Here is a partial and dynamic "wish list" of Linux programs proposed by
members of the SEUL-sci mailing list. Comments, suggestions and pointers to
programs filling these niches are always welcome, as are new items for this
Given its ability to run on a wide variety of hardware, Linux
should be an ideal platform for data acquisition, especially in a networked
enviornment. What DAC packages are there presently available for Linux?
'Why Linux for Science'
What makes Linux a good operating system for scientific uses? Why use
Linux over Windows9x or NT?
Meta-Analysis Tool (aka 'data-stealer')
This would be used to facilitate reuse of data from several datasets taken
from the literature. This program would take a scanned image of a scatter
plot, and allow the user to obtain an approximation of the data in the plot.
Some code to do this in visual basic already exists
and it should be fairly adaptable.
A linking program between the R Statistical package and the
GNUmeric spreadsheet (for that matter, any Linux spreadsheet) would be useful.
GUI-Based QBE Grid
This program would get information from a database, expose the tables
and fields within that db, and allow the user to create an SQL query while
permitting but not requiring hand-coding the SQL. Loath as we are to admit it,
the Access database has a pretty good QBE grid ... which is good because MS
Query's QBE left much to be desired. Related projects worth examining include gASQL
and GNOME Transcript
Generalized Analysis Manager
This is something that's been under consideration for some time now, more
out of need than anything else. However, no serious development has yet been started. This is not so much a program as it is an
interface between existing software packages: a graphing utility (say
gnuplot), statistical analysis package (R?), database for data storage, and
spreadsheet for fine data manipulation. The app itself would log all data
transactions, graphs and analyses. This latter function would be useful
because it would allow the researcher to modify their assumptions or
approach somewhat (ie. remove outliers) and rapidly re-do the same analyses
and graphing done previously.
Please contact Pete St. Onge (firstname.lastname@example.org) for additions