In October 2025, Microsoft's Edge browser had a market share of ***** percent in the United States. Edge was first publicly released in July 2015, with the consumer release of Windows 10. However, Chrome held a majority of the market share, with around ** percent in the same month. What are web browsers? A web browser is a software application for visualizing websites, documents and data. The most popular current browsers are Google Chrome, Apple’s Safari, Microsoft Edge, and Firefox. Historically one of the large players in the segment, Internet Explorer has unfortunately lost its tight grip on the web browser market.As shown by the graph at hand, Google Chrome has been the most popular browser in the United States since December 2013. In other countries, Google Chrome has also taken up a dominating role. In the European browser market, Chrome and Safari have established strong market positions with ** and **** percent, respectively. On a worldwide scale, Chrome provided a share of around ** percent in the global web browser market as of December 2021.

As of October 2025, Google Chrome had the largest market share in the United States, with over 59 percent, followed by Apple's Safari, with 25.1 percent. Furthermore, Microsoft's Edge browser had a United States market share of 6.4 percent. The new Microsoft Edge was based on Chromium and was released in January 2020. Web browsers Web browsers serve as the application software through which users from across the globe access the contents of the World Wide Web. Browsers are available on a range of devices: desktop PCs, laptops, tablets, smartphones, and consoles. Given the popularity of smartphones, mobile devices have become the primary way to access the internet, overtaking PCs. Google Chrome has been the most popular web browser worldwide in the past decade, holding almost two-thirds of the market in 2023. Safari followed, occupying around 19 percent of the market. Safari turns 20 years old Safari is a web browser developed by Apple and first launched in January 2003. With regular updates, Safari is integrated into iOS, macOS, and iPadOS, the operating systems of iPhones, Macs, and iPads. Thanks to the popularity of Apple devices worldwide, Safari is used as a web browser at different rations in the United States and in many European countries. For instance, Safari held over 29 percent of the UK internet browser market in August 2022 but only 11 percent of the German web browser market in November 2022.

Independent trust and security comparison of browser-use and Knowledge Graph Memory

Motivation: Predicting microbial gene fitness across environmental conditions remains a central challenge for predictive phenomics and autonomous experimentation. Fitness assays generate large volumes of genotype–phenotype measurements difficult to integrate with experimental metadata and biological function in a form that supports mechanistic reasoning. Knowledge graphs offer a semantic framework for unifying modalities and enabling context-aware inference. Results: We build GIMME (Graph Inference for Microbial Metabolism Exploration), a semantically grounded knowledge graph that unifies gene fitness measurements spanning 10 Pseudomonas species with experimental metadata and biological context. Media are decomposed into chemical components and experiments carry structured links to natural-language descriptions. The resulting graph supports two inference modes: (1) symbolic graph traversal to surface candidate gene–environment and gene–chemical associations, and (2) learned inference using heterogeneous graph neural networks that propagate information across neighborhoods. We formulate link regression over (gene, media, experiment) triplets, combining learned gene embeddings with pretrained LLM sourced text embeddings of node descriptions to predict gene fitness. We then augment a baseline MLP with an auxiliary message-passing encoder (GraphSAGE/GAT) that propagates information over gene–protein–function and media–chemical subgraphs, and fuse the two pathways with a gated residual connection. This approach produces strong agreement with held-out fitness measurements (GraphSAGE Pearson r 0.74) while also highlighting inference challenges in extreme-fitness regimes. We aggregate GAT edge-attention weights by relation type and layer to estimate which biological and environmental relations most influence fitness predictions. Conclusion: This work explores using knowledge graphs as “context graphs” for microbial phenotype prediction. They provide a rich substrate which enables explainable retrieval of supporting evidence, and provides a natural bridge to autonomous workflows that prioritize the next experiment.

Additional file 3: Table S1. List of MoMI-G features.

The Highway Statistics Series consists of annual reports containing analyzed statistical information on motor fuel, motor vehicle registrations, driver licenses, highway user taxation, highway mileage, travel, and highway finance. These information are presented in tables as well as selected charts. It has been published annually since 1945.

Realtional Dataset - Downlaod Dump.sql - Before importing Dump.sql icrease your max_allowed_packet=4M to 16M in your MySQL initialization usually located at C:\ProgramData\MySQL\MySQL Server 8.0\my.ini - Import Dump.sql to your local MySQL server by MySQL Workbech->Server->Data Import -> Import from Self-Contained File -> Start Import --------------------------------------------------------------------------------------------------------------------------------------- # Graph Dataset via graphs_cypher # Download Neo4j Desktop Download link can be found here https://neo4j.com/ # Add Graph in Neo4j Desktop Run Neo4j Desktop application and follow instructions: - Click on Add Graph - Set desired name and password - Click on Create - Start the Graph database # Loading Cypher scripts to Graph database Once the graph database is running, start Neo4j browser and execute cypher script for each project. as show in the figure below The script will insert the data of the graph in the database. OR # Loading from graph.db - Download the graph.db - graph.db contains subfolder DeveloperInteractionGraph.db and SubsystemInteractionGraph.db - To visualize any desired graph from the graph.db/DeveloperInteractionGraph.db or SubsystemInteractionGraph.db - Create a graph in Neo4j - Click on Manage - Click on Open Folder - Paste the desired (project).db inside the data/databases folder - Restart DB - Neo4j credentials: userid = neo4j password = admin ## Visualize graph The resultant graph can be visualized using following query: Show only 25 nodes $ MATCH (n) RETURN n LIMIT 25 Show all nodes $ MATCH (n) RETURN n

Additional file 7: Table S4. Breakends in the opposite end of the CCDC6-RET inversion.

To cite, click the "cite" button and select the citation style through the "datacite" drop-down button to the right.

This paper is presented at the International Seminar "Language Maintenance and Shift IV" (LAMAS IV) on the 18th of November 2014 in Semarang, Central Java, Indonesia.

RELEASE V3.0.2 KNOWLEDGE GRAPH BENCHMARK ARCHIVE

Website: https://github.com/callahantiff/PheKnowLator/wiki/Benchmarks-and-Builds

The goal of this PheKnowLator benchmark is to provide knowledge graph builds that represent human disease mechanisms, including the central dogma.

Google Cloud Storage Bucket Access:

• https://console.cloud.google.com/storage/browser/pheknowlator

Build Data:

Google Cloud Storage

• Original Data
• Processed Data

Zenodo

• Original Data
• Processed Data

Knowledge Graph Data:

v1.0.0

Google Cloud Storage

• https://console.cloud.google.com/storage/browser/pheknowlator/archived_builds/release_v1.0.0

Zenodo

• 10.5281/zenodo.7030200

All Other Versions

Google Cloud Storage

• Class-based Builds
• Instance-Based Builds

Zenodo

• Class-based Knowledge Model + Standard Relations
  • OWL
  • OWL-NETS
• Class-based Knowledge Model + Inverse Relations
  • OWL
  • OWL-NETS
• Instance-based Knowledge Model + Standard Relations
  • OWL
  • OWL-NETS
• Instance-based Knowledge Model + Inverse Relations
  • OWL
  • OWL-NETS

Knowledge Graph Embeddings:

Google Cloud Storage

• v1.0.0_03SEP2019

Zenodo:

• v1.0.0_03SEP2019

The search graph of Colonial Collections. It contains a selection of the information from the knowledge graph. The search graph enables full-text searching the information and is used by the frontend applications of Colonial Collections.

Feel free to use this dataset, but please be aware that it can change at any time, depending on the requirements of the frontend applications. If you want to have a more stable dataset, use the knowledge graph instead.

Bar Chart Buffer (#450) — 10 fields, 0 records. Other table in Business Central. Primary key: Series No.. 0 FlowFields, 2 relations. Free schema browser.

Additional file 8: Table S5. Called SVs in LC-2/ad nuclear DNA from Illumina reads.

Celsius to Fahrenheit conversion table (−273.15 to 500 °C). Pre-calculated reference tables for common temperature conversions. Bookmark this page for quick lookups — includes key temperature landmarks.

Celsius to Kelvin conversion table (−273.15 to 500 °C). Pre-calculated reference tables for common temperature conversions. Bookmark this page for quick lookups — includes key temperature landmarks.

Kelvin to Celsius conversion table (0 to 773.15 K). Pre-calculated reference tables for common temperature conversions. Bookmark this page for quick lookups — includes key temperature landmarks.

RELEASE V2.1.0 KNOWLEDGE GRAPH: ORIGINAL DATA SOURCES

Release: v2.1.0

The goal of this build was to create a knowledge graph that represented human disease mechanisms and included the central dogma. The data sources utilized in this release include many of the sources used in the initial release, as well as some new data made available by the Comparative Toxicogenomics Database and experimental data from the Human Protein Atlas.

Data sources are listed by type (Ontology and Data not represented in an ontology [Database Sources]). Additional details are provided for each data source below. Please see documentation on the primary release (https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources) for additional details on each data source as well as citation information.

Data Access:

• https://console.cloud.google.com/storage/browser/pheknowlator/archived_builds/release_v2.1.0/build_01MAY2021

ONTOLOGIES

• Cell Ontology
• Cell Line Ontology
• Chemical Entities of Biological Interest (ChEBI) Ontology
• Gene Ontology
• Human Phenotype Ontology
• Mondo Disease Ontology
• Pathway Ontology
• Protein Ontology
• Relations Ontology
• Sequence Ontology
• Uber-Anatomy Ontology
• Vaccine Ontology

Cell Ontology (CL)

Homepage: GitHub
Citation:

Bard J, Rhee SY, Ashburner M. An ontology for cell types. Genome Biology. 2005;6(2):R21

Usage: Utilized to connect transcripts and proteins to cells. Additionally, the edges between this ontology and its dependencies are utilized:

• ChEBI
• GO
• PATO
• PRO
• RO
• UBERON

Cell Line Ontology (CLO)

Homepage: http://www.clo-ontology.org/
Citation:

Sarntivijai S, Lin Y, Xiang Z, Meehan TF, Diehl AD, Vempati UD, Schürer SC, Pang C, Malone J, Parkinson H, Liu Y. CLO: the cell line ontology. Journal of Biomedical Semantics. 2014;5(1):37

Usage: Utilized this ontology to map cell lines to transcripts and proteins. Additionally, the edges between this ontology and its dependencies are utilized:

• CL
• DOID
• NCBITaxon
• UBERON

Chemical Entities of Biological Interest (ChEBI)

Homepage: https://www.ebi.ac.uk/chebi/
Citation:

Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, Turner S, Swainston N, Mendes P, Steinbeck C. ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Research. 2015;44(D1):D1214-9

Usage: Utilized to connect chemicals to complexes, diseases, genes, GO biological processes, GO cellular components, GO molecular functions, pathways, phenotypes, reactions, and transcripts.

Gene Ontology (GO)

Homepage: http://geneontology.org/
Citations:

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA. Gene ontology: tool for the unification of biology. Nature Genetics. 2000;25(1):25

The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Research. 2018;47(D1):D330-8

Usage: Utilized to connect biological processes, cellular components, and molecular functions to chemicals, pathways, and proteins. Additionally, the edges between this ontology and its dependencies are utilized:

• CL
• NCBITaxon
• RO
• UBERON

Other Gene Ontology Data Used: goa_human.gaf.gz

Human Phenotype Ontology (HPO)

Homepage: https://hpo.jax.org/
Citation:

Köhler S, Carmody L, Vasilevsky N, Jacobsen JO, Danis D, Gourdine JP, Gargano M, Harris NL, Matentzoglu N, McMurry JA, Osumi-Sutherland D. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Research. 2018;47(D1):D1018-27

Usage: Utilized to connect phenotypes to chemicals, diseases, genes, and variants. Additionally, the edges between this ontology and its dependencies are utilized:

• CL
• ChEBI
• GO
• UBERON

Files

• Other Human Phenotype Ontology Data Used: phenotype.hpoa

Mondo Disease Ontology (Mondo)

Homepage: https://mondo.monarchinitiative.org/
Citation:

Mungall CJ, McMurry JA, Köhler S, Balhoff JP, Borromeo C, Brush M, Carbon S, Conlin T, Dunn N, Engelstad M, Foster E. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Research. 2017;45(D1):D712-22

Usage: Utilized to connect diseases to chemicals, phenotypes, genes, and variants. Additionally, the edges between this ontology and its dependencies are utilized:

• CL
• NCBITaxon
• GO
• HPO
• UBERON

Pathway Ontology (PW)

Homepage: rgd.mcw.edu
Citation:

Petri V, Jayaraman P, Tutaj M, Hayman GT, Smith JR, De Pons J, Laulederkind SJ, Lowry TF, Nigam R, Wang SJ, Shimoyama M. The pathway ontology–updates and applications. Journal of Biomedical Semantics.

A graph, G=(V,E), consists of a non empty finite set V of elements called vertices and a set E of pairs of elements of V called edges. The number of vertices N=|G|=|V| is the order of the graph. If (x,y) is an edge of E, we say that x and y (or y and x) are adjacent and this is usually written x --> y. It is also said that x and y are the endvertices of the edge (x,y). The degree of a vertex δ(x) is the number of vertices adjacent to x. The degree of G is Δ=max_{x ∈ V} δ(x). A graph is regular of degree Δ or Δ - regular if the degree of all vertices equal Δ. The distance between two vertices x and y, d(x,y) , is the number of edges of a shortest path between x and y , and its maximum value over all pair of vertices, D=max_{x, y ∈ V}d(x,y) , is the diameter of the graph. A (Δ,D) graph is a graph with maximum degree Δ and diameter at most D. The order of a graph with degree Δ, Δ > 2), of diameter D is easily seen to be bounded by

1 + Δ + Δ (Δ-1) + ...+ Δ (Δ-1) D-1 = (Δ (Δ-1)D -2) / (Δ-2) = N(Δ, D)

Hoffman and Singleton introduced the concept of Moore graphs, named after Edward Forrest Moore, as graphs that attain this value, known as Moore bound. They also showed that, for D ≥ 2 and Δ ≥ 3, Moore graphs exist for D=2 and Δ =3,7 , and (perhaps) 57. In this context, it is of great interest to find graphs that, for a given maximum diameter and maximum degree, have a number of vertices as close as possible to the Moore bound.

Download the .zip package, unpack it and open in a browser the file
table_degree_diameter.html or the file taula_delta_d.html.

The table on that page presents the state of the art, as of January 2026, for the largest known (Δ, D)-graphs. Entries in boldface are optimal. Clicking on a position provides more information about that entry, including graph construction details, the Moore bound, the author, references, and more. Entries with a border include a SageMath script for computing their relevant properties. Adjacency lists are available for most graphs with fewer than 20,000 vertices. By clicking on entry (8,3) = 253, you can access a ZIP file containing the programs used to obtain the results for this graph, as well as for the graphs (3,5), (6,8), (7,6), (7,7), (8,5), (9,4), (10,4), (10,5), (11,5), (12,5), (13,5), (14,5), and (15,5) , all found by the author in 2024. The C program used is the same as the one that produced the entry (8,3) in 1994, with only minor modifications to the output. Journal publications associated with this data: F. Comellas. Table of large graphs with given degree and diameter. arXiv:2406.18994 [math.CO]. doi: 10.48550/arXiv.2406.18994v2 F. Comellas. New results on the degree-diameter problem for undirected graphs. Electron. J. Graph Theory Appl. 13 (1) (2025), 211-215. doi:10.5614/ejgta.2025.13.1.14.

Chart Definition (#1310) — 8 fields, 8 records. Other table in Business Central. Primary key: Code Unit ID, Chart Name. 0 FlowFields, 2 relations. Free schema browser.

Additional file 5: Table S2. Summary of the Oxford Nanopore sequencing data of LC-2/ad.