Update croissant.json

Update croissant.json since dataset was move to a different location

croissant.json

@@ -54,29 +54,138 @@
       "@id": "repo",
       "name": "repo",
       "description": "The Hugging Face git repository.",
-      "contentUrl": "https://huggingface.co/datasets/madivanhorn/cfc26/tree/refs%2Fconvert%2Fparquet",
+      "contentUrl": "https://huggingface.co/datasets/perona-lab/cfc26/tree/refs%2Fconvert%2Fparquet",
       "encodingFormat": "git+https",
       "sha256": "https://github.com/mlcommons/croissant/issues/80"
+    },
+    {
+      "@type": "cr:FileSet",
+      "@id": "parquet-files-for-config-default",
+      "containedIn": {
+        "@id": "repo"
+      },
+      "encodingFormat": "application/x-parquet",
+      "includes": "default/*/*.parquet"
+    }
+  ],
+  "recordSet": [
+    {
+      "@type": "cr:RecordSet",
+      "dataType": "cr:Split",
+      "key": {
+        "@id": "default_splits/split_name"
+      },
+      "@id": "default_splits",
+      "name": "default_splits",
+      "description": "Splits for the default config.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "default_splits/split_name",
+          "dataType": "sc:Text"
+        }
+      ],
+      "data": [
+        {
+          "default_splits/split_name": "train"
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "default",
+      "description": "perona-lab/cfc26 - 'default' subset (first 5GB)",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "default/split",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-default"
+            },
+            "extract": {
+              "fileProperty": "fullpath"
+            },
+            "transform": {
+              "regex": "default/(?:partial-)?(train)/.+parquet$"
+            }
+          },
+          "references": {
+            "field": {
+              "@id": "default_splits/split_name"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "default/png",
+          "dataType": "sc:ImageObject",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-default"
+            },
+            "extract": {
+              "column": "png"
+            },
+            "transform": {
+              "jsonPath": "bytes"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "default/__key__",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-default"
+            },
+            "extract": {
+              "column": "__key__"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "default/__url__",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-default"
+            },
+            "extract": {
+              "column": "__url__"
+            }
+          }
+        }
+      ]
     }
   ],
-  "recordSet": [],
   "conformsTo": "http://mlcommons.org/croissant/1.1",
   "name": "cfc26",
-  "description": "madivanhorn/cfc26 dataset hosted on Hugging Face and contributed by the HF Datasets community",
+  "description": "\n\t\n\t\t\n\t\tCFC26 Dataset Card\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nCFC26 is a large-scale benchmark dataset for fish detection and counting in underwater ARIS sonar video. Data were collected across 16+ field locations in Alaska, California, Washington, and British Columbia, spanning a wide range of environmental conditions, fish species, and deployment configurations. The dataset is designed to support research in object detection, multi-object tracking, and ecological counting in acoustically… See the full description on the dataset page: https://huggingface.co/datasets/perona-lab/cfc26.",
   "alternateName": [
-    "madivanhorn/cfc26"
+    "perona-lab/cfc26"
   ],
   "creator": {
-    "@type": "Person",
-    "name": "Madison Van Horn Mitchell",
-    "url": "https://huggingface.co/madivanhorn"
+    "@type": "Organization",
+    "name": "Perona Lab",
+    "url": "https://huggingface.co/perona-lab"
   },
   "keywords": [
     "mit",
+    "1M - 10M",
+    "webdataset",
+    "Image",
+    "Text",
+    "Datasets",
+    "WebDataset",
+    "Croissant",
     "🇺🇸 Region: US"
   ],
   "license": "https://choosealicense.com/licenses/mit/",
-  "url": "https://huggingface.co/datasets/madivanhorn/cfc26",
+  "url": "https://huggingface.co/datasets/perona-lab/cfc26",
   "rai:dataLimitations": "The dataset is limited to sonar-based fish counting in riverine environments and may not generalize to other sensing modalities (e.g., optical video), marine/open-ocean settings, or species with substantially different motion or morphology. While expanded to nine river systems, geographic and ecological coverage remains incomplete (North America only), and certain river conditions (e.g., extreme turbidity, rare seasonal events, or atypical flow regimes) may be underrepresented. Annotation quality may be affected by low signal-to-noise ratios and ambiguity in fish visibility, particularly in dense or occluded scenes. The dataset is not recommended for tasks requiring fine-grained species classification, individual fish re-identification, or appearance-based recognition independent of motion cues.",
   "rai:dataBiases": "The dataset may exhibit selection bias toward rivers and deployment sites where sonar systems and data collection is feasible, potentially over-representing current sites of interest in North America. Environmental conditions (e.g., typical flow, sonar artifacts) and species distributions may be unevenly sampled, leading to skewed representations of fish behavior and density. Label bias may arise from human annotation under low-visibility conditions, where temporal cues are required and ambiguous cases may be inconsistently labeled. These biases may cause models to overfit to common environmental patterns or fish movement dynamics and underperform in rare or extreme conditions.",
   "rai:personalSensitiveInformation": "The dataset does not contain personal or human-related sensitive information. It consists of sonar imagery of fish in natural river environments.",
@@ -86,28 +195,34 @@
   "prov:wasDerivedFrom": [
     {
       "@id": "https://data.caltech.edu/records/1y23m-j8r69",
-      "prov:label": "The Caltech Fish Counting Dataset (CFC22)",
+      "prov:label": "Caltech Fish Counting Dataset (CFC22)",
       "sc:license": "MIT"
     }
   ],
   "prov:wasGeneratedBy": [
     {
       "@type": "prov:Activity",
-      "prov:type": "Data Collection",
+      "prov:type": {
+        "@id": "https://www.wikidata.org/wiki/Q4929239"
+      },
       "prov:label": "Collection",
-      "prov:description": "Data was collected via sonar camera deployments in river systems across four U.S. states and two countries. The collection method is observations using fixed-position sonar sensors capturing continuous video streams over extended durations under varying environmental conditions."
+      "sc:description": "Data was collected via sonar camera deployments in river systems across four U.S. states and two countries. The collection method is observations using fixed-position sonar sensors capturing continuous video streams over extended durations under varying environmental conditions."
     },
     {
       "@type": "prov:Activity",
-      "prov:type": "Preprocessing",
+      "prov:type": {
+        "@id": "https://www.wikidata.org/wiki/Q5227332"
+      },
       "prov:label": "Preprocessing",
-      "prov:description": "Video streams were segmented into clips, filtered for quality, and normalized for consistent formatting. Clips with unusable signal quality or severe artifacts may have been excluded. Standardization steps include temporal clipping, echogram visualizations, and formatting for downstream annotation and evaluation."
+      "sc:description": "Video streams were segmented into clips, filtered for quality, and normalized for consistent formatting. Clips with unusable signal quality or severe artifacts may have been excluded. Standardization steps include temporal clipping, echogram visualizations, and formatting for downstream annotation and evaluation."
     },
     {
       "@type": "prov:Activity",
-      "prov:type": "Annotation",
+      "prov:type": {
+        "@id": "https://www.wikidata.org/wiki/Q109719325"
+      },
       "prov:label": "Annotation",
-      "prov:description": "Human annotators labeled fish instances using bounding boxes, with counting derived from crossing events. Annotation guidelines emphasized temporal consistency and handling of ambiguous detections in low signal-to-noise conditions. Quality control included review processes and consistency checks across annotators; inter-annotator agreement metrics are not reported."
+      "sc:description": "Human annotators labeled fish instances using bounding boxes, with counting derived from crossing events. Annotation guidelines emphasized temporal consistency and handling of ambiguous detections in low signal-to-noise conditions. Quality control included review processes and consistency checks across annotators; inter-annotator agreement metrics are not reported."
     }
   ]
 }