dataesr/scientific-paragraphs-categorization huggingface dataset:
Hepatica in the Americas, despite the well-documented hyperendemic areas in this region  [132]. Due to the limited availability of national prevalence data, we instead extracted the study prevalence from each record, using the mean study prevalence when multiple prevalences were recorded. However, as study size and methodology varied greatly between records, and there was a wide range of study prevalences reported even between geographically close areas with similar risk factors, this data cannot be used to reliably estimate national prevalence. Nevertheless, the data gaps recognised here demonstrate the need for continued improvement of mapping and surveillance to confirm focal points of disease that should be targeted for public health interventions. While disease burden can be estimated within assumptions and available data  [6] , the limited knowledge available on FBTs in many areas highlights the need for more research and the promotion of successful frameworks, guidelines, and control programmes for surveillance and reporting of FBTs.
These efforts have helped to improve surveillance and case detection  [22, 135] , map FBT endemic areas  [140] , understand and reduce transmission and infection risk  [138, 139, 147] , lower FBT prevalence in humans and intermediate hosts  [116, 147] , and helped to prioritise where resources should be most effectively used in control programmes  [135, 136, 139]. Such initiatives could help inform future studies in other endemic areas to identify within country variation of the endemic areas and delineate treatment strategies according to the level of geographical co-endemicity. Several themes were highlighted from the qualitative data extracted from the literature; however; as these data reflect what each record discussed and were not necessarily supported by strong epidemiological data, the trends identified in this review may not align with the true epidemiological situation in communities.
Conclusions FBTs are targeted for control as part of the WHO NTD road map 2021-2030, with mapping and surveillance and capacity, awareness, and advocacy building identified as critical actions required to reach the 2030 targets. FBTs, like other NTDs, impact the most impoverished populations and lack the surveillance systems and tools to adequately ascertain their true burden. The continued high burden of FBTs identified in this review, and the mismatch between the data that are reported to WHO and that which are reported in the literature, demonstrates the need for programmes to improve mapping and surveillance in order to target public health interventions. Integrated One Health approaches across environmental, animal, and human health sectors are needed to meet the 2030 goals.
Data Collection During the locomotor examination, as part of the clinical routine, horses were systematically equipped with the EQUISYM ® system (Arioneo, LIM France, Le Bouscat, France), described by Macaire et al. and Timmerman et al. [15, 28]. Data were recorded during approximately 20 s of trot per direction (clockwise/counterclockwise). The trot is of particular interest because a stride is composed of two diagonal beats whether on a straight line or circling to left or right.
Data Processing The data were processed following the methods described by Macaire et al. [15]. Briefly, based on the vertical displacement of the head (_H) and the withers (_W) occurring along a stride, the following asymmetry indices (AIs), expressed as a percentage of the maximal range of motion within a stride, were used to compare the left vs. right part of the stride (Figure  1 ): AI-Min, AI-Max and AI-up. Additionally, AI-Tmax was calculated as a time-related index, representing the left-right difference in the duration of the down-up cycle (the time between the two maxima) of the vertical displacement occurring before and after the stance phase. AI-Tmax was expressed as a percentage of the maximal time between two consecutive maxima. A positive AI value indicated a smaller movement amplitude or duration during the right stance than during the left stance, and a negative AI value indicated the opposite.
Automated methods for detecting and tracking animals through computer vision techniques can mitigate some of the challenges of behavior analysis. However, the development of these techniques heavily relies on the availability of high-quality datasets for training and evaluation. Despite the existence of livestock detection datasets, there is still a dearth of comprehensive, top-notch datasets especially for goat detection. Moreover, the accuracy of detection is closely tied to the quality of the dataset 5. Hence, the creation of a high-quality dataset for goat detection is crucial for improving the performance of computer vision algorithms in the field of precision agriculture. To address this gap, we introduce the first dataset for goat detection that contains 6160 annotated images of goats captured under varying environmental conditions.
The images were collected from field surveys. Each image was annotated with bounding boxes around the goat's body. All bounding boxes stick at pixel level around body (when possible: feet on leaves, fur that create blur, etc). The annotations were performed by an expert in the field of computer vision, ensuring high accuracy and consistency. The dataset contains images of goats in various poses and orientations, including standing, grazing, and lying down. The images were captured under different lighting conditions, including few bright sunlight and low light. The dataset is intended for use in developing machine learning algorithms for goat detection, with potential applications in precision agriculture, wildlife conservation, animal welfare, and animal husbandry. The dataset can also be used as a benchmark for evaluating existing detection methods.
Existing datasets for object detection Common objects in context. Microsoft COCO 6 is a large-scale image recognition, segmentation, and captioning dataset that contains more than 328,000 images with over 2. 5 million object instances labeled and segmented across 80 different categories. COCO is one of the most widely used benchmark dataset for object detection and instance segmentation, and it has been used for animal detection as well. Researchers have used COCO to train and evaluate animal detection models, including detecting animals in the wild, on farms, and in.
zoos. The animal categories in COCO include bird, cat, dog, horse, sheep, cow, and elephant, among others, but no goat class. The coco dataset include bounding boxes, segmentation and other information as depicted in Fig. 1. 7  is a benchmark dataset and competition for object detection and recognition in natural images. It includes a set of image classification, detection, and segmentation challenges, with the goal of advancing the state-of-the-art in computer vision research. The dataset includes annotated images of 21 objects classes, including animals, such as cat, cow, dog, horse, and sheep, but no goat class. Figure  2  shows some examples from the dataset.
Roboflow. Roboflow is a computer vision startup that provides tools and services for training, deploying, and improving computer vision models  8. One of their initiatives is the Universe project, which is a collection of datasets curated from "open-source" image and video datasets. The project aims to provide a single place where developers and researchers can access a diverse set of computer vision datasets. Within Roboflow, only five datasets have a goat class. Figure  3  shows some pictures taken from different Roboflow goat datasets that exhibit erroneous annotation. Limitation of existing dataset. The COCO dataset has been criticized  5  for its inaccurate annotations and low quality. One of the reasons for the inaccuracy is that the annotations are done by crowd-workers who have varying levels of expertise in object detection.
Additionally, some objects may be missed or incorrectly labeled due to the complexity of the scene or the object's appearance. This is illustrated in Fig. 1 , where several number of false negatives are present. Similar to the COCO dataset, PASCAL VOC also suffers from limitations in terms of accuracy  9. While the bounding box alignment may seem better than COCO, there still exist a wide number of false negatives, as shown in Fig. 2. Finally as illustrated in Fig. 3 , Roboflow datasets are largely affected by quality issues, such as inaccuracy of bounding box fit, resulting in objects being cropped, missed entirely or fused on the whole picture. Finally, some datasets may have licensing restrictions that could limit their use in certain case. To summarize, it is critical to recognize potential quality issues in available datasets, such as inaccurate bounding box fitting and licensing issues.
Most of the datasets available online for livestock monitoring are not   reliable sources of data, particularly for goat detection. Despite the lack of datasets dedicated to goat detection, pre-training object detection models on these datasets can still be beneficial to provide a good initialization. importance of dataset quality. The quality of a dataset plays a crucial role in predicting the accuracy of a machine learning algorithm trained on it  10, 11. In the case of goat detection, a high quality dataset such as the one in this work, with accurate and consistent bounding box annotations, diverse and representative images, and minimal noise and errors, can greatly improve the accuracy and robustness of the trained object detector  5. This can be considered as data-centric approach. On the other hand, a low quality dataset with incorrect or inconsistent annotations, limited or biased images, and noisy or erroneous data can negatively affect the accuracy and generalization ability of the trained detector  12.
In addition, animals that are close to each other may be misdetected or merged due to the fact that the detector has been trained on a data set with these examples. Therefore, it is important to ensure that the training dataset is of high quality and representative of the real-world scenarios where individuals are present. This can help improve the accuracy and robustness of the detector and enable more accurate analysis and monitoring of goat behavior in different settings. It also means that a high quality dataset allows greater generalizability than a dataset with more data but lower quality, so quality should always be prioritized over quantity when possible  11.
The dataset contains a mix of white sheep, goats, and mainly Creole sheep, that are mostly practically indistinguishable in appearance to European goats. phantom3. In a previous study  18  , Creole goats grazing on two distinct pastures (G1 + G2) were recorded using a Phantom3 UAV drone equipped with a 12-megapixel camera sensor. The camera had a 94-degree field of view lens, which enabled wide-angle shots, and images were captured at a maximum resolution of 4000 × 3000 px. The study was conducted over four successive days in April 2017 at the INRA-PTEA farm (16° 2 N; 61° 2 W), and a total of 47 images were re-annotated to include kids. To account for the small size of the animals and the large image size, each image was subdivided into smaller ones, resulting in 696 images with dimensions of 1000 × 750 px.
Additionally, two videos were recorded on April 13, 2017, using the same drone and providing different perspectives of the pasture. Table  2  show the number of acquisition per pasture (G1 + G2) and date. Time-lapse camera. We used construction time-lapse cameras (TLC2000 pro, year 2018, brand Brinno) on various previous studies  [19] [20] [21] [22]. These cameras record at 1. 3 Mpx with a resolution of 1280 × 720 px using jpeg compression. Those studies are presented bellow: The initial research conducted as part of a master's thesis  19  set up different experimental plots to study the detection and tracking of goats and sheep. The first subset includes seven Creole sheep raised indoor with identical reddish coats. The second data set contains one Creole sheep with a brown coat. The third contains nine goats near the camera, six of them have dark coats, while the others have red coats.
All of these previous research have resulted in a large amount of images being collected. Some of the data was extracted and carefully re-annotated. Table  3  summarizes the number of images annotated within this subset: Tracking series. A CCTV camera (ENEO -IPD-75M2713M5A) with a resolution of 2592 × 1944 px was used to capture 17 videos of goats grazing on pasture in 2022, over 5 different days, to develop a tracking algorithm  23. Each movie has been subsampled to obtain 98 images, with 50 showing non-overlapping goats and 50 featuring at least 2 goats overlapping. These videos were recorded at two different places of the INRAE-PTEA facility in Guadeloupe, French West Indies. In early 2023, 6 new videos were generated over 3 separate days at the experimental plot in Duclos, featuring male goats with distinct coat colors: dark, white, russet, and dark-russet.
Data Records The authors of this study have publicly released CherryChèvre dataset, which is available in VGG format at https://doi. org/10. 57745/QEZBNA  24  (subsets are available separately in dedicated TGZ archives for each of them) and in YOLO format with a train/validation/test split at https://doi. org/10. 57745/4C03OG  25   Table  3. Number of annotated images by date for the TLC2000 device. 2. 0 CC-BY and Open Data Commons Attribution. This compatibility allows for extensive reuse and modification of the data, provided that the original authors are properly attributed.
Statistics of the dataset. CherryChèvre is composed of five distinct subsets and contains a total of 6160 images. These images were carefully annotated with 35381 unique bounding boxes. The number of bounding boxes varies across the subsets, as detailed in Table  6. The Timelapse and Tracking subsets contain 77 of the labeled images, showing that CherryGoat is well balanced for these two subsets but the others are less represented. Figure  4  displays the normalized centroid distribution for the entire dataset, obtained by dividing the bounding box position (centroid) by the size of the image. The first blue bin on the x-axis, ranging from 0. 0 to 0. 052, shows that 1770 bounding boxes appear near the left border of the image (between 0% and 5. 2% of the image size). Similarly, the last blue bin shows 453 bounding boxes appear near the right side of the images.
Thus Figure  4  highlights that the presence of sky and ground areas leads to uneven sampling of goat positions along the vertical dimension. To address this issue, data augmentation techniques  26  can be used to add random displacement (20 40%) along this axis. This approach will facilitate sampling of all screen positions during the training phase. Figure  5  displays the distribution of the normalized size, which includes both width and height, for the entire dataset, obtained by dividing the bounding box size by the size of the image. The results indicate that both dimensions are evenly distributed. Moreover, it reveals that larger objects are less represented in the dataset. Therefore, models trained on this dataset may not detect a goat that appears in an entire image. However, medium and small-sized goats are well-represented, making the dataset suitable for monitoring goats in a wide area.
For those who wish to detect goats in full screen, the present dataset can be reused by dividing the images into several pieces, each piece containing one or more individuals but excluding most of the backgrounds. This can also be implemented as a data augmentation technique  26. Figure  6 , is presented to reinforce the previously mentioned observations. It showcases the total sum of normalized bounding boxes across the entire dataset, revealing that most of the annotations are concentrated in certain areas while there is a comparatively lower density of annotations towards the edges. This could be attributed to the placement of the fence and the fixed position and orientation of the camera in most cases. To address this issue, a simple vertical and horizontal flip could be implemented as a data augmentation technique, along with a random translation, to efficiently sample all screen positions.
Examples of annotated images. This section provides visual examples of the annotations made on images within the dataset. It showcases how bounding boxes were drawn around the goats and provides insight into the   performance evaluation. In order to evaluate the performance of the proposed dataset for goat detection a YOLOv8 object detection model  16  (yolov8x6 and yolov8l, respectively with 97. 321. 636 and 43. 630. 611 parameters) has been trained. To ensure a representative distribution of data, the entire dataset was randomly divided into training (80%, or 4907 images), validation (10%, or 616 images), and test (10%, or 637 images) sets. This split was carefully design to include each source of input (by date of acquisition) in the same proportion for training, validation and test. The models were trained using default hyper-parameters on an RTX 3060 12GB GPU.
To accommodate for memory limitations, the batch size was adjusted to 2 and 12 for yolov8x6 and yolov8l, respectively. Also the number of epochs differ due to excessive learning time, 200 and 330 was respectively set  The results of this experiment provide insight into the effectiveness of the proposed dataset and its potential when coupled with YOLOv8. The evaluation was done with an open-source tool  27  that extract most relevant metrics. Such as "COCO Average Precision" and "COCO Average Recall" which measures the quality of object detection by comparing the predicted bounding boxes to the ground truth bounding boxes of the objects in the images. The Average Precision (AP) metric evaluates a model's ability to identify relevant objects by measuring the percentage of True Positive detection.
This data set is therefore important, especially for those who wish to work with low resolution cameras, for wildlife conservation or theft prevention. 96 × 96, and medium objects are those in between. Concerning the AR metric, the recall is computed for 1, 10 and 100 maximum detections, for all IoU threshold and then averaged respectively for AR1, AR10 and AR100. These metrics were computed for both models, YOLOv8x6 (as shown in Table  7 ) and YOLOv8l (as shown in Table  8 ). YOLOv8x6 with an input size of 1280 × 1280 outperforms YOLOv8l with an input size of 640 × 640, exhibiting better results for all metrics, especially for small objects. The training and validation performances were included to demonstrate that there was no overfitting. The models were evaluated on a test set that was not previously encountered during training or validation to assess their generalization capability.
The outcomes indicate that both YOLOv8x6 and YOLOv8l attained high APs and ARs for all IoU thresholds and object sizes, except for small objects that are typically distant. Indicating their effectiveness in detecting goats in outdoor environments, especially for monitoring a grazing area. The detections generated by the models are not always perfect and may result in missing parts of the animal, such as the tail, legs or head, or confusing parts between overlapping individuals. These observations are probably not related to the quality of the dataset, but more certainly to the limitation of the models. This may highlight the need for further development and improvement of object detection models.
Usage Notes This data paper present the first fine-grained annotated goat dataset for outdoor goat detection in natural environments. The dataset contains a total of 6160 images, captured by a Trecker X2, a CCTV camera, a time-lapse camera and a drone. The images and videos feature different breeds, colours, and genders of goats grazing on different pastures of different sizes, mostly in Guadeloupe, French West Indies. The dataset was carefully annotated by expert, providing ground truth labels for goat detection. This dataset can be used to train and evaluate computer vision algorithms for goat detection and tracking, which can have significant applications in precision livestock farming. The dataset and annotations are made publicly available, with the authors aiming to encourage collaborations and accelerate progress in the field.
The authors hope that this dataset will stimulate further studies on animal behavior analysis and serve as a cornerstone for a new generation of computer vision applications in agriculture. implications and potential applications of the dataset. CherryChèvre offers vast potential for applications in precision agriculture, such as monitoring animal welfare, behavior, and health  28. It can be used to optimize breeding programs, enhance productivity, diagnose and treat medical conditions in livestock and pets  29. The dataset can also be used to monitor animal welfare in transportation, prevent livestock theft, monitor habitat usage and detect poaching in wildlife conservation  30. Furthermore, researchers can use it to study animal behavior, cognition, social interaction  31  , and self-medication  32.
Limitations and possible improvements of the dataset. The proposed dataset in this paper has few limitations that must be taken into consideration. One of the main limitations is that the dataset was acquired at a specific location, which means that it may not be representative of all natural environments. For example, there are no goats near sea, lake, forest or in a snowy background. Another limitation is that the data set contains few distinct goats, each possessing a unique coat color. While this may be sufficient for most studies, it limits the generalizability of the dataset. Furthermore, the dataset mostly contains outdoor goat images, which means that the learned model may lose accuracy in detecting goats indoors. Within the dataset, some birds are visible (mainly Bubulcus ibis, Corvus corax and Quiscalus lugubris), otherwise no other species are present, thus a detector trained on this dataset may detect other animals, like dogs, cows, horses, cats, etc.
• Grouping: In situations where animals are in groups or herds, it can be challenging to separate individual animals and accurately annotate them with bounding boxes. Especially on sheep, as they often huddle closely together and have similar body shapes and sizes, making it difficult to distinguish one from another. In such cases, alternative annotation methods such as segmentation or keypoint annotations may be more appropriate. Such annotation could add awesome value to the dataset by providing more detailed information about the animal's behavior and movements. For example, segmentation can help track the movement of individual body parts, which can be valuable for studying animal behavior, locomotion, and biomechanics. Keypoint detection can be used to analyze an animal's posture, facial expressions, and social interactions, providing insights into their communication and social behavior.
Incorporating a depth order for each annotated bounding box is an additional enhancement that can greatly improve the accuracy of tracking in animal behavior analysis. This enhancement would enable the prediction of overlapping bounding boxes and their relative order, providing valuable information about the spatial relationship between objects in the scene, including which object is in front of the camera when multiple objects overlap. In terms of improvements, future studies could focus on expanding the dataset by including more diverse environments and goat individuals. Additionally, the dataset could be extended to include additional attributes or labels beyond simple detection, such as age, gender, weight or behavior, to support more complex studies. The author awaiting for collaboration on these subjects.