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2 results for “fluorometers”
Underway discrete chlorophyll and post-calibrated underway fluorometer data during NES-LTER Transect cruises, ongoing since 2019
The continuous underway fluorescence, induced by in vivo chlorophyll-a (Chl-a), of surface waters of the Northeast U.S. shelf is compared to discrete Chl-a samples for post-calibration, collected ship-board as part of the Northeast U.S. Shelf Long-Term Ecological Research (NES LTER). Chl-a values derived from the manufacturer-calibrated sensors (hereafter, “continuous fluorescence”) and collected continuously are often different from the precise Chl-a concentrations obtained from discrete, extracted samples. Moreover, underway fluorometers and manufacturer calibrations differ per cruise. Thus, post-calibration of the continuous fluorescence signals using discrete Chl-a measurements is essential to standardize and compare the high-resolution underway Chl-a data along cruise tracks. For six cruises aboard the R/V Endeavor between summer 2019 and summer 2021, 12 to 22 discrete samples were collected from the underway system to measure Chl-a concentrations. These discrete Chl-a concentrations were then compared, using simple linear regressions (Model I least square fit), to corresponding continuous fluorescence values recorded by the two independent fluorometers installed with the underway system. For each cruise a preferred fluorometer was identified based on the best fit of the linear regression between discrete Chl-a concentrations and continuous fluorescence values. The slope and the intercept of the linear regression were used to post-calibrate continuous fluorescence values into standardized and intercomparable Chl-a concentration. This data package includes a table for the underway discrete Chl-a values and a table for the 1-min post-calibrated continuous fluorescence values for the preferred underway fluorometer per cruise.
Sensor data associated with Lucius et al. 2020 – Using machine learning to correct for nonphotochemical quenching in high-frequency in vivo fluorometer data.
This document describes a dataset used to produce Using machine learning to correct for nonphotochemical quenching in high-frequency, in vivo fluorometer data, as reported in: Lucius, M.A., Johnston, K.E., Eichler, L.W., Farrell, J.L., Moriarty, V.W. and Relyea, R.A. (2020), Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data. Limnol Oceanogr Methods, 18: 477-494. https://doi.org/10.1002/lom3.10378 The dataset consists of high-frequency water quality and meterological sensor data collected from two autonomous vertical profiling platforms deployed on Lake George, NY during the ice-free months of 2017-2019. Water quality data include depth-referenced measurements of chlorophyll fluorescence, water temperature and dissolved oxygen. Meteorological data include surface-incident total radiation as well as two derived values: solar azimuth and 1-hr rolling average of total radiation. Finally, using interpolated data from regularly collected subsurface profiles of photosynthetically active radiation, estimates of subsurface total radiation were estimated and included in this dataset. This dataset does not include raw data. The data used were subjected to quality control procedures of the Jefferson Project, as well as additional outlier removal measures and the creation of derived data (as previously described and described in detail in Lucius et al. 2020).
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.