Dataset Overview

This dataset provides detailed information on website traffic, including page views, session duration, bounce rate, traffic source, time spent on page, previous visits, and conversion rate.

Dataset Description

• Page Views: The number of pages viewed during a session.
• Session Duration: The total duration of the session in minutes.
• Bounce Rate: The percentage of visitors who navigate away from the site after viewing only one page.
• Traffic Source: The origin of the traffic (e.g., Organic, Social, Paid).
• Time on Page: The amount of time spent on the specific page.
• Previous Visits: The number of previous visits by the same visitor.
• Conversion Rate: The percentage of visitors who completed a desired action (e.g., making a purchase).

Data Summary

• Total Records: 2000
• Total Features: 7

Key Features

1. Page Views: This feature indicates the engagement level of the visitors by showing how many pages they visit during their session.
2. Session Duration: This feature measures the length of time a visitor stays on the website, which can indicate the quality of the content.
3. Bounce Rate: A critical metric for understanding user behavior. A high bounce rate may indicate that visitors are not finding what they are looking for.
4. Traffic Source: Understanding where your traffic comes from can help in optimizing marketing strategies.
5. Time on Page: This helps in analyzing which pages are retaining visitors' attention the most.
6. Previous Visits: This can be used to analyze the loyalty of visitors and the effectiveness of retention strategies.
7. Conversion Rate: The ultimate metric for measuring the effectiveness of the website in achieving its goals.

Usage

This dataset can be used for various analyses such as:

• Identifying key drivers of engagement and conversion.
• Analyzing the effectiveness of different traffic sources.
• Understanding user behavior patterns and optimizing the website accordingly.
• Improving marketing strategies based on traffic source performance.
• Enhancing user experience by analyzing time spent on different pages.

Acknowledgments

This dataset was generated for educational purposes and is not from a real website. It serves as a tool for learning data analysis and machine learning techniques.

This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10.

The original dataset contains missing values. They have been simply replaced by zeros.

Comprehensive dataset analyzing Walmart.com's daily website traffic, including 16.7 million daily visits, device distribution, geographic patterns, and competitive benchmarking data.

Context

This file contains 5 years of daily time series data for several measures of traffic on a statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors.

Content

The variables are daily counts of page loads, unique visitors, first-time visitors, and returning visitors to an academic teaching notes website. There are 2167 rows of data spanning the date range from September 14, 2014, to August 19, 2020. A visit is defined as a stream of hits on one or more pages on the site on a given day by the same user, as identified by IP address. Multiple individuals with a shared IP address (e.g., in a computer lab) are considered as a single user, so real users may be undercounted to some extent. A visit is classified as "unique" if a hit from the same IP address has not come within the last 6 hours. Returning visitors are identified by cookies if those are accepted. All others are classified as first-time visitors, so the count of unique visitors is the sum of the counts of returning and first-time visitors by definition. The data was collected through a traffic monitoring service known as StatCounter.

Inspiration

This file and a number of other sample datasets can also be found on the website of RegressIt, a free Excel add-in for linear and logistic regression which I originally developed for use in the course whose website generated the traffic data given here. If you use Excel to some extent as well as Python or R, you might want to try it out on this dataset.

This is the aggregated version of the daily dataset used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-05, after aggregating them into weekly.

The original dataset contains missing values. They have been simply replaced by zeros before aggregation.

This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

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?

Comprehensive dataset analyzing Amazon's daily website visits, traffic patterns, seasonal trends, and comparative analysis with other ecommerce platforms based on May 2025 data.

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

Per the Federal Digital Government Strategy, the Department of Homeland Security Metrics Plan, and the Open FEMA Initiative, FEMA is providing the following web performance metrics with regards to FEMA.gov.rnrnInformation in this dataset includes total visits, avg visit duration, pageviews, unique visitors, avg pages/visit, avg time/page, bounce ratevisits by source, visits by Social Media Platform, and metrics on new vs returning visitors.rnrnExternal Affairs strives to make all communications accessible. If you have any challenges accessing this information, please contact FEMAWebTeam@fema.dhs.gov.

This dataset originates from DataCamp. Many users have reposted copies of the CSV on Kaggle, but most of those uploads omit the original instructions, business context, and problem framing. In this upload, I’ve included that missing context in the About Dataset so the reader of my notebook or any other notebook can fully understand how the data was intended to be used and the intended problem framing.

Note: I have also uploaded a visualization of the workflow I personally took to tackle this problem, but it is not part of the dataset itself. Additionally, I created a PowerPoint presentation based on my work in the notebook, which you can download from here:
PPTX Presentation

Recipe Site Traffic

From: Head of Data Science
Received: Today
Subject: New project from the product team

Hey!

I have a new project for you from the product team. Should be an interesting challenge. You can see the background and request in the email below.

I would like you to perform the analysis and write a short report for me. I want to be able to review your code as well as read your thought process for each step. I also want you to prepare and deliver the presentation for the product team - you are ready for the challenge!

They want us to predict which recipes will be popular 80% of the time and minimize the chance of showing unpopular recipes. I don't think that is realistic in the time we have, but do your best and present whatever you find.

You can find more details about what I expect you to do here. And information on the data here.

I will be on vacation for the next couple of weeks, but I know you can do this without my support. If you need to make any decisions, include them in your work and I will review them when I am back.

Good Luck!

From: Product Manager - Recipe Discovery
To: Head of Data Science
Received: Yesterday
Subject: Can you help us predict popular recipes?

Hi,

We haven't met before but I am responsible for choosing which recipes to display on the homepage each day. I have heard about what the data science team is capable of and I was wondering if you can help me choose which recipes we should display on the home page?

At the moment, I choose my favorite recipe from a selection and display that on the home page. We have noticed that traffic to the rest of the website goes up by as much as 40% if I pick a popular recipe. But I don't know how to decide if a recipe will be popular. More traffic means more subscriptions so this is really important to the company.

Can your team: - Predict which recipes will lead to high traffic? - Correctly predict high traffic recipes 80% of the time?

We need to make a decision on this soon, so I need you to present your results to me by the end of the month. Whatever your results, what do you recommend we do next?

Look forward to seeing your presentation.

About Tasty Bytes

Tasty Bytes was founded in 2020 in the midst of the Covid Pandemic. The world wanted inspiration so we decided to provide it. We started life as a search engine for recipes, helping people to find ways to use up the limited supplies they had at home.

Now, over two years on, we are a fully fledged business. For a monthly subscription we will put together a full meal plan to ensure you and your family are getting a healthy, balanced diet whatever your budget. Subscribe to our premium plan and we will also deliver the ingredients to your door.

Example Recipe

This is an example of how a recipe may appear on the website, we haven't included all of the steps but you should get an idea of what visitors to the site see.

Tomato Soup

Servings: 4
Time to make: 2 hours
Category: Lunch/Snack
Cost per serving: $

Nutritional Information (per serving) - Calories 123 - Carbohydrate 13g - Sugar 1g - Protein 4g

Ingredients: - Tomatoes - Onion - Carrot - Vegetable Stock

Method: 1. Cut the tomatoes into quarters….

Data Information

The product manager has tried to make this easier for us and provided data for each recipe, as well as whether there was high traffic when the recipe was featured on the home page.

As you will see, they haven't given us all of the information they have about each recipe.

You can find the data here.

I will let you decide how to process it, just make sure you include all your decisions in your report.

Don't forget to double check the data really does match what they say - it might not.

Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

This dataset represents a collection of records for users' visits to a website, where certain variables related to these users are studied to determine whether they clicked on a particular ad or not. Here’s a detailed description of the data:

Daily Time Spent on Site: The number of minutes the user spends on the website daily.

Age: The age of the user in years.

Area Income: The average annual income of the area where the user resides, measured in U.S. dollars.

Daily Internet Usage: The number of minutes the user spends on the internet daily.

Ad Topic Line: The headline or main topic of the ad that was shown to the user.

City: The city where the user resides.

Male: An indicator of the user's gender, where 1 represents male and 0 represents female.

Country: The country where the user resides.

Timestamp: The date and time when this record was logged.

Clicked on Ad: An indicator of whether the user clicked on the ad, where 1 means the user clicked on the ad, and 0 means they did not.

In summary, this data is used to analyze users' behavior on the website based on a set of demographic and usage factors, with a focus on whether they clicked on a particular ad or not.

Collation of data from Radio Éireann log books, at RTÉ, Donnybrook, Dublin 4.

Dataset originally created 2016 UPDATE: Packaged on 02/10/2025

I. About this Data Set

This data set is a result of close reading conducted by Patrick Egan (Pádraig Mac Aodhgáin) at Radio Teilifís Éireann log books relating to Seán Ó Riada.

Research was conducted between 2014-2018. It contains a combination of metadata from searches of the Boole Library catalogue and Seán Ó Riada Collection finding aid (or "descriptive list"), relating to music-related projects that were involving Seán Ó Riada. The PhD project was published in 2020, entitled, “Exploring ethnography and digital visualisation: a study of musical practice through the contextualisation of music related projects from the Seán Ó Riada Collection”, and a full listing of radio broadcasts is added to the dataset named "The Ó Riada Projects" at https://doi.org/10.5281/zenodo.15348617

You are invited to use and re-use this data with appropriate attribution.

The "RÉ Logs Dataset" dataset consists of 90 rows.

II. What’s included? This data set includes:

A search of log books of radio broadcasts to find all instances of shows that involved Seán Ó Riada.

III. How Was It Created? These data were created by daily visits to Radio Teilifís Éireann in Dublin, Ireland.

IV. Data Set Field Descriptions

Column headings have not been added to the dataset.

Column A - blank
Column B - type of broadcast
Column C - blank
Column D - date of broadcast
Column E - blank
Column F - blank
Column G - blank
Column H - blank
Column I - description of broadcast
Column J - blank
Column K - blank
Column J - length of broadcast

V. Rights statement The text in this data set was created by the researcher and can be used in many different ways under creative commons with attribution. All contributions to this PhD project are released into the public domain as they are created. Anyone is free to use and re-use this data set in any way they want, provided reference is given to the creator of this dataset.

VI. Creator and Contributor Information

Creator: Patrick Egan (Pádraig Mac Aodhgáin)

VII. Contact Information Please direct all questions and comments to Patrick Egan via his website at www.patrickegan.org. You can also get in touch with the Library via UCC website.

Dataset originally created 03/01/2019 UPDATE: Packaged on 04/18/2019 UPDATE: Edited README on 04/18/2019

I. About this Data Set This data set is a snapshot of work that is ongoing as a collaboration between Kluge Fellow in Digital Studies, Patrick Egan and an intern at the Library of Congress in the American Folklife Center. It contains a combination of metadata from various collections that contain audio recordings of Irish traditional music. The development of this dataset is iterative, and it integrates visualizations that follow the key principles of trust and approachability. The project, entitled, “Connections In Sound” invites you to use and re-use this data.

The text available in the Items dataset is generated from multiple collections of audio material that were discovered at the American Folklife Center. Each instance of a performance was listed and “sets” or medleys of tunes or songs were split into distinct instances in order to allow machines to read each title separately (whilst still noting that they were part of a group of tunes). The work of the intern was then reviewed before publication, and cross-referenced with the tune index at www.irishtune.info. The Items dataset consists of just over 1000 rows, with new data being added daily in a separate file.

The collections dataset contains at least 37 rows of collections that were located by a reference librarian at the American Folklife Center. This search was complemented by searches of the collections by the scholar both on the internet at https://catalog.loc.gov and by using card catalogs.

Updates to these datasets will be announced and published as the project progresses.

II. What’s included? This data set includes:

The Items Dataset – a .CSV containing Media Note, OriginalFormat, On Website, Collection Ref, Missing In Duplication, Collection, Outside Link, Performer, Solo/multiple, Sub-item, type of tune, Tune, Position, Location, State, Date, Notes/Composer, Potential Linked Data, Instrument, Additional Notes, Tune Cleanup. This .CSV is the direct export of the Items Google Spreadsheet

III. How Was It Created? These data were created by a Kluge Fellow in Digital Studies and an intern on this program over the course of three months. By listening, transcribing, reviewing, and tagging audio recordings, these scholars improve access and connect sounds in the American Folklife Collections by focusing on Irish traditional music. Once transcribed and tagged, information in these datasets is reviewed before publication.

IV. Data Set Field Descriptions

IV

a) Collections dataset field descriptions

ItemId – this is the identifier for the collection that was found at the AFC
Viewed – if the collection has been viewed, or accessed in any way by the researchers.
On LOC – whether or not there are audio recordings of this collection available on the Library of Congress website.
On Other Website – if any of the recordings in this collection are available elsewhere on the internet
Original Format – the format that was used during the creation of the recordings that were found within each collection
Search – this indicates the type of search that was performed in order that resulted in locating recordings and collections within the AFC
Collection – the official title for the collection as noted on the Library of Congress website
State – The primary state where recordings from the collection were located
Other States – The secondary states where recordings from the collection were located
Era / Date – The decade or year associated with each collection
Call Number – This is the official reference number that is used to locate the collections, both in the urls used on the Library website, and in the reference search for catalog cards (catalog cards can be searched at this address: https://memory.loc.gov/diglib/ihas/html/afccards/afccards-home.html)
Finding Aid Online? – Whether or not a finding aid is available for this collection on the internet

b) Items dataset field descriptions

id – the specific identification of the instance of a tune, song or dance within the dataset
Media Note – Any information that is included with the original format, such as identification, name of physical item, additional metadata written on the physical item
Original Format – The physical format that was used when recording each specific performance. Note: this field is used in order to calculate the number of physical items that were created in each collection such as 32 wax cylinders.
On Webste? – Whether or not each instance of a performance is available on the Library of Congress website
Collection Ref – The official reference number of the collection
Missing In Duplication – This column marks if parts of some recordings had been made available on other websites, but not all of the recordings were included in duplication (see recordings from Philadelphia Céilí Group on Villanova University website)
Collection – The official title of the collection given by the American Folklife Center
Outside Link – If recordings are available on other websites externally
Performer – The name of the contributor(s)
Solo/multiple – This field is used to calculate the amount of solo performers vs group performers in each collection
Sub-item – In some cases, physical recordings contained extra details, the sub-item column was used to denote these details
Type of item – This column describes each individual item type, as noted by performers and collectors
Item – The item title, as noted by performers and collectors. If an item was not described, it was entered as “unidentified”
Position – The position on the recording (in some cases during playback, audio cassette player counter markers were used)
Location – Local address of the recording
State – The state where the recording was made
Date – The date that the recording was made
Notes/Composer – The stated composer or source of the item recorded
Potential Linked Data – If items may be linked to other recordings or data, this column was used to provide examples of potential relationships between them
Instrument – The instrument(s) that was used during the performance
Additional Notes – Notes about the process of capturing, transcribing and tagging recordings (for researcher and intern collaboration purposes)
Tune Cleanup – This column was used to tidy each item so that it could be read by machines, but also so that spelling mistakes from the Item column could be corrected, and as an aid to preserving iterations of the editing process

V. Rights statement The text in this data set was created by the researcher and intern and can be used in many different ways under creative commons with attribution. All contributions to Connections In Sound are released into the public domain as they are created. Anyone is free to use and re-use this data set in any way they want, provided reference is given to the creators of these datasets.

VI. Creator and Contributor Information

Creator: Connections In Sound

Contributors: Library of Congress Labs

VII. Contact Information Please direct all questions and comments to Patrick Egan via www.twitter.com/drpatrickegan or via his website at www.patrickegan.org. You can also get in touch with the Library of Congress Labs team via LC-Labs@loc.gov.

Statistics on the visits to the websites of the institutions located on the single platform of the websites of national and local authorities. Statistics do not reflect all website visitors, but only those who have consented to statistical cookies.

Research data on traffic exchange limitations including low-quality traffic characteristics, search engine penalty risks, and comparison with effective alternatives like SEO and content marketing strategies.

README

Link to the Publication

🔗 Read the Paper

Details related to access to the data

Data user agreement

The terms and conditions for using this dataset are specified in the [LICENCE](LICENCE) file included in this repository. Please review these terms carefully before accessing or using the data.

Contact person

For additional information about the dataset, please contact:
- Name: Angela Lombardi
- Affiliation: Department of Electrical and Information Engineering, Politecnico di Bari
- Email: angela.lombardi@poliba.it

Practical information to access the data

The dataset can be accessed through our dedicated web platform. To request access:

1. Visit the main dataset page at: https://sisinflab.poliba.it/neurosense-dataset-request/
2. Follow the instructions on the website to submit your access request
3. Upon approval, you will receive further instructions for downloading the data

Please ensure you have read and agreed to the terms in the data user agreement before requesting access.

Overview

EEG Emotion Recognition - Muse Headset
2023-2024

The experiment consists in 40 sessions per user. During each session, users are asked to watch a
music video with the aim to understand their emotions.
Recordings are performed with a Muse EEG headset at a 256 Hz sampling rate.
Channels are recorded as follows:
- Channel 0: AF7
- Channel 1: TP9
- Channel 2: TP10
- Channel 3: AF8

The chosen songs have various Last.fm tags in order to create different feelings. The title of every track
can be found in the "TaskName" field of sub-ID***_ses-S***_task-Default_run-001_eeg.json, while the author,
the Last.fm tag and additional information in "TaskDescription".

Methods

Subjects

The subject pool is made of 30 college students, aged between 18 and 35. 16 of them are males, 14 females.

Apparatus

The experiment was performed using the same procedures as those to create
[Deap Dataset](https://www.eecs.qmul.ac.uk/mmv/datasets/deap/), which is a dataset to recognize emotions via a Brain
Computer Interface (BCI).

Task organization

Firstly, music videos were selected. Once 40 songs were picked, the protocol was chosen and the self-assessment
questionnaire was created.

Task details

In order to evaluate the stimulus, Russell's VAD (Valence-Arousal-Dominance) scale was used.
In this scale, valenza-arousal space can be divided in four quadrants:
- Low Arousal/Low Valence (LALV);
- Low Arousal/High Valence (LAHV);
- High Arousal/Low Valence (HALV);
- High Arousal/High Valence (HAHV).

Experimental location

The experiment was performed in a laboratory located at DEI Department of
[Politecnico di Bari](https://www.poliba.it/).

Missing data

Data recorded during session S019 - Session 2, ID021 - Session 23, user was corrupted, therefore is missing.
Sessions S033 and S038 of ID015 user show a calculated effective sampling rate lower than 256 Hz:
- ID015_ses-S033 has 226.1320 Hz
- ID015_ses-S038 has 216.9549 Hz

Dataset containing metrics and parameters for free website traffic distribution, including Nano credit system details, eligibility criteria (6000 hits/month, domain restrictions), and manual renewal requirements.

Bacteriophages are the most abundant biological entity on the planet, but at the same time do not account for much of the genetic material isolated from most environments due to their small genome sizes. They also show great genetic diversity and mosaic genomes making it challenging to analyze and understand them. Here we present MetaPhinder, a method to identify assembled genomic fragments (i.e.contigs) of phage origin in metagenomic data sets. The method is based on a comparison to a database of whole genome bacteriophage sequences, integrating hits to multiple genomes to accomodate for the mosaic genome structure of many bacteriophages. The method is demonstrated to out-perform both BLAST methods based on single hits and methods based on k-mer comparisons. MetaPhinder is available as a web service at the Center for Genomic Epidemiology https://cge.cbs.dtu.dk/services/MetaPhinder/, while the source code can be downloaded from https://bitbucket.org/genomicepidemiology/metaphinder or https://github.com/vanessajurtz/MetaPhinder.