China Number of Graduate: Postgraduate: Master Degree data was reported at 927.629 Person th in 2023. This records an increase from the previous number of 779.845 Person th for 2022. China Number of Graduate: Postgraduate: Master Degree data is updated yearly, averaging 334.613 Person th from Dec 1997 (Median) to 2023, with 27 observations. The data reached an all-time high of 927.629 Person th in 2023 and a record low of 38.051 Person th in 1998. China Number of Graduate: Postgraduate: Master Degree data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GD: No of Graduate: Higher Education: By Region.

Participants

This dataset includes 25 native Mandarin Chinese speakers (14 females, mean age = 24.04 ± 2.28 years) who participated in both EEG and fMRI experiments. The participants were all right-handed, with no reported history of neurological disorders. They were enrolled in undergraduate or graduate programs in Shanghai. All participants gave informed consent, and the experiments were approved by the Ethics Committee of the Ninth People's Hospital, affiliated with Shanghai Jiao Tong University School of Medicine (SH9H-2019-T33-2 and SH9H-2022-T379-2).

In the case of French participants, due to legal constraints, additional session considerations were taken into account, such as shorter session durations.

Experiment Procedure

MRI Scanning Sessions

Participants underwent both EEG and fMRI experiments while listening to the Chinese version of Le Petit Prince. During the MRI session, participants were instructed to maintain fixation on a crosshair on the screen and minimize eye movements and head motions. The task involved attending to different talkers in the multitalker condition (single male, single female, mixed male, and mixed female talkers).

Session Breakdown

• The entire session lasted approximately 70 minutes for fMRI participants, including a series of 4 conditions (single-talker, mixed-attended, and mixed-unattended conditions).
• Quiz questions were administered after each run to assess participants' comprehension of the narrative.

In the French cohort, due to legal time constraints, the experiment durations were adjusted.

Stimuli

The stimuli were selected excerpts from the Chinese version of Le Petit Prince (available at xiaowangzi.org). These audio clips were previously used in both EEG (Li et al., 2024) and fMRI (Li et al., 2022) studies.

The English and Chinese versions were enhanced with visual stimuli (e.g., images of scenes from the book) to align with the storyline. However, visual stimuli were not presented in the French version to comply with legal restrictions.

Acquisition

MRI Hardware & Scanning Parameters

• EEG: Data were collected using a 64-channel actiCAP system, sampled at 500 Hz, and filtered between 0.016 and 80 Hz.

• fMRI: Scanning was performed on a 7.0 T Terra Siemens MRI scanner at the Zhangjiang International Brain Imaging Centre. The scanning parameters differed slightly between the English/Chinese and French studies due to equipment availability.

  • Functional MRI: 85 interleaved axial slices (1.6×1.6×1.6 mm voxel size, TR = 1000 ms, TE = 22.2 ms)
  • Anatomical MRI: MP-RAGE sequence, T1-weighted images (voxel size = 0.7×0.7×0.7 mm).

Preprocessing

MRI Data Processing

1. DICOM to NIfTI Conversion: All raw MRI data were converted to NIfTI format using dcm2niix (version 1.0.20220505) and processed using the fMRIPrep pipeline (version 20.2.0).
2. Anatomical Preprocessing:
   • Skull stripping
   • Segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF)
   • Registration to the Montreal Neurological Institute (MNI) space using MNI152NLin2009cAsym:res-2 template.
3. Functional Preprocessing:
   • Motion correction
   • Slice-timing correction
   • Multi-echo ICA for denoising
   • Voxel resampling to native and MNI spaces.

Note: Visual stimuli processing for the English and Chinese conditions was handled separately to avoid potential biases in the analysis.

BackgroundTaenia solium cysticercosis affects millions of impoverished people worldwide and can cause neurocysticercosis, an infection of the central nervous system which is potentially fatal. Children may represent an especially vulnerable population to neurocysticercosis, due to the risk of cognitive impairment during formative school years. While previous epidemiologic studies have suggested high prevalence in rural China, the prevalence in children as well as risk factors and impact of disease in low-resource areas remain poorly characterized.Methodology/Principal findingsUtilizing school based sampling, we conducted a cross-sectional study, administering a questionnaire and collecting blood for T. solium cysticercosis antibodies in 2867 fifth and sixth grade students across 27 schools in west Sichuan. We used mixed-effects logistic regression models controlling for school-level clustering to study associations between risk factors and to characterize factors influencing the administration of deworming medication. Overall prevalence of cysticercosis antibodies was 6%, but prevalence was significantly higher in three schools which all had prevalences of 15% or higher. Students from households owning pigs (adjusted odds ratio [OR] 1.81, 95% CI 1.08–3.03), from households reporting feeding their pigs human feces (adjusted OR 1.49, 95% CI 1.03–2.16), and self-reporting worms in their feces (adjusted OR 1.85, 95% CI 1.18–2.91) were more likely to have cysticercosis IgG antibodies. Students attending high prevalence schools were more likely to come from households allowing pigs to freely forage for food (OR 2.26, 95% CI 1.72–2.98) and lacking a toilet (OR 1.84, 95% CI 1.38–2.46). Children who were boarding at school were less likely to have received treatment for gastrointestinal worms (adjusted OR 0.58, 95% CI 0.42–0.80).Conclusions/SignificanceOur study indicates high prevalences of cysticercosis antibodies in young school aged children in rural China. While further studies to assess potential for school-based transmission are needed, school-based disease control may be an important intervention to ensure the health of vulnerable pediatric populations in T. solium endemic areas.

Student Performance Dataset 2024 Overview This dataset comprises detailed information about high school students in China, collected from various universities and schools. It is designed to analyze the factors influencing student performance, well-being, and engagement. The data includes a wide range of features such as demographic details, academic performance, health status, parental support, and more. The participating institutions include prominent universities such as Tsinghua University, Peking University, Fudan University, Shanghai Jiao Tong University, and Zhejiang University.

Dataset Description Features Student ID: Unique identifier for each student. Gender: Gender of the student (Male/Female). Age: Age of the student. Grade Level: The grade level of the student (e.g., 9, 10, 11, 12). Attendance Rate: The percentage of days the student attended school. Study Hours: Average number of hours the student spends studying daily. Parental Education Level: The highest level of education attained by the student's parents. Parental Involvement: The level of parental involvement in the student's education (High, Medium, Low). Extracurricular Activities: Whether the student participates in extracurricular activities (Yes/No). Socioeconomic Status: Socioeconomic status of the student's family (High, Medium, Low). Previous Academic Performance: Previous academic performance level (High, Medium, Low). Class Participation: The level of participation in class (High, Medium, Low). Health Status: General health status of the student (Good, Average, Poor). Access to Learning Resources: Whether the student has access to necessary learning resources (Yes/No). Internet Access: Whether the student has access to the internet (Yes/No). Learning Style: Preferred learning style of the student (Visual, Auditory, Kinesthetic). Teacher-Student Relationship: Quality of the relationship between the student and teachers (Positive, Neutral, Negative). Peer Influence: Influence of peers on the student's behavior and performance (Positive, Neutral, Negative). Motivation Level: Student's level of motivation (High, Medium, Low). Hours of Sleep: Average number of hours the student sleeps per night. Diet Quality: Quality of the student's diet (Good, Average, Poor). Transportation Mode: Mode of transportation used by the student to commute to school (Bus, Car, Walk, Bike). School Type: Type of school attended by the student (Public, Private). School Location: Location of the school (Urban, Rural). Homework Completion Rate: The rate at which the student completes homework assignments. Reading Proficiency: Proficiency level in reading. Math Proficiency: Proficiency level in mathematics. Science Proficiency: Proficiency level in science. Language Proficiency: Proficiency level in language. Physical Activity Level: The level of physical activity (High, Medium, Low). Screen Time: Average daily screen time in hours. Bullying Incidents: Number of bullying incidents the student has experienced. Special Education Services: Whether the student receives special education services (Yes/No). Counseling Services: Whether the student receives counseling services (Yes/No). Learning Disabilities: Whether the student has any learning disabilities (Yes/No). Behavioral Issues: Whether the student has any behavioral issues (Yes/No). Attendance of Tutoring Sessions: Whether the student attends tutoring sessions (Yes/No). School Climate: Overall perception of the school's environment (Positive, Neutral, Negative). Parental Employment Status: Employment status of the student's parents (Employed, Unemployed). Household Size: Number of people living in the student's household. Performance Score: Overall performance score of the student (Low, Medium, High).