The open-source data annotation tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market's expansion is fueled by several key factors: the rising adoption of AI across various industries (including automotive, healthcare, and finance), the need for efficient and cost-effective data annotation solutions, and a growing preference for flexible, customizable tools offered by open-source platforms. While cloud-based solutions currently dominate the market due to scalability and accessibility, on-premise deployments remain significant for organizations with stringent data security requirements. The competitive landscape is dynamic, with numerous established players and emerging startups vying for market share. The market is segmented geographically, with North America and Europe currently holding the largest shares due to early adoption of AI technologies and robust research & development activities. However, the Asia-Pacific region is projected to witness significant growth in the coming years, driven by increasing investments in AI infrastructure and talent development. Challenges remain, such as the need for skilled annotators and the ongoing evolution of annotation techniques to handle increasingly complex data types. The forecast period (2025-2033) suggests continued expansion, with a projected Compound Annual Growth Rate (CAGR) – let's conservatively estimate this at 15% based on typical growth in related software sectors. This growth will be influenced by advancements in automation and semi-automated annotation tools, as well as the emergence of novel annotation paradigms. The market is expected to see further consolidation, with larger players potentially acquiring smaller, specialized companies. The increasing focus on data privacy and security will necessitate the development of more robust and compliant open-source annotation tools. Specific application segments like healthcare, with its stringent regulatory landscape, and the automotive industry, with its reliance on autonomous driving technology, will continue to be major drivers of market growth. The increasing availability of open-source datasets and pre-trained models will indirectly contribute to the market’s expansion by lowering the barrier to entry for AI development.

The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in various AI applications. The market's expansion is fueled by several key factors: the rising adoption of machine learning and deep learning algorithms across industries, the need for efficient and cost-effective data annotation solutions, and a growing preference for customizable and flexible tools that can adapt to diverse data types and project requirements. While proprietary solutions exist, the open-source ecosystem offers advantages including community support, transparency, cost-effectiveness, and the ability to tailor tools to specific needs, fostering innovation and accessibility. The market is segmented by tool type (image, text, video, audio), deployment model (cloud, on-premise), and industry (automotive, healthcare, finance). We project a market size of approximately $500 million in 2025, with a compound annual growth rate (CAGR) of 25% from 2025 to 2033, reaching approximately $2.7 billion by 2033. This growth is tempered by challenges such as the complexities associated with data security, the need for skilled personnel to manage and use these tools effectively, and the inherent limitations of certain open-source solutions compared to their commercial counterparts. Despite these restraints, the open-source model's inherent flexibility and cost advantages will continue to attract a significant user base. The market's competitive landscape includes established players like Alecion and Appen, alongside numerous smaller companies and open-source communities actively contributing to the development and improvement of these tools. Geographical expansion is expected across North America, Europe, and Asia-Pacific, with the latter projected to witness significant growth due to the increasing adoption of AI and machine learning in developing economies. Future market trends point towards increased integration of automated labeling techniques within open-source tools, enhanced collaborative features to improve efficiency, and further specialization to cater to specific data types and industry-specific requirements. Continuous innovation and community contributions will remain crucial drivers of growth in this dynamic market segment.

AdA Project Public Data Release

This repository holds public data provided by the AdA project (Affektrhetoriken des Audiovisuellen - BMBF eHumanities Research Group Audio-Visual Rhetorics of Affect).

See: http://www.ada.cinepoetics.fu-berlin.de/en/index.html The data is made accessible under the terms of the Creative Commons Attribution-ShareAlike 3.0 License. The data can be accessed also at the project's public GitHub repository: https://github.com/ProjectAdA/public

Further explanations of the data can be found on our AdA project website: https://projectada.github.io/. See also the peer-reviewed data paper for this dataset that is in review to be published in NECSUS_European Journal of Media Studies, and will be available from https://necsus-ejms.org/ and https://mediarep.org

The data currently includes:

AdA Filmontology

The latest public release of the AdA Filmontology: https://github.com/ProjectAdA/public/tree/master/ontology

A vocabulary of film-analytical terms and concepts for fine-grained semantic video annotation.

The vocabulary is also available online in our triplestore: https://ada.cinepoetics.org/resource/2021/05/19/eMAEXannotationMethod.html

Advene Annotation Template

The latest public release of the template for the Advene annotation software: https://github.com/ProjectAdA/public/tree/master/advene_template

The template provides the developed semantic vocabulary in the Advene software with ready-to-use annotation tracks and predefined values.

In order to use the template you have to install and use Advene: https://www.advene.org/

Annotation Data

The latest public releases of our annotation datasets based on the AdA vocabulary: https://github.com/ProjectAdA/public/tree/master/annotations

The dataset of news reports, documentaries and feature films on the topic of "financial crisis" contains more than 92.000 manual & semi-automatic annotations authored in the open-source-software Advene (Aubert/Prié 2005) by expert annotators as well as more than 400.000 automatically generated annotations for wider corpus exploration. The annotations are published as Linked Open Data under the CC BY-SA 3.0 licence and available as rdf triples in turtle exports (ttl files) and in Advene's non-proprietary azp-file format, which allows instant access through the graphical interface of the software.

The annotation data can also be queried at our public SPARQL Endpoint: http://ada.filmontology.org/sparql

• The dataset contains furthermore sample files for two different export capabilities of the web application AdA Annotation explorer: 1) all manual annotations of the type "field size" throughout the corpus as csv files. 2) static html exports of different queries conducted in the AdA Annotation Explorer.

Manuals

The data set includes different manuals and documentations in German and English: https://github.com/ProjectAdA/public/tree/master/manuals

"AdA Filmontology – Levels, Types, Values": an overview over all analytical concepts and their definitions.

"Manual: Annotating with Advene and the AdA Filmontology". A manual on the usage of Advene and the AdA Annotation Explorer that provides the basics for annotating audiovisual aesthetics and visualizing them.

"Notes on collaborative annotation with the AdA Filmontology"

This dataset was recorded in the Virtual Annotated Cooking Environment (VACE), a new open-source virtual reality dataset (https://sites.google.com/view/vacedataset) and simulator (https://github.com/michaelkoller/vacesimulator) for object interaction tasks in a rich kitchen environment. We use the Unity-based VR simulator to create thoroughly annotated video sequences of a virtual human avatar performing food preparation activities. Based on the MPII Cooking 2 dataset, it enables the recreation of recipes for meals such as sandwiches, pizzas, fruit salads and smaller activity sequences such as cutting vegetables. For complex recipes, multiple samples are present, following different orderings of valid partially ordered plans. The dataset includes an RGB and depth camera view, bounding boxes, object masks segmentation, human joint poses and object poses, as well as ground truth interaction data in the form of temporally labeled semantic predicates (holding, on, in, colliding, moving, cutting). In our effort to make the simulator accessible as an open-source tool, researchers are able to expand the setting and annotation to create additional data samples.

The research leading to these results has received funding from the Austrian Science Fund (FWF) under grant agreement No. I3969-N30 InDex and the project Doctorate College TrustRobots by TU Wien. Thanks go out to Simon Schreiberhuber for sharing his Unity expertise and to the colleagues at the TU Wien Center for Research Data Management for data hosting and support.

The video tutorial creation software market, currently valued at $251 million in 2025, is projected to experience robust growth, driven by the increasing demand for online learning and the rising popularity of eLearning platforms. A Compound Annual Growth Rate (CAGR) of 5.3% from 2025 to 2033 indicates a significant expansion of this market. This growth is fueled by several factors, including the accessibility of user-friendly software, the proliferation of online courses and educational content, and the increasing need for businesses to provide effective employee training. The market is further boosted by advancements in screen recording technology, offering enhanced features like video editing capabilities, annotation tools, and interactive elements. While competition among established players like iSpring Suite, Camtasia, and Panopto is fierce, opportunities exist for niche players catering to specific needs, such as those offering specialized integrations with learning management systems (LMS) or those focused on accessibility features. The market segmentation, while not explicitly provided, can be reasonably inferred to include categories based on software pricing (free, freemium, subscription-based), functionality (basic screen recording, advanced editing, interactive features), target audience (individuals, educators, businesses), and operating system compatibility (Windows, macOS, web-based). Geographic segmentation would likely show significant market share held by North America and Europe initially, with growth potential in emerging Asian and Latin American markets as internet penetration and digital literacy improve. Challenges to market growth could include the availability of free or open-source alternatives, the need for ongoing software updates and technical support, and the potential for market saturation in certain segments. However, the overall trend points towards a continued expansion of the video tutorial creation software market driven by ongoing innovation and increased demand for engaging and effective online educational resources.

This dataset is the Ego Perspective version of the Virtual Annotated Cooking Environment Dataset (DOI: 10.48436/r5d7q-bdn48) and was recorded in the Virtual Annotated Cooking Environment (VACE), a new open-source virtual reality dataset (https://sites.google.com/view/vacedataset) and simulator (https://github.com/michaelkoller/vacesimulator) for object interaction tasks in a rich kitchen environment. We use the Unity-based VR simulator to create thoroughly annotated video sequences of a virtual human avatar performing food preparation activities. Based on the MPII Cooking 2 dataset, it enables the recreation of recipes for meals such as sandwiches, pizzas, fruit salads and smaller activity sequences such as cutting vegetables. For complex recipes, multiple samples are present, following different orderings of valid partially ordered plans. The dataset includes an RGB and depth camera view, bounding boxes, object masks segmentation, human joint poses and object poses, as well as ground truth interaction data in the form of temporally labeled semantic predicates (holding, on, in, colliding, moving, cutting). In our effort to make the simulator accessible as an open-source tool, researchers are able to expand the setting and annotation to create additional data samples.

The research leading to these results has received funding from the Austrian Science Fund (FWF) under grant agreement No. I3969-N30 InDex and the project Doctorate College TrustRobots by TU Wien. Thanks go out to Simon Schreiberhuber for sharing his Unity expertise and to the colleagues at the TU Wien Center for Research Data Management for data hosting and support.

The global video capture software market is experiencing robust growth, driven by the increasing demand for screen recording and video editing capabilities across various sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors. The rise of remote work and online collaboration has significantly increased the need for efficient tools to create and share video tutorials, presentations, and training materials. Furthermore, the expanding adoption of cloud-based solutions offers scalability, accessibility, and enhanced collaboration features, boosting market expansion. The market is segmented by deployment (cloud-based and web-based) and application (large enterprises and SMEs). Cloud-based solutions are gaining traction due to their flexibility and cost-effectiveness, while large enterprises are the primary consumers due to their higher budgets and greater need for comprehensive video capture solutions. The competitive landscape comprises both established players and emerging startups, leading to continuous innovation and improved functionality in the market. Growth is further accelerated by the increasing use of video content across various platforms, including social media, e-learning, and corporate communications. However, market growth faces certain restraints, such as the availability of free or open-source alternatives, and the potential security concerns associated with cloud-based solutions. Despite these challenges, the overall market outlook remains positive, with continuous innovation in areas like AI-powered video editing, screen annotation, and enhanced collaboration features expected to drive further expansion in the coming years. The Asia-Pacific region is anticipated to witness significant growth, driven by increasing internet penetration and rising adoption of digital technologies. North America, however, is likely to retain its leading position due to the high adoption of advanced technologies and the presence of major market players.

Understanding the biological underpinnings of movement and action requires the development of tools for quantitative measurements of animal behavior. Drosophila melanogaster provides an ideal model for developing such tools: the fly has unparalleled genetic accessibility and depends on a relatively compact nervous system to generate sophisticated limbed behaviors including walking, reaching, grooming, courtship, and boxing. Here we describe a method that uses active contours to semi-automatically track body and leg segments from video image sequences of unmarked, freely behaving D. melanogaster. We show that this approach yields a more than 6-fold reduction in user intervention when compared with fully manual annotation and can be used to annotate videos with low spatial or temporal resolution for a variety of locomotor and grooming behaviors. FlyLimbTracker, the software implementation of this method, is open-source and our approach is generalizable. This opens up the possibility of tracking leg movements in other species by modifications of underlying active contour models.