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13 results for “concentration indices”
Dissolved Organic Carbon Concentration, Dissolved Organic Matter Optical Properties, and Water Quality Indicators in the Plum Island Estuary (PIE), Massachusetts, USA (2018-2023)
This is a data set of paired in situ measurements of water quality parameters, total suspended solids concentration, and concentration and optical properties (absorption coefficient spectra and fluorescence indices) of dissolved organic matter (DOM) collected between 2018 and 2023 in the Plum Island Estuary and nearshore waters. In situ water quality measurements (salinity, temperature, optical dissolved oxygen saturation, turbidity, and dissolved organic matter fluorescence) were collected with a water quality sonde from the surface (top 1 m of water column), along with corresponding samples that were processed and analyzed in the lab for dissolved organic carbon (DOC) concentration, chromophoric DOM (CDOM), absorption coefficient spectra, DOM excitation-emission matrix (EEM) fluorescence, and total suspended sediment (TSS) concentration. The data were used in multiple studies (see manuscripts listed below) focusing on the dynamics of DOC and CDOM in the Plum Island Estuary.
Text-fig. 2. A – First small excavation trench in the newly discovered fossiliferous outcrop area of the Ploužnice lake horizon, locality "Small Ravine" south of the village Ploužnice on the slope of road No. 286. Typical are the red, violet and varicoloured tuffaceous siltstones. Semily Formation, Late Stephanian, Krkonoše Piedmont Basin. B – Basal part of fossiliferous 12 cm to14 cm thick reddish to purple-red tuffaceous siltstone bed from the trench in Text-fig. 2A. Internal bedding is expressed as horizontal but with diffuse changes in grain size from fine silt to coarse, fine sandy silt. Intercalated in places are 3 to 5 mm thick layers (indicated by arrows) with patchy concentrations of isolated fish remains, especially in the first 3 centimetres of this siltstone layer. The large white spot at 2.5 cm is a coprolite fragment. in Fossil Fauna And Flora Of A Re-Discovered Locality In The Late Carboniferous Ploužnice Horizon Of The Krkonoše Piedmont Basin, Bohemian Massif
Text-fig. 2. A – First small excavation trench in the newly discovered fossiliferous outcrop area of the Ploužnice lake horizon, locality "Small Ravine" south of the village Ploužnice on the slope of road No. 286. Typical are the red, violet and varicoloured tuffaceous siltstones. Semily Formation, Late Stephanian, Krkonoše Piedmont Basin. B – Basal part of fossiliferous 12 cm to14 cm thick reddish to purple-red tuffaceous siltstone bed from the trench in Text-fig. 2A. Internal bedding is expressed as horizontal but with diffuse changes in grain size from fine silt to coarse, fine sandy silt. Intercalated in places are 3 to 5 mm thick layers (indicated by arrows) with patchy concentrations of isolated fish remains, especially in the first 3 centimetres of this siltstone layer. The large white spot at 2.5 cm is a coprolite fragment.
Global data on crop nutrient concentration and harvest indices
<div> <div> <div> <p>Estimates of crop nutrient removal (as crop products and crop residues) are an important component of crop nutrient balances. Crop nutrient removal can be estimated through multiplication of the quantity of crop products or crop residues (removed) by the nutrient concentration of those crop products and crop residue components respectively. Data for quantities of crop products removed at a country level are available through FAOSTAT (<a href="https://www.fao.org/faostat/en/">https://www.fao.org/faostat/en/</a>), but equivalent data for quantities of crop residues are not available at a global level. However, quantities of crop residues can be estimated if the relationship between quantity of crop residues and crop products is known. Harvest index (HI) provides one such indication of the relationship between quantity of crop products and crop residues. HI is the proportion of above-ground biomass as crop products and can be used to estimate quantity of crop residues based on quantity of crop products. Previously, meta-analyses or surveys have been performed to estimate nutrient concentrations of crop products and crop residues and harvest indices (collectively known as crop coefficients). The challenges for using these coefficients in global nutrient balances include the representativeness of world regions or countries. Moreover, it may be unclear which countries or crop types are actually represented in the analyses of data. In addition, units used among studies differ which makes comparisons challenging. To overcome these challenges, data from meta-analyses and surveys were collated in one dataset with standardised units and referrals to the original region and crop names used by the sources of data. Original region and crop names were converted into internationally recognised names, and crop coefficients were summarised into two Tiers of data, representing the world (Tier 1, with single coefficient values for the world) and specific regions or countries of the world (Tier 2, with single coefficient values for each country). This dataset will aid both global and regional analyses for crop nutrient balances. </p> </div> </div> </div>
Field experimental data of crop yields, nutrient concentrations, and harvest indices from around the world
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Global data on crop nutrient concentration and harvest indices
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Biomarker indices and concentrations and biomarker-based temperature estimates from the Iberian Margin core MD95-2042, composite atmospheric temperature record from Greenland, and stacks of delta 18Oice and atmospheric temperature records from three Antarctic sites
<p>Core MD95-2042 alkenone and GDGT data: This dataset provides the following information for core MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT-2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. Sediment samples were taken every 5 cm from core MD95-2042 and homogenized before lipid extraction. The lipid extracts were splitted into two fractions: one for alkenone analysis by gas chromatography coupled to a flame ionization detector, and the other for GDGT analysis by high-performance liquid chromatography coupled to mass spectrometry. All GDGT analyses were done in duplicate. The 1σ analytical uncertainties from 37 replicate analyses of the core catcher sample from core MD95-2042 are 0.007 (0.4 °C) for RI-OH, 0.008 (0.2 °C) for RI-OH′, 0.003 (0.2 °C) for TEX<sub>86</sub>, 0.238 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for U<sup>K′</sup><sub>37</sub>. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX<sub>86</sub><sup>H</sup>-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX<sub>86</sub><sup>H</sup> + 33.0 (Darfeuil et al., 2016). U<sup>K′</sup><sub>37</sub>-SST estimates are from the following global calibration: SST = 29.876 × U<sup>K′</sup><sub>37</sub> − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX<sub>86</sub>-SST and TEX<sub>86</sub>-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for U<sup>K′</sup><sub>37</sub>-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Alkenone data covering the 160–70 and 70–0 ka BP periods are from Davtian et al. (2021) and Darfeuil et al. (2016), respectively. GDGT data covering the 160–45 ka BP period are from Davtian et al. (2021). The age model of core MD95-2042 for the 160–43 and 43–0 ka BP periods was obtained by tuning to Chinese speleothems (Cheng et al., 2016) and by recalibrating existing <sup>14</sup>C ages with the Marine20 calibration curve (Heaton et al., 2020), respectively. MIS, Marine Isotope Stage; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available.</p> <p>Greenland atmospheric temperature record: This dataset consists in a composite Greenland atmospheric temperature record, which was built with the following records: the GISP2 atmospheric temperature record by Kobashi et al. (2017) for the 10–0 ka BP period, the NGRIP atmospheric temperature record by Kindler et al. (2014) for the 120–10 ka BP period, and the NEEM atmospheric temperature record by NEEM community members (2013) for the 129–120 ka BP period. The NEEM temperature anomalies obtained by NEEM community members (2013) were shifted by –31 °C to obtain absolute air temperatures. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p> <p>Antarctic δ<sup>18</sup>O<sub>ice</sub> and atmospheric temperature stacks: This dataset consists in two stacks of three Antarctic records (EDC, EDML, and WD), one for δ<sup>18</sup>O<sub>ice</sub> and the other for atmospheric temperature: both stacks are provided with their stacking uncertainties. To build the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were resampled every 10 years before centering to zero means and normalization to unit standard deviations over the 140–0 ka BP period (68–0 ka BP for WD). To optimize the continuity between the portions with and without the WD ice core, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were centered to zero means over the 68–67 ka BP period. The resulting Antarctic δ<sup>18</sup>O<sub>ice</sub> records were then averaged and stacking uncertainties were calculated as the pooled standard deviation of the stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records divided by the square root of the number of stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records. The final Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, expressed in ‰, has the same standard deviation as the δ<sup>18</sup>O<sub>ice</sub> record from EDML over the 140–0 ka BP period, and has a zero mean over the 1–0 ka BP. The Antarctic atmospheric temperature stack was built like the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, except that the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were corrected for seawater δ<sup>18</sup>O<sub>ice</sub> variations before conversion into atmospheric temperature. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p>
Figure S1: Nutrient concentration of lettuce (Lactuca sativa 'Rex') plants grown at different total incident light levels in deep water culture hydroponics. Lines show multiple regression analysis results, indicating no significant interactions. Each data point represents one plant. N = nitrogen, P = phosphorus, K = potassium, Ca = calcium, Mg = magnesium, S = sulfur, B = boron, Cu = copper, Fe = iron, Mn = manganese, and Zn = zinc.
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The data of precipitation, stream discharge, groundwater level, and DOC and DIC concentrations and DOM optical indices of water in the Hulugou catchemnt, upper reaches of Heihe River
<p>Here we provide the original data of the precipitation, stream discharge, air temperature, dissolved organic carbon concentrations and dissolved inorganic carbon concentrations and DOM optical indices of water at different locations in the Hulugou catchment, upper reaches of Heihe River, Northeastern Tibet Plateau, China.</p>
The Evaluation of Injection Site Pain and Adherence in Patients Switching from a Low to High Concentration Adalimumab (AVT-02) Across Multiple Indications.
ClinicalTrials.gov study NCT05913817. IPD Sharing: NO. Countries: 1. Publications: 0.
Biomarker indices and concentrations and biomarker-based temperature estimates from Iberian Margin cores MD99-2331, MD95-2040 and MD95-2042 (160–45 ka BP)
<p>Biomarker metadata and data for cores MD99-2331, MD95-2040, and MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT 2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. The 1σ analytical uncertainties are 0.008 (0.5 °C) for RI-OH, 0.007 (0.2 °C) for RI-OH′, 0.003 (0.1 °C) for TEX<sub>86</sub>, 0.263 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for U<sup>K′</sup><sub>37</sub>. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX<sub>86</sub><sup>H</sup>-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX<sub>86</sub><sup>H</sup> + 33.0 (Darfeuil et al., 2016). U<sup>K′</sup><sub>37</sub>-SST estimates are from the following global calibration: SST = 29.876 × U<sup>K′</sup><sub>37</sub> − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX<sub>86</sub>-SST and TEX<sub>86</sub>-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for U<sup>K′</sup><sub>37</sub>-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Core MD95-2040 U<sup>K′</sup><sub>37</sub> and summed di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentration data are from Pailler and Bard (2002). Core MD95-2042 U<sup>K′</sup><sub>37</sub> and summed di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentration data covering the 70–45 ka BP period are from Darfeuil et al. (2016). GDGT data for core MD95-2042 samples with TEX<sub>86</sub> values below 0.4 are deemed anomalous and are thus excluded from any description and analysis. Cmbsf, centimeters below sea floor; MIS, marine isotope stage; DO, Dansgaard–Oeschger event; GS, Greenland stadial; H, Heinrich event; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available.</p>
Impact of the Preparation Method of Red Cell Concentrates on Transfusion Indices in Thalassemic Patients
ClinicalTrials.gov study NCT03992001. IPD Sharing: YES. Countries: 1. Publications: 0.
Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007
The Change in Chlorophyll-a Concentrations 1998-2007 component of the Indicators of Coastal Water Quality Collection represents a tabular time series of the chlorophyll-a concentrations for each grid cell, derived from the "Indicators of Coastal Water Quality: Annual Chlorophyll-a Concentration 1998-2007" data set. Chlorophyll-a concentrations are derived from NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The grid cells are organized by country, and the percentage of change from 1998-2007 is calculated for each cell. Each time series was assessed with a linear regression, and cells with statistically significant trends in chlorophyll-a concentrations are identified. The rows of the table are linked to a sequence grid from the "Indicators of Coastal Water Quality: Ancillary Data" collection to facilitate the mapping of the trend values for selected countries and areas of interest. The data are processed and compiled by the Columbia University Center for International Earth Science Information Network (CIESIN).
Indicators of Coastal Water Quality: Annual Chlorophyll-a Concentration 1998-2007
The Annual Chlorophyll-a Concentrations component of the Indicators of Coastal Water Quality Collection consists of gridded satellite measurements of chlorophyll-a concentrations (in nanogram/cubic meter) in a band extending between 10 and 100 km from the shoreline. Chlorophyll-a concentrations are derived from NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The grids are based on annual composites of SeaWiFS satellite data provided by the SeaWiFS Project, NASA/Goddard Space Flight Center and GeoEye in the form of HDF files at a resolution of 5 arc-minutes (approximately 9 x 9 km at the equator). The source files are true-color images generated from sub-sampled, calibrated, Rayleigh-corrected level-2 data, which are derived from raw radiance counts by applying sensor calibration, atmospheric corrections, and bio-optical algorithms. To arrive at chlorophyll-a concentrations, radiance counts were converted using the conversion formula provided as part of the original data files. The gridding is done by the Columbia University Center for International Earth Science Information Network (CIESIN).
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OpenNeuro
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