• Bonferroni adjust for multiple comparisons.

Chlorophyll-a is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production. As an essential source of energy in the marine environment, the extent and availability of phytoplankton biomass can be highly influential for fisheries production and dictate trophic structure in marine ecosystems. Changes in phytoplankton biomass are predominantly effected by changes in nutrient availability, through either natural (e.g., turbulent ocean mixing) or anthropogenic (e.g., agricultural runoff) processes. This layer represents the standard deviation of the 8-day time series of chlorophyll-a (mg/m3) from 2002-2013. Monthly and 8-day 4-km (0.0417-degree) spatial resolution data were obtained from the MODIS (Moderate-resolution Imaging Spectroradiometer) Aqua satellite instrument from the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov). The standard deviation was calculated over all 8-day chlorophyll-a data from 2002-2013 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Formula for converting median and interquartile range (IQR) into mean and standard deviation (SD).

Bioaccumulation of trace metals in carbonate shells of mussels and clams was investigated at seven hydrothermal vent fields of the Mid-Atlantic Ridge (Menez Gwen, Snake Pit, Rainbow, and Broken Spur) and the Eastern Pacific (9°N and 21°N at the East Pacific Rise and the southern trough of Guaymas Basin, Gulf of California). Mineralogical analysis showed that carbonate skeletons of mytilid mussel Bathymodiolus sp. and vesicomyid clam Calyptogena m. are composed mainly of calcite and aragonite, respectively. The first data were obtained for contents of a variety of chemical elements in bivalve carbonate shells from various hydrothermal vent sites. Analyses of chemical compositions (including Fe, Mn, Zn, Cu, Cd, Pb, Ag, Ni, Cr, Co, As, Se, Sb, and Hg) of 35 shell samples and 14 water samples from mollusk biotopes revealed influences of environmental conditions and some biological parameters on bioaccumulation of metals. Bivalve shells from hydrothermal fields with black smokers are enriched in Fe and Mn by factor of 20-30 relative to the same species from the Menez Gwen low-temperature vent site. It was shown that essential elements (Fe, Mn, Ni, and Cu) more actively accumulated during early ontogeny of the shells. High enrichment factors of most metals (n x 100 - n x 10000) indicate efficient accumulation function of bivalve carbonate shells. Passive metal accumulation owing to adsorption on shell surfaces was estimated to be no higher than 50% of total amount and varied from 14% for Fe to 46% for Mn.

Techical Information: GRADBENTHICRF (2015). Study of benthic community of Ria Formosa

Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student’s t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.

The TReCCA Analyser is conceived to facilitate, speed up and intensify the analysis and representation of your time-resolved data, more specically in the case of cell culture assays. Without having to type any formula, it will perform at wish the following calculations: Control condition normalisation. Technical replicate averaging and standard deviation calculation. Smoothing and slope calculation of the data in order to obtain the rate of change. IC50/EC50 determination of a substance in a time-resolved fashion.

This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1).

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record.

Techical Information: NEWSLUISINLET (1997). Study of the evolution and displacement of the new S.Lu s inlet (Ancao inlet)

Techical Information: INLET-IPTM (2005). Inlet monitoring

Techical Information: Project CRIDA 'Consequences of River Discharge Modifications on Coastal Zone and Continental Shelf 'Reference: POCTI/P/MAR/15289/99

The application of radiogenic isotopes to the study of Cenozoic circulation patterns in the South Pacific Ocean has been hampered by the fact that records from only equatorial Pacific deep water have been available. We present new Pb and Nd isotope time series for two ferromanganese crusts that grew from equatorial Pacific bottom water (D137-01, 'Nova', 7219 m water depth) and southwest Pacific deep water (63KD, 'Tasman', 1700 m water depth). The crusts were dated using 10Be/9Be ratios combined with constant Co-flux dating and yield time series for the past 38 and 23 Myr, respectively. The surface Nd and Pb isotope distributions are consistent with the present-day circulation pattern, and therefore the new records are considered suitable to reconstruct Eocene through Miocene paleoceanography for the South Pacific. The isotope time series of crusts Nova and Tasman suggest that equatorial Pacific deep water and waters from the Southern Ocean supplied the dissolved trace metals to both sites over the past 38 Myr. Changes in the isotopic composition of crust Nova are interpreted to reflect development of the Antarctic Circumpolar Current and changes in Pacific deep water circulation caused by the build up of the East Antarctic Ice Sheet. The Nd isotopic composition of the shallower water site in the southwest Pacific appears to have been more sensitive to circulation changes resulting from closure of the Indonesian seaway.

Four samples of Nauru Basin basalts (Cores 94 to 109 of Hole 462A, sub-bottom depth 1077-1209 m) have 87Sr/86Sr ratios in the range 0.7037 to 0.7038, which is distinctly higher than the ratios of N-type MORB. The Rb contents of the samples are depleted in comparison with those of MORB and ocean-island basalts. These chemical and isotopic characteristics are identical to those of the basalts previously drilled during Leg 61 (Cores 75 to 90 of Hole 462A), and are explained in terms of inhomogeneity of the source region in the mantle or later alteration effects. Sr/Ca-Ba/Ca systematics of 15 samples from Cores 462A-94 to 462A-109 and 14 samples from Cores 462A-75 to 462A-90 suggest that the Nauru Basin basalts are derived from a mantle peridotite by 20 to 30% partial melting with subsequent Plagioclase crystallization.

This dataset contains ERA5.1 surface level analysis parameter data ensemble means over the period 2000-2006. ERA5.1 is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project re-run for 2000-2006 to improve upon the cold bias in the lower stratosphere seen in ERA5 (see technical memorandum 859 in the linked documentation section for further details). The ensemble means are calculated from the ERA5.1 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data. Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). The main ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data, ERA5t, are also available upto 5 days behind the present. A limited selection of data from these runs are also available via CEDA, whilst full access is available via the Copernicus Data Store.

Histograms and results of k-means and Ward's clustering for Hidden Room game

The fileset contains information from three sources:

1. Histograms files:
* Lexical_histogram.png (histogram of lexical error ratios)
* Grammatical_histogram.png (histogram of grammatical error ratios)

2. K-means clustering files:
* elbow-lex kmeans.png (clustering by lexical aspects: error curves obtained for applying elbow method to determinate the optimal number of clusters)
* cube-lex kmeans.png (clustering by lexical aspects: a three-dimensional representation of clusters obtained after applying k-means method)
* Lexical_clusters (table) kmeans.xls (clustering by lexical aspects: centroids, standard deviations and number of instances assigned to each cluster)
* elbow-gram kmeans.png (clustering by grammatical aspects: error curves obtained for applying elbow method to determinate the optimal number of clusters)
* cube-gramm kmeans.png (clustering by grammatical aspects: a three-dimensional representation of clusters obtained after applying k-means method)
* Grammatical_clusters (table) kmeans.xls (clustering by grammatical aspects: centroids, standard deviations and number of instances assigned to each cluster)
* elbow-lexgram kmeans.png (clustering by lexical and grammatical aspects: error curves obtained for applying elbow method to determinate the optimal number of clusters)
* Lexical_Grammatical_clusters (table) kmeans.xls (clustering by lexical and grammatical aspects: centroids, standard deviations and number of instances assigned to each cluster)
* Grammatical_clusters_number_of_words (table) kmeans.xls : number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying k-means clustering to grammatical error ratios.
* Lexical_clusters_number_of_words (table) kmeans.xls : number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying k-means clustering to lexical error ratios.
* Lexical_Grammatical_clusters_number_of_words (table) kmeans.xls : number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying k-means clustering to lexical and grammatical error ratios.

3. Ward’s Agglomerative Hierarchical Clustering files:
* Lexical_Cluster_Dendrogram_ward.png (clustering by lexical aspects: dendrogram obtained after applying Ward's clustering method).
* Grammatical_Cluster_Dendrogram_ward.png (clustering by grammatical aspects: dendrogram obtained after applying Ward's clustering method)
* Lexical_Grammatical_Cluster_Dendrogram_ward.png (clustering by lexical and grammatical aspects: dendrogram obtained after applying Ward's clustering method)
* Lexical_Grammatical_clusters (table) ward.xls: Centroids (from column 2 to 7) and cluster sizes (last column) obtained by applying Ward's agglomerative hierarchical clustering to lexical and grammatical error ratios.
* Grammatical_clusters (table) ward.xls: Centroids (from column 2 to 4) and cluster sizes (last column) obtained by applying Ward's agglomerative hierarchical clustering to grammatical error ratios.
* Lexical_clusters (table) ward.xls: Centroids (from column 2 to 4) and cluster sizes (last column) obtained by applying Ward's agglomerative hierarchical clustering to lexical error ratios.
* Lexical_clusters_number_of_words (table) ward.xls: number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying Ward's agglomerative hierarchical clustering to lexical error ratios.
* Grammatical_clusters_number_of_words (table) ward.xls: number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying Ward's agglomerative hierarchical clustering to grammatical error ratios.
* Lexical_Grammatical_clusters_number_of_words (table) ward.xls: number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying Ward's agglomerative hierarchical clustering to lexical and grammatical error ratios.

Means and standard deviations of variables.

Mean standard deviation (across the three replication tracings) of the goodness of fit (MSE) and parameter values for each condition.

A series of 1-atm. melting experiments on basaltic flows collected from Holes 918D and 989B in the oceanic succession of the East Greenland continental margin can be used to define possible phase equilibria and liquid lines of descent. A sample from Site 918 (Section 152-918D-108R-2) shows the melting order low-Ca pyroxene (1153ºC), augite (1182ºC), olivine (1192ºC), and plagioclase (1192ºC). A sample from Site 989 (Section 163-989B-10R-7) melts in the order of low-Ca pyroxene (1113ºC), augite (1167ºC), plagioclase (1177ºC), and olivine (1184ºC). In particular, the relatively early appearance of low-Ca pyroxene distinguishes the melting relations for the oceanic succession from those observed for the basaltic continental succession at Site 917. A basaltic andesite flow from the Middle Series at Site 917 (Section 152-917A-27R-4) shows the melting order of low-Ca pyroxene (1142ºC), plagioclase (1173ºC), and olivine (1173ºC). This melting order is difficult to reconcile with the observed large compositional variations in SiO2 and FeO for the Middle Series, which imply early magnetite fractionation. Major element considerations and rare-earth element modeling of the dacites of the Middle Series suggest that they formed by low extent of melting (<20%) of continental hydrated gabbroic or mafic amphibolite at pressures <8 kbar. These crustally derived melts represent possible contaminants of basaltic magmas of the Lower and Middle Series at Site 917.

GDA (Geochemic al Data Analysis) is a comprehensive IBM PC-based geochemical data processing system. It is designed to use whole-rock geochemical data retrieved from the ORACLE database, but can be adapted for other databases, or data can be entered into files from the keyboard. The programs are written in FORTRAN 77 (microsoft compiler) and use the MicroGlyph Systems SciPlot graphics package for plotting. The system includes facilities for generating plots (histograms, XY plots, triangular plots, spidergrams, box-whisker plots, etc.), calculating statistical functions (e.g., mean, standard deviation, regression lines, correlation coefficients and cluster analysis) and CIPW norms, printing tables, and carrying out petrogenetic modelling calculations. Plots can be displayed on a PC screen for inspection and editing before being output to a plotter or other device. Other programs allow samples to be assigned to groups for plotting purposes, and allow editing and merging of datafiles.

This is a supplementary dataset for the publication:

I. M. Tanash and T. Riihonen, "Tight Logarithmic Approximations and Bounds for Generic Capacity Integrals and Their Applications to Statistical Analysis of Wireless Systems," in IEEE Transactions on Communications, 2022, doi: 10.1109/TCOMM.2022.3198435.

The dataset contains the sets of optimized coefficients for the novel minimax approximations of the Nakagami and lognormal capacity integrals in terms of absolute error. The proposed approximations have the form of a weighted sum of logarithmic functions. The optimized coefficients are found for a wide range of the corresponding fading parameters, namely m for the Nakagami capacity integral and σ (standard deviation) for the lognormal capacity integral. Please note that the optimized coefficients in the provided dataset for the lognormal capacity integral are calculated for σdB (standard deviation in decibels) so σ=0.1 log_e(10) σdB in Eq. 5.

The Matlab function (func_extract_coef.m) extracts the required set of optimal coefficients from the provided dataset according to the selected capacity integral, the parameter's value, and the number of terms. See help func_extract_coef for more information.

The Matlab script (general_any_func) implements the theory presented in the corresponding journal paper: More specifically, it implements solving Eq. 22 to calculate the optimized coefficients of Eq. 7 for the Nakagami capacity integral. The code also provides general comments on how to generalize it to obtain the optimized coefficients of any communication system in terms of absolute error. Number of supplementary Matlab functions (general_any_func, func_abs_gen_any_func, calc_d_gen, calc_Cappr_gen, calc_d_gen_derivative, calc_Cappr_gen_derivative, Gauss_Laguerre, and peakseek) are provided herein and are used in the main Matlab script.

A Matlab script (Example.m) is also provided as an example to illustrate the use of the provided Matlab function (func_extract_coef.m) in extracting the required coefficients from the dataset, to calculate and plot the corresponding absolute error which is shown by figure Example.jpg.