This dataset is a univariate time-series dataset that records environmental noise levels along roads. It can be used for anomaly detection and forecasting tasks. The dataset includes numerical noise level data along with corresponding anomaly labels.

📉**Data Details**

• Number of Samples: 417,000
• Sampling Period: 200ms (Noise levels were recorded at 200-millisecond intervals.)
• Column Information:
  • noise level (float64): Noise level (dB)
  • label (int64): Anomaly label
  • 0: Road traffic noise (normal traffic noise, not an anomaly)
  • 1: Non-road traffic noise (classified as anomalous noise, e.g., bird chirping, construction noise, sirens, etc.)

In this dataset, normal road traffic noise is assigned the label 0, while other anomalous sounds (non-road traffic noise) are assigned the label 1. This dataset can be used for noise analysis and anomaly detection in accordance with environmental standards.

Note:The teacher labels of the noise level data may not fully reflect fine variations in sound, potentially containing some degree of error. For example, even within a segment labeled as an anomaly, there may be a mix of periods when the anomalous sound is actually present and when it is absent.

💻**Usage**

This dataset can be utilized in the following research and experimental applications:

Time-Series Forecasting

• Predicting future noise levels using past noise data
• Can be used as training data for time-series models such as LSTM, Transformer, ARIMA, etc.

Anomaly Detection

• Classifying normal road traffic noise and anomalous sounds
• Enables the construction of anomaly detection models using label 0 (normal noise) and label 1 (anomalous noise)

Environmental Noise Analysis

• Analyzing variation patterns of urban road noise
• Data analysis for noise regulations and environmental standards

📋**Data Format**

• Filename: noise_level_data.csv
• Format: CSV
• Features per sample:
  • Each row contains one noise level value and its corresponding label
  • Consecutive data points can be treated as a time series

💡**Usage Example**

import pandas as pd
# Load the data
df = pd.read_csv("noise_level_data.csv")
# Check the first few rows
print(df.head())

*Dominant models were applied for these SNPs, hence coefficients reflect the difference in methylation level for carriers of the minor allele compared to major allele homozgyotes (reference group).†Females were compared to males (reference group).‡Additive models were applied for these SNPs, hence coefficients reflect the difference in methylation level for each additional copy of the minor allele compared to major allele homozygotes (reference group).ΦRecessive models were applied for these SNPs, hence coefficients reflect the difference in methylation level for minor allele homozygotes compared to carriers of the major allele (reference group).łReduced numbers in multiple regression models are due to limited maternal genotype data and removal of outliers, consequently, these reduced numbers may in part account for the lack of significance seen with some predictor variables. Note also that mean methylation levels were utilized for multiple regression modelling despite not always demonstrating the strongest effect size with individual predictors. Standardised beta coefficients are obtained by first standardizing all variables to have a mean of 0 and a standard deviation of 1, they denote the increase in methylation for a standard deviation increase in the predictor variables. Multiple regression analysis was not performed for ZNT5 associations as mean methylation was not considered across this locus.

Objective(s): The 2024 Pediatric Sepsis Data Challenge provides an opportunity to address the lack of appropriate mortality prediction models for LMICs. For this challenge, we are asking participants to develop a working, open-source algorithm to predict in-hospital mortality and length of stay using only the provided synthetic dataset. The original data used to generate the real-world data (RWD) informed synthetic training set available to participants was obtained from a prospective, multisite, observational cohort study of children with suspected sepsis aged 6 months to 60 months at the time of admission to hospitals in Uganda. For this challenge, we have created a RWD-informed synthetically generated training data set to reduce the risk of re-identification in this highly vulnerable population. The synthetic training set was generated from a random subset of the original data (full dataset A) of 2686 records (70% of the total dataset - training dataset B). All challenge solutions will be evaluated against the remaining 1235 records (30% of the total dataset - test dataset C). Data Description: Report describing the comparison of univariate and bivariate distributions between the Synthetic Dataset and Test Dataset C. Additionally, a report showing the maximum mean discrepancy (MMD) and Kullback–Leibler (KL) divergence statistics. Synthetic training dataset and data dictionary for the synthetic dataset containing 138 variables. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

NOTE. aCategorical data are no.(%) of subject, continuous data are expressed as mean (SD) or median [quartiles].bAll variables included in the final multivariable model are shown.cOnly significant (p<0.05) and selected non-significant variables in univariate analysis are shown.OR = odds ratio; CI = confidence interval; TAR = time at risk; ICU = intensive care unit; APACHE II = Acute Physiology and Chronic Health Evaluation II; SD = standard deviation.

Note: HR-heart rate, BMI-body mass index, WC-waist circumference, SBP-systolic blood pressure, DBP-diastolic blood pressure, FPG- fasting plasma glucose, PBG- plasma blood glucose, IR-insulin resistance, TG- triglyceride, PH- Hypertension, DM- Diabetes.

Counts shown here are out of a total of 347,709,284 individual EHR documents and 204, 413 total unique patient records.

Notes:*Early breast cancers include stage 0, stage I, and stage II cancers;**Carcinomas in situ includes lobular and ductal carcinomas in situ, microinvasive carcinoma, and Paget's disease; ***Infiltrative non-specific cancers are invasive ductal and lobular carcinomas and mixed ductal carcinoma; ****Special carcinomas are tubular carcinoma, medullary carcinoma, and mucinous carcinoma.a:Northeast, Central, Northwest, and Southwest areas; b:Northern, Southern, and Eastern areas; c: junior high school and below:primary school, junior high school, and illiteracy;senior high school and above: senior high school, junior college, and above degree;d: business staff, manual workers, housewives, soldiers, and others.

Note. Ns are for individuals.*p <.05; 95% confidence intervals are in brackets.Descriptive Statistics, Intraclass Correlations, and Univariate Genetic (a²), Shared Environmental (c²), and Nonshared Environmental (e²) Components of Variance for Reading Comprehension.

Note—Data are numbers or mean ± standard deviation of each variable.Univariate analysis of clinical and CT features of encapsulated and invasive thymomas.

Note: SD: standard deviation; SVR: sustained virological response; RVR: rapid virological response; EVR, early virological response. AST: aspartate aminotransferase; ALT: alanine aminotransferase; APRI: aspartate aminotransferase-to-platelet ratio index.* defined as patients who had received 24 weeks of peginterferon/ribavirin

Note:–: p value not available.

Note: 1. there were three hospitals sampled in this survey. A=hospital A, C=hospital C.2. D=diploma degree; B=bachelor degree.3. EBN is abbreviation for Evidence-based Nursing.

Top reasons for the presence of the word ‘porph’ found in the clinical note.

Note: Parameter estimates whose 95% CI exclude 0 for systolic blood pressure and exclude 1 for the hypertension outcomes are in bold highlight. Multivariable analyses controlled for all variables listed in the above columns and also: response to unfair treatment; social desirability; body mass index; waist to hip ratio; cigarette smoking (current and smoked within 8 hrs of exam, current did not smoke within 8 hrs of exam; ex-smoker, never smoker); alcohol within 8 hrs of exam (yes; no); food within 8 hrs of exam (yes; no); taking anti-hypertensive medication (yes; no).

NOTE. Multivariate model controlled for race, treatment arm assignment, date of study entry, education level, and geographic region. CI, confidence interval; HR, hazards ratio; UAI, unprotected anal intercourse.

Abbreviations and Note: OS, overall survival; NA, not adopted; NS, not significant; AFP, α-fetoprotein; HBsAg, hepatitis B surface antigen; TNM, tumor-node-metastasis; 95%CI, 95% confidence interval; HR, Hazard ratio; Cox proportional hazards regression model.

Note:1HR: hazard ratio; 2CI: confidence interval. Statistical significance was defined as * P

NOTE: HAART, highly-active antiretroviral therapy; QALY, quality-adjusted life years. Each estimate based on 10 simulated randomized trials with 1000 women per trial.* Simulated through 0.75 log10 reduction in viral load in 65% of women, with 0.25 log10 response in the remainder.† Highest probability of vertical transmission incorporated upper-bound transmission probability for each maternal viral load, and lower-bound estimate for effectiveness of Caesarean section, while lowest probability incorporated lower-bound transmission probabilities and upper-bound estimate for effectiveness of Caesarean section.‡ A health care intervention is “dominated” if it costs more, but provides less health benefit, than a competing intervention. A dominated health intervention is never preferred [50]. A health care intervention is considered to be “cost-saving” when it costs less a competing intervention; “highly cost-effective” when it costs less than the GDP per capita; and “cost-effective” when it is between one and three times a country's GDP per capita, given that the intervention provides more health benefit than a competing intervention [49], [50].§ Discounted to present value at 3% per annum.¶ Incorporated upper- and lower-bound estimates for costs of highly-active antiretroviral therapy (HAART), peripartum zidovudine therapy, and delivery of directly observed HAART.∥ Incorporated upper- and lower-bound estimates for costs of vaginal delivery and Caesarean section.