PyNIDM: Neuroimaging Data Model in Python

A Python library to manipulate the Neuroimaging Data Model.

Dependencies

Git-annex
Graphviz (native package):
- Fedora: dnf install graphviz
- OS-X: brew install graphviz

Installation

$ pip install pynidm

Contributing to the Software

This software is open source and community developed. As such, we encourage anyone and everyone interested in semantic web and neuroimaging to contribute. To begin contributing code to the repository, please fork the main repo into your user space and use the pull request GitHub feature to submit code for review. Please provide a reasonably detailed description of what was changed and why in the pull request.

Reporting Issues or Problems

If you encounter a bug, you can directly report it in the issues section. Please describe how to reproduce the issue and include as much information as possible that can be helpful for fixing it. If you would like to suggest a fix, please open a new pull request or include your suggested fix in the issue.

Support and Feedback

We would love to hear your thoughts on our Python toolbox. Feedback, questions, or feature requests can also be submitted as issues. Note, we are a small band of researchers who mostly volunteer our time to this project. We will respond as quickly as possible.

NIDM Model Details

NIDM files (typically nidm.ttl) are RDF Turtle documents that represent neuroimaging study data using the W3C PROV provenance data model. Every entity, activity, and agent is identified by a URI and connected by typed RDF triples, making NIDM data machine-readable, semantically rich, and interoperable across sites and tools.

The terms and classes used in NIDM documents are formally defined in the NIDM-Experiment ontology. Community-based management of the controlled vocabulary used to annotate data elements is described in Keator et al., Frontiers in Neuroinformatics 2023 and maintained in the NIDM-Terms repository.

A formal LinkML schema documenting the complete graph structure is provided at src/nidm/experiment/schema/nidm_schema.yaml.

Graph Hierarchy

A NIDM graph is organized as a hierarchy of W3C PROV objects. Each node carries one or more rdf:type assertions — one NIDM-specific type giving its scientific role, and one PROV type giving its provenance role:

Project  (nidm:Project + prov:Activity)
│
├── Session  (nidm:Session + prov:Activity)          [dct:isPartOf → Project]
│    │
│    └── Acquisition  (nidm:Acquisition + prov:Activity)
│         │                                          [dct:isPartOf → Session]
│         └── AcquisitionObject  (nidm:AcquisitionObject + prov:Entity)
│                                    [prov:wasGeneratedBy → Acquisition]
│                                    [variable values stored as RDF properties]
│
├── DataElement  (nidm:DataElement / nidm:PersonalDataElement + prov:Entity)
│
└── Derivative  (nidm:Derivative + prov:Activity)   [dct:isPartOf → Project]
     │
     └── DerivativeObject  (prov:Entity)            [prov:wasGeneratedBy → Derivative]
                                [derived values stored as RDF properties]

Project is the top-level container for a study or dataset, holding title, license, funding, and versioning metadata.

Session groups the acquisitions for one participant visit.

Acquisition represents a single data-collection event — an MRI scan, a questionnaire, or a demographic entry. Imaging acquisitions carry nidm:hadAcquisitionModality, nidm:hadImageContrastType, and nidm:hadImageUsageType.

AcquisitionObject is the entity produced by an Acquisition. For imaging data it stores the filename and checksum; for assessments and demographics it stores measured values as RDF properties, using DataElement URIs as predicates.

Derivative / DerivativeObject represent post-processing pipelines (FreeSurfer, FSL, ANTs, etc.) and the analysis results they produce.

Participant Linkage

Participants are prov:Person agents linked to Acquisitions through PROV’s qualified-association pattern:

Acquisition
  └── prov:qualifiedAssociation
        └── prov:Association  (blank node)
              ├── prov:agent    ──►  Person
              │                       └── ndar:src_subject_id  "sub-001"
              └── prov:hadRole  ──►  sio:Subject

ndar:src_subject_id on the Person node is the primary human-readable participant identifier across all PyNIDM query operations.

DataElements and Measurement Values

DataElements define the semantics of every measured variable — its label, data type, units, valid range, and linkage to a shared ontology concept via nidm:isAbout. Linking variables to concepts from the NIDM-Experiment ontology or community registries such as InterLex enables federated queries across datasets that use different local variable names for the same underlying concept.

DataElement URIs serve a dual role in the graph:

As subjects — the DataElement URI carries all metadata about the variable (label, units, ontology mapping, etc.).
As predicates — the same URI is used as the RDF predicate on AcquisitionObjects and DerivativeObjects to store actual measured values.

A PersonalDataElement (demographic or assessment variable) in Turtle:

niiri:gender_hrg8rh  a nidm:PersonalDataElement, prov:Entity ;
    rdfs:label              "gender" ;
    dct:description         "Gender of participant" ;
    nidm:sourceVariable     "gender" ;
    nidm:isAbout            ilx:ilx_0101292 ;
    nidm:valueType          xsd:complexType ;
    nidm:minValue           "NA" ;
    nidm:maxValue           "NA" ;
    reproschema:choices     [ rdfs:label "male"   ; reproschema:value "1" ],
                            [ rdfs:label "female" ; reproschema:value "2" ] ;
    ilx:ilx_0739289         "NIDM" .

# Same DataElement URI used as a predicate to store a subject's value:
niiri:acqobj_abc123  prov:wasGeneratedBy niiri:acq_456 ;
                     niiri:gender_hrg8rh  "1"^^xsd:string .

An imaging pipeline DataElement (e.g. from FreeSurfer):

fs:fs_000003  a nidm:DataElement ;
    rdfs:label           "Brain Segmentation Volume (mm^3)" ;
    nidm:isAbout         obo:UBERON_0000955 ;
    nidm:measureOf       ilx:ilx_0112559 ;
    nidm:datumType       ilx:ilx_0738276 ;
    nidm:unitCode        "mm^3" ;
    nidm:hasLaterality   "Bilateral" .

DataElement Property Reference

Property	RDF Predicate	Description
type	`rdf:type`	`nidm:PersonalDataElement` (demographic / assessment) or `nidm:DataElement` (imaging pipeline CDE), always combined with `prov:Entity`
label	`rdfs:label`	Human-readable variable name
description	`dct:description`	Free-text description of the variable
definition	`rdfs:comment`	Longer formal definition (used when importing terms from external registries)
sourceVariable	`nidm:sourceVariable`	Original column / variable name in the source dataset
isAbout	`nidm:isAbout`	URI of the ontology concept this variable represents (e.g. `ilx:ilx_0100400` for age). The key property enabling cross-dataset concept-based federated queries. See the NIDM-Experiment ontology and InterLex
valueType	`nidm:valueType`	XSD datatype URI: `xsd:float`, `xsd:integer`, `xsd:string`, `xsd:boolean`, or `xsd:complexType` for categorical variables
minValue	`nidm:minValue`	Minimum allowed value (`"NA"` if not applicable)
maxValue	`nidm:maxValue`	Maximum allowed value (`"NA"` if not applicable)
unitCode	`nidm:unitCode`	Unit of measurement string (e.g. `"mm^3"`, `"years"`, `"vertex"`)
choices	`reproschema:choices`	Categorical response options. Each choice is a blank node with `rdfs:label` (display text) and `reproschema:value` (stored code), or a plain literal string for simple enumerations
measureOf	`nidm:measureOf`	URI of the physical / biological property being measured (e.g. `ilx:ilx_0112559` for volume, `obo:PATO_0001323` for surface area). Used primarily in imaging pipeline CDEs
datumType	`nidm:datumType`	URI of the measurement datum type (e.g. `ilx:ilx_0738276` for scalar, `ilx:ilx_0102597` for count). Used primarily in imaging pipeline CDEs
hasLaterality	`nidm:hasLaterality`	Brain laterality: `"Left"`, `"Right"`, or `"Bilateral"`. Used in imaging pipeline CDEs
url	`nidm:url`	URL linking to this variable’s entry in a terminology registry (e.g. InterLex / SciCrunch)
sameAs	`nidm:sameAs`	URI of an equivalent term in another vocabulary
allowableValues	`bids:allowableValues`	Allowable values for BIDS-sourced variables
associatedWith	`ilx:ilx_0739289`	Terminology provenance tag (e.g. `"NIDM"`) indicating which controlled vocabulary sourced this term

Key Namespaces

nidm:          http://purl.org/nidash/nidm#
prov:          http://www.w3.org/ns/prov#
niiri:         http://iri.nidash.org/              (instance identifiers)
ndar:          https://ndar.nih.gov/api/datadictionary/v2/dataelement/
dct:           http://purl.org/dc/terms/
dctypes:       http://purl.org/dc/dcmitype/
sio:           http://semanticscience.org/ontology/sio.owl#
obo:           http://purl.obolibrary.org/obo/
onli:          http://neurolog.unice.fr/ontoneurolog/v3.0/instrument.owl#
reproschema:   http://schema.repronim.org/
ilx:           http://uri.interlex.org/
freesurfer:    https://surfer.nmr.mgh.harvard.edu/
fsl:           http://purl.org/nidash/fsl#
ants:          http://stnava.github.io/ANTs/
bids:          http://bids.neuroimaging.io/

Example SPARQL Queries

List all projects and their titles:

PREFIX nidm:    <http://purl.org/nidash/nidm#>
PREFIX dctypes: <http://purl.org/dc/dcmitype/>

SELECT ?project ?title WHERE {
  ?project a nidm:Project .
  OPTIONAL { ?project dctypes:title ?title }
}

List all subjects and their source IDs:

PREFIX prov: <http://www.w3.org/ns/prov#>
PREFIX ndar: <https://ndar.nih.gov/api/datadictionary/v2/dataelement/>

SELECT ?person ?subject_id WHERE {
  ?person a prov:Person ;
          ndar:src_subject_id ?subject_id .
}

Retrieve values for a variable (e.g. AGE_AT_SCAN) across all subjects:

PREFIX prov:  <http://www.w3.org/ns/prov#>
PREFIX ndar:  <https://ndar.nih.gov/api/datadictionary/v2/dataelement/>
PREFIX rdfs:  <http://www.w3.org/2000/01/rdf-schema#>

SELECT ?subject_id ?value WHERE {
  ?de rdfs:label "AGE_AT_SCAN" .
  ?acq_obj ?de ?value ;
           prov:wasGeneratedBy ?acq .
  ?acq prov:qualifiedAssociation ?assoc .
  ?assoc prov:agent ?person .
  ?person ndar:src_subject_id ?subject_id .
}

Find all DataElements about a given concept using nidm:isAbout (enables cross-dataset federated queries):

PREFIX nidm:  <http://purl.org/nidash/nidm#>
PREFIX rdfs:  <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?de ?label ?sourceVar WHERE {
  { ?de a nidm:DataElement } UNION { ?de a nidm:PersonalDataElement }
  ?de nidm:isAbout <http://uri.interlex.org/ilx_0100400> ;
      rdfs:label ?label .
  OPTIONAL { ?de nidm:sourceVariable ?sourceVar }
}

NIDM-Experiment Tools

BIDS MRI Conversion to NIDM

This program will convert a BIDS MRI dataset to a NIDM-Experiment RDF document. It will parse phenotype information and simply store variables/values and link to the associated json data dictionary file. To use this tool please set your INTERLEX_API_KEY environment variable to your unique API key. To get an Interlex API key you visit SciCrunch, register for an account, then click on “MyAccount” and “API Keys” to add a new API key for your account.

$ bidsmri2nidm -d [ROOT BIDS DIRECT] -bidsignore

# Write one NIDM file per subject as BIDS_ROOT/sub-<id>/nidm.ttl:
$ bidsmri2nidm -d [ROOT BIDS DIRECT] --per_subject

# Or direct the per-subject files under a different base output directory:
$ bidsmri2nidm -d [ROOT BIDS DIRECT] --per_subject -o [OUTPUT DIRECTORY]

usage: bidsmri2nidm [-h] -d DIRECTORY [-jsonld] [-bidsignore] [-no_concepts]
                 [-json_map JSON_MAP] [-log LOGFILE] [-o OUTPUTFILE]
                 [-per_subject]

This program will represent a BIDS MRI dataset as a NIDM RDF document and provide user with opportunity to annotate
the dataset (i.e. create sidecar files) and associate selected variables with broader concepts to make datasets more
FAIR.

Note, you must obtain an API key to Interlex by signing up for an account at scicrunch.org then going to My Account
and API Keys.  Then set the environment variable INTERLEX_API_KEY with your key.

optional arguments:
  -h, --help            show this help message and exit
  -d DIRECTORY          Full path to BIDS dataset directory
  -jsonld, --jsonld     If flag set, output is json-ld not TURTLE
  -bidsignore, --bidsignore
                     If flag set, tool will add NIDM-related files to .bidsignore file
  -no_concepts, --no_concepts
                     If flag set, tool will no do concept mapping
  -log LOGFILE, --log LOGFILE
                     Full path to directory to save log file. Log file name is bidsmri2nidm_[basename(args.directory)].log
  -o OUTPUTFILE         Output turtle file path; defaults to nidm.ttl in the BIDS directory.  Accepts an absolute or
                        relative path (relative paths resolve against the current working directory; missing parent
                        directories are created).  In --per_subject mode this is the base output directory, beneath
                        which one sub-<id>/nidm.ttl is written per subject.
  -per_subject, --per_subject
                     If flag set, a separate NIDM turtle file named nidm.ttl is written into each subject's BIDS
                     directory, i.e. BIDS_ROOT/sub-<id>/nidm.ttl.  By default these go under the BIDS directory; use
                     -o to specify a different base output directory.  When combined with -bidsignore, each
                     sub-<id>/nidm.ttl is added to the BIDS dataset's .bidsignore file (when the output lies inside
                     the BIDS tree).

map variables to terms arguments:
  -json_map JSON_MAP, --json_map JSON_MAP
                     Optional full path to user-suppled JSON file containing data element definitions.

CSV File to NIDM Conversion

This program will load in a CSV file and iterate over the header variable names performing an elastic search of https://scicrunch.org/nidm-terms for NIDM-ReproNim tagged terms that fuzzy match the variable names. The user will then interactively pick a term to associate with the variable name. The resulting annotated CSV data will then be written to a NIDM data file. To use this tool please set your INTERLEX_API_KEY environment variable to your unique API key. To get an Interlex API key you visit SciCrunch, register for an account, then click on “MyAccount” and “API Keys” to add a new API key for your account.

usage: csv2nidm [-h] -csv CSV_FILE [-json_map JSON_MAP | -redcap REDCAP]
                [-nidm NIDM_FILE] [-no_concepts] [-log LOGFILE] -out
                OUTPUT_FILE

This program will load in a CSV file and iterate over the header variable
names performing an elastic search of https://scicrunch.org/ for NIDM-ReproNim
tagged terms that fuzzy match the variable names. The user will then
interactively pick a term to associate with the variable name. The resulting
annotated CSV data will then be written to a NIDM data file. Note, you must
obtain an API key to Interlex by signing up for an account at scicrunch.org
then going to My Account and API Keys. Then set the environment variable
INTERLEX_API_KEY with your key.  The tool supports import of RedCap data
dictionaries and will convert relevant information into a json-formatted
annotation file used to annotate the data elements in the resulting NIDM file.

optional arguments:
  -h, --help            show this help message and exit
  -csv CSV_FILE         Full path to CSV file to convert
  -json_map JSON_MAP    Full path to user-supplied JSON file containing
                        variable-term mappings.
  -csv_map CSV_MAP      Full path to a user-supplied CSV data dictionary with
                        columns: source_variable, label, description,
                        valueType, measureOf, isAbout, unitCode, minValue,
                        maxValue. Mutually exclusive with -json_map/-redcap.
  -redcap REDCAP        Full path to a user-supplied RedCap formatted data
                        dictionary for csv file.
  -nidm NIDM_FILE       Optional full path of NIDM file to add CSV->NIDM
                        converted graph to
  -no_concepts          If this flag is set then no concept associations will
                        be asked of the user. This is useful if you already
                        have a -json_map specified without concepts and want to
                        simply run this program to get a NIDM file without
                        user interaction to associate concepts.
  -log LOGFILE, --log LOGFILE
                        Full path to directory to save log file. Log file name
                        is csv2nidm_[arg.csv_file].log
  -dataset_id DATASET_ID
                        Optional dataset identifier (e.g. a DOI). When
                        provided, unique data element IDs incorporate this
                        value as part of their hash, ensuring CDE URIs are
                        globally unique across datasets.
  -derivative DERIVATIVE_METADATA
                        If set, indicates the CSV contains derivative data.
                        The value must be the path to a software metadata CSV
                        with columns: title, description, version, url,
                        cmdline, platform, ID. The CSV must also include
                        columns ses, task, run, and source_url.
  -out OUTPUT_FILE      Full path with filename to save NIDM file

convert

This function will convert NIDM files to various RDF-supported formats and name then / put them in the same place as the input file.

Usage: pynidm convert [OPTIONS]

Options:
  -nl, --nidm_file_list TEXT      A comma separated list of NIDM files with
                                  full path  [required]
  -t, --type [turtle|jsonld|xml-rdf|n3|trig]
                                  Output RDF serialization format  [required]
  -out, --outdir TEXT             Optional directory to save converted file.
                                  Defaults to the same directory as the input.
  --help                          Show this message and exit.

concatenate

This function will concatenate NIDM files. Warning, no merging will be done so you may end up with multiple prov:agents with the same subject id if you’re concatenating NIDM files from multiple visits of the same study. If you want to merge NIDM files on subject ID see pynidm merge

Usage: pynidm concat [OPTIONS]

Options:
  -nl, --nidm_file_list TEXT  A comma separated list of NIDM files with full
                            path  [required]
  -o, --out_file TEXT         File to write concatenated NIDM files
                            [required]
  --help                      Show this message and exit.

visualize

This command produces a visualization of the supplied NIDM files as a directed provenance graph, written to the same directory as each input file.

Usage: pynidm visualize [OPTIONS]

Options:
  -nl, --nidm_file_list TEXT    A comma-separated list of NIDM files with
                                full path  [required]
  -fmt, --format [svg|png|pdf]  Output format (default: svg). SVG opens in
                                any web browser with unlimited scroll and
                                zoom. PNG produces a high-resolution raster.
                                PDF is vector but may clip very large graphs.
  --help                        Show this message and exit.

merge

This function will merge NIDM files. See command line parameters for supported merge operations.

Usage: pynidm merge [OPTIONS]

Options:
  -nl, --nidm_file_list TEXT  A comma separated list of NIDM files with full
                           path  [required]
  -s, --s                     If parameter set then files will be merged by
                           ndar:src_subjec_id of prov:agents
      -o, --out_file TEXT         File to write concatenated NIDM files
                           [required]
      --help                      Show this message and exit.

Query

This function provides query support for NIDM graphs. Exactly one query-type option is required (the group is mutually exclusive).

Usage: pynidm query [OPTIONS]

Options:
  -nl, --nidm_file_list TEXT      Comma-separated NIDM inputs.  Each entry
                                  may be a NIDM file, a directory (recursed
                                  for **/nidm.ttl), a manifest text file
                                  (.txt/.list, one entry per line), a glob,
                                  or an http(s) URL  [required]
  -nc, --cde_file_list TEXT       A comma separated list of NIDM CDE files
                                  with full path. Can also be set in the
                                  CDE_DIR environment variable
  -wc, --with_cdes                Seed the CDE cache with the bundled
                                  FreeSurfer/FSL/ANTS CDE files (no -nc
                                  needed)

  Query Type (pick exactly one):
  -q, --query_file FILENAME       Text file containing a SPARQL query to
                                  execute
  -p, --get_participants          Return participant IDs and prov:agent
                                  entity IDs
  -i, --get_instruments           Return list of
                                  onli:assessment-instrument entries
  -iv, --get_instrument_vars      Return variables for all
                                  onli:assessment-instrument entries
  -de, --get_dataelements         Return all DataElements in NIDM file
  -debv, --get_dataelements_brainvols
                                  Return all brain volume DataElements with
                                  details
  -bv, --get_brainvols            Return all brain volume data elements and
                                  values with participant IDs
  -gf, --get_fields TEXT          Return data for a comma-separated list of
                                  field names across all NIDM files
                                  (e.g. -gf age,fs_000003)
  -u, --uri TEXT                  A REST API URI query

  -o, --output_file TEXT          Optional output file (CSV) to store
                                  results of query
  -j / -no_j                      Return result of a uri query as JSON
  -bg, --blaze TEXT               Base URL of a Blazegraph SPARQL endpoint
                                  (e.g. http://localhost:9999/blazegraph/sparql)
  -v, --verbosity TEXT            Verbosity level 0-5, 0 is default
  --help                          Show this message and exit.

QueryAI — AI-Assisted Natural Language Query

This tool translates natural-language questions about your NIDM data into SPARQL queries using an LLM — either a cloud API (Anthropic Claude or OpenAI GPT) or an optional local LLM server (e.g. llama.cpp or Ollama) for fully offline use. It uses a two-phase approach:

Phase 1 — Concept Resolution: The AI extracts variable concepts (e.g. “age”, “left hippocampus volume”) from your question. The tool then resolves each concept to the exact DataElement URI in your NIDM files by matching on nidm:isAbout (preferred) or nidm:sourceVariable properties. If multiple DataElements match, you are prompted to select the correct one(s).
Phase 2 — SPARQL Generation: For a plain “retrieve these variables” question the tool builds the SPARQL itself, in code (no LLM) from the resolved URIs — a person-anchored, zero-padding-tolerant query that joins every variable back to the same subject. This is fully reproducible regardless of which model ran Phase 1, and avoids the cartesian products and cross-file subject-id mismatches that an LLM-authored join is prone to. For analytical questions (counts, averages, group-by, filtering) the LLM generates the query instead, using the NIDM graph structure loaded from the bundled nidm_schema.json. Either way the query is executed locally against your NIDM files via rdflib — no subject data leaves your machine. See Query modes below.

Usage: pynidm queryai [OPTIONS]

Options:
  -nl, --nidm_file_list TEXT  Comma-separated NIDM inputs.  Each entry may be
                              a NIDM file, a directory (recursed for
                              **/nidm.ttl), a manifest text file (.txt/.list,
                              one entry per line, # comments allowed), a glob,
                              or an http(s) URL  [required]
  -wc, --with_cdes            Also load the bundled FreeSurfer/FSL/ANTS CDE
                              files so brain-volume data elements resolve
                              without listing them
  -q, --question TEXT         Natural-language question to ask about the
                              NIDM data.  If not provided, enters
                              interactive mode.
  -o, --output_file PATH      Optional output file for results (CSV format)
  -s, --show_query            Show the generated SPARQL query before
                              executing it
  -m, --mode [auto|deterministic|llm]
                              How Phase 2 builds the SPARQL.  'deterministic'
                              assembles a person-anchored, zero-padding-
                              tolerant query in code from the resolved URIs;
                              'llm' always asks the AI; 'auto' (default) uses
                              the deterministic builder for plain retrieval
                              questions and the AI for analytical ones.
  --help                      Show this message and exit.

Supplying many NIDM files. Instead of a long comma-separated path list, -nl accepts a directory (recursed for **/nidm.ttl), a manifest text file (one path / directory / glob / URL per line, # comments allowed), a shell glob, or an http(s) URL — entries may be mixed and are de-duplicated. Add -wc to pull in the bundled FreeSurfer/FSL/ANTS CDE files automatically:

# every per-subject file under a study tree, plus the CDEs, in one shot
pynidm queryai -nl /data/abide/derivatives -wc -q "Retrieve age, sex, and left and right hippocampus volume"

# or a manifest file listing files/dirs/URLs
pynidm queryai -nl nidm_files.txt -wc -q "..."

This also applies to pynidm query (-nl and -wc behave the same way there).

Prerequisites — choose an LLM provider.

queryai can use a cloud API (Anthropic or OpenAI) or an optional local LLM server, so it can run fully offline with no API key. The provider is selected by the PYNIDM_AI_PROVIDER environment variable (anthropic | openai | llama, case-insensitive); if it is unset the provider is auto-detected from whichever of the variables below is present.

Anthropic (Claude):

export ANTHROPIC_API_KEY=sk-ant-...

OpenAI (GPT):

export OPENAI_API_KEY=sk-...

Optional local server (no API key, runs offline). queryai can talk to any local OpenAI-compatible LLM server, so it can run with no cloud account and with nothing leaving your machine. Two common backends are llama.cpp (its llama-server) and Ollama.

With llama.cpp, install it and start llama-server with an instruct model. -hf downloads a GGUF from HuggingFace and caches it on first run, -ngl 99 offloads layers to the GPU (Apple Silicon / CUDA), and -c sets the context size (use a generous value — the Phase 2 prompt includes the NIDM schema):

brew install llama.cpp                                          # macOS; see llama.cpp for other platforms

llama-server -hf bartowski/Qwen2.5-14B-Instruct-GGUF:Q4_K_M -c 16384 -ngl 99

This serves an OpenAI-compatible API on http://localhost:8080. Then point queryai at it and run as usual:

export PYNIDM_AI_PROVIDER=llama
export PYNIDM_LLAMA_URL=http://localhost:8080/v1
export PYNIDM_LLAMA_MODEL=local-model     # llama.cpp ignores this; any value is fine

pynidm queryai -nl data/nidm.ttl -q "How many subjects are there?" -s

To use Ollama instead, run ollama serve and ollama pull llama3, then set PYNIDM_LLAMA_URL=http://localhost:11434/v1 and PYNIDM_LLAMA_MODEL=llama3 (the model tag).

With a local server nothing leaves your machine — not the question, the schema, or your data. Model choice matters: a small 7B model only handles the simplest queries, so we recommend a 14B instruct model (Qwen2.5-14B-Instruct-GGUF:Q4_K_M, shown above) for the multi-variable queries typical of real use — larger still if you have the memory. Local models are generally less reliable than Claude/GPT at producing valid SPARQL, so always inspect the generated query with -s.

A cloud provider + key may instead be stored in a config file at ~/.pynidm/config.json:

{"provider": "anthropic", "api_key": "sk-ant-..."}

Example 1 — Count subjects:

pynidm queryai -nl data/nidm.ttl -q "How many subjects are there?" -s

Example 2 — Average age:

pynidm queryai -nl data/nidm.ttl -q "What is the average age of all subjects?" -s

Example 3 — Complex cross-entity query with brain volumes:

pynidm queryai \
    -nl nidm.ttl,fs_cde.ttl,fsl_cde.ttl,ants_cde.ttl \
    -q "For each subject, get their ID, age, sex, diagnosis, and the left hippocampus volume from FreeSurfer, FSL, and/or ANTS with a column indicating the software tool that produced it" \
    -s

When multiple DataElements match a concept (e.g. left hippocampus volume from both FreeSurfer and ANTs), you will be prompted to select:

Multiple DataElements match 'left hippocampus volume':
  [1] ants:ants_000040 | label="Left-Hippocampus Volume (mm^3)" | laterality=Left
  [2] fs:fs_003343 | label="Left-Hippocampus Volume_mm3 (mm^3)" | laterality=Left
  [a] Select all
  [0] Skip this variable

Enter one number, multiple numbers separated by commas (e.g. 2,3),
'a' for all, or 0 to skip.

Example 4 — Interactive mode:

pynidm queryai -nl data/nidm.ttl

NIDM AI Query - Interactive Mode
Type your question and press Enter. Type 'quit' to exit.

Question: How many subjects have a diagnosis of autism?
...

Query modes (-m).

By default (-m auto) a plain “retrieve / list / show these variables” question is answered by the deterministic builder: the SPARQL is assembled in code from the resolved DataElement URIs, so the result is identical no matter which model (cloud or local) ran Phase 1, and is immune to the cartesian-product and cross-file subject-id problems an LLM-written join can introduce. Each variable is joined back to the same subject through the NIDM provenance backbone (entity → prov:wasGeneratedBy → activity → prov:qualifiedAssociation → prov:agent → Person), and subject ids are matched after stripping leading zeros so a demographics file (50772) lines up with a FreeSurfer/FSL derivative file (0050772). Coded values are translated to labels only when the DataElement defines value levels (reproschema:choices); otherwise the raw value is returned and a note is printed — queryai never fabricates a mapping. Analytical questions (counts, averages, group-by, filtering) are routed to the LLM. Use -m deterministic or -m llm to force a path.

# deterministic, reproducible, runs even with a small local model
pynidm queryai -nl demographics.ttl,freesurfer_cde.ttl \
  -q "Retrieve age, sex, diagnosis, and left and right hippocampus volume" -s

Query engine (Oxigraph).

rdflib’s built-in SPARQL engine is pure-Python and can be very slow on large multi-file graphs (many sites + CDEs). PyNIDM therefore depends on oxrdflib (the Rust-backed Oxigraph engine), installed automatically with the package, and runs SPARQL through it by default. This speeds up both pynidm query and pynidm queryai (including LLM-generated queries, not just the deterministic ones). Subject joins are matched on a normalized (leading-zero-stripped) id materialized at load, so cross-file joins use a term index rather than a per-row REPLACE filter.

No setup is needed. To force the engine for a single run, set PYNIDM_QUERY_ENGINE (auto (default) | oxigraph | rdflib); use rdflib only to fall back to the built-in engine:

PYNIDM_QUERY_ENGINE=rdflib pynidm query -nl data/nidm.ttl -q query.rq

Demo Script:

A demo script that automatically downloads sample NIDM data from public GitHub repositories and runs several example queries is included at src/nidm/experiment/tools/examples/queryai_demo.sh. See the queryai_demo.sh source for details.

chmod +x src/nidm/experiment/tools/examples/queryai_demo.sh
./src/nidm/experiment/tools/examples/queryai_demo.sh       # run all queries
./src/nidm/experiment/tools/examples/queryai_demo.sh 1 3   # run queries 1 and 3

linear_regression

This function provides linear regression support for NIDM graphs.

Usage: pynidm linear-regression [OPTIONS]

Options:
  -nl, --nidm_file_list TEXT      A comma-separated list of NIDM files with
                                  full path  [required]
  -model, --ml TEXT               An equation representing the linear
                                  regression. The dependent variable comes
                                  first, followed by "=" or "~", followed by
                                  the independent variables separated by "+"
                                  (Ex: -model "fs_003343 = age*sex + sex +
                                  age + group + age*group + bmi") [required]
  -contrast, --ctr TEXT           Parameter, if set, will return differences
                                  in variable relationships by group. One or
                                  multiple parameters can be used (separate
                                  with commas) (Ex: -contrast group,age)
  -r, --regularization TEXT       If set, applies L1 or L2 regularization
                                  and returns the maximum likelihood weight.
                                  Prevents overfitting. (Ex: -r L1)
  -o, --output_file TEXT          Optional output file (TXT) to store results
  --help                          Show this message and exit.

To use the linear regression algorithm successfully, structure, syntax, and querying is important. Here is how to maximize the usefulness of the tool:

First, use pynidm query to discover the variables to use. PyNIDM allows for the use of either data elements (PIQ_tca9ck), specific URLs (http://uri.interlex.org/ilx_0100400), or source variables (DX_GROUP).

An example of a potential query is:

pynidm query -nl /simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_a/nidm.ttl,/simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_b/nidm.ttl -u /projects?fields=fs_000008,DX_GROUP,PIQ_tca9ck,http://uri.interlex.org/ilx_0100400

You can also do:

pynidm query -nl /simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_a/nidm.ttl,/Users/Ashu/Downloads/simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_b/nidm.ttl -gf fs_000008,DX_GROUP,PIQ_tca9ck,http://uri.interlex.org/ilx_0100400

The query looks in the two files specified in the -nl parameter for the variables specified. In this case, we use fs_000008 and DX_GROUP (source variables), a URL (http://uri.interlex.org/ilx_0100400), and a data element (PIQ_tca9ck). The output of the file is slightly different depending on whether you use -gf or -u. With -gf, it will return the variables from both files separately, while -u combines them.

Now that we have selected the variables, we can perform a linear regression. In this example, we will look at the effect of DX_GROUP, age at scan, and PIQ on supratentorial brain volume.

The command to use for this particular data is:

pynidm linear-regression -nl /simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_a/nidm.ttl,/simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_b/nidm.ttl -model "fs_000008 = DX_GROUP + PIQ_tca9ck + http://uri.interlex.org/ilx_0100400" -contrast "DX_GROUP" -r L1

-nl specifies the file(s) to pull data from, while -model is the model to perform a linear regression model on. In this case, the variables are fs_000008 (the dependent variable, supratentorial brain volume), DX_GROUP (diagnostic group), PIQ_tca9ck (PIQ), and http://uri.interlex.org/ilx_0100400 (age at scan). The -contrast parameter says to contrast the data using DX_GROUP, and then do a L1 regularization to prevent overfitting.

Details on the REST API URI format and usage can be found below.

PyNIDM: REST API and Command Line Usage

Introduction

There are two main ways to interact with NIDM data using the PyNIDM REST API. First, the pynidm query command line utility will accept queries formatted as REST API URIs. Second, the rest-server.py script can be used to run a HTTP server to accept and process requests. This script can either be run directly or using a docker container defined in the docker directory of the project.

Example usage:

$ pynidm query -nl "cmu_a.ttl,cmu_b.ttl" -u /projects

dc1bf9be-10a3-11ea-8779-003ee1ce9545
ebe112da-10a3-11ea-af83-003ee1ce9545

Installation

To use the REST API query syntax on the command line, follow the PyNIDM installation instructions.

The simplest way to deploy a HTTP REST API server would be with the provided docker container. You can find instructions for that process in the README.md file in the docker directory of the Github repository.

URI formats

Here is a list of the current operations.

- /projects
- /projects/{project_id}
- /projects/{project_id}/subjects
- /projects/{project_id}/subjects
- /projects/{project_id}/subjects/{subject_id}
- /projects/{project_id}/subjects/{subject_id}/instruments
- /projects/{project_id}/subjects/{subject_id}/instruments/{instrument_id}
- /projects/{project_id}/subjects/{subject_id}/derivatives/
- /projects/{project_id}/subjects/{subject_id}/derivatives/{derivative_id}
- /subjects
- /subjects/{subject_id}
- /statistics/projects/{project_id}
- /dataelements
- /dataelements/{dataelement_id}

You can append the following query parameters to many of the operations:

- filter
- field

Operations

/projects

Get a list of all project IDs available.

Supported optional query parameters: fields

/projects/{project_id}

See some details for a project. This will include project summary information (acquisition modality, contrast type, image usage, etc) as well as a list of subject IDs and data elements used in the project.

When a fields parameters are provided, all instrument/derivative data in the project matching the field list will be returned as a table.

When a filter parameter is provided, the list of subjects returned will only include subjects that have data passing the filter

Supported optional query parameters: filter, fields

/projects/{project_id}/subjects

Get the list of subjects in a project

When a filter parameter is provided only subjects matching the filter will be returned.

Supported optional query parameters: filter

/projects/{project_id}/subjects/{subject_id}

Get the details for a particular subject. This will include the results of any instrumnts or derivatives associated with the subject, as well a list of the related activities.

Supported query parameters: none

/projects/{project_id}/subjects/{subject_id}/instruments

Get a list of all instruments associated with that subject.

Supported query parameters: none

/projects/{project_id}/subjects/{subject_id}/instruments/{instrument_id}

Get the values for a particular instrument

Supported query parameters: none

/projects/{project_id}/subjects/{subject_id}/derivatives

Get a list of all instruments associated with that subject.

Supported query parameters: none

/projects/{project_id}/subjects/{subject_id}/derivatives/{derivative_id}

Get the values for a particular derivative

Supported query parameters: none

/subjects

Returns the UUID and Source Subject ID for all subjects available.

If the fields parameter is provided, the result will also include a table of subjects along with the values for the supplied fields in any instrument or derivative

Supported query parameters: fields

/subjects/{subject_id}

Get the details for a particular subject. This will include the results of any instrumnts or derivatives associated with the subject, as well a a list of the related activities.

Supported query parameters: none

/statistics/projects/{project_id}

See project statistics. You can also use this operation to get statsitcs on a particular instrument or derivative entry by use a field query option.

Supported query parameters: filter, field

/statistics/projects/{project_id}/subjects/{subject_id}

See some details for a project. This will include the list of subject IDs and data elements used in the project

Supported query parameters: none

/dataelements/{identifier}

Returns a table of details on the dataelement that has any synonym matching the provided identifier. The system will attempt to match the data element label, isAbout URI, or data element URI. The return result will also provide a list of projects where the data element is in use.

Supported query parameters: none

Query Parameters

filter

The filter query parameter is used when you want to receive data only on subjects that match some criteria. The format for the filter value should be of the form:

identifier op value [ and identifier op value and ... ]

Identifiers should be formatted as either a simple field, such as “age”, or if you want to restrict the match to just instruments or derivatives format it ia “derivatives.ID” or “derivatives.Subcortical gray matter volume (mm^3)”

You can use any value for identifier that is shown in the data_elements section of the project details. For a derivative ID, you can use the last component of a derivative field URI (ex. for the URI http://purl.org/nidash/fsl#fsl_000007, the ID would be “fsl_000007”) or the exact label shown when viewing derivative data (ex. “Left-Caudate (mm^3)”).

The op can be one of “eq”, “gt”, “lt”.

Example filters:: ?filter=instruments.AGE_AT_SCAN gt 30 ?filter=instrument.AGE_AT_SCAN eq 21 and derivative.fsl_000007 lt 3500

fields

The fields query parameter is used to specify what fields should be detailed. The matching rules are similar to the filter parameter.

Example field query:: http://localhost:5000/statistics/projects/abc123?field=AGE_AT_SCAN,derivatives.fsl_000020

For identifiers in both the fields and filters, when PyNIDM is trying to match your provided value with data in the file a list of synonyms will be created to facilitate the match. This allows you to use the exact identifier, URI, data element label, or an “is about” concept URI if available.

Return Formatting

By default the HTTP REST API server will return JSON formatted objects or arrays. When using the pynidm query command line utility the default return format is text (when possible) or you can use the -j option to have the output formatted as JSON.

Examples

Get the UUID for all the projects at this location

curl http://localhost:5000/projects

Example response:

[
    "dc1bf9be-10a3-11ea-8779-003ee1ce9545"
]

Get the project summary details

curl http://localhost:5000/projects/dc1bf9be-10a3-11ea-8779-003ee1ce9545

Example response:

{
  "AcquisitionModality": [
    "MagneticResonanceImaging"
  ],
  "ImageContrastType": [
    "T1Weighted",
    "FlowWeighted"
  ],
  "ImageUsageType": [
    "Anatomical",
    "Functional"
  ],
  "Task": [
    "rest"
  ],
  "sio:Identifier": "1.0.1",
  "dctypes:title": "ABIDE CMU_a Site",
  "http://www.w3.org/1999/02/22-rdf-syntax-ns#type": "http://www.w3.org/ns/prov#Activity",
  "prov:Location": "file://datasets.datalad.org/abide/RawDataBIDS/CMU_a",
  "subjects": [
    "fdb6c8bc-67aa-11ea-ba45-003ee1ce9545",
    "b276ebb6-67aa-11ea-ba45-003ee1ce9545",
    "a38c4e42-67aa-11ea-ba45-003ee1ce9545",
    "a2ff751c-67aa-11ea-ba45-003ee1ce9545",
    "cfce5728-67aa-11ea-ba45-003ee1ce9545",
    "f165e7ae-67aa-11ea-ba45-003ee1ce9545",
    "cf4605ee-67aa-11ea-ba45-003ee1ce9545",
    "a1efa78c-67aa-11ea-ba45-003ee1ce9545",
    "d0de8ebc-67aa-11ea-ba45-003ee1ce9545",
    "a4a999ba-67aa-11ea-ba45-003ee1ce9545",
    "a0555098-67aa-11ea-ba45-003ee1ce9545",
    "b41d75f2-67aa-11ea-ba45-003ee1ce9545",
    "be3fbff0-67aa-11ea-ba45-003ee1ce9545",
    "eec5a0ca-67aa-11ea-ba45-003ee1ce9545"
  ],
  "data_elements": [
    "SCQ_TOTAL", "VIQ", "VINELAND_WRITTEN_V_SCALED", "WISC_IV_VCI", "ADOS_COMM", "FILE_ID", "WISC_IV_BLK_DSN_SCALED",
    "WISC_IV_SYM_SCALED", "ADI_R_SOCIAL_TOTAL_A", "WISC_IV_INFO_SCALED", "ADOS_GOTHAM_SEVERITY",
    "VINELAND_COMMUNICATION_STANDARD", "VINELAND_PERSONAL_V_SCALED", "SUB_ID", "ADOS_GOTHAM_TOTAL",
    "ADI_R_VERBAL_TOTAL_BV", "VINELAND_COPING_V_SCALED", "VINELAND_DOMESTIC_V_SCALED", "SRS_COGNITION",
    "FIQ_TEST_TYPE", "WISC_IV_PSI", "OFF_STIMULANTS_AT_SCAN", "VINELAND_PLAY_V_SCALED", "AGE_AT_MPRAGE",
    "VIQ_TEST_TYPE", "ADI_RRB_TOTAL_C", "WISC_IV_DIGIT_SPAN_SCALED", "FIQ", "DSM_IV_TR", "DX_GROUP",
    "VINELAND_INTERPERSONAL_V_SCALED", "VINELAND_SUM_SCORES", "ADOS_STEREO_BEHAV", "ADI_R_ONSET_TOTAL_D",
    "ADOS_GOTHAM_SOCAFFECT", "ADOS_GOTHAM_RRB", "CURRENT_MED_STATUS", "VINELAND_EXPRESSIVE_V_SCALED",
    "AGE_AT_SCAN", "WISC_IV_PRI", "SEX", "SRS_RAW_TOTAL", "ADOS_RSRCH_RELIABLE", "WISC_IV_SIM_SCALED",
    "WISC_IV_CODING_SCALED", "SRS_MANNERISMS", "AQ_TOTAL", "HANDEDNESS_SCORES", "HANDEDNESS_CATEGORY",
    "SRS_VERSION", "ADI_R_RSRCH_RELIABLE", "EYE_STATUS_AT_SCAN", "MEDICATION_NAME", "ADOS_SOCIAL",
    "ADOS_MODULE", "VINELAND_RECEPTIVE_V_SCALED", "VINELAND_DAILYLVNG_STANDARD", "VINELAND_ABC_STANDARD",
    "PIQ", "VINELAND_SOCIAL_STANDARD", "SITE_ID", "COMORBIDITY", "BMI", "VINELAND_COMMUNITY_V_SCALED",
    "ADOS_TOTAL", "VINELAND_INFORMANT", "WISC_IV_WMI", "WISC_IV_MATRIX_SCALED", "WISC_IV_LET_NUM_SCALED",
    "PIQ_TEST_TYPE", "SRS_COMMUNICATION", "WISC_IV_VOCAB_SCALED", "SRS_AWARENESS", "WISC_IV_PIC_CON_SCALED",
    "SRS_MOTIVATION"
  ]
}

Get Left-Pallidum volume (fsl_0000012) values for all subjects in a project

pynidm query -nl ttl/cmu_a.ttl -u /projects/cc305b3e-67aa-11ea-ba45-003ee1ce9545?fields=fsl_000012

-----------------------------------------------  -----------------------------------------------------
AcquisitionModality                              ["MagneticResonanceImaging"]
ImageContrastType                                ["FlowWeighted", "T1Weighted"]
ImageUsageType                                   ["Functional", "Anatomical"]
Task                                             ["rest"]
sio:Identifier                                   "1.0.1"
dctypes:title                                    "ABIDE CMU_a Site"
http://www.w3.org/1999/02/22-rdf-syntax-ns#type  "http://www.w3.org/ns/prov#Activity"
prov:Location                                    "file://datasets.datalad.org/abide/RawDataBIDS/CMU_a"
-----------------------------------------------  -----------------------------------------------------

subjects
------------------------------------
fdb6c8bc-67aa-11ea-ba45-003ee1ce9545
b276ebb6-67aa-11ea-ba45-003ee1ce9545
a38c4e42-67aa-11ea-ba45-003ee1ce9545
a2ff751c-67aa-11ea-ba45-003ee1ce9545
cfce5728-67aa-11ea-ba45-003ee1ce9545
f165e7ae-67aa-11ea-ba45-003ee1ce9545
cf4605ee-67aa-11ea-ba45-003ee1ce9545
a1efa78c-67aa-11ea-ba45-003ee1ce9545
d0de8ebc-67aa-11ea-ba45-003ee1ce9545
a4a999ba-67aa-11ea-ba45-003ee1ce9545
a0555098-67aa-11ea-ba45-003ee1ce9545
b41d75f2-67aa-11ea-ba45-003ee1ce9545
be3fbff0-67aa-11ea-ba45-003ee1ce9545
eec5a0ca-67aa-11ea-ba45-003ee1ce9545

data_elements
-------------------------------
SCQ_TOTAL
VIQ
...
WISC_IV_PIC_CON_SCALED
SRS_MOTIVATION

subject                               field       datumType    label                   value  units
------------------------------------  ----------  -----------  --------------------  -------  -------
fdb6c8bc-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     1630  mm^3
b276ebb6-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     2062  mm^3
a38c4e42-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     1699  mm^3
a2ff751c-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     1791  mm^3
cfce5728-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     2017  mm^3
f165e7ae-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     2405  mm^3
cf4605ee-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     2062  mm^3
a1efa78c-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     1961  mm^3
d0de8ebc-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     1568  mm^3
a4a999ba-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     1948  mm^3
a0555098-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     1764  mm^3
b41d75f2-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     2031  mm^3
be3fbff0-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     1935  mm^3
eec5a0ca-67aa-11ea-ba45-003ee1ce9545  fsl_000012  ilx_0738276  Left-Pallidum (mm^3)     1806  mm^3

Get the subjects in a project

pynidm query -nl "cmu_a.nidm.ttl" -u http://localhost:5000/projects/dc1bf9be-10a3-11ea-8779-003ee1ce9545/subjects

Example response:

deef8eb2-10a3-11ea-8779-003ee1ce9545
df533e6c-10a3-11ea-8779-003ee1ce9545
ddbfb454-10a3-11ea-8779-003ee1ce9545
df21cada-10a3-11ea-8779-003ee1ce9545
dcfa35b2-10a3-11ea-8779-003ee1ce9545
de89ce4c-10a3-11ea-8779-003ee1ce9545
dd2ce75a-10a3-11ea-8779-003ee1ce9545
ddf21020-10a3-11ea-8779-003ee1ce9545
debc0f74-10a3-11ea-8779-003ee1ce9545
de245134-10a3-11ea-8779-003ee1ce9545
dd5f2f30-10a3-11ea-8779-003ee1ce9545
dd8d4faa-10a3-11ea-8779-003ee1ce9545
df87cbaa-10a3-11ea-8779-003ee1ce9545
de55285e-10a3-11ea-8779-003ee1ce9545

Use the command line to get statistics on a project for the AGE_AT_SCAN and a FSL data element

pynidm query -nl ttl/cmu_a.nidm.ttl -u /statistics/projects/dc1bf9be-10a3-11ea-8779-003ee1ce9545?fields=instruments.AGE_AT_SCAN,derivatives.fsl_000001

Example response:

-------------------------------------------------  ---------------------------------------------
"http://www.w3.org/1999/02/22-rdf-syntax-ns#type"  http://www.w3.org/ns/prov#Activity
"title"                                            ABIDE CMU_a Site
"Identifier"                                       1.0.1
"prov:Location"                                    /datasets.datalad.org/abide/RawDataBIDS/CMU_a
"NIDM_0000171"                                     14
"age_max"                                          33.0
"age_min"                                          21.0

  gender
--------
       1
       2

handedness
------------
R
L
Ambi

subjects
------------------------------------
de89ce4c-10a3-11ea-8779-003ee1ce9545
deef8eb2-10a3-11ea-8779-003ee1ce9545
dd8d4faa-10a3-11ea-8779-003ee1ce9545
ddbfb454-10a3-11ea-8779-003ee1ce9545
de245134-10a3-11ea-8779-003ee1ce9545
debc0f74-10a3-11ea-8779-003ee1ce9545
dd5f2f30-10a3-11ea-8779-003ee1ce9545
ddf21020-10a3-11ea-8779-003ee1ce9545
dcfa35b2-10a3-11ea-8779-003ee1ce9545
df21cada-10a3-11ea-8779-003ee1ce9545
df533e6c-10a3-11ea-8779-003ee1ce9545
de55285e-10a3-11ea-8779-003ee1ce9545
df87cbaa-10a3-11ea-8779-003ee1ce9545
dd2ce75a-10a3-11ea-8779-003ee1ce9545

-----------  ------------------  --------
AGE_AT_SCAN  max                 33
AGE_AT_SCAN  min                 21
AGE_AT_SCAN  median              26
AGE_AT_SCAN  mean                26.2857
AGE_AT_SCAN  standard_deviation   4.14778
-----------  ------------------  --------

----------  ------------------  -----------
fsl_000001  max                 1.14899e+07
fsl_000001  min                 5.5193e+06
fsl_000001  median              7.66115e+06
fsl_000001  mean                8.97177e+06
fsl_000001  standard_deviation  2.22465e+06
----------  ------------------  -----------

Get details on a subject

Use -j for a JSON formatted response

pynidm query -j -nl "cmu_a.nidm.ttl" -u http://localhost:5000/projects/dc1bf9be-10a3-11ea-8779-003ee1ce9545/subjects/df21cada-10a3-11ea-8779-003ee1ce9545

Example response:

 {
"uuid": "df21cada-10a3-11ea-8779-003ee1ce9545",
"id": "0050665",
"activity": [
  "e28dc764-10a3-11ea-a7d3-003ee1ce9545",
  "df28e95a-10a3-11ea-8779-003ee1ce9545",
  "df21c76a-10a3-11ea-8779-003ee1ce9545"
],
"instruments": {
  "e28dd218-10a3-11ea-a7d3-003ee1ce9545": {
    "SRS_VERSION": "nan",
    "ADOS_MODULE": "nan",
    "WISC_IV_VCI": "nan",
    "WISC_IV_PSI": "nan",
    "ADOS_GOTHAM_SOCAFFECT": "nan",
    "VINELAND_PLAY_V_SCALED": "nan",
    "null": "http://www.w3.org/ns/prov#Entity",
    "VINELAND_EXPRESSIVE_V_SCALED": "nan",
    "SCQ_TOTAL": "nan",
    "SRS_MOTIVATION": "nan",
    "PIQ": "104.0",
    "FIQ": "109.0",
    "WISC_IV_PRI": "nan",
    "FILE_ID": "CMU_a_0050665",
    "VIQ": "111.0",
    "WISC_IV_VOCAB_SCALED": "nan",
    "VINELAND_DAILYLVNG_STANDARD": "nan",
    "WISC_IV_SIM_SCALED": "nan",
    "WISC_IV_DIGIT_SPAN_SCALED": "nan",
    "AGE_AT_SCAN": "33.0"
    }
 },
"derivatives": {
    "b9fe0398-16cc-11ea-8729-003ee1ce9545": {
       "URI": "http://iri.nidash.org/b9fe0398-16cc-11ea-8729-003ee1ce9545",
       "values": {
         "http://purl.org/nidash/fsl#fsl_000005": {
           "datumType": "ilx_0102597",
           "label": "Left-Amygdala (voxels)",
           "value": "1573",
           "units": "voxel"
         },
         "http://purl.org/nidash/fsl#fsl_000004": {
           "datumType": "ilx_0738276",
           "label": "Left-Accumbens-area (mm^3)",
           "value": "466.0",
           "units": "mm^3"
         },
         "http://purl.org/nidash/fsl#fsl_000003": {
           "datumType": "ilx_0102597",
           "label": "Left-Accumbens-area (voxels)",
           "value": "466",
           "units": "voxel"
         }
       },
       "StatCollectionType": "FSLStatsCollection"
    }
 }

version

Print the installed PyNIDM version.

Usage: pynidm version

PyNIDM: Neuroimaging Data Model in Python

Dependencies

Installation

Contributing to the Software

Reporting Issues or Problems

Support and Feedback

NIDM Model Details

Graph Hierarchy

Participant Linkage

DataElements and Measurement Values

DataElement Property Reference

Key Namespaces

Example SPARQL Queries

NIDM-Experiment Tools

BIDS MRI Conversion to NIDM

CSV File to NIDM Conversion

convert

concatenate

visualize

merge

Query

QueryAI — AI-Assisted Natural Language Query

linear_regression

PyNIDM: REST API and Command Line Usage

Introduction

Installation

URI formats

Operations

Query Parameters

Return Formatting

Examples

Get the UUID for all the projects at this location

Get the project summary details

Get Left-Pallidum volume (fsl_0000012) values for all subjects in a project

Get the subjects in a project

Use the command line to get statistics on a project for the AGE_AT_SCAN and a FSL data element

Get details on a subject

version

Indices and tables