Add files using upload-large-folder tool

README.md

@@ -12,7 +12,7 @@ pretty_name: 3D Brain Structure MRI Scans
 ---
 
 ## 🧠 Dataset Summary
-3794 3D structural MRI brain scans (T1-weighted MPRAGE NIfTI files) from 2607 individuals included in five publicly available datasets: [IXI](https://brain-development.org/ixi-dataset/), [DLBS](https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html), [NKI-RS](https://fcon_1000.projects.nitrc.org/indi/enhanced/sharing_neuro.html), [OASIS-1](https://sites.wustl.edu/oasisbrains/home/oasis-1/), and [OASIS-2](https://sites.wustl.edu/oasisbrains/home/oasis-2/). Subjects have a mean age of 45 ± 24. 3773 scans come from cognitively normal individuals and 261 scans from individuals with an Alzheimer's disease clinical diagnosis. Scans dimensions are 113x137x113, 1.5mm^3 resolution, aligned to MNI152 space (see methods).
+3794 3D structural MRI brain scans (T1-weighted MPRAGE NIfTI files) from 2607 individuals included in five publicly available datasets: [DLBS](https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html), [IXI](https://brain-development.org/ixi-dataset/), [NKI-RS](https://fcon_1000.projects.nitrc.org/indi/enhanced/sharing_neuro.html), [OASIS-1](https://sites.wustl.edu/oasisbrains/home/oasis-1/), and [OASIS-2](https://sites.wustl.edu/oasisbrains/home/oasis-2/). Subjects have a mean age of 45 ± 24. 3773 scans come from cognitively normal individuals and 261 scans from individuals with an Alzheimer's disease clinical diagnosis. Scans dimensions are 113x137x113, 1.5mm^3 resolution, aligned to MNI152 space (see methods).
 
 Scans have been processed and all protected health information (PHI) is excluded. Only the skull-stripped scan, integer age, biological sex, clinical diagnosis, and scan metadata are included. [Radiata](https://radiata.ai/) compiles and processes publicly available neuroimaging datasets to create this open, unified, and harmonized dataset. For more information see https://radiata.ai/public-studies. Example uses including developing foundation-like models or tailored models for brain age prediction and disease classification.
 

brain-structure.py

@@ -1,14 +1,14 @@
 import os
 import json
-import datasets
 import logging
+import datasets
 
 logger = logging.getLogger(__name__)
 
 _DESCRIPTION = """
 This dataset contains T1-weighted .nii.gz structural MRI scans in a BIDS-like arrangement.
-Each scan has an associated JSON sidecar with metadata, including fields such as subject
-demographics, scanner information, and a 'split' field indicating train/validation/test.
+Each scan has an associated JSON sidecar with metadata, including a 'split' field indicating
+whether it's train, validation, or test.
 """
 
 _CITATION = """
@@ -16,7 +16,7 @@ _CITATION = """
   author    = {Jesse Brown and Clayton Young},
   title     = {Brain-Structure: A Collection of Processed Structural MRI Scans},
   year      = {2025},
-  url          = {https://huggingface.co/datasets/radiata-ai/brain-structure},
+  url       = {https://huggingface.co/datasets/radiata-ai/brain-structure},
   note      = {Version 1.0},
   publisher = {Hugging Face}
 }
@@ -25,21 +25,21 @@ _CITATION = """
 _HOMEPAGE = "https://huggingface.co/datasets/radiata-ai/brain-structure"
 _LICENSE = "ODC-By v1.0"
 
+
 class BrainStructureConfig(datasets.BuilderConfig):
     """
     Configuration class for the Brain-Structure dataset.
-    You can define multiple configurations if needed (e.g. different subsets).
+    You can define multiple configurations if needed (e.g., different subsets).
     """
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
 
+
 class BrainStructure(datasets.GeneratorBasedBuilder):
     """
-    A dataset loader for T1 .nii.gz files plus JSON sidecars.
-    Each sidecar includes a 'split' field identifying whether the scan
-    belongs to the train, validation, or test set.
+    A dataset loader for T1 .nii.gz files plus JSON sidecars indicating splits
+    (train, validation, test). Usage example:
 
-    Usage Example:
         ds = load_dataset(
             "radiata-ai/brain-structure",
             name="all",
@@ -49,22 +49,20 @@ class BrainStructure(datasets.GeneratorBasedBuilder):
     """
 
     VERSION = datasets.Version("1.0.0")
+
+    # If you do NOT need multiple configs, you can define just one here:
     BUILDER_CONFIGS = [
         BrainStructureConfig(
             name="all",
             version=VERSION,
-            description=(
-                "All structural MRI data in a BIDS-like arrangement, labeled "
-                "with train/validation/test splits."
-            ),
+            description="All structural MRI data in a BIDS-like arrangement, labeled with train/val/test splits."
         ),
     ]
     DEFAULT_CONFIG_NAME = "all"
 
     def _info(self):
         """
-        Provides metadata about the dataset, including feature types
-        and general dataset information.
+        Returns DatasetInfo, including feature types and other meta information.
         """
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -102,34 +100,35 @@ class BrainStructure(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager: datasets.DownloadManager):
         """
-        Returns SplitGenerators for 'train', 'validation', and 'test'.
-        Each split is identified by matching the 'split' field in the JSON sidecar.
+        Creates SplitGenerators for train, validation, and test.
+        No remote download is performed here. Instead, we reference
+        the local directory containing this script.
         """
-        data_dir = dl_manager.dataset_dir
+        # Typically, we use dl_manager.download_and_extract(...) for remote data,
+        # but here we assume the data is already in the same repo as this script.
+
+        # Path to the folder containing this script (and presumably the data).
+        data_dir = os.path.abspath(os.path.dirname(__file__))
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"data_dir": data_dir, "desired_split": "train"},
+                gen_kwargs={"data_dir": data_dir, "desired_split": "train"}
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"data_dir": data_dir, "desired_split": "validation"},
+                gen_kwargs={"data_dir": data_dir, "desired_split": "validation"}
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"data_dir": data_dir, "desired_split": "test"},
+                gen_kwargs={"data_dir": data_dir, "desired_split": "test"}
             ),
         ]
 
     def _generate_examples(self, data_dir, desired_split):
         """
-        Recursively scan the data_dir, locate JSON sidecar files, and yield
-        examples whose 'split' field matches desired_split.
-
-        Each yielded example includes:
-         - 'nii_filepath': pointing to the corresponding .nii.gz file
-         - 'metadata': dictionary of subject and scan information
+        Recursively walks data_dir, locates JSON sidecar files, and yields
+        examples that match the specified 'desired_split'.
         """
         id_ = 0
         for root, dirs, files in os.walk(data_dir):
@@ -139,26 +138,21 @@ class BrainStructure(datasets.GeneratorBasedBuilder):
                     with open(sidecar_path, "r") as f:
                         sidecar = json.load(f)
 
-                    # Only yield if 'split' matches the desired split
+                    # Only yield if 'split' matches the requested split
                     if sidecar.get("split") == desired_split:
-                        # Attempt to locate the matching .nii.gz file
-                        # Typically the sidecar is named sub-xxx_ses-xxx_scandata.json
-                        # and the NIfTI file: sub-xxx_ses-xxx_T1w.nii.gz
-                        possible_nii_prefix = fname.replace("_scandata.json", "_T1w")
+                        # Locate corresponding NIfTI .nii.gz
+                        nii_prefix = fname.replace("_scandata.json", "_T1w")
                         nii_filepath = None
                         for potential_file in files:
-                            if (potential_file.startswith(possible_nii_prefix) 
+                            if (potential_file.startswith(nii_prefix) 
                                 and potential_file.endswith(".nii.gz")):
                                 nii_filepath = os.path.join(root, potential_file)
                                 break
 
                         if not nii_filepath:
-                            logger.warning(
-                                f"No corresponding .nii.gz file found for {sidecar_path}"
-                            )
+                            logger.warning(f"No .nii.gz found for {sidecar_path}")
                             continue
 
-                        # Build the example
                         yield id_, {
                             "id": str(id_),
                             "nii_filepath": nii_filepath,
@@ -167,7 +161,7 @@ class BrainStructure(datasets.GeneratorBasedBuilder):
                                 "participant_id": sidecar.get("participant_id", ""),
                                 "session_id": sidecar.get("session_id", ""),
                                 "study": sidecar.get("study", ""),
-                                "age": sidecar.get("age", 0),  # default to 0 if missing
+                                "age": sidecar.get("age", 0),
                                 "sex": sidecar.get("sex", ""),
                                 "clinical_diagnosis": sidecar.get("clinical_diagnosis", ""),
                                 "scanner_manufacturer": sidecar.get("scanner_manufacturer", ""),