TagX data annotation services are a set of tools and processes used to accurately label and classify large amounts of data for use in machine learning and artificial intelligence applications. The services are designed to be highly accurate, efficient, and customizable, allowing for a wide range of data types and use cases.

The process typically begins with a team of trained annotators reviewing and categorizing the data, using a variety of annotation tools and techniques, such as text classification, image annotation, and video annotation. The annotators may also use natural language processing and other advanced techniques to extract relevant information and context from the data.

Once the data has been annotated, it is then validated and checked for accuracy by a team of quality assurance specialists. Any errors or inconsistencies are corrected, and the data is then prepared for use in machine learning and AI models.

TagX annotation services can be applied to a wide range of data types, including text, images, videos, and audio. The services can be customized to meet the specific needs of each client, including the type of data, the level of annotation required, and the desired level of accuracy.

TagX data annotation services provide a powerful and efficient way to prepare large amounts of data for use in machine learning and AI applications, allowing organizations to extract valuable insights and improve their decision-making processes.

The size and share of the market is categorized based on Type (Image/video, Text, Audio) and Application (IT & Telecom, BFSI, Healthcare, Retail, Automotive, Agriculture, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Global Data Annotation Tools Market size at US$ 102.38 Billion in 2023, set to reach US$ 908.57 Billion by 2032 at a CAGR of 24.4% from 2024 to 2032.

The Data Annotation Tools Market Report is Segmented by Component (Text, Image, Other Types), by Type (Manual, Semi-Supervised, Automatic), by End-User (BFSI, IT and Telecom, Retail, Healthcare, Government, Other End-Users), by Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The coral reef benthic community data described here result from the automated annotation (classification) of benthic images collected during photoquadrat surveys conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD, formerly the Coral Reef Ecosystem Division) as part of NOAA's ongoing National Coral Reef Monitoring Program (NCRMP). SCUBA divers conducted benthic photoquadrat surveys in coral reef habitats according to protocols established by ESD and NCRMP during the ESD-led NCRMP mission to the islands and atolls of the Pacific Remote Island Areas (PRIA) and American Samoa from June 8 to August 11, 2018. Still photographs were collected with a high-resolution digital camera mounted on a pole to document the benthic community composition at predetermined points along transects at stratified random sites surveyed only once as part of Rapid Ecological Assessment (REA) surveys for corals and fish (Ayotte et al. 2015; Swanson et al. 2018) and permanent sites established by ESD and resurveyed every ~3 years for climate change monitoring. Overall, 30 photoquadrat images were collected at each survey site. The benthic habitat images were quantitatively analyzed using the web-based, machine-learning, image annotation tool, CoralNet (https://coralnet.ucsd.edu; Beijbom et al. 2015; Williams et al. 2019). Ten points were randomly overlaid on each image and the machine-learning algorithm "robot" identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to functional group (Tier 1: hard coral, soft coral, sessile invertebrate, macroalgae, crustose coralline algae, and turf algae) for coral, algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). These benthic data can ultimately be used to produce estimates of community composition, relative abundance (percentage of benthic cover), and frequency of occurrence.

Pacific Labeled Corals is an aggregate dataset containing 2318 coral reef survey images from four Pacific monitoring projects in Moorea (French Polynesia), the northern Line Islands, Nanwan Bay (Taiwan) and Heron Reef (Australia). Pacific Labeled Corals contain a total of 318828 expert annotations across 4 pacific reef locations, and can be used as a benchmark dataset for evaluating object recognition methods and texture descriptors as well as for domain transfer learning research. The images have all been annotated using a random point annotation tool by a coral reef expert. In addition, 200 images from each location have been cross-annotatoed by 6 experts, for a total of 7 sets of annotations for each image. These data will be published in Beijbom O., et al., 'Transforming benthic surveys through automated image annotation' (in submission). These data are a subset of the raw data from which knb-lter-mcr.4 is derived.

The presented dataset used for the experiments is described in the article "Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges" (doi:https://doi.org/10.48550/arXiv.2403.17725). The dataset consists of images of steel bridge structures and pixel-wise fatigue crack annotations. Some of the images contain bridge structures with cracks or corrosion, while others capture structures without any defect.

The images are provided by bridge infrastructure owners "Rijkswatersaat" and "ProRail" and by "Nebest" engineering company. The annotation of images was made using a semi-automatic annotation tool described in the article "Segmentation Tool for Images of Cracks" (doi:https://doi.org/10.1007/978-3-031-35399-4_8) and which implementation is available at https://github.com/akomp22/crack-segmentation-tool.

The dataset consists of high-resolution images and is stored in the folder "entire images". The images are divided into test and train sets. Images that capture cracks are stored in the folder "crack_train" and "crack_test". Images capturing structure without a crack are stored in folders "nocrack_train" and "nocrack_test". For each image, a .json file is stored in the same folder and under the same name as the corresponding image. The .json file stores the position (x,y) of pixels on the image, which lie in a crack region. An example of a code to generate a binary segmentation map from the .json files is given in the "read_json_annotation.py" file.

Additional patch datasets were generated from the entire images. The patch datasets are stored in the “patch dataset” folder. The multiple patch datasets differ by the patch size, number of patches, and fraction of patches that do not contain cracks among all patches of the particular dataset.

For more explanations, please refer to the article: https://doi.org/10.48550/arXiv.2403.17725

Background: The composition of tissue types present within a wound is a useful indicator of its healing progression and could be helpful in guiding its treatment. Additionally, this measure is clinically used in wound healing tools (e.g. BWAT) to assess risk and recommend treatment. However, the identification of wound tissue and the estimation of their relative composition is highly subjective and variable. This results in incorrect assessments being reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists. Objective: To measure inter-and intra-rater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians. To determine if an objective assessment of tissue types (i.e., size, amount) can be achieved using a deep convolutional neural network that predicts wound tissue types. The proposed objective measurement by machine learning model’s performance is reported in terms of mean intersection over union (mIOU) between model prediction and the ground truth labels. Finally, to compare the performance of the model wound tissue identification by a cohort of wound care clinicians. Methods: A dataset of 58 anonymized wound images of various types of chronic wounds from Swift Medical’s Wound Database was used to conduct the inter-rater and intra-rater agreement study. The dataset was split into 3 subsets, with 50% overlap between subsets to measure intra-rater agreement. Four different tissue types (epithelial, granulation, slough and eschar) within the wound bed were independently labelled by the 5 wound clinicians using a browser-based image annotation tool. Each subset was labelled at one-week intervals. Inter-rater and intra rater agreement was computed. Next, two separate deep convolutional neural networks architectures were developed for wound segmentation and tissue segmentation and are used in sequence in the proposed workflow. These models were trained using 465,187 wound image-label pairs and 17,000 image-label pairs respectively. This is by far the largest and most diverse reported dataset of labelled wound images used for training deep learning models for wound and wound tissue segmentation. This allows our models to be robust, unbiased towards skin tones and generalize well to unseen data. The deep learning model architectures were designed to be fast and nimble to allow them to run in near real-time on mobile devices. Results: We observed considerable variability when a cohort of wound clinicians was tasked to label the different tissue types within the wound using a browser-based image annotation tool. We report poor to moderate inter-rater agreement in identifying tissue types in chronic wound images. A very poor Krippendorff alpha value of 0.014 for inter-rater variability when identifying epithelization has been observed, while granulation is most consistently identified by the clinicians. The intra-rater ICC(3,1) (Intra-Class Correlation) however indicates raters are relatively consistent when labelling the same image multiple times over a period of time. Our deep learning models achieved a mean intersection over union (mIOU) of 0.8644 and 0.7192 for wound and tissue segmentation respectively. A cohort of wound clinicians, by consensus, rated 91% of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality. Conclusions: Our inter-rater agreement study validates that clinicians may exhibit considerable variability when identifying and visually estimating tissue proportion within the wound bed. The proposed deep learning model provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately. Our solution works on off-the-shelf mobile devices and was trained with the largest and most diverse chronic wound dataset ever reported and leading to a robust model when deployed. The proposed solution brings us a step closer to more accurate wound documentation and may lead to improved healing outcomes when deployed at scale.

This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below).

It contains ~250 000 images from 250 cellular lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image.

Besides, this folder also contains a .mat file containing 250 timeseries of classes corresponding to the lifespan of the 200 cells.

The dataset used for training (200 cellular lifespans) is in the folder /trainingset, while the dataset used for testing (50cellular lifespans) is in the folder /testset

It has been used to train the network doi.org/10.5281/zenodo.5553862 from the software DetecDiv: github.com/gcharvin/DetecDiv

biorxiv.org/content/10.1101/2021.10.05.463175v1

Data type: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat)

Microscopy data type: Brightfield images with 3 stacks, each stack representing a color of RGB.

Imaging: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS

Cell type: Budding yeast wild type cell (BY4742)

File format: .tif (16-bit RGB, 1 color per z-stack) + .mat

Image size: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm)

Author(s): Théo, ASPERT

Contact email: theo.aspert@gmail.com

Affiliation: IGBMC, Université de Strasbourg

Funding bodies: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.

This dataset comprises the following components:

1. SHdataset: It contains 12,051 microscopic images taken from 103 urine samples, along with their corresponding segmentation masks manually annotated for Schistosoma haematobium eggs. The dataset is randomly partitioned into 80-20 train-test splits.

2. diagnosis_test_dataset: This dataset includes 65 clinical urine samples. Each sample consists of 117 Field-of-View (FoV) images required to capture the entire filter membrane. Additionally, the dataset includes the diagnosis results provided by an expert microscopist.

Samples were obtained from school-age children who had observed the presence of blood in their urine. These clinical urine samples were collected in 20 mL sterile universal containers as part of a field study conducted in the Federal Capital Territory (FCT), Abuja, Nigeria, in collaboration with the University of Lagos, Nigeria. The study received ethical approval from the Federal Capital Territory Health Research Ethics Committee (FCT-HREC) Nigeria (Reference No. FHREC/2019/01/73/18-07-19).

The standard urine filtration procedure was used to process the clinical urine samples. Specifically, 10 mL of urine was passed through a 13 mm diameter filter membrane with a pore size of 0.2 μm. After filtration, the membrane was placed on a microscopy glass slide and covered with a coverslip to enhance the flatness of the membrane for image capture. The images were acquired using a digital microscope called the Schistoscope and were saved in PNG format with a resolution of 2028 X 1520 pixels and a size of approximately 2 MB.

The annotation and microscopy analysis were performed by a team of two experts from the ANDI Centre of Excellence for Malaria Diagnosis, College of Medicine, University of Lagos, and Centre de Recherches Medicales des Lambaréné, CERMEL, Lambarene. The experts used the coco annotation tool to annotate the 12,051 images, creating polygons around the Schistosoma haematobium eggs. The output of the annotation process was a JSON file containing specific details about the image storage location, size, filename, and coordinates of all annotated regions.

The segmentation mask images were generated from the JSON file using a Python program. The SHdataset was used to develop an automated diagnosis framework for urogenital schistosomiasis, while the diagnosis_test_dataset was used to compare the performance of the developed framework with the results from the expert microscopist.

For further details about the dataset, more information can be found in the following articles:

1. Oyibo, P., Jujjavarapu, S., Meulah, B., Agbana, T., Braakman, I., van Diepen, A., Bengtson, M., van Lieshout, L., Oyibo, W., Vdovine, G., and Diehl, J.C. (2022). "Schistoscope: an automated microscope with artificial intelligence for detection of Schistosoma haematobium eggs in resource-limited settings." Micromachines, 13(5), p.643.

2. Oyibo, P., Meulah, B., Bengtson, M., van Lieshout, L., Oyibo, W., Diehl, J.C., Vdovine, G., and Agbana, T. (2023). "Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings." Journal of Medical Imaging. [Accepted Manuscript]

This dataset is materials for a paper entitled "Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans"https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-020-0745-2(Preprint on bioRxiv: https://www.biorxiv.org/content/10.1101/698241v2)This dataset includes following materials:Additional file 0: Dataset S1 (zip): Neuron ID dataset (contains positions and expression patterns) and corresponding static 3D images.Additional file 1: Table S1 (xlsx): Summary of neuron ID dataset including expression patterns of the promoters. Additional file 2: Figure S1 (pdf): Correction of posture of the worms.Additional file 3: Figure S2 (pdf): Performance and robustness of the posture correction.Additional file 4: Table S2 (xlsx): Summary statistics for Additional file 3: Figure S2. Additional file 5: Figure S3 (pdf): Movements of the cells during time-lapse imaging.Additional file 6: Table S3 (xlsx): Summary statistics for Additional file 5: Figure S3. Additional file 7: Figure S4 (pdf): Overlay plot of cell positions for all worms Additional file 8: Figure S5 (pdf): Specific-cell-centered landscape. Additional file 9: Figure S6 (pdf): Less varying neuron pairs.Additional file 10: Figure S7 (pdf): Position of posterior pharyngeal bulb affects cell positionsAdditional file 11: Table S4 (xlsx): Summary statistics for Additional file 10: Figure S7.Additional file 12: Figure S8 (pdf): Stability and sparseness of expression pattern.Additional file 13: Table S5 (xlsx): Evaluation result of promoter combinations.Additional file 14: Figure S9 (pdf): An example fluorescent image of JN3039 strain and annotated cell names.Additional file 15 (zip): Dataset S2: Positions of nuclei and expression patterns of landmark fluorescence in the whole-brain imaging strains as the test data for automatic annotation and corresponding static 3D images.Additional file 16: Figure S10 (pdf): Health of 4D strains.Additional file 17: Table S6 (xlsx): Summary statistics for Additional file 16: Figure S10.Additional file 18: Figure S11 (pdf): Comparison of the synthetic atlas and the neuron ID datasetAdditional file 19: Figure S12 (pdf): Variation of relative position of cell pairsAdditional file 20: Figure S13 (pdf): Error rate of each bipartite matching and majority voting.Additional file 21: Figure S14 (pdf): Relationship between error rate of automatic annotation for JN3039 and detected count in the neuron ID datasetAdditional file 22: Figure S15 (pdf): Error rates of the automatic annotation method for the animals in a microfluidic chip Additional file 23: Dataset S3 (zip): A tutorial for semi-automatic annotation using our software.Additional file 24: Figure S16 (pdf): Correct ratio of automatic annotation and its improvement by manual annotationAdditional file 25: Figure S17 (pdf): Most valuable cells for improving accuracy of automatic annotation Additional file 26: Note S1 (docx): Optimization of parameters for atlas generationAdditional file 27: Dataset S4 (zip): Sequences of the promoters in Table 1.Additional file 28: Dataset S5 (zip): All codes for the GUI RoiEdit3D and analysis pipeline to make figures.

“Mobile mapping data” or “geospatial videos”, as a technology that combines GPS data with videos, were collected from the windshield of vehicles with Android Smartphones. Nearly 7,000 videos with an average length of 70 seconds were recorded in 2019. The smartphones collected sensor data (longitude and latitude, accuracy, speed and bearing) approximately every second during the video recording. Based on the geospatial videos, we manually identified and labeled about 10,000 parking violations in data with the help of an annotation tool. For this purpose, we defined six categorical variables (see PDF). Besides parking violations, we included street features like street category, type of bicycle infrastructure, and direction of parking spaces. An example for a street category is the collector street, which is an access street with primary residential use as well as individual shops and community facilities. Obviously, the labeling is a step that can (partly) be done automatically with image recognition in the future if the labeled data is used as a training dataset for a machine learning model.

Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.

Our website: https://www.reta-benchmark.org

A range of macroalgal indices have been proposed for subtidal environments that utilise morphological and biological traits of species or groups. Table 1 shows how CATAMI macroalgal classifications aligned with life-history characteristics often used in the derivation of indicators, and with the ecological status groups proposed by Orfandis et al. for monitoring the health of marine systems [88].Ecological state group I—late successional species, ecological state group II—opportunistic species.CATAMI macroalgal classification aligned with life-history characteristics often used in the derivation of indicators, and with the ecological status groups for monitoring the health of marine systems.

Highlighted (bold) papers made applications publicly available to other researchers. NOAA protocol stands for National Oceanic and Atmospheric Administration protocol [16].