- 물질별 국제 화학 식별자(International Chemical Identifier, InChI) 표기법 등의 기본정보를 비롯하여, 유전독성 속성별 실험에 관한 가공 데이터와 과학적 근거 및 기타 메타데이터를 포함하는 데이터

Excel files containing raw data used to generate figures throughout manuscript. This dataset is associated with the following publication: Dean , T., D. Betancourt , J. Kim, L. Harvey, A. Evans, and B. Grace. Biological Responses of Raw 264.7 Macrophage Exposed to Two Strains of Stachybotrys chartarum Spores Grown on Four Different Wallboard Types. INHALATION TOXICOLOGY. Taylor & Francis, Inc., Philadelphia, PA, USA, 28(7): 303-307, (2016).

These datasets outline the previously published studies, the parameters measured and optimized, and the results of the PEST optimization of SWMM. NOTE: This dataset has been removed from public access due to revocation. Please refer inquiries regarding this dataset to the listed contact person. This dataset is not publicly accessible because: Data removed from dataset due to revocation. Please refer inquiries regarding this dataset to the listed contact person. It can be accessed through the following means: N/A. Format: N/A. This dataset is associated with the following publication: Platz, M., M. Simon, and M. Tryby. Testing of Storm Water Management Model Low Impact Development Modules. JAWRA. American Water Resources Association, Middleburg, VA, USA, 56(2): 283-296, (2020).

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Datasets and code for analyses and generation of figures are available via FigShare (doi: 10.6084/m9.figshare.22266046) and a public GitHub repository (https://github.com/rumschsl/MacroBiodivTrends). This dataset is associated with the following publication: Rumschlag, S., M. Mahon, D. Jones, W. Battaglin, J. Behrens, E. Bernhardt, P. Bradley, E. Brown, F. De Laender, R. Hill, S. Kunz, S. Lee, E. Rosi, R. Schafer, T. Schmidt, M. Simonin, K. Smalling, K. Voss, and J. Rohr. Density declines, richness increases, and composition shifts in stream macroinvertebrates. Science Advances. American Association for the Advancement of Science (AAAS), Washington, DC, USA, 9(18): eadf4896, (2023).

The dataset includes mean +/- standard deviation of each experiment ex vitro (in vitro) rat, in vivo rat, ex vitro (in vitro) human, calculated human. The rows show each fraction or factor in the experiment (e.g., skin wash, tape strip, etc.). The ex vivo data is from work completed at EPA-RTP. The in vivo data was from work completed at NIEHS. This dataset is associated with the following publication: Knudsen, G., A. Trexler, A. Richiards, M. Hughes, and L. Birnbaum. 2,4,6-Tribromophenol disposition and kinetics in rodents: effects of dose, route, sex, and species. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 169(1): 167-179, (2019).

농림축산식품부에서 제공하는 시설원예 농작물(가지) 병해충에 대한 데이터로 병, 해충의 이미지 데이터를 제공합니다.

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 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).

PubMed was searched for the terms microRNA and biomarker in addition to blood (blue), urine (orange), saliva (grey), or cerebrospinal fluid (yellow). The graph in the paper displays the number of total annual publications over the past 16 years and the actual numbers used generate this graph are in incuded in the attached spreadsheet. A near annual doubling of publications occurred from the years 2009 until 2015, whereas only a mere 4% increase in annual publications from 2015 until 2018 was noted. While a number of factors can contribute to these publication trends, this indicates an overall cooling of research interest in biofluid-based microRNA biomarker development. This dataset is associated with the following publication: Chorley, B., E. Atabakhsh, G. Doran, J. Gautier, H. Ellinger-Ziegelbauer, D. Jackson, T. Sharapova, P. Yuen, R. Church, P. Couttet, R. Froetschl, J. McDuffie, V. Martine, P. Pande, L. Peel, C. Rafferty, F. Simutis, and A. Harrill. Methodological considerations for measuring biofluid-based microRNA biomarkers. CRITICAL REVIEWS IN TOXICOLOGY. Taylor & Francis Group, London, UK, 51(3): 264-282, (2021).

PubMed was searched for the terms microRNA and biomarker in addition to blood (blue), urine (orange), saliva (grey), or cerebrospinal fluid (yellow). The graph in the paper displays the number of total annual publications over the past 16 years and the actual numbers used generate this graph are in incuded in the attached spreadsheet. A near annual doubling of publications occurred from the years 2009 until 2015, whereas only a mere 4% increase in annual publications from 2015 until 2018 was noted. While a number of factors can contribute to these publication trends, this indicates an overall cooling of research interest in biofluid-based microRNA biomarker development. This dataset is associated with the following publication: Chorley, B., E. Atabakhsh, G. Doran, J. Gautier, H. Ellinger-Ziegelbauer, D. Jackson, T. Sharapova, P. Yuen, R. Church, P. Couttet, R. Froetschl, J. McDuffie, V. Martine, P. Pande, L. Peel, C. Rafferty, F. Simutis, and A. Harrill. Methodological considerations for measuring biofluid-based microRNA biomarkers. CRITICAL REVIEWS IN TOXICOLOGY. Taylor & Francis Group, London, UK, 51(3): 264-282, (2021).