An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Pyla-Koutsopetria Archaeological Project I: Pedestrian Survey" data publication.

R and R markdown source code, and model output files for the manuscript: Saravia LA. (2014) A new method to analyze species abundances in space using generalized dimensions. PeerJ PrePrints 2:e745v1http://dx.doi.org/10.7287/peerj.preprints.745v1 Other code used in the manuscript is - The neutral/hierarchical model: https://github.com/lsaravia/neutral - The generalized dimension estimation software: https://github.com/lsaravia/mfsba

The table elaborates the five different personality dimensions included in the personality test given to the participants.

Reliable optical critical dimension (OCD) metrology in the regime where the inspection wavelength λ is much larger than the critical dimensions (CDs) of the measurand is only possible using a model-based approach. Due to the complexity of the models involved, that often require solving Maxwell's equations, many applications use a library based look-up approach. Here, the best experiment-to-theory fit is found by comparing the measurement data to a library consisting of pre-calculated simulations. One problem with this approach is that it makes the accuracy of the solution dependent on the refinement of the grid. Interpolating between library values requires a uniform grid in most cases, and can also be very time-consuming. We present an approach based on radial basis functions that is fast, accurate and most importantly works on arbitrary grids. The method is implemented in a application based on the programming language R, that additionally allows for Bayesian data analysis, and provides multiple diagnostics.

Introduction

This dataset corresponds to the simulation data used within AI methods in _"Fast proton transport and neutron production in proton therapy using Fourier neural operators"_ [CITE]. It has been extracted from the corresponding PHITS dataset [1] related to the same work, and is used by the codebase provided in [2] implementing all important AI methods within the paper.

The purpose of this entry is to provide a more easily accessible version of the data in [2] ready to be used for AI applications. The size of the dataset has been greatly reduced, and put into a format allowing the access of the phase space density at each individual depth in the phantom for both protons and neutrons and in the form of discretized histograms.

A concise description of the simulation setup is provided in [2] please refer to the paper for detailed discussion, description, analysis, and further results derived from this dataset.

General information

The phase space density data is divided into discretized histograms as defined in the related paper. This follows the approximation within said paper where only 4 dimensions are kept, related to the depth, radial distance (R), energy (E) and azimuthal divergence (θ) of the particles. The depth dimension is considered as a pseudo-time dimension, meaning that time is not provided within the data. In order to simulate examples of different beams propagatng through different materials, a total of 47 phantoms have been simulated, each with a unique starting energy. Phantoms have been divided into slabs along the depth dimension which are assumed to be of homogeneous material along the dimensions perpendicular to the beam axis, but are composed of different materials among them. The proton density is provided as the Monte Carlo simulated protons appropriately binned into the defined discretizations whenever one of the surfaces of each slab is crossed. When it comes to the neutron phase space density, this is instead provided as the angle, energy and radius distributions of secondary neutrons produced within each slab. Both densities are to be considered as integrated with respect to time. For each slab, also the energy deposited by the proton is provided, coming as an energy deposition probability distribution along E and R. Moreover, each of the 47 phantoms has been irradiated according to three different sets of treatment head paramenter, leading to the creation of three dataset: ES8, ES9 and NES8. For the sake of reproducibility, weights for each of the models discussed in [2] are also provided.

Parametrization

The densities are observed through discretizations as identified in the paper. Within this work, the resolution along the beam depth is fixed to 0.5mm, the energy resolution is set to 1 and 2 MeV for the proton and neutron fluences respectively, while the radial distance and angle is handled differently among the two particles. For protons these are discretized in logarithmically spaced bins, with the first bin also comprising 0, and ranging up to 95.9 mm and 58.76 ° respectively. Instead, for neutrons both dimensions are uniformly discretized, ranging from 0 up to 60 mm and 180 ° respectively. The R, E and θ dimensions are divided into 30x250x30 bins within the proton data, and into 30x125x30 in the case of the neutrons, which are provided at each discretized depth. Data about energy deposition follows the same radial binning as in the case of the proton density, but the energy binning is instead logarithmic ranging from 1.0e-3 up to 97.7 MeV.

As already mentioned, the ES8, ES9 and NES8 datasets differ in terms of the treatment head parameters. More details about the specifics of each dataset can be found in [1]. As ES8 and ES9 share the same treatment head parameters with the exception of the intensity, the proton density is not provided for the ES9 dataset to limit storage size.

Model weights for each surrogate trained on each of the provided datasets (called MES8, MES9 and MNES8) are also provided, abiding to the surrogate structure defined in [2]. In particular, each surrogate is composed of a proton and neutron model for both density and intensity prediction. Models can be used as detailed in the GitHub repository [3] related to [2].

File description

Both the aforementioned density discretizations are named internally as "phits_logfull" and "hn_phits" for the proton and neutrons respectively, with the energy deposition one following the same convention as the protons. All files contained within this datasets are therefore named according to the discretizations as either "phits_logfull_cube_protons_\

Surrogates are provided in separate .zip files. Each surrogate contains 4 subfolders related to each surrogate component. The PDF components come in the form of pytorch checkpoints encapsulating Fourier Neural Operator models defined through package `neuraloperator` [4] [5] with version 0.3.0. Intensity components are instead .pickle files containing XGBoostRegressor objects defined through package `XGBoost` [6]. Each component also comes with a pickled dictionary containing important metadata related to model hyperparameters.

Folder Structure

The provided data consists of three different .zip files, each related to the ES8, ES9 and the NES8 datasets. Each .zip file comes already divided within the train, validation and test split on the basis of the starting energy. Within each split folder, simulations are represented through folders named in the format "\

It should be noted that, although the total size of the proposed dataset is of around 7GB, uncompressing the files requires a total size of 180.2 GB.

References

[1] H. N. Ratliff, F. Blangiardi, PHITS simulations of neutron and gamma-ray production from and transport of 70–250 MeV protons in hetero-geneous 1D tissue phantoms, Rodare, (in preparation for submission)(2025).

[2] "Fast proton transport and neutron production in proton therapy using Fourier neural operators" (to be filled)

[3] Blangiardi, F. (2025). AI_phase_space_PT [Computer software]. GitHub. [https://github.com/f-blan/AI_phase_space_PT](https://github.com/f-blan/AI_phase_space_PT)

[4] J. Kossaifi, N. Kovachki, Z. Li, D. Pitt, M. Liu-Schiaffini, R. J. George, B. Bonev, K. Azizzadenesheli, J. Berner, A. Anandkumar, A library for learning neural operators (2024). arXiv:2412.10354.

[5] N. B. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. M. Stuart, A. Anandkumar, Neural operator: Learning maps between function spaces, CoRR abs/2108.08481 (2021).

[6] T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, ACM, 2016, p. 785–794. doi:10.1145/2939672.2939785. URL http://dx.doi.org/10.1145/2939672.2939785

Acknowledgements

The NOVO project has received funding from the European Innovation Council (EIC) under grant agreement No. 101130979. The EIC receives support from the European Union's Horizon Europe research and innovation programme. Partners from The University of Manchester has received funding from UK Research and Innovation under grant agreement No. 10102118

Data and code belonging to the manuscript: The availability and completeness of open funder metadata - Case study for publications funded by the Dutch Research Council

Abstract: Research funders spend considerable efforts collecting information on outcomes of the research they fund. To help funders track publication output associated with their funding, Crossref initiated FundRef in 2013, enabling publishers to register funding information using persistent identifiers. However, it is hard to assess the coverage of funder metadata because it is unknown how many articles are the result of funded research and therefore should include funder metadata.

In this paper we looked at 5,004 publications reported by researchers to be the result of funding by a specific funding agency: the Dutch Research Council NWO. Only 67% of these articles contain funding information in Crossref, with a subset acknowledging NWO as funder name and/or Funder IDs linked to NWO (53% and 45%, respectively).

Web of Science (WoS), Scopus and Dimensions are all able to infer additional funding information from funding statements in the full text of the articles. Funding information in Lens largely corresponds to that in Crossref, with some additional funding information likely taken from PubMed.

We observe interesting differences between publishers in the coverage and completeness of funding metadata in Crossref compared to proprietary databases, highlighting potential to increase the quality of open metadata on funding.

This dataset contains the following files:

DOIs_unique_CR_Lens_Wos_Scopus_Dim.csv - Dataset of unique DOIs (n= 5,004) with collected information from Crossref and presence/absence of funder information in Lens, Web of Science, Scopus and Dimensions

NWO_funder_names_Crossref.txt - List of funder name variants for NWO found in Crossref

Google_Apps_Script.js - Google Apps Script for retrieving information from Crossref and processing Dimensions results

DOI_cleaning.R - R script for cleaning DOIs

The table shows the mean scores for the personality test and the test for health related quality of life, comparing each cohort separately, the VPT individuals and finally all participants.**p<0.01;*p<0.05; ns = not significant;aVPT (N = 119), control (N = 104);bVPT (N = 99), control (N = 99);cVPT (N = 218), control (N = 203);dVPT (N = 194), control (N = 191); VPT, very preterm; SF12-PCS, Short Form 12- Physical Component Summary; SF12-MCS, Short Form 12-Mental Component Summary.

Tropical ecologists have long recognized rainfall as the key climate filter shaping tropical ecosystem structure and function across space and time. Still, tropical ecologists have historically had a limited toolkit for characterizing rainfall, largely relying on simple metrics like mean annual precipitation (MAP) and dry season length to characterize rainfall regimes that vary along many more dimensions. Here, we review methods for quantifying dimensions of rainfall variability on multiple time scales, with a focus on ecological applications of these methods. We also discuss key considerations for tropical ecologists looking to use rainfall metrics that better align with hypothesized biological or ecological mechanisms or that more effectively describe rainfall variability in the systems we study, and provide a toolkit (R scripts and gridded datasets) to do so. We argue that incorporating more sophisticated approaches to quantify rainfall variability into study design and statistical a...

Length, symphyseal length, angle and width for these beam models is based upon the morphology of specimens listed in Table 2.1. Note that these measurements are 1/100th of the ‘volume scaled’ high resolution meshes, not actual specimen size.

Aim: Understanding how the landscape influences gene flow is important in explaining biodiversity, especially when co-distributed taxa across heterogeneous landscapes exhibit species-specific habitat associations. Here, we test predictions about the effects of forest-type on population connectivity in two sympatric species of spiny rats that differ in their forest associations. Specifically, we evaluate the hypothesis that seasonal floodplain forests (várzea) provide linear connectivity, facilitating gene flow among individuals, while non-flooded forests (terra-firme) may diminish the functional connectivity. Location: Western Amazon, South America. Taxon: Proechimys simonsi (non-flooded forests, terra-firme) and Proechimys steerei (seasonal floodplain forests, várzea). Methods: We analyze about 13,000 SNPs along with characterizations of landscape heterogeneity for two forest types to test for differences in the functional connectivity. Influence of the landscape and environmental vari..., , Supporting Information for the manuscript: Functional connectivity in sympatric spiny rats reflects different dimensions of Amazonian forest-association Including:-Raw Data: Fastq files by individuals after Stacks processing reads (.fq.gz);-Genetic Data: Raw and Filtered VCF by species (.vcf);-Spatial Data: coordinates of all localities by the individuals (.csv);-MLPE Tables: input for IBR analyses in MLPE mixed models (.csv);-Supporting_Information: Table S1.1 with details on geographic information for each individual (.csv); and the Supporting Information file (Appendix S1, S2, and S3) in a single file (.pdf); R scripts are available in:https://github.com/jdalapicolla/LanGen_pipeline_version2https://github.com/jdalapicolla/IBD_models.Rhttps://github.com/jdalapicolla/MLPE.R, # Data from: Functional connectivity in sympatric spiny rats reflects different dimensions of Amazonian forest-association

https://doi.org/10.5061/dryad.4qrfj6qbf

Description of the data and file structure

Supporting Information files for the paper "Functional connectivity in sympatric spiny rats reflects different dimensions of Amazonian forest-association" (https://doi.org/10.1111/jbi.14281)

1) Raw Data: Fastq files by individuals after Stacks processing reads (.fq.gz). Samples were sequenced using the ddRAD-Seq technique in one lane of a HiSeq2500 (Illumina, San Diego, CA, USA) at the Center for Applied Genomics in Toronto, Canada to generate 150 bp, single-end reads. More details can be found in "Functional connectivity in sympatric spiny rats reflects different dimensions of Amazonian forest-association" (https://doi.org/10.1111/jbi.14281)

**2) Genetic Da...

Survey objectives: \r

\r The community appreciation of biodiversity (CAB) indicator is one of the measures in the NSW Government's Biodiversity Indicator Program reporting. The indicator is based on a set of survey questions to assess and track changes in community understanding and support of biodiversity conservation across 3 key dimensions:\r \r * cognitive appreciation – whether people are aware of biodiversity and its benefits or values\r * affective appreciation – how much people value biodiversity and whether they care about it\r * behavioural appreciation – whether people are engaged in actions that protect or benefit biodiversity.\r \r More information about the Biodiversity Indicator Program and the latest 2024 Biodiversity Outlook Report is available here: https://www.environment.nsw.gov.au/topics/animals-and-plants/biodiversity/biodiversity-indicator-program\r \r \r

Enhanced survey instrument: \r

\r The CAB indicator was conceptualised and developed by an external group of researchers from University of Queensland, Queensland University of Technology and CSIRO. The first assessment of the indicator repurposed data from the 2015 ‘Who cares about the environment?’ survey to help understand community appreciation of biodiversity across the 3 dimensions. The findings were published in 2021. \r \r The same external team of researchers developed an enhanced CAB indicator method for future use. The second assessment in 2022 adopted the same 3 dimensions as the first assessment, but using a purpose-built survey tool. The enhanced survey instrument retained the 22 'Who cares' survey questions used in the first assessment, for comparison and continuity, and incorporated 52 additional questions which allow the indicator to be more comprehensively assessed. \r The attached 'Developing enhanced measures' report describes the development and features of this enhanced indicator. The report is also available here:\r https://www.environment.nsw.gov.au/research-and-publications/publications-search/community-appreciation-of-biodiversity-indicator-developing-enhanced-measures\r \r Since 2022, the NSW DCCEEW Social Science team have collected data using the enhanced CAB indicator survey on an annual basis, to track trends over time.\r \r \r

Methodology and reporting:\r

\r The enhanced CAB survey is issued as a 12-minute online questionnaire to a total of approximately 2,000 residents of NSW aged 18 and over. The survey was built and is hosted using the Qualtrics survey platform. A number of data quality checks are conducted at launch of each survey, and on delivery of the final data.\r \r Qualtrics is responsible for sourcing participants from several market research panel providers. Quotas have been set by key demographics to ensure a representative sample. The final results are weighted by age group, gender, regional proportions, and Aboriginal status for NSW population. It is acknowledged that some groups may be underrepresented in the final sample - such as residents with limited English skills, residents with low or no formal education, those with limited access to internet etc. \r \r External events - such as Covid-19 pandemic related public health orders, extreme weather events in NSW - so far have not impacted the ability to gather sample for the study. However, as this is a social research dataset, it is expected that such external events may have an impact on the environmental attitudes and behaviours that the survey has been designed to collect information on, and may explain some of the variance in the results over time.\r \r Results are reported on an aggregated level in order to protect the privacy and anonymity of individual respondents, to meet social research industry standards, and to ensure the robustness of the results. \r \r At the aggregate NSW level, the survey has high levels of accuracy, due to the large sample size of approximately n=2,000 responses per wave. Typically, at the 95% confidence level, the margin of error (MoE) on survey results reported on population level is approximately +/- 2.2% points or less. \r \r \r ----\r \r Please contact Social Science Team at SocialResearch@environment.nsw.gov.au with any questions or feedback.\r

ABSTRACT Objective: To investigate whether there is a relationship between nutritional status and limitations in activities of daily living in patients with chronic obstructive pulmonary disease. Methods: A cross sectional study was conducted from July to December 2011 in Santa Catarina. Seventeen chronic obstructive pulmonary disease patients [age (years) = 67±8; forced expiratory volume in one second (% of the predicted value) = 38.6±16.1; body mass index (kg/m2) = 24.7±5.4] underwent the assessments: pulmonary function (spirometry); functional status (London Chest Activity of Daily Living scale, physical activities in daily life, and Glittre ADL-Test; nutritional status (anthropometry and dual-energy X-Ray absorptiometry). Results: The total score of the London Chest Activity of Daily Living scale correlated with fat-free mass (r=-0.50; p=0.04) and lean mass (r=-0.50; p=0.04). The lying time in physical activities in daily life correlated with bone mineral content (r=-0.50; p=0.04). Nutricional status was not correlated with time spent on Glittre ADL-test. Conclusion: Variables that reflect muscle mass depletion are related to variables of self-reported limitation in activities of daily living. Bone mineral content is correlated with time patients spend lying, reflecting the impact of inactive postures on the nutritional status of these patients.

Beaks of granivorous songbirds are adapted to dehusk seeds fast and efficiently. This is reflected in the large variety of beak shapes and sizes among species specialized in different seed types. Generally, larger beaks improve the dehusking of larger seeds by transmitting and withstanding higher bite forces. Meanwhile, smaller beaks are better suited for processing smaller seeds by allowing faster beak movements and better seed handling dexterity. These patterns are presumably the result of a trade-off between force and velocity inherent to lever systems. Since beak shape also varies among individuals of the same species, we investigated whether beak shape relates to variation in feeding performance and beak kinematics in the Domestic Canary (Serinus canaria). We analysed beak morphology of 87 individuals through both traditional size measurements and 3D-landmark analysis to capture metrics such as beak depth, length, width and curvature. We related these metrics of morphology to data ..., Image data of birds with their beaks held in a closed resting position were selected from the recordings of Andries et al. (2023). Image data was calibrated using synchronized images of a calibration object with known dimensions in XMAlab. Landmarks were annotated on the calibrated images in XMAlab. 3D-coordinates of the landmarks were extracted and used to calculate beak size metrics in Microsoft Excel. To analyze beak curvature, 3D-coordinates of the landmarks on the upper beak's curve were further processed and analyzed in R (version 4.3.3.). Morphological data were related to feeding performance, beak kinematics and seed handling skill metrics, directly taken from Andries et al. (2023)., , # Data from: Beak dimensions affect feeding performance within a granivorous songbird species

https://doi.org/10.5061/dryad.d51c5b0dm

Description of the data and file structure

This dataset contains all the necessary data for reproducing the results of the research, as described in the manuscript with the name: 'Beak dimensions affect feeding performance within a granivorous songbird species'

The data includes beak morphology data obtained from landmark analysis and size measurmeents on calibrated images and data on feeding performance, skills and beak kinematics obtained from Andries et al. (2023).

Files and variables

This dataset includes 5 .csv files with landmark and measurement data, one R-file containing the code for the landmark analysis, one R-file containing the code for statistical analysis and associated .txt files (see below; code/software) and the raw image data used in the anaylses.

image_data.zip: File folder contai...

*Estimated measurements.

Bird checklist for Arthur R Marshall Loxahatchee National Wildlife Refuge

Loadings of Items, Eigenvalue and Percentage of Explained Variance for the Dimensions of the Dutch Version of the R-SPQ-2F for Maximum Likelihood Factor Analysis with Oblique Rotation (Loadings between −0.40 and 0.40 omitted).

In the field of psychopathy, there is an ongoing debate about the core traits that define the disorder, and that therefore must be present to some extent in all psychopaths. The main controversy of this debate concerns criminal behaviour, as some researchers consider it a defining trait, while others disagree. Using a representative sample of 204 Spanish convicted inmates incarcerated at the Pereiro de Aguiar Penitentiary in Ourense, Spain, we tested two competing models, the Psychopathy Checklist-Revised (PCL-R), which includes criminal behaviour items, versus the Comprehensive Assessment of Psychopathic Personality (CAPP), which does not. We used two different PCL-R models, one that includes criminal items and another that does not. PCL-R factors, facets, and testlets from both models and CAPP dimensions were correlated and compared. Two different PCL-R cut-off scores, 25 or more and 30 or more, were used for the analysis. Overall, a strong correlation was found between PCL-R and CAPP scores in the whole sample, but as scores increased and inmates became more psychopathic, the correlations weakened. All these data indicate that psychopathy, understood to mean having high scores on the PCL-R and CAPP, is a multidimensional entity, and inmates can develop the disorder and then receive the diagnosis through different dimensions. The CAPP domains showed better correlations when compared with the PCL-R factors from both models, showing that an instrument for the assessment of psychopathy without a criminal dimension is valuable for clinical assessment and research purposes.

This collection contains supplementary datasets generated during the machine learning–assisted bibliometric workflow for metabolomics and phytochemical research. The datasets represent sequential outputs derived from the integration and harmonisation of bibliographic metadata from Scopus, Web of Science (WoS), and Dimensions, processed via R and Python environments.The datasets were produced through distinct workflow stages:Dataset 1A (merged_dataset2.xlsx): Consolidated metadata produced in R from the merged raw bibliographic exports of Scopus, WoS, and Dimensions.Dataset 1B (sampled_data.xlsx): A stratified random sample generated in Python for pretraining and manual annotation.Dataset 1C (sample_data_pretrained.xlsx): Annotated sample dataset manually screened according to inclusion and exclusion criteria.Dataset 1D (highlighted_full_data_with_predictions.xlsx): The complete harmonised dataset automatically classified using the trained XGBoost model.Dataset 1E (absolute_metabolomics_data.xlsx): Final curated dataset of relevant records extracted from the ML-filtered corpus.Importantly, the file names of each dataset presented here were renamed from their original Google Drive file paths (referenced in the Python Google Colab scripts) to ensure sequential, descriptive, and logically ordered naming. This adjustment enhances clarity, reproducibility, and cross-reference consistency across all linked repositories.

Online data collection was carried out in a nationally representative online sample by a Hungarian company (Social Research Ltd.), an expert in social surveys and data collection. 16,000 members of the company’s research community were invited to participate voluntarily in the research via email, in which no preliminary information was given on the subject of the survey. The voluntary sample was stratified according to gender, age, education, and settlement type. Following the stratification, the respondents were randomly selected in order to obtain a final, nationally representative sample. There were five age groups (18-29, 30-39, 40-49, and 50-59 years of age, and 60 years or older), and three categories of education level (primary and vocational school, secondary, and higher education). The stratification of the sample was completed by adjusting the true proportions of settlement type (capital, cities/towns, and villages) and regions (Central, Eastern, and Western Hungary).The total sample size was N = 993, but the number of respondents to the ECR-R-HU questions was N = 958, as the questionnaire was not offered to participants who had never been in a romantic relationship. Four months after the first wave of data collection in December 2018, a second wave was carried out in a smaller subsample (N = 98) of the original sample. The number of male and female participants in the whole sample (Wave 1) was approximately equal. The average age of participants was M = 47.89 (SD = 13.85) with all age groups from 18 to 89 appropriately represented and almost half of the sample was middle-aged (between 40 and 60 years old). Somewhat more than half of the respondents lived in country towns, the remaining 20% and 23% lived in the capital and in villages, respectively. Two thirds of the participants graduated at least from secondary school. Most respondents (78.2%) were either in a romantic relationship or married at the time of the survey. The demographic characteristics of the Wave 2 subsample proved not to be representative. Participants used an online platform to complete the 25-minute survey. In addition to socio-demographic questions and the ECR-R-HU, they were given the Hungarian version of four questionnaires (WHO Well-being Questionnaire (WBI-5), Perceived Stress Scale-4 (PSS-4), Depression Scale Questionnaire (DS1K), Family Assessment Device (FAD)) in order to test the convergent validity of the ECR-R-HU. ECR-R-HU is the Hungarian translation of the ECR-R (Fraley et al., 2000), a 36-item self-report measure of adult attachment. The instrument includes two subscales (Avoidance, Anxiety) that assess attachment-related anxiety and avoidance, each with 18 items. Participants used a 7-point Likert-type scale to indicate their level of agreement with each item, where 1 = strongly disagree and 7 = strongly agree. Participants received the following instructions: “Please take a moment to think about your previous and current romantic experiences and indicate the level of your agreement with each statement”.The survey was approved by the Research Ethics Committee of Semmelweis University Budapest, Hungary, license number RKEB: 197/2018.

Reference: Dupont K, Gervai J, Danis I, Tóth I, Koren R, Török S: Factor Structure, Psychometric Properties, and Validation of the Hungarian Version of the Experiences in Close Relationships Revised (ECR-R-HU) Questionnaire in a Nationally Representative Community Sample. Journal of Personality Assessment. (manuscript accepted for publication)

This article introduces a graphical goodness-of-fit test for copulas in more than two dimensions. The test is based on pairs of variables and can thus be interpreted as a first-order approximation of the underlying dependence structure. The idea is to first transform pairs of data columns with the Rosenblatt transform to bivariate standard uniform distributions under the null hypothesis. This hypothesis can be graphically tested with a matrix of bivariate scatterplots, Q-Q plots, or other transformations. Furthermore, additional information can be encoded as background color, such as measures of association or (approximate) p-values of tests of independence. The proposed goodness-of-fit test is designed as a basic graphical tool for detecting deviations from a postulated, possibly high-dimensional, dependence model. Various examples are given and the methodology is applied to a financial dataset. An implementation is provided by the R package copula. Supplementary material for this article is available online, which provides the R package copula and reproduces all the graphical results of this article.