This dataset includes observations of trackers present on the top 500 pages popular among Finnish web users as per Alexa. The data collection was conducted using TrackerTracker in five separate requests for five subsets of 100 sites each between 19.8.2017 and 20.8.2017. The tool used a tracker database from March 24, 2017. More methodology details are described in the associated journal article https://doi.org/10.23978/inf.87841

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

This anonymized data set consists of one month's (October 2018) web tracking data of 2,148 German users. For each user, the data contains the anonymized URL of the webpage the user visited, the domain of the webpage, category of the domain, which provides 41 distinct categories. In total, these 2,148 users made 9,151,243 URL visits, spanning 49,918 unique domains. For each user in our data set, we have self-reported information (collected via a survey) about their gender and age.

We acknowledge the support of Respondi AG, which provided the web tracking and survey data free of charge for research purposes, with special thanks to François Erner and Luc Kalaora at Respondi for their insights and help with data extraction.

The data set is analyzed in the following paper:

• Kulshrestha, J., Oliveira, M., Karacalik, O., Bonnay, D., Wagner, C. "Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data." Proceedings of the International AAAI Conference on Web and Social Media. 2021. https://arxiv.org/abs/2012.15112.

The code used to analyze the data is also available at https://github.com/gesiscss/web_tracking.

If you use data or code from this repository, please cite the paper above and the Zenodo link.

This dataset contains a list of 3654 Dutch websites that we considered the most popular websites in 2015. This list served as whitelist for the Newstracker Research project in which we monitored the online web behaviour of a group of respondents.

The research project 'The Newstracker' was a subproject of the NWO-funded project 'The New News Consumer: A User-Based Innovation Project to Meet Paradigmatic Change in News Use and Media Habits'.

For the Newstracker project we aimed to understand the web behaviour of a group of respondents. We created custom-built software to monitor their web browsing behaviour on their laptops and desktops (please find the code in open access at https://github.com/NITechLabs/NewsTracker). For reasons of scale and privacy we created a whitelist with websites that were the most popular websites in 2015. We manually compiled this list by using data of DDMM, Alexa and own research. The dataset consists of 5 columns:
- the URL
- the type of website: We created a list of types of websites and each website has been manually labeled with 1 category
- Nieuws-regio: When the category was 'News', we subdivided these websites in the regional focus: International, National or Local
- Nieuws-onderwerp: Furthermore, each website under the category News was further subdivided in type of news website. For this we created an own list of news categories and manually coded each website
- Bron: For each website we noted which source we used to find this website.

The full description of the research design of the Newstracker including the set-up of this whitelist is included in the following article: Kleppe, M., Otte, M. (in print), 'Analysing & understanding news consumption patterns by tracking online user behaviour with a multimodal research design', Digital Scholarship in the Humanities, doi 10.1093/llc/fqx030.

In March 2024, close to 4.4 billion unique global visitors had visited Wikipedia.org, slightly down from 4.4 billion visitors since August of the same year. Wikipedia is a free online encyclopedia with articles generated by volunteers worldwide. The platform is hosted by the Wikimedia Foundation.

Most migratory birds return every year to the same breeding sites and some species show a similarly high fidelity to wintering grounds as well. Fidelity to stopover sites during migration has been much less studied and is usually found to be lower. Here, we investigate site fidelity and distance to previously visited sites throughout the annual cycle in the common cuckoo, a nocturnal trans-Saharan migrant, based on satellite-tracking data from repeated annual migrations of thirteen adult males. All birds (100%) returned to the same breeding grounds, with a median shortest distance of only 1 km from the locations in previous year. This was in strong contrast to a much lower and much less precise site fidelity at non-breeding sites during the annual cycle: In only 18% of the possible cases in all non-breeding regions combined, did the cuckoos return to within 50 km of a previously visited non-breeding site, with no significant differences among the main staging regions (Europe in autumn, ..., , , # Recurrence, fidelity, and proximity to previously visited sites throughout the annual cycle in a trans-Saharan migrant, the Common Cuckoo

https://doi.org/10.5061/dryad.r4xgxd2mv

Description of the data and file structure

The file contains satellite-tracking data of the best daily position from 13 adult Common Cuckoos that were tracked during two, three, or four successive years, from June 2010 to February 2015 as described in Willemoes et al. 2014 and Hewson et al. 2016.

Data fields are individual ID (ID), date and time (timestamp), geographical location (location-long, location-lat), geographical identification of stopover (Stopover), stopover region (Major_Stop), duration of stopover (DaysStop), year tracked (Migration_cycle) and Argos location quality (argos:lc). If no stopover region (Major_Stop) could be assigned to a location, the cell was left empty.

Hewson, C. M., Thorup, K., Pearce-Higgins, J. W. and Atkinson, P. W. 2016. *Po...

Scientists are increasingly engaging the web to provide formal and informal science education opportunities. Despite the prolific growth of web-based resources, systematic evaluation and assessment of their efficacy remains limited. We used clickstream analytics, a widely available method for tracking website visitors and their behavior, to evaluate >60,000 visits over three years to an educational website focused on ecology. Visits originating from search engine queries were a small proportion of the traffic, suggesting the need to actively promote websites to drive visitation. However, the number of visits referred to the website per social media post varied depending on the social media platform and the quality of those visits (e.g., time on site and number of pages viewed) was significantly lower than visits originating from other referring websites. In particular, visitors referred to the website through targeted promotion (e.g., inclusion in a website listing classroom teaching resources) had higher quality visits. Once engaged in the site's core content, visitor retention was high; however, visitors rarely used the tutorial resources that serve to explain the site's use. Our results demonstrate that simple changes in website design, content and promotion are likely to increase the number of visitors and their engagement. While there is a growing emphasis on using the web to broaden the impacts of biological research, time and resources remain limited. Clickstream analytics provides an easily accessible, relatively fast and quantitative means by which those engaging in educational outreach can improve upon their efforts.

In this cross-sectional study, we extracted the uniform resource locator (URL) of each National Abortion Federation member facility on May 6, 2022. We visited each unique URL using webXray (Timothy Libert), which detects third-party tracking. For each web page, we recorded data transfers to third-party domains. Transfers typically include a user’s IP (internet protocol) address and the web page being visited. We also recorded the presence of third-party cookies, data stored on a user’s computer that can facilitate tracking across multiple websites.

This public use dataset has 11 data elements reflecting COVID-19 community levels for all available counties. This dataset contains the same values used to display information available at https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels-county-map.html. CDC looks at the combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days — to determine the COVID-19 community level. The COVID-19 community level is determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge. Using these data, the COVID-19 community level is classified as low, medium , or high. COVID-19 Community Levels can help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals. See https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels.html for more information. Visit CDC’s COVID Data Tracker County View* to learn more about the individual metrics used for CDC’s COVID-19 community level in your county. Please note that county-level data are not available for territories. Go to https://covid.cdc.gov/covid-data-tracker/#county-view. For the most accurate and up-to-date data for any county or state, visit the relevant health department website. *COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

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.

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 Behaviour: 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.

SIMS is a tablet based site visit software used for PEPFAR funded activities. Technically, it is composed of mobile data collection tablets running COTS software (where GH fills in the question protocol); secondly compiling and storing the data in the AIDnet environment in a MySQL database; thirdly transfer of the data on a quarterly basis to PEPFAR's DATUM system. The goal of PEPFAR's Site Improvement Through Monitoring System (SIMS) is to increase the impact of PEPFAR programs on the HIV epidemic through standardized monitoring of the quality of PEPFAR support at the site level (e.g., health facility; ward, district, etc.), focusing on key program area elements. SIMS aims to systematize and broaden ongoing PEPFAR site monitoring processes and improve documentation of this oversight; this is accomplished through administration of standard tools that assess adherence to PEPFAR standards of care and service delivery, as well as within entities that support and guide service delivery.

Records of species and habitat ad hoc sightings collected in the freshwater environment. This includes, but it not limited to aquatic plants, amphibians and certain easily identified invertebrates. Data collection will be on-going during the monitoring and other survey work.

TO VIEW A GLOSSARY OF TERMS FOR THE DIRECTORY - CLICK HERE

WHAT IS THIS TRACKER FOR?

The City of Boston is committed to increasing transparency in the processes around the Zoning Board of Appeal (ZBA). The Inspectional Services Department (ISD) at the City is tasked with ensuring compliance with the zoning code. If an application for a permit is refused because of a zoning violation, applicants are able to appeal the decision to the ZBA and ask for an exception, sometimes known as a “variance.” If the ZBA grants relief, then the appellant is able to continue with the process of obtaining a permit.

In order to provide greater transparency in the ZBA process, the City of Boston Zoning Board of Appeal tracker is now available on Analyze Boston. Each record in this tracker represents an appeal of a denied permit application; the original permit application is known as the “parent application.” To find out more information about the original permit application, visit our Permit Finder tool. To view a map of this data, visit our ZBA Tracker Map Tool.

To learn more about the ZBA process and how to file an appeal, visit our website.

WHAT DOES EACH STATUS MEAN?

Appeal Submitted - indicates that an appeal of a zoning refusal was successfully submitted into ISD’s tracking system, either in-person at ISD (1010 Massachusetts Ave.) or through the online application portal.

More Information:

• For detailed information on how to file an appeal, see this page on the ISD website.

Next steps:

• The appeal will undergo an initial review by City staff to ensure that all necessary information and documentation has been included.
• The appellant must pay a fee.

Community Process - indicates that City staff have completed their review and signed off for the appeal process to move onto getting community feedback.

Contact Information:

• Use the link below to find the Neighborhood Liaison from the Office of Neighborhood Services for the application’s location.

Next steps:

• The appellant will work with the Mayor’s Office of Neighborhood Services to engage with people who own adjacent properties, members of the local community, and other relevant stakeholders.

• Depending on the type of project, the Boston Planning and Development Agency may also conduct a review.

Hearing Scheduled - indicates that the appeal has been scheduled for a committee or subcommittee meeting of the ZBA. For this to take place, the Mayor’s Office of Neighborhood Services has notified ISD that the appellant has adequately engaged with the community that would be affected, should the zoning relief be granted.

Attendance Information:

• View a schedule of hearings at the ZBA webpage. Select an Upcoming Hearing to find the list of topics and how to attend.

Next steps:

• The appellant will attend the hearing in person (or through the virtual meeting). The appellant will provide the ZBA with reasons why an exception or variance to the zoning code should be granted and answer any questions from the ZBA.

• At the hearing, members of the public will be able to testify in support or against the appeal.

• The ZBA will discuss the appeal and vote to approve or deny.

Alternatively:

• The appellant can request a deferral; if allowed by the ZBA, during the hearing the appeal will receive a new hearing date.

• The appellant can withdraw the application; if allowed by the ZBA, it can be withdrawn without prejudice.

Hearing Rescheduled - indicates that the appeal’s scheduled committee meeting has been changed. This can happen for several reasons. For example, the appellant can request a deferral if they need more time to complete or update plans, or the board can defer an appeal if a quorum isn’t present (perhaps due to a recusal). A request for deferral is approved by the board, which also selects a new hearing date.

Next steps:

• Same as Hearing Scheduled above.

Hearing Concluded - indicates that the hearing has taken place. The appeal could have been approved, denied, deferred, or withdrawn, with or without additional requirements.

Additional Information:

• Meeting minutes from each committee meeting are available to download from the ZBA webpage.

Next steps:

• The ZBA Executive Secretary finalizes the Board’s decision in writing or the appellant’s attorney submits a finalized decision for ISD Legal Team review.

ZBA Decision Finalized - indicates the date on the ZBA’s written decision letter. The decision is listed under the ‘result’ field.

Next steps:

• Neighboring property owners are notified of the decision shortly after this date

• Beginning on the Final Decision Date, neighboring property owners and other involved parties who disagree with the ZBA’s decision have twenty days to file an appeal in Suffolk County Superior Court or Boston Housing Court. (For detailed information on the Zoning Commission and appeal process, please refer to Chapter 665 of the Acts of 1956, available here)

Appeal Closed - indicates the appeal’s outcome has been finalized and the twenty day Appeal period has ended.

Next steps:

• Depending on the ZBA decision, the appellant may or may not be able to continue the process for seeking the permit for which zoning relief was requested.

• If the ZBA approved or sustained with proviso, the appellant must take additional steps before continuing the permitting process.

WHAT DOES EACH RESULT MEAN?

Approved - means the zoning relief requested has been granted.

Approved with Proviso - means the zoning relief requested has been granted, with some conditions that must be fulfilled before the permitting process can continue. These conditions will be detailed in the written decision of the ZBA. Examples of such conditions could include: having the Boston Planning and Development Agency review updated plans; submitting more detailed plans; or obtaining additional engineer reports.

Denied - means the zoning relief requested was not granted. The appellant must wait a year before submitting a new appeal on a project for the same site.

Denied without Prejudice - means the zoning relief requested was not granted. However, the appellant only has to wait thirty days before submitting an appeal on a new project at the same site.

Withdrawn - means the appellant has chosen to remove the appeal from the ZBA’s consideration. The appellant does not have to wait a year to appeal the same zoning violations.

Note: If there is no result listed, it means that the ZBA has not issued its final written decision on the appeal. This may be the case even for appeals that have been heard by the ZBA.

HOW DO I USE THIS TRACKER?

This tracker is designed for members of the public and City of Boston employees to be able to quickly search for a specific appeal that has been submitted to the ZBA, or to search for appeals based on criteria such as location or primary contact, in order to identify the status of the appeal.

Below, under the "Data and Resources" header, you will see the "Zoning Board of Appeal Tracker" dataset:

• To look at the directory - click the "Preview" button and you will be taken to a spreadsheet-like view of the directory data.

• To expand the number of applications available to scroll through, click the "Show _ Entries" drop down at the top left of the data table and select your desired number. Alternatively, you can scroll to the bottom right of the dataset and select your desired page number.

• To search the tracker - use the search box to the top right of the data table to search for any keyword in the dataset. For example, if you are looking for a certain contact, type the name into the search box and see what comes back.

• To filter the data, click the blue "Add Filter" link at the top left of the data table, select the field you would like to filter on, and select the corresponding value of that field that you would like to display. For example - if you wanted to show applications for properties in Charlestown, you would click "Add Filter", select the "city" field, and select "Charlestown". You can add multiple filters.

• To sort the data based on a specific field, click the arrows next to the field name to sort in either ascending or descending order.

• To hide columns that aren't relevant to you, click the blue "Hide/Unhide Columns" button at the top right of the data table, and click on the desired column names. Hidden column names will be highlighted in white. To unhide a column, simply click it again.

• The Data Dictionary - which explains what each field means and what the values of each field mean - is available as a table below the directory, and is also

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

Abstract copyright UK Data Service and data collection copyright owner.

Renaissance was the Museums, Libraries and Archives Council's (MLA) programme to transform England's regional museums. The programme has received over £300 million since 2002 which has been allocated across nine regional museum hubs. Regional museum hubs are a cluster of four-five museums which receive government investment in order to develop as centres of excellence and as leaders of their regional museum communities.

MLA has been gathering data from the nine regional museum hubs from 2002-2003 to 2007-2008. The Renaissance Data Collection is a quarterly return of data from each site participating in the Renaissance in the Regions Programme. The data returns contain information on numbers of: visits; priority group visits; child visits; website visits; school visits; Higher Education visits; adult and child on-site participation; and outreach activity. The data returns support Programme management and monitoring and forms the basis of the Renaissance Museums Performance Indicator statistical series.

From the 30th June 2011, the regional Renaissance hub structure ceases to exist. 2011-12 is a transitional year for Renaissance, in which £37.6 million of grant funding, previously known as museum hub funding, has been made available instead directly to 45 museum services.

Further information about Renaissance can be found on the MLA's Renaissance Data Collection web page.

Main Topics:
Data were recorded by hubs and submitted to the MLA on a quarterly basis, following a financial year cycle e.g. Q1 (April to June).

Data were submitted in an Excel workbook (Data Collection Template) that consists of six worksheets, covering six different areas of museums activity:

• Template 1: number of self-directed visits by children and young people in formal education
• Template 2: number of facilitated visits by children and young people in formal education
• Template 3: number of instances of children, young people and adults participating in museums’ outreach activities
• Template 4: number of instances of teachers in contact with museums
• Template 5: number of instances of children, young people and adults participating in organised activities at museums
• Template 6: visits, child visits, web visits and loan venues

Within each template there are a number of different measures, with hubs reporting on a total of 67 performance indicators. A breakdown of each template measure and accompanying guidelines can be found in the Audience Data Collection Manual 2008.
Data were not collected for templates 2 and parts of templates 3 and 5, in 2004-2005. In addition, some template sections were not introduced until January 2005. See individual data files for details.

Introduction

Digital Footprint Statistics: A digital footprint is the trail of information people leave behind when using the internet. It includes everything from social media posts to online searches, websites visited, and emails sent. Some of this data is shared intentionally, like posting on Facebook, while other parts are collected automatically, like tracking cookies from websites.

A digital footprint can be active, meaning data is shared by choice, or passive, meaning it is collected without you realizing it. It's important to manage your digital footprint because it can affect your privacy, reputation, and even job opportunities in the future. Understanding it helps you stay safe online.

Measurements of macroinvertebrate diversity, biotic index, richness, and species percentages of 4 composite sites in Stevensville Brook and Browns River

The WellNet database contains information related to sites for surface water measurements. These data are used by NDWR to assess the condition of the groundwater and surface water systems over time and are available to the public on NDWR’s website. Surface water measurement sites are chosen based on availability of dedicated measurement equipment, permit terms, and where additional flow information is required. This dataset is updated every day from a non-spatial SQL Server database using lat/long coordinates to display location. The feature class participates in a relationship class with a surface water measure table joined using the sitename field. This dataset contains both active and inactive sites. Measurement data is provided by reporting agencies and by regular site visits from NDWR staff. For website access, please see the Stream/Spring site at http://water.nv.gov/SpringAndStreamFlow.aspx.

This dataset contains the automatically collected data used for the overview paper about the GenABEL Project (Karssen et al, 2016, DOI:10.12688/f1000research.8733.1). Some data used for the paper was collected manually and is therefore not included in this dataset.

The file "tracker_report-2016-04-16.csv" is an export of the bug reports from the GenABEL R-forge bug tracker on the date listed in the file name.

The file "Analytics www.genabel.org Locatie Lennart 20150428-20160428.csv" is a custom export of the Google Analytics data for visits to the GenABEL website (www.genabel.org) in the period marked by the dates listed in the file name. The columns contain the ISO code of the country, city, number of sessions, number of new viewers, bounce percentage, pages per session and average session duration, respectively.

The file analysis_GenABELpaper.org contains the source code used for the automated data extraction for this paper in Emacs Org mode literate programming format (http://orgmode.org, Schulte 2012, doi:10.18637/jss.v046.i03)