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croissant.json

@@ -5,7 +5,8 @@
     "cr": "http://mlcommons.org/croissant/",
     "sc": "https://schema.org/",
     "dct": "http://purl.org/dc/terms/",
-    "citeAs": "cr:citeAs",
+    "rai": "http://mlcommons.org/croissant/RAI/",
+    "prov": "http://www.w3.org/ns/prov#",
     "column": "cr:column",
     "conformsTo": "dct:conformsTo",
     "data": {
@@ -45,9 +46,16 @@
   "url": "https://huggingface.co/datasets/enscg/calvdb",
   "license": "https://creativecommons.org/licenses/by/4.0/",
   "version": "1.0.0",
-  "datePublished": "2025-05-06",
+  "datePublished": "2026-05-01",
   "conformsTo": "http://mlcommons.org/croissant/1.0",
-  "citeAs": "@inproceedings{YOUR_CITATION_KEY, title={SvalCalvingDB}, author={YOUR NAME}, year={2025}}",
+  "rai:dataLimitations": "The dataset is restricted to 123 marine-terminating glaciers in the Svalbard archipelago (RGI 6.0, region 07) and covers only 2013–2023. The prediction task is fixed to an annual (365-day) horizon; sub-annual forecasting is out of scope. The dataset focuses on calving-front position as the target variable, so other aspects of glacier dynamics (e.g., surface velocity, mass balance) are not represented.",
+  "rai:dataBiases": "Geographic bias: all 123 glaciers are located in Svalbard, which has a distinct climate regime, fjord geometry, and ocean-forcing characteristics compared with other Arctic and Antarctic calving systems; models trained solely on this dataset may not transfer to other regions without fine-tuning.",
+  "rai:personalSensitiveInformation": "This dataset contains no personal or sensitive information. All data are derived entirely from satellite remote-sensing imagery, geophysical measurements, and climate reanalysis products. No information relating to human subjects is present.",
+  "rai:dataUseCases": "The dataset is designed to measure a model's ability to predict the spatial location of a marine-terminating glacier's calving front 365 days in the future, given a sequence of five past calving-front locations together with co-registered multispectral imagery, geophysical covariates, and climate forcing.",
+  "rai:dataSocialImpact": "The dataset enables data-driven monitoring of glacier calving dynamics, advancing scientific understanding of ice–ocean interactions and sea-level rise projections relevant to climate-change adaptation and coastal planning. Releasing the benchmark publicly lowers the barrier for the machine-learning and remote-sensing communities to engage with cryosphere science.",
+  "rai:hasSyntheticData": false,
+  "prov:wasDerivedFrom": "Full details of data sources are provided in Section 3 (Dataset) of the accompanying paper.",
+  "prov:wasGeneratedBy": "Full details of data collection, preprocessing, annotation, and benchmark construction are provided in Section 4 (CalvingDB Benchmark) of the accompanying paper.",
   "distribution": [
     {
       "@type": "cr:FileObject",