Regression results of the Callaway method. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Regression results of crowding out effects. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Article data. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Overidentification test results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Quantile regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Instrumental variable regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Other robust regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Baseline regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Results of propensity score matching. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Parallel trend test. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Placebo test. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Descriptive statistics of variables. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Data set. by Belachew Tekleyohannes Wogayehu (19470257)

“…Even though hairdressing is a significant source of wealth, it is linked to several kinds of medical problems mainly skin conditions. …”

Results of iterative calculation of reservoir data in different water cut stages. by Mengyun Li (7490870)

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Relative permeability curve of a well in Gudong test area. by Mengyun Li (7490870)

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The distribution of residual oil in the numerical simulation model when the water drive front breaks through the well. by Mengyun Li (7490870)

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Volume proportion of weak water-flushed zone in low permeability formation at different permeability ratio. by Mengyun Li (7490870)

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The volume proportion of weak water-flushed zone in low permeability layer varies with the permeability ratio. by Mengyun Li (7490870)

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History matching of the water cut and oil production rate. by Mengyun Li (7490870)

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The distribution of residual oil in the numerical simulation model when the water cut reaches 98%. by Mengyun Li (7490870)

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