The CRM-geothermal database was created within the Horizon Europe CRM-geothermal project (Grant Agreement No. 101058163) to support the assessment of geothermal systems as sources of both renewable energy and critical raw materials (CRMs). The primary purpose of data collection was to compile, harmonise, and make openly available geoscientific and geochemical data relevant to the occurrence, enrichment, and potential co-production of CRMs from geothermal environments in Europe and East Africa.
The database integrates legacy data compiled from peer-reviewed literature, national geological and geothermal databases, and previous European research projects (notably REFLECT), together with new data generated by project partners through field sampling and laboratory analyses. Sampling campaigns targeted geothermal wells and surface manifestations in selected regions, including Türkiye, the East African Rift (Kenya, Tanzania, Malawi), Cornwall (UK), and Iceland. Laboratory analyses include major ion chemistry, trace and critical element concentrations, mineralogical composition, and gas data, determined using methods such as ICP-MS, XRF, and XRD.
All records were harmonised using a unified metadata schema, standardised units, and consistent reporting formats. Quality control involved automated validation routines and manual expert review. Each record includes spatial coordinates, sampling context, analytical method, references, and a quality flag indicating data origin and traceability.
The database is provided as a structured Excel file and contains interconnected datasets on geothermal wells, fluids, rocks, gases, and mineral precipitates. In total, the dataset comprises 9,773 records covering a wide range of geological settings, from volcanic and metamorphic systems to sedimentary basins. The CRM-geothermal database is FAIR-aligned, openly available, and intended for reuse in geothermal research, resource assessment, and studies on the sustainable co-production of geothermal energy and critical raw materials.
Method:
The CRM-geothermal database was compiled using a combined approach integrating literature-based data collection, database harmonisation, and new data generation through field sampling and laboratory analysis.
Legacy data were collected from peer-reviewed scientific publications, national geological and geothermal databases, technical reports, and previous European research projects, with a particular emphasis on the REFLECT project. Relevant parameters were manually extracted, digitised where necessary, and cross-checked against original sources to ensure consistency and traceability.
New data were generated within the CRM-geothermal project through targeted sampling campaigns at selected geothermal sites in Europe and Eastern Africa. Samples of geothermal fluids, rocks, gases, and mineral precipitates were collected from wells and surface manifestations following standard geochemical sampling protocols.
Laboratory analyses were performed by project partner institutions using established analytical techniques, including inductively coupled plasma mass spectrometry (ICP-MS) for trace and critical elements, X-ray fluorescence (XRF) for bulk chemical composition, and X-ray diffraction (XRD) for mineralogical characterisation. Gas compositions were determined using gas chromatography and noble gas mass spectrometry where applicable. Detection limits and analytical uncertainties follow laboratory-specific standards and are documented where available.
All data were harmonised using a unified metadata schema. Units, parameter names, and reporting formats were standardised, and spatial information was converted to WGS 84 decimal degrees. Quality control was applied through automated validation scripts checking metadata completeness, coordinate validity, and numerical plausibility, followed by manual expert review to ensure scientific coherence and correct sample attribution.
The final dataset was organised into interconnected thematic tables (wells, fluids, rocks, gases, and scales) and exported as a structured Excel file for dissemination. Each record includes references, analytical method information, and a quality flag indicating data origin and traceability.
Technical Info:
The CRM-geothermal data publication is provided as a structured multi-sheet Excel (XLSX) file representing a curated snapshot of the CRM-geothermal database at the time of publication. The dataset was generated through controlled export workflows following data validation and harmonisation.
The Excel file contains separate worksheets for thematic data tables (wells, fluids, rocks, gases, and mineral precipitates). Each worksheet preserves unique identifiers, standardised metadata fields, and cross-references between related records, allowing the dataset to be used independently of any external system or software platform.
The EU funded project CRM-geothermal aims to establish an overview of the potential for critical raw materials (CRM) in geothermal fluids across the EU and third countries (Ref). Within this framework, the geothermal sites of Tuzla, Seferihisar and Dikili in eastern Turkey have been visited in March 2023. To estimate the potential of CRM at these sites, a comprehensive sampling program was performed. Rock samples (drill gravel) of the production borehole and scaling from gas-water separators were obtained. Furthermore, sampling of geothermal fluids (gas and brine) and precipitates (salt) along the production line was performed.
Here, the results of the geochemical analyses of solid sample materials (drill gravel, scales and salt) are presented.
All analyses were performed in the ElMiE-Lab (Elements and Minerals of the Earth Laboratory) at German Research Centre for Geosciences Potsdam, Germany (https://labinfrastructure.geo-x.net/laboratories/8). For their major and minor element compositions, bulk samples of drill gravel and scales were analyzed with XRF and ICP-MS, respectively. Salt precipitates were analyzed for dry loss and mineral composition using XRD.
Most of spatial databases that exist today have been designed to serve multiple purposes and hence concentrate on the 'least common denominator'. These general purpose spatial databases are rich in geometry, yet they are poor in semantics - in particular with regards to the representation of higher order semantic concepts that extend beyond the semantics of individual, discrete objects. While such higher level semantic concepts are not explicitly coded in current cartographic databases, they are nevertheless implicitly contained, owing to the fact that there often exists a relationship between the form (i.e. geometry) and function (i.e. semantics) of real-world phenomena, particularly in the built environment. Hence, it is possible - at least to some extent - to 'enrich' cartographic databases retrospectively, making implicitly contained higher level semantic concepts explicit through cartographic pattern recognition processes. The main goal of our project is therefore to develop automated methods to make this hidden information explicit. There are a number of solutions for the enrichment of cartographic/spatial databases, especially in the domain of automated cartographic generalisation. We argue, however, that the versatility and reusability of these solutions is often rather limited, since they were developed for specific databases and geospatial concepts, and encapsulated in algorithms. In our work, we have aimed to provide a more general approach by formalising the definition of semantic concepts through ontologies, and investigate how these formal definitions can be used to drive cartographic pattern recognition processes in order to enrich spatial databases. We argue that following this approach, enhanced understanding of generated patterns, easier adaptibility for different patterns, and enhanced interoperability can be provided. To this end, following issues have been adressed in our research: 1) Identification and formalisation of relevant urban concepts and their spatial properties. 2) Transformation from ontologies to algorithms that allow their automatic detection in existing spatial databases. 3) Design of intuitive human-computer interaction methods with the pattern recognition system: How can a human operator define concepts and how can he/she explore generated patterns/relations? 4) Evaluation of the enriched database, in order to demonstrate the utility of ontology-enriched databases. Objective 1 has been addressed by extracting knowledge from various sources about urban morphology, urban design, and city guides, and using this knowledge to define ontologies. Concerning objective 2, a methodology and framework for ontology-driven pattern recognition has been developed and published. It builds on a formalisation of the pattern recognition process by relating geographic concepts to cartographic measures and to other geographic concepts. (abridged text)