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

Experience

[robbyjo]

I also did lots of comp sci empirical experiments. My experience is that the tools used for experimenting itself is very ad-hoc and not easily scriptable. Most of the times we are required to tend the hour-long experiments to see what happened on the output and decide what to do next. And... the decision is often times not clear cut. Some sort of heuristic is needed. Not to mention about the frustations when the errors occur (especially when the tool is buggy, which is very often in research settings). So, considering this, what I would do is to construct a script and do the experiments in phases. Run it and see the result several days after.

I also noticed that from one experiment to another is sometimes radically different that I would doubt it is easily manageable.

Re:Experience

[jkauzlar]

I agree with the parent post after giving the problem a little thought. There may tools available, but I think what you need is to set up scripts for your experiments.

What comes to mind when I think about experiment management software is unit testing software. Correct me if I'm wrong, but when you run empirical software experiments, you are essentially unit testing the software.

Something like Python, Perl, or TCL (probably Python-- powerful, easy to read) should suit you ideally. Other options include Make utilities like make or Ant (w/ JUnit would work great!).

With any of these you could make use of any existing command-line or scriptable utilities for conversion or producing data files or database data.

Just my 2 cents. Hope this helps.

Re:Experience

[robbyjo]

Sorry, but I must disagree. Most of the times, research experiment != unit testing.

To illustrate: Take for example a data mining project. The first phase is data preparation -- which is easily scriptable. But how to prepare the data is different story. We must examine the raw data case by case to decide how to treat it. For example: When to discretize and using what method (linear scale, log scale, etc), when to reduce dimensionality, etc etc. This requires human supervision.

Even after we do the data prep, we look at the result. If the cooked data contains too much loss of information due to prep stage, we have to do it again using different parameters. This is painful.

Then, next on the pipeline: What algorithm to use. This is, again, depend on the characteristics of the cooked data. You know, some "experts" (read: grad students) will determine it using some "random" heuristics of their mind given some reasonable explanations.

If after the result is out and is not desirable, we might go back for different algorithm or choose different data prep parameters, and so forth...

Given this settings, I doubt that there is a silver bullet for this problem...

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/

Piracy is Your Only Option

[use_compress]

1. You cannot (?) afford commercial software.
2. It is impractical for you to continue writing your own software.
3. You cannot find open source software.
-------
Conclusion: Steal commercial software! -)

Re:Piracy is Your Only Option

[NewbieProgrammerMan]

must...resist....

4. Profit!!!!

Sorry, I've been reading slashdot too much and must append such an item to all lists I encounter. :P

And it's not stealing, it's copyright infringement. ;)

Seriously, though, I think using commercial software still won't cover all the bases. Alea said, "I can find commercial solutions (often specific to a particular domain)..." which I would assume means that there don't appear to be any general-purpose experiment packages.

As some others have already posted, 'experiments' can cover a wide range of things, and I can imagine that making a general-purpose experiment harness would be a tall order. Having such a thing would be useful for some of the work I do, but I have not had the time (and probably don't have the ability) to try to put together something that can help manage and automate experiments (or sensor data processing jobs, in my case). This is one of those problems which I 'feel' has to have a solution, but I know it's currently beyond my capability to figure out how it should work.

dependencies (but not make)

[Anonymous Coward]

I'm also an empirical computer scientist, and another aspect I would look for is handling dependencies. Make is the standard tool for doing this, but it's not up to this task.

Ideally, I'd type make paper and it would start from the beginning stages of the experiment and go all the way through creating the paper. Moreover, if anything died along the way, I could fix the problem, type make again, and it would more or less pick up where it left off, not re-running things it had already done (unless they were affected by my fix).

But after playing with this for a few days, I became convinced that make wasn't up to snuff for what I wanted. I have these sort of `attribute-value' dependency constraints. From one raw initial dataset, I create several cross-validation folds, each of which contains one training set and a couple varieties of test set. the filenames might look like

base.fold0.testA.test base.fold0.testB.test base.fold0.train

Now suppose that the way I actually run an experiment involves passing a test set and the corresponding training set to the model I'm testing, a command like:

modelX base.fold0.testA.test base.fold0.train > base.modelX.fold0.testA.run

Since, however, I have to run this over several folds (and other variations that I'm glossing over), I'd like to write an 'implicit rule' in the Makefile. This involves pattern-matching the filenames. But it's a very simple pattern-matching: you get to insert one .* (spelled %) in each string, which corresponds to the same thing. Given that, there's noway I can specify the command I have above.

You might be thinking, you could do

%.modelX.testA.run : %.testA.test %.train

but then I have to copy this rule several times for each sort of test set, even if the command they run is the same.

The underlying problem, I think, is that the pattern-matching in make's implicit rules is too simple. What I would rather have is some kind of attribute-value thing, so I could say something like

{ fileid=$1 model=modelX test=$2 filetype=run } : {fileid=$1 test=$2 filetype=test } { fileid=$1 filetype=train }

where fileid corresponds to 'base.fold0' and whatever other file identifying information is needed.

This notation is sort of based on a natural language attribute-value grammar.

Anyway, if anyone has any suggestions as to this aspect of the problem, I would be grateful

Re:dependencies (but not make)

[Anonymous Coward]

I ran into this problem when I was in graduate school, too. What I eventually did was to abandon make because of the limitations you are running into, and construct a special-purpose experiment running utility that would know about all the predecessors, etc. It turned out not to be too hard, actually. However, if you don't know perl or another language that gives you good pattern matching and substring extraction capability, then this will be very hard to do.

I just wrote two functions. (I wrote them in the shell, but if I were doing it again, I'd probably do it in perl.) construct() simply makes a file if it is out of date (see example below). Construct() is where are of your rules go: it knows how to transform a target filename into a list of dependencies and a command.

It uses a function called up_to_date() which simply calls construct() for each dependency, then returns false if the target is not up to date with respect to each dependency. If you don't do anything very sophisticated here, up_to_date will only be a few lines of code.

"construct" will basically replace your makefile. For example, if you did it in perl, you could write it something like this:

sub construct {
local $_ = $_[0]; # Access the argument.

if (/^base\.model(.)\.fold(\d+)\.test(.).run$/) {
@dependencies = ("base.fold$2.test$3.test",
"base.fold$2.train");
if (!up_to_date($_, # Output file.
@dependencies, # Input files.
"model$1")) { # Rerun if prog changed, too.
system("model$1 @dependencies > $_");
}
}
elsif (/^....$/) { # .. Check other patterns. ...
}
}

What you've gained from this is a much, much more powerful way of constructing the rule and the dependencies from the target filename. Of course, your file will be a little harder to read than a Makefile--that's what you pay for the extra power. But instead of having many duplicate rules in a makefile, you can use regular expressions or whatever kind of pattern matching capability you want to construct the rules.

R?

[Elektroschock]

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

Ant, with some tweaking.

[Xerithane]

We do something that almost parallels this, and we still haven't had the time to complete the Ant setup. The basic gist of it is that Ant has properties files that can contain any number of parameters, along with embedded XSLT functionality. This allows Ant to generate new build.xml files (The Ant build file) and run it, on the fly, given a set of user-entered commands, environment variables, or file parameters. The parameter files are easy to modify and update, and combined with CVS you can even do version control on the different experiments.

What I would end up doing is setup an Ant build file for each experiment, under each algorithm.

Algorithm/experiment_dataset1.properties
Algori thm/experiment_dataset2.properties

And then you can update property files, using a quick shell script, or something along those lines at the end of the data set, as well as having build/run times that Ant can retrieve for you. Good solution, and you aren't reinventing the wheel.

Requires Java, which depending upon your ideology is either a good thing or a curse. :)

AppLeS?

[kst]

Something like the AppLeS Parameter Sweep Template software might suit your needs. I've never used it myself, but it looks like it might be close to what you're looking for.

See here for other projects from the GRAIL lab at SDSC and UCSD.

Uh-huh

[Ryvar]

I don't mean to sound cynical, but this seems to come across to me as a very nicely written:

Ne3D H3lp WIt M4H H4x0RiN!!!!!

I mean, let's face it, much of what modern hacking closed-sourced software consists of is throwing a variety of shit against a variety of programs in a variety of configurations and seeing what breaks and then following up to make an exploit out of it.

While this probably isn't the case here, it's very hard to read that note and not snicker just a tiny, tiny bit . . .

Oh that's easy....

[Anonymous Coward]

The Academic Community, especially those strange AI people, have long sought complicated programs and machinery that could automate all of their work and projects, keep track of complicated "parameter sets, datasets, etc....".

But what you are looking for, sir, is the cheap labor commonly known as a Graduate Student

• Many of these "grads" [as they are commonly known] have INDEED been able to " 'execute' experiments and report their status", as well as "writing out results (available in multiple languages)".
• The Graduate Student is often known for their abilities to create and distribute notes in lieu of bringing that onerous burden upon more high-ranking academic officials
• ...you don't even have to dream about doing "clustered work" or "outputing results to spreadsheets, Matlab, LaTeX tables, etc....". These fancy machines can definately do that...
• Of course, there are several "graduate students" that provide a fancy front end (and rear end, for that matter). I think that I would agree with your assesment that they do not need to have that feature, although it might make your days a bit more... ermm... *pleasant* :-)
• As well, most graduate students have the capability of performing "basic stats", although most don't have an extensive faculty for performing such calculations...
• And don't you even worry about the price -- you'll see that they're quite affordable.

To conclude, you say that "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". I would wholeheartedly have to agree with you: just about every discipline can do more and see farther by standing on the backs of their graduate students.
In fact, I'm afraid to report that you are a bit behind the times in this department as these "Graduate Student" devices are quite common at universities and research labs.

Re:Oh that's easy....

[Alea]

Ah, you see... there's the problem... I am, in fact, a cheap alternative to the much vaunted "Graduate Student". I'm a "Lazy Graduate Student" (TM), with slow update rates, poor accuracy, and long downtimes. Eventually, I'll probably break down completely into a "Professor", in which case someone will have to find some "Graduate Students" to get the work done...

Re:Oh that's easy....

[quantaman]

Of course, there are several "graduate students" that provide a fancy front end (and rear end, for that matter). I think that I would agree with your assesment that they do not need to have that feature, although it might make your days a bit more... ermm... *pleasant* :-)

That does have it's advantages though you should be cautious. In my experience those models often have a large number of bugs in their systems and tend to be a lot more likely to pick up viruses as well.
This shouldn't be a problem for most operations but ocassionally if you try to interface them with your other components you may find your other systems becoming infected as well. In extreme cases you may also find interfacing with these systems can cause additional child processes to be created. These child processes are extremely hard to get rid of, early on you may be able to simply kill them but this command becomes extremely impratical after a few months of operation. These processes are known to take up huge amounts of resources and maintainance and often take the better part of 2 decades to subside (they're still present but resource demands drop considerably). Of course many of these risks can be alliviated by using a proper wrapper class while working with this "graduate student" systems.

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/.

Re:Perl is only useful for maintaining your job

[asciirock]

Just admit it. Perl slept with your wife. That's what this is really about, isn't it?

tcltest

[trb]

It might not satisfy all your requirements out of the box, but could you put something together with tcltest?

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.

schema

[Tablizer]

Draft relational schema:

Table: experiments
----
exprmntID
exprmntWhen // date-time stamp
exprmntDescr // description
outcome

Table: params
----
paramID // auto-num
exprmntRef // foreign key to experiments table
paramName
paramValue

Table: dataSet
----
dataSetID // auto-num
filePath
datasetDescr
isGenerated&nbsp ; // "True" if from experiment
CRC // ASCII check-sum to make sure not changed

Table: dataSetUsed
----
exprmntRef // foreign key to experiments table
dataSetRef // foreign key to dataSet table

Table: softwareVersion
----
svID
softwareTitle
svVers ion

Table: softwareVersionUsed
----
svRef // foreign key to softwareVersion
exprmntRef // foreign key to experiments table

Just use something like MySQL or MS-Access, and perhaps some kind of CRUD[1] tool to create front ends. You can expand from there based on new needs you encounter.

[1] CRUD = typical Create, Read (list), Update, Delete screens.

(Note: slashdot's filter scrambles certain variable names.)

you need...

[Alpha_Nerd]

It looks like you need - da da da da! - [b]EXTREME[/b] PROGRAMMING!

configuration management, build scripts, etc...

[foog]

The features I would want would be:

management of all details of an experiment, including parameter sets, datasets, and the resulting data


This can be handled by an ad-hoc database, a flat file in most cases. If you were a Windows power user, you'd spend an hour or two putting together something in Access for it.

ability to "execute" experiments and report their status

make with a little scripting, or whatever you use as a build system.

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

Again, an ad-hoc database would be your friend.

ability to differentiate versions of software

This is conventionally handled with a configuration management system like CVS, Sourcesafe, or Clearcase.

I hate reinventing the wheel, too, and I'd love to see a good book on using standard free Unix tools like make, CVS, Postgres, perl or some other common scripting language, TeX, etc for cleanly and efficiently
automating complex computing processes and producing nice reports from them.

PAW and ROOT look interesting though they look like overkill for many apps.

Also, get a copy of Writing The Laboratory Notebook, some hardbound buffered laboratory notebooks, and Sakura 05 Pigma Micron archival pigment pens to keep your paper records. You'll thank me.

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".

Re:that's what UNIX is there for

[ExoticMandibles]

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.

What is it intrinsically about Windows that makes it "no good for this sort of thing"? Windows provides all the system services you need to do these tasks, and all the tools you mention are available natively for Windows. Come to think of it, they're available for OS/2, QNX, Mac OS X, and nearly every other desktop operating system out there. One could erase every mention of UNIX-specificness from your post, and not only would your post still hold true, it would be an improvement. Your knee-jerk UNIX advocacy, nestled in and disguised as helpful advice, is a disservice to the original poster.

Suggesting that the original poster must be using UNIX in order to get their work done is wrong in several senses of the word; it is not factual, and it is irresponsible. On the contrary--I am certain that their current choice of operating system is entirely up to the task. He or she should feel absolutely no onus to switch.

Experimental tools

[clusterboy]

Have you considering using python? researchers at DIKU have created a benchmark tool for . It seems usefull. Python is also excellent for gluing modules together.
A wellthought example on how to setup your code for experimental work is the lemur toolkit from CMU. This toolkit has a concept of "parameter" files that is very handy :) Generally I would recommend against exporting directly to spreadsheet formats - I tend to export to flat files (with field separators). Postgres' COPY command is extremely handy from scripts. I also find gnu-plot handy. Remember .. these tools might seem un-intuitive at first .. but when you have used them for earlier experiments you can re-use code between experiements :) I hope this helps

SMIRP

[sco08y]

I'm one of the principal designers of a system called SMIRP.

It started out as a very simple system that didn't act as much more than a set of tables with some simple linking structures. On top of that is an alerting system, (so you can track new experiments being done) a full text index, bots for automating certain procedures, and a system for transferring data to Excel.

What's surprising is that for the most part, the underlying structure stayed exactly the same even though we've been running all the operations in an inorganic chemistry lab on for, oh, four years now. I've been chewing over ways of rewriting it because, honestly, it's still the same prototype. I'd love to go with an all Perl solution... but the damned thing just works and I have other stuff to do.

Some lessons I've learned, problems I've run into:

A general interface. You really need a flexible structure because scientists never know what parameters they're going to use until they do the experiment. Our big success has been such a simple structure that people can throw a SMIRPSpace together in minutes.

Browser based interface. It's great because it's ubiquitous, but it's painful because of the inflexibility of forms. One big win with it is that you can get a horde of workstudies to form a pipeline. For example, a grad student might put a request in the system for an article, a workstudy recieves a notification of the change and hits the web to fill in details, another then gets notified and sends a request to the library, another gets notified and scans the result and finally the grad student sees a scanned copy of the article.

Excel based interface. It's great because people can play with data, but it's Excel...

XML is garbage. There's nothing you can do in XML that you can't do better with a flat file + regexes, or a SQL DBMS. XML is utterly, completely worthless.

Proprietary products. This won't be a huge surprise to /.'ers, but we got seriously screwed when the prototype we did in Cold Fusion became production code and we realised that Allaire (and later Macromedia) would not computer redistribution for less than 10,000 units. I could try to get it running on another CF implementation (I think there's some Blue Dragon or something) but honestly, I'd rather rewrite the whole thing.

Reporting. This is *hard* to do. We still don't have any serious system for handling reports beyond "import the data to Excel and do it manually."

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".

I Develop This Kind of Software

[spirality]

The Computer Aided Engineering (CAE) world has much the same problem you do.

They model their products with several different analysis codes, each with its own input and output format. This generates a gob of data, and is currently managed in ad hoc ways, is not easy to integrate with other results and wastes the time of lots of engineers.

The product we've come up with to manage both the models, the process for executing the models, and the data generated by running the models is a software framework called CoMeT (Computational Modeling Toolkit).

We are also capable of managing different versions of the model, parameter studies, and some basic data mining. The whole thing is scriptable with Scheme.

Unfortunately, we are a commercial software company, and the software is still under development, although everything I mentioned above can currently be done. We are mostly working on a front end now, although we still need to make a few improvements to the framework and add support for many analysis codes.

The reason I'm replying to this is that your list of requirements is a perfect subset of ours. We are aiming our product at CAE in the mechanical and electrical domains (Mechatronics).

I know, it's not free, but we feel we've done some very innovative things and it has taken several people many years of low pay to get this far. We really want to make some money off it eventually.... :)

If you want more information check out the web-site or email me here. We're in need of proving this technology in a production environment so maybe we can work something out.

-Craig.

Might be suitable?

[gowdy]

http://roofit.sourceforge.net/

ExpLab

[The Visiting Priest]

I'm in precisely the same situation as Alea, so I read the suggestions here with considerable interest.

I'd like to mention ExpLab.

Though I haven't used ExpLab yet, these folks have been associated with other very high quality work (CGAL) so I expect good things. Here are three goals they list for the project:

• to provide a simple way to set up and run computational experiments;
• to provide a means of automatically documenting the environment in which an experiment is run so the experiment can be easily rerun (provided the same environment is still available) and the results can be more accurately compared to the results of other computational experiments;
• to eliminate some of the tedium involved in collecting and analyzing output by providing basic text output processing tools.