## 5 Popular Datasets for ASD Detection or Classification
The autism screening on the adult dataset consists of 21 attributes and a total of 704 participants in the age group of 17-64 years. The ASD children traits dataset consists of 19 attributes and 1054 instances where the age range of the participants is from 12 to 36 months. The Autism Screening data for toddlers dataset comprises 1985 instances and 28 attributes. The age range of participants consists of 1 to 18 years. These datasets have a few limitations; first, the datasets have been developed by compiling user responses. It is analyzed that few outliers exist within these datasets caused by typing errors or user mistakes while entering data, causing a lack of data integrity. The datasets are limited to detailed information as they consist of a few questions but do not include any attributes regarding medical ASD tests carried out by experts like MRI or EEG signals, which lowers the impact of ASD classification. Moreover, all these datasets consist of class imbalance in terms of data samples as these datasets consist of fewer samples of ASD patients and more instances of non-ASD individuals, making it less effective for ASD detection [137; 138]  
**The eye tracking dataset. ** The eye tracking dataset is an open-source resource publicly available in the Figshare repository. It was created to assist researchers in further research on ASD by sharing detailed records of gaze behavior in children. The dataset comprises 59 participants and is limited to children aged 3 to 12 years, including ASD and TD participants, collected from educational institutions in Hauts-de-France. The gender distribution of participants was 64% male and 36% female. Eye movements, such as fixations and saccades, pupil diameter, and point-of-gaze coordinates, have been recorded for each participant utilizing a screen-based SMI RED-M eye tracker at a sampling rate of 60 Hz. At the same time, this dataset provides valuable opportunities for ASD researchers to explore more research on ASD detection using it. However, limitations exist, such as small sample size, limited diversity, gender imbalance, and potential measurement errors [139; 140]. **NDAR dataset. ** The National Database for Autism Research (NDAR) is a scalable and adaptable data informatics platform that facilitates research on ASD. Data from all layers of biological and behavioral structures, such as synthetics, genes, brain tissue, personality, and relationships with the environment and society, are supported. Different sorts of data are also supported by NDAR, including text, pictures, time series, and numerical values, as NDAR consists of multi-dimensional data types like behavioral data, genetic data, and neuroimaging data that may be in raw format. It adds more complexity to data analysis; therefore, extensive data preprocessing techniques are required to develop benchmark datasets requiring higher computational resources like GPU. Moreover, the datasets are limited to fewer samples and need more diversity, adding biases towards specific human races and age groups [141; 142]. **SFARI dataset. ** The SFARI Autism Inpatient Collection dataset was released in 2013 and consists of behavioral and genetics-based samples belonging to ASD individuals with 527 instances. Some limitations are found in the SFARI dataset. First, the number of instances of this dataset is significantly low and requires more data samples. The genetic dataset is based on the socioeconomic and demographic factors of participants from specific countries with different diagnosis criteria for individuals with ASD, limiting overall diversity and generalizability. Moreover, the behavioral dataset consists of missing values as data is collected via parents and caregivers so human error can reduce data integrity [143; 144; 145]. **EEG datasets.