{"id":{"repo_id":"vilnius","oai_identifier":"oai:vu.lt:elaba:210578161"},"canonical_url":"https://search.dev.ndltd.org/etd/vilnius/oai:vu.lt:elaba:210578161","repository":{"repo_id":"vilnius","name":"Vilnius University","base_url":"https://epublications.vu.lt/oai"},"display":{"title":"Kvantinės chemijos skaičiavimų rezultatų duomenų bazės ir informacinės sistemos panaudojimas karotenoidų analizei /","abstract":"Phytochemicals such as carotenoids are important subjects of research because of their potential as medicines for various diseases. Dietary intake of carotenoids such as lycopene, beta-carotene, and alpha-carotene may reduce the risk of cancer and heart disease. In addition to their health benefits, carotenoids contribute to the photosynthetic process, giving plants their bright colors and protecting them from photo-oxidative stress and heat. They are therefore an important subject of research in the modern scientific world. As the number of carotenoid computations increases, the need for efficient management and handling of quantum chemistry data is growing. The aim of this work was to develop a new, efficient information system to facilitate the processing of quantum chemistry calculations related to carotenoids. The VU supercomputer ,,HPC Sauletekis“, located at the Faculty of Physics of Vilnius University, was used for the work. The main objective of this work has been successfully achieved. Using the Python programming language and the SQLite software library, an information system and a database, ,,Python_DB“, have been developed for storing quantum chemistry calculations on carotenoids. The system provides a user-friendly interface which is executed via the command line. The main functionality of the database implemented in the ,,Python_DB“ system includes uploading, searching, downloading, and deleting data. In addition, the system implements additional functionality to allow the study of the energies of excited states of H-dimer structure carotenoids as well as Raman and optical absorption spectra of various carotenoids. Moreover, the Jmol software has been integrated for data visualization. Additionally, the possibility for cross-dependency representations was integrated, where data were extracted from the computational files using the CCLib parser. The database and the ,,Python_DB“ information system are publicly available on the GitHub platform: https://github.com/Ariadna21/Python_DB/tree/main.","abstract_html":"Phytochemicals such as carotenoids are important subjects of research because of their potential as medicines for various diseases. Dietary intake of carotenoids such as lycopene, beta-carotene, and alpha-carotene may reduce the risk of cancer and heart disease. In addition to their health benefits, carotenoids contribute to the photosynthetic process, giving plants their bright colors and protecting them from photo-oxidative stress and heat. They are therefore an important subject of research in the modern scientific world. As the number of carotenoid computations increases, the need for efficient management and handling of quantum chemistry data is growing. The aim of this work was to develop a new, efficient information system to facilitate the processing of quantum chemistry calculations related to carotenoids. The VU supercomputer ,,HPC Sauletekis“, located at the Faculty of Physics of Vilnius University, was used for the work. The main objective of this work has been successfully achieved. Using the Python programming language and the SQLite software library, an information system and a database, ,,Python_DB“, have been developed for storing quantum chemistry calculations on carotenoids. The system provides a user-friendly interface which is executed via the command line. The main functionality of the database implemented in the ,,Python_DB“ system includes uploading, searching, downloading, and deleting data. In addition, the system implements additional functionality to allow the study of the energies of excited states of H-dimer structure carotenoids as well as Raman and optical absorption spectra of various carotenoids. Moreover, the Jmol software has been integrated for data visualization. Additionally, the possibility for cross-dependency representations was integrated, where data were extracted from the computational files using the CCLib parser. The database and the ,,Python_DB“ information system are publicly available on the GitHub platform: https://github.com/Ariadna21/Python_DB/tree/main.","abstract_has_math":false,"creators":["Šamatovič, Ariadna,"],"institution":"Institutional Repository of Vilnius University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-24T05:55:48Z","subjects":[],"languages":["lit"],"rights":["info:eu-repo/semantics/openAccess"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.vu.lt/VU:ELABAETD210578161&prefLang=en_US","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Šamatovič, Ariadna,"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024"]},{"key":"dc:publisher","label":"Institution","values":["Institutional Repository of Vilnius University"]},{"key":"dc:relation","label":"Dc Relation","values":["https://epublications.vu.lt/object/elaba:210578161/210578161.pdf"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/bachelorThesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["lit"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://repository.vu.lt/VU:ELABAETD210578161&prefLang=en_US"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Phytochemicals such as carotenoids are important subjects of research because of their potential as medicines for various diseases. Dietary intake of carotenoids such as lycopene, beta-carotene, and alpha-carotene may reduce the risk of cancer and heart disease. In addition to their health benefits, carotenoids contribute to the photosynthetic process, giving plants their bright colors and protecting them from photo-oxidative stress and heat. They are therefore an important subject of research in the modern scientific world. As the number of carotenoid computations increases, the need for efficient management and handling of quantum chemistry data is growing. The aim of this work was to develop a new, efficient information system to facilitate the processing of quantum chemistry calculations related to carotenoids. The VU supercomputer ,,HPC Sauletekis“, located at the Faculty of Physics of Vilnius University, was used for the work. The main objective of this work has been successfully achieved. Using the Python programming language and the SQLite software library, an information system and a database, ,,Python_DB“, have been developed for storing quantum chemistry calculations on carotenoids. The system provides a user-friendly interface which is executed via the command line. The main functionality of the database implemented in the ,,Python_DB“ system includes uploading, searching, downloading, and deleting data. In addition, the system implements additional functionality to allow the study of the energies of excited states of H-dimer structure carotenoids as well as Raman and optical absorption spectra of various carotenoids. Moreover, the Jmol software has been integrated for data visualization. Additionally, the possibility for cross-dependency representations was integrated, where data were extracted from the computational files using the CCLib parser. The database and the ,,Python_DB“ information system are publicly available on the GitHub platform: https://github.com/Ariadna21/Python_DB/tree/main."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Kvantinės chemijos skaičiavimų rezultatų duomenų bazės ir informacinės sistemos panaudojimas karotenoidų analizei /","Usage of quantum chemistry calculation results database and information system for carotenoids analysis."]}]}],"canonical_facts":{"dc:creator":["Šamatovič, Ariadna,"],"dc:date":["2024"],"dc:description":["Phytochemicals such as carotenoids are important subjects of research because of their potential as medicines for various diseases. Dietary intake of carotenoids such as lycopene, beta-carotene, and alpha-carotene may reduce the risk of cancer and heart disease. In addition to their health benefits, carotenoids contribute to the photosynthetic process, giving plants their bright colors and protecting them from photo-oxidative stress and heat. They are therefore an important subject of research in the modern scientific world. As the number of carotenoid computations increases, the need for efficient management and handling of quantum chemistry data is growing. The aim of this work was to develop a new, efficient information system to facilitate the processing of quantum chemistry calculations related to carotenoids. The VU supercomputer ,,HPC Sauletekis“, located at the Faculty of Physics of Vilnius University, was used for the work. The main objective of this work has been successfully achieved. Using the Python programming language and the SQLite software library, an information system and a database, ,,Python_DB“, have been developed for storing quantum chemistry calculations on carotenoids. The system provides a user-friendly interface which is executed via the command line. The main functionality of the database implemented in the ,,Python_DB“ system includes uploading, searching, downloading, and deleting data. In addition, the system implements additional functionality to allow the study of the energies of excited states of H-dimer structure carotenoids as well as Raman and optical absorption spectra of various carotenoids. Moreover, the Jmol software has been integrated for data visualization. Additionally, the possibility for cross-dependency representations was integrated, where data were extracted from the computational files using the CCLib parser. 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