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README.md

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 ### Supported Tasks and Leaderboards
 
-This dataset is intended to train image classification or regression models to predict the time period in which color photographs were taken. The task could be approached either as a classification task or could be approached as an image regression task. 
+This dataset is intended to train image classification or regression models to predict the time period in which color photographs were taken. This task could be approached either as a classification task or could be approached as an image regression task. 
 
 
 ## Dataset Structure
@@ -96,21 +96,19 @@ There is a single training split since the original dataset doesn't define a tra
 
 #### Initial Data Collection and Normalization
 
-[More Information Needed]
-
-#### Who are the source language producers?
+> Beginning with a collection of approximately 230,000 Flickr images taken prior to 1980, we perform automated removal of monochromatic images. The remaining images are manually inspected to remove non-photographic content (e.g. scans of vintage artwork) and any remaining monochromatic images. Finally, a random subsampling and decimation is performed to create a dataset containing an equal number of historical color images for each decade (1,375 images total).
 
-[More Information Needed]
 
 ### Annotations
 
 #### Annotation process
 
-[More Information Needed]
+Annotations are based on metadata available on Flickr.
+
 
 #### Who are the annotators?
 
-[More Information Needed]
+It appears that annotations are sourced via Flickr, so the annotators are presumed to be those uploading the images to Flickr. This will include individuals as well as cultural heritage institutions.
 
 ### Personal and Sensitive Information
 
@@ -146,4 +144,4 @@ There is a single training split since the original dataset doesn't define a tra
 
 ### Contributions
 
-Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
+Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.