Update README.md

README.md

@@ -17,13 +17,13 @@ This dataset is a detailed assembly of 11,000 annotated images for advanced anal
 ### Dataset Description
 
 <!-- Provide a longer summary of what this dataset is. -->
-Machine learning (ML) algorithms have shown potential in automatically detecting and measuring stomata. However, ML algorithms require substantial data to efficiently train and optimize models, but their potential is restricted by the limited availability and quality of stomatal images. To overcome this obstacle, this dataset was established. It consists of around 11,000 unique images of hardwood leaf stomata collected from projects conducted between 2015 and 2022. Within the dataset, there are more than 7,000 images of 17 common hardwood species, such as oak, maple, ash, elm, and hickory. Additionally, the dataset contains over 3,000 images of 55 genotypes from seven Populus taxa. For each image, Inner_guard_cell_walls were labeled as “0” and whole_stomata (stomatal aperture and guard cells) were labeled as “1” and had a corresponding YOLO label file that can be converted into other annotation formats. 
+This dataset consists of around 11,000 unique images of hardwood leaf stomata collected from projects conducted between 2015 and 2022. Within the dataset, there are more than 7,000 images of 17 common hardwood species, such as oak, maple, ash, elm, and hickory. Additionally, the dataset contains over 3,000 images of 55 genotypes from seven Populus taxa. For each image, it is represented with image_id, species, scientific_name, image_path, image_magnification, width, height, and resolution  and annotations. Within annotations, there are category id and information about the bounded box of the image.
 
 - **Curated by:** [Jiaxin Wang, Heidi J. Renninger and Qin Ma]
 - **Language(s) (NLP):** [English]
 - **License:** [http://creativecommons.org/licenses/by/4.0/]
 
-### Dataset Sources [optional]
+### Dataset Sources 
 
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@@ -48,12 +48,12 @@ Machine learning (ML) algorithms have shown potential in automatically detecting
 
 <!-- Motivation for the creation of this dataset. -->
 
-[More Information Needed]
+Machine learning (ML) algorithms have shown potential in automatically detecting and measuring stomata. However, ML algorithms require substantial data to efficiently train and optimize models, but their potential is restricted by the limited availability and quality of stomatal images. To overcome this obstacle, this dataset was established.
 
 ### Source Data
 
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+The study utilized stomatal images from two datasets: Hardwood and Populus spp., acquired from 2015 to 2022. The Hardwood dataset contained 16 species, including American elm (Ulmus americana Planch), cherrybark oak (Quercus pagoda Raf.), Nuttall oak (Quercus texana Buckley), shagbark hickory (Carya ovata (Mill.) K. Koch), Shumard oak (Quercus shumardii Buckley), swamp chestnut oak (Quercus michauxii Nutt.), water oak (Quercus nigra L.), willow oak (Quercus phellos L.), ash (Fraxinus L.), black gum (Nyssa sylvatica Marshall), deerberry (Vaccinium stamineum Linneaus), leatherwood (Dirca palustris L.), red maple (Acer rubrum L.), post oak (Quercus stellata Wangenh.), willow (Salix spp.), and winged elm (Ulmus alata Michx.), with the age of seedlings ranging from 1–3 years for Nuttall oak, water oak, and Shumard oak, and 30–50 years for the rest. Using a compound light microscope (Olympus, Tokyo, Japan) equipped with a digital microscope camera (MU300, AmScope, USA) with a 5 mm lens and a fixed microscope adapter (FMA050, AmScope), over 10,000 stomatal images were captured. The Populus dataset consisted of over 3,000 images from 55 genotypes of seven taxa of hybrid poplar and eastern cottonwood (Populus deltoides), which were 4 to 5 years old.
 #### Data Collection and Processing
 
 <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->