Add all repo links for side panel, update text to recognize end of challenge

README.md

@@ -141,15 +141,19 @@ This dataset contains images of pinned carabid beetle specimens collected by the
     - Wei-Lun Chao, The Ohio State University, Columbus, OH, USA
     - Eric R. Sokol, National Ecological Observatory Network (NEON), Boulder, CO, USA
     - Sydne Record, University of Maine, Orono, ME, USA
-- **Homepage:** https://www.nsfhdr.org/mlchallenge-y2
-- **Repository:** https://github.com/Imageomics/HDR-SMood-Challenge
+- **Homepage:** [nsfhdr.org/mlchallenge-y2](https://www.nsfhdr.org/mlchallenge-y2)
+- **Challenge Repository:** [Imageomics/HDR-SMood-Challenge](https://github.com/Imageomics/HDR-SMood-Challenge)
+- **Sample Repository:** [Imageomics/HDR-SMood-Challenge-sample](https://github.com/Imageomics/HDR-SMood-Challenge-sample)
+- **Data Generation Repository:** [Imageomics/CarabidImaging](https://github.com/Imageomics/CarabidImaging)
 <!-- - **Paper:**  TBD -->
 
 This dataset contains high-resolution images and metadata for pinned specimens of carabid beetles collected across U.S. ecosystems by the [National Ecological Observatory Network (NEON)](https://www.neonscience.org/). Each image is linked to specimen-level metadata (e.g., collection date, site, taxonomic identification) and environmental context, including drought severity indicators ([Standardized Precipitation Evapotranspiration Index (SPEI)](https://spei.csic.es/)) calculated over multiple timescales using remote sensing data. This dataset is designed to support research on the relationship between ecological traits and climate stress, and is intended for training and evaluating machine learning models that predict environmental conditions-particularly drought status—from biological imagery. It was created for the Imageomics portion of the [second HDR ML Challenge](https://www.nsfhdr.org/mlchallenge-y2), which emphasizes model generalization across sites with different climates, land cover types, and beetle species pools. Not all available information is provided as part of the challenge, but it will be added at the end of the challenge to allow for broader use beyond the challenge. This includes smaller beetles, which were entirely excluded from the challenge, but will be added to this dataset later.
 
 ### Supported Tasks and Leaderboards
 
-Leaderboard is available on the [Codabench Challenge page](https://www.codabench.org/competitions/9854#/results-tab).
+Development Phase leaderboard is available on the [Codabench Challenge page](https://www.codabench.org/competitions/9854#/results-tab).
+
+The Final Challenge Leaderboard is hosted on the [HDR SMood Challenge Website](https://www.nsfhdr.org/html/mlchallenge-y2/winners.html).
 
 ## Dataset Structure
 
@@ -205,7 +209,7 @@ Each record in `train.csv` or `val.csv` corresponds to a single pinned carabid b
 
 Metadata include an identifier for a collection event (`eventID`), the date of the collection event (`collectDate`), an anonymized identifier of the domain (`domainID`) and site (`siteID`) where the collection event took place, taxonomic information (`scientificName`), unique beetle image identifier (`public_id`), a link to the beetle image file (`relative_img_loc`), a link to the color palette image (`colorpicker_path`), a link to the scale image (`scalebar_path`), and Standardized Precipication Evapotranspiratoin Index (SPEI) values that correspond with the location and time that the specimen was collected. The SPEI values were calculated for the 30 day (`SPEI_30d`), 1 year (`SPEI_1y`), and 2 year (`SPEI_2y`) time windows preceding the time of collection at each location for each beetle specimen in the dataset. 
 
-The `train.csv` and `val.csv` files are to be used for training models for submission and reflect the information that will be given during testing sans `siteID`, `collectDate`, and the target variables `SPEI_30d`, `SPEI_1y`, and `SPEI_2y`. Please see the [challenge sample repository](https://github.com/Imageomics/HDR-SMood-Challenge-sample) for an example of how these were used in training the baseline submission. After the challenge the testings files `seen_domain.csv`, `seen_domain_challenge.csv`, `unseen_domain.csv`, `unseen_domain_challenge.csv` will be included with the same columns as `train.csv` and `val.csv`.
+The `train.csv` and `val.csv` files are to be used for training models for submission and reflect the information that will be given during testing sans `siteID`, `collectDate`, and the target variables `SPEI_30d`, `SPEI_1y`, and `SPEI_2y`. Please see the [challenge sample repository](https://github.com/Imageomics/HDR-SMood-Challenge-sample) for an example of how these were used in training the baseline submission. Now that the challenge has closed, the testings files `seen_domain.csv`, `seen_domain_challenge.csv`, `unseen_domain.csv`, `unseen_domain_challenge.csv` have been included with the same columns as `train.csv` and `val.csv`.
 
 The `data/` folder contains the dataset in parquet format, where `train` prefix indicates it corresponds to the images and metadata in `train.csv`, while `validation` corresponds to `val.csv`. Similarly: