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).

This dataset contains the data, code and associated files for the manuscript Authored by Marable et al. This dataset is associated with the following publication: Marable, C., C. Frank, R. Seim, S. Hester, M. Henderson, B. Chorley, and T. Shafer. Integrated Omic Analyses Identify Pathways and Transcriptomic Regulators Associated with Chemical Alterations of in vitro Neural Network Formation. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 186(1): 118-133, (2022).

Tables with data on the macroinvertebrate communities at multiple sites. Data list how many individuals of each of several taxa were retrieved from each site/ station. This dataset is associated with the following publication: Yeardley, R., B. Duffy, K. Kimbrough, J. Lazorchak, M. Mills, and E. Johnson. A Comparison of Two Macroinvertebrate Multi-Plate Sampling Methods to Inform Great Lakes Monitoring and Remediation Efforts. Journal of Environmental Protection. Scientific Research Publishing, Inc., Irvine, CA, USA, 14(12): 933-953, (2023).

Gene Expression Omnibus and ArrayExpress archived study numbers of microarray data used in the study. This dataset is associated with the following publication: Rooney, J., B. Chorley, S. Hiemstra, S. Wink, X. Wang, D. Bell, B. van de Water, and J. Corton. Mining a human transcriptome database for chemical modulators of Nrf2 (PLOS ONE). PLoS ONE. Public Library of Science, San Francisco, CA, USA, 15(9): e0239367, (2020).

Analytical chemistry and toxicological assessment of microcystin-LR toxin standards from seven vendors; a comparison of mass, purity and toxicity based on label mass. This dataset is associated with the following publication: Lang, J., D. Jenkins-Hill, N. Chernoff, J. Schmid, M. Strynar, J. McCord, C. Rosal, and T.L. Thanh. Variability of Microcystin-LR Standards Available from Seven Commercial Vendors. Toxins. MDPI, Basel, SWITZERLAND, 14(10): 705, (2022).

These data are associated with the figures and tables presented in the manuscript "Early microRNA responses in rodent liver mediated by furan exposure establish dose thresholds for later adverse outcomes". Each spreadsheet contains the data for each Figure and metadata describes each column and/or row of data. For access to the smallRNA-seq files, please follow link provided and supply the reviewer key code "mjsnewymzlollmz".

Data used to generate Figures 2, 3, and 4 as we ll as Table 3. This dataset is associated with the following publication: Banerji, A., M. Bagley, J. Shoemaker, D. Tettenhorst, C. Nietch, J. Allen, and J. Santodomingo. Evaluating putative ecological drivers of microcystin spatiotemporal dynamics using metabarcoding and environmental data. Harmful Algae. Elsevier B.V., Amsterdam, NETHERLANDS, 86: 84-95, (2019).

These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number. The datasets are organized in the files by instrument type: PTA (particle tracking analysis), RMM (resonant mass measurement), ESZ (electrical sensing zone), and OTH (other techniques not covered in the three largest groups, including holographic particle characterization, laser diffraction, flow imaging, and flow cytometry). In the OTH group, the specific instrument type for each dataset is noted. Each instrument type (PTA, RMM, ESZ, OTH) has a dedicated file. Included in the data files for each dataset are: (1) the cumulative particle number concentration (PNC, (1/mL)); (2) the concentration distribution density (CDD, (1/mL·nm)) based upon five bins centered at each particle population peak diameter; (3) the CDD in higher resolution, varied-width bins. The lower-diameter bin edge (µm) is given for (2) and (3). Additionally, the PTA, RMM, and ESZ files each contain unweighted mean cumulative particle number concentrations and concentration distribution densities calculated from all datasets reporting values. The associated standard deviations and standard errors of the mean are also given. In the OTH file, the means and standard deviations were calculated using only data from one of the sub-groups (holographic particle characterization) that had n = 3 paired datasets. Where necessary, datasets not using the common bin resolutions are noted (PTA, OTH groups). The data contained here are presented and discussed in a manuscript to be submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.

These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number. The datasets are organized in the files by instrument type: PTA (particle tracking analysis), RMM (resonant mass measurement), ESZ (electrical sensing zone), and OTH (other techniques not covered in the three largest groups, including holographic particle characterization, laser diffraction, flow imaging, and flow cytometry). In the OTH group, the specific instrument type for each dataset is noted. Each instrument type (PTA, RMM, ESZ, OTH) has a dedicated file. Included in the data files for each dataset are: (1) the cumulative particle number concentration (PNC, (1/mL)); (2) the concentration distribution density (CDD, (1/mL·nm)) based upon five bins centered at each particle population peak diameter; (3) the CDD in higher resolution, varied-width bins. The lower-diameter bin edge (µm) is given for (2) and (3). Additionally, the PTA, RMM, and ESZ files each contain unweighted mean cumulative particle number concentrations and concentration distribution densities calculated from all datasets reporting values. The associated standard deviations and standard errors of the mean are also given. In the OTH file, the means and standard deviations were calculated using only data from one of the sub-groups (holographic particle characterization) that had n = 3 paired datasets. Where necessary, datasets not using the common bin resolutions are noted (PTA, OTH groups). The data contained here are presented and discussed in a manuscript to be submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.

These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number. The datasets are organized in the files by instrument type: PTA (particle tracking analysis), RMM (resonant mass measurement), ESZ (electrical sensing zone), and OTH (other techniques not covered in the three largest groups, including holographic particle characterization, laser diffraction, flow imaging, and flow cytometry). In the OTH group, the specific instrument type for each dataset is noted. Each instrument type (PTA, RMM, ESZ, OTH) has a dedicated file. Included in the data files for each dataset are: (1) the cumulative particle number concentration (PNC, (1/mL)); (2) the concentration distribution density (CDD, (1/mL·nm)) based upon five bins centered at each particle population peak diameter; (3) the CDD in higher resolution, varied-width bins. The lower-diameter bin edge (µm) is given for (2) and (3). Additionally, the PTA, RMM, and ESZ files each contain unweighted mean cumulative particle number concentrations and concentration distribution densities calculated from all datasets reporting values. The associated standard deviations and standard errors of the mean are also given. In the OTH file, the means and standard deviations were calculated using only data from one of the sub-groups (holographic particle characterization) that had n = 3 paired datasets. Where necessary, datasets not using the common bin resolutions are noted (PTA, OTH groups). The data contained here are presented and discussed in a manuscript to be submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.