The Very Long-term Iberian Fish Database

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Dataset information

Country of origin
Updated
2025.12.09 00:00
Created
2025.11.25
Available languages
English
Keywords
Quality scoring

Dataset description

Humans have inhabited the sea shores and rivers of the Iberian Peninsula for thousands of years, leaving behind numerous traces and a wide variety of fish remains. These findings provide valuable data for tracking changes in fish species distribution over time as well as identifying regional variations. While a large amount of information is available for the area, most of it is scattered throughout a variety of sources. The Very Long-term Iberian Fish Database (VLT_IberianFISH), is a comprehensive compilation of 94,204 fish remains from 441 samples across 225 archaeological sites throughout Iberia. The identified portion of the assemblages (NISP = 81,312) comprises identifications at the species, genus, family, and order or higher taxonomic levels. Spanning approximately 90,000 years – from the Middle Palaeolithic to the 19th century – the VLT_IberianFISH Database offers a resource for a range of disciplines concerned with aquatic environments, human occupancy and resource exploitation. Three supplementary files accompany the Dataset, each providing essntial contextual or supporting information: Dictionary (VLT_IberianFISH_Dictionary_SDATA-25-05229A.gpkg_: Excel worksheet detailing each field and its expected format  Taxonomic list (VLTIberianFISH_Taxonomic list_SDATA-25-05229A.gpkg: list of taxa recorded to the dataset, the number of remains documented for each category (NISP), the updated taxonomic nomenclature, and the corresponding Order and Family. Notes were appended for taxa requiring clarification - for example, in cases involving synonymy, extinct or extirpated populations, or taxa with restricted or shifting natural ranges. Geopackage (VLTIberianFISH_SDATA-25-05229A.gpkg): this file enables users to visualize site distributions, conduct spatial analyses, integrate the dataset with other environmental or archaeological layers, and reproduce the spatial components of the dataset.  
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32
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2019
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