2026-04-28T04:37:33Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/111992026-03-09T11:01:20Zcom_10259_6168com_10259_5086com_10259_2604com_10259_3924col_10259_6775col_10259_7224col_10259_5684
Carreira Barral, Israel
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600
0000-0002-4835-8752
García-Moral, Ana
24a1cb2d-5bfd-46bd-a5cd-de88f7012062
600
Iñigo-Martínez, María Emilia
1edf4c19-54e4-4c14-bf40-3574b74b4bef
600
Díez Hernández, Julieta
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600
0000-0001-9935-077X
Ibáñez Porras, Jesús
788
600
0000-0002-6271-4266
Alonso-Terán, Mario
f818dfc6-6531-480f-8197-d4c526ddb89c
600
Fuente Gamero, Patricia de la
965
600
0000-0002-6048-1534
Barros García, Rocío
787
600
0000-0002-0392-8504
Martel Martín, Sonia
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600
0000-0001-9080-0877
2026-01-12T12:34:41Z
2026-01-12T12:34:41Z
2025-12-15
Carreira-Barral, I., García Moral, A., Iñigo Martinez, M. E., Díez-Hernández, J., Ibanez, J., Mario Alonso Terán, De La Fuente, P., Barros Garcia, R., & Martel-Martín, S. (2025). Dataset of the work 'A five-indicator methodology for early-stage sustainable selection of metal scraps and raw materials: application in the steel industry' [Data set]. Universidad de Burgos. https://doi.org/10.71486/MCTS-0K15
https://hdl.handle.net/10259/11199
10.71486/mcts-0k15
This dataset contains the data provided by ArcelorMittal España (commercial names, quality and elemental composition of the studied metal scraps and raw materials) in the context of the 'IRIDISCENTE' research project, and the analysis, results and conclusions for each of the 11 scenarios considered in the work 'A five-indicator methodology for early-stage sustainable selection of metal scraps and raw materials: application in the steel industry'
This work was funded by the Spanish Agencia Estatal de Investigación (AEI) through the ‘Artificial Intelligence for the sustainable design of efficient alloys and processes (IRIDISCENTE)’ research project (PLEC2023-010190)
text/plain
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eng
Universdad de Burgos
https://hdl.handle.net/10259/11472
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PLEC2023-010190/ES/Inteligencia artificial para el diseño sostenible de aleaciones y procesos eficientes/IRIDISCENTE/
Atribución-NoComercial 4.0 Internacional
http://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
Circularity assessment
Criticality assessment
Early-stage evaluation
Metal scrap
Raw material
Steel
Sustainability index
Industria siderúrgica
Residuos metálicos
Steel industry and trade
Scrap metals
Dataset of the work 'A five-indicator methodology for early-stage sustainable selection of metal scraps and raw materials: application in the steel industry'
dataset
info:eu-repo/semantics/acceptedVersion
2025
LICENSE
license.txt
license.txt
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https://riubu.ubu.es/bitstream/10259/11199/4/license.txt
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CC-LICENSE
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https://riubu.ubu.es/bitstream/10259/11199/3/license_rdf
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ORIGINAL
readme.txt
readme.txt
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https://riubu.ubu.es/bitstream/10259/11199/1/readme.txt
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Scraps_raw_materials_SI.xlsx
Scraps_raw_materials_SI.xlsx
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https://riubu.ubu.es/bitstream/10259/11199/2/Scraps_raw_materials_SI.xlsx
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10259/11199
oai:riubu.ubu.es:10259/11199
2026-03-09 12:01:20.329
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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