Why does one fail but the other succeeds?

> DataFrame(ID = LETTERS[1:3], score = list(2:4, 6, 7:8))
Error in DataFrame(ID = LETTERS[1:3], score = list(2:4, 6, 7:8)) : 
  cannot coerce class "list" to a DataFrame
> data <- DataFrame(ID = LETTERS[1:3])
> data$score <- list(2:4, 6, 7:8)
> data
DataFrame with 3 rows and 2 columns
           ID  score
  <character> <list>
1           A  2,3,4
2           B      6
3           C    7,8

You need to protect the list by wrapping it in I(), otherwise the DataFrame() constructor interprets it as a list of 3 columns (so it tries to turn it into a DataFrame and fails because the columns don't have the same length):

library(S4Vectors)

DataFrame(ID = LETTERS[1:3], score = I(list(2:4, 6, 7:8)))
DataFrame with 3 rows and 2 columns
#            ID  score
#   <character> <list>
# 1           A  2,3,4
# 2           B      6
# 3           C    7,8

Note that this is no different from what happens with ordinary data frames:

data.frame(ID = LETTERS[1:3], score = list(2:4, 6, 7:8))
# Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE,  : 
#   arguments imply differing number of rows: 3, 1, 2

data.frame(ID = LETTERS[1:3], score = I(list(2:4, 6, 7:8)))
#   ID   score
# 1  A 2, 3, 4
# 2  B       6
# 3  C    7, 8

If you don't protect, data.frame() and DataFrame() want to interpret the list as a list of columns (so 3 columns in this case):

data.frame(ID = LETTERS[1:3], score = list(A=2:4, B=letters[1:3], C=7:9))
#  ID score.A score.B score.C
# 1  A       2       a       7
# 2  B       3       b       8
# 3  C       4       c       9

DataFrame(ID = LETTERS[1:3], score = list(A=2:4, B=letters[1:3], C=7:9))
# DataFrame with 3 rows and 4 columns
#            ID   score.A     score.B   score.C
#   <character> <integer> <character> <integer>
# 1           A         2           a         7
# 2           B         3           b         8
# 3           C         4           c         9

Best,

H.