The "Dataset_Graph.zip" file contains the graph models of the trees in the dataset. These graph models are saved in the "pickle" format, which is a binary format used for serializing Python objects. The graph models capture the structural information and relationships of the cylinders in each tree, representing the hierarchical organization of the branches.

Results of the 'Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis' papers. There are 2 datasets:

• 'exact_matches.csv': contains detailed information about the concepts present both in OpenAlex and OpenAIRE.
• 'related_matches.csv': contains detailed information about the concepts from OpenAlex and OpenAIRE that were not present in both KGs but got aligned following the algorithm presented in the paper.

The detailed information refers to the following column:

• Category1: name of the first category
• Source1: source of the first category ('OpenAlex' or 'OpenAIRE')
• Category2: name of the second category
• Source2: source of the first category ('OpenAlex' or 'OpenAIRE')
• Similarity: semantic similarity value of the two categories
• PapersInC1: number of papers from the collected dataset belonging to the first category
• PapersInC2: number of papers from the collected dataset belonging to the second category
• PapersInBoth: number of papers from the collected dataset belonging to both of the categories
• Agreement: the value of the agreement of the categories in the tw KGs (Intersection over Union)

Quantitative data underlying graphs published in Figs 1–5.

BackgroundPsychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. Methodology/Principal FindingsTo quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. Conclusions/SignificanceThe results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.

“Variable-oriented quantitative empirical research: systemic framework and procedures” is extracted in this project as a structured knowledge graph, representing the core variables, hypothesized relationships, and analytical steps as nodes and edges, so that they can be easily used for automatic retrieval, modeling, and visual analysis.

Wikipedia is the largest and most read online free encyclopedia currently existing. As such, Wikipedia offers a large amount of data on all its own contents and interactions around them, as well as different types of open data sources. This makes Wikipedia a unique data source that can be analyzed with quantitative data science techniques. However, the enormous amount of data makes it difficult to have an overview, and sometimes many of the analytical possibilities that Wikipedia offers remain unknown. In order to reduce the complexity of identifying and collecting data on Wikipedia and expanding its analytical potential, after collecting different data from various sources and processing them, we have generated a dedicated Wikipedia Knowledge Graph aimed at facilitating the analysis, contextualization of the activity and relations of Wikipedia pages, in this case limited to its English edition. We share this Knowledge Graph dataset in an open way, aiming to be useful for a wide range of researchers, such as informetricians, sociologists or data scientists.

There are a total of 9 files, all of them in tsv format, and they have been built under a relational structure. The main one that acts as the core of the dataset is the page file, after it there are 4 files with different entities related to the Wikipedia pages (category, url, pub and page_property files) and 4 other files that act as "intermediate tables" making it possible to connect the pages both with the latter and between pages (page_category, page_url, page_pub and page_link files).

The document Dataset_summary includes a detailed description of the dataset.

Thanks to Nees Jan van Eck and the Centre for Science and Technology Studies (CWTS) for the valuable comments and suggestions.

Network of 43 papers and 72 citation links related to "A Qualitative and Quantitative Analysis of Real Time Traffic Information Providers".

This dataset provides a five-level, fine-grained, and structurally normalized knowledge-graph representation of a qualitative methods text corpus (Research with Qualitative Data), treated purely as text data rather than as a bibliographic object. Each record corresponds to a node at one of five hierarchical levels—macro-section (level 1), meso-section (level 2), paragraph (level 3), sentence (level 4), and keyword/media snippet (level 5)—with explicit parent–child links (e.g., sentence → paragraph, paragraph → meso-section), forming a well-closed, acyclic tree structure. For all machine-readable content in the source PDF, the dataset decomposes the corpus into independent nodes while preserving page locators and section titles, so that any fragment of text can be traced back to its exact position in the original file. Keyword nodes are automatically extracted from sentences to enhance search, thematic mapping, and downstream modeling without altering or compressing the underlying text. For tables and images, the dataset stores captions, surrounding textual context, and row-level data_points where applicable, enabling full reconstruction of tabular and visual information at the text level. Under the assumption that “all machine-readable text in the PDF is the reference universe,” the collection achieves a practically lossless representation of the qualitative methods corpus and has been independently checked for level completeness, parent–child consistency, and content integrity, supporting its designation as a five-level, completely lossless text-based knowledge-graph dataset suitable for advanced qualitative methodology research, knowledge-graph engineering, and large-language-model retrieval and reasoning experiments.

Index Time Series for Alpha Architect U.S. Quantitative Momentum ETF. The frequency of the observation is daily. Moving average series are also typically included. Under normal circumstances,the fund will invest at least 80% of its net assets (plus any borrowings for investment purposes) in U.S.- listed companies that meet the Sub-Adviser"s definition of momentum ("Momentum Companies "). The Sub-Adviser employs a multi-step, quantitative, rules-based methodology to identify a portfolio of approximately 50 to 200 equity securities with the highest relative momentum.

Pancreatic islets of Langerhans consist of endocrine cells, primarily α, β and δ cells, which secrete glucagon, insulin, and somatostatin, respectively, to regulate plasma glucose. β cells form irregular locally connected clusters within islets that act in concert to secrete insulin upon glucose stimulation. Due to the central functional significance of this local connectivity in the placement of β cells in an islet, it is important to characterize it quantitatively. However, quantification of the seemingly stochastic cytoarchitecture of β cells in an islet requires mathematical methods that can capture topological connectivity in the entire β-cell population in an islet. Graph theory provides such a framework. Using large-scale imaging data for thousands of islets containing hundreds of thousands of cells in human organ donor pancreata, we show that quantitative graph characteristics differ between control and type 2 diabetic islets. Further insight into the processes that shape and maintain this architecture is obtained by formulating a stochastic theory of β-cell rearrangement in whole islets, just as the normal equilibrium distribution of the Ornstein-Uhlenbeck process can be viewed as the result of the interplay between a random walk and a linear restoring force. Requiring that rearrangements maintain the observed quantitative topological graph characteristics strongly constrained possible processes. Our results suggest that β-cell rearrangement is dependent on its connectivity in order to maintain an optimal cluster size in both normal and T2D islets.

This dataset comprises a five-level, fine-grained, lossless knowledge graph (Version 2) constructed around full-text papers on advanced qualitative research and mixed-methods research methodologies. The source texts are complete, lengthy academic works covering the philosophical foundations of qualitative research, research design, specific methodological operations, and diverse case studies and mixed-methods practices. This dataset no longer preserves the original formatting and layout details. Instead, it systematically transforms the knowledge content into structured data organized as “whole-chapter-paragraph-sentence-keyword/heterogeneous node,” supporting methodological meta-research, instructional design, knowledge graph and GraphRAG modeling, as well as the development of intelligent retrieval and reasoning systems for academic texts. Version 2 significantly enhances paragraph-level representation, chart data preservation, and metadata annotation capabilities over its predecessor, balancing readability, computability, and methodological rigor.

Index Time Series for Alpha Architect U.S. Quantitative Value ETF. The frequency of the observation is daily. Moving average series are also typically included. The Sub-Adviser employs a multi-step, quantitative, rules-based methodology to identify a portfolio of approximately 50 to 200 undervalued U.S. equity securities with the potential for capital appreciation. A security is considered to be undervalued when it trades at a price below the price at which the Sub-Adviser believes it would trade if the market reflected all factors relating to the company"s worth.

This dataset is constructed based on the leading research methodology, Research Methods for Business Students, forming a five-level granular “lossless knowledge graph”-type structured text dataset: The original content is disaggregated across multiple levels—chapters, page numbers, paragraphs, sentences, and keywords. Each record corresponds to a knowledge unit (e.g., sentence, keyword, table, or diagram) while preserving precise positional indices within the source text (chapter_no, page_number, paragraph_index, sentence_index, keyword_index, etc.). This enables complete digital representation without semantic or contextual loss. The dataset includes both complete sentence texts and normalized keyword sequences extracted from sentences. It can be widely applied in teaching business research methods (e.g., questionnaire design examples, content analysis and coding exercises), text mining and natural language processing experiments, knowledge graph and vector retrieval algorithm development, as well as quantitative analysis and visualization modeling around the meta-knowledge domain of “research methods.” The dataset is designed to provide educators and researchers with a machine-readable, structurally transparent, and reproducible benchmark sample to support AI-enhanced teaching, methodological tool validation, and interdisciplinary research.

Use of vectors in financial graphs By using mathematical vectors calculations as financial modeling then further into a new form of quantitative analysis instrument for linear financial computation graphs. A new tool in financial data analysis as an indicator.

Network of 31 papers and 36 citation links related to "Quantitative Analysis".

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

We propose a novel approach to predict saturation vapor pressures using group contribution-assisted graph convolutional neural networks (GC2NN), which use both, molecular descriptors like molar mass and functional group counts, as well as molecular graphs containing atom and bond features, as representations of molecular structure. Molecular graphs allow the ML model to better infer molecular connectivity and spatial relations compared to methods using other, non-structural embeddings. We achieve best results with an adaptive-depth GC2NN, where the number of evaluated graph layers depends on molecular size. We apply the model to compounds relevant for the formation of SOA, achieving strong agreement between predicted and experimentally-determined vapor pressure. In this study, we present two models: a general model with broader scope, achieving a mean absolute error (MAE) of 0.69 log-units (R2 = 0.86), and a specialized model focused on atmospheric compounds (MAE = 0.37 log-units, R2 = 0.94).

In the "Dataset_pointcloud.zip," you will find two files related to the point clouds in the dataset: "Dataset_building_other.zip" and "Dataset_tree.zip." The "Dataset_building_other.zip" file contains separate text files for each project, specifically for the "Buildings" and "Other" point clouds. On the other hand, the "Dataset_tree.zip" file includes all the point cloud files for the trees in each project. These files are in TXT format and consist of four main numbers representing each point in the point clouds. The first three numbers represent the location coordinates of the point. These coordinates typically correspond to the X, Y, and Z coordinates in a 3D space, indicating the position of the point within the project. The fourth number in each line represents the intensity value of the point.

The projects map file is provided in .kml format, allowing users to view the locations of the 40 projects on Earth browsers such as Google Earth. This file serves as a guide for locating each project based on their respective project names.

Index Time Series for Invesco Quantitative Strats Glbl Eq Lw Vol Lw Crbn UCITS ETF Acc EUR. The frequency of the observation is daily. Moving average series are also typically included. NA