[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

[seul-sci] Pete's wish list :)



Here are some of my thoughts on what I feel to be gaps in useful scientific
applications and documentation for Linux, or at least apps that I'm not yet
aware of. I would greatly appreciate any suggestions for items to add to
this list, and especially any URLs to programs that you may know of that
fill these gaps; both will go onto a web page shortly.


'Why Linux for Science' DOC
     What makes Linux a good operating system for scientific uses? Why use
Lonux over Windows9x or NT?

Meta-Analysis Tool (aka 'data-stealer')
     This is 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
(http://blaze.trentu.ca/~erpds/software.html) and it should be
fairly adaptable

R-Gnumeric linkage
     A linking program between the R Statistical package and the GNUmeric
spreadsheet and for that matter any other 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 I am 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.

Generalized Analysis Manager
     This is something I've been thinking about for some time now, mostly
out of need than anything else. 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.


BIAS ALERT: My training is in ecology, so it will probably be very evident
my choices of 'useful apps' are going to be aimed in that direction.
However, irrespective of the discipline, I'd love to add more entries in
this list

-- 
Pete St. Onge
pete@seul.org