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)

Coverage of plant species in each plot. by Xuemei Xiang (20756894)

“…Plant factors (Grasses importance value, leaf nitrogen weighted mean, specific leaf area-weighted mean, leaf area-weighted mean, and leaf weight weighted mean) and soil environmental factors (soil total nitrogen and soil carbon-nitrogen ratio) directly or indirectly affect plant community diversity under warming and nitrogen deposition.…”

Specifications and effects of heating devices. by Xuemei Xiang (20756894)

“…Plant factors (Grasses importance value, leaf nitrogen weighted mean, specific leaf area-weighted mean, leaf area-weighted mean, and leaf weight weighted mean) and soil environmental factors (soil total nitrogen and soil carbon-nitrogen ratio) directly or indirectly affect plant community diversity under warming and nitrogen deposition.…”

Study variables. by Amit Timilsina (15203538)

“…The mean tree cover percentage also decreased from 21% in 2011 to 19% in 2016. …”

Descriptive statistics. by Amit Timilsina (15203538)

“…The mean tree cover percentage also decreased from 21% in 2011 to 19% in 2016. …”

Data. by Aroon La-up (14095691)

“…The Linear Mixed-Effects Model analysis revealed significant associations between BMD and several factors: increasing age, female sex, diabetes status and BMI. …”

Characteristics of JIA patients. by Yasmine Makhlouf (17409866)

“…Twelve studies published between 2003 and 2018 were analyzed, encompassing 1513 patients with a mean age of 11.4 years. Tumor necrosis factor alpha inhibitors were the predominant biologic agents used (75.8%), with a mean follow-up duration of 2 years post-biologic therapy initiation. …”

List of excluded articles. by Yasmine Makhlouf (17409866)

“…Twelve studies published between 2003 and 2018 were analyzed, encompassing 1513 patients with a mean age of 11.4 years. Tumor necrosis factor alpha inhibitors were the predominant biologic agents used (75.8%), with a mean follow-up duration of 2 years post-biologic therapy initiation. …”

Flow Chart of Study Participant Selection. by Zhi Jin (3742471)

“…Notably, individuals with long sleep duration (>9 hours) had a significantly decreased risk of CVD (OR: 0.36, 95% CI: 0.15–0.85, P = 0.02) compared to those with shorter sleep durations.…”

Loss of RBM-26 causes mitochondria dysfunction in PLM. by Tamjid A. Chowdhury (14149846)

Clinical characteristics. by Clemens Plattner (21567706)

“…Survival curves were estimated using the Kaplan-Meier plot and compared using the Log-rank test.</p><p>Results</p><p>Mean age at T2D diagnosis was significantly lower in the FHD group, while time to insulin initiation was independent from FHD status. …”

Blood Pressure and LDL-C During Follow-up. by Karl Ingard (22582091)

“…The risk of cardiovascular death was significantly decreased (HR 0.64, 95% CI 0.41–0.998, <i>P</i> = 0.049) and the risk of fracture non-significantly increased (HR 1.47, 95% CI 0.95–2.27, <i>P</i> = 0.08) in the intervention group compared to the control group.…”

Baseline Characteristics of Included Patients. by Karl Ingard (22582091)

“…The risk of cardiovascular death was significantly decreased (HR 0.64, 95% CI 0.41–0.998, <i>P</i> = 0.049) and the risk of fracture non-significantly increased (HR 1.47, 95% CI 0.95–2.27, <i>P</i> = 0.08) in the intervention group compared to the control group.…”

Datasets used in the study. by Rajon Banik (12066099)

“…</p><p>Conclusion</p><p>The findings indicate a significant increase in the availability of health facilities offering modern family planning services in Bangladesh; however, a slight decline has been observed in their overall mean readiness score. …”

<i>SLC2A2</i> is essential for liver differentiation in developing vertebrates by Yejin Kim (740789)

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Test plan. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”