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buds-lab · Feb 16, 2024 · 0516551 · 0516551
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@@ -27,7 +27,7 @@ BUDS Lab is in the [Department of the Built Environment](https://cde.nus.edu.sg/
 [Project HEATS](https://www.linkedin.com/company/project-heats/){:target="_blank"} -- HEATS (Heat Exposure, AcTivity, and Sleep) is a three-year, multi-institutional project focused on improving sleep outcomes for people exposed to heat. Funded by the Singapore National Research Foundation, the project develops technological and behavioral solutions to minimize heat exposure for improved sleep using the Cozie Apple platform. It is a collaboration with [Prof. Stefano Schiavon](https://ced.berkeley.edu/ced/faculty-staff/stefano-schiavon){:target="_blank"} from UC Berkeley, [Assoc. Prof. Jason Lee](https://medicine.nus.edu.sg/phys/research/research-programs/human-potential/jason-kai-wei-lee/){:target="_blank"} at the NUS Dept. of Physiology, and [Dr. Tom Parkinson](https://www.sydney.edu.au/architecture/about/our-people/academic-staff/thomas-parkinson.html){:target="_blank"} from the University of Sydney.
 
 ![Project Cozie Apple](buds-lab.github.io/cozie-apple.png)
-[Cozie Apple Watch](https://cozie-apple.app){:target="_blank"} ([ResearchGate](https://www.researchgate.net/project/Cozie-App){:target="_blank"}) -- The latest version of the Cozie clockface application for the Apple Watch platform. This open-source project is led by [Dr. Mario Frei](https://www.linkedin.com/in/mario-frei/?originalSubdomain=sg){:target="_blank"} and [Yun Xuan Chua](https://www.linkedin.com/in/chuayunxuan/?originalSubdomain=sg){:target="_blank"} and is a collaboration with [Prof. Stefano Schiavon](https://ced.berkeley.edu/ced/faculty-staff/stefano-schiavon){:target="_blank"}'s team at the [UC Berkeley Center for the Built Environment](https://cbe.berkeley.edu/){:target="_blank"} in Singapore.
+[Cozie Apple Watch](https://cozie-apple.app){:target="_blank"} -- The Apple Watch version of the Cozie platform allows researcher to collect physiological, environmental and subjective data for built environment research. This open-source project is led by [Dr. Mario Frei](https://www.linkedin.com/in/mario-frei/?originalSubdomain=sg){:target="_blank"} and [Yun Xuan Chua](https://www.linkedin.com/in/chuayunxuan/?originalSubdomain=sg){:target="_blank"} and is a collaboration with [Prof. Stefano Schiavon](https://ced.berkeley.edu/ced/faculty-staff/stefano-schiavon){:target="_blank"}'s team at the [UC Berkeley Center for the Built Environment](https://cbe.berkeley.edu/){:target="_blank"} in Singapore.
 
 ![IoB](buds-lab.github.io/iob.png)
 [The Internet-of-Buildings (IoB) Project](http://bit.ly/nus-pfm){:target="_blank"} -- The IoB project focuses on the technology and data convergence of the various researchers in the NUS Dept. of the Built Environment. This project is a collaboration with [Prof. Daniel Wong](https://www.sde.nus.edu.sg/bdg/wp-content/uploads/sites/5/2019/12/staff_bdgdan_082018.pdf){:target="_blank"} and it ties together several projects from within the department.
@@ -36,10 +36,10 @@ BUDS Lab is in the [Department of the Built Environment](https://cde.nus.edu.sg/
 [Data Science for Construction, Architecture and Engineering Online EDx Course](https://www.edx.org/course/Data-Science-for-Construction-Architecture-and-Engineering){:target="_blank"} -- The BUDS Lab hosts the first online data science course focused **specifically on data analytics from the various phases of the building life cycle - design, construction, and operations.** This is an introductory course that adds [Python](https://www.python.org/){:target="_blank"}, the [Pandas Data Analytics library](https://pandas.pydata.org/){:target="_blank"} and various visualization and machine learning techniques to the toolbox of architects, engineers, operations, and other industry professionals. Over 15,000 participants have taken this course since April 2020.
 
 ![BDG2](buds-lab.github.io/buildingdatagenome2.png)
-[The Building Data Genome Project 2 (BDG2)](https://github.com/buds-lab/building-data-genome-project-2){:target="_blank"} ([ResearchGate](http://bit.ly/2mWDM5r){:target="_blank"}) -- The BDG2 Project is an open data set made up of 3,053 energy meters from 1,636 buildings. The data set is two full years (2016 and 2017) of hourly frequency measurements from electricity, heating and cooling water, steam, and irrigation meters. A subset of the data was used in the [Great Energy Predictor III (GEPIII) competition](https://www.kaggle.com/c/ashrae-energy-prediction){:target="_blank"} hosted by the ASHRAE organization in late 2019. 
+[The Building Data Genome Project 2 (BDG2)](https://github.com/buds-lab/building-data-genome-project-2){:target="_blank"} -- The BDG2 Project is an open data set made up of 3,053 energy meters from 1,636 buildings. The data set is two full years (2016 and 2017) of hourly frequency measurements from electricity, heating and cooling water, steam, and irrigation meters. A subset of the data was used in the [Great Energy Predictor III (GEPIII) competition](https://www.kaggle.com/c/ashrae-energy-prediction){:target="_blank"} hosted by the ASHRAE organization in late 2019. 
 
 ![ASHRAE Kaggle](buds-lab.github.io/ashraekaggle.png)
-[The ASHRAE Great Energy Predictor III (GEPIII) Competition on Kaggle](https://www.kaggle.com/c/ashrae-energy-prediction){:target="_blank"} ([ResearchGate](https://www.researchgate.net/project/ASHRAE-Great-Energy-Predictor-III-Kaggle-Competition){:target="_blank"}) -- The BUDS Lab was the leading technical organizer of the biggest building energy-related machine learning competition ever held with over 3,614 teams who submitted 39,402 predictions. The competition included data from 2,380 energy meters collected from 1,448 buildings in 16 sites and had US$25,000 in prize money for the top five winners. The competition ran from Oct.-Dec. 2019 and was sponsored by [ASHRAE](https://www.ashrae.org/){:target="_blank"} and hosted on the [Kaggle](https://www.kaggle.com/){:target="_blank"} platform. This competition is a resurrection of [prediction challenges hosted by ASHRAE in the mid-1990's](https://www.kaggle.com/c/great-energy-predictor-shootout-i){:target="_blank"}
+[The ASHRAE Great Energy Predictor III (GEPIII) Competition on Kaggle](https://www.kaggle.com/c/ashrae-energy-prediction){:target="_blank"} -- The BUDS Lab was the leading technical organizer of the biggest building energy-related machine learning competition ever held with over 3,614 teams who submitted 39,402 predictions. The competition included data from 2,380 energy meters collected from 1,448 buildings in 16 sites and had US$25,000 in prize money for the top five winners. The competition ran from Oct.-Dec. 2019 and was sponsored by [ASHRAE](https://www.ashrae.org/){:target="_blank"} and hosted on the [Kaggle](https://www.kaggle.com/){:target="_blank"} platform. This competition is a resurrection of [prediction challenges hosted by ASHRAE in the mid-1990's](https://www.kaggle.com/c/great-energy-predictor-shootout-i){:target="_blank"}
 
 ![SpaceMatch](buds-lab.github.io/spacematch.png)
 [SpaceMatch](https://www.spacematch.me/){:target="_blank"} -- SpaceMatch is the Uber&reg; of flexible workspaces -- an AI-enhanced spatial recommendation engine that matches building occupants to suitable workspaces based on comfort preferences. A live demonstration was implemented in the SDE4 building at NUS and is the focus of a team within the [NUS GRIP Program](https://nus.edu.sg/grip/){:target="_blank"} in early 2021.