertise.

This asset is a filter (derived view of a dataset) based on the system dataset, 'Site Analytics: Catalog Search Terms' which is automatically generated by the City of Austin Open Data Portal (data.austintexas.gov). It provides data on the words and phrases entered by site users of in search bars that look through the data catalog for relevant information. Catalog searches using the Discovery API are not included.

Each row in the dataset indicates the number of catalog searches made using the search term from the specified user segment during the noted hour.

Data are segmented into the following user types: • site member: users who have logged in and have been granted a role on the domain • community user: users who have logged in but do not have a role on the domain • anonymous: users who have not logged in to the domain

Data are updated by a system process at least once a day, if there is new data to record.

Data provided by: Tyler Technologies Creation date of data source: January 31, 2020

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

The website speed test market has witnessed significant growth, reaching a market size of XXX million in 2025. This growth is primarily attributed to the increasing demand for faster and more reliable internet speeds, driven by the widespread adoption of streaming video, online gaming, and cloud-based applications. The CAGR of the market is projected to remain strong over the forecast period from 2023 to 2033, reaching a value of XXX million by 2033. Key market trends include the growing adoption of 5G networks and the increasing popularity of fiber optic internet, both of which offer significantly faster speeds compared to traditional copper-based connections. In terms of segmentation, the market for website speed test can be divided into two main types: cable internet and fiber optic internet. Cable internet is currently the most widely used type of broadband internet connection, but fiber optic internet is rapidly gaining popularity due to its superior speed and reliability. Other types of broadband internet connections include fixed wireless internet, satellite internet, and DSL internet. The market can also be segmented based on its application, with individuals and businesses being the two primary user groups. Businesses typically require faster and more reliable internet speeds than individuals, and are therefore more likely to invest in higher-end solutions such as fiber optic internet. Major companies in the website speed test market include Fusion Connect, Bandwidth Place, Ookla, Netflix, and Measurement Lab, among others.

The global split testing tools market is experiencing robust growth, projected to reach $914.5 million in 2025. While the exact Compound Annual Growth Rate (CAGR) isn't provided, considering the rapid adoption of digital marketing strategies and the increasing need for data-driven optimization, a conservative estimate places the CAGR between 15% and 20% during the forecast period (2025-2033). This growth is fueled by several key drivers. Businesses across all sizes—from small and medium-sized enterprises (SMEs) to large enterprises—are increasingly leveraging A/B testing and multivariate testing to enhance website conversion rates, improve user experience, and ultimately boost revenue. The rise of sophisticated cloud-based solutions offering scalable and user-friendly interfaces further contributes to market expansion. Trends such as personalization, AI-powered testing, and integration with other marketing automation tools are also driving demand. However, factors such as the initial investment required for implementing split testing tools and the complexity associated with interpreting results can act as restraints. The market is segmented by deployment type (web-based, cloud-based) and application (large enterprises, SMEs), reflecting the diverse needs of different user groups. The competitive landscape is dynamic, with established players like Optimizely, Adobe, and VWO alongside emerging innovative companies vying for market share. This competition fosters innovation and drives down prices, making split testing tools increasingly accessible to a wider range of businesses. The market's geographic distribution is diverse, with North America, Europe, and Asia Pacific representing significant revenue contributors. The growth in these regions is propelled by the high concentration of businesses that heavily invest in digital marketing and the presence of a strong technological infrastructure. While precise regional breakdowns are unavailable, we can predict that North America will maintain a significant market share due to early adoption and technological advancement. Europe and Asia Pacific are expected to witness considerable growth driven by increasing digitalization and the expansion of e-commerce. The forecast period (2025-2033) promises continued expansion, driven by ongoing technological advancements and the expanding adoption of data-driven decision-making in marketing and website optimization. The market's trajectory suggests a continued upward trend, underscoring the critical role split testing plays in the modern digital marketing landscape.

According to the results of a survey conducted worldwide in 2023, nearly half of responding digital marketers believed artificial intelligence (AI) would have a positive impact on website search traffic in the next five years. Some 20 percent stated AI would have a neutral effect, while 30 percent agreed that the technology would negatively impact search traffic.

The global job search site market is projected to reach a valuation of million by 2033, exhibiting a CAGR of XX% during the forecast period of 2025-2033. The market growth is attributed to the increasing adoption of cloud-based and web-based job search platforms, rising demand for skilled professionals, and the growing popularity of remote work. The market is segmented based on type (cloud-based, web-based) and application (large enterprises, SMEs, individuals). Cloud-based job search platforms are gaining traction due to their scalability, flexibility, and cost-effectiveness. Large enterprises are the primary users of these platforms, as they offer features such as candidate management, automated screening, and data analytics. Individuals are also increasingly using job search platforms to find suitable job opportunities, leading to the growth of the SME and individual segments. Key players in the market include LinkedIn, Indeed, ZipRecruiter, Hired, Monster, and Glassdoor. Regional analysis reveals that North America and Europe dominate the market due to the presence of a large number of job seekers and employers. Asia Pacific is expected to witness significant growth in the coming years, driven by the rising adoption of online recruitment and job search platforms in developing countries like India and China.

In November 2024, the website with the highest reach on PCs in South Korea was naver.com, as around 77.4 percent of computer users in the country accessed that site. Naver.com is the search portal website of South Korean internet giant Naver Corporation, offering multiple services from social media, shopping, and blogs to search engines and more. Naver as a search engine Naver generated most of its revenue in 2022 from its function as an internet search platform. Naver’s biggest competitor in the online search market was Google. The domain google.com had a website reach on PCs around the mid-40 percent mark, but overall Google as a search engine effortlessly claimed the highest market share on the search engine market in South Korea. Still, looking at the number of monthly users, Naver was the most used search engine among consumers.  Internet usage in South Korea In general, the South Korean population is very well connected to the internet, as over 97 percent had access to it as of 2021. Accordingly, internet usage in the country has thoroughly expanded into various aspects of the average person’s day-to-day life. Additionally, the government also continues to push further development of utilizing the internet.

Delve into Serpstat's comprehensive website ratings, providing an in-depth analysis of websites across 1K+ industries and 229 countries. Our datasets offer a wealth of valuable metrics, including domain global rating, domain rating within the category, and domain category (e.g., Shopping/Apparel/Footwear). Gain insights into domain estimated search traffic, domain visibility (an indicator of domain visibility in the top 20 Google results), number of domain SEO keywords, number of referring domains, number of backlinks, and Serpstat Domain Rank (a domain authority indicator).

With these robust metrics, our datasets empower businesses to make informed decisions and optimize their online strategies effectively. Whether you're conducting competitor analysis, market research, or SEO optimization, Serpstat's website ratings provide the comprehensive insights needed to drive success in today's digital landscape. Plus, our datasets are refreshed on demand, ensuring that you always have access to the most up-to-date information for strategic decision-making.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.

Methods

RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data were downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

Following data processing describe below, on ingest into RAMP an additional field, citableContent, is added to the page level data.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, data are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above.

The data in these CSV files include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the index field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data follow the format 2018-01_RAMP_all.csv. Using this example, the file 2018-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2018.

Data Collection from August 19, 2018 Onward

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository

The Enterprise SEO Platforms market is experiencing robust growth, driven by the increasing reliance of businesses on digital channels for customer acquisition and brand building. The market size in 2025 is estimated at $5 billion, with a Compound Annual Growth Rate (CAGR) of 15% projected from 2025 to 2033. This growth is fueled by several key factors. Firstly, the ever-increasing complexity of search engine algorithms necessitates sophisticated SEO tools capable of managing large-scale website optimization efforts. Secondly, the rise of mobile-first indexing and the growing importance of voice search are forcing companies to adopt advanced SEO strategies, which in turn fuels demand for robust enterprise-grade platforms. Finally, the need for precise measurement and reporting of SEO performance is driving adoption of platforms that offer comprehensive analytics and data visualization capabilities. The market is segmented by platform type (navigational, transactional, informational) and user application (SMEs, large enterprises), with large enterprises representing a significant portion of the market due to their higher budgets and more complex SEO needs. The competitive landscape is highly dynamic, with established players like SEMrush and Ahrefs alongside emerging specialized platforms continuously innovating to meet evolving market demands. Geographic distribution shows a strong concentration in North America and Europe, reflecting the higher digital maturity and adoption rates in these regions. However, Asia-Pacific is expected to witness significant growth in the coming years as businesses in this region increasingly invest in digital marketing strategies. The future of the Enterprise SEO Platforms market appears promising, with continued growth driven by technological advancements and evolving search engine landscapes. The increasing demand for data-driven decision-making in SEO will further propel the adoption of advanced analytical tools. Furthermore, integration with other marketing platforms, such as CRM and marketing automation systems, will become increasingly crucial for seamless workflow and data-driven campaign optimization. This necessitates the development of more integrated and user-friendly platforms, placing further pressure on vendors to innovate and provide comprehensive, value-added services. Despite these positive trends, challenges remain, including the need for greater transparency in SEO reporting and the ongoing evolution of search engine algorithms, requiring constant adaptation and platform updates.

As of 2019, direct traffic accounts for the largest percentage of website traffic worldwide, with a share of 55 percent. Additionally, search traffic accounts for 29 percent of worldwide website traffic.

This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

SEO Software Market size was valued at USD 274.95 Million in 2024 and is projected to reach USD 790.95 Million by 2031, growing at a CAGR of 14.12% from 2024 to 2031.

Global SEO Software Market Drivers

Growing Importance of Online Presence: As more and more people use the internet and become more digitally literate, companies from all sectors are realizing how critical it is to have a strong online presence. SEO software helps companies become more visible on search engines, increasing brand awareness and bringing in organic traffic to their websites.

Updates to Search Engine Algorithms: In order to provide consumers with more relevant and superior search results, search engines such as Google regularly improve their algorithms. The need for SEO software, which enables companies to modify their tactics to satisfy the most recent search engine standards and preserve or raise their search ranks, is being driven by these algorithm changes.

Increasing Rivalry in Digital Marketing: As more companies engage in digital marketing, there is growing rivalry for online exposure and search engine results. In order to stay ahead of the competition, firms can use the tools and analytics provided by SEO software to analyze their rivals, spot possibilities, and improve their SEO tactics.

Concentrate on material Marketing: Since relevant, high-quality material is necessary to draw in and hold the attention of readers, content marketing is an important component of SEO. In order to help organizations generate and optimize content that appeals to their target audience, SEO software frequently includes capabilities for keyword research, content optimization, and content performance tracking.

Mobile Search Optimization: As more people browse the internet on mobile devices, businesses are placing a premium on mobile search optimization. In order to guarantee a flawless user experience and higher search ranks on mobile search results pages, SEO software provides tools and insights to optimize websites for mobile devices.

Data-driven Decision Making: SEO software gives organizations access to insightful statistics and data that help them decide on the best SEO tactics. SEO software helps organizations to assess success, spot trends, and improve their SEO strategies for greater outcomes. It does this through keyword analysis, backlink monitoring, and performance tracking, among other features.

Concentrate on Local SEO: Local SEO is crucial for drawing clients in certain regions for companies that serve local markets or have a physical presence. To assist businesses become more visible in local search results, SEO software frequently includes capabilities for local keyword research, citation management, and local business listing optimization.

Version update: The originally uploaded versions of the CSV files in this dataset included an extra column, "Unnamed: 0," which is not RAMP data and was an artifact of the process used to export the data to CSV format. This column has been removed from the revised dataset. The data are otherwise the same as in the first version.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2019. For a description of the data collection, processing, and output methods, please see the "methods" section below.

Methods

Data Collection

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above. Also as noted above, daily data are downloaded for each IR in two sets which cannot be combined. One dataset includes the URLs of items that appear in SERP. The second dataset is aggregated by combination of the country from which a search was conducted and the device used.

As a result, two CSV datasets are provided for each month of published data:

page-clicks:

The data in these CSV files correspond to the page-level data, and include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the previous field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data end with “page-clicks”. For example, the file named 2019-01_RAMP_all_page-clicks.csv contains page level click data for all RAMP participating IR for the month of January, 2019.

country-device-info:

The data in these CSV files correspond to the data aggregated by country from which a search was conducted and the device used. These include the following fields:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.
index: The Elasticsearch index corresponding to country and device access information data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the previous field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data end with “country-device-info”. For example, the file named 2019-01_RAMP_all_country-device-info.csv contains country and device data for all participating IR for the month of January, 2019.

References

Google, Inc. (2021). Search Console APIs. Retrieved from https://developers.google.com/webmaster-tools/search-console-api-original.