Data

Aquaculture monitoring increasingly relies on computer vision to evaluate fish behavior and welfare under farming conditions. This dataset was collected in a commercial recirculating aquaculture system (RAS) integrated with hydroponics in Queretaro, Mexico, to support the development of robust visual models for Nile tilapia (Oreochromis niloticus). More than ten hours of underwater recordings were curated into 31 clips of 30 s each, a duration selected to balance representativeness of fish activity with a manageable size for annotation and training. Videos were captured using commercial action cameras at multiple resolutions (1920 × 1080 to 5312 × 4648 px), frame rates (24–60 fps), depths, and lighting configurations, reproducing real-world challenges such as turbidity, suspended solids, and variable illumination. For each recording, physicochemical parameters were measured, including temperature, pH, dissolved oxygen and turbidity, and are provided in a structured CSV file. In addition to the raw videos, the dataset includes 3520 extracted frames annotated using a polygon-based JSON format, enabling direct use for training object detection and behavior recognition models. This dual resource of unprocessed clips and annotated images enhances reproducibility, benchmarking, and comparative studies. By combining synchronized environmental data with annotated underwater imagery, the dataset contributes a non-invasive and versatile resource for advancing aquaculture monitoring through computer vision.

The development of autonomous train systems relies heavily on machine learning (ML) models, which in turn depend on large, high-quality annotated datasets for training and evaluation. The railway domain lacks adequate public datasets and efficient annotation tools. To address this gap, we present Labels4Rails, a tool designed specifically for the annotation of railway scenes. It captures track topology, switch states including switch directions, and informational tags regarding the images’ content and leverages consistent camera perspectives and the fixed track geometries inherent to railways for annotation efficiency. We used Labels4Rails to create the L4R_NLB reference dataset from Norwegian railway footage. The dataset contains 10,253 annotated images across four seasons, including 1415 switch annotations. Both the tool and dataset are publicly available.

The AlimurgITA portal is a user-friendly and effective tool for researching Wild Edible Plants (WEPs). It provides valuable information on alimurgic plant species, aiding conservation and potential applications (agricultural, food, etc.). Users can interact with authors to report errors and contribute to the knowledge base regarding local uses. The authors will update the site every six months to include new data. Currently, the online database contains data on 1116 taxa used in 20 Italian regions: updated scientific name and link to the site Acta Plantarum, family, main synonyms, common name in Italian and regional dialect, chorotype, life form, a map showing the regions where it is known to be used, the part used, how it is used, and the bibliography. From the home page, you can search for taxa by scientific name, and there are pages dedicated to summaries of the entries: scientific name, family, chorotype, life form, method of use, and part used. Additionally, within the FuD WE PIC Project, the AlimurgITA entity list is being integrated with Italian vegetation data from the European Vegetation Archive to model WEPs richness, identify diversity hotspots, and explore the relationship between WEPs diversity and habitat types.