schemasheets - structuring your data using spreadsheets
Create a data dictionary / schema for your data using simple spreadsheets - no coding required.
- Author your schema as a google sheet or excel spreadsheet
- Generate schemas:
- LinkML
- SHACL and ShEx
- JSON-Schema
- SQL DDL
- OWL
- Validate data automatically
See the test google sheets for examples
See also the examples folder which has an end-to-end example
How it works
The following example shows a schema sheet for a schema that is focused around the concept of a Person. The rows in the sheet describe either classes or slots (fields)
record | field | key | multiplicity | range | desc | schema.org |
---|---|---|---|---|---|---|
> class |
slot | identifier | cardinality | range | description | exact_mappings: {curie_prefix: sdo} |
- | id | yes | 1 | string | any identifier | identifier |
- | description | no | 0..1 | string | a textual description | description |
Person | n/a | n/a | n/a | a person,living or dead | Person | |
Person | id | yes | 1 | string | identifier for a person | identifier |
Person, Organization | name | no | 1 | string | full name | name |
Person | age | no | 0..1 | decimal | age in years | - |
Person | gender | no | 0..1 | decimal | age in years | - |
Person | has medical history | no | 0..* | MedicalEvent | medical history | - |
MedicalEvent | n/a | n/a | n/a | - | - |
The sheet is structured as follows:
- The first line is a header line. You get to decide the column headers
- Subsequent lines starting with
>
are column descriptors- these provide a way to interpret the columns
- descriptors can be drawn from the linkml vocabulary
- Remaining rows are elements of your schema
- Each element gets its own row
- A row can represent a class (record, table), field (column), enumeration, or other element types
The most basic schema concepts are classes and slots
- classes represent record types, similar to tables in a database or sheets in a spreadsheet
- slots represent fields, similar to columns in a database or spreadsheet
These can be used in combination:
- If a class is provided, but a slot is not, then the row represents a class.
- If a slot is provided, but a class is not, then the row represents a slot (field)
- If both class and slot are provided, then the row represents the usage of a slot in the context of a class
Generating schemas
Assuming your schema is arranged as a set of sheets (TSV files) in the src
folder:
sheets2project -d . src/*.tsv
This will generate individual folders for jsonschema, shacl, ... as well as a website that can be easily hosted on github.
To create only LinkML yaml:
schemasheets -o my.yaml src/*.tsv
Simple data dictionaries
This framework allows you to represent complex relation-style schemas using spreadsheets/TSVs. But it also allows for representation of simple "data dictionaries" or "minimal information lists". These can be thought of as "wide tables", e.g. representing individual observations or observable units such as persons or samples.
TODO
Prefixes
If you specify a column descriptor of prefix
, then rows with that column populated will represent prefixes. The prefix expansion is specified using prefix_reference
Example:
prefix | URI |
---|---|
> prefix |
prefix_reference |
sdo | http://schema.org/ |
personinfo | https://w3id.org/linkml/examples/personinfo/ |
famrel | https://example.org/FamilialRelations# |
GSSO | http://purl.obolibrary.org/obo/GSSO_ |
We recommend you specify prefixes in their own sheet.
If prefixes are not provided, and you do not specify --no-repair
then prefixes will be inferred using bioregistry
Schema-level metadata
If you specify a column descriptor of schema
, then rows with that column populated will represent schemas.
Example:
Schema | uri | Desc | Schema Prefix |
---|---|---|---|
> schema |
id | description | default_prefix |
PersonInfo | https://w3id.org/linkml/examples/personinfo | Information about people, based on schema.org | personinfo |
The list of potential descriptors for a schema can be found by consulting SchemaDefinition in the LinkML metamodel.
Both id
and name
are required, these will be auto-filled if you don't fill this in.
Populating the fields description
and license
is strongly encouraged.
Currently multiple schemas are not supported, we recommend providing a single-row sheet for schema metadata
Enums
Two descriptors are provided for enumerations:
enum
permissible_value
These can be used in combination:
- If
enum
is provided, andpermissible_value
is not, then the row represents an enumeration - If both
enum
andpermissible_value
are provided, the row represents a particular enum value
The following example includes two enums:
ValueSet | Value | Mapping | Desc |
---|---|---|---|
> enum |
permissible_value | meaning | description |
FamilialRelationshipType | - | - | familial relationships |
FamilialRelationshipType | SIBLING_OF | famrel:01 | share the same parent |
FamilialRelationshipType | PARENT_OF | famrel:02 | biological parent |
FamilialRelationshipType | CHILD_OF | famrel:03 | inverse of parent |
GenderType | - | - | gender |
GenderType | nonbinary man | GSSO:009254 | - |
GenderType | nonbinary woman | GSSO:009253 | - |
... | ... | ... | - |
All other descriptors are optional, but we recommend you provide descriptions of both the enumeration and the meaning descriptor which maps the value to a vocabulary or ontology term.
For more on enumerations, see the linkml tutorial
Specifying cardinality
See configschema.yaml for all possible vocabularies, these include:
- UML strings, e.g. '0..1'
- text strings matching the cardinality vocabulary, e.g. 'zero to one'
- codes used in cardinality vocabulary, e.g. O, M, ...
The vocabulary maps to underlying LinkML primitives:
Slot-class grids
If you have a large number of fields/columns, with varying applicability/cardinality across different classes, it can be convenient to specify this as a grid.
An example is a minimal information standard that includes different packages or checklists, e.g. MIxS.
For example:
term | title | desc | mi_patient | mi_mod | mi_terrestrial | mi_marine | mi_extraterrestrial |
---|---|---|---|---|---|---|---|
> slot |
title | description | cardinality | cardinality | cardinality | cardinality | cardinality |
> |
applies_to_class: MI patient |
applies_to_class: MI model organism |
applies_to_class: MI terrestrial sample |
applies_to_class: MI marine sample |
applies_to_class: MI extraterrestrial sample |
||
id | unique identifier | a unique id | M | M | M | M | M |
alt_ids | other identifiers | any other identifiers | O | O | O | O | O |
body_site | body site | location where sample is taken from | M | R | - | - | - |
disease | disease status | disease the patient had | M | O | - | - | - |
age | age | age | M | R | - | - | - |
depth | depth | depth in ground or water | - | - | R | R | R |
alt | altitude | height above sea level | R | R | R | ||
salinity | salinity | salinity | R | R | R | ||
porosity | porosity | porosity | |||||
location | location | location on earth | |||||
astronomical_body | astronomical body | planet or other astronomical object where sample was collected | M |
Here the applies_to_class
descriptor indicates that the column value for the slot indicated in the row is interpreted as slot usage for that class.
Metatype fields
In all of the examples above, distinct descriptors are used for class names, slot names, type names, enum names, etc
An alternative pattern is to mix element types in a single sheet, indicate the name of the element using name
and the type using metatype
.
For example:
type | item | applies to | key | multiplicity | range | parents | desc | schema.org | wikidata | belongs | status | notes |
---|---|---|---|---|---|---|---|---|---|---|---|---|
> metatype |
name | class | identifier | cardinality | range | is_a | description | exact_mappings: {curie_prefix: sdo} |
exact_mappings: {curie_prefix: wikidata} |
in_subset | status | ignore |
> vmap: {C: class, F: slot} |
||||||||||||
> |
curie_prefix: wikidata | vmap: {T: testing, R: release} |
||||||||||
F | id | yes | 1 | string | any identifier | identifier | ||||||
F | name | Person, Organization | no | 1 | string | full name | name | |||||
F | description | no | 0..1 | string | a textual description | description | ||||||
F | age | Person | no | 0..1 | decimal | age in years | ||||||
F | gender | Person | no | 0..1 | decimal | age in years | ||||||
F | has medical history | Person | no | 0..* | MedicalEvent | medical history | T | |||||
C | Person | a person,living or dead | Person | Q215627 | R | |||||||
C | Event | grouping class for events | Q1656682 | a | R | |||||||
C | MedicalEvent | Event | a medical encounter | b | T | |||||||
C | ForProfit | Organization | ||||||||||
C | NonProfit | Organization | Q163740 | foo |
Formal specification
In progress. The following is a sketch. Please refer to the above examples for elucidation.
- The first line is a HEADER line.
- Each column must be non-null and unique
- In future grouping columns may be possible
- Subsequent lines starting with
>
are column configurations- A column configuration can be split over multiple lines
- Each line must be a valid yaml string (note that a single token is valid yaml for that token)
- The first config line must include a descriptor
- Subsequent lines are settings for that descriptor
- A descriptor can be one of:
- Any LinkML metamodel slot (e.g. description, comments, required, recommended, multivalued)
- The keyword
cardinality
- An element metatype (schema, prefix, class, enum, slot, type, subset, permissible_value)
- Setting can be taken from configschema.yaml
- vmap provides a mapping used to translate column values. E.g. a custom "yes" or "no" to "true" or "false"
- various keys provide ways to auto-prefix or manipulate strings
- Remaining rows are elements of your schema
- Each element gets its own row
- A row can represent a class (record, table), field (column), enumeration, or other element types
- The type of the row is indicated by whether columns with metatype descriptors are filled
- E.g. if a column header "field" has a descriptor "slot" then any row with a non-null value is interpreted as a slot
- If a
metatype
descriptor is present then this is used - A row must represent exactly one element type
- If both class and slot descriptors are present then the row is interpreted as a slot in the context of that class (see slot_usage)
- All sheets/TSVs are combined together into a single LinkML schema as YAML
- This LinkML schema can be translated to other formats as per the LinkML generators
Working with files / google sheets
This tool takes as input a collection of sheets, which are stored as TSV files.
You can make use of various ways of managing/organizing these:
- TSVs files maintained in GitHub
- Google sheets
- Excel spreadsheets
- SQLite databases
Tips for each of these and for organizing your information are provided below
Multiple sheets vs single sheets
It is up to you whether you represent your schema as a single sheet or as multiple sheets
However, if your schema includes a mixture of different element types, you may end up with a lot of null values if you have a single sheet. It can be more intuitive to "normalize" your schema description into different sheets:
- sheets for classes/slots
- sheets for enums
- sheets for types
Currently schemasheets has no built in facilities for interacting directly with google sheets - it is up to you to both download and upload these
TODO: scripts for merging/splitting sheets
Manual upload/download
Note that you can create a URL from a google sheet to the TSV download - TODO
COGS
We recommend the COGS framework for working with google sheets
A common pattern is a single sheet document for a schema, with different sheets/tabs for different parts of the schema
TODO: example
Working with Excel spreadsheets
Currently no direct support, it is up to you to load/save as individual TSVs