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The mesoscale Hydrological Model - mHM

The mesoscale Hydrological Model - mHM

REpository for the BESTMAP biodiversity models´ code.

Conceptual model to incorporate intensification traps into resource-use modelling

This project consists of codes to extract gridded total N surplus (both agricultural and non-agricultural soils) dataset at multiple sub-country level (i.e. NUTS 1, NUTS 2) and to derive mean and standard deviations of 16 N surplus estimates from 1850-2019 across Europe.

code: public

Rappbode sampling campaign DIABLO

This repository contains the code required to replicate the analyses as published in Beckmann et al. (2022): "Archetypes of agri-environmental potentialsystems: a multi-scale typology for spatial stratification and upscaling in Europe"

Repository for the BESTMAP Deliverable D4.1 'Agent-Based Models for each CS'.

The mesoscale Hydrological Model - mHM

Provided are functions suitable for the analysis of mass spectrometry-based proteomics data, providing global information on protein abundance and abundances of post-translational modifications (PTMs). However, at least in parts, this package can be also used to analyse other omics, e.g. metabolomics and transcriptomics. The package includes processing of fold changes (e.g. from TMT or SILAC experiments) and intensities (e.g. from LFQ experiments). Processing includes checking reproducibility (PCAs, correlation bubble plots, sample-2-sample distance plots), identification of outliers (based on Mahalanobis distance), log2 transformation, median normalization, filtering for reliably identified proteins/sites, variance stabilization (based on the 'DEP' package), imputation (based on the 'DEP' package), calculation of average log2(fold changes), p-values, and adjusted p-values. These basic results are visualized (volcano plots, stacked bar charts of significant changes, numbers of identified proteins). Enrichment analyses (using 'clusterProfiler') are conducted based on the gene sets stored in the MSigDB (using 'msigdbr') and are visualized subsequently. Also enrichment results obtained using the 'Ingenuity Pathway Analysis' tool (Qiagen) are visualized. Weighted Gene Correlation Network Analysis ('WGCNA', including module formation, module-trait correlation, identification of hub genes/key drivers, visualization of results) is conducted. For PTM data, site intensities are extracted based on Proteome Discoverer's Peptide Isoform table. Furthermore, kinase enrichment for phosphoproteomics data using 'KinSwingR' can be conducted.