We Do A Little Annotating

## Overview

We Do A Little Annotating is a dataset for object detection tasks - it contains Brown Cells annotations for 261 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Annotating

## Overview

Annotating is a dataset for object detection tasks - it contains Face annotations for 3,915 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The annotating software market is booming, projected to reach over $1 billion by 2033. Discover key trends, regional insights, and leading companies driving this growth in our comprehensive market analysis. Explore web-based vs. on-premise solutions and their applications in education, business, and machine learning.

The global Data Annotation Services market for Artificial Intelligence (AI) and Machine Learning (ML) is projected for robust expansion, estimated at USD 4,287 million in 2025, with a compelling Compound Annual Growth Rate (CAGR) of 7.8% expected to persist through 2033. This significant market value underscores the foundational role of accurate and high-quality annotated data in fueling the advancement and deployment of AI/ML solutions across diverse industries. The primary drivers for this growth are the escalating demand for AI-powered applications, particularly in rapidly evolving sectors like autonomous vehicles, where precise visual and sensor data annotation is critical for navigation and safety. The healthcare industry is also a significant contributor, leveraging annotated medical images for diagnostics, drug discovery, and personalized treatment plans. Furthermore, the surge in e-commerce, driven by personalized recommendations and optimized customer experiences, relies heavily on annotated data for understanding consumer behavior and preferences. The market encompasses various annotation types, including image annotation, text annotation, audio annotation, and video annotation, each catering to specific AI model training needs. The market's trajectory is further shaped by emerging trends such as the increasing adoption of sophisticated annotation tools, including active learning and semi-supervised learning techniques, aimed at improving efficiency and reducing manual effort. The rise of cloud-based annotation platforms is also democratizing access to these services. However, certain restraints, including the escalating cost of acquiring and annotating massive datasets and the shortage of skilled data annotators, present challenges that the industry is actively working to overcome through automation and improved training programs. Prominent companies such as Appen, Infosys BPM, iMerit, and Alegion are at the forefront of this market, offering comprehensive annotation solutions. Geographically, North America, particularly the United States, is anticipated to lead the market due to early adoption of AI technologies and substantial investment in research and development, followed closely by the Asia Pacific region, driven by its large data volumes and growing AI initiatives in countries like China and India. Here is a unique report description for Data Annotation Services for AI and ML, incorporating your specified parameters:

This comprehensive report delves into the dynamic landscape of Data Annotation Services for Artificial Intelligence (AI) and Machine Learning (ML). From its foundational stages in the Historical Period (2019-2024), through its pivotal Base Year (2025), and into the expansive Forecast Period (2025-2033), this study illuminates the critical role of high-quality annotated data in fueling the advancement of intelligent technologies. We project the market to reach significant valuations, with the Estimated Year (2025) serving as a crucial benchmark for current market standing and future potential. The report analyzes key industry developments, market trends, regional dominance, and the competitive strategies of leading players, offering invaluable insights for stakeholders navigating this rapidly evolving sector.

Critically reading and evaluating claims made in the primary literature are vital skills for the future professional and personal lives of undergraduate students. However, the formal presentation of intricate content in primary research articles presents a challenge to inexperienced readers. During the fall 2020 semester, I introduced a Collaborative Annotation Project (CAP) into my online 400-level developmental neurobiology course to help students critically read eight research papers. During CAP, students used collaborative annotation software asynchronously to add clarifying comments, descriptions of and links to appropriate websites, and pose and answer questions on assigned papers. Student work was guided and assessed using a CAP grading rubric. Responses to anonymous surveys revealed students found CAP helpful for reading the primary literature and the rubric clarified expectations for the project. Here, I describe how I introduced, used, and assessed CAP in my online class, and I share the detailed CAP instructions and rubric.

Primary image: A moment of levity while annotating primary literature. Sample student annotations from the Collaborative Annotation Project. Student #1 compares immunofluorescence data to Christmas lights, an observation appreciated by student #2. Student names have been removed.

The Data Annotation Tools Market size was valued at USD 1.31 billion in 2023 and is projected to reach USD 6.72 billion by 2032, exhibiting a CAGR of 26.3 % during the forecasts period. Recent developments include: In November 2023, Appen Limited, a high-quality data provider for the AI lifecycle, chose Amazon Web Services (AWS) as its primary cloud for AI solutions and innovation. As Appen utilizes additional enterprise solutions for AI data source, annotation, and model validation, the firms are expanding their collaboration with a multi-year deal. Appen is strengthening its AI data platform, which serves as the bridge between people and AI, by integrating cutting-edge AWS services. , In September 2023, Labelbox launched Large Language Model (LLM) solution to assist organizations in innovating with generative AI and deepen the partnership with Google Cloud. With the introduction of large language models (LLMs), enterprises now have a plethora of chances to generate new competitive advantages and commercial value. LLM systems have the ability to revolutionize a wide range of intelligent applications; nevertheless, in many cases, organizations will need to adjust or finetune LLMs in order to align with human preferences. Labelbox, as part of an expanded cooperation, is leveraging Google Cloud's generative AI capabilities to assist organizations in developing LLM solutions with Vertex AI. Labelbox's AI platform will be integrated with Google Cloud's leading AI and Data Cloud tools, including Vertex AI and Google Cloud's Model Garden repository, allowing ML teams to access cutting-edge machine learning (ML) models for vision and natural language processing (NLP) and automate key workflows. , In March 2023, has released the most recent version of Enlitic Curie, a platform aimed at improving radiology department workflow. This platform includes Curie|ENDEX, which uses natural language processing and computer vision to analyze and process medical images, and Curie|ENCOG, which uses artificial intelligence to detect and protect medical images in Health Information Security. , In November 2022, Appen Limited, a global leader in data for the AI Lifecycle, announced its partnership with CLEAR Global, a nonprofit organization dedicated to ensuring access to essential information and amplifying voices across languages. This collaboration aims to develop a speech-based healthcare FAQ bot tailored for Sheng, a Nairobi slang language. .

Fluid Annotation is an intuitive human-machine collaboration interface for annotating the class label and outline of every object and background region in an image.

Ruler Annotating

## Overview

Ruler Annotating is a dataset for instance segmentation tasks - it contains Ruler Cj5i annotations for 500 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

3D Mapping Annotation

## Overview

3D Mapping Annotation is a dataset for object detection tasks - it contains Ramps Steps annotations for 806 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Working with AG to annotate recording 'TBC1-017'. Language as given: Megiar

This document describes a vocabulary for annotating descriptions of vocabularies with examples and usage notes.

Each image in seperate image set has a unique six-digit number such as 000001.jpg. A corresponding annotation file in txt format is provided in annotation set such as 000001.txt. Each annotation file is organized as below:

1. category_id: a number which corresponds to the category name. In category_id, 1 represents short sleeve top, 2 represents long sleeve top, 3 represents short sleeve outwear, 4 represents long sleeve outwear, 5 represents vest, 6 represents sling, 7 represents shorts, 8 represents trousers, 9 represents skirt, 10 represents short sleeve dress, 11 represents long sleeve dress, 12 represents vest dress and 13 represents sling dress.

2. bounding_box: [x1,y1,x2,y2]，where x1 and y_1 represent the upper left point coordinate of bounding box, x_2 and y_2 represent the lower right point coordinate of bounding box. (width=x2-x1;height=y2-y1)

The dataset is split into a training set (10K images), a validation set (2k images)

Global Annotating Software comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2024 - 2032. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

In this study, we explore to what extent language users agree about what kind of stances are expressed in natural language use or whether their interpretations diverge. In order to perform this task, a comprehensive cognitive-functional framework of ten stance categories was developed based on previous work on speaker stance in the literature. A corpus of opinionated texts, where speakers take stance and position themselves, was compiled, the Brexit Blog Corpus (BBC). An analytical interface for the annotations was set up and the data were annotated independently by two annotators. The annotation procedure, the annotation agreement and the co-occurrence of more than one stance category in the utterances are described and discussed. The careful, analytical annotation process has by and large returned satisfactory inter- and intra-annotation agreement scores, resulting in a gold standard corpus, the final version of the BBC.

Purpose:

The aim of this study is to explore the possibility of identifying speaker stance in discourse, provide an analytical resource for it and an evaluation of the level of agreement across speakers in the area of stance-taking in discourse.

The BBC is a collection of texts from blog sources. The corpus texts are thematically related to the 2016 UK referendum concerning whether the UK should remain members of the European Union or not. The texts were extracted from the Internet from June to August 2015. With the Gavagai API (https://developer.gavagai.se), the texts were detected using seed words, such as Brexit, EU referendum, pro-Europe, europhiles, eurosceptics, United States of Europe, David Cameron, or Downing Street. The retrieved URLs were filtered so that only entries described as blogs in English were selected. Each downloaded document was split into sentential utterances, from which 2,200 utterances were randomly selected as the analysis data set. The final size of the corpus is 1,682 utterances, 35,492 words (169,762 characters without spaces). Each utterance contains from 3 to 40 words with a mean length of 21 words.

For the data annotation process the Active Learning and Visual Analytics (ALVA) system (https://doi.org/10.1145/3132169 and https://doi.org/10.2312/eurp.20161139) was used. Two annotators, one who is a professional translator with a Licentiate degree in English Linguistics and the other one with a PhD in Computational Linguistics, carried out the annotations independently of one another.

The data set can be downloaded in two different formats: a standard Microsoft Excel format and a raw data format (ZIP archive) which can be useful for analytical and machine learning purposes, for example, with the Python library scikit-learn. The Excel file includes one additional variable (utterance word length). The ZIP archive contains a set of directories (e.g., "contrariety" and "prediction") corresponding to the stance categories. Inside of each such directory, there are two nested directories corresponding to annotations which assign or not assign the respective category to utterances (e.g., inside the top-level category "prediction" there are two directories, "prediction" with utterances which were labeled with this category, and "no" with the rest of the utterances). Inside of the nested directories, there are textual files containing individual utterances.

When using data from this study, the primary researcher wishes citation also to be made to the publication: Vasiliki Simaki, Carita Paradis, Maria Skeppstedt, Magnus Sahlgren, Kostiantyn Kucher, and Andreas Kerren. Annotating speaker stance in discourse: the Brexit Blog Corpus. In Corpus Linguistics and Linguistic Theory, 2017. De Gruyter, published electronically before print. https://doi.org/10.1515/cllt-2016-0060

An individual’s likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due to their time-consuming expert curation process, job exposure matrices currently cover only a subset of possible workplace exposures and may not be regularly updated. Scientific literature articles describing exposure studies provide important supporting evidence for developing and updating job exposure matrices, since they report on exposures in a variety of occupational scenarios. However, the constant growth of scientific literature is increasing the challenges of efficiently identifying relevant articles and important content within them. Natural language processing methods emulate the human process of reading and understanding texts, but in a fraction of the time. Such methods can increase the efficiency of both finding relevant documents and pinpointing specific information within them, which could streamline the process of developing and updating job exposure matrices. Named entity recognition is a fundamental natural language processing method for language understanding, which automatically identifies mentions of domain-specific concepts (named entities) in documents, e.g., exposures, occupations and job tasks. State-of-the-art machine learning models typically use evidence from an annotated corpus, i.e., a set of documents in which named entities are manually marked up (annotated) by experts, to learn how to detect named entities automatically in new documents. We have developed a novel annotated corpus of scientific articles to support machine learning based named entity recognition relevant to occupational substance exposures. Through incremental refinements to the annotation process, we demonstrate that expert annotators can attain high levels of agreement, and that the corpus can be used to train high-performance named entity recognition models. The corpus thus constitutes an important foundation for the wider development of natural language processing tools to support the study of occupational exposures.

Global Annotating Software Market is segmented by Application (AI_ machine learning_ data labeling_ healthcare_ research), Type (Image annotation_ text annotation_ audio annotation_ AI-assisted annotation_ automated tagging), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Context

Objectives: Chronic obstructive pulmonary disease (COPD) phenotypes cover a range of lung abnormalities. To allow text mining methods to identify pertinent and potentially complex information about these phenotypes from textual data, we have developed a novel annotated corpus, which we use to train a neural network-based named entity recognizer to detect fine-grained COPD phenotypic information. Materials and methods: Since COPD phenotype descriptions often mention other concepts within them (proteins, treatments, etc.), our corpus annotations include both outermost phenotype descriptions and concepts nested within them. Our neural layered bidirectional long short-term memory conditional random field (BiLSTM-CRF) network firstly recognizes nested mentions, which are fed into subsequent BiLSTM-CRF layers, to help to recognize enclosing phenotype mentions. Results: Our corpus of 30 full papers (available at: http://www.nactem.ac.uk/COPD) is annotated by experts with 27 030 phenotype-related concept mentions, most of which are automatically linked to UMLS Metathesaurus concepts. When trained using the corpus, our BiLSTM-CRF network outperforms other popular approaches in recognizing detailed phenotypic information. Discussion: Information extracted by our method can facilitate efficient location and exploration of detailed information about phenotypes, for example, those specifically concerning reactions to treatments. Conclusion: The importance of our corpus for developing methods to extract fine-grained information about COPD phenotypes is demonstrated through its successful use to train a layered BiLSTM-CRF network to extract phenotypic information at various levels of granularity. The minimal human intervention needed for training should permit ready adaption to extracting phenotypic information about other diseases.

Content

1. Description

The COPD corpus is a semantically annotated corpus, focussed on phenotypic information, consisting of 30 full-text articles. The corpus has been manually annotated with named entities, using a fine-grained annotation scheme, which aims to capture detailed information about COPD phenotypes. In particular, the annotations may be "nested" within each other. This is to take into account the potentially complex and nested nature of phenotype descriptions.

The annotations in the COPD corpus correspond to both:

• complete phrases that constitute COPD phenotypes
• other types of concepts frequently mentioned within COPD phenotype phrases, and/or which are mentioned within the context of these phenotypes.

The scheme used to annotate the COPD corpus is aimed at supporting:

• automated location and categorisation of COPD phenotypes, e.g., those identified through tests, or those constituting risk-raising individual behaviours (such as smoking)
• detailed investigations about the nature of these phenotypes, such as finding those affecting specific anatomical locations, or those concerning different results of specific tests, etc.

The COPD corpus annotations consist of the following:

• Named entities that describe phenotypes, which appear within phenotype descriptions, or which are otherwise relevant in the characterization of phenotypes.
• Links between named entity annotations and the specific concepts that they describe in the UMLS Metathesaurus, through the assignment of UMLS concept identifiers (CUIs). These links are established using an automatic normalisaton method. Hence, while the majority of entity annotations are linked to CUIs, some are not.

For further information, please see: http://nactem.ac.uk/COPD/

2. Annotation Format

The corpus consists of:

• The configuration files need to display the annotations in brat: annotation.conf, visual.conf and tools.conf (see http://brat.nlplab.org/configuration.html for more details)
• Three directories (“train†, “dev†and “test†) which contain the annotated data, split into the training, development and test sets that were used in the experiments described in the associated article (see the end of this file for details). Each of the directories contains the following two types of files:
  • A set of text files (.txt), each corresponding to a paragraph in a full-text article.
  • A set of annotation files (.ann), containing the manually-added annotationsassociated with each paragraph file

The text file and associated annotation files have the same base name, which denotes the article's PMCID. The naming convention is as follows: [PMCID]_[paragraphNumber]

The paragraphs are numbered consecutively, starting from 0. So, for example, the file PMC2528206_0.txt contains the text of the first paragraph of the full text in the article with the PMCID PMC2528206, while the file PMC2740954_25.ann contains the annotations associated with the 26th paragraph of the ful...

This dataset contains 35 of 39 taxonomies that were the result of a systematic review. The systematic review was conducted with the goal of identifying taxonomies suitable for semantically annotating research data. A special focus was set on research data from the hybrid societies domain.

The following taxonomies were identified as part of the systematic review:

Filename

Taxonomy Title

acm_ccs

ACM Computing Classification System [1]

amec

A Taxonomy of Evaluation Towards Standards [2]

bibo

A BIBO Ontology Extension for Evaluation of Scientific Research Results [3]

cdt

Cross-Device Taxonomy [4]

cso

Computer Science Ontology [5]

ddbm

What Makes a Data-driven Business Model? A Consolidated Taxonomy [6]

ddi_am

DDI Aggregation Method [7]

ddi_moc

DDI Mode of Collection [8]

n/a

DemoVoc [9]

discretization

Building a New Taxonomy for Data Discretization Techniques [10]

dp

Demopaedia [11]

dsg

Data Science Glossary [12]

ease

A Taxonomy of Evaluation Approaches in Software Engineering [13]

eco

Evidence & Conclusion Ontology [14]

edam

EDAM: The Bioscientific Data Analysis Ontology [15]

n/a

European Language Social Science Thesaurus [16]

et

Evaluation Thesaurus [17]

glos_hci

The Glossary of Human Computer Interaction [18]

n/a

Humanities and Social Science Electronic Thesaurus [19]

hcio

A Core Ontology on the Human-Computer Interaction Phenomenon [20]

hft

Human-Factors Taxonomy [21]

hri

A Taxonomy to Structure and Analyze Human–Robot Interaction [22]

iim

A Taxonomy of Interaction for Instructional Multimedia [23]

interrogation

A Taxonomy of Interrogation Methods [24]

iot

Design Vocabulary for Human–IoT Systems Communication [25]

kinect

Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors [26]

maco

Thesaurus Mass Communication [27]

n/a

Thesaurus Cognitive Psychology of Human Memory [28]

mixed_initiative

Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey [29]

qos_qoe

A Taxonomy of Quality of Service and Quality of Experience of Multimodal Human-Machine Interaction [30]

ro

The Research Object Ontology [31]

senses_sensors

A Human-Centered Taxonomy of Interaction Modalities and Devices [32]

sipat

A Taxonomy of Spatial Interaction Patterns and Techniques [33]

social_errors

A Taxonomy of Social Errors in Human-Robot Interaction [34]

sosa

Semantic Sensor Network Ontology [35]

swo

The Software Ontology [36]

tadirah

Taxonomy of Digital Research Activities in the Humanities [37]

vrs

Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions [38]

xdi

Cross-Device Interaction [39]

We converted the taxonomies into SKOS (Simple Knowledge Organisation System) representation. The following 4 taxonomies were not converted as they were already available in SKOS and were for this reason excluded from this dataset:

1) DemoVoc, cf. http://thesaurus.web.ined.fr/navigateur/ available at https://thesaurus.web.ined.fr/exports/demovoc/demovoc.rdf

2) European Language Social Science Thesaurus, cf. https://thesauri.cessda.eu/elsst/en/ available at https://zenodo.org/record/5506929

3) Humanities and Social Science Electronic Thesaurus, cf. https://hasset.ukdataservice.ac.uk/hasset/en/ available at https://zenodo.org/record/7568355

4) Thesaurus Cognitive Psychology of Human Memory, cf. https://www.loterre.fr/presentation/ available at https://skosmos.loterre.fr/P66/en/

References

[1] “The 2012 ACM Computing Classification System,” ACM Digital Library, 2012. https://dl.acm.org/ccs (accessed May 08, 2023).

[2] AMEC, “A Taxonomy of Evaluation Towards Standards.” Aug. 31, 2016. Accessed: May 08, 2023. [Online]. Available: https://amecorg.com/amecframework/home/supporting-material/taxonomy/

[3] B. Dimić Surla, M. Segedinac, and D. Ivanović, “A BIBO ontology extension for evaluation of scientific research results,” in Proceedings of the Fifth Balkan Conference in Informatics, in BCI ’12. New York, NY, USA: Association for Computing Machinery, Sep. 2012, pp. 275–278. doi: 10.1145/2371316.2371376.

[4] F. Brudy et al., “Cross-Device Taxonomy: Survey, Opportunities and Challenges of Interactions Spanning Across Multiple Devices,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, in CHI ’19. New York, NY, USA: Association for Computing Machinery, Mai 2019, pp. 1–28. doi: 10.1145/3290605.3300792.

[5] A. A. Salatino, T. Thanapalasingam, A. Mannocci, F. Osborne, and E. Motta, “The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas,” in Lecture Notes in Computer Science 1137, D. Vrandečić, K. Bontcheva, M. C. Suárez-Figueroa, V. Presutti, I. Celino, M. Sabou, L.-A. Kaffee, and E. Simperl, Eds., Monterey, California, USA: Springer, Oct. 2018, pp. 187–205. Accessed: May 08, 2023. [Online]. Available: http://oro.open.ac.uk/55484/

[6] M. Dehnert, A. Gleiss, and F. Reiss, “What makes a data-driven business model? A consolidated taxonomy,” presented at the European Conference on Information Systems, 2021.

[7] DDI Alliance, “DDI Controlled Vocabulary for Aggregation Method,” 2014. https://ddialliance.org/Specification/DDI-CV/AggregationMethod_1.0.html (accessed May 08, 2023).

[8] DDI Alliance, “DDI Controlled Vocabulary for Mode Of Collection,” 2015. https://ddialliance.org/Specification/DDI-CV/ModeOfCollection_2.0.html (accessed May 08, 2023).

[9] INED - French Institute for Demographic Studies, “Thésaurus DemoVoc,” Feb. 26, 2020. https://thesaurus.web.ined.fr/navigateur/en/about (accessed May 08, 2023).

[10] A. A. Bakar, Z. A. Othman, and N. L. M. Shuib, “Building a new taxonomy for data discretization techniques,” in 2009 2nd Conference on Data Mining and Optimization, Oct. 2009, pp. 132–140. doi: 10.1109/DMO.2009.5341896.

[11] N. Brouard and C. Giudici, “Unified second edition of the Multilingual Demographic Dictionary (Demopaedia.org project),” presented at the 2017 International Population Conference, IUSSP, Oct. 2017. Accessed: May 08, 2023. [Online]. Available: https://iussp.confex.com/iussp/ipc2017/meetingapp.cgi/Paper/5713

[12] DuCharme, Bob, “Data Science Glossary.” https://www.datascienceglossary.org/ (accessed May 08, 2023).

[13] A. Chatzigeorgiou, T. Chaikalis, G. Paschalidou, N. Vesyropoulos, C. K. Georgiadis, and E. Stiakakis, “A Taxonomy of Evaluation Approaches in Software Engineering,” in Proceedings of the 7th Balkan Conference on Informatics Conference, in BCI ’15. New York, NY, USA: Association for Computing Machinery, Sep. 2015, pp. 1–8. doi: 10.1145/2801081.2801084.

[14] M. C. Chibucos, D. A. Siegele, J. C. Hu, and M. Giglio, “The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations,” in The Gene Ontology Handbook, C. Dessimoz and N. Škunca, Eds., in Methods in Molecular Biology. New York, NY: Springer, 2017, pp. 245–259. doi: 10.1007/978-1-4939-3743-1_18.

[15] M. Black et al., “EDAM: the bioscientific data analysis ontology,” F1000Research, vol. 11, Jan. 2021, doi: 10.7490/f1000research.1118900.1.

[16] Council of European Social Science Data Archives (CESSDA), “European Language Social Science Thesaurus ELSST,” 2021. https://thesauri.cessda.eu/en/ (accessed May 08, 2023).

[17] M. Scriven, Evaluation Thesaurus, 3rd Edition. Edgepress, 1981. Accessed: May 08, 2023. [Online]. Available: https://us.sagepub.com/en-us/nam/evaluation-thesaurus/book3562

[18] Papantoniou, Bill et al., The Glossary of Human Computer Interaction. Interaction Design Foundation. Accessed: May 08, 2023. [Online]. Available: https://www.interaction-design.org/literature/book/the-glossary-of-human-computer-interaction

[19] “UK Data Service Vocabularies: HASSET Thesaurus.” https://hasset.ukdataservice.ac.uk/hasset/en/ (accessed May 08, 2023).

[20] S. D. Costa, M. P. Barcellos, R. de A. Falbo, T. Conte, and K. M. de Oliveira, “A core ontology on the Human–Computer Interaction phenomenon,” Data Knowl. Eng., vol. 138, p. 101977, Mar. 2022, doi: 10.1016/j.datak.2021.101977.

[21] V. J. Gawron et al., “Human Factors Taxonomy,” Proc. Hum. Factors Soc. Annu. Meet., vol. 35, no. 18, pp. 1284–1287, Sep. 1991, doi: 10.1177/154193129103501807.

[22] L. Onnasch and E. Roesler, “A Taxonomy to Structure and Analyze Human–Robot Interaction,” Int. J. Soc. Robot., vol. 13, no. 4, pp. 833–849, Jul. 2021, doi: 10.1007/s12369-020-00666-5.

[23] R. A. Schwier, “A Taxonomy of Interaction for Instructional Multimedia.” Sep. 28, 1992. Accessed: May 09, 2023. [Online]. Available: https://eric.ed.gov/?id=ED352044

[24] C. Kelly, J. Miller, A. Redlich, and S. Kleinman, “A Taxonomy of Interrogation Methods,”

Automated Image Annotation for Microscopy Market Outlook

According to our latest research, the global Automated Image Annotation for Microscopy market size reached USD 542.7 million in 2024, reflecting robust adoption across life sciences and healthcare research. The market is projected to expand at a CAGR of 18.2% from 2025 to 2033, with the total market value anticipated to reach USD 2,464.8 million by 2033. This remarkable growth is being driven by the increasing demand for high-throughput, accurate, and scalable image analysis solutions in medical diagnostics, pharmaceutical research, and academic settings.

The primary growth factor propelling the Automated Image Annotation for Microscopy market is the exponential rise in the volume and complexity of microscopy image data generated in life sciences research and clinical diagnostics. As advanced imaging modalities such as confocal, super-resolution, and electron microscopy become commonplace, researchers face mounting challenges in manually annotating vast datasets. Automated image annotation platforms, leveraging artificial intelligence and deep learning, provide significant efficiency gains by streamlining annotation workflows, minimizing human error, and enabling reproducible data labeling at scale. This technological leap is particularly critical in fields like cell biology, pathology, and neuroscience, where precise annotation is essential for downstream analysis, disease modeling, and biomarker discovery.

Another key driver is the growing integration of automated annotation tools into end-to-end digital pathology and drug discovery pipelines. Pharmaceutical and biotechnology companies are increasingly investing in automation to accelerate preclinical research, reduce time-to-market for new therapeutics, and enhance the reliability of high-content screening assays. Automated image annotation not only expedites the identification and classification of cellular structures but also supports quantitative analysis required for regulatory submissions and clinical trials. Furthermore, the rising adoption of cloud-based platforms is democratizing access to advanced annotation tools, enabling collaboration across geographically dispersed research teams and facilitating the aggregation of large annotated datasets for AI model training.

The market is also benefitting from significant advancements in machine learning algorithms, including semantic segmentation, instance segmentation, and object detection, which have dramatically improved annotation accuracy and versatility. These innovations are reducing the barriers for adoption among academic and research institutions, which often operate under tight resource constraints. Additionally, the increasing prevalence of open-source annotation frameworks and interoperability standards is fostering an ecosystem where automated annotation solutions can be seamlessly integrated with existing microscopy workflows. As a result, the Automated Image Annotation for Microscopy market is poised for sustained growth, with emerging applications in personalized medicine, digital pathology, and precision oncology further expanding its addressable market.

From a regional perspective, North America currently leads the global Automated Image Annotation for Microscopy market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to the high concentration of pharmaceutical companies, advanced healthcare infrastructure, and significant investments in AI-driven healthcare solutions. However, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by increasing R&D expenditure, expanding biotechnology sectors, and rising adoption of digital pathology solutions in countries such as China, Japan, and India. This regional diversification is expected to fuel market expansion and foster innovation in automated image annotation technologies worldwide.

Component Analysis

The Automated Image Annotation for

An individual’s likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due to their time-consuming expert curation process, job exposure matrices currently cover only a subset of possible workplace exposures and may not be regularly updated. Scientific literature articles describing exposure studies provide important supporting evidence for developing and updating job exposure matrices, since they report on exposures in a variety of occupational scenarios. However, the constant growth of scientific literature is increasing the challenges of efficiently identifying relevant articles and important content within them. Natural language processing methods emulate the human process of reading and understanding texts, but in a fraction of the time. Such methods can increase the efficiency of both finding relevant documents and pinpointing specific information within them, which could streamline the process of developing and updating job exposure matrices. Named entity recognition is a fundamental natural language processing method for language understanding, which automatically identifies mentions of domain-specific concepts (named entities) in documents, e.g., exposures, occupations and job tasks. State-of-the-art machine learning models typically use evidence from an annotated corpus, i.e., a set of documents in which named entities are manually marked up (annotated) by experts, to learn how to detect named entities automatically in new documents. We have developed a novel annotated corpus of scientific articles to support machine learning based named entity recognition relevant to occupational substance exposures. Through incremental refinements to the annotation process, we demonstrate that expert annotators can attain high levels of agreement, and that the corpus can be used to train high-performance named entity recognition models. The corpus thus constitutes an important foundation for the wider development of natural language processing tools to support the study of occupational exposures.