  Context: Tables are ubiquitous formats for data. Therefore, techniques for
writing correct programs over tables, and debugging incorrect ones, are vital.
Our specific focus in this paper is on rich types that articulate the
properties of tabular operations. We wish to study both their expressive power
and _diagnostic quality_.
  Inquiry: There is no "standard library" of table operations. As a result,
every paper (and project) is free to use its own (sub)set of operations. This
makes artifacts very difficult to compare, and it can be hard to tell whether
omitted operations were left out by oversight or because they cannot actually
be expressed. Furthermore, virtually no papers discuss the quality of type
error feedback.
  Approach: We combed through several existing languages and libraries to
create a "standard library" of table operations. Each entry is accompanied by a
detailed specification of its "type," expressed independent of (and hence not
constrained by) any type language. We also studied and categorized a corpus of
(student) program edits that resulted in table-related errors. We used this to
generate a suite of erroneous programs. Finally, we adapted the concept of a
datasheet to facilitate comparisons of different implementations.
  Knowledge: Our benchmark creates a common ground to frame work in this area.
Language designers who claim to support typed programming over tables have a
clear suite against which to demonstrate their system's expressive power. Our
family of errors also gives them a chance to demonstrate the quality of
feedback. Researchers who improve one aspect -- especially error reporting --
without changing the other can demonstrate their improvement, as can those who
engage in trade-offs between the two. The net result should be much better
science in both expressiveness and diagnostics. We also introduce a datasheet
format for presenting this knowledge in a methodical way.
  Grounding: We have generated our benchmark from real languages, libraries,
and programs, as well as personal experience conducting and teaching data
science. We have drawn on experience in engineering and, more recently, in data
science to generate the datasheet.
  Importance: Claims about type support for tabular programming are hard to
evaluate. However, tabular programming is ubiquitous, and the expressive power
of type systems keeps growing. Our benchmark and datasheet can help lead to
more orderly science. It also benefits programmers trying to choose a language.
