Add example describing the structure of the JSON/dict input format fo…

…r Dataset object

examples/01_datasets/01_plot_dataset_io.py

@@ -23,7 +23,8 @@
 # Datasets are stored as json or pkl[.gz] files
 # -----------------------------------------------------------------------------
 # Json files are used to create Datasets, while generated Datasets are saved
-# to, and loaded from, pkl[.gz] files.
+# to, and loaded from, pkl[.gz] files. Find more information on the JSON file format
+# here :ref:`datasets_json` section.
 # We use jsons because they are easy to edit, and thus build by hand, if
 # necessary.
 # We then store the generated Datasets as pkl.gz files because an initialized

examples/01_datasets/06_plot_dataset_json.py

@@ -0,0 +1,164 @@
+"""
+
+.. _datasets_json:
+
+===============================================
+Create a NiMARE Dataset object from a JSON file
+===============================================
+
+Here, we present the structure of the JSON/dict input format to create a
+:class:`~nimare.dataset.Dataset` class from scratch.
+"""
+
+###############################################################################
+# Data Structure
+# -----------------------------------------------------------------------------
+# A JSON file is organized as a nested dictionary structure containing neuroimaging
+# study data. Each study may contain one or multiple contrasts. Each contrast could
+# have coordinates, associated images, metadata, and text information. The data will
+# be mapped to five core DataFrames: annotations, coordinates, images, metadata, and texts.
+
+###############################################################################
+# Study Level
+# -----------------------------------------------------------------------------
+# - Every study is assigned a unique identifier (study_id), which is defined by
+#   the user (e.g., "pain_01.nidm").
+# - Every study contains a ``contrasts`` dictionary holding all the contrasts for that
+#   study. Each contrast is assigned a unique identifier (contrast_id), which is
+#   also defined by the user (e.g., "1").
+#
+# .. code-block:: python
+#
+#    {
+#        "<study_id>": {
+#            "contrasts": {
+#                "<contrast_id>": {
+#                    "annotations": {...},
+#                    "coords": {...},
+#                    "images": {...},
+#                    "metadata": {...},
+#                    "text": {...},
+#                }
+#            }
+#        }
+#    }
+
+###############################################################################
+# Contrast Level
+# -----------------------------------------------------------------------------
+# Each contrast contains five main dictionaries:
+
+###############################################################################
+# 1. **Annotations Dictionary** (`annotations`)
+# `````````````````````````````````````````````````````````````````````````````
+# - Contains labels and annotations.
+# - Optional for studies.
+#
+# .. code-block:: python
+#
+#    "annotations": {
+#        "label": str,  # Label for the contrast
+#        "description": str  # Description of the contrast
+#    }
+
+###############################################################################
+# 2. **Coordinates Dictionary** (`coords`)
+# `````````````````````````````````````````````````````````````````````````````
+# - Includes space information and x, y, and z coordinates.
+#
+# .. code-block:: python
+#
+#    "coords": {
+#        "space": str,  # e.g., "MNI"
+#        "x": List[float],  # x-coordinates
+#        "y": List[float],  # y-coordinates
+#        "z": List[float]   # z-coordinates
+#    }
+
+
+###############################################################################
+# 3. **Images Dictionary** (`images`)
+# `````````````````````````````````````````````````````````````````````````````
+# - Contains paths to statistical maps. Possible keys are "beta", "se", "t", and "z".
+#
+# .. code-block:: python
+#
+#    "images": {
+#        "beta": str,     # Path to contrast image
+#        "se": str,       # Path to standard error image
+#        "t": str,        # Path to t-statistic image
+#        "z": str         # Path to z-statistic image
+#    }
+
+###############################################################################
+# 4. **Metadata Dictionary** (`metadata`)
+# `````````````````````````````````````````````````````````````````````````````
+# - Contains study-specific metadata.
+# - Flexible schema for user-defined metadata.
+#
+# .. code-block:: python
+#
+#    "metadata": {
+#        "sample_sizes": List[int]
+#    }
+
+###############################################################################
+# 5. **Text Dictionary** (`text`)
+# `````````````````````````````````````````````````````````````````````````````
+# - Contains study/contrast text information.
+# - Contains keys associated with the linked publication.
+#
+# .. code-block:: python
+#
+#    "text": {
+#        "title": str,    # Study title
+#        "keywords": str, # Study keywords
+#        "abstract": str, # Study abstract
+#        "body": str      # Main study text/content
+#    }
+
+###############################################################################
+# Example JSON
+# -----------------------------------------------------------------------------
+# Load the example dataset JSON file
+
+import json
+import os
+
+from nimare.utils import get_resource_path
+
+dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json")
+
+with open(dset_file, "r") as f_obj:
+    data = json.load(f_obj)
+
+###############################################################################
+# Example of accessing coordinates for a study
+study_coords = data["pain_01.nidm"]["contrasts"]["1"]["coords"]
+x_coords = study_coords["x"]
+y_coords = study_coords["y"]
+z_coords = study_coords["z"]
+print(x_coords[:5], y_coords[:5], z_coords[:5])
+
+###############################################################################
+# Example of accessing image paths
+study_images = data["pain_01.nidm"]["contrasts"]["1"]["images"]
+beta_image_path = study_images["beta"]
+t_stat_path = study_images["t"]
+print(beta_image_path, t_stat_path)
+
+###############################################################################
+# Example of accessing metadata
+study_metadata = data["pain_01.nidm"]["contrasts"]["1"]["metadata"]
+sample_size = study_metadata["sample_sizes"][0]
+print(sample_size)
+
+###############################################################################
+# .. note::
+#    Find more information about the Dataset class that can be created from this JSON file
+#    in :ref:`datasets_object`.
+
+###############################################################################
+# Example JSON Structure
+# -----------------------------------------------------------------------------
+print(json.dumps(data, indent=4))