Common standards for data import file formats for statistics software? I'm looking for common standards (file formats) for how most statistics software packages import data. 
The reason is that I'm going to make an export function for a simulation software package.
The data to be exported consists of time-series of (1 or more) values for all members of a population. The population size can be in the 100,000's. The length of the time-series is typically in the 1,000's.  The number of values per member per timestep can range from 1 to 20.
How is such data typically imported by statistics packages?
 A: To expand on @King's answer a bit:
A convenient 'lowest common-denominator' format is something like:


*

*Comma-separated values (CSV)

*Variable names in first 'header' line

*variable names alphanumeric starting with a letter, not case dependent, no spaces or underscores or dots, 16 characters max

*same number of comma-separated fields on each line

*'Long' format, i.e. multiple lines per subject, one line for each timepoint within each subject


e.g.:
 personid,time,vara,varb
 1001,1,4.322,6
 1001,2,5.645,7
 1001,3,6.332,10
 1003,1,8.434,2
 1003,2,5.342,4
 ...

A: I'm working in a department where different people use different softwares (R, Stata, SAS, SPSS, etc); we are quite happy with passing data around using CSVs.
A: CSV is a time tested format. But avoid a world of headaches and Use the IETF RFC 4180 standard for CSV files, particularly if data comes from external sources where you don't control the string formats on input.  i.e. 


*

*if the value has a comma or line break, surround it with double quotes

*If it has a double quote, put a double  quote before it to
escape the double quote. etc.  
(Or globally strip out linebreaks, commmas and quotes).  
That said, CSV loses critical information, and you should consider SPSS (.sav or .por) format or XML W3C standard for relational tables.  Both include meta data that CSV loses, like variable types (i.e. zip codes are actually strings with leading zeros, but look like integers, and the leading zeros get lost in input downstream).  SPSS data files also include variable and value labels. 
