In our everyday lives, we are required to make decisions based upon our statistical intuitions. Often, these involve the comparison of two groups, such as luxury versus family cars and their suitability. Research has shown that the mean difference affects judgements where two sets of data are compared, but the variability of the data has only a minor influence, if any at all. However, prior research has tended to present raw data as simple lists of values. Here, we investigated whether displaying data visually, in the form of parallel dot plots, would lead viewers to incorporate variability information. In Experiment 1, we asked a large sample of people to compare two fictional groups (children who drank ‘Brain Juice’ versus water) in a one-shot design, where only a single comparison was made. Our results confirmed that only the mean difference between the groups predicted subsequent judgements of how much they differed, in line with previous work using lists of numbers. In Experiment 2, we asked each participant to make multiple comparisons, with both the mean difference and the pooled standard deviation varying across data sets they were shown. Here, we found that both sources of information were correctly incorporated when making responses. Taken together, we suggest that increasing the salience of variability information, through manipulating this factor across items seen, encourages viewers to consider this in their judgements. Such findings may have useful applications for best practices when teaching difficult concepts like sampling variation.

These data are the standard error calculated from the AVISO Level 4 Absolute Dynamic Topography for Climate Model Comparison Number of Observations data set ( in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/aviso/L4/abs_dynamic_topo ). This data set is not meant to be used alone, but with the absolute dynamic topography data. These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison). The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid. These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).

Compared items’ means and standard deviations, and dimensions’ reliability estimates of Portuguese (Morais et al., 2014) and Spanish versions of the IBCP instrument

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

Mean genetic standard deviation (Genetic SD) of parents in cycle 20 and loss in genetic standard deviation in cycle 20 in comparison to the genetic standard deviation in cycle 0 (Loss over cycle 0) for trait 1 (T1), trait 2 (T2) and the index trait using either optimal independent culling or index selection with different levels of accuracy, unfavourably correlated traits, and T2 relative economic importance of 1.0.

Comparison of mean and standard deviation for the manual versus automatic results of phagocytosis ratios.

About this dataset

Context

The dataset presents median household incomes for various household sizes in South Range, MI, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, South Range did not include 4, 5, 6, or 7-person households. Across the different household sizes in South Range the mean income is $51,844, and the standard deviation is $18,238. The coefficient of variation (CV) is 35.18%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $31,226. It then further increased to $65,869 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/south-range-mi-median-household-income-by-household-size.jpeg" alt="South Range, MI median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range median household income. You can refer the same here

Average standard deviations of the overlaps across different serialisations in the top 5% of the rules in both corpora. Bolded values denote the ranking mechanisms that deliver the best diversity within each corpus – i.e., the larger the average standard deviation, the better the cross-type diversity.

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.

About this dataset

Context

The dataset presents median household incomes for various household sizes in State Line City, IN, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, State Line City did not include 4, 5, 6, or 7-person households. Across the different household sizes in State Line City the mean income is $64,968, and the standard deviation is $33,244. The coefficient of variation (CV) is 51.17%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $36,144. It then further increased to $101,336 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/state-line-city-in-median-household-income-by-household-size.jpeg" alt="State Line City, IN median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State Line City median household income. You can refer the same here

Genic standard deviation (Genic SD) of parents in cycle 20 and loss in genic standard deviation in cycle 20 in comparison to the genic standard deviation in cycle 0 (Loss over cycle 0) for trait 1 (T1), trait 2 (T2) and the index trait using either optimal independent culling or index selection with different levels of accuracy, unfavourably correlated traits, and T2 relative economic importance of 1.0.

Analysis of ‘Datasets from an interlaboratory comparison to characterize a multi-modal polydisperse sub-micrometer bead dispersion’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7f7e5222-e579-486e-b5d7-c02d511d1964 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

About this dataset

Context

The dataset presents median household incomes for various household sizes in United States, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, all of the household sizes were found in United States. Across the different household sizes in United States the mean income is $94,149, and the standard deviation is $26,829. The coefficient of variation (CV) is 28.50%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $38,463. It then further increased to $114,329 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/united-states-median-household-income-by-household-size.jpeg" alt="United States median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States median household income. You can refer the same here

Techical Information: Be-10 concentrations are based on Be isotopic measurements normalized to pre-2007 values of the KNSTD standard series. Multiply by 1.106 for values compatible with post-2007 normalization. See Nishiizumi et al. (2007, doi:10.1016/j.nimb.2007.01.297). Simple exposure ages assume a steady production rate; altitude corrections per Lal (1991, doi:10.1016/0012-821X(91)90220-C) as implemented in the CRONUS calculator (http://hess.ess.washington.edu/math/). Standard deviation = external errors including all known analytical and production rate contributions propagated at the 1sigma level. Internal errors do not include contributions to uncertainty in the final exposure ages common to all samples, such as production rate and scaling errors. External errors should be used when comparing these ages to ages obtained with other dating methods. Internal errors should be used to assess the consistency of exposure ages at a given site. Range of exposure ages returned by the four paleomagnetically corrected production rate scaling methods implemented in the CRONUS calculator. As noted before, these ranges are appropriate for external comparisons with other dating methods, and should not be used to assess the internal consistency of ages from a given site.

Score means and standard deviations and comparison of participants’ performance in experimental phases.

Contents of rare earth elements (REE) in standard samples of Fe-Mn nodules (SDO-5, 6), Fe-Mn crust (SDO-7), and red clay (SDO-9) have been determined by ICP-MS and instrumental neutron activation analysis. Reproducibility of ICP-MS was 5-6%. These results are discussed and compared with other data. It has been found that distribution of REE in the standard samples of ocean Fe-Mn ores and red clay is highly homogenous.

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

We have studied the sedimentary and basaltic inputs of lithium to subduction zones. Various sediments from DSDP and ODP drill cores in front of the Mariana, South Sandwich, Banda, East Sunda and Lesser Antilles island arcs have been analysed and show highly variable Li contents and d7Li values. The sediment piles in front of the Mariana and South Sandwich arcs largely consist of pelagic sediments (clays and oozes). The pelagic clays have high Li contents (up to 57.3 ppm) and Li isotope compositions ranging from +1.3? to +4.1?. The oozes have lower Li contents (7.3-16 ppm) with d7Li values of the diatom oozes from the South Sandwich lower (+2.8? to +3.2?) than those of the radiolarian oozes from the Mariana arc (+8.1? to +14.5?). Mariana sediment also contains a significant portion of volcanogenic material, which is characterised by a moderate Li content (14 ppm) and a relatively heavy isotope composition (+6.4?). Sediments from the Banda and Lesser Antilles contain considerable amounts of continental detritus, and have high Li contents (up to 74.3 ppm) and low d7Li values (around 0?), caused by weathering of continental bedrock. East Sunda sediments largely consist of calcareous oozes. These carbonate sediments display intermediate to high Li contents (2.4-41.9 ppm) and highly variable d7Li values (-1.6? to +12.8?). Basaltic oceanic crust samples from worldwide DSDP and ODP drill cores are characterised by enrichment of Li compared to fresh MORB (6.6-33.1 vs. 3.6-7.5 ppm, respectively), and show a large range in Li isotope compositions (+1.7? to +11.8?). The elemental and isotopic enrichment of Li in altered basalts is due to the uptake of isotopically heavy seawater Li during weathering. However, old oceanic crust samples from Sites 417/418 exhibit lighter Li isotope compositions compared to young basaltic crust samples from Sites 332B and 504B. This lighter Li isotope signature in old crust is unexpected and further research is needed to explore this issue.

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 laboratory experiment was devoted to teaching descriptive statistics and comparing independent groups to STEAM (Science, Technology, Engineering, Arts, and Mathematics) students using open-source and graphical user interface software. Students answered 21 questions using JAMOVI in previously published data sets to learn fundamental statistics concepts. It was divided into four parts. In the first part, descriptive statistics were carried out (mean, median, standard deviation, interquartile range, data normality, and skewness). In the second part, data normality was checked by using visual inspection of plots (histograms and Q–Q plots). In the third part, two independent groups were compared. In the fourth part, more than two independent groups were compared. Normally, comparisons between two or more groups are presented in many textbooks, and a normal and homogeneous distribution of the data is assumed. Only parametric tests were taught, while nonparametric tests were not presented. Thus, data normality was checked using hypothesis tests (Shapiro–Wilk, Kolmogorov–Smirnov, and Anderson–Darling tests). Then, homogeneity was checked using Levene’s and Bartlett’s tests. Normality and homogeneity were also checked using a visual inspection of plots. Once normality and homogeneity were checked, parametric tests were used (t test and ANOVA). If the normality of the data was not checked, nonparametric tests were used (Mann–Whitney and Kruskal–Wallis tests).