Yes, here is a minimal example solution with just the first 20 rows of your data.

```
library(lme4)
#> Loading required package: Matrix
CBM <- data.frame(
sid = c(210103, 210103, 210103, 210103, 210103, 210103, 210103,
210105, 210105, 210105, 210105, 210105, 210105, 210105,
210202, 210202, 210202, 210202, 210202, 210202),
Time = c(1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6),
TestScore = c(-1.45496720585195, -0.260188842483168, -1.45496720585195,
-1.75366179669414, -1.45496720585195, -0.857578024167559,
-1.45496720585195, -1.75366179669414, 0.337200339201222,
-0.857578024167559, -1.15627261500975, -0.558883433325363,
0.0385057483590271, -0.857578024167559, -0.857578024167559, 1.23328411172781,
-1.15627261500975, -1.45496720585195, -1.15627261500975,
0.635894930043418)
)
base <- lmer(CBM$TestScore ~ 1 + CBM$Time + (1 + CBM$Time | CBM$sid),
data=head(CBM,20), REML=FALSE)
#> singular fit
coef(base)$`CBM$sid`$`(Intercept)`
#> [1] -0.8681693 -0.8129559 -0.8007325
coef(base)$`CBM$sid`$`CBM$Time`
#> [1] -0.004295916 0.007774753 0.010447016
```

^{Created on 2019-02-02 by the reprex package (v0.2.1)}