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README.md

@@ -262,7 +262,7 @@ The dataset contains individual GCC and RCC curves for the PUUM site. It was cre
 ### Source Data
 [PhenoCam images](https://phenocam.nau.edu/webcam/) in [PUUM](https://phenocam.nau.edu/webcam/sites/NEON.D20.PUUM.DP1.00033/) site were downloaded. This data product is developed upon the proposed [GCC generation pipeline](https://github.com/Imageomics/phenology-project-HI/tree/main/gcc_generation) to generate phenological time-series based on the images. 
 
-To produce this dataset, images are first gathered from [PUUM PhenoCam](https://phenocam.nau.edu/webcam/sites/NEON.D20.PUUM.DP1.00033/). We use the images from 10am to 2pm every day for best lighting conditions. We then manually label individual masks for one example image (see [below](#annotation-process) for more details). The raw time-series are generated by directly applying the mask to the image. Then we conduct K-means clustering on the individual pixels of every image. As the flowers of Koa trees are of white color, we only keep the pixels with the largest brightness to perform GCC calculation. And finally, we apply discrete wavelet transform to get the smoothed time-series. 
+To produce this dataset, images are first gathered from [PUUM PhenoCam](https://phenocam.nau.edu/webcam/sites/NEON.D20.PUUM.DP1.00033/). We use the images from 10am to 2pm every day for best lighting conditions. Please create an account in the PhenoCam page, and download the image data between 10am to 2pm. To fully replicate our results, please download the data from 2019-04-28 to 2025-04-24. We then manually label individual masks for one example image (see [below](#annotation-process) for more details). The raw time-series are generated by directly applying the mask to the image. Then we conduct K-means clustering on the individual pixels of every image. As the flowers of Koa trees are of white color, we only keep the pixels with the largest brightness to perform GCC calculation. And finally, we apply discrete wavelet transform to get the smoothed time-series. 
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 <!-- #### Data Collection and Processing -->
@@ -338,7 +338,7 @@ This dataset (the compilation) has been marked as dedicated to the public domain
 **BibTeX:**
 ```
 @misc{phenology_normal_hawaii
-    author = {Jianyang Gu and Faye Xie and Colby Stakun-Pickering},
+    author = {Jianyang Gu and Faye Xie and Colby Stakun-Pickering and Adam Young and Chris Florian},
     title = {Hawaii PUUM Individual Phenology Dataset},
     year = {2025},
     url = {https://huggingface.co/datasets/imageomics/phenology_normal_hawaii},
@@ -401,6 +401,8 @@ This work was supported by both the [Imageomics Institute](https://imageomics.or
 
 This material is based in part upon work supported by the National Ecological Observatory Network (NEON), a program sponsored by the U.S. National Science Foundation (NSF) and operated under cooperative agreement by Battelle. Data used in this research were provided by the [PhenoCam Network](https://phenocam.nau.edu/webcam/), which has been supported by the National Science Foundation, the [Long-Term Agroecosystem Research (LTAR)](https://ltar.ars.usda.gov/) network which is supported by the United States Department of Agriculture (USDA), the U.S. Department of Energy, the U.S. Geological Survey, the Northeastern States Research Cooperative, and the USA National Phenology Network. We thank the PhenoCam Network collaborators, including site PIs and technicians, for publicly sharing the data that were used in this paper.
 
+We would also like to thank Daniel Rubenstein for the guidance, and Mike Long and his team for the generous assistance during the field work. 
+
 <!-- You may also want to credit the source of your data, i.e., if you went to a museum or nature preserve to collect it. -->
 
 <!-- ## Glossary  -->