Raw data. by Changzhi Liu (518454)

“…The incidence of fractures was significantly greater among participants with low RC levels than among those with high RC levels (6.4% vs. 3.1%, P < 0.01). …”

Baseline characteristics of the subjects. by Changzhi Liu (518454)

“…The incidence of fractures was significantly greater among participants with low RC levels than among those with high RC levels (6.4% vs. 3.1%, P < 0.01). …”

Evaluation results. by Briya Tariq (19666901)

“…It focuses on identifying and quantifying clinically significant materials in the presence of metal objects. …”

Dataset with steel insert. by Briya Tariq (19666901)

“…It focuses on identifying and quantifying clinically significant materials in the presence of metal objects. …”

Reference dataset. by Briya Tariq (19666901)

“…It focuses on identifying and quantifying clinically significant materials in the presence of metal objects. …”

Dataset with aluminium insert. by Briya Tariq (19666901)

“…It focuses on identifying and quantifying clinically significant materials in the presence of metal objects. …”

Correlation in site-level DNA methylation estimates between TMS and RRBS data after permutation. by Amy Longtin (21409957)

GO and KEGG analysis of DEPs. by Huiyang Jia (14444661)

Simulation datasets. by Xiao Mo (2430355)

“…These results offer significant theoretical guidance for the design and improvement of needle-free injection.…”

Grid division diagram. by Ming Zhang (9736)

“…When the height is 2.5m and above, the windbreak efficiency is greater than 90%, and the windbreak effect is significantly improved. (2) The change of sand barrier height has a significant effect on the windbreak efficiency between the second and third sand barriers. (3) Among the three sand-blocking fences, when the height of the sand-blocking fence is 2.5m, the thickness of the sand is 50.51% and 58.33% higher than that of the 2m high sand-blocking fence, and the sand-blocking effect is the most significant. …”

Model calculation diagram. by Ming Zhang (9736)

“…When the height is 2.5m and above, the windbreak efficiency is greater than 90%, and the windbreak effect is significantly improved. (2) The change of sand barrier height has a significant effect on the windbreak efficiency between the second and third sand barriers. (3) Among the three sand-blocking fences, when the height of the sand-blocking fence is 2.5m, the thickness of the sand is 50.51% and 58.33% higher than that of the 2m high sand-blocking fence, and the sand-blocking effect is the most significant. …”

Grid independence verification. by Ming Zhang (9736)

“…When the height is 2.5m and above, the windbreak efficiency is greater than 90%, and the windbreak effect is significantly improved. (2) The change of sand barrier height has a significant effect on the windbreak efficiency between the second and third sand barriers. (3) Among the three sand-blocking fences, when the height of the sand-blocking fence is 2.5m, the thickness of the sand is 50.51% and 58.33% higher than that of the 2m high sand-blocking fence, and the sand-blocking effect is the most significant. …”

Antibodies used in this study. by Kevin J. Kokesh (19859571)

“…Protein kinase C (PKC) activity was greater in the CF cells compared to the non-CF cells, but the activity was significantly attenuated in both cell types after infection with <i>M</i>. …”

Time and flowrate used for proteomics. by Kevin J. Kokesh (19859571)

“…Protein kinase C (PKC) activity was greater in the CF cells compared to the non-CF cells, but the activity was significantly attenuated in both cell types after infection with <i>M</i>. …”

S1 Graphical abstract - by Kevin J. Kokesh (19859571)

“…Protein kinase C (PKC) activity was greater in the CF cells compared to the non-CF cells, but the activity was significantly attenuated in both cell types after infection with <i>M</i>. …”

Results of longitudinal analysis of mental health in HCWs. by A. H. Ayesha Lavell (16734002)

Mean scores and 95% CI of mental health symptoms, COVID-19 admissions and effect size during follow-up. by A. H. Ayesha Lavell (16734002)

Metric correlation matrix. by Chao Song (379006)

Mean parameter values for the selected crops. by Gourab Saha (8987405)

“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”

Performance comparison of ML models. by Gourab Saha (8987405)

“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”