Open Source Experiment Management Software?

Alea asks: "I do a lot of empirical computer science, running new algorithms on hundreds of datasets, trying many combinations of parameters, and with several versions of many pieces of software. Keeping track of these experiments is turning into a nightmare and I spend an unreasonable amount of time writing code to smooth the way. Rather than investing this effort over and over again, I have been toying with writing a framework to manage everything, but don't want to reinvent the wheel. I can find commercial solutions (often specific to a particular domain) but does anyone know of an open source effort? Failing that, does anyone have any thoughts on such a beast?"

"The features I would want would be:

• management of all details of an experiment, including parameter sets, datasets, and the resulting data
• ability to "execute" experiments and report their status
• an API for obtaining parameter values and writing out results (available to multiple languages)
• additionally (alternately?) a standard format for transferring data (XDF might be good)
• ability to extract selected results from experimental data
• ability to add notes
• ability to differentiate versions of software

In my dreamworld, it would also (via plugin architecture?) provide these:

• automatically run experiments over several parameters values
• distribute jobs and data over a cluster
• output to various formats (spreadsheets, Matlab, LaTeX tables, etc.)

Things I don't think it needs to do:

• provide a fancy front-end (that can be done separately - I'm thinking mainly in terms of libraries)
• visualize data
• statistical analysis (although some basic stats would be handy)

The amount of output data I'm dealing with doesn't necessitate database software (some sort of structured markup is ok for me), but some people would probably like more powerful storage backends. I can see it as experiment management 'middleware'. There's no reason such software should be limited to computer science (nothing I'm contemplating is very domain specific). I can imagine many disciplines that would benefit."

Comments

Object Modeling System

[Anonymous Coward]

Take a look at the object modeling system. It is currently being developed by Agricultural Research Service but many other agencies are cooperating.

http://oms.ars.usda.gov/

R?

[Elektroschock]

Did you consider R, a Splus clone? For Scientific Statistics a very flexible solution. http://www.r-project.org

ROOT

[kenthorvath]

http://root.cern.ch/

We experimental high-energy physics folk have been using it (and PAW) for some time. It offers scripting and histogramming and analysis and a bunch of other features. And it's open source. Check it out.

suggest jdb for managing individual experiments

[john_heidemann]

I've been very happy using jdb (see below) to handle individual experiments, and directories and shell scripts to handle sets of experiments.

JDB is a package of commands for manipulating flat-ASCII databases from shell scripts. JDB is useful to process medium amounts of data (with very little data you'd do it by hand, with megabytes you might want a real database). JDB is very good at doing things like:

• extracting measurements from experimental output
• re-examining data to address different hypotheses
• joining data from different experiments
• eliminating/detecting outliers
• computing statistics on data (mean, confidence intervals, histograms, correlations)
• reformatting data for graphing programs

For more details, see http://www.isi.edu/~johnh/SOFTWARE/JDB/.

Sounds like High Energy Physics

[Anonymous Coward]

What you describe does indeed sound like High Energy Physics.

And the "middleware" you need are the GNU tools gluing together the specialized programs that do the specific things you want.

We have been using unix for a long time, and many of us prefer the combination of small targeted tools philosophy rather than a single monolithic package.

I will repeat, and you can stop reading now if you want. The GNU tools, unix, and specialized scriptable programs are already the "middleware" you seek.

If you are just missing some of the tools in the middle, here are the ones used in HEP. You might find more appropriate ones closer to whatever discipline you work in.

All the basic unix text processing tools and shells.
bash. csh. Perl. grep. sed. and so on.

Filename schemes ranging from appropriate to clever to bizarre.
(See other posts here)

Make it so that all the inputs you want to change can be done on the command line or with an input steering text file.

Same tools combined with some simple c-code to produce formats for spreadsheets or PAW or ROOT or whatever visualization or post-processing thing you need done. Has ntuple and histogram support automatically, which might be all you need.

Almost always I choose space delimited text for simple output to push into PAW, ROOT, or spreadsheets. I keep a directory of templates to help me out here.

Some people use full blown databases to manage output. For a long time there have been databases specific to the HEP needs. I recently have started using XML-style data formats to encapsulate such things in text files if the resulting output is more complicated than a single line. You mention XDF, sure, that sounds like the same idea.

CONDOR (U Wisconsin) has worked nicely for me for clustering and batch job submission when I need to tool through 100 data files or 100 diffrent parameter lists on tens of computers. The standard unix "at" is good enough in a pinch if you play on only 5 computers or so.

HEP folks use things like PAW and ROOT (find them at CERN) which contain many statistical analysis things and monstrous computation algorithsm. Or at least ntuples, histograms, averages, and standard deviations. You could go commercial or the gsl here if you prefer such things.

CVS or similar to take care of code versions.
Don't forget to comment your code.

We write our own code and compile from fortran or c or c++ for most everything else.

Output all plots to postscript or eps.

LaTeX is scriptable.

And use shells, grep, perl to glue it all together. Did I mention those already?
I get a good night's sleep more often than not.

And decide what to do next after coffee the following morning.
This is where you put your brain, and if you have done the above well enough, this is where you spend most of your time.
The answer I get each morning (as another post suggests) is always so suprising that I need to start from scratch anyway.

I bet that is what you are doing already. Probably no monolithic software will be as efficient as that in a dynamic research environment.

What did I miss from your question?

Oh, yes. Get a ten-pack of computation notebook with 11 3/4 x 9 1/4 inch pages (if you print things with standard US letter paper). And lots of pens. And scotch tape to tape plots into that notebook. Laser printer and photocopier. Post-it notes to remind yourself what you wanted to do next (or e-mail memos to yourself). Maybe I should have listed this first.

Good luck.

that's what UNIX is there for

[g4dget]

Managing and organizing really huge amounts of data is one of the big strengths of UNIX--you just have to learn how to use it well:

• Consider using "make" or "mk" for automating complex processing steps. "make" also lets you parallelize complex experiments (by figuring out which jobs can be run safely in parallel), and some versions of "make" are capable of dealing with compute clusters. If you need to try something with multiple parameter values, write make rules and put the parameter values in there as dependencies.
• Organize your data into directory hierarchies; pick meaningful and self-explanatory names. Don't let directories become too big. Keep related data files and results together in the same directory, and keep different data files in different directories.
• Keep scripts and programs along with the data, not in completely separate source trees.
• Write scripts that summarize the data and give them obvious names; you can figure out later from that what needs looking at and what it means.
• Use textual data files as much as possible and have your programs add information to those files as comments that document what they did.
• If you generated important result, keep a snapshot of the sources that generated it along with it.
• Leave copious README files everywhere, containing notes to yourself, so that you can figure out what you did.
• If you generate junk during some trial runs, delete it, or at least rename it to something like "results.junk", otherwise you'll trip over it later.
• Back things up.
• Learn the core UNIX command line tools, tools like "sort", "uniq", "awk", "cut", "paste", "find", "xargs", etc.; they are really powerful. You probably also want to learn Perl, but don't get into the habit of trying to do everything in Perl--the traditional UNIX tools are often simpler.
• If you are using Windows, switch to UNIX. Windows may be good for starting up MS Office, but it is no good for this sort of thing. If you absolutely must use Windows for data analysis, stick your data into a relational database or Excel spreadsheets.
• Learn to use environment variables.
• Learn to use the Bourne/Korn/Bash shell; the C-shell is no good for this sort of thing.
• For certain kinds of automation, expect is also very handy.
• For visualizing data, write scripts that analyze your data and automatically generate the plots/graphs--you will run them again and again.

Distribution of jobs, running things with multiple parameter values, etc., all can be handed smoothly from the shell. This is really the sort of thing that UNIX was designed for, and the entire UNIX environment is your "experiment management software".

there are many projects developing such software!

[edeljoe]

Funding agencies in the USA (NSF, NIH) and Europe have recently decided to target the construction of such software, and many competing projects have been given grants, most of which involve the production of open source software.

Relevant keywords are "eScience", "Experimental Data Management", "Experimental Metadata", and to some extent "Grid Computing".

Here is a paper which lays out the program of research.

I work for one such NSF & NIH funded project at Dartmouth College. We're developing such a tool : Java-based, completely open, available at sourceforge, currently in alpha, to be released for fMRI use in July, but designed from the start to be generalizable for all of experimental science. This is built on top of a pre-existing framework for semantic data management and modeling from Stanford.

I'll try to list some of the features relevant to your needs:

• the thing will organize all your data across all experiments and sports a nice Java API, annotations, a set of interchangable & sophisticated query engines, and java plugins for supporting, among other things, application specific tasks, application specific rendering widgets for data, and new backend data formats.
• currently supported backend formats include: RDF, DAML+OIL, XML, text files, and SQL databases.
• we should have cluster job submission support integrated in by july, but it depends on your cluster set-up. currently this is presented to the user by way of executing "processing pipelines" for data. If this metaphor doesn't work for you, you may have to write some additional code for us!
• since the experimental designs are represented in a prolog-style knowledge-base, it would be very simple to put some intelligence in about how to "run" or "execute" a given class of experimental designs and do a lot of automatic reasoning or planning re: dependencies. In fact, I think that someone at Stanford has already done this, but I'd have to look into it.

Finally, I would like to stress that our project is one of many, and that if it doesn't meet your needs, within a year there will be many competing "eScience" toolkits.

You may contact me for more information by reversing the following string: "ude.htuomtrad@exj".