Adding test data to dataset (parquet) (part 00001-of-00002)

README.md

@@ -3,7 +3,8 @@ license: cc-by-4.0
 language:
 - en
 - la
-pretty_name: "Beetles as Sentinel Taxa: Predicting drought conditions from NEON specimen imagery"
+pretty_name: 'Beetles as Sentinel Taxa: Predicting drought conditions from NEON specimen
+  imagery'
 task_categories:
 - image-feature-extraction
 dataset_info:
@@ -40,13 +41,25 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 14289671472.3
+    num_bytes: 14087525685.19
     num_examples: 22370
   - name: validation
-    num_bytes: 1742043556.572
+    num_bytes: 1559423695.07
     num_examples: 2486
-  download_size: 15078112217
-  dataset_size: 16031715028.872
+  - name: seen
+    num_bytes: 2923805033.104
+    num_examples: 7028
+  - name: seen_challenge
+    num_bytes: 3306626373.775
+    num_examples: 6725
+  - name: unseen
+    num_bytes: 1343141646.385
+    num_examples: 2795
+  - name: unseen_challenge
+    num_bytes: 1434282817.54
+    num_examples: 3106
+  download_size: 20021233279
+  dataset_size: 24654805251.064
 tags:
 - biology
 - image
@@ -69,7 +82,20 @@ configs:
     path: data/train-*
   - split: validation
     path: data/validation-*
-description: "Images of pinned carabid beetle specimens collected by the National Ecological Observatory Network (NEON) from ecological sites across the U.S., along with associated metadata and drought severity indices (Standardized Precipitation Evapotranspiration Index (SPEI)). It was developed to for the second HDR ML Challenge, and is intended to support the development of machine learning models to predict environmental conditions—specifically drought status—from organismal traits captured in specimen imagery."
+  - split: seen
+    path: data/seen-*
+  - split: seen_challenge
+    path: data/seen_challenge-*
+  - split: unseen
+    path: data/unseen-*
+  - split: unseen_challenge
+    path: data/unseen_challenge-*
+description: Images of pinned carabid beetle specimens collected by the National Ecological
+  Observatory Network (NEON) from ecological sites across the U.S., along with associated
+  metadata and drought severity indices (Standardized Precipitation Evapotranspiration
+  Index (SPEI)). It was developed to for the second HDR ML Challenge, and is intended
+  to support the development of machine learning models to predict environmental conditions—specifically
+  drought status—from organismal traits captured in specimen imagery.
 ---
 
 

data/unseen_challenge-00001-of-00003.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5670b3ab94a31fb946287702f66680fcf75b77e719f94f274ac424bda791f3bd
+size 269580616

data/unseen_challenge-00002-of-00003.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c42ba48049f058505bfe45efab20bbffc302cff479532fa74f39c07fe214e2b6
+size 272214642