This part of the data release includes graphical representation (figures) of data from sediment cores collected in 2009 offshore of Palos Verdes, California. This file graphically presents combined data for each core (one core per page). Data on each figure are continuous core photograph, CT scan (where available), graphic diagram core description (graphic legend included at right; visual grain size scale of clay, silt, very fine sand [vf], fine sand [f], medium sand [med], coarse sand [c], and very coarse sand [vc]), multi-sensor core logger (MSCL) p-wave velocity (meters per second) and gamma-ray density (grams per cc), radiocarbon age (calibrated years before present) with analytical error (years), and pie charts that present grain-size data as percent sand (white), silt (light gray), and clay (dark gray). This is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Figures.pdf. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

Measuring the quality of Question Answering (QA) systems is a crucial task to validate the results of novel approaches. However, there are already indicators of a reproducibility crisis as many published systems have used outdated datasets or use subsets of QA benchmarks, making it hard to compare results. We identified the following core problems: there is no standard data format, instead, proprietary data representations are used by the different partly inconsistent datasets; additionally, the characteristics of datasets are typically not reflected by the dataset maintainers nor by the system publishers. To overcome these problems, we established an ontology---Question Answering Dataset Ontology (QADO)---for representing the QA datasets in RDF. The following datasets were mapped into the ontology: the QALD series, LC-QuAD series, RuBQ series, ComplexWebQuestions, and Mintaka. Hence, the integrated data in QADO covers widely used datasets and multilinguality. Additionally, we did intensive analyses of the datasets to identify their characteristics to make it easier for researchers to identify specific research questions and to select well-defined subsets. The provided resource will enable the research community to improve the quality of their research and support the reproducibility of experiments.

Here, the mapping results of the QADO process, the SPARQL queries for data analytics, and the archived analytics results file are provided.

Up-to-date statistics can be created automatically by the script provided at the corresponding QADO GitHub RDFizer repository.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into “pictures,” and develop interactive visualizations, for example, with the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, for example, Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education. Supplementary materials containing code and data for the assignments are available online.

New Zealand's official employment and unemployment statistics are sourced from the Household Labour Force Survey. Data on the number of people employed in New Zealand and the unemployment rate is available from 1970.

This repository contains numerical data that underlies graphs and summary statistics for a manuscript originally posted as:
Yuriko Harigaya, Nana Matoba, Brandon D. Le, Jordan M. Valone, Jason L. Stein, Michael I. Love*, William Valdar*. "Probabilistic classification of gene-by-treatment interactions on molecular count phenotypes." doi: https://doi.org/10.1101/2024.08.03.605142 (* These authors contributed equally to this work.)

The data corresponds to the release v0.1.1 of the GitHub repository at https://github.com/yharigaya/classifygxt-paper. See main-figs.csv, supp-figs.csv, and tables.csv for the correspondence between the figures/tables and the numerical data files. The supp-figs.zip and supp-tables.zip contain the files for the supplementary figures and tables, respectively. The ClassifyGxT software is available from https://github.com/yharigaya/classifygxt.

Notes:

• The individual genotype data that underlie Fig5A, Fig5C, Fig5E, Fig5G, Fig6A, Fig6C, Fig6E, S17 FigA, S17 FigC, S17 FigE, S17 FigG, S18 FigA, S18 FigC, and S18 FigE can be accessed via the Database of Genotypes and Phenotypes (dbGap) at https://www.ncbi.nlm.nih.gov/gap/ with the accession number phs003642.v1.p1 upon request and approval.
• The partial ROC curves in S5 Fig and S12 Fig can be drawn using the data for the full ROC curves in S2 Fig and S11 Fig, respectively.
• The numerical data underlying S7.2 Fig and S7.4 Fig are included in S7_Fig.csv and S7.3_Fig.csv, respectively.
• In S3_Table_Data.csv and S4_Table_Data.csv, the "Index" column contains the indices for five randomly selected feature-SNP pairs.
• In S5_Table_Data.csv and S6_Table_Data.csv, the "Marginal likelihood" column contains relative values of the sum of log-marginal likelihood across feature-SNP pairs.

Algebraic graphs for interpretations, with statistics is a book. It was written by Gordon Lindores Bell and published by Harrap in 1965.

About this dataset

Context

The dataset tabulates the Gate household income by age. The dataset can be utilized to understand the age-based income distribution of Gate income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Gate, OK annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Gate, OK household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Gate income distribution by age. You can refer the same here

Tables and charts have long been seen as effective ways to convey data. Much attention has been focused on improving charts, following ideas of human perception and brain function. Tables can also be viewed as two-dimensional representations of data, yet it is only fairly recently that we have begun to apply principles of design that aid the communication of information between the author and reader. In this study, we collated guidelines for the design of data and statistical tables. These guidelines fall under three principles: aiding comparisons, reducing visual clutter, and increasing readability. We surveyed tables published in recent issues of 43 journals in the fields of ecology and evolutionary biology for their adherence to these three principles, as well as author guidelines on journal publisher websites. We found that most of the over 1,000 tables we sampled had no heavy grid lines and little visual clutter. They were also easy to read, with clear headers and horizontal orient..., Once we had established the above principles of table design, we assessed their use in issues of 43 widely read ecology and evolution journals (SI 2). Between January and July 2022, we reviewed the tables in the most recent issue published by these journals. For journals without issues (such as Annual Review of Ecology, Evolution, and Systematics, or Biological Conservation), we examined the tables in issues published in a single month or in the entire most recent volume if few papers were published in that journal on a monthly basis. We reviewed only articles in a traditionally typeset format and published as a PDF or in print. We did not examine the tables in online versions of articles. Having identified all tables for review, we assessed whether these tables followed the above-described best practice principles for table design and, if not, we noted the way in which these tables failed to meet the outlined guidelines. We initially both reviewed the same 10 tables to ensure that we a..., , # Design of tables for the presentation and communication of data in ecological and evolutionary biology

Once we had established the above principles of table design, we assessed their use in issues of 43 widely read ecology and evolution journals (SI 2). Between January and July 2022, we reviewed the tables in the most recent issue published by these journals. For journals without issues (such as Annual Review of Ecology, Evolution, and Systematics, or Biological Conservation), we examined the tables in issues published in a single month or in the entire most recent volume if few papers were published in that journal on a monthly basis. We reviewed only articles in a traditionally typeset format and published as a PDF or in print. We did not examine the tables in online versions of articles.

Having identified all tables for review, we assessed whether these tables followed the above-described best practice principles for table design and, if not, we noted the way in which these ...

A prior distribution for the underlying graph is introduced in the framework of Gaussian graphical models. Such a prior distribution induces a block structure in the graph’s adjacency matrix, allowing learning relationships between fixed groups of variables. A novel sampling strategy named Double Reversible Jumps Markov chain Monte Carlo is developed for learning block structured graphs under the conjugate G-Wishart prior. The algorithm proposes moves that add or remove not just a single edge of the graph but an entire group of edges. The method is then applied to smooth functional data. The classical smoothing procedure is improved by placing a graphical model on the basis expansion coefficients, providing an estimate of their conditional dependence structure. Since the elements of a B-Spline basis have compact support, the conditional dependence structure is reflected on well-defined portions of the domain. A known partition of the functional domain is exploited to investigate relationships among portions of the domain and improve the interpretability of the results. Supplementary materials for this article are available online.

Context

This knowledge graph is constructed to aid research in scholarly data analysis. It can serve as a standard benchmark dataset for several tasks, including knowledge graph embedding, link prediction, recommendation systems, and question answering about high quality papers from 20 top computer science conferences.

This has been introduced and used in the PhD thesis Multi-Relational Embedding for Knowledge Graph Representation and Analysis and TPDL'19 paper Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space.

Content

Construction protocol

Scholarly data

From the Microsoft Academic Graph dataset, we extracted high quality computer science papers published in top conferences between 1990 and 2010. The top conference list are based on the CORE ranking A* conferences. The data was cleaned by removing conferences with less than 300 publications and papers with less than 20 citations. The final list includes 20 top conferences: AAAI, AAMAS, ACL, CHI, COLT, DCC, EC, FOCS, ICCV, ICDE, ICDM, ICML, ICSE, IJCAI, NIPS, SIGGRAPH, SIGIR, SIGMOD, UAI, and WWW.

Knowledge graph

The scholarly dataset was converted to a knowledge graph by defining the entities, the relations, and constructing the triples. The knowledge graph can be seen as a labeled multi-digraph between scholarly entities, where the edge labels express there relationships between the nodes. We use 5 intrinsic entity types including Paper, Author, Affiliation, Venue, and Domain. We also use 5 intrinsic relation types between the entities including author_in_affiliation, author_write_paper, paper_in_domain, paper_cite_paper, and paper_in_venue.

Benchmark data splitting

The knowledge graph was split uniformly at random into the training, validation, and test sets. We made sure that all entities and relations in the validation and test sets also appear in the training set so that their embeddings can be learned. We also made sure that there is no data leakage and no redundant triples in these splits, thus, constitute a challenging benchmark for link prediction similar to WN18RR and FB15K-237.

Data content

File format

All files are in tab-separated-values format, compatible with other popular benchmark datasets including WN18RR and FB15K-237. For example, train.txt includes "28674CFA author_in_affiliation 075CFC38", which denotes the author with id 28674CFA works in the affiliation with id 075CFC38. The repo includes these files: - all_entity_info.txt contains id name type of all entities - all_relation_info.txt contains id of all relations - train.txt contains training triples of the form entity_1_id relation_id entity_2_id - valid.txt contains validation triples - test.txt contains test triples

Statistics

Data statistics of the KG20C knowledge graph:

Acknowledgements

For the dataset and semantic query method, please cite: - Hung Nghiep Tran and Atsuhiro Takasu. Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space. In Proceedings of International Conference on Theory and Practice of Digital Libraries (TPDL), 2019.

For the MEI knowledge graph embedding model, please cite: - Hung Nghiep Tran and Atsuhiro Takasu. Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion. In Proceedings of the European Conference on Artificial Intelligence (ECAI), 2020.

For the baseline results and extended semantic query method, please cite: - Hung Nghiep Tran. Multi-Relational Embedding for Knowledge Graph Representation and Analysis. PhD Dissertation, The Graduate University for Advanced Studies, SOKENDAI, Japan, 2020.

For the Microsoft Academic Graph dataset, please cite: - Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. An Overview of Microsoft Academic Service (MAS) and Applications. In Proceedings of the International Conference on World Wide Web (WWW), 2015.

Inspiration

We include the baseline results for two tasks on the KG20C dataset, link prediction and semantic queries. Link prediction is a relational query task given a relation and the head or tail entity to predict the corresponding tail or head entities. Semantic queries include human-friendly query on the scholarly data. MRR is the mean reciprocal rank, Hit@k is the percentage of correct predictions at top k.

For more information, please refer to the citations.

Link prediction results

We report results for 4 methods. Random, which is just random guess to show the task difficulty. Word2vec, which is the popular embedding method. SimplE/CP and MEI are two recent knowledge graph embedding methods.

All models are in small size settings, equivalent to total embedding size 100 (50x2 for Word2vec and SimplE/CP, 10x10 for MEI).

Semantic queries results

The following results demonstrate semantic queries on knowledge graph embedding space, using the above MEI (small) model.

Statistics illustrates consumption, production, prices, and trade of Tubes; data/graphic display, black and white or other monochrome in Sweden from 2007 to 2024.

Statistics illustrates consumption, production, prices, and trade of Tubes; data/graphic display, black and white or other monochrome in GCC from 2007 to 2024.

Statistics illustrates consumption, production, prices, and trade of Tubes; data/graphic display, black and white or other monochrome in Greece from 2007 to 2024.

Statistics illustrates consumption, production, prices, and trade of Tubes; data/graphic display, black and white or other monochrome in the World from 2007 to 2024.

This workflow focuses on analyzing diverse soil datasets using PCA to understand their physicochemical properties. It connects to a MongoDB database to retrieve soil samples based on user-defined filters. Key objectives include variable selection, data quality improvement, standardization, and conducting PCA for data variance and pattern analysis. The workflow generates graphical representations, such as covariance and correlation matrices, scree plots, and scatter plots, to enhance data interpretability. This facilitates the identification of significant variables, data structure exploration, and optimal component determination for effective soil analysis. Background - Understanding the intricate relationships and patterns within soil samples is crucial for various environmental and agricultural applications. Principal Component Analysis (PCA) serves as a powerful tool in unraveling the complexity of multivariate soil datasets. Soil datasets often consist of numerous variables representing diverse physicochemical properties, making PCA an invaluable method for: ∙Dimensionality Reduction: Simplifying the analysis without compromising data integrity by reducing the dimensionality of large soil datasets. ∙Identification of Dominant Patterns: Revealing dominant patterns or trends within the data, providing insights into key factors contributing to overall variability. ∙Exploration of Variable Interactions: Enabling the exploration of complex interactions between different soil attributes, enhancing understanding of their relationships. ∙Interpretability of Data Variance: Clarifying how much variance is explained by each principal component, aiding in discerning the significance of different components and variables. ∙Visualization of Data Structure: Facilitating intuitive comprehension of data structure through plots such as scatter plots of principal components, helping identify clusters, trends, and outliers. ∙Decision Support for Subsequent Analyses: Providing a foundation for subsequent analyses by guiding decision-making, whether in identifying influential variables, understanding data patterns, or selecting components for further modeling. Introduction The motivation behind this workflow is rooted in the imperative need to conduct a thorough analysis of a diverse soil dataset, characterized by an array of physicochemical variables. Comprising multiple rows, each representing distinct soil samples, the dataset encompasses variables such as percentage of coarse sands, percentage of organic matter, hydrophobicity, and others. The intricacies of this dataset demand a strategic approach to preprocessing, analysis, and visualization. This workflow introduces a novel approach by connecting to a MongoDB, an agile and scalable NoSQL database, to retrieve soil samples based on user-defined filters. These filters can range from the natural site where the samples were collected to the specific date of collection. Furthermore, the workflow is designed to empower users in the selection of relevant variables, a task facilitated by user-defined parameters. This flexibility allows for a focused and tailored dataset, essential for meaningful analysis. Acknowledging the inherent challenges of missing data, the workflow offers options for data quality improvement, including optional interpolation of missing values or the removal of rows containing such values. Standardizing the dataset and specifying the target variable are crucial, establishing a robust foundation for subsequent statistical analyses. Incorporating PCA offers a sophisticated approach, enabling users to explore inherent patterns and structures within the data. The adaptability of PCA allows users to customize the analysis by specifying the number of components or desired variance. The workflow concludes with practical graphical representations, including covariance and correlation matrices, a scree plot, and a scatter plot, offering users valuable visual insights into the complexities of the soil dataset. Aims The primary objectives of this workflow are tailored to address specific challenges and goals inherent in the analysis of diverse soil samples: ∙Connect to MongoDB and retrieve data: Dynamically connect to a MongoDB database, allowing users to download soil samples based on user-defined filters. ∙Variable selection: Empower users to extract relevant variables based on user-defined parameters, facilitating a focused and tailored dataset. ∙Data quality improvement: Provide options for interpolation or removal of missing values to ensure dataset integrity for downstream analyses. ∙Standardization and target specification: Standardize the dataset values and designate the target variable, laying the groundwork for subsequent statistical analyses. ∙PCA: Conduct PCA with flexibility, allowing users to specify the number of components or desired variance for a comprehensive understanding of data variance and patterns. ∙Graphical representations: Generate visual outputs, including covariance and correlation matrices, a scree plot, and a scatter plot, enhancing the interpretability of the soil dataset. Scientific questions - This workflow addresses critical scientific questions related to soil analysis: ∙Facilitate Data Access: To streamline the retrieval of systematically stored soil sample data from the MongoDB database, aiding researchers in accessing organized data previously stored. ∙Variable importance: Identify variables contributing significantly to principal components through the covariance matrix and PCA. ∙Data structure: Explore correlations between variables and gain insights from the correlation matrix. ∙Optimal component number: Determine the optimal number of principal components using the scree plot for effective representation of data variance. ∙Target-related patterns: Analyze how selected principal components correlate with the target variable in the scatter plot, revealing patterns based on target variable values.

Statistics illustrates consumption, production, prices, and trade of Tubes; data/graphic display, black and white or other monochrome in Costa Rica from 2007 to 2024.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

This repository contains network graphs and network metadata from Moviegalaxies, a website providing network graph data from about 773 films (1915–2012). The data includes individual network graph data in Graph Exchange XML Format and descriptive statistics on measures such as clustering coefficient, degree, density, diameter, modularity, average path length, the total number of edges, and the total number of nodes.

Statistics illustrates consumption, production, prices, and trade of Tubes; data/graphic display, black and white or other monochrome in French Southern Territories from 2007 to 2024.