The original contributions presented in the study are included in the article and online through the TAME Toolkit, available at: https://uncsrp.github.io/Data-Analysis-Training-Modules/, with underlying code and datasets available in the parent UNC-SRP GitHub website (https://github.com/UNCSRP). This dataset is associated with the following publication: Roell, K., L. Koval, R. Boyles, G. Patlewicz, C. Ring, C. Rider, C. Ward-Caviness, D. Reif, I. Jaspers, R. Fry, and J. Rager. Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 4: 893924, (2022).

The EPA GitHub repository PAU4ChemAs as described in the README.md file, contains Python scripts written to build the PAU dataset modules (technologies, capital and operating costs, and chemical prices) for tracking chemical flows transfers, releases estimation, and identification of potential occupation exposure scenarios in pollution abatement units (PAUs). These PAUs are employed for on-site chemical end-of-life management. The folder datasets contains the outputs for each framework step. The Chemicals_in_categories.csv contains the chemicals for the TRI chemical categories. The EPA GitHub repository PAU_case_study as described in its readme.md entry, contains the Python scripts to run the manuscript case study for designing the PAUs, the data-driven models, and the decision-making module for chemicals of concern and tracking flow transfers at the end-of-life stage. The data was obtained by means of data engineering using different publicly-available databases. The properties of chemicals were obtained using the GitHub repository Properties_Scraper, while the PAU dataset using the repository PAU4Chem. Finally, the EPA GitHub repository Properties_Scraper contains a Python script to massively gather information about exposure limits and physical properties from different publicly-available sources: EPA, NOAA, OSHA, and the institute for Occupational Safety and Health of the German Social Accident Insurance (IFA). Also, all GitHub repositories describe the Python libraries required for running their code, how to use them, the obtained outputs files after running the Python script modules, and the corresponding EPA Disclaimer. This dataset is associated with the following publication: Hernandez-Betancur, J.D., M. Martin, and G.J. Ruiz-Mercado. A data engineering framework for on-site end-of-life industrial operations. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA, 327: 129514, (2021).

바이러스질병과 수의과학기술편람(2016-2022)에 대한 데이터로 동물질병방제 표준기술 지침, 미래성장동력 표준기술 지침등이 수록되어 있다.

All of the code used to analyze and report the data as well as build confidence in the approach is available as a supplementary RMarkdown report, and a tool to derive PODBioactivity and PODRead-Across is available as an RShiny web-application. The data used in the workflow are either available on public databases or are included in the supplementary material to allow for reproducibility of results. The results and output of the workflow (i.e., chemical info, PODs, etc.) are provided in the supplementary material (available as a download from the journal article). This dataset is associated with the following publication: Beal, M., M. Gagne, S. Kulkarni, G. Patlewicz, R. Thomas, and T. Barton-Maclaren. Implementing in vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories. ALTEX. Society ALTEX Edition, Kuesnacht, SWITZERLAND, 39(1): 123-139, (2022).

감염병의 예방 및 신속한 대응을 위한 극복 수단과 과학적 근거를 마련하기 위해, 국립보건연구원 소속 감염병연구센터를 확대·개편하여 설립된 감염병 전문 국가 연구기관의 간행물 정보

본데이터는 오송첨단의료산업진흥재단의 사업관리시스템에서 제공하는 지원사업 공고이력에 대한 정보를 제공하는 공공데이터입니다. 국내 바이오·의료산업 분야의 기업에서 사업 공고에 지원하기 위해 구축된 중요한 정보 자원입니다. 이 데이터에는 사업 공고년도, 공고명, 공고일 등에 대한 정보를 비롯해 사업 지원 시 필요한 정보에 대해 전반적인 내용을 이해하는 데 도움이 되는 다양한 항목이 포함되어 있습니다. 본 데이터는 사업에 지원을 희망하는 기업, 기관 등의 실질적인 활용을 위해 제공됩니다.

국립호남권생물자원관에서 수행한 연구 결과를 바탕으로 발간된 연구 논문에 대한 데이터(논문명, 년도, 상세내용, 학술명, 링크, 저자 등)