PR = Paraná; PA = Pantanal; AR = Araguaia; AM = Amazon; n = number of samples for each source in each basin. Shared superscript lowercase letters indicate lack of significant differences for the Tukey post-hoc test.

Some of the SNK rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - The first set of files represents projections of the number of historical (1901-1981) standard deviations (SD) above the historical mean for each of three future decades (2020-2029, 2050-2059, 2060-2069) temperature and precipitation levels.

The second set of files represents projections of the proportion of years in a future decade when monthly temperature or precipitation levels are at least two historical SDs above the historical mean.

Temperature and precipitation are monthly means and totals, respectively.

The spatial extent is clipped to a Seward REA boundary bounding box.

In the first set of files, each file, referred to as SDclasses, consists of ordered categorical (factor) data, with three unique classes (factor levels), coded 0, 1 and 2. Within each file, raster grid cells categorized as 0 represent those where the future decadal mean temperature or precipitation value does not exceed one historical SD above the historical mean. Cells categorized as 1 represent those where future decadal values exceed the historical mean by at least one but less than two historical SDs. Cells categorized as 2 represent those where future decadal values exceed the historical mean by at least two historical SDs.

In the second set of files, each file, referred to as annProp, consists of numerical data. Within each file, raster grid cell values are proportions, ranging from zero to one, representing the proportion of years in a future decade when monthly mean temperature or monthly total precipitation are at least two historical SD above the historical mean. Proportions are calculated on five GCMs and then averaged rather than calculated on the five-model composite directly.

Overview:

The historical monthly mean is calculated for each month as the 1901-1981 interannual mean, i.e., the mean of 82 annual monthly values.

The historical SD is calculated for each month as the 1901-1981 interannual SD, i.e., the SD of 82 annual monthly values.

2x2 km spatial resolution downscaled CRU 3.1 data is used as the historical baseline.

A five-model composite (average) of the Alaska top five AR4 2x2 km spatial resolution downscaled global circulation models (GCMs), using the A2 emissions scenario, is used for the future decadal datasets. This 5 Model Average is referred to by the acronym 5modelavg.

For a description of the model selection process, please see Walsh et al. 2008. Global Climate Model Performance over Alaska and Greenland. Journal of Climate. v. 21 pp. 6156-6174.

Emmission scenarios in brief:

The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) created a range of scenarios to explore alternative development pathways, covering a wide range of demographic, economic and technological driving forces and resulting greenhouse gas emissions. The B1 scenario describes a convergent world, a global population that peaks in mid-century, with rapid changes in economic structures toward a service and information economy. The Scenario A1B assumes a world of very rapid economic growth, a global population that peaks in mid-century, rapid introduction of new and more efficient technologies, and a balance between fossil fuels and other energy sources. The A2 scenario describes a very heterogeneous world with high population growth, slow economic development and slow technological change.

These files are bias corrected and downscaled via the delta method using PRISM (http:prism.oregonstate.edu) 1961-1990 2km data as baseline climate. Absolute anomalies are utilized for temperature variables. Proportional anomalies are utilized for precipitation variables. Please see http:www.snap.uaf.edumethods.php for a description of the downscaling process.

File naming scheme:

[variable]_[metric]_[groupModel]_[timeFrame].[fileFormat]

[variable] pr, tas [metric] SDclasses, annProp [groupModel] 5modelAvg [timeFrame] decade_month [fileFormat] tif

examples:

pr_SDclasses_5modelAvg_2020s_01.tif

This file represents a spatially explicit map of the number of January total precipitation historical SDs above the January total precipitation historical mean level, for projected 2020-2029 decadal mean January total precipitation, where cell values are binned in classes less than one, at least one, less than two, and greater than two, labeled as 0, 1, and 2, respectively.

tas_annProp_5modelAVg_2060s_06.tif

This file represents a spatially explicit map of the proportion of years in the period 2060-2069 when June mean temperature projections are at least two historical SDs above the June mean temperature historical mean level, where cell values are proportions ranging from zero to one.

tas = near-surface air temperature

pr = precipitation including both liquid and solid phases

• Bonferroni adjust for multiple comparisons.

Note. Means in the same row with different superscripts are significantly different at p

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.

n is the number of replicates at each time point and represents data from at least 4 mice.*P-value calculated by Student's t-test for the difference between each time point and pre-treatment value. SD is the standard deviation. N/A = not applicable.

Pathway Multi-Omics Simulated Data

These are synthetic variations of the TCGA COADREAD data set (original data available at http://linkedomics.org/data_download/TCGA-COADREAD/). This data set is used as a comprehensive benchmark data set to compare multi-omics tools in the manuscript "pathwayMultiomics: An R package for efficient integrative analysis of multi-omics datasets with matched or un-matched samples".

There are 100 sets (stored as 100 sub-folders, the first 50 in "pt1" and the second 50 in "pt2") of random modifications to centred and scaled copy number, gene expression, and proteomics data saved as compressed data files for the R programming language. These data sets are stored in subfolders labelled "sim001", "sim002", ..., "sim100". Each folder contains the following contents: 1) "indicatorMatricesXXX_ls.RDS" is a list of simple triplet matrices showing which genes (in which pathways) and which samples received the synthetic treatment (where XXX is the simulation run label: 001, 002, ...), (2) "CNV_partitionA_deltaB.RDS" is the synthetically modified copy number variation data (where A represents the proportion of genes in each gene set to receive the synthetic treatment [partition 1 is 20%, 2 is 40%, 3 is 60% and 4 is 80%] and B is the signal strength in units of standard deviations), (3) "RNAseq_partitionA_deltaB.RDS" is the synthetically modified gene expression data (same parameter legend as CNV), and (4) "Prot_partitionA_deltaB.RDS" is the synthetically modified protein expression data (same parameter legend as CNV).

Supplemental Files

The file "cluster_pathway_collection_20201117.gmt" is the collection of gene sets used for the simulation study in Gene Matrix Transpose format. Scripts to create and analyze these data sets available at: https://github.com/TransBioInfoLab/pathwayMultiomics_manuscript_supplement

According to an academic study conducted in 2020, the main reasons for divergence between scores provided by five of the major environmental, social and governance (ESG) ratings agencies is because of scope and measurement. Scope refers to which issues are factored into the score, while measurement is how these performance on these issues is quantified. Based on 2017 data, there was an average of 0.5 standard deviations between providers for these two categories. However, it should be noted that the same study with 2014 data showed average values of 0.48 for scope and 0.54 for measurement, indicating the latter may be slightly more of a reason for ESG score divergence. The weight placed on each issue did not have as much of an effect, with an average of 0.31 standard deviations.

The ESG frameworks included in the study were Sustainalytics, Vigeo, RobecoSAM, Asset4 and MSCI.

Note: Empty cells mean no such genotypes were found in our sample. Maj: Major allele; Het: Heterozygote; Min: Minor allele.aResults (p values) of post hoc comparisons. mh = Maj versus Het, mm = Maj versus Min, hm = Het versus Min.bPost hoc comparison was not run because there were only 2 groups for this locus.

The mean ( standard deviation) values of the parameters from nested sampling (NS), MCMC and the point estimates from simulated annealing (SA). The data came from with additional noise and from Jaeger et al. (Figure 7 [67]) to which we fit three models: Linear ; Quadratic ; Sigmoid .

To assess geographic distributions of elements in the Arctic we compared essential and non-essential elements in the livers of polar bears (Ursus maritimus) collected from five regions within Canada in 2002, in Alaska between 1994 and 1999 and from the northwest and east coasts of Greenland between 1988 and 2000. As, Hg, Pb and Se varied with age, and Co and Zn with gender, which limited spatial comparisons across all populations to Cd, which was highest in Greenland bears. Collectively, geographic relationships appeared similar to past studies with little change in concentration over time in Canada and Greenland for most elements; Hg and Se were higher in some Canadian populations in 2002 as compared to 1982 and 1984. Concentrations of most elements in the polar bears did not exceed toxicity thresholds, although Cd and Hg exceeded levels correlated with the formation of hepatic lesions in laboratory animals.

This document contains the results of the CMIP5 quality control level 2 (NetCDF) for experiment "cmip5 output1 NCAR CCSM4 decadal2003".

Used error flags are: -1 -- Not checked

0 -- No error found

1 testTimeStep() ^0, ^5 Error: negative time step

2 testTimeStep() ^0, ^5 Error: missing time step

4 testTimeStep() ^0, ^5 Error: identical time step

8 testCalendarTimeBounds() Error: negative/zero time bounds range

16 testCalendarTimeBounds() ^0 Error: overlapping time bounds ranges

32 testCalendarTimeBounds() ^0 Warning: gap between time bounds ranges

100 testData() Warning: found a record entirely with filling value

200 testData() Warning: found a record entirely with constant value

400 testData() Warning: suspect minimum Note: No table comparison; inferred from the data

800 testData() Warning: suspect maximum Note: No table comparison; inferred from the data

1600 testData() Warning: undefined standard deviation

3200 testData() Warning: suspecting a replicated record Note: A record of min., max., ave., and std. dev. is identical to a previous one. Note: Fields of constant or filling value excluded.

This document contains the results of the CMIP5 quality control level 2 (NetCDF) for experiment "cmip5 output1 MIROC MIROC5 sstClim".

Used error flags are: -1 -- Not checked

0 -- No error found

1 testTimeStep() ^0, ^5 Error: negative time step

2 testTimeStep() ^0, ^5 Error: missing time step

4 testTimeStep() ^0, ^5 Error: identical time step

8 testCalendarTimeBounds() Error: negative/zero time bounds range

16 testCalendarTimeBounds() ^0 Error: overlapping time bounds ranges

32 testCalendarTimeBounds() ^0 Warning: gap between time bounds ranges

100 testData() Warning: found a record entirely with filling value

200 testData() Warning: found a record entirely with constant value

400 testData() Warning: suspect minimum Note: No table comparison; inferred from the data

800 testData() Warning: suspect maximum Note: No table comparison; inferred from the data

1600 testData() Warning: undefined standard deviation

3200 testData() Warning: suspecting a replicated record Note: A record of min., max., ave., and std. dev. is identical to a previous one. Note: Fields of constant or filling value excluded.

Antarctic terrestrial ecosystems have poorly developed soils and currently experience one of the greatest rates of climate warming on the globe. We investigated the responsiveness of organic matter decomposition in Maritime Antarctic terrestrial ecosystems to climate change, using two study sites in the Antarctic Peninsula region (Anchorage Island, 67°S; Signy Island, 61°S), and contrasted the responses found with those at the cool temperate Falkland Islands (52°S). Our approach consisted of two complementary methods: (1) Laboratory measurements of decomposition at different temperatures (2, 6 and 10 °C) of plant material and soil organic matter from all three locations. (2) Field measurements at all three locations on the decomposition of soil organic matter, plant material and cellulose, both under natural conditions and under experimental warming (about 0.8 °C) achieved using open top chambers. Higher temperatures led to higher organic matter breakdown in the laboratory studies, indicating that decomposition in Maritime Antarctic terrestrial ecosystems is likely to increase with increasing soil temperatures. However, both laboratory and field studies showed that decomposition was more strongly influenced by local substratum characteristics (especially soil N availability) and plant functional type composition than by large-scale temperature differences. The very small responsiveness of organic matter decomposition in the field (experimental temperature increase <1 °C) compared with the laboratory (experimental increases of 4 or 8 °C) shows that substantial warming is required before significant effects can be detected.

The concentrations of mercury (Hg) and other trace metals (Ni, Cu, Zn, Mo, Ba, Re, U) and the Hg isotopic composition were examined across a dramatic redox and productivity transition in a mid-Pleistocene Mediterranean Sea sapropel sequence. Characteristic trace metal enrichment in organic-rich layers was observed, with organic-rich sapropel layers ranging in Hg concentration from 314 to 488 ng/g (avg = 385), with an average enrichment in Hg by a factor of 5.9 compared to organic-poor background sediments, which range from 39 to 94 ng/g Hg (avg = 66). Comparison of seawater concentrations and sapropel accumulations of trace metals suggests that organic matter quantitatively delivers Hg to the seafloor. Near complete scavenging of Hg from the water column renders the sapropel Hg isotopic composition representative of mid-Pleistocene Mediterranean seawater. Sapropels have an average d202Hg value of -0.91 per mil ± 0.15 per mil (n = 5, 1 SD) and D199Hg value of 0.11 per mil ± 0.03 per mil (n = 5, 1 SD). Background sediments have an average d202Hg of -0.76 per mil ± 0.16 per mil (n = 5, 1 SD) and D199Hg of 0.05 per mil ± 0.01 per mil (n = 5, 1 SD), which is indistinguishable from the sapropel values. We suggest that the sapropel isotopic composition is most representative of the mid-Pleistocene Tyrrhenian Sea.

This document contains the results of the CMIP5 quality control level 2 (NetCDF) for experiment "cmip5 output1 BCC bcc-csm1-1-m rcp85".

Used error flags are: -1 -- Not checked

0 -- No error found

1 testTimeStep() ^0, ^5 Error: negative time step

2 testTimeStep() ^0, ^5 Error: missing time step

4 testTimeStep() ^0, ^5 Error: identical time step

8 testCalendarTimeBounds() Error: negative/zero time bounds range

16 testCalendarTimeBounds() ^0 Error: overlapping time bounds ranges

32 testCalendarTimeBounds() ^0 Warning: gap between time bounds ranges

100 testData() Warning: found a record entirely with filling value

200 testData() Warning: found a record entirely with constant value

400 testData() Warning: suspect minimum Note: No table comparison; inferred from the data

800 testData() Warning: suspect maximum Note: No table comparison; inferred from the data

1600 testData() Warning: undefined standard deviation

3200 testData() Warning: suspecting a replicated record Note: A record of min., max., ave., and std. dev. is identical to a previous one. Note: Fields of constant or filling value excluded.

This is supporting data for the manuscript entitled 'DFENS: Diffusion chronometry using Finite Elements and Nested Sampling' by E. J. F. Mutch, J. Maclennan, O. Shorttle, J. F. Rudge and D. Neave. Preprint here: https://doi.org/10.1002/essoar.10503709.1 Data Set S1. ds01.csv Electron probe microanalysis (EPMA) profile data of olivine crystals used in this study. Standard deviations are averaged values of standard deviations from counting statistics and repeat measurements of secondary standards. Data Set S2. ds02.csv Plagioclase compositional profiles used in this study, including SIMS, EPMA and step scan data. Standard deviations for EPMA analyses are averaged values of standard deviations from counting statistics and repeat measurements of secondary standards. Standard deviations for SIMS and step scan analyses are based on analytical precision of secondary standards. Data Set S3. ds03.csv Angles between the EPMA profile and the main olivine crystallographic axes measured by electron backscatter diffraction (EBSD). 'angle100X' is the angle between the [100] crystallographic axis and the x direction of the EBSD map, 'angle100Y' is the angle between [100] crystallographic axis and the y direction of the EBSD map, and 'angle100Z' is the angle between the [100] crystallographic axis and the z direction in the EBSD map etc. 'angle100P' is the angle between the EPMA profile and the [100] crystallographic axis, 'angle010P' is the angle between the EPMA profile and the [010] crystallographic axis, and 'angle100P' is the angle between the EPMA profile and the [001] crystallographic axis. All angles are in degrees. Data Set S4. ds04.csv Median timescales and 1 sigma errors from the olivine crystals of this study. The +1 sigma (days) is the quantile value calculated at 0.841 (i.e. 0.5 + (0.6826 / 2)). The -1 sigma (days) is therefore the quantile calculated at approximately 0.158 (which is 1 - 0.841). The 2 sigma is basically the same but it is 0.5 + (0.95/2). The value quoted as the +1 sigma (error) is the difference between the upper 1 sigma quantile and the median. Likewise the -1 sigma (error) is the difference between the median and the lower 1 sigma quantile. Data Set S5. ds05.xlsx Median timescales and 1 sigma errors from the plagioclase crystals of this study. Results from each of the parameterisations of the Mg-in-plagioclase diffusion data are included: Faak et al, (2013), Van Orman et al., (2014) and a combined expression. Data Set S6. ds06.xlsx Spreadsheet containing the regression parameters and covariance matrices used in this study and in Mutch et al. (2019). Additional versions of the olivine regressions where the ln fO2 is expressed in Pa have been made for completeness. We recommend using the versions where ln fO2 is expressed in its native form (bars).

This dataset contains the prioritization provided by a panel of 15 experts to a set of 28 barriers categories for 8 different roles of the future energy system. A Delphi method was followed and the scores provided in the three rounds carried out are included. The dataset also contains the scripts used to assess the results and the output of this assessment. A list of the information contained in this file is: data folder: this folders includes the scores given by the 15 experts in the 3 rounds. Every round is in an individual folder. There is a file per expert that has the scores between -5 (not relevant at all) to 5 (completely relevant) per barrier (rows) and actor (columns). There is also a file with the description of the experts in terms of their position in the company, the type of company and the country. fig folder: this folder includes the figures created to assess the information provided by the experts. For each round, the following figures are created (in each respective folder): Boxplot with the distribution of scores per barriers and roles. Heatmap with the mean scores per barriers and roles. Boxplots with the comparison of the different distributions provided by the experts of each group (depending on the keywords) per barrier and role. Heatmap with the mean score per barrier weighted depeding on the importance of the role in each use case and the final prioritization. Finally, bar plots with the mean scores differences between rounds and boxplot with comparisons of the scores distributions are also provided. stat folder: this folder includes the files with the results of the different statistical assessment carried out. For each round, the following figures are created (in each respective folder): The statistics used to assess the scores (Intraclass correlation coefficient, Inter-rater agreement, Inter-rater agreement p-value, Homogeneity of Variances, Average interquartile range, Standard Deviation of interquartile ranges, Friedman test p-value Average power post hoc) per barrier and per role. The results of the post hoc of the Friedman Test per berries and per roles. The average score per barrier and per role. The mean value of the scores provided by the experts grouped by the keywords per barrier and role. P-value of the comparison of these two values. The end prioritization of the barrier for the use case (averaging the scores or fuzzy merging of the critical sets) Finally, the differences between the mean and standard deviations of the scores between two consecutive rounds are provided.

The topographic survey of the studied outcrops is based on several thousands of measurements per study site and the measurement of the sample elevation with reference to sea level using a real-time kinematic GPS Trimble R8. The maximum vertical (Z) and horizontal (X and Y) elevation errors are of ± 2.0 cm and a few millimetres, respectively. During the measurement, the surveys were related to the French Polynesian Geodetic Network (Réseau Géodésique de Polynésie Française; RGPF), to operating tide gauges or tide gauge data sets, to probes that were deployed during the field work, to the instantaneous sea level or to modern adjacent microatolls growing in a similar environment than their fossil counterparts. In the absence of geodetic datum or tide gauges, probes were deployed for four to five days in order to measure the sea-level position and to compare the data to the elevation of modern microatolls. The relative sea-level curve, which is presented in this paper, is based on data acquired on islands for which longer tidal records and geodetic data are available. After acquisition, the raw data were processed with the aims: 1) to estimate the elevation of individual dated fossil microatolls based on local tide gauge parameters, and 2) to compare the elevation of all dated fossil microatolls according to the same vertical reference. The link between tide gauge data and the position of the living and fossil microatolls can be established using RGPF. However, a topographic reference at the scale of French Polynesia (4,167 km^2), which is mandatory to achieve the second objective, does not exist, as tide gauge observations are incomplete and the NGPF (Nivellement Général de Polynésie Française) vertical datum that is associated to the RGPF is not homogeneous at this regional scale. The official geodetic system in French Polynesia is the RGPF, which is associated with the NGPF vertical datum. The French Polynesian Geodetic Network is a semi-dynamic system with different levels established by the Naval Hydrographic and Oceanographic Service (Service Hydrographique et Océanographique de la Marine; SHOM) in cooperation with the National Geographic Institute (Institut Géographique National; IGN). The selection of microatolls for dating has been based on the lack of erosion features, the absence of local moating effects and their mineralogical preservation, demonstrating that our database is robust. The chemical preparation, mass-spectrometer measurements and age dating were performed in the years 2014 to 2016 mostly directly after field collection. The data are presented in Supplementary Table 2 following recommendations from Dutton et al. (2017). The best-preserved samples, as indicated by X-ray Powder Diffraction (XRD) measurements, comprise 97.5% aragonite on average (n = 281). Additionally, no secondary aragonite or calcite crystals were revealed by thin section and Scanning Electron Microscope (SEM) observations.

In-situ proton-microprobe analyses are presented for glasses, plagioclases, pyroxenes, olivines, and spinels in eleven samples from Sites 834-836, 839, and 841 (vitrophyric rhyolite), plus a Tongan dacite. Elements analyzed are Mn, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Pb, and Sn (in spinels only). The data are used to calculate two sets of partition coefficients, one set based on the ratio of element in mineral/element in coexisting glass. The second set of coefficients, thought to be more robust, is corrected by application of the Rayleigh fractionation equations, which requires additional use of modal data. Data are presented for phenocryst core-rim phases and microphenocryst-groundmass phases from a few samples. Comparison with published coefficients reveals an overall consistency with those presented here, but with some notable anomalies. Examples are relatively high Zr values for pyroxenes and abnormally low Mn values in olivines and clinopyroxenes from Site 839 lavas. Some anomalies may reflect kinetic effects, but interpretation of the coefficients is complicated, especially in olivines from Sites 836 and 839, by possible crystal-liquid disequilibrium resulting from mixing processes.