Classifying tree species in satellite images is an important task for environmental monitoring and forest management. Here we present a dataset containing Sentinel-2 satellite pixel time series of individual trees intended for training machine learning models. The dataset was created by merging information from the German National Forest Inventory in 2012 with satellite data. It sparsely covers the whole of Germany for the years 2015 to 2022 and comprises 48 species and 3 species groups.

Agricultural, fishery, forestry and agro-processing activities produce large quantities of residues, by-products and waste materials every year. Here, we present a global organic matter database (OMD), the first of its kind, consolidating estimates of residues and by-products potentially available for use in a circular bio-economy. It also provides definitions, typologies and methods to aid consistent classification, estimation and reporting of the various residues and by-products.

Nitrogen is crucial in ocean food webs, but only some microbes can fix N₂ gas into a bioavailable form. Most are known only by their nifH gene sequence. We created a software workflow for nifH data and ran it on 944 ocean samples, producing a database (DB) that captures the global diversity of N₂-fixing marine microbes and the environmental factors that influence them. The workflow and DB can standardize analyses of past and future nifH datasets to enable insights into marine communities.