About

This dataset provides insights into user behavior and online advertising, specifically focusing on predicting whether a user will click on an online advertisement. It contains user demographic information, browsing habits, and details related to the display of the advertisement. This dataset is ideal for building binary classification models to predict user interactions with online ads.

Features

• id: Unique identifier for each user.
• full_name: User's name formatted as "UserX" for anonymity.
• age: Age of the user (ranging from 18 to 64 years).
• gender: The gender of the user (categorized as Male, Female, or Non-Binary).
• device_type: The type of device used by the user when viewing the ad (Mobile, Desktop, Tablet).
• ad_position: The position of the ad on the webpage (Top, Side, Bottom).
• browsing_history: The user's browsing activity prior to seeing the ad (Shopping, News, Entertainment, Education, Social Media).
• time_of_day: The time when the user viewed the ad (Morning, Afternoon, Evening, Night).
• click: The target label indicating whether the user clicked on the ad (1 for a click, 0 for no click).

Goal

The objective of this dataset is to predict whether a user will click on an online ad based on their demographics, browsing behavior, the context of the ad's display, and the time of day. You will need to clean the data, understand it and then apply machine learning models to predict and evaluate data. It is a really challenging request for this kind of data. This data can be used to improve ad targeting strategies, optimize ad placement, and better understand user interaction with online advertisements.

Click Web Traffic Combined with Transaction Data: A New Dimension of Shopper Insights

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Click enhances the unparalleled accuracy of CE Transact by allowing investors to delve deeper and browse further into global online web traffic for CE Transact companies and more. Leverage the unique fusion of web traffic and transaction datasets to understand the addressable market and understand spending behavior on consumer and B2B websites. See the impact of changes in marketing spend, search engine algorithms, and social media awareness on visits to a merchant’s website, and discover the extent to which product mix and pricing drive or hinder visits and dwell time. Plus, Click uncovers a more global view of traffic trends in geographies not covered by Transact. Doubleclick into better forecasting, with Click.

Consumer Edge’s Click is available in machine-readable file delivery and enables: • Comprehensive Global Coverage: Insights across 620+ brands and 59 countries, including key markets in the US, Europe, Asia, and Latin America. • Integrated Data Ecosystem: Click seamlessly maps web traffic data to CE entities and stock tickers, enabling a unified view across various business intelligence tools. • Near Real-Time Insights: Daily data delivery with a 5-day lag ensures timely, actionable insights for agile decision-making. • Enhanced Forecasting Capabilities: Combining web traffic indicators with transaction data helps identify patterns and predict revenue performance.

Use Case: Analyze Year Over Year Growth Rate by Region

Problem A public investor wants to understand how a company’s year-over-year growth differs by region.

Solution The firm leveraged Consumer Edge Click data to: • Gain visibility into key metrics like views, bounce rate, visits, and addressable spend • Analyze year-over-year growth rates for a time period • Breakout data by geographic region to see growth trends

Metrics Include: • Spend • Items • Volume • Transactions • Price Per Volume

Inquire about a Click subscription to perform more complex, near real-time analyses on public tickers and private brands as well as for industries beyond CPG like: • Monitor web traffic as a leading indicator of stock performance and consumer demand • Analyze customer interest and sentiment at the brand and sub-brand levels

Consumer Edge offers a variety of datasets covering the US, Europe (UK, Austria, France, Germany, Italy, Spain), and across the globe, with subscription options serving a wide range of business needs.

Consumer Edge is the Leader in Data-Driven Insights Focused on the Global Consumer

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 2020. 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 2020-01_RAMP_all_page-clicks.csv contains page level click data for all RAMP participating IR for the month of January, 2020.

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 2020-01_RAMP_all_country-device-info.csv contains country and device data for all participating IR for the month of January, 2020.

References

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

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 2021. For a description of the data collection, processing, and output methods, please see the "methods" section below.

The record will be revised periodically to make new data available through the remainder of 2021.

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 2021-01_RAMP_all_page-clicks.csv contains page level click data for all RAMP participating IR for the month of January, 2021.

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 2021-01_RAMP_all_country-device-info.csv contains country and device data for all participating IR for the month of January, 2021.

References

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

This dataset captures how many voter registration applications each agency has distributed, how many applications agency staff sent to the Board of Elections, how many staff each agency trained to distribute voter registration applications, whether or not the agency hosts a link to voting.nyc on its website and if so, how many clicks that link received during the reporting period.

Analysis of ‘Climate Change Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/climate-change-datae on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Data from World Development Indicators and Climate Change Knowledge Portal on climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use.

In addition to the data available here and through the Climate Data API, the Climate Change Knowledge Portal has a web interface to a collection of water indicators that may be used to assess the impact of climate change across over 8,000 water basins worldwide. You may use the web interface to download the data for any of these basins.

Here is how to navigate to the water data:

• Go to the Climate Change Knowledge Portal home page (http://climateknowledgeportal.worldbank.org/)
• Click any region on the map Click a country In the navigation menu
• Click "Impacts" and then "Water" Click the map to select a specific water basin
• Click "Click here to get access to data and indicators" Please be sure to observe the disclaimers on the website regarding uncertainties and use of the water data.

Attribution: Climate Change Data, World Bank Group.

World Bank Data Catalog Terms of Use

Source: http://data.worldbank.org/data-catalog/climate-change

This dataset was created by World Bank and contains around 10000 samples along with 2009, 1993, technical information and other features such as: - 1994 - Series Code - and more.

How to use this dataset

• Analyze 1995 in relation to Scale
• Study the influence of 1998 on Country Code
• More datasets

Acknowledgements

If you use this dataset in your research, please credit World Bank

--- Original source retains full ownership of the source dataset ---

The data on the use of the data sets on the OGD portal BL (data.bl.ch) are collected and published by the specialist and coordination office OGD BL. Contains the day the usage was measured.dataset_title: The title of the dataset_id record: The technical ID of the dataset.visitors: Specifies the number of daily visitors to the record. Visitors are recorded by counting the unique IP addresses that recorded access on the day of the survey. The IP address represents the network address of the device from which the portal was accessed.interactions: Includes all interactions with any record on data.bl.ch. A visitor can trigger multiple interactions. Interactions include clicks on the website (searching datasets, filters, etc.) as well as API calls (downloading a dataset as a JSON file, etc.).RemarksOnly calls to publicly available datasets are shown.IP addresses and interactions of users with a login of the Canton of Basel-Landschaft - in particular of employees of the specialist and coordination office OGD - are removed from the dataset before publication and therefore not shown.Calls from actors that are clearly identifiable as bots by the user agent header are also not shown.Combinations of dataset and date for which no use occurred (Visitors == 0 & Interactions == 0) are not shown.Due to synchronization problems, data may be missing by the day.

Context This large dataset with users interactions logs (page views) from a news portal was kindly provided by Globo.com, the most popular news portal in Brazil, for reproducibility of the experiments with CHAMELEON - a meta-architecture for contextual hybrid session-based news recommender systems. The source code was made available at GitHub.

The first version (v1) (download) of this dataset was released for reproducibility of the experiments presented in the following paper:

Gabriel de Souza Pereira Moreira, Felipe Ferreira, and Adilson Marques da Cunha. 2018. News Session-Based Recommendations using Deep Neural Networks. In 3rd Workshop on Deep Learning for Recommender Systems (DLRS 2018), October 6, 2018, Vancouver, BC, Canada. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3270323.3270328

A second version (v2) (download) of this dataset was made available for reproducibility of the experiments presented in the following paper. Compared to the v1, the only differences are:

Included four additional user contextual attributes (click_os, click_country, click_region, click_referrer_type) Removed repeated clicks (clicks in the same articles) within sessions. Those sessions with less than two clicks (minimum for the next-click prediction task) were removed

Gabriel de Souza Pereira Moreira, Dietmar Jannach, and Adilson Marques da Cunha. 2019. Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks. arXiv preprint arXiv:1904.10367, 49 pages

You are not allowed to use this dataset for commercial purposes, only with academic objectives (like education or research). If used for research, please cite the above papers.

Content The dataset contains a sample of user interactions (page views) in G1 news portal from Oct. 1 to 16, 2017, including about 3 million clicks, distributed in more than 1 million sessions from 314,000 users who read more than 46,000 different news articles during that period.

It is composed by three files/folders:

clicks.zip - Folder with CSV files (one per hour), containing user sessions interactions in the news portal. articles_metadata.csv - CSV file with metadata information about all (364047) published articles articles_embeddings.pickle Pickle (Python 3) of a NumPy matrix containing the Article Content Embeddings (250-dimensional vectors), trained upon articles' text and metadata by the CHAMELEON's ACR module (see paper for details) for 364047 published articles. P.s. The full text of news articles could not be provided due to license restrictions, but those embeddings can be used by Neural Networks to represent their content. See this paper for a t-SNE visualization of these embeddings, colored by category.

Acknowledgements I would like to acknowledge Globo.com for providing this dataset for this research and for the academic community, in special to Felipe Ferreira for preparing the original dataset by Globo.com.

Dataset banner photo by rawpixel on Unsplash

Inspiration This dataset might be very useful if you want to implement and evaluate hybrid and contextual news recommender systems, using both user interactions and articles content and metadata to provide recommendations. You might also use it for analytics, trying to understand how users interactions in a news portal are distributed by user, by article, or by category, for example.

If you are interested in a dataset of user interactions on articles with the full text provided, to experiment with some different text representations using NLP, you might want to take a look in this smaller dataset.

Introduction to static monitoring - PAMGuard tutorial dataset

GENERAL INFORMATION

This is a dataset to be used with the PAMGuard tutorial Introduction to static monitoring in PAMGuard. The dataset is from a single autonomous acoustic logger (SoundTrap) recording off the Scottish West Coast and contains a wealth of acoustic data including dolphin whistles and clicks, sonar, baleen whales and porpoises. The tutorial text and other related files can be found on the PAMGuardLearning GitHub page or PAMGuard website.

Year of data collection:

2018

Geographic location of data collection:

Stanton Banks, Scotland

Funding sources that supported the collection of the data:

The COMPASS project has been supported by the EU’s INTERREG VA Programme, managed by the Special EU Programmes Body. The views and opinions expressed in this document do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).

Recommended citation for this dataset:

Risch, D., Quer, S., Edwards, E., Beck, S., Macaulay, J., Calderan, S. (2018). Acoustic data from the Scottish west coast recorded with a single-element recording unit doi: 10.5281/zenodo.13880212

DATA & FILE OVERVIEW

Description of dataset

The dataset contains three days of sample recordings from a deployment of an acoustic recording device (SoundTrap ST300) with a battery pack. The SoundTrap was running an on-device click detector on data at 576kHz sample rate and saving 96kHz raw acoustic data. The click detector detects any transient sound and saves a snippet of the waveform.

The dataset also contains processed data from the entire deployment. There data have been processed in PAMGuard software (www.pamguard.org) for low frequency moans, dolphin whistles, clicks noise and longterm spectral averages. The detection and soundscape data has been saved in and raw acoustic recordings discarded.

Files

There are two directories - _audio_ and _viewer_. *audio* contains .sud files whicha re compressed audio and metadata files. They contain raw audio, click detection data and metadata. Data from sud files can be decompressed using SoundTrap Host software (www.oceaninstruments.co.nz).

The _viewer_ folder contains a PAMGuard database and associated detection files in the _PAMBinary_ folder and sub folders. The database contains PAMGuard settings and some basic metadata. The detection .pgdf files are non human readable files that contain detection data such as detected clicks, frequency contours of whistles, moans and other tonal sounds and a time series of soundscape metrics. These files can be opened with PAMGuard software (www.pamguard.org), MATLAB (https://github.com/PAMGuard/PAMGuardMatlab) and R (https://github.com/TaikiSan21/PamBinaries).

The folder structure is as follows

├── audio #Three example days of acoustic recordings
│ ├── D2_SB_189411
│ ├── D2_SB_189415
│ └── D2_SB_189428
├── README.md #The readme file
└── viewer #Processed data from a whole deployment
├── compass_database_D2_stanton.sqlite3 #PAMGuard database
└── PAMBinary
├── 20180301 #Folders containing detection files
├── 20180302
├── '''

SHARING AND ACCESS

These data are open source under Creative Commons Attribution 4.0 International. This means that these data can be used in other tutorials as long as the original authors are credited. Using these data for scientific purposes requires the permission of the authors.

Click Here : Bitdefender Customer Service

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In today's fast-paced digital world, one of the most critical things for people and organizations to do is keep their cyber security up to date. Bitdefender is a well-known firm that provides strong antivirus and internet security software. Like with any other service or product, users may have problems or questions about their subscriptions, features, billing, or installation. At this point, Bitdefender's customer service is a very significant aspect of the support system. This complete guide is called "How Do I Get in Touch with 'Bitdefender Customer Service'?" This easy-to-follow article will show you how to get in touch with Bitdefender's support team in a few different ways so you can obtain fast, useful, and skilled help.

Understanding How Important Bitdefender Customer Service Is When it comes to cybersecurity services, customer service is highly vital for keeping people pleased. Both new and long-time customers may have problems that pop up out of the blue. These can be issues with installation, activation keys, system compatibility, payment, or security. Bitdefender has a number of help options that are tailored to these situations. If you know how to reach them customer care, you may get your problems fixed quickly and with as little hassle as possible.

Here are some things you should know before you call Bitdefender Support. You may speed up the process by doing a few things before you call Bitdefender's customer service. Be ready with the following information:

Peacock Tv Login Peacock Tv Sign in Bitdefender Login Account Bitdefender Sign in Account Norton Login Norton Sign in

The email address for your Bitdefender account

Your Bitdefender Central login details

The key or code that allows you utilize your product

The device or operating system that is having the difficulty

A full explanation of the problem or error message you are getting

Being ready implies that the support crew can help you right away without having to call you back several times.

First, you need go to Bitdefender Central. When you think, "How do I reach 'Bitdefender Customer Service'?" First, you need to go to Bitdefender Central. This online dashboard lets you keep track of your account, installations, devices, and subscriptions. You can also use customer assistance options like live chat, sending tickets, and articles that help you fix difficulties.

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Help with Bitdefender for Business Users You might need more specific advice if your firm uses Bitdefender GravityZone or other corporate solutions. Business users can access dedicated enterprise help through the GravityZone portal. Enterprise users can report issues, start conversations, and seek for more help that is tailored to their security and infrastructure needs.

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Diverse learning theories have been constructed to understand learners' internal states through various tangible predictors. We focus on self-regulatory actions that are subconscious and habitual actions triggered by behavior agents' 'awareness' of their attention loss. We hypothesize that self-regulatory behaviors (i.e., attention regulation behaviors) also occur in e-reading as 'regulators' as found in other behavior models (Ekman, P., & Friesen, W. V., 1969). In this work, we try to define the types and frequencies of attention regulation behaviors in e-reading. We collected various cues that reflect learners' moment-to-moment and page-to-page cognitive states to understand the learners' attention in e-reading.

The text 'How to make the most of your day at Disneyland Resort Paris' has been implemented on a screen-based e-reader, which we developed in a pdf-reader format. An informative, entertaining text was adopted to capture learners' attentional shifts during knowledge acquisition. The text has 2685 words, distributed over ten pages, with one subtopic on each page. A built-in webcam on Mac Pro and a mouse have been used for the data collection, aiming for real-world implementation only with essential computational devices. A height-adjustable laptop stand has been used to compensate for participants' eye levels.

Thirty learners in higher education have been invited for a screen-based e-reading task (M=16.2, SD=5.2 minutes). A pre-test questionnaire with ten multiple-choice questions was given before the reading to check their prior knowledge level about the topic. There was no specific time limit to finish the questionnaire. We collected cues that reflect learners' moment-to-moment and page-to-page cognitive states to understand the learners' attention in e-reading. Learners were asked to report their distractions on two levels during the reading: 1) In-text distraction (e.g., still reading the text with low attentiveness) or 2) out-of-text distraction (e.g., thinking of something else while not reading the text anymore). We implemented two noticeably-designed buttons on the right-hand side of the screen interface to minimize possible distraction from the reporting task. After triggering a new page, we implemented blur stimuli on the text in the random range of 20 seconds. It ensures that the blur stimuli occur at least once on each page. Participants were asked to click the de-blur button on the text area of the screen to proceed with the reading. The button has been implemented in the whole text area, so participants can minimize the effort to find and click the button. Reaction time for de-blur has been measured, too, to grasp the arousal of learners during the reading. We asked participants to answer pre-test and post-test questionnaires about the reading material. Participants were given ten multiple-choice questions before the session, while the same set of questions was given after the reading session (i.e., formative questions) with added subtopic summarization questions (i.e., summative questions). It can provide insights into the quantitative and qualitative knowledge gained through the session and different learning outcomes based on individual differences. A video dataset of 931,440 frames has been annotated with the attention regulator behaviors using an annotation tool that plays the long sequence clip by clip, which contains 30 frames. Two annotators (doctoral students) have done two stages of labeling. In the first stage, the annotators were trained on the labeling criteria and annotated the attention regulator behaviors separately based on their judgments. The labels were summarized and cross-checked in the second round to address the inconsistent cases, resulting in five attention regulation behaviors and one neutral state. See WEDAR_readme.csv for detailed descriptions of features.

The dataset has been uploaded 1) raw data, which has formed as we collected, and 2) preprocessed, that we extracted useful features for further learning analytics based on real-time and post-hoc data.

Reference

Ekman, P., & Friesen, W. V. (1969). The repertoire of nonverbal behavior: Categories, origins, usage, and coding. semiotica, 1(1), 49-98.

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The Worldwide Soundscapes project is a global, open inventory of spatio-temporally replicated soundscape datasets. This Zenodo entry comprises the data tables that constitute its (meta-)database, as well as their description.

The overview of all sampling sites can be found on the corresponding project on ecoSound-web, as well as a demonstration collection containing selected recordings. More information on the project can be found here and on ResearchGate.

The audio recording criteria justifying inclusion into the meta-database are:

• Stationary (no transects, towed sensors or microphones mounted on cars)
• Passive (unattended, no human disturbance by the recordist)
• Ambient (no spatial or temporal focus on a particular species or direction)
• Spatially and/or temporally replicated (multiple sites sampled at least at one common daytime or multiple days sampled at least in one common site)

The individual columns of the provided data tables are described in the following. Data tables are linked through primary keys; joining them will result in a database.

datasets

• dataset_id: incremental integer, primary key
• name: name of the dataset. if it is repeated, incremental integers should be used in the "subset" column to differentiate them.
• subset: incremental integer that can be used to distinguish datasets with identical names
• collaborators: full names of people deemed responsible for the dataset, separated by commas
• contributors: full names of people who are not the main collaborators but who have significantly contributed to the dataset, and who could be contacted for in-depth analyses, separated by commas.
• date_added: when the datased was added (DD/MM/YYYY)
• URL_open_recordings: if recordings (even only some) from this dataset are openly available, indicate the internet link where they can be found.
• URL_project: internet link for further information about the corresponding project
• DOI_publication: DOI of corresponding publications, separated by comma
• core_realm_IUCN: The core realm of the dataset. Datasets may have multiple realms, but the main one should be listed. Datasets may contain sampling sites from different realms in the "sites" sheet. IUCN Global Ecosystem Typology (v2.0): https://global-ecosystems.org/
• medium: the physical medium the microphone is situated in
• protected_area: Whether the sampling sites were situated in protected areas or not, or only some.
• GADM0: For datasets on land or in territorial waters, Global Administrative Database level0
  https://gadm.org/
• GADM1: For datasets on land or in territorial waters, Global Administrative Database level1
  https://gadm.org/
• GADM2: For datasets on land or in territorial waters, Global Administrative Database level2
  https://gadm.org/
• IHO: For marine locations, the sea area that encompassess all the sampling locations according to the International Hydrographic Organisation. Map here: https://www.arcgis.com/home/item.html?id=44e04407fbaf4d93afcb63018fbca9e2
• locality: optional free text about the locality
• latitude_numeric_region: study region approximate centroid latitude in WGS84 decimal degrees
• longitude_numeric_region: study region approximate centroid longitude in WGS84 decimal degrees
• sites_number: number of sites sampled
• year_start: starting year of the sampling
• year_end: ending year of the sampling
• deployment_schedule: description of the sampling schedule, provisional
• temporal_recording_selection: list environmental exclusion criteria that were used to determine which recording days or times to discard
• high_pass_filter_Hz: frequency of the high-pass filter of the recorder, in Hz
• variable_sampling_frequency: Does the sampling frequency vary? If it does, write "NA" in the sampling_frequency_kHz column and indicate it in the sampling_frequency_kHz column inside the deployments sheet
• sampling_frequency_kHz: frequency the microphone was sampled at (sounds of half that frequency will be recorded)
• variable_recorder:
• recorder: recorder model used
• microphone: microphone used
• freshwater_recordist_position: position of the recordist relative to the microphone during sampling (only for freshwater)
• collaborator_comments: free-text field for comments by the collaborators
• validated: This cell is checked if the contents of all sheets are complete and have been found to be coherent and consistent with our requirements.
• validator_name: name of person doing the validation
• validation_comments: validators: please insert the date when someone was contacted
• cross-check: this cell is checked if the collaborators confirm the spatial and temporal data after checking the corresponding site maps, deployment and operation time graphs found at https://drive.google.com/drive/folders/1qfwXH_7dpFCqyls-c6b8RZ_fbcn9kXbp?usp=share_link

datasets-sites

• dataset_ID: primary key of datasets table
• dataset_name: lookup field
• site_ID: primary key of sites table
• site_name: lookup field

sites

• site_ID: unique site IDs, larger than 1000 for compatibility with ecoSound-web
• site_name: name or code of sampling site as used in respective projects
• latitude_numeric: exact numeric degrees coordinates of latitude
• longitude_numeric: exact numeric degrees coordinates of longitude
• topography_m: for sites on land: elevation. For marine sites: depth (negative). in meters
• freshwater_depth_m
• realm: Ecosystem type according to IUCN GET https://global-ecosystems.org/
• biome: Ecosystem type according to IUCN GET https://global-ecosystems.org/
• functional_group: Ecosystem type according to IUCN GET https://global-ecosystems.org/
• comments

deployments

• dataset_ID: primary key of datasets table
• dataset_name: lookup field
• deployment: use identical subscript letters to denote rows that belong to the same deployment. For instance, you may use different operation times and schedules for different target taxa within one deployment.
• start_date_min: earliest date of deployment start, double-click cell to get date-picker
• start_date_max: latest date of deployment start, if applicable (only used when recorders were deployed over several days), double-click cell to get date-picker
• start_time_mixed: deployment start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording start time for continuous recording deployments. If multiple start times were used, you should mention the latest start time (corresponds to the earliest daytime from which all recorders are active). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")
• permanent: is the deployment permanent (in which case it would be ongoing and the end date or duration would be unknown)?
• variable_duration_days: is the duration of the deployment variable? in days
• duration_days: deployment duration per recorder (use the minimum if variable)
• end_date_min: earliest date of deployment end, only needed if duration is variable, double-click cell to get date-picker
• end_date_max: latest date of deployment end, only needed if duration is variable, double-click cell to get date-picker
• end_time_mixed: deployment end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording end time for continuous recording deployments.
• recording_time: does the recording last from the deployment start time to the end time (continuous) or at scheduled daily intervals (scheduled)? Note: we consider recordings with duty cycles to be continuous.
• operation_start_time_mixed: scheduled recording start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")
• operation_duration_minutes: duration of operation in minutes, if constant
• operation_end_time_mixed: scheduled recording end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")
• duty_cycle_minutes: duty cycle of the recording (i.e. the fraction of minutes when it is recording), written as "recording(minutes)/period(minutes)". For example: "1/6" if the recorder is active for 1 minute and standing by for 5 minutes.
• sampling_frequency_kHz: only indicate the sampling frequency if it is variable within a particular dataset so that we need to code different frequencies for different deployments
• recorder
• subset_sites: If the deployment was not done in all the sites of the

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The data.bris Research Data Repository is maintained by the University of Bristol Research Data Service. The Service is a Library-led collaboration with IT Services and RED. The data.bris Research Data Repository is an online digital repository of multi-disciplinary research datasets produced at the University of Bristol. Data published through the repository are all openly available under a Non-Commercial Government Licence for public sector information, and each deposit is assigned a unique Digital Object Identifier (DOI). If you have discovered material that you believe to be a potential breach of copyright, or a violation of any law (including but not limited to laws on copyright, patent, trademark, confidentiality, data protection, obscenity, defamation or libel), please refer to the Research Data Repository Notice and Take Down Policy. Some deposits consist of a great number of individual files. For this reason the Repository has adopted an organisational principle similar to the familiar folder/sub-folder/file approach. Listed at the bottom of each dataset page are either ‘Data Resources’ or ‘Sub-levels’. Data Resources are files associated with the dataset (you can download them from the dataset page or click ‘Explore’ then ’More information’ to see the file’s own deposit information). Where a depositor has used folders, you will be presented with a list of Sub-levels, which are synonymous with ‘folders’ and ‘sub-folders’. By clicking on a sub-level you can ‘drill down’ into the dataset and will either be presented with a list of Data Resources (associated files), further Sub-levels, or a mixture of both. All sub-levels are shown on the top level page. You can elect to go ‘Up one level’ or go ‘Back to top level’ (back to the initial deposit record) at any stage.

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

Source:

Mariusz Šapczyński, Cracow University of Economics, Poland, lapczynm '@' uek.krakow.pl Sylwester Białowąs, Poznan University of Economics and Business, Poland, sylwester.bialowas '@' ue.poznan.pl

Data Set Information:

The dataset contains information on clickstream from online store offering clothing for pregnant women. Data are from five months of 2008 and include, among others, product category, location of the photo on the page, country of origin of the IP address and product price in US dollars.

Attribute Information:

The dataset contains 14 variables described in a separate file (See 'Data set description')

Relevant Papers:

N/A

Citation Request:

If you use this dataset, please cite:

Šapczyński M., Białowąs S. (2013) Discovering Patterns of Users' Behaviour in an E-shop - Comparison of Consumer Buying Behaviours in Poland and Other European Countries, “Studia Ekonomiczne†, nr 151, “La société de l'information : perspective européenne et globale : les usages et les risques d'Internet pour les citoyens et les consommateurs†, p. 144-153

Data description ìe-shop clothing 2008î

Variables:

1. YEAR (2008)

========================================================

2. MONTH -> from April (4) to August (8)

========================================================

3. DAY -> day number of the month

========================================================

4. ORDER -> sequence of clicks during one session

========================================================

5. COUNTRY -> variable indicating the country of origin of the IP address with the

following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

========================================================

6. SESSION ID -> variable indicating session id (short record)

========================================================

7. PAGE 1 (MAIN CATEGORY) -> concerns the main product category:

1-trousers 2-skirts 3-blouses 4-sale

========================================================

8. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product

(217 products)

========================================================

9. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

========================================================

10. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

========================================================

11. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

========================================================

12. PRICE -> price in US dollars

========================================================

13. PRICE 2 -> variable informing whether the price of a particular product is higher than

the average price for the entire product category

1-yes 2-no

========================================================

14. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

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 2017. For a description of the data collection, processing, and output methods, please see the "methods" section below.

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

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 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 2017-01_RAMP_all.csv. Using this example, the file 2017-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2017.

References

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

a - it is not fair to disclose individual’s personal details to the world and is likely to cause damage or distress. b - these details are not of sufficient interest to the public to warrant an intrusion into the privacy of the individual. Please click the below web link to see the exemption in full. www.legislation.gov.uk/ukpga/2000/36/section/40 Breach of confidentiality Please note that the identification of individuals is also a breach of the common law duty of confidence. An individual who has been identified could make a claim against the NHSBSA for the disclosure of the confidential information. The information requested is therefore being withheld as it falls under the exemption in section 41(1) ‘Information provided in confidence’ of the Freedom of Information Act. Please click the below web link to see the exemption in full.

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