oxigraph

RDF graph CLI tool and SPARQL HTTP server

Oxigraph CLI is a graph database implementing the SPARQL standard. It is packaged as a command-line tool allowing to manipulate RDF files and query them using SPARQL, and also allows to spawn a HTTP server on top of the database.

php-matomo-component-network

component providing Network tools

This package contains a component that provide network tools. Especially it can be used to manipulate IP addresses.

postgresql-17-pgpcre

Perl Compatible Regular Expressions (PCRE) extension for PostgreSQL

This is a module for PostgreSQL that exposes Perl-compatible regular expressions (PCRE) functionality as functions and operators. It is based on the popular PCRE library.

postgresql-17-asn1oid

ASN.1 OID data type for PostgreSQL

This plugin provides the necessary support functions to store ASN.1 OIDs in a PostgreSQL database.

postgresql-17-http

HTTP client for PostgreSQL, retrieve a web page from inside the database

PostgreSQL extension to make HTTP requests from within the database, returning results for usage in SQL queries.

r-bioc-densvis

density-preserving data visualization via non-linear dimensionality reduction

Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) <doi:10.1101/2020.05.12.077776>. The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space.