KupferDigital mechanical testing dataset from BAM for the Brinell hardness test data of sample E
KupferDigital mechanical testing dataset from BAM for the Vickers hardness test data of sample D
hardness measurements on a diffusion specimen
KupferDigital mechanical testing dataset from BAM for the Vickers hardness test data of sample C
Some example CSV files for the hands-on during the workshop.
eds measurements on a diffusion specimen
Machine protocol for continuous casting of the alloy CuSn12 in the project KupferDigital.
machine protocol for casting of alloy CuSn12
KupferDigital mechanical testing dataset from BAM for the tensile test data of sample F1
KupferDigital mechanical testing dataset from BAM for the Vickers hardness test data of sample F
KupferDigital mechanical testing dataset from BAM for the Vickers hardness test data of sample E
KupferDigital mechanical testing dataset from BAM for the Brinell hardness test data of sample b
KupferDigital mechanical testing dataset from BAM for the Brinell hardness test data of sample D
Machine protocol for continuous casting of the alloy 9 CuSn9Ni2
machine protocol for casting of alloy 9 CuSn9Ni2
Machine protocol for continuous casting of the alloy 7 CuZn25Si4
machine protocol for casting of alloy 7 CuZn25Si4
KupferDigital mechanical testing dataset from BAM for the Vickers hardness test data of sample B
KupferDigital mechanical testing dataset from BAM for the tensile test data of sample F2
measured temperature during the precipitation annealing
measured temperature during the diffusion annealing
Machine protocol for continuous casting of the alloy 6 CuZn40
machine protocol for casting of alloy 6 CuZn40
KupferDigital mechanical testing dataset from BAM for the tensile test data of sample B2
KupferDigital mechanical testing dataset from BAM for the tensile test data of sample D2
Machine protocol for continuous casting of the alloy 2 CuSn6
machine protocol for casting of alloy 2 CuSn6
KupferDigital mechanical testing dataset from BAM for the Brinell hardness test data of sample A
KupferDigital mechanical testing dataset from BAM for the tensile test data of sample B1
Machine protocol for continuous casting of the alloy 3 CuNi20
machine protocol for casting of alloy 3 CuNi20
Machine protocol for continuous casting of the alloy 5 CuZn25
machine protocol for casting of alloy 5 CuZn25
KupferDigital mechanical testing dataset from BAM for the tensile test data of sample A2
KupferDigital mechanical testing dataset from BAM for the tensile test data of sample E1
Stahl Digital Process Ontology, uses EMMO and PMDco-0.1
Vickers hardness data for alloy CuZn38As, test performed according to the DIN EN ISO 6507-1:2018 standard.
Visual Knowledge Graph creation tool, based on DrawIO
This dataset demonstrates how material information from two independent industrial dataspaces — **Catena-X** and **Manufacturing-X** — can be jointly represented and queried as a unified **[PMDCo](https://github.com/materialdigital/core-ontology)** knowledge graph. ## Scenario The object of interest is a **tension pulley assembly** composed of two parts from different manufacturers and dataspaces. **Part 1 — Pulley Wheel (Catena-X)** Material: PA 66 GF 30 — see [cross-project-use-case](https://dataportal.material-digital.de/dataset/cross-project-use-case) **Part 2 — Tensioner Bracket (Manufacturing-X)** Material: Steel AISI 304 — see [steel-inspection-document-mapro](https://dataportal.material-digital.de/dataset/steel-inspection-document-mapro) The [assembly graph](https://dataportal.material-digital.de/dataset/203bde74-4e5f-4a74-a1d8-261e0f8ca84a/resource/76296e9a-a97d-42e7-91cd-657bfc70c12d/download/tension_pully.ttl) represents the tension pulley using PMDCo and BFO part-whole relations. Component IRIs link into the material graphs from both source datasets. All three graphs are loaded into this dataset's [Fuseki triplestore](https://dataportal.material-digital.de/dataset/203bde74-4e5f-4a74-a1d8-261e0f8ca84a/fuseki/$/sparql), enabling joint SPARQL queries that traverse from the physical object through its parts into material properties originating from two separate dataspaces. An [example SPARQL query](https://dataportal.material-digital.de/dataset/203bde74-4e5f-4a74-a1d8-261e0f8ca84a/resource/6f9909fd-10a5-47b0-9098-097a756b8b11/download/cross_dataspace_query.sparql) demonstrates this joint querying capability. Or a simple on directly here [Query](https://dataportal.material-digital.de/sparklis/?title=pmd-x-cross-dataspace-demo&endpoint=https%3A//dataportal.material-digital.de/dataset/203bde74-4e5f-4a74-a1d8-261e0f8ca84a/fuseki/%24/sparql&sparklis-query=%5BVId%5DReturn%28Det%28An%28161%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28Rel%28%22http%3A//purl.obolibrary.org/obo/BFO_0000051%22%2CFwd%2CNOr%28Det%28Term%28URI%28%22https%3A//catena-x.net/edc/assets/urn%3Auuid%3A3f5a8c2d-1234-4b6e-9abc-def012345678%22%29%29%2CSome%28Rel%28%22http%3A//purl.obolibrary.org/obo/BFO_0000051%22%2CFwd%2CDet%28An%28169%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28Rel%28%22http%3A//purl.obolibrary.org/obo/RO_0000086%22%2CFwd%2CDet%28An%28205%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28Rel%28%22http%3A//purl.obolibrary.org/obo/IAO_0000417%22%2CFwd%2CDet%28An%28241%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28Rel%28%22http%3A//purl.obolibrary.org/obo/OBI_0001938%22%2CFwd%2CDet%28An%28277%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28And%28Rel%28%22https%3A//qudt.org/schema/qudt/numericValue%22%2CFwd%2CDet%28An%28314%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22https%3A//qudt.org/schema/qudt/unit%22%2CFwd%2CDet%28An%28313%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29%29%29%29%29%29%29%29%29%29%29%29%2CDet%28Term%28URI%28%22https%3A//mfg-x.2024.2de/dsp/assets/component-87654321%22%29%29%2CSome%28Rel%28%22http%3A//purl.obolibrary.org/obo/RO_0000086%22%2CFwd%2CDet%28An%28322%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28Rel%28%22http%3A//purl.obolibrary.org/obo/OBI_0001938%22%2CFwd%2CDet%28An%28358%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28And%28Rel%28%22https%3A//w3id.org/pmd/co/PMD_0000006%22%2CFwd%2CDet%28An%28395%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22http%3A//purl.obolibrary.org/obo/IAO_0000039%22%2CFwd%2CDet%28An%28394%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29%29%29%29%29%29%29%29%29%29%29&sparklis-path=D&entity_lexicon_select=http%3A//www.w3.org/2000/01/rdf-schema%23label&concept_lexicons_select=http%3A//www.w3.org/2000/01/rdf-schema%23label) ## Full Pipeline ![Cross-Dataspace Pipeline Diagram](https://dataportal.material-digital.de/dataset/203bde74-4e5f-4a74-a1d8-261e0f8ca84a/resource/578476fe-f010-4c2c-90d9-35cd6db55360/download/pipeline-diagram.svg) ## Part of Cross-Dataspace Demonstration This dataset is part of a three-dataset series: - [cross-project-use-case](https://dataportal.material-digital.de/dataset/cross-project-use-case) — Catena-X SAMM → PMDCo conversion - [steel-inspection-document-mapro](https://dataportal.material-digital.de/dataset/steel-inspection-document-mapro) — Manufacturing-X AAS → PMDCo conversion - **This dataset** — joint assembly graph and cross-dataspace SPARQL queries
PMDCo material graph for stainless steel 316/4401 (Manufacturing-X dataspace component). Loaded into this dataset's Fuseki triplestore to enable joint querying with the Catena-X material data. Source: dataset [steel-inspection-document-mapro](https://dataportal.material-digital.de/dataset/steel-inspection-document-mapro).
PMDCo-conformant RDF knowledge graph for PA6GF30, produced by applying [pmdco-mapping-insert.sparql](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/30780c7d-e08b-4607-b83a-c2dd20e44e89/download/pmdco-mapping-insert.sparql) to the Fuseki triplestore loaded with [material_data_test_pa6gf30.json](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/5f7d5a69-f8f5-49d1-9780-c3ecdc0e11ec/download/material_data_test_pa6gf30.json). Material properties are represented as PMDCo quality entities linked to QUDT-typed measurement datums. This is the output of the Catena-X → PMDCo conversion pipeline.
Example SPARQL SELECT query demonstrating joint querying over the tension pulley assembly. Given `ex:TensionPulley_001`, retrieves both components and their material properties from the merged Catena-X and Manufacturing-X PMDCo graphs. Run against this dataset's [Fuseki endpoint](https://dataportal.material-digital.de/dataset/203bde74-4e5f-4a74-a1d8-261e0f8ca84a/fuseki/$/sparql).
PMDCo/BFO-conformant RDF graph describing a tension pulley assembly (`ex:TensionPulley_001`) composed of two parts from different dataspaces. **Part 1 — Pulley Wheel (Catena-X)** Material: PA6GF30 — see [cross-project-use-case](https://dataportal.material-digital.de/dataset/cross-project-use-case) **Part 2 — Tensioner Bracket (Manufacturing-X)** Material: Steel 316/4401 — see [steel-inspection-document-mapro](https://dataportal.material-digital.de/dataset/steel-inspection-document-mapro) Each component links to its material entity via `obo:BFO_0000051` (has part), pointing directly into the PMDCo material graphs loaded in this dataset's [Fuseki triplestore](https://dataportal.material-digital.de/dataset/203bde74-4e5f-4a74-a1d8-261e0f8ca84a/fuseki/$/sparql). This enables joint SPARQL queries that traverse from the assembly through its parts into material properties originating from two separate dataspaces.
Using the payload to PMD knowledge graph pipeline developed at IWM (Thomas Hanke, Matthias Büschelberger) for Material Data transfer BASF - Bosch
Example payload from EngineeringData - UsageData -LoadCollective (specified)
YAML rules for SAMM knowledge graph (V1), without pVT entry
Example payload from EngineeringData - UsageData - ServiceLife (specified)
Payload of a Catena-X data transfer (BASF - Bosch), executed in 02/2026
Extended YAML file for mapping of Engineering Data (UsageData and ServiceLife)
YAML rules for SAMM knowledge graph (V2), with pVT entry
Aspect Model for Material Data (not included in data transfer in 02/2026)
The GlasDigital Ontology was developed for the GlasDigital project (https://www.materialdigital.de/project/4). It describes the robotic melting process at Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin and also contains the terminology of the SciGlass database (The data has become open source since 2019. See: https://github.com/epam/SciGlass). A practical use case of the ontology is the Semantic Knowledge Base (SKB) developed with Fast Ontodocker (https://git.material-digital.de/ya-fanchen/fast-ontodocker), which covers the entire glass manufacturing process parameters and glass property searching, as well as the integration of SciGlass data. It is based on the PMD Core Ontology (PMDco) v1.
Vickers Test Ontology (VTO) has been developed for representing the Vickers testing process, testing equipment requirements, test pieces characteristics, and related testing parameters and their measurement procedure according to DIN EN ISO 6507-1 standard. VTO v3.0.0 (2025.11.03) reused BFO, RO, PMDco V3.0.0-alpha4, NFDIcore V3.0.0 and MWO V3.0.0 ontologies.
This is an ontology for the PMD-project SmaDi, designed for smart materials and with the focus on using it in the context of ontology-based data access (OBDA).
Short Tutorial on User Interaction with the Material Digital Dataportal
Ontology created and used in Know-Now Project used to describe data acquisition, refinement, and utilization in the development of a multilayer ferrite inductor. More: * An Automatized Simulation Workflow for Powder Pressing Simulations Using SimStack | Bjoern Mieller, Masood Valavi, Celso Ricardo Caldeira Rêgo (2025) | https://doi.org/10.1002/adem.202400872 * Ontology-Based Data Acquisition, Refinement, and Utilization in the Development of a Multilayer Ferrite Inductor | Björn Mieller, Sahar Ben Hassine, Jörg Töpfer, Christoph Priese, Arne Bochmann, Beate Capraro, Sebastian Stark, Uwe Partsch, Carina Fresemann (2025) | DOI: https://doi.org/10.1002/adem.202401042
map for the catena x material data aspect model from Schaeffler
Diese Ontologie beschreibt Faserverbundwerkstoffe entsprechend den Anforderungen im Projekt SensoTwin, sie ist weiterhin in Entwicklung. Sie kümmert sich um: verwendete Materialien, Produktionsprozesse, Prüfverfahren, Parameter und Ergebnisse, Simulation und Bauteilgeometrie. Enthaltene Instanzen dienen als Proof of Concept bzw. als beispielhafte Anwendungen.
Modules of the ODE_AM Application Ontologies using basic formal ontology BFO as top level and Common Core Ontology CCO as mid level modules. More: * Ontologies for FAIR Data in Additive Manufacturing: A Use Case-Based Evaluation | Thomas Bjarsch, Klaus Drechsler, Johannes Schilp (2025) | DOI: https://doi.org/10.1002/adem.202401528
Link to the Folder containing the ontology modules
This dataset documents the conversion of a steel product inspection document from the **Manufacturing-X dataspace** into a **[PMDCo](https://github.com/materialdigital/core-ontology)**-conformant knowledge graph. The source data is an [Asset Administration Shell (AAS) inspection document](https://dataportal.material-digital.de/dataset/a725c4a1-a463-47f8-bad3-fe84b73ef0d3/resource/25076900-3a83-4580-a354-47055958a22c/download/inspectiondocument_316_4401_alloy.json) for cold-rolled stainless steel 316/4401, conforming to the EN 10204 standard as used in the Manufacturing-X dataspace. ## Conversion Pipeline 1. The [AAS inspection document](https://dataportal.material-digital.de/dataset/a725c4a1-a463-47f8-bad3-fe84b73ef0d3/resource/25076900-3a83-4580-a354-47055958a22c/download/inspectiondocument_316_4401_alloy.json) is loaded into a triplestore using the [AAS2KG tool](https://github.com/MI-FraunhoferIWM/AAS2KG-inspection-documents-of-steel-products) 2. A [SPARQL CONSTRUCT query](https://dataportal.material-digital.de/dataset/a725c4a1-a463-47f8-bad3-fe84b73ef0d3/resource/93e50b44-f325-417b-80dd-83fc2664ce6b/download/inspectiondocument_aas2kg_mapping.sparql) transforms the AAS RDF into PMDCo-conformant triples, extracting mechanical properties (tensile strength, yield strength, elongation) and chemical composition (9 elements) as PMDCo quality entities with QUDT-typed measurement datums 3. The [output graph](https://dataportal.material-digital.de/dataset/a725c4a1-a463-47f8-bad3-fe84b73ef0d3/resource/929c0b99-ebfb-4aaf-99c5-ae29285ace71/download/inspectiondocument_316_4401_alloy_pmdcore.ttl) is a standalone PMDCo knowledge graph of the stainless steel material ## Part of Cross-Dataspace Demonstration This dataset is part of a three-dataset series: - [cross-project-use-case](https://dataportal.material-digital.de/dataset/cross-project-use-case) — Catena-X SAMM → PMDCo conversion - **This dataset** — Manufacturing-X AAS → PMDCo conversion - [pmd-x-cross-dataspace-demo](https://dataportal.material-digital.de/dataset/pmd-x-cross-dataspace-demo) — joint assembly graph and cross-dataspace SPARQL queries The pipeline is demonstrated here https://github.com/MI-FraunhoferIWM/AAS2KG-inspection-documents-of-steel-products and the RDF data generated is exported into DSMS platform https://pmdx.materials-data.space/knowledge/dataset/inspectiondocument316-4401bb791e14-2532-46c7-a7ea-82d332bd35d3-bfbb73ef
3.1 Inspection certificate represented as AAS. This data was used as an input to AAS2RDF converter along with SPARQL Construct mapping to generate RDF data. The AAS JSON includes only Product information, Material properties and Chemical composition values for demonstrative purpose.
PMDCore knowledge graph representation of Steel inspection document. This turtle file comes as output from AAS2KG converter pipeline.
SPARQL construct mapping to convert steel inspection document AAS into PMDCore based knowledge graph. This serves as input to AAS2KG converter along with AAS json.
pmd3.x Version der 42CrMoS4 Zugversuchsdaten (https://github.com/materialdigital/demodata_tensiletest_42CrMoS4/) Results of tensile tests on round bar specimens are reported. The material tested is the quenched and tempered steel 42CrMoS4. The aim of the test series is to determine the influence of different heat treatments on the tensile strength and behavior under tensile loading. Exemplary micrographs and microstructure information are provided. The compilation of all related information is still ongoing. The processes of specimen machining, heat treatment, tensile testing, and microstructure analysis have initially been described in PMDCO-2.07 based on the PMD Tensile Test Ontology (TTO). Within this dataset, a mapping in PMDCO-3.x is executed. However, the results are not officially approved. Examples of SPARQL queries are provided.
Description of the process the 42CrMoS4 round bars have undergone. This process was modelled in the PMD Core Ontology (pmdco)
Start here to get a first impression of the data set.
Zipkomprimierte Daten mit korrekter Verzeichnisstruktur zur Ausführung des Python-Skripts.
Knowledge Graph of the process the 42CrMoS4 round bars have undergone. This Knowledge Graph aligns with the PMD Core Ontology (pmdco3.0.0-alpha4).
Mapping Template für das Mapping der Zugversuchsdaten im CSV-Format.
Mapping Template für das Mapping der Metallografiebilder.
Python-Skript für das Mapping der Zugversuchsdaten von CSV zu TTL. Das Skript erzeugt, gegeben des Templates, der Metadaten und der CSVs, eine TTL-Datei pro CSV und erzeugt eine TTL-Datei welche alle Daten gemeinsam enthält
This pptx contains diagrams of stress-strain-curves of 42CrMoS4 after different heat treatments. Information on specimen geometry is provided.
Zusätzliche Metadaten: IDs der Proben, Austenitisierungstemperaturen
The following images depict the microscopic fracture pattern analysis of the samples from test series two, which underwent various surface pre-treatments: PP-AC, PP-LT, PP-SB, LT-AC, LT-LT, LT-SB, SB-AC, SB-LT and SB-SB. The thickness of both samples was 2 mm.
Fracture pattern analysis using a white-light microscope for test series two.
The following images depict the microscopic structure of the CFRP samples from test series two, which underwent various surface pre-treatments: PP-AC, PP-LT, PP-SB, LT-AC, LT-LT, LT-SB, SB-AC, SB-LT and SB-SB.
The following images depict the microscopic structure of the steel samples from test series two, which underwent various surface pre-treatments: PP-AC, PP-LT, PP-SB, LT-AC, LT-LT, LT-SB, SB-AC, SB-LT and SB-SB.
This are the microscop pictures for the steel part of the combination PP-AC
Ontology Created in the DIGITRUBBER Project. Mostly uses OBI Ontologies like IAO and Chebi.
- Link to the Ontology. - Edited with Protégé.
Dataset demonstrating the conversion of a aas payload referencing the aas inspection document sub model to pmdco knowledge graph and the other way round.
AAS Inspection Document Submodel Usecase
This ontology represents the data model for the Asset Administration Shell according to the specification version V3.1.
IDTA 02032-1-0_Template Inspection Documents of Steel Products.json
The following images depict the microscopic fracture pattern analysis of the aged samples from test series three, which underwent various surface pre-treatments: PP-AC, LT-LT, SB-SB.
Fracture pattern analysis using a white-light microscope for ageing samples from test series three.
The following images depict the microscopic fracture pattern analysis of the aged LT-LT samples from test series three.
The following images depict the microscopic fracture pattern analysis of the aged SB-SB samples from test series three.
The following images depict the microscopic fracture pattern analysis of the aged PP-AC samples from test series three.
This dataset contains about 2000 results from hybrid-specimen tensile tests (both experimental and simulated). Each specimen (identified by `hyb` and optionally a human-readable `hyb_name`) is described by a structured set of metadata covering **geometry** (e.g., overlap length, ply/metal/adhesive thickness, bolt diameter), **joining concept** (binary flags for `bolt` and `adhesive`), and **process/condition information** (e.g., CFRP and metal surface preparation classes, aging state, bolt torque/prestress, friction coefficients, adhesive curing temperatures). Material properties are included for the involved constituents—**CFRP** (tensile strengths in 0°/90°), **metal** (modulus and strength), **bolt** (modulus and strength), and **adhesive** (modulus and strength)—with values coming from datasheets for experiments or serving as input parameters for simulations. Mechanical response is captured through aligned tensile-test statistics (min/max/mean) for **force**, **strain**, and **stress**, plus the full **stress–strain curve** (`stress_val` array) and an overall performance metric, the **hybrid tensile strength** (`hyb_tens_strength_val`, max stress). A boolean `simulated` indicates whether a row originates from simulation or experiment.
Simulated and Experimental Tensile Test Results for Hybrid Materials (CFRP-Steel)
### Brief explanation of each variable * **`hyb`**: Unique identifier for the hybrid specimen. * **`hyb_name`**: Human-readable specimen label (for easier identification; not used for training). * **`base_plate_id`**: Identifier of the CFRP base plate (may be missing; not available for simulations). * **`ply_thickness_val`**: Thickness of a single CFRP ply (mm). * **`overlap_len_mm`**: Overlap length of the joint region (mm). * **`cfrp_surf_prep_class`**: Surface preparation category applied to the CFRP. * **`bolt`**: Flag indicating a bolted joint (1 = yes, 0 = no). * **`adhesive`**: Flag indicating an adhesive joint (1 = yes, 0 = no). * **`aging`**: Flag indicating aging treatment (1 = aged, 0 = not aged). * **`cfrp_tens_stren_90_dgr_mpa`**: CFRP tensile strength in 90° direction (MPa). * **`cfrp_tens_stren_0_dgr_mpa`**: CFRP tensile strength in 0° direction (MPa). * **`bolt_tens_strength_val`**: Bolt tensile strength (MPa). * **`bolt_tens_mod_val`**: Bolt Young’s modulus (MPa). * **`adhesive_tens_strength_val`**: Adhesive tensile strength (MPa). * **`adhesive_tens_mod_val`**: Adhesive Young’s modulus (MPa). * **`bolt_diameter_val`**: Bolt diameter (mm). * **`adhesive_thickness_val`**: Thickness of the adhesive layer (mm). * **`torque_val_Nm`**: Applied bolt pretension torque (N·m); null if no bolt. * **`metal_surf_prep_class`**: Surface preparation category applied to the metal part (usually missing for simulations). * **`metal_tens_mod_val`**: Metal Young’s modulus (MPa). * **`metal_tens_strength_val`**: Metal tensile strength (MPa). * **`metal_thick_val`**: Metal thickness (mm). * **`force_val_min`**: Minimum force value in the test curve (N), aligned/interpolated to strain/stress. * **`force_val_max`**: Maximum force value in the test curve (N), aligned/interpolated to strain/stress. * **`force_val_mean`**: Mean force value over the curve (N), aligned/interpolated to strain/stress. * **`strain_val_min`**: Minimum measured strain (dimensionless), aligned to force/stress. * **`strain_val_max`**: Maximum measured strain (dimensionless), aligned to force/stress. * **`strain_val_mean`**: Mean strain over the curve (dimensionless), aligned to force/stress. * **`stress_val`**: Full stress history/curve as an array (MPa), derived from force and adhesion area. * **`stress_val_min`**: Minimum stress value over the curve (MPa), aligned to force/strain. * **`stress_val_max`**: Maximum stress value over the curve (MPa), aligned to force/strain. * **`stress_val_mean`**: Mean stress value over the curve (MPa), aligned to force/strain. * **`hyb_tens_strength_val`**: Hybrid specimen tensile strength (MPa), typically the maximum stress. * **`simulated`**: Data source flag (`true` = simulation, `false` = experiment). * **`adh_temp_val_min`**: Minimum adhesive cure temperature during autoclave cycle (°C). * **`adh_temp_val_max`**: Maximum adhesive cure temperature during autoclave cycle (°C). * **`adh_temp_val_mean`**: Mean adhesive cure temperature during autoclave cycle (°C). * **`friction_steel_steel_val`**: Friction coefficient between screw/bolt and metal plate (dimensionless). * **`friction_steel_cfrp_val`**: Friction coefficient between screw/bolt and CFRP plate (dimensionless). * **`prestress_val_N`**: Bolt pre-stress (pretension force) applied to the joint (N).
The extracted Turtle (TTL) file is an RDF export of the knowledge graph created in the HybridDigital project. It contains the project’s graph content in the form of subject–predicate–object triples, i.e., explicit semantic statements rather than tabular records. The graph represents knowledge about hybrid material specimens and related research outputs, covering both experimental and simulated data. It captures the involved physical entities (e.g., specimens and their constituents), the relevant processes (e.g., manufacturing and testing activities), and the resulting information artifacts (e.g., measurements, computed results, and derived assessments). For semantic alignment, the graph follows a layered ontology approach: BFO (Basic Formal Ontology) is used as the Top-Level Ontology (TLO) to provide the foundational categories for entities and processes. CCO (Common Core Ontologies) is used as the Middle-Level Ontology (MLO) to supply reusable mid-level concepts and relations that structure scientific/engineering information, measurement representations, and artifact-related modeling. Domain-specific terms (project/manufacturing/materials concepts) extend this foundation where needed.
Triples for material and processing information about hybrid materials
Gitlab Repo with full data pipeline (Web Application) from Raw Data to OWL
Link to Gitlab Repo with the full data pipeline
An Ontology for the Digitization of a process chain for the production, characterization and prototypical application of magnetocaloric alloys. This ontology was modelled via OTTR Templates and is automatically generated. You can find the templates under the URL: https://www.dipromag.de/ottr-templates/
The following images depict the microscopic fracture pattern analysis of the samples from test series three, which underwent various surface pre-treatments: PP-AC, LT-LT, SB-SB and AC-AC
Fracture pattern analysis using a white-light microscope for test series three.
The following images depict the microscopic structure of the AC-AC samples from test series three.
The following images depict the microscopic structure of the PP-AC samples from test series three.
The following images depict the microscopic structure of the SB-SB samples from test series three.
The following images depict the microscopic structure of the LT-LT samples from test series three.
demo data set von PT500 für HTO applications Ontologien
VisGraph is an interactive editor for RDF knowledge graphs and ontologies built on the Reactodia canvas. It visualises RDF subjects as nodes and predicates as edges, provides full authoring (add nodes, create and edit links, rename namespaces), applies OWL-RL reasoning with visual differentiation of inferred knowledge, and automatically clusters large graphs for readability — all directly in the browser against an in-memory RDF store backed by Web Workers.
VisGraph — interactive RDF / ontology canvas
Use this link and hit the resonig indicator on the to right to start reasoning. The infered information will be presented in highlighted text and links betwen nodes.
A set of stress–strain curves obtained from simulated tensile tests of hybrid material specimens. Two joining mechanisms are evaluated: an adhesive joint and a bolted joint. Each result consists of (1) a JSON file describing the simulated specimen (geometry and materials) and (2) a linked CSV file containing the corresponding simulated stress–strain curve.
Simulated Static Tensile Test for Joined Hybrid Specimens (CFRP-Steel)
Simulated static tensile test results for joined hybrid material specimens (cfrp-steel)
Fracture pattern analysis using a white-light microscope for the screwed samples. This section contains the fracture images from the bolted tests. There were two test series. The first test series investigated the influence of the overlap length on the tensile shear strength of the bolted connection, while the second investigated the influence of the hole diameter on the tensile shear strength of the bolted connection.
Fracture pattern analysis using a white-light microscope for the screwed samples.
The fracture patterns of the bolted connection with different overlap lengths are shown here. These vary between 100, 112.5, 125, and 137.5 mm.
Here are the fracture patterns of the bolted connection with different hole diameters. These vary between 4, 5, 8, and 10 mm.
The Concrete Production and Testing Ontology (CPTO) is created in the project LeBeDigital. It describes the production chain of concrete, starting with the mix design through curing and testing. It consist of the domain specific ontology CPTO as a basis for a set of knowledge graph templates to describe process steps like mixing process, hardening and storage or compressive strength testing. The CPTO is based on PMDco 2.07
Dataset to show off the CKAN Views this Platform overs
This dataset documents the conversion of polymer material data from the **Catena-X dataspace** into a **[PMDCo](https://github.com/materialdigital/core-ontology)**-conformant knowledge graph. The source data is a PA6GF30 (30% glass-filled polyamide 6) material property dataset conforming to the [SAMM `MaterialData` aspect model](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/2c5241c7-d6dc-46f7-b606-f85db755e49a/download/materialdataam3010.ttl) as used in the Catena-X dataspace standard. ## Conversion Pipeline 1. [Instance data](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/5f7d5a69-f8f5-49d1-9780-c3ecdc0e11ec/download/material_data_test_pa6gf30.json) is retrieved from a Catena-X dataspace via an Eclipse Dataspace Connector (EDC) 2. The JSON is converted to RDF using the RDFConverter tool with a SAMM-specific YARRRML mapping, producing the [SAMM semantic document](PLACEHOLDER_DATASET1_JOINED_TTL) 3. A [SPARQL INSERT query](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/30780c7d-e08b-4607-b83a-c2dd20e44e89/download/pmdco-mapping-insert.sparql) transforms the SAMM representation into PMDCo-conformant triples, mapping SAMM units to QUDT equivalents and material properties to PMDCo quality classes 4. The [output graph](PLACEHOLDER_DATASET1_PMDCO_TTL) is a standalone PMDCo knowledge graph of the PA6GF30 material The [SAMM aspect model schema](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/2c5241c7-d6dc-46f7-b606-f85db755e49a/download/materialdataam3010.ttl) and the [mapping diagram](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/32121b96-7360-468b-a65b-d775f4685345/download/samm-pmdco.png) illustrate the conceptual mapping between the two representations. The [Jupyter notebook](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/34889e74-c5da-4545-aabc-e8a12f96cfd3/download/cross_project_usecase.ipynb) demonstrates the full pipeline end-to-end. ## Part of Cross-Dataspace Demonstration This dataset is part of a three-dataset series: - **This dataset** — Catena-X SAMM → PMDCo conversion - [steel-inspection-document-mapro](https://dataportal.material-digital.de/dataset/steel-inspection-document-mapro) — Manufacturing-X AAS → PMDCo conversion - [pmd-x-cross-dataspace-demo](https://dataportal.material-digital.de/dataset/pmd-x-cross-dataspace-demo) — joint assembly graph and cross-dataspace SPARQL queries An example query on the SAMM graph can be found here: [SAMM-Query](https://dataportal.material-digital.de/sparklis/?title=cross-project-use-case&endpoint=https%3A//dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/fuseki/%24/sparql&sparklis-query=%5BVId%5DReturn%28Det%28An%288%2CModif%28Select%2CUnordered%29%2CClass%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23MaterialData%22%29%29%2CSome%28And%28Rel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23materialInformation%22%2CFwd%2CDet%28An%2848%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28And%28Rel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23materialIdentifier%22%2CFwd%2CDet%28An%28122%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23materialName%22%2CFwd%2CDet%28An%28121%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23mechanicalProperty%22%2CFwd%2CDet%28An%2847%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28And%28Rel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23flexuralModulus%22%2CFwd%2CDet%28An%28163%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23flexuralStrength%22%2CFwd%2CDet%28An%28162%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23impactStrength%22%2CFwd%2CDet%28An%28161%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23strainAtBreak%22%2CFwd%2CDet%28An%28160%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23stressAtBreak%22%2CFwd%2CDet%28An%28159%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23youngsModulus%22%2CFwd%2CDet%28An%28158%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23physicalProperty%22%2CFwd%2CDet%28An%2846%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28Rel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23density%22%2CFwd%2CDet%28An%28199%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23thermophysicalProperty%22%2CFwd%2CDet%28An%2845%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28And%28Rel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23glassTransitionTemperature%22%2CFwd%2CDet%28An%28240%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23humidity%22%2CFwd%2CDet%28An%28239%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23linearThermalExpansionCoefficient%22%2CFwd%2CDet%28An%28238%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28And%28Rel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23linearThermalExpansionCoefficientParallel%22%2CFwd%2CDet%28An%28277%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23linearThermalExpansionCoefficientTransverse%22%2CFwd%2CDet%28An%28276%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23meltingTemperature%22%2CFwd%2CDet%28An%28237%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23pvt%22%2CFwd%2CDet%28An%28236%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28And%28Rel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23pressure%22%2CFwd%2CDet%28An%28315%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23specificVolume%22%2CFwd%2CDet%28An%28314%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23temperature%22%2CFwd%2CDet%28An%28313%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29%2CRel%28%22urn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23waterAbsorption%22%2CFwd%2CDet%28An%28235%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29%2CRel%28%22http%3A//www.w3.org/1999/02/22-rdf-syntax-ns%23type%22%2CFwd%2CDet%28An%2844%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28Rel%28%22http%3A//www.w3.org/1999/02/22-rdf-syntax-ns%23type%22%2CFwd%2CDet%28An%2885%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29%29%29%29&sparklis-path=DDDRRRDDDRRRRDD&entity_lexicon_select=http%3A//www.w3.org/2000/01/rdf-schema%23label&concept_lexicons_select=http%3A//www.w3.org/2000/01/rdf-schema%23label) After running the INSERT SPARQL query with the [HTML helper](https://dataportal.material-digital.de/dataset/cross-project-use-case/resource/32e3bc55-a022-4edd-b5bc-7402c8ff5991/) on the update query endpoint, the SAMM document is transformed into a simple PMDCo knowledge graph. The PMDCo graph can be queried with this example: [PMDCo-Query](https://dataportal.material-digital.de/sparklis/?title=Core%20English%20DBpedia&endpoint=https%3A//dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/fuseki/%24/sparql&sparklis-query=%5BVId%5DReturn%28Det%28An%288%2CModif%28Select%2CUnordered%29%2CClass%28%22http%3A//purl.obolibrary.org/obo/BFO_0000040%22%29%29%2CSome%28And%28Rel%28%22http%3A//purl.obolibrary.org/obo/RO_0000086%22%2CFwd%2CDet%28An%2845%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28And%28Rel%28%22http%3A//www.w3.org/1999/02/22-rdf-syntax-ns%23type%22%2CFwd%2CDet%28An%2882%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22http%3A//purl.obolibrary.org/obo/IAO_0000417%22%2CFwd%2CDet%28An%2881%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28Rel%28%22http%3A//purl.obolibrary.org/obo/OBI_0001938%22%2CFwd%2CDet%28An%28118%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28And%28Rel%28%22https%3A//qudt.org/schema/qudt/numericValue%22%2CFwd%2CDet%28An%28155%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22https%3A//qudt.org/schema/qudt/unit%22%2CFwd%2CDet%28An%28154%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29%29%29%29%29%29%29%29%2CRel%28%22http%3A//www.w3.org/2000/01/rdf-schema%23label%22%2CFwd%2CDet%28An%28193%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CRel%28%22http%3A//www.w3.org/1999/02/22-rdf-syntax-ns%23type%22%2CFwd%2CDet%28An%28191%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29&sparklis-path=D&entity_lexicon_select=http%3A//www.w3.org/2000/01/rdf-schema%23label&concept_lexicons_select=http%3A//www.w3.org/2000/01/rdf-schema%23label) The original JSON payload can be reconstructed from the SAMM triples using this [JSON-Query](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/fuseki/%24/sparql?query=PREFIX%20rdf%3A%20%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0APREFIX%20n1%3A%20%3Curn%3Asamm%3Aio.catenax.material_data%3A1.0.0%23%3E%0APREFIX%20n3%3A%20%3Curn%3Asamm%3Aorg.eclipse.esmf.samm%3Ameta-model%3A2.2.0%23%3E%0A%0ASELECT%20%28CONCAT%28%0A%20%20%27%7B%27%2C%0A%20%20%20%20%3FsectionsJson%2C%20%27%2C%27%2C%0A%20%20%20%20%27%22thermophysicalProperty%22%3A%7B%27%2C%20%3FthermoScalarsJson%2C%0A%20%20%20%20%20%20%27%2C%22linearThermalExpansionCoefficient%22%3A%7B%27%2C%20%3FltecJson%2C%20%27%7D%7D%2C%27%2C%0A%20%20%20%20%27%22pvt%22%3A%27%2C%20%3FpvtJson%2C%0A%20%20%27%7D%27%0A%29%20AS%20%3Fjson%29%0A%0AWHERE%20%7B%0A%0A%20%20%7B%0A%20%20%20%20SELECT%20%3Fmat%0A%20%20%20%20%20%20%28CONCAT%28%27%5B%27%2C%20GROUP_CONCAT%28%3FpvtEntry%20%3B%20SEPARATOR%3D%27%2C%27%29%2C%20%27%5D%27%29%20AS%20%3FpvtJson%29%0A%20%20%20%20WHERE%20%7B%0A%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20SELECT%20%3Fmat%20%3Fpvt%0A%20%20%20%20%20%20%20%20%20%20%28CONCAT%28%27%7B%27%2C%20GROUP_CONCAT%28%0A%20%20%20%20%20%20%20%20%20%20%20%20CONCAT%28%27%22%27%2C%20STRAFTER%28STR%28%3Fp%29%2C%20%27%23%27%29%2C%20%27%22%3A%27%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20IF%28REGEX%28STR%28%3Fv%29%2C%20%27%5E-%3F%5B0-9%5D%2B%28%5C%5C.%5B0-9%5D%2B%29%3F%28%5BeE%5D%5B%2B-%5D%3F%5B0-9%5D%2B%29%3F%24%27%29%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20STR%28%3Fv%29%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20CONCAT%28%27%22%27%2C%20STR%28%3Fv%29%2C%20%27%22%27%29%29%29%0A%20%20%20%20%20%20%20%20%20%20%20%20%3B%20SEPARATOR%3D%27%2C%27%29%0A%20%20%20%20%20%20%20%20%20%20%2C%20%27%7D%27%29%20AS%20%3FpvtEntry%29%0A%20%20%20%20%20%20%20%20WHERE%20%7B%0A%20%20%20%20%20%20%20%20%20%20%3Fmat%20a%20n1%3AMaterialData%20.%0A%20%20%20%20%20%20%20%20%20%20%3Fmat%20n1%3AthermophysicalProperty%2Fn1%3Apvt%20%3Fpvt%20.%0A%20%20%20%20%20%20%20%20%20%20%3Fpvt%20%3Fp%20%3Fv%20.%0A%20%20%20%20%20%20%20%20%20%20FILTER%28isLiteral%28%3Fv%29%20%26%26%20%3Fp%20%21%3D%20rdf%3Atype%29%0A%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%20%20GROUP%20BY%20%3Fmat%20%3Fpvt%0A%20%20%20%20%20%20%7D%0A%20%20%20%20%7D%0A%20%20%20%20GROUP%20BY%20%3Fmat%0A%20%20%7D%0A%0A%20%20%7B%0A%20%20%20%20SELECT%20%3Fmat%0A%20%20%20%20%20%20%28GROUP_CONCAT%28%0A%20%20%20%20%20%20%20%20CONCAT%28%27%22%27%2C%20STRAFTER%28STR%28%3Fp%29%2C%20%27%23%27%29%2C%20%27%22%3A%27%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20IF%28REGEX%28STR%28%3Fv%29%2C%20%27%5E-%3F%5B0-9%5D%2B%28%5C%5C.%5B0-9%5D%2B%29%3F%28%5BeE%5D%5B%2B-%5D%3F%5B0-9%5D%2B%29%3F%24%27%29%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20STR%28%3Fv%29%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20CONCAT%28%27%22%27%2C%20STR%28%3Fv%29%2C%20%27%22%27%29%29%29%0A%20%20%20%20%20%20%20%20%3B%20SEPARATOR%3D%27%2C%27%29%20AS%20%3FltecJson%29%0A%20%20%20%20WHERE%20%7B%0A%20%20%20%20%20%20%3Fmat%20a%20n1%3AMaterialData%20.%0A%20%20%20%20%20%20%3Fmat%20n1%3AthermophysicalProperty%2Fn1%3AlinearThermalExpansionCoefficient%20%3Fltec%20.%0A%20%20%20%20%20%20%3Fltec%20%3Fp%20%3Fv%20.%0A%20%20%20%20%20%20FILTER%28isLiteral%28%3Fv%29%20%26%26%20%3Fp%20%21%3D%20rdf%3Atype%29%0A%20%20%20%20%7D%0A%20%20%20%20GROUP%20BY%20%3Fmat%0A%20%20%7D%0A%0A%20%20%7B%0A%20%20%20%20SELECT%20%3Fmat%0A%20%20%20%20%20%20%28GROUP_CONCAT%28%0A%20%20%20%20%20%20%20%20CONCAT%28%27%22%27%2C%20STRAFTER%28STR%28%3Fp%29%2C%20%27%23%27%29%2C%20%27%22%3A%27%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20IF%28REGEX%28STR%28%3Fv%29%2C%20%27%5E-%3F%5B0-9%5D%2B%28%5C%5C.%5B0-9%5D%2B%29%3F%28%5BeE%5D%5B%2B-%5D%3F%5B0-9%5D%2B%29%3F%24%27%29%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20STR%28%3Fv%29%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20CONCAT%28%27%22%27%2C%20STR%28%3Fv%29%2C%20%27%22%27%29%29%29%0A%20%20%20%20%20%20%20%20%3B%20SEPARATOR%3D%27%2C%27%29%20AS%20%3FthermoScalarsJson%29%0A%20%20%20%20WHERE%20%7B%0A%20%20%20%20%20%20%3Fmat%20a%20n1%3AMaterialData%20.%0A%20%20%20%20%20%20%3Fmat%20n1%3AthermophysicalProperty%20%3Fthermo%20.%0A%20%20%20%20%20%20%3Fthermo%20%3Fp%20%3Fv%20.%0A%20%20%20%20%20%20FILTER%28isLiteral%28%3Fv%29%20%26%26%20%3Fp%20%21%3D%20rdf%3Atype%29%0A%20%20%20%20%7D%0A%20%20%20%20GROUP%20BY%20%3Fmat%0A%20%20%7D%0A%0A%20%20%7B%0A%20%20%20%20SELECT%20%3Fmat%20%28GROUP_CONCAT%28%3FsectionJson%20%3B%20SEPARATOR%3D%27%2C%27%29%20AS%20%3FsectionsJson%29%0A%20%20%20%20WHERE%20%7B%0A%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20SELECT%20%3Fmat%20%3FsectionName%0A%20%20%20%20%20%20%20%20%20%20%28CONCAT%28%27%22%27%2C%20%3FsectionName%2C%20%27%22%3A%7B%27%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20GROUP_CONCAT%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20CONCAT%28%27%22%27%2C%20STRAFTER%28STR%28%3Fp%29%2C%20%27%23%27%29%2C%20%27%22%3A%27%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20IF%28REGEX%28STR%28%3Fv%29%2C%20%27%5E-%3F%5B0-9%5D%2B%28%5C%5C.%5B0-9%5D%2B%29%3F%28%5BeE%5D%5B%2B-%5D%3F%5B0-9%5D%2B%29%3F%24%27%29%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20STR%28%3Fv%29%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20CONCAT%28%27%22%27%2C%20STR%28%3Fv%29%2C%20%27%22%27%29%29%29%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3B%20SEPARATOR%3D%27%2C%27%29%2C%0A%20%20%20%20%20%20%20%20%20%20%27%7D%27%29%20AS%20%3FsectionJson%29%0A%20%20%20%20%20%20%20%20WHERE%20%7B%0A%20%20%20%20%20%20%20%20%20%20%3Fmat%20a%20n1%3AMaterialData%20.%0A%20%20%20%20%20%20%20%20%20%20n1%3AMaterialData%20n3%3Aproperties%2Frdf%3Arest%2A%2Frdf%3Afirst%20%3FsectionProp%20.%0A%20%20%20%20%20%20%20%20%20%20BIND%28STRAFTER%28STR%28%3FsectionProp%29%2C%20%27%23%27%29%20AS%20%3FsectionName%29%0A%20%20%20%20%20%20%20%20%20%20FILTER%28%3FsectionName%20%21%3D%20%27thermophysicalProperty%27%29%0A%20%20%20%20%20%20%20%20%20%20%3Fmat%20%3FsectionProp%20%3FsectionInst%20.%0A%20%20%20%20%20%20%20%20%20%20%3FsectionInst%20%3Fp%20%3Fv%20.%0A%20%20%20%20%20%20%20%20%20%20FILTER%28isLiteral%28%3Fv%29%20%26%26%20%3Fp%20%21%3D%20rdf%3Atype%29%0A%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%20%20GROUP%20BY%20%3Fmat%20%3FsectionName%0A%20%20%20%20%20%20%7D%0A%20%20%20%20%7D%0A%20%20%20%20GROUP%20BY%20%3Fmat%0A%20%20%7D%0A%0A%7D)
small helper page running sparql querys from file
Jupyter notebook demonstrating the full Catena-X → PMDCo conversion pipeline. Retrieves material data from a Catena-X dataspace via an Eclipse Dataspace Connector (EDC), converts it to RDF using the [RDFConverter](https://github.com/materialdigital/RDFConverter) tool with a SAMM-specific YARRRML mapping, cleans provenance triples, and loads the result into a triplestore. Requires EDC access credentials and a running RDFConverter instance — see notebook comments for configuration.
RDF representation of the PA6GF30 material instance data, converted from [material_data_test_pa6gf30.json](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/5f7d5a69-f8f5-49d1-9780-c3ecdc0e11ec/download/material_data_test_pa6gf30.json) using the RDFConverter tool with a SAMM-specific YARRRML mapping. Conforms to the [MaterialData SAMM aspect model](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/2c5241c7-d6dc-46f7-b606-f85db755e49a/download/materialdataam3010.ttl). This is the intermediate representation in the pipeline — the input to [pmdco-mapping-insert.sparql](https://dataportal.material-digital.de/dataset/f2bca6a2-04df-47cb-9439-589e46ba60e2/resource/30780c7d-e08b-4607-b83a-c2dd20e44e89/download/pmdco-mapping-insert.sparql) which produces the final PMDCo output graph.
Demo of creation and query sematic documents created on the basis of catena-x samm model payloads. Uses a YARRRML mapping, found here https://dataportal.material-digital.de/dataset/samm-batch-map that created the necessary tripples. To test this urself copy the link to the mapping into the mapping url field of the application found here https://rdfconverter.matolab.org/. This Dataportal has triple store integration and u can find a query on the resuting data here [Example Query](https://dataportal.material-digital.de/sparklis/?title=batch&endpoint=https%3A//dataportal.material-digital.de/fuseki/f789ccb0-c887-4e2e-a988-6da379bf7d7a&sparklis-query=%5BVId%5DReturn%28Det%28An%288%2CModif%28Select%2CUnordered%29%2CClass%28%22urn%3Asamm%3Aio.catenax.batch%3A3.0.1%23Batch%22%29%29%2CSome%28Triple%28S%2CDet%28An%2845%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%2CDet%28An%2846%2CModif%28Select%2CUnordered%29%2CThing%29%2CSome%28And%28Rel%28%22http%3A//www.w3.org/1999/02/22-rdf-syntax-ns%23type%22%2CFwd%2CDet%28An%28144%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%2CTriple%28S%2CDet%28An%28148%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%2CDet%28An%28149%2CModif%28Select%2CUnordered%29%2CThing%29%2CNone%29%29%29%29%29%29%29%29%29&sparklis-path=DDDRDDRDR)
The KupferDigital Copper Ontology aims to represent the generic constructs (including classes and properties) related to the domain of material science and engineering of copper products and its life-cycle. The Materials Ontology uses BFO as the top-level ontology and IOF Core as the mid-level ontology. The purpose of the ontology is to serve as a foundation for ensuring consistency and interoperability across various material science and engineering ontologies.
Institut für Angewandte Informatik (InfAI) e. V.