Dataset

This dataset was created by K Scott Mader

Contents

With the rapid advancement of high throughput sequencing, large numbers of genetic markers can be readily and cheaply acquired, but most current software packages for genetic map construction cannot handle such dense input. Modern computer architectures and server farms represent untapped resources that can be used to enable higher marker densities to be processed in tractable time. Here we present a pipeline using a modified version of OneMap that parallelizes over bottleneck functions and achieves substantial speedups for producing a high density linkage map (N = 20,000). Using simulated data we show that the outcome is as accurate as the traditional pipeline. We further demonstrate that there is a direct relationship between the number of markers used and the level of deviation between true and estimated order, which in turn impacts the final size of a genetic map.

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Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

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

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance applications,

Dataset

This dataset was created by SeaTigre

Contents

CAMEO is the umbrella name for a system of software applications used widely to plan for and respond to chemical emergencies. All of the programs in the suite work interactively to share and display critical information in a timely fashion; the programs can also be used individually. ALOHA (Areal Locations of Hazardous Atmospheres) is a hazard modeling tool used to evaluate the atmospheric dispersion of hazardous chemical vapors, as well as some fires and explosions. ALOHA prompts the user to enter basic scenario information (such as weather conditions and details about how the chemical is escaping), and ALOHA will create a threat zone estimate of the area where a hazard (such as toxicity or thermal radiation) has exceeded a user-specified Level of Concern (LOC). CAMEO is a database application (with eight modules) designed to assist with the data management requirements under the Emergency Planning and Community Right-to-Know Act (EPCRA). CAMEO Chemicals features an extensive database of chemical datasheets that provide critical response information, including physical properties, health hazards, air and water hazards, and recommendations for firefighting, first aid, and spill response. Additionally, it also has a reactivity prediction tool that can be used to estimate what hazards (such as explosions or chemical fires) could occur if chemicals were to mix together. MARPLOT (Mapping Applications for Response, Planning, and Local Operational Tasks) is a mapping program; users can quickly view and modify maps, and they can create their own objects to place on the maps. ALOHA threat zones can be displayed on a map in MARPLOT, and objects on the map (such as facilities) can be linked to related records in the CAMEO program. Tier2 Submit is an adjunct program in the suite, which allows users to complete EPCRA Tier II forms electronically. The facility, chemical inventory, and contact information entered into Tier2 Submit can also be imported into the corresponding modules in CAMEO.

The INTERPNT method can be used to produce accurate maps of trees based solely on tree diameter and tree-to-tree distance measurements. For additional details on the technique please see the published paper (Boose, E. R., E. F. Boose and A. L. Lezberg. 1998. A practical method for mapping trees using distance measurements. Ecology 79: 819-827). Additional information is contained in the documentation that accompanies the program. The Abstract from the paper is reproduced below. "Accurate maps of the locations of trees are useful for many ecological studies but are often difficult to obtain with traditional surveying methods because the trees hinder line of sight measurements. An alternative method, inspired by earlier work of F. Rohlf and J. Archie, is presented. This "Interpoint method" is based solely on tree diameter and tree-to-tree distance measurements. A computer performs the necessary triangulation and detects gross errors. The Interpoint method was used to map trees in seven long-term study plots at the Harvard Forest, ranging from 0.25 ha (200 trees) to 0.80 ha (889 trees). The question of accumulation of error was addressed though a computer simulation designed to model field conditions as closely as possible. The simulation showed that the technique is highly accurate and that errors accumulate quite slowly if measurements are made with reasonable care (e.g., average predicted location errors after 1,000 trees and after 10,000 trees were 9 cm and 15 cm, respectively, for measurement errors comparable to field conditions; similar values were obtained in an independent survey of one of the field plots). The technique requires only measuring tapes, a computer, and two or three field personnel. Previous field experience is not required. The Interpoint method is a good choice for mapping trees where a high level of accuracy is desired, especially where expensive surveying equipment and trained personnel are not available."

Context

I would like to write a quest scraper. A Tool that takes a look at an image of a Heroquest quest map and can derive all symbols with their positions correctly; turning the "dead" image once again into an editable quest file. On Heroscribe.org a great java-based tool for editing quest files can be downloaded. In ideal case, my tool can take an image and output the Heroscribe format.

That's a task for later. Today, we just want to do the recognition.

I took around 100 Maps from the ancient game Heroquest, cut them down to single square images and used them as training data set for a neural net. The incredible imbalance in the data set made it necessary that I made 100 more maps, to boost the underrepresented symbol appearances. All of the maps have been made in Heroscribe (downloadable at Heroscribe.org) and exported as png; like that they have the same size.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1711994%2F9050fb998965fcf24ef4b76d4c9fe4d7%2F11-BastionofChaos_EU.png?generation=1570256920345210&alt=media" alt="EU format Heroquest map">

Now I have 13 thousand snippets of Heroquest Quest Maps, in three cut out factors (78, 42 and 34 pixel). In each sample, there can be one or more of the following things: Monsters, Furniture, Doors, and rooms. For each snippet, the position information is already preserved in the data set: It was taken during the cropping process. You know where you cut the image right now, so why not keeping that information right away?

In the easiest case, there is just one symbol in a square. In some cases there are two or three of them at the same time; like there can be one or more door, one monster, and the square itself is discolored because the room is a special room. So here we have do recognize several symbols at the same time.

The first (roughly half) of the dataset contains real data from real maps, in the second half I've made up data to fill gaps in the data coverage.

The Y-Labels

Y-Data is provided in an excel-formatted spreadsheet. One column is for single-square-items and furniture; four for doors and one for rooms. If there were too many items in one square, or sometimes when I was tired from labelling all the data, it could happen that I was putting a label in the wrong column or even put the wrong label. I guess that currently, around 0.5% of the data is mislabelled; except for the room symbol column; which is not at all well labeled.

I tried to train a resnet to recognize the Y-Data given and it was surprisingly difficult. The current best working solution has four convolutional layers and one dense layer; has nothing to do with the current state-of-the-art deep learning. The advantage is, it is trainable under an hour on any laptop; the disadvantage is does not yet always work as intended.

See some examples for the images and the difficulties: The "center pic" of a "table" symbol: It is difficult to recognize anything here.

https://i.imgur.com/yCP4pF9.png" alt="Table, small cutout">

And the same square in the "pic" cutout:

https://i.imgur.com/9a9scVN.png" alt="Table, big cutout">

"center pic" of a Treasure Chest: Sufficient to recognize it; easily!

https://i.imgur.com/KjX1QUV.png" alt="Treasure Chest, small cutout">

Big cutout of the same Treasure Chest: Distracting details in the surrounding.

https://i.imgur.com/OPBlWHV.png" alt="Treasure Chest, big cutout">

For each symbol, I also extracted the two main colors. There are maps in the EU format, which are completely black and white (see above picture). The other half of the maps is in US format: Monsters are green, furniture is dark red, traps and trapped furniture have a orange or turquoise background instead of white; Hero symbols are bright red. There is real information in those colors.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1711994%2F3900bd109f86618a48e619ec00ce892d%2F11-BastionofChaos_US.png?generation=1570257035192555&alt=media" alt="US format Heroquest map">

The symbols in the data set are black and white, all of them. The columns 'min_color' and 'max_color' preserve the color information. I planned to give it as an auxiliary input to the neural net, but didn't yet get round to do it. The color information can be distracting, too: In the US map format, sometimes otherwise normal furniture symbols are marked with trap colors when they thought about some special event for it.

Target acceptance rates

Those are quite easy images on one side. Noiseless, size-fixed, no skew or zoom coming from photography... I even bootstrapped my data set by using K-Means to bulk-label some images. Yes, K-Means. It is easy to classify this data beyond the 95% recognition. So what's the catch?

First of all, the number of classes. It's not a single-class recognition problem; in this data set we have around 100 class...

Free downloads of about 26,900 classic maps issued by the Survey of India and its descendant organizations in Pakistan, Bangladesh and Burma. The collection includes maps of Pakistan, India, Nepal, Bhutan, Bangladesh and Burma dating from the 1880s through to the 2010s, as well as some even older historical maps.

The "Map Selection and Download Spreadsheet" file below can be downloaded to provide an easy-to-use tool to view the file names of all the maps available on this website. Each of the filenames in the spreadsheet is a link to the map file, and a click on the file name will download the map to the viewers computer. This file can be stored by the viewer for future use, or, as the collection grows, an updated file can be obtained periodically from this website. The file is issued in an MS Excel format, but it can be opened by Google Sheets or other spreadsheet software.

The map collection is broken down into 19 different categories based on topic, scale and geographic area. A tab at the bottom of the spreadsheet opens the page for each category.

Occupancy Grid Mapping Software Market Outlook

According to our latest research, the global occupancy grid mapping software market size reached USD 1.24 billion in 2024, reflecting robust adoption across multiple industries. The market is expected to expand at a CAGR of 13.7% from 2025 to 2033, projecting a value of USD 4.11 billion by 2033. This impressive growth trajectory is primarily driven by the increasing integration of autonomous systems and robotics in both industrial and commercial sectors, alongside rapid advancements in sensor technologies and artificial intelligence.

The surge in demand for occupancy grid mapping software is fundamentally linked to the proliferation of autonomous vehicles, robotics, and drones, where real-time environment mapping is crucial for safe and efficient navigation. This software enables machines to interpret their surroundings by creating digital grids that represent occupied and free spaces, thus facilitating advanced decision-making capabilities. The expansion of smart manufacturing, logistics, and warehousing operations is further accelerating the adoption of occupancy grid mapping solutions, as businesses seek to optimize workflows, enhance safety, and reduce operational costs. Moreover, the growing trend of Industry 4.0 and the Internet of Things (IoT) is pushing organizations to deploy sophisticated mapping tools to support automation and digital transformation initiatives.

Another significant growth factor is the continuous improvement in sensor technologies, such as LiDAR, radar, and computer vision systems, which form the backbone of occupancy grid mapping software. These advancements have resulted in higher mapping accuracy, improved spatial resolution, and faster data processing, enabling real-time mapping even in complex and dynamic environments. The integration of artificial intelligence and machine learning algorithms into occupancy grid mapping software is also enhancing its capability to predict and adapt to changing conditions, making it indispensable for autonomous navigation and robotics applications. As industries increasingly prioritize safety, efficiency, and automation, the demand for reliable and scalable mapping solutions is expected to rise exponentially.

The occupancy grid mapping software market is also benefiting from the increasing focus on research and development, especially within academia and research institutions. These organizations are leveraging advanced mapping software to develop next-generation autonomous systems, robotics, and AI-powered applications. Collaborative initiatives between industry players and academic institutions are fostering innovation, leading to the creation of more robust, flexible, and interoperable mapping solutions. Furthermore, supportive government policies and funding for smart infrastructure and autonomous technology development are creating a favorable ecosystem for market growth, particularly in regions with strong technology adoption rates.

Regionally, North America continues to dominate the occupancy grid mapping software market, driven by early adoption of autonomous vehicles, strong presence of leading technology companies, and substantial investments in research and development. Europe is following closely, propelled by stringent safety regulations and the rapid digitalization of the automotive and manufacturing sectors. The Asia Pacific region is witnessing the fastest growth, fueled by expanding industrial automation, increasing adoption of robotics, and government initiatives supporting smart city projects. Latin America and the Middle East & Africa are gradually catching up, as local industries begin to recognize the benefits of advanced mapping technologies for operational efficiency and safety.

Component Analysis

The occupancy grid mapping software market is segmented by component into software and services, each playing a distinct role in the overall value proposition. The software segment accounts for the largest share, driven by the growing need for advanced mapping algorithms, real-time data processing, and seamless integration with various hardware platforms. Modern occupancy grid mapping software offers a range of features, including multi-sensor fusion, dynamic obstacle detection, and adaptive path planning, which are essential for autonomous systems operating in unpredictable e

The Computer Animation & Modeling Software market is experiencing robust growth, driven by increasing demand across diverse sectors like construction, automotive, and entertainment. The market's size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. The proliferation of high-performance computing, the rise of virtual and augmented reality applications, and the increasing adoption of cloud-based software solutions are all contributing to market expansion. Furthermore, the growing need for realistic simulations and visualizations in various industries, coupled with advancements in 3D scanning and modeling techniques, are driving demand for sophisticated software solutions. The market segmentation reveals strong growth across various application areas, with the construction and automotive industries leading the charge, followed by video entertainment and transportation. The market's competitive landscape is characterized by a mix of established players like Autodesk and newer entrants offering specialized solutions. The geographical distribution shows robust growth across North America and Europe, with significant emerging market potential in Asia-Pacific. This continued market expansion is expected to be sustained throughout the forecast period (2025-2033), propelled by ongoing technological innovation and the integration of AI and machine learning capabilities within animation and modeling software. While challenges remain, such as the need for skilled professionals and high initial investment costs for advanced software, the overall market outlook remains positive. The increasing adoption of subscription-based models and the emergence of user-friendly interfaces are also driving accessibility and wider market penetration. The convergence of 3D modeling and animation software with other technologies, such as game engines and virtual production pipelines, is set to further expand the market's capabilities and appeal to a broader range of users.

Data set: The name of the data set; Markers/chr: Number of markers simulated on each chromosome; Total markers: The total number of markers; Genetic map: Number of markers in the genetic map after filtering for informative markers (and the corresponding percentage of all simulated markers); Markers/LG: The average number of markers on each LG (and the marker density range).

The PALEOMAP project produces paleogreographic maps illustrating the Earth's plate tectonic, paleogeographic, climatic, oceanographic and biogeographic development from the Precambrian to the Modern World and beyond.

A series of digital data sets has been produced consisting of plate tectonic data, climatically sensitive lithofacies, and biogeographic data. Software has been devloped to plot maps using the PALEOMAP plate tectonic model and digital geographic data sets: PGIS/Mac, Plate Tracker for Windows 95, Paleocontinental Mapper and Editor (PCME), Earth System History GIS (ESH-GIS), PaleoGIS(uses ArcView), and PALEOMAPPER.

Teaching materials for educators including atlases, slide sets, VHS animations, JPEG images and CD-ROM digital images.

Some PALEOMAP products include: Plate Tectonic Computer Animation (VHS) illustrating motions of the continents during the last 850 million years.

Paleogeographic Atlas consisting of 20 full color paleogeographic maps. (Scotese, 1997).

Paleogeographic Atlas Slide Set (35mm)

Paleogeographic Digital Images (JPEG, PC/Mac diskettes)

Paleogeographic Digital Image Archive (EPS, PC/Mac Zip disk) consists of the complete digital archive of original digital graphic files used to produce plate tectonic and paleographic maps for the Paleographic Atlas.

GIS software such as PaleoGIS and ESH-GIS.

As a first step in understanding law enforcement agencies' use and knowledge of crime mapping, the Crime Mapping Research Center (CMRC) of the National Institute of Justice conducted a nationwide survey to determine which agencies were using geographic information systems (GIS), how they were using them, and, among agencies that were not using GIS, the reasons for that choice. Data were gathered using a survey instrument developed by National Institute of Justice staff, reviewed by practitioners and researchers with crime mapping knowledge, and approved by the Office of Management and Budget. The survey was mailed in March 1997 to a sample of law enforcement agencies in the United States. Surveys were accepted until May 1, 1998. Questions asked of all respondents included type of agency, population of community, number of personnel, types of crimes for which the agency kept incident-based records, types of crime analyses conducted, and whether the agency performed computerized crime mapping. Those agencies that reported using computerized crime mapping were asked which staff conducted the mapping, types of training their staff received in mapping, types of software and computers used, whether the agency used a global positioning system, types of data geocoded and mapped, types of spatial analyses performed and how often, use of hot spot analyses, how mapping results were used, how maps were maintained, whether the department kept an archive of geocoded data, what external data sources were used, whether the agency collaborated with other departments, what types of Department of Justice training would benefit the agency, what problems the agency had encountered in implementing mapping, and which external sources had funded crime mapping at the agency. Departments that reported no use of computerized crime mapping were asked why that was the case, whether they used electronic crime data, what types of software they used, and what types of Department of Justice training would benefit their agencies.

maps.ie is ranked #126384 in US with 155.06K Traffic. Categories: Computer Software and Development, Information Technology. Learn more about website traffic, market share, and more!

Global Computer Animation & Modeling Software market size 2021 was recorded $229.891 Billion whereas by the end of 2025 it will reach $280.5 Billion. According to the author, by 2033 Computer Animation & Modeling Software market size will become $417.594. Computer Animation & Modeling Software market will be growing at a CAGR of 5.1% during 2025 to 2033.

HD Mapless Navigation Software Market Outlook

According to our latest research, the global HD Mapless Navigation Software market size reached USD 1.32 billion in 2024, with robust momentum driven by technological advancements and the proliferation of autonomous systems across industries. The market is expected to expand at a remarkable CAGR of 18.9% from 2025 to 2033, propelling the market value to an estimated USD 6.43 billion by 2033. This impressive growth trajectory is largely attributed to increasing adoption of autonomous vehicles, drones, and robotics, coupled with the demand for real-time, high-precision navigation solutions that do not rely on pre-generated high-definition maps.

One of the primary growth factors fueling the HD Mapless Navigation Software market is the rapid evolution and deployment of autonomous vehicles in both consumer and commercial sectors. Unlike traditional navigation systems that depend on static, pre-mapped data, HD mapless solutions leverage real-time sensor data, artificial intelligence, and machine learning to interpret and navigate complex environments dynamically. This capability is particularly crucial for next-generation vehicles operating in ever-changing urban landscapes, where pre-mapped information can quickly become outdated due to ongoing construction, road closures, or temporary obstacles. The flexibility and adaptability offered by HD mapless navigation software significantly enhance safety, reliability, and operational efficiency, making it a preferred choice for autonomous mobility solutions worldwide.

Another significant driver is the expanding use of robotics and drones across various industries, including logistics, agriculture, surveillance, and infrastructure inspection. These applications demand navigation systems that can operate reliably in GPS-denied environments or areas where HD maps are unavailable or impractical to maintain. HD mapless navigation software addresses this challenge by enabling real-time environmental perception and decision-making, reducing dependency on external mapping data. The integration of advanced sensor fusion, computer vision, and edge computing technologies further strengthens the performance and accuracy of these systems, thereby accelerating their adoption in both indoor and outdoor settings.

The market is also benefitting from growing investments in artificial intelligence and machine learning research, which are critical to the ongoing advancement of mapless navigation algorithms. Leading technology companies and automotive OEMs are pouring resources into the development of AI-powered perception systems that can interpret complex visual and spatial data on the fly. As these technologies mature, they are expected to further reduce the costs and technical barriers associated with deploying autonomous systems at scale. Additionally, regulatory bodies are gradually updating safety and operational standards to accommodate the unique requirements of mapless navigation, fostering a more supportive ecosystem for innovation and commercialization.

From a regional perspective, North America currently leads the HD Mapless Navigation Software market, driven by a concentration of technology innovators, autonomous vehicle pilots, and robust investment in smart mobility infrastructure. However, Asia Pacific is emerging as a high-growth region, propelled by rapid urbanization, government-led smart city initiatives, and a burgeoning robotics sector. Europe also plays a pivotal role, with strong regulatory frameworks and a focus on sustainable transportation solutions. As these regions continue to invest in digital infrastructure and autonomous mobility, the global market is poised for sustained expansion through 2033.

Component Analysis

The Component segment of the HD Mapless Navigation Software market is broadly categorized into Software and Services. The software sub-segment dominates the market, accounting for a substantial share of overall revenue in 2024. This dominance is primarily due to the core function of mapless navigation, which relies heavily on sophisticated algorithms, sensor fusion, and AI-driven perception modules. Software solutions are continuously evolving, integrating advancements in deep learning, real-time data processing, and edge computing to deliver precise navigation capabilities without the need for static HD maps. As the demand for flexible and scalable navigation so

TDEC is continuously striving to create better business practices through GIS and one way that we have found to provide information and answer some question is utilizing an interactive map. An interactive map is a display of geospatial data that allows you to manipulate and query the contents to get the information needed using a set of provided tools. Interactive maps are created using GIS software, and then distributed to users, usually over a computer network. The TDEC Land and Water interactive map will allow you to do simple tasks such as pan, zoom, measure and find a lat/long, while also giving you the capability of running simple queries to locate land and waters by name, entity, and number. With the ability to turn off and on back ground images such as aerial imagery (both black and white as well as color), we hope that you can find much utility in the tools provided.

Unlock enhanced gaming control! Explore the booming controller mapping tool market, projected to reach $461.2 million by 2033. Learn about key trends, leading companies like Xpadder & reWASD, and regional market share insights in this comprehensive analysis. Boost your gaming performance today!

Electronic supplement for a mapping study on acceptance behavior theories and models in SE

Please refer to the following paper when you cite/use this data:

Jürgen Börstler, Nauman bin Ali, Kai Petersen, Emelie Engström (2024).Acceptance behavior theories and models in software engineering — A mapping study.Information and Software Technology.https://doi.org/10.1016/j.infsof.2024.107469

Overview of the electronic supplement

An overview-file (README.docx) comprising the following:

An overview of the supplements.

A plain list of the theories and models of acceptance behavior used for constructing the search string.

A plain list of the software engineering venues used for constructing the search string.

A ready-to-use search string for Scopus (plain text).

A plain list of the 47 included primary studies.

A separate Bibtex-file (p1-p47.bib) with all 47 included primary studies.

A separate Excel-file (data extraction.xlsx) comprising the following sheets:

The data extracted for the 47 included primary studies.

The 27 primary studies excluded during full-text reading and data extraction.