For K4 and Km-e graphs, a coloring type (K4,Km-e;n) is such an edge coloring of the full Kn graph, which does not have the K4 subgraph in the first color (representing by no edges in the graph) or the Km-e subgraph in the second color (representing by edges in the graph). Km-e means the full Km graph with one edge removed.The Ramsey number R(K4,Km-e) is the smallest natural number n such that for any edge coloring of the full Kn graph there is an isomorphic subgraph with K4 in the first color (no edge in the graph) or isomorphic with Km-e in the second color (exists edge in the graph). Coloring types (K4,Km-e;n) exist for n<R(K4,Km-e).The dataset consists of:a) 5 files containing all non-isomorphic graphs that are coloring types (K4,K3-e;n) for 1<n<7,b) 9 files containing all non-isomorphic graphs that are coloring types (K4,K4-e;n) for 1<n<11.

Currently, in the field of chart datasets, most existing resources are mainly in English, and there are almost no open-source Chinese chart datasets, which brings certain limitations to research and applications related to Chinese charts. This dataset draws on the construction method of the DVQA dataset to create a chart dataset focused on the Chinese environment. To ensure the authenticity and practicality of the dataset, we first referred to the authoritative website of the National Bureau of Statistics and selected 24 widely used data label categories in practical applications, totaling 262 specific labels. These tag categories cover multiple important areas such as socio-economic, demographic, and industrial development. In addition, in order to further enhance the diversity and practicality of the dataset, this paper sets 10 different numerical dimensions. These numerical dimensions not only provide a rich range of values, but also include multiple types of values, which can simulate various data distributions and changes that may be encountered in real application scenarios. This dataset has carefully designed various types of Chinese bar charts to cover various situations that may be encountered in practical applications. Specifically, the dataset not only includes conventional vertical and horizontal bar charts, but also introduces more challenging stacked bar charts to test the performance of the method on charts of different complexities. In addition, to further increase the diversity and practicality of the dataset, the text sets diverse attribute labels for each chart type. These attribute labels include but are not limited to whether they have data labels, whether the text is rotated 45 °, 90 °, etc. The addition of these details makes the dataset more realistic for real-world application scenarios, while also placing higher demands on data extraction methods. In addition to the charts themselves, the dataset also provides corresponding data tables and title text for each chart, which is crucial for understanding the content of the chart and verifying the accuracy of the extracted results. This dataset selects Matplotlib, the most popular and widely used data visualization library in the Python programming language, to be responsible for generating chart images required for research. Matplotlib has become the preferred tool for data scientists and researchers in data visualization tasks due to its rich features, flexible configuration options, and excellent compatibility. By utilizing the Matplotlib library, every detail of the chart can be precisely controlled, from the drawing of data points to the annotation of coordinate axes, from the addition of legends to the setting of titles, ensuring that the generated chart images not only meet the research needs, but also have high readability and attractiveness visually. The dataset consists of 58712 pairs of Chinese bar charts and corresponding data tables, divided into training, validation, and testing sets in a 7:2:1 ratio.

These data were used to examine grammatical structures and patterns within a set of geospatial glossary definitions. Objectives of our study were to analyze the semantic structure of input definitions, use this information to build triple structures of RDF graph data, upload our lexicon to a knowledge graph software, and perform SPARQL queries on the data. Upon completion of this study, SPARQL queries were proven to effectively convey graph triples which displayed semantic significance. These data represent and characterize the lexicon of our input text which are used to form graph triples. These data were collected in 2024 by passing text through multiple Python programs utilizing spaCy (a natural language processing library) and its pre-trained English transformer pipeline. Before data was processed by the Python programs, input definitions were first rewritten as natural language and formatted as tabular data. Passages were then tokenized and characterized by their part-of-speech, tag, dependency relation, dependency head, and lemma. Each word within the lexicon was tokenized. A stop-words list was utilized only to remove punctuation and symbols from the text, excluding hyphenated words (ex. bowl-shaped) which remained as such. The tokens’ lemmas were then aggregated and totaled to find their recurrences within the lexicon. This procedure was repeated for tokenizing noun chunks using the same glossary definitions.

For K6-e and Km-e graphs, the type coloring (K6-e,Km-e;n) is such an edge coloring of the full Kn graph, which does not have the K6-e subgraph in the first color (no edge in the graph) or the Km-e subgraph in the second color (exists edge in the graph). Km-e means the full Km graph with one edge removed. The Ramsey number R(K6-e,Km-e) is the smallest natural number n such that for any edge coloring of the full Kn graph there is an isomorphic subgraph with K6-e in the first color (no edge in the graph) or isomorphic with Km-e in the second color (exists edge in the graph). Coloring types (K6-e,Km-e;n) exist for n<R(K6-e,Km-e).

The archive file contains files describing every bi-equivalent planar graph.
Each folder of the type LM contains the graph of valencies L and M.
Each sub-folder in these folder correspond to 1 graph.
Their content is:
- f.par : the PGC for the graph
- a txt file: file describing the connectivity of the graph (see below for the format).
- a svg file: vector graphic file for the graph.

Syntax of the txt file:
Line 1 : the number of nodes for the graph
Line 2 : the nodes for the outside face (usually "0 1 2 P-1")
Following lines except last 2 lines: "n1 n2" : the indices of 2 nodes linked together.
Line -2 from the end: "N0 n1 n2 ... nn" : the list of nodes of the 1st type.
Last line: "N1 n1 n2 ... nn", the list of nodes of the 2nd type.

DRAKO is a leader in providing Device Graph Data, focusing on understanding the relationships between consumer devices and identities. Our data allows businesses to create holistic profiles of users, track engagement across platforms, and measure the effectiveness of advertising efforts.

Device Graph Data is essential for accurate audience targeting, cross-device attribution, and understanding consumer journeys. By integrating data from multiple sources, we provide a unified view of user interactions, helping businesses make informed decisions.

Key Features: - Comprehensive device mapping to understand user behaviour across multiple platforms - Detailed Identity Graph Data for cross-device identification and engagement tracking - Integration with Connected TV Data for enhanced insights into video consumption habits - Mobile Attribution Data to measure the effectiveness of mobile campaigns - Customizable analytics to segment audiences based on device usage and demographics - Some ID types offered: AAID, idfa, Unified ID 2.0, AFAI, MSAI, RIDA, AAID_CTV, IDFA_CTV

Use Cases: - Cross-device marketing strategies - Attribution modelling and campaign performance measurement - Audience segmentation and targeting - Enhanced insights for Connected TV advertising - Comprehensive consumer journey mapping

Data Compliance: All of our Device Graph Data is sourced responsibly and adheres to industry standards for data privacy and protection. We ensure that user identities are handled with care, providing insights without compromising individual privacy.

Data Quality: DRAKO employs robust validation techniques to ensure the accuracy and reliability of our Device Graph Data. Our quality assurance processes include continuous monitoring and updates to maintain data integrity and relevance.

Dataset

This dataset was created by Anshuman_Tiwari2005

Released under CC0: Public Domain

Contents

Hungary - Distribution of population by household types: Single person was 13.80% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Hungary - Distribution of population by household types: Single person - last updated from the EUROSTAT on November of 2025. Historically, Hungary - Distribution of population by household types: Single person reached a record high of 14.50% in December of 2017 and a record low of 9.20% in December of 2010.

CLARAThis deposit is part of the CLARA project. The CLARA project aims to empower teachers in the task of creating new educational resources. And in particular with the task of handling the licenses of reused educational resources. The present deposit contains the RDF files created using an RDF mapping (RML) and a mapper (Morph-KGC). It also contains the files JSON used as input. The corresponding pipeline can be found on Gitlab. The data used in that pipeline originate from X5GON, a European project aiming to generate and gather open educational resources. Knowledge graph contentThe present Knowledge Graph contains information about 45K Educational Resources (ERs) and 135K subjects (extracted from DBpedia).That information contains

the author, its title and description the license, a URL to the resource itself, the language of the ER, its mimetype, and finally which subject it talks about, and to what extent. That extent is given by two scores: a PageRank score and a Cosinus score. A particularity of the knowledge graph is its heavy use of RDF reification, across large multi-valued properties.Thus four versions of the knowledge graph exist, using Standard reification, Singleton property, Named graphs, and RDF-star. The Knowledge Graph also contains categories originating from DBpedia. They help precise the subjects that are also extracted from DBpedia. The KG.zip files contain five types of files:

Authors_[X].nt - Those contain the authors' nodes, their type, and name. ER_[X].nt/nq/ttl - Those contain the ERs and their information using the respective RDF reification model. categories_skos_[X].ttl - Those contain the hierarchy of DBpedia categories. categories_labels.ttl - This file contains additional information about the categories. categories_article.ttl - This file contains the RDF triples that link the DBpedia subjects to the DBpedia categories.

JSON content The original dataset was cut into multiple JSON files in order to make its processing easier. DBpedia categories were extracted as RDF and aren't present in the JSON files.There are two types of files in the input-json.zip file:

authors_[X].json - Which lists the authors names ER_[X].json - Which lists the ERs and their related information.That information contains:

their title. their description. their language (and language_detected, only the first one is used in the pipeline here). their license. their mimetype. the authors. the date of creation of the resource. a url linking to the resource itself. the subjects (named concepts) associated with the resource. With the corresponding scores.

If you do use this dataset, you can cite this repository:

Kieffer, M., Fakih, G., & Serrano Alvarado, P. (2023). CLARA Knowledge Graph of licensed educational resources [Data set]. Semantics, Leipzig, Germany. Zenodo. https://doi.org/10.5281/zenodo.8403142 Or the corresponding paper

Kieffer, M., Fakih, G. & Serrano-Alvarado, P. (2023). Evaluating Reification with Multi-valued Properties in a Knowledge Graph of Licensed Educational Resources. Semantics, Leipzig, Germany.

This data set provides the graph data used for evaluation of the graph watermarking and data hiding algorithms. It includes 160 different graphs (in .mat format) and the readme file with all relevant information and instructions to use.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Users can download the data set and static graphs, tables and charts regarding cancers in the United States. Background The United States Cancer Statistics is web-based report created by the Centers for Disease Control and Prevention, in partnership with the National Cancer Institute (NCI) and the North American Association of Central Cancer Registries (NAACCR). The site contains cancer incidence and cancer mortality data. Specific information includes: the top ten cancers, state vs. national comparisons, selected cancers, childhood cancer, cancers grouped by state/ region, cancers gr ouped by race/ ethnicity and brain cancers by tumor type. User Functionality Users can view static graphs, tables and charts, which can be downloaded. Within childhood cancer, users can view by year and by cancer type and age group or by cancer type and racial/ ethnic group. Otherwise, users can view data by female, male or male and female. Users may also download the entire data sets directly. Data Notes The data sources for the cancer incidence data are the CD C's National Program for Cancer Registries (NPCR) and NCI's Surveillance, Epidemiology and End Result (SEER). CDC's National Vital Statistics System (NVSS) collects the data on cancer mortality. Data is available for each year between 1999 and 2007 or for 2003- 2007 combined. The site does not specify when new data becomes available.

Sweden - Distribution of population by household types: Single person was 22.20% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Sweden - Distribution of population by household types: Single person - last updated from the EUROSTAT on December of 2025. Historically, Sweden - Distribution of population by household types: Single person reached a record high of 24.10% in December of 2023 and a record low of 19.80% in December of 2012.

Dataset

This dataset was created by Brayan Alejandro Valencia lopez

Released under Apache 2.0

Contents

oag-cs, oag-eng, oag-chem are new heterogeneous networks composed of subsets of the Open Academic Graph (OAG). Each of the datasets contains papers from three different subject domains -- computer science, engineering, and chemistry. These datasets also contain four types of entities -- papers, authors, institutions, and fields of study. Each paper is associated with a 768-dimensional feature vector generated from a pre-trained XLNet applying on the paper titles. The representation of each word in the title are weighted by each word's attention to get the title representation for each paper. Each paper node is labeled with its published venue (paper or conference). We split the papers published up to 2016 as the training set, papers published in 2017 as the validation set, and papers published in 2018 and 2019 as the test set. The publication year of each paper is also included in these datasets. This means those datasets can also be converted to use the publication year as class labels.

Laos Imports of silicones, in primary forms from Japan was US$19 during 2023, according to the United Nations COMTRADE database on international trade. Laos Imports of silicones, in primary forms from Japan - data, historical chart and statistics - was last updated on October of 2025.

One table and 11 figures. Table 1 shows XLORE2 statistics. Figure 1 shows the framework of XLORE2. Figure 2 is an example of cross-lingual knowledge linking. Figure 3 presents the framework of cross-lingual knowledge linking. Figure 4 is an example of cross-lingual property matching (attribute matching). Figure 5 shows the framework of cross-lingual property matching. Figure 6 presents an example of mistakenly derived facts. Figure 7 is the framework of cross-lingual knowledge validation. Figure 8 shows an example of fine-grained type inference. Figure 9 depicts the framework of fine-grained type inference. Figure 10 is an illustration of XLink. Figure 11 shows the interface of XLORE2 and XLink.

Dataset

This dataset was created by Thida Khim

Contents

United States Exports of commodities not specified according to kind was US$79.23 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Exports of commodities not specified according to kind - data, historical chart and statistics - was last updated on December of 2025.

Dataset

This dataset was created by Terry James

Released under Other (specified in description)

Contents