# FAQ¶

This is a collection of frequently asked questions (and their answers) that might help new users avoid common mistakes or provide useful hints to more experienced users.

## How do I design a good schema?¶

There is really no good answer on how to generally design a good schema because it is heavily dependent on the domain and the specific application. Nonetheless, there are some basic rules worth following:

1. Be descriptive. Although we are using short variable names in the tutorial, in general metadata keys should be as long as necessary for a third party to understand their meaning without needing to ask someone.
2. Any parameter which is likely to be varied at some point during the study should be part of the metadata right from the start to avoid needing to modify the schema later.
3. Take advantage of grouping keys! The job metadata mapping may be nested, just like any other Python dict.
4. Even if you don’t use “official” schemas, consider to work out standardized schemas among your peers or with your collaborators.
5. Use the state point to define the identity of each job, use the document to store additional metadata.

## What is the difference between the job state point and the job document?¶

The state point defines the identity of each job in form of the job id. Conceptually, all data related to a job should be a function of the state point. That means that any metadata that could be changed without invalidating the data, should in principle be placed in the job document.

Important

The state point defines the identity of each job, the job document is data.

## How do I avoid replicating metadata in filenames?¶

Many users, especially those new to signac, fall into the trap of storing metadata in filenames within a job’s workspace even though that metadata is already encoded in the job itself.

Using the Tutorial project as an example, we might have stored the volume corresponding to the job at pressure 4 in a file called volume_pressure_4.txt. However, this is completely unnecessary since that information can already be accessed through the job via job.sp.p. Furthermore, creating files this way causes additional complications, such as the need to modify filenames whenever we operate on the data space. For example, extracting the volume from a particular job originally consisted of doing this:

volume = float(open(job.fn('volume.txt')).read())


volume = float(open(job.fn('volume_pressure_{}.txt'.format(job.sp.p))).read())

Whenever you find yourself hard-coding ids into your code, consider replacing it with a function that uses the find_jobs() function instead.