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.

In this paper, we present new detailed data on the trace metal content of more than 200 shallow polar snow samples collected at various depths in numerous locations mainly in Antarctica and Greenland. The samples were collected in ultraclean plexiglass or teflon tubes from the walls of hand dug pits, using stringent contamination free techniques controlled by severe blank tests. They were then analysed for Na, Mg, K, Ca, Fe, Al, Mn, Pb, Cd, Cu, Zn and Ag in clean room conditions by flameless atomic absorption, after a preconcentration step (by non boiling evaporation in teflon bulbs) which includes dissolving any solid particles by concentrated nitric and hydrofluoric acids. The overall precision on the measured concentrations is of the order of 10 % for all the metals except Pb (20 %) and Cd (35 %), using 95 % confidence limits. The data obtained are compared with those published previously in the literature. Part of these previous data are shown to be erroneously too high, probably because of con-tamination problems both during field collection and analysis.

Comparison of the estimates for the mean and variance of the normal distribution using Eq (15) versus maximum likelihood estimation, from 100 simulations with true value of μ = 0 and σ2 = 1.

Sample sizes and descriptive statistics (mean ± standard deviation) for chronological age (CA) at prediction, observed maturity offset and predicted maturity offset, predicted ages at PHV and the difference of predicted age at PHV minus observed ages at PHV (criterion) with the original (Mirwald) and modified (Moore) equations at each observation in players classified as advanced, average and delayed based on observed ages at PHV†.

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.

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

Background Microarray experiments offer a potent solution to the problem of making and comparing large numbers of gene expression measurements either in different cell types or in the same cell type under different conditions. Inferences about the biological relevance of observed changes in expression depend on the statistical significance of the changes. In lieu of many replicates with which to determine accurate intensity means and variances, reliable estimates of statistical significance remain problematic. Without such estimates, overly conservative choices for significance must be enforced. Results A simple statistical method for estimating variances from microarray control data which does not require multiple replicates is presented. Comparison of datasets from two commercial entities using this difference-averaging method demonstrates that the standard deviation of the signal scales at a level intermediate between the signal intensity and its square root. Application of the method to a dataset related to the β-catenin pathway yields a larger number of biologically reasonable genes whose expression is altered than the ratio method. Conclusions The difference-averaging method enables determination of variances as a function of signal intensities by averaging over the entire dataset. The method also provides a platform-independent view of important statistical properties of microarray data.

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X4, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing datathey are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the “critical pair,” which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

Productivity changes in the tropical-subtropical East-Atlantic are reconstructed using a multiproxy approach. This involves comparing the barium content of the marine surface sediment and sediment core records with other export productivity proxies. Recent biogenic barium accumulation rates (Babio-AR) are shown to generally reflect export productivity (Pexp) in the modern East Atlantic. This relationship may also depend on the planktonic ecology of the surface water, which is dominated by the Ba-rich skeletons (celestite of acantharians) in the open marine environment. Coastal and open marine transfer functions are derived by calibrating Babio-AR with export productivity. However, the sedimentary Ba signal is found to decrease with depth below 1850 m, probably due to increased dissolution. Results also show that Ba preservation is not dependent on the amount of fine fraction (< 2µm). lt remains uncertain whether increased silica dissolution through open marine aeolian Fe-input (Bishop, 1988; Takeda, 1998) favours a decreased Ba-flux. Longterm Pexp-changes determined using the new Ba transfer functions significantly agree with those derived from other methods (i.e. TOC) in open marine locations in the northern margin of the North Atlantic subtropical gyre over the last 40 ka and the last 330 ka in the equatorial divergence zone. Export productivity is shown to be 2-3 times higher than today during cold periods in the equatorial high productivity zone and possibly up to 6 times higher during stages 6 and 8. Productivity cycles are evidently controlled by the lateral advection of nutrient-rich intermediate waters from high southern latitudes.

The geometry of the Tonga Arc implies that it has rotated approximately 17° clockwise away from the Lau Ridge as the Lau Basin formed in between. Questions have arisen about the timing of the opening, whether the arc behaved rigidly, and whether the opening occurred instead from motion of the Lau Ridge, the remanent arc. We undertook to address these questions by taking paleomagnetic samples from sediment cores drilled on the Tonga Arc at Sites 840 and 841, orienting the samples in azimuth, and comparing the paleodeclinations to expected directions. Advanced hydraulic piston corer (APC) cores from Holes 840C and 841A were oriented during drilling with a tool based on a magnetic compass and attached to the core barrel. Samples from Hole 841B were drilled with a rotary core barrel (RCB) and therefore are azimuthally unoriented. They were oriented by identifying faults and dipping beds in the core and aligning them with the same features in the Formation MicroScanner (FMS) wireline logs, which were themselves oriented with a three-axis magnetometer in the FMS tool. The best results came from the APC cores, which yielded a mean pole at -69.0°S, 112.2°E for an age of 4 Ma. This pole implies a declination anomaly of 20.8° ± 12.6° (95% confidence limit), which appears to have occurred by tectonic rotation of the Tonga Arc. This value is almost exactly that expected from the geometry of the arc and implies that it did indeed rotate clockwise as a rigid body. The large uncertainty in azimuth results from core orientation errors, which have an average standard deviation of 18.6°. The youngest cores used to calculate the APC pole contain sediments deposited during Subchron 2A (2.48-3.40 Ma), and their declinations are indistinguishable from the others. This observation suggests that most of the rotation occurred after their deposition; this conclusion must be treated with caution, however, because of the large azimuthal orientation errors. Poles from late and early Miocene sediments of Hole 841B are more difficult to interpret. Samples from this hole are mostly normal in polarity, fail a reversal test, and yield poles that suggest that the normal-polarity directions may be a recent overprint. Late Miocene reversed-polarity samples may be unaffected by this overprint; if so, they imply a declination anomaly of 51.1° ± 11.5°. This observation may indicate that, for older sediments, Tonga forearc rotations are larger than expected.

1Mean (standard deviation)28 missing data3proportion (95% confident interval).N/A: not available; BSR: Brazilian Society of Rheumatology.Description of the study sample and comparison between respondents and nonrespondents.

The robust rank-order test was designed to be appropriate when the samples being compared have unequal variance. However, it tends to be excessively liberal when the samples are asymmetric because the test statistic is assumed to have a standard normal distribution for sample sizes > 12. This work proposes an on-the-fly method to estimate the distribution of the test statistic. The method of likelihood maximization is used to estimate the parameters of the parent distributions of the given sample-pair. Subsequently, the null distribution of the test statistic is obtained by the Monte-Carlo method. Simulations are performed to compare this method with that of standard normal approximation of the test statistic. For small sample sizes (<= 20), the Monte-Carlo method performs better, especially for low values of significance levels (< 5%). Additionally, when the smaller sample has the larger standard deviation, the Monte-Carlo method performs better even for large sample sizes (= 40/60). The two methods do not differ in power. Finally, a Monte-Carlo sample size of 10^4 is found to be sufficient to obtain the aforementioned improvements. The results of this study pave the way for development of a toolbox to perform the robust rank-order test in a distribution-free manner.

Vertical permeability and sediment consolidation measurements were taken on seven whole-round drill cores from Sites 1253 (three samples), 1254 (one sample), and 1255 (three samples) drilled during Ocean Drilling Program Leg 205 in the Middle America Trench off of Costa Rica's Pacific Coast. Consolidation behavior including slopes of elastic rebound and virgin compression curves (Cc) was measured by constant rate of strain tests. Permeabilities were determined from flow-through experiments during stepped-load tests and by using coefficient of consolidation (Cv) values continuously while loading. Consolidation curves and the Casagrande method were used to determine maximum preconsolidation stress. Elastic slopes of consolidation curves ranged from 0.097 to 0.158 in pelagic sediments and 0.0075 to 0.018 in hemipelagic sediments. Cc values ranged from 1.225 to 1.427 for pelagic carbonates and 0.504 to 0.826 for hemipelagic clay-rich sediments. In samples consolidated to an axial stress of ~20 MPa, permeabilities determined by flow-through experiments ranged from a low value of 7.66 x 10**-20 m**2 in hemipelagic sediments to a maximum value of 1.03 x 10**-16 m**2 in pelagic sediments. Permeabilities calculated from Cv values in the hemipelagic sediments ranged from 4.81 x 10**-16 to 7.66 x 10**-20 m**2 for porosities 49.9%-26.1%.

Mean, within-run standard deviation (SDw-run) and within-run coefficient of variation (CVw-run) in percent from one blood sample analyzed for each of the 3 analyzers (cobas b 123, VetStat and epoc) and compared to published precision targets [21].

MiRNAs significantly overexpressed (p < 0.05) by at least 2 standard deviations in tumors compared to control samples.

Spherical Standard Deviation of the Normals This data is part of the Global Ensemble Digital Terrain Model (GEDTM30) dataset. Check the related identifiers section below to access other parts of the dataset. Disclaimer This is the first release of the Multiscale Land Surface Parameters (LSPs) of Global Ensemble Digital Terrain Model (GEDTM30). Use for testing purposes only. This work was funded by the European Union. However, the views and opinions expressed are solely those of the author(s) and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is." The Open-Earth-Monitor project consortium, along with its suppliers and licensors, hereby disclaims all warranties of any kind, express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and non-infringement. Neither the Open-Earth-Monitor project consortium nor its suppliers and licensors make any warranty that the website will be error-free or that access to it will be continuous or uninterrupted. You understand that you download or otherwise obtain content or services from the website at your own discretion and risk. Description LSPs are derivative products of the GEDTM30 that represent measures of local topographic position, curvature, hydrology, light, and shadow. A pyramid representation is implemented to generate multiscale resolutions of 30m, 60m, 120m, 240m, 480m, and 960m for each LSP. The parametrization is powered by Whitebox Workflows in Python. To see the documentation, please visit our GEDTM30 GitHub (https://github.com/openlandmap/GEDTM30). Dataset Contents This dataset includes: Global Spherical Standard Deviation of the Normals 120m Global Spherical Standard Deviation of the Normals 240m Global Spherical Standard Deviation of the Normals 480m Global Spherical Standard Deviation of the Normals 960m Due to Zenodo's storage limitations, the high resolution LSP data are provided via external links: Global Spherical Standard Deviation of the Normals 30m Global Spherical Standard Deviation of the Normals 60m Related Identifiers Digital Terrain Model: GEDTM30 Landform: Slope in Degree, Geomorphons Light and Shadow: Positive Openness, Negative Openness, Hillshade Curvature: Minimal Curvature, Maximal Curvature, Profile Curvature, Tangential Curvature, Ring Curvature, Shape Index Local Topographic Position: Difference from Mean Elevation, Spherical Standard Deviation of the Normals Hydrology: Specific Catchment Area, LS Factor, Topographic Wetness Index Data Details Time period: static. Type of data: properties derived from Digital Terrain Model How the data was collected or derived: The data was derived using Whitbox Workflows. Methods used: LSP algorithms. Limitations or exclusions in the data: The dataset does not include data Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180, -65, 180, 85) Spatial resolution: 120m, 240m, 480m, 960m Image size: 360,000P x 178,219L; 180,000P x 89,110L; 45,000L x 22,282L File format: Cloud Optimized Geotiff (COG) format. Additional information: Layer Scale Data Type No Data Difference from Mean Elevation 100 Int16 32,767 Geomorphons 1 Byte 255 Hillshade 1 UInt16 65,535 LS Factor 1,000 UInt16 65,535 Maximal Curvature 1,000 Int16 32,767 Minimal Curvature 1,000 Int16 32,767 Negative Openness 100 UInt16 65,535 Positive Openness 100 UInt16 65,535 Profile Curvature 1,000 Int16 32,767 Ring Curvature 10,000 Int16 32,767 Shape Index 1,000 Int16 32,767 Slope in Degree 100 UInt16 65,535 Specific Catchment Area 1,000 UInt16 65,535 Spherical Standard Deviation of the Normals 100 Int16 32,767 Tangential Curvature 1,000 Int16 32,767 Topographic Wetness Index 100 Int16 32,767 Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue here Naming convention To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, for twi_edtm_m_120m_s_20000101_20221231_go_epsg.4326_v20241230.tif, the fields are: generic variable name: twi = topographic wetness index variable procedure combination: edtm = derivative direct from global ensemble digital terrain model Position in the probability distribution/variable type: m = measurement Spatial support: 120m Depth reference: s = surface Time reference begin time: 20000101 = 2000-01-01 Time reference end time: 20211231 = 2021-12-31 Bounding box: go = global EPSG code: EPSG:4326 Version code: v20241230 = version from 2024-12-30

Two silicate-rich dust layers were found in the Dome Fuji ice core in East Antarctica, at Marine Isotope Stages 12 and 13. Morphologies, textures, and chemical compositions of constituent particles reveal that they are high-temperature melting products and are of extraterrestrial origin. Because similar layers were found ~2000 km east of Dome Fuji, at EPICA (European Project for Ice Coring in Antarctica)-Dome C, particles must have rained down over a wide area 434 and 481 ka. The strewn fields occurred over an area of at least 3 × 10**6 km**2. Chemical compositions of constituent phases and oxygen isotopic composition of olivines suggest that the upper dust layer was produced by a high-temperature interaction between silicate-rich melt and water vapor due to an impact explosion or an aerial burst of a chondritic meteoroid on the inland East Antarctic ice sheet. An estimated total mass of the impactor, on the basis of particle flux and distribution area, is at least 3 × 10**9 kg. A possible parent material of the lower dust layer is a fragment of friable primitive asteroid or comet. A hypervelocity impact of asteroidal/cometary material on the upper atmosphere and an explosion might have produced aggregates of sub-µm to µm-sized spherules. Total mass of the parent material of the lower layer must exceed 1 × 10**9 kg. The two extraterrestrial horizons, each a few millimeters in thickness, represent regional or global meteoritic events not identified previously in the Southern Hemisphere.

Open Access# Data from: Burrowing crab effects on the properties and functions of coastal soft sediments https://doi.org/10.5061/dryad.3bk3j9kt3 Effect size calculations (including means, sample sizes, and standard deviation) of crab burrowing effects (i.e., high density vs low density) on the properties, nutrient stocks, and functions of coastal sediments. Data comes from studies conducted across Africa, Asia, Australia, North America, and South America. ## Description of the data and file structure File list: 1. Rinehart_et_al.202X_Effectsizes CSV file containing the Hedges d effect size calculations (including the raw means, sample sizes, and standard deviations) for each extracted comparison/study from all 59 manuscripts. Additional extracted data (e.g., crab taxa, experimental conditions, habitat, burrow density) are also included for each comparison/study. 2. Rinehart_et_al.202X_Publicationbias CSV file containing the pooled standard deviation and the Hedges d effect size calculation for each comparison/study. This datafile was used to conduct analyses of publication bias for a resulting systematic meta-analysis. Data-specific information for: (1) Rinehart_et_al.202X_Effectsizes Number of variables: 47 Number of cases/rows: 1423 Variable List: 1. id: the unique code assigned to each data row. 2. reference: author, year, and journal for each data source. 3. pub_year: year of reference publication. One in preparation study was included in the dataset (Rinehart et al. 20XX), it's publication year is denoted as 20XX. 4. paper id: the unique code assigned to each manuscript included in the dataset. 5. continent: the continent where the data was collected. 6. country: the country where the data was collected. 7. state: the state (united states only) where the data was collected. 8. estuary: the name of the estuary where the data was collected. 9. latitude_dd: the latitude associated with the data collected in decimal degrees (dd). 10. longitude_dd: the longitude associated with the data collected in decimal degrees (dd). 11. ecosystem: the type of ecosystem (e.g., salt marsh, mangrove forest, tidal flat) associated with the collected data. 12. vegetation: categorical variable noting the presence (vegetated) or absence (not unvegetated) of any vegetation. 13. ecosystem_type: categorical variable noting if the ecosystem was restored, created, or natural. 14. relative_salinity: categorical variable noting the relative salinity in the ecosystem where the data was collected. 15. tidal_amplitude_m: the tidal amplitude (in meters) in the ecosystem where the data was collected. 16. tidal_cycle: categorical variable noting the type of tidal cycle (e.g., diurnal) in the ecosystem where the data was collected. 17. soil_type: categorical variable noting the soil type (e.g., sand) in the ecosystem where the data was collected. 18. elevation_m: the elevation (in meters) of the ecosystem where the data was collected. 19. study_duration_d: the length of time (in days) that the study ran (applies mainly to manipulative studies). 20. study_timing: the seasons or months during which the study was run. 21. dominant_plant_genus: the genus of the dominant plant present in the ecosystem where the data was collected. 22. dominant_plant_species: the species of the dominant plant present in the ecosystem where the data was collected. 23. dominant_plant_functional_group: categorical variable noting the functional group (e.g., grass) of the dominant plant species in the ecosystem where the data was collected. 24. crab_genus: the genus of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 25. crab_species: the species of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 26. crab_diet: categorical variable noting the main feeding strategy (e.g., herbivore, detritivore) used by the dominant crab species. 27. crab_superfamily: the superfamily of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 28. mean_burrow_diameter_high_crab_treatment_mm: the mean burrow diameter in the study's high crab treatment in mm. 29. mean_burrow_diameter_low_crab_treatment_mm: the mean burrow diameter in the study's low crab treatment in mm. 30. mean_burrow_depth_cm: the mean burrow depth in cm reported by the study. 31. burrow_density_high_crab_m^2: the mean crab burrow density per meter-squared reported in the study's high crab treatment. 32. burrow_density_low_crab_m^2: the mean crab burrow density per meter-squared reported in the study's low crab treatment. 33. experiment_type: categorical variable noting if the study used observational or manipulative methodologies. 34. experiment_setting: categorical variable noting if the study was conducted in a laboratory or field setting. Laboratory studies also include outdoor mesocosm studies. 35. field_location: categorical variable noting where studies conducted in the field placed their study relative to the shoreline. Specifically, we noted if studied sampled in the ecosystem interior (far from shoreline) or at the ecosystem edge (adjacent to the shoreline). 36. soil_depth_cm: the depth, in cm, within the soil profile from which the sediment samples were collected. 37. soil_characteristic_measured: categorical variable identifying the specific sediment property, nutrient stock, or function that was quantified by the study. 38. soil_characteristic_units: the original units used to quantify the soil characteristic within the study. 39. mean_low_crab: the mean value of the soil characteristic measured in the low crab treatment within the study. 40. sd_low_crab: the standard deviation of the soil characteristic measured in the low crab treatment within the study. 41. n_low_crab: the sample size of the soil characteristic measured in the low crab treatment within the study. 42. mean_high_crab: the mean value of the soil characteristic measured in the high crab treatment within the study. 43. sd_high_crab: the standard deviation of the soil characteristic measured in the high crab treatment within the study. 44. n_high_crab: the sample size of the soil characteristic measured in the high crab treatment within the study. 45. crab_density: categorical variable noting if the study documented relative burrowing crab density within their study using burrow density (burrow) or counts of individuals (individuals). 46. hedges_d: the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d values were calculated in OpenMee software (see code/software below). Positive effect sizes indicate that burrowing crabs increased the value of the sediment measurement, while negative effect sized indicate that burrowing crabs decreased the value of the sediment measurement. 47. hedges_d_var: the variation of the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d variation values were calculated in OpenMee software (see code/software below). Missing data codes: na Data-specific information for: (2) Rinehart_et_al.202X_Publicationbias Number of variables: 22 Number of cases/rows: 1423 Variable List: 1. id: the unique code assigned to each data row. 2. reference: author, year, and journal for each data source. 3. pub_year: year of reference publication. One in preparation study was included in the dataset (Rinehart et al. 20XX), it's publication year is denoted as 20XX. 4. paper id: the unique code assigned to each manuscript included in the dataset. 5. ecosystem: the type of ecosystem (e.g., salt marsh, mangrove forest, tidal flat) associated with the collected data. 6. vegetation: categorical variable noting the presence (vegetated) or absence (not unvegetated) of any vegetation. 7. crab_superfamily: the superfamily of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 8. burrow_density_high_crab_m^2: the mean crab burrow density per meter-squared reported in the study's high crab treatment. 9. experiment_type: categorical variable noting if the study used observational or manipulative methodologies. 10. experiment_setting: categorical variable noting if the study was conducted in a laboratory or field setting. Laboratory studies also include outdoor mesocosm studies. 11. soil_characteristic_measured: categorical variable identifying the specific sediment property, nutrient stock, or function that was quantified by the study. 12. soil_characteristic_units: the original units used to quantify the soil characteristic within the study. 13. mean_low_crab: the mean value of the soil characteristic measured in the low crab treatment within the study. 14. sd_low_crab: the standard deviation of the soil characteristic measured in the low crab treatment within the study. 15. n_low_crab: the sample size of the soil characteristic measured in the low crab treatment within the study. 16. mean_high_crab: the mean value of the soil characteristic measured in the high crab treatment within the study. 17. sd_high_crab: the standard deviation of the soil characteristic measured in the high crab treatment within the study. 18. n_high_crab: the sample size of the soil characteristic measured in the high crab treatment within the study. 19. pooled_sd: the pooled standard deviation of the high and low crab treatments for each study. 20. crab_density: categorical variable noting if the study documented relative burrowing crab density within their study using burrow density (burrow) or counts of individuals (individuals). 21. hedges_d: the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d values were calculated in OpenMee software (see code/software

The table shows data at the level of individual workers, both for upper arm and trunk observation. Within workers (n = 28), data are listed for each measured shift (n = 3), and within shifts for each observer (n = 3). The table shows the data used for estimation of group means and variance components (Table 2) after eliminating shifts with more than 50% computer work (marked in red). All 28 workers are included in the table, even workers #1, #7, #14 and #15, who were completely disqualified. (XLSX)

We present a simple, spreadsheet-based method to determine the statistical significance of the difference between any two arbitrary curves. This modified Chi-squared method addresses two scenarios: A single measurement at each point with known standard deviation, or multiple measurements at each point averaged to produce a mean and standard error. The method includes an essential correction for the deviation from normality in measurements with small sample size, which are typical in biomedical sciences. Statistical significance is determined without regard to the functionality of the curves, or the signs of the differences. Numerical simulations are used to validate the procedure. Example experimental data are used to demonstrate its application. An Excel spreadsheet is provided for performing the calculations for either scenario.