Estimated impact of missing data imputation on total antibiotic use in dogs and cats, 2015–2017. by Sarin Suwanpakdee (119683)

Class distribution balanced through NearMiss undersampling. by Seongil Han (20490232)

Sociodemographic characteristics of stroke patients including lost to follow-up/missing data (n = 188). by Farayi Kaseke (19560197)

Factors associated with decreased total and cause-specific maternal mortality in Eastern and Western China during 2004–2012, using Bayesian Kernel Machine Regression hierarchical variable selection with missing data imputed by MICE. by Xiaojing Zeng (2577715)

Performance enhancements of classification models using the undersampling method (NearMiss). by Seongil Han (20490232)

Missing value percentage for variables. by Xin Liu (43569)

Length of data collection in district-year by world region. by Stephan Dietrich (6979985)

Hybrid imputation of missing values using KNN on MEWMA-based adaptive process control by Yijun Jiang (1852231)

“…<p>Missing data, a common issue in production processes due to factors like sample contamination and equipment malfunctions, can lead to a decrease in the recognition accuracy of control charts, especially in cases of shifting. …”

Decreased expression of tight junction proteins and disruption of intestinal barrier integrity in DSS-induced colitis mouse model. by Mariam Wed Eladham (21439745)

SPL course of subject 10, which was excluded from analysis since it misses a crescendo part during the fast MdV task. by Jonas Kirsch (20642910)

Data collection timepoints. by Hugo Hille (11294179)

Subjects:

Clinical baseline data. by Jie Zhao (49409)

“…</p><p>Method</p><p>Clinical data from MIMIC-IV and a hospital validation set were retrospectively analyzed. …”

Clinical baseline data after PSM. by Jie Zhao (49409)

“…</p><p>Method</p><p>Clinical data from MIMIC-IV and a hospital validation set were retrospectively analyzed. …”

Baseline data of clinical ICU patients. by Jie Zhao (49409)

“…</p><p>Method</p><p>Clinical data from MIMIC-IV and a hospital validation set were retrospectively analyzed. …”

Patient demographics by BMI missingness in the United Kingdom NJR data and Danish data. by Epaminondas Markos Valsamis (8194083)

Subjects:

Experiences of conducting six-monthly multi-month refills as scored by healthcare workers. by Rachel Chamanga (14492705)

Subjects:

Summary of barriers and facilitators identified in this study. by Rachel Chamanga (14492705)

Subjects:

Characteristics of participant in the healthcare worker surveys. by Rachel Chamanga (14492705)

Subjects:

Demographic characteristics of health care workers who provided key informant interviews. by Rachel Chamanga (14492705)

Subjects:

This is the data set that we used to reach the conclusions drawn in the manuscript with related metadata and methods. by Farayi Kaseke (19560197)