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The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.\n<br></br>\nThe usage of pyThermoML is demonstrated in the following <a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb">example workflow</a> and can be utilized to read the given ThermoML file.', 'num_resources': 1, 'num_tags': 10, 'organization': {'id': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'name': 'darus', 'title': 'DaRUS', 'type': 'repository', 'description': 'Chemistry collection from DaRUS, the data repository of the University of Stuttgart.', 'image_url': 'logoDarusKreis.png', 'created': '2023-05-03T09:01:04.791551', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'private': False, 'related_molecule': [], 'state': 'active', 'title': 'Self-diffusion coefficients of simulated aqueous methanol mixtures', 'type': 'dataset', 'extras': [{'key': 'creator', 'value': 'Gültig, Matthias'}, {'key': 'date', 'value': '2022-10-28T00:00:00'}, {'key': 'identifier', 'value': 'https://doi.org/10.18419/darus-3114'}, {'key': 'metadata_modified', 'value': '2022-11-29T01:00:05'}, {'key': 'set_spec', 'value': 'all'}, {'key': 'harvest_object_id', 'value': 'e935fb8d-7472-4b8a-88e0-7442f8441dc1'}, {'key': 'harvest_source_id', 'value': '8ba5ef26-d024-46cd-8099-94f1e74e7a36'}, {'key': 'harvest_source_title', 'value': 'Darus Test Harvest'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2023-05-08T19:14:00.323927', 'format': 'HTML', 'hash': '', 'id': '9bef3f2b-efd6-4039-b73a-c924342ed03c', 'last_modified': None, 'metadata_modified': '2023-05-08T19:14:00.297216', 'mimetype': None, 'mimetype_inner': None, 'name': 'Self-diffusion coefficients of simulated aqueous methanol mixtures', 'package_id': 'doi-10-18419-darus-3114', 'position': 0, 'resource_type': 'HTML', 'size': None, 'state': 'active', 'url': 'https://doi.org/10.18419/darus-3114', 'url_type': None}], 'tags': [{'display_name': 'chemistry', 'id': '20e4e978-2a22-4286-a18b-4ae22d1ffca1', 'name': 'chemistry', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'engineering', 'id': '3ff6cbc9-08ad-4fd1-aa1e-6676db9d1e1c', 'name': 'engineering', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'excess-properties', 'id': '4f0d3070-54f9-4e54-aa93-2ce8eefb5511', 'name': 'excess-properties', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'fair', 'id': 'f478468d-4177-45bd-910b-eb1eeec855fd', 'name': 'fair', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'fair-data-principles', 'id': '70904371-5f99-455f-af39-2ff1d5b1ea6b', 'name': 'fair-data-principles', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'liquid-mixtures', 'id': 'c6f22099-46e7-4e66-9cf9-ded2359d790f', 'name': 'liquid-mixtures', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'mathematical-sciences', 'id': 'e46bf35a-29e9-4b40-a5c8-db6a64e96d7d', 'name': 'mathematical-sciences', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'physics', 'id': '820fb04d-8f9c-45ca-9a54-9054d91e527b', 'name': 'physics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'thermoml', 'id': 'c831467c-748e-4806-8359-ff9216511379', 'name': 'thermoml', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'transferability-of-force-fields', 'id': '0e4eb12c-951b-43dd-b6df-d7268d534adc', 'name': 'transferability-of-force-fields', 'state': 'active', 'vocabulary_id': None}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Gültig, Matthias, Range, Jan Peter, Schmitz, Benjamin, Pleiss, Jürgen', 'author_email': None, 'creator_user_id': '1be646ae-ab26-47b8-8835-e4b27f11961e', 'id': 'doi-10-18419-darus-3113', 'isopen': False, 'license_id': '', 'license_title': '', 'maintainer': 'DaRUS', 'maintainer_email': None, 'metadata_created': '2023-05-08T19:13:59.936551', 'metadata_modified': '2023-05-08T19:13:59.936558', 'name': 'doi-10-18419-darus-3113', 'notes': 'In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. \n<br></br>\nThe feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. <a href="https://github.com/FAIRChemistry/pyThermoML">PyThermoML</a> enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.\n<br></br>\nThe usage of pyThermoML is demonstrated in the following <a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb">example workflow</a> and can be utilized to read the given ThermoML file.', 'num_resources': 1, 'num_tags': 10, 'organization': {'id': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'name': 'darus', 'title': 'DaRUS', 'type': 'repository', 'description': 'Chemistry collection from DaRUS, the data repository of the University of Stuttgart.', 'image_url': 'logoDarusKreis.png', 'created': '2023-05-03T09:01:04.791551', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'private': False, 'related_molecule': [], 'state': 'active', 'title': 'Densities of simulated aqueous glycerol mixtures', 'type': 'dataset', 'extras': [{'key': 'creator', 'value': 'Gültig, Matthias'}, {'key': 'date', 'value': '2022-10-28T00:00:00'}, {'key': 'identifier', 'value': 'https://doi.org/10.18419/darus-3113'}, {'key': 'metadata_modified', 'value': '2022-11-29T01:00:05'}, {'key': 'set_spec', 'value': 'all'}, {'key': 'harvest_object_id', 'value': 'a708588e-c6a1-42b9-9940-7baed1996ba5'}, {'key': 'harvest_source_id', 'value': '8ba5ef26-d024-46cd-8099-94f1e74e7a36'}, {'key': 'harvest_source_title', 'value': 'Darus Test Harvest'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2023-05-08T19:13:59.952325', 'format': 'HTML', 'hash': '', 'id': '26ae5db8-3a08-4e05-8554-5b69f997544a', 'last_modified': None, 'metadata_modified': '2023-05-08T19:13:59.919894', 'mimetype': None, 'mimetype_inner': None, 'name': 'Densities of simulated aqueous glycerol mixtures', 'package_id': 'doi-10-18419-darus-3113', 'position': 0, 'resource_type': 'HTML', 'size': None, 'state': 'active', 'url': 'https://doi.org/10.18419/darus-3113', 'url_type': None}], 'tags': [{'display_name': 'chemistry', 'id': '20e4e978-2a22-4286-a18b-4ae22d1ffca1', 'name': 'chemistry', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'engineering', 'id': '3ff6cbc9-08ad-4fd1-aa1e-6676db9d1e1c', 'name': 'engineering', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'excess-properties', 'id': '4f0d3070-54f9-4e54-aa93-2ce8eefb5511', 'name': 'excess-properties', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'fair', 'id': 'f478468d-4177-45bd-910b-eb1eeec855fd', 'name': 'fair', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'fair-data-principles', 'id': '70904371-5f99-455f-af39-2ff1d5b1ea6b', 'name': 'fair-data-principles', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'liquid-mixtures', 'id': 'c6f22099-46e7-4e66-9cf9-ded2359d790f', 'name': 'liquid-mixtures', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'mathematical-sciences', 'id': 'e46bf35a-29e9-4b40-a5c8-db6a64e96d7d', 'name': 'mathematical-sciences', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'physics', 'id': '820fb04d-8f9c-45ca-9a54-9054d91e527b', 'name': 'physics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'thermoml', 'id': 'c831467c-748e-4806-8359-ff9216511379', 'name': 'thermoml', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'transferability-of-force-fields', 'id': '0e4eb12c-951b-43dd-b6df-d7268d534adc', 'name': 'transferability-of-force-fields', 'state': 'active', 'vocabulary_id': None}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Gültig, Matthias, Range, Jan Peter, Schmitz, Benjamin, Pleiss, Jürgen', 'author_email': None, 'creator_user_id': '1be646ae-ab26-47b8-8835-e4b27f11961e', 'id': 'doi-10-18419-darus-3112', 'isopen': False, 'license_id': '', 'license_title': '', 'maintainer': 'DaRUS', 'maintainer_email': None, 'metadata_created': '2023-05-08T19:13:59.793471', 'metadata_modified': '2023-05-08T19:13:59.793484', 'name': 'doi-10-18419-darus-3112', 'notes': 'In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. \n<br></br>\nThe feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. <a href="https://github.com/FAIRChemistry/pyThermoML">PyThermoML</a> enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.\n<br></br>\nThe usage of pyThermoML is demonstrated in the following <a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb">example workflow</a> and can be utilized to read the given ThermoML file.', 'num_resources': 1, 'num_tags': 10, 'organization': {'id': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'name': 'darus', 'title': 'DaRUS', 'type': 'repository', 'description': 'Chemistry collection from DaRUS, the data repository of the University of Stuttgart.', 'image_url': 'logoDarusKreis.png', 'created': '2023-05-03T09:01:04.791551', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'private': False, 'related_molecule': [], 'state': 'active', 'title': 'Densities of simulated aqueous methanol mixtures', 'type': 'dataset', 'extras': [{'key': 'creator', 'value': 'Gültig, Matthias'}, {'key': 'date', 'value': '2022-10-28T00:00:00'}, {'key': 'identifier', 'value': 'https://doi.org/10.18419/darus-3112'}, {'key': 'metadata_modified', 'value': '2022-11-29T01:00:05'}, {'key': 'set_spec', 'value': 'all'}, {'key': 'harvest_object_id', 'value': '8e7b3c9f-0427-4f41-9284-54de0f087363'}, {'key': 'harvest_source_id', 'value': '8ba5ef26-d024-46cd-8099-94f1e74e7a36'}, {'key': 'harvest_source_title', 'value': 'Darus Test Harvest'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2023-05-08T19:13:59.795372', 'format': 'HTML', 'hash': '', 'id': 'a671476d-a67c-4914-a5d9-e339266e8e23', 'last_modified': None, 'metadata_modified': '2023-05-08T19:13:59.779358', 'mimetype': None, 'mimetype_inner': None, 'name': 'Densities of simulated aqueous methanol mixtures', 'package_id': 'doi-10-18419-darus-3112', 'position': 0, 'resource_type': 'HTML', 'size': None, 'state': 'active', 'url': 'https://doi.org/10.18419/darus-3112', 'url_type': None}], 'tags': [{'display_name': 'chemistry', 'id': '20e4e978-2a22-4286-a18b-4ae22d1ffca1', 'name': 'chemistry', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'engineering', 'id': '3ff6cbc9-08ad-4fd1-aa1e-6676db9d1e1c', 'name': 'engineering', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'excess-properties', 'id': '4f0d3070-54f9-4e54-aa93-2ce8eefb5511', 'name': 'excess-properties', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'fair', 'id': 'f478468d-4177-45bd-910b-eb1eeec855fd', 'name': 'fair', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'fair-data-principles', 'id': '70904371-5f99-455f-af39-2ff1d5b1ea6b', 'name': 'fair-data-principles', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'liquid-mixtures', 'id': 'c6f22099-46e7-4e66-9cf9-ded2359d790f', 'name': 'liquid-mixtures', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'mathematical-sciences', 'id': 'e46bf35a-29e9-4b40-a5c8-db6a64e96d7d', 'name': 'mathematical-sciences', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'physics', 'id': '820fb04d-8f9c-45ca-9a54-9054d91e527b', 'name': 'physics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'thermoml', 'id': 'c831467c-748e-4806-8359-ff9216511379', 'name': 'thermoml', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'transferability-of-force-fields', 'id': '0e4eb12c-951b-43dd-b6df-d7268d534adc', 'name': 'transferability-of-force-fields', 'state': 'active', 'vocabulary_id': None}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Zimmermann, Nils Edvin Richard, Guevara-Carrion, Gabriela, Vrabec, Jadran, Hansen, Niels', 'author_email': None, 'creator_user_id': '1be646ae-ab26-47b8-8835-e4b27f11961e', 'id': 'doi-10-18419-darus-2996', 'isopen': False, 'license_id': '', 'license_title': '', 'maintainer': 'DaRUS', 'maintainer_email': None, 'metadata_created': '2023-05-08T19:13:56.780040', 'metadata_modified': '2023-05-08T19:13:56.780047', 'name': 'doi-10-18419-darus-2996', 'notes': "<p>Supplementary material for 'Predicting and rationalizing the Soret coefficient of binary Lennard-Jones mixtures in the liquid state' (N. E. R. Zimmermann, G. Guevara-Carrion, J. Vrabec, N. Hansen, Adv. Theory Simul., 2022) containing scripts, packages, and files to re-create and to submit LAMMPS simulations performed for non-equilibrium molecular dynamics (NEMD) simulations and to post-process these simulations to obtain the final results. Furthermore, scripts are provided to calculate Soret coefficients with the Shukla-Firoozabadi model (Ind. Eng. Chem. Res., 1998, 37, 8, 3331) and the Reith-Müller-Plathe model (J. Chem. Phys., 2000, 112, 5, 2436). Finally, all data from the present study are provided, where as much of the raw data of the NEMD simulations are provided as reasonably possible (e.g., trajectory files could not be included due to memory constraints, but temperature profiles and mole fraction profiles are provided in tar archives).</p>\n\n<p>Note that each directory of this dataset contains a README.txt to outline the content found in the directory and, possibly, its subdirectories.</p>\n\n<p>The general directory structure is as follows:</p>\n<pre>\n|\n|- code\n| |\n| |- nemd\n| |\n| |- reith\n| |\n| '- shukla\n|\n'- data\n| |\n| |- emd\n| |\n| |- nemd\n| |\n| |- reith\n| |\n| '- shukla\n|\n'- movie\n |\n '- nemd\n</pre>", 'num_resources': 1, 'num_tags': 13, 'organization': {'id': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'name': 'darus', 'title': 'DaRUS', 'type': 'repository', 'description': 'Chemistry collection from DaRUS, the data repository of the University of Stuttgart.', 'image_url': 'logoDarusKreis.png', 'created': '2023-05-03T09:01:04.791551', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'private': False, 'related_molecule': [], 'state': 'active', 'title': "Supplementary material for 'Predicting and rationalizing the Soret coefficient of binary Lennard-Jones mixtures in the liquid state'", 'type': 'dataset', 'extras': [{'key': 'contributor', 'value': 'Zimmermann, Nils Edvin Richard'}, {'key': 'creator', 'value': 'Zimmermann, Nils Edvin Richard'}, {'key': 'date', 'value': '2022-08-03T00:00:00'}, {'key': 'identifier', 'value': 'https://doi.org/10.18419/darus-2996'}, {'key': 'metadata_modified', 'value': '2022-11-29T01:00:05'}, {'key': 'set_spec', 'value': 'all'}, {'key': 'harvest_object_id', 'value': '4cfb8f05-1d3b-4592-8825-aad3b378c974'}, {'key': 'harvest_source_id', 'value': '8ba5ef26-d024-46cd-8099-94f1e74e7a36'}, {'key': 'harvest_source_title', 'value': 'Darus Test Harvest'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2023-05-08T19:13:56.782471', 'format': 'HTML', 'hash': '', 'id': 'c939cadc-8eef-4551-bf3a-0a2065c89e8f', 'last_modified': None, 'metadata_modified': '2023-05-08T19:13:56.763804', 'mimetype': None, 'mimetype_inner': None, 'name': "Supplementary material for 'Predicting and rationalizing the Soret coefficient of binary Lennard-Jones mixtures in the liquid state'", 'package_id': 'doi-10-18419-darus-2996', 'position': 0, 'resource_type': 'HTML', 'size': None, 'state': 'active', 'url': 'https://doi.org/10.18419/darus-2996', 'url_type': None}], 'tags': [{'display_name': 'chemistry', 'id': '20e4e978-2a22-4286-a18b-4ae22d1ffca1', 'name': 'chemistry', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'engineering', 'id': '3ff6cbc9-08ad-4fd1-aa1e-6676db9d1e1c', 'name': 'engineering', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'green-kubo-relations', 'id': 'db223b89-d173-46e5-85a3-413d91ba3252', 'name': 'green-kubo-relations', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'lennard-jones-potential', 'id': '5bd83026-b886-4867-ab66-7b7f7307ca72', 'name': 'lennard-jones-potential', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'liquid', 'id': '24a3e911-30b8-4856-8c77-2dd5b82b104b', 'name': 'liquid', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'mathematical-model', 'id': '9fc27b56-68a3-434f-94a1-670ac3b843d3', 'name': 'mathematical-model', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'mixture', 'id': '35c216a5-3748-42af-9207-15cd32156761', 'name': 'mixture', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'molecular-dynamics-simulation', 'id': '0040c50a-44c0-4113-b945-716272c88419', 'name': 'molecular-dynamics-simulation', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'molecular-simulation', 'id': 'c7631893-732e-4a1a-b8a9-d6e9921a3085', 'name': 'molecular-simulation', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'non-equilibrium-statistical-mechanics', 'id': '1ee6466a-e5a6-4d96-9ce9-f94c14715b62', 'name': 'non-equilibrium-statistical-mechanics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'physics', 'id': '820fb04d-8f9c-45ca-9a54-9054d91e527b', 'name': 'physics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'statistical-mechanics', 'id': '5dc2bc42-1b76-4a76-ab9b-65026eb877c4', 'name': 'statistical-mechanics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'thermophoresis', 'id': '5a508dec-750f-4a7a-9caa-6cbd0e70ae47', 'name': 'thermophoresis', 'state': 'active', 'vocabulary_id': None}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Kessler, Christopher, Schuldt, Robin, Emmerling, Sebastian, Lotsch, Bettina, Kästner, Johannes, Gross, Joachim, Hansen, Niels', 'author_email': None, 'creator_user_id': '1be646ae-ab26-47b8-8835-e4b27f11961e', 'id': 'doi-10-18419-darus-2308', 'isopen': False, 'language': 'English', 'license_id': '', 'license_title': '', 'maintainer': 'DaRUS', 'maintainer_email': None, 'metadata_created': '2023-05-08T19:13:41.625425', 'metadata_modified': '2023-05-08T19:13:41.625431', 'name': 'doi-10-18419-darus-2308', 'notes': '<p>This dataset contains results from Grand Canonical Monte Carlo (GCMC) Simulation (data/isotherms_sim/) and experiment (data/isotherms/exp). </p>\n\n<p>All Data is presented in a <a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=108370">jupyter notebook</a> and for a fast overview without executing the notebook also as <a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=108427">pdf-file</a>.<br>\nFurthermore the dataset contains the modified cif files of COF LZU-1, including partial charges obtained with DDEC method (data/cif/). Force field (data/forcefield/) and input files for raspa-code (data/input_files/) are also available. Results from simulations of material properties e.g. specific surface area can be found in data/material_properties/ and pore size distributions from raspa simulations are listed in data/output_psd/. The folder \'progs\' simply contains functions to make the jupyter-notebook clearer.</p>\n\nWe recommend viewing the data by choosing the option "Tree".', 'num_resources': 1, 'num_tags': 13, 'organization': {'id': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'name': 'darus', 'title': 'DaRUS', 'type': 'repository', 'description': 'Chemistry collection from DaRUS, the data repository of the University of Stuttgart.', 'image_url': 'logoDarusKreis.png', 'created': '2023-05-03T09:01:04.791551', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'private': False, 'related_molecule': [], 'state': 'active', 'title': "Supplementary material for 'Influence of Layer Slipping on Adsorption of Light Gases in Covalent Organic Frameworks: A Combined Experimental and Computational Study'", 'type': 'dataset', 'extras': [{'key': 'contributor', 'value': 'Kessler, Christopher'}, {'key': 'creator', 'value': 'Kessler, Christopher'}, {'key': 'date', 'value': '2021-12-17T00:00:00'}, {'key': 'identifier', 'value': 'https://doi.org/10.18419/darus-2308'}, {'key': 'metadata_modified', 'value': '2022-11-29T01:00:04'}, {'key': 'set_spec', 'value': 'all'}, {'key': 'harvest_object_id', 'value': '35322780-93c3-4f33-ae9f-707a1d44d6cc'}, {'key': 'harvest_source_id', 'value': '8ba5ef26-d024-46cd-8099-94f1e74e7a36'}, {'key': 'harvest_source_title', 'value': 'Darus Test Harvest'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2023-05-08T19:13:41.627383', 'format': 'HTML', 'hash': '', 'id': 'a57a0a4a-700e-4a78-96b7-93e98fb0487c', 'last_modified': None, 'metadata_modified': '2023-05-08T19:13:41.609095', 'mimetype': None, 'mimetype_inner': None, 'name': "Supplementary material for 'Influence of Layer Slipping on Adsorption of Light Gases in Covalent Organic Frameworks: A Combined Experimental and Computational Study'", 'package_id': 'doi-10-18419-darus-2308', 'position': 0, 'resource_type': 'HTML', 'size': None, 'state': 'active', 'url': 'https://doi.org/10.18419/darus-2308', 'url_type': None}], 'tags': [{'display_name': 'adsorption', 'id': '93d0d375-e7a2-476a-b7b9-8bb74b45107b', 'name': 'adsorption', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'chemistry', 'id': '20e4e978-2a22-4286-a18b-4ae22d1ffca1', 'name': 'chemistry', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'covalent-organic-framework', 'id': '5a6dcae2-b0ae-424e-84a4-f3c428400320', 'name': 'covalent-organic-framework', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'density-functional-theory', 'id': '05ed3b04-b36f-438a-9fb9-739fe4c6e183', 'name': 'density-functional-theory', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'engineering', 'id': '3ff6cbc9-08ad-4fd1-aa1e-6676db9d1e1c', 'name': 'engineering', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'force-fields-for-simulations', 'id': '4127b176-3908-47f0-b018-1861790956a6', 'name': 'force-fields-for-simulations', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'grand-canonical-monte-carlo-simulation', 'id': '312b8f2d-978e-49eb-9bb6-5e435696f384', 'name': 'grand-canonical-monte-carlo-simulation', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'inputfiles-for-simulations', 'id': 'b7fb12e0-1221-4ee4-bacd-eebc04650b08', 'name': 'inputfiles-for-simulations', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'methods-to-evaluate-and-visualize-data', 'id': 'ae54652d-f61a-4fa7-9960-dd04217de1a8', 'name': 'methods-to-evaluate-and-visualize-data', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'physics', 'id': '820fb04d-8f9c-45ca-9a54-9054d91e527b', 'name': 'physics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'results-from-experiments', 'id': 'fbfedacb-831e-4483-9d0f-79269d692da1', 'name': 'results-from-experiments', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'results-from-simulations', 'id': '39bd9f6d-8659-4ce9-89d9-873c7c72c2e3', 'name': 'results-from-simulations', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'structural-data', 'id': '4239c14f-9bda-4422-8daa-7c43ad2b0505', 'name': 'structural-data', 'state': 'active', 'vocabulary_id': None}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Fleck, Maximilian, Markthaler, Daniel, Stankiewicz, Bartosz, Hansen, Niels', 'author_email': None, 'creator_user_id': '1be646ae-ab26-47b8-8835-e4b27f11961e', 'id': 'doi-10-18419-darus-2132', 'isopen': False, 'language': 'English', 'license_id': '', 'license_title': '', 'maintainer': 'DaRUS', 'maintainer_email': None, 'metadata_created': '2023-05-08T19:13:33.706372', 'metadata_modified': '2023-05-08T19:13:33.706377', 'name': 'doi-10-18419-darus-2132', 'notes': "Supplementary material for 'Exploring the Effect of Enhanced Sampling on Protein Stability Prediction' containing files to (re-)execute GROMACS simulations performed during the mutation study. This dataset contains simulation input files in GROMACS format accompanying the mentioned publication. Structure, topology, and simulation parameter-files are provided for the simulations discussed in the paper.<br>\nDue to the high number of files, the study is provided as a .zip-file. Please check the README.", 'num_resources': 1, 'num_tags': 6, 'organization': {'id': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'name': 'darus', 'title': 'DaRUS', 'type': 'repository', 'description': 'Chemistry collection from DaRUS, the data repository of the University of Stuttgart.', 'image_url': 'logoDarusKreis.png', 'created': '2023-05-03T09:01:04.791551', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'private': False, 'related_molecule': [], 'state': 'active', 'title': "Supplementary material for 'Exploring the Effect of Enhanced Sampling on Protein Stability Prediction'", 'type': 'dataset', 'extras': [{'key': 'contributor', 'value': 'Fleck, Maximilian'}, {'key': 'creator', 'value': 'Fleck, Maximilian'}, {'key': 'date', 'value': '2022-05-04T00:00:00'}, {'key': 'identifier', 'value': 'https://doi.org/10.18419/darus-2132'}, {'key': 'metadata_modified', 'value': '2022-11-29T01:00:04'}, {'key': 'relation', 'value': 'Kowalski, J. A.; Liu, K.; Kelly, J. W. (2009) The NMR solution structure of the isolated Apo Pin1 WW domain. <a href="https://doi.org/10.2210/pdb2KCF/pdb">doi: 10.2210/pdb2KCF/pdb</a>'}, {'key': 'set_spec', 'value': 'all'}, {'key': 'harvest_object_id', 'value': 'd6a9f9cb-07de-49ce-aa3d-bf37767c856c'}, {'key': 'harvest_source_id', 'value': '8ba5ef26-d024-46cd-8099-94f1e74e7a36'}, {'key': 'harvest_source_title', 'value': 'Darus Test Harvest'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2023-05-08T19:13:33.714269', 'format': 'HTML', 'hash': '', 'id': '5445ae04-24b9-4e5b-a149-a1e65ab0ebd4', 'last_modified': None, 'metadata_modified': '2023-05-08T19:13:33.696610', 'mimetype': None, 'mimetype_inner': None, 'name': "Supplementary material for 'Exploring the Effect of Enhanced Sampling on Protein Stability Prediction'", 'package_id': 'doi-10-18419-darus-2132', 'position': 0, 'resource_type': 'HTML', 'size': None, 'state': 'active', 'url': 'https://doi.org/10.18419/darus-2132', 'url_type': None}], 'tags': [{'display_name': 'chemistry', 'id': '20e4e978-2a22-4286-a18b-4ae22d1ffca1', 'name': 'chemistry', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'engineering', 'id': '3ff6cbc9-08ad-4fd1-aa1e-6676db9d1e1c', 'name': 'engineering', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'gromos-force-field', 'id': 'c0216f82-8018-4f7d-80cf-53c0370dcd7d', 'name': 'gromos-force-field', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'molecular-dynamics-simulation', 'id': '0040c50a-44c0-4113-b945-716272c88419', 'name': 'molecular-dynamics-simulation', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'physics', 'id': '820fb04d-8f9c-45ca-9a54-9054d91e527b', 'name': 'physics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'protein-stability', 'id': '36bb5b73-cae1-4d08-acf2-02f18a200268', 'name': 'protein-stability', 'state': 'active', 'vocabulary_id': None}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Fauser, Dominik, Kuhn, Moritz, Steeb, Holger', 'author_email': None, 'creator_user_id': '1be646ae-ab26-47b8-8835-e4b27f11961e', 'id': 'doi-10-18419-darus-2024', 'isopen': False, 'license_id': '', 'license_title': '', 'maintainer': 'DaRUS', 'maintainer_email': None, 'metadata_created': '2023-05-08T19:13:27.515082', 'metadata_modified': '2023-05-08T19:13:27.515088', 'name': 'doi-10-18419-darus-2024', 'notes': 'This data contains diffusion measurements of Shape Memory Polymers (SMP) immersed in demineralized water. \nThe SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc.\nThe SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands).\n<p>\n<p>\nThe samples were dried in a drying oven for 10 days prior to the absorption measurements. The determination of the water absorption capacity is carried out at two different temperatures (30 °C and 60 °C). The samples are immersed in a demineralized water bath, which is stored in a drying oven. The time-dependent diffusive process is carried out with weight gain measurements.\nAt each measuring point, the sample is taken out of the water bath and the surface water is wiped off with a lint-free cloth. \nAttention was paid to keep the measuring time (t < 30 sec) at each measuring point as short as possible in order not to influence the diffusive process.\nThe measurement interval was set at the beginning of the measurement at 30 °C to 60 min and at 60 °C to 10 min. It was measured at daytime and working hours.\n<p> \n<p>\nAfter the samples have been completely saturated, the desorption experiment starts. The saturated samples were stored in a drying oven at 30 °C and the weight loss was measured. The measurement parameters correspond to the absorption measurements at 30 °C.', 'num_resources': 1, 'num_tags': 8, 'organization': {'id': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'name': 'darus', 'title': 'DaRUS', 'type': 'repository', 'description': 'Chemistry collection from DaRUS, the data repository of the University of Stuttgart.', 'image_url': 'logoDarusKreis.png', 'created': '2023-05-03T09:01:04.791551', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'private': False, 'related_molecule': [], 'state': 'active', 'title': 'Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements', 'type': 'dataset', 'extras': [{'key': 'contributor', 'value': 'Fauser, Dominik'}, {'key': 'creator', 'value': 'Fauser, Dominik'}, {'key': 'date', 'value': '2021-05-08T00:00:00'}, {'key': 'identifier', 'value': 'https://doi.org/10.18419/darus-2024'}, {'key': 'metadata_modified', 'value': '2022-11-29T01:00:04'}, {'key': 'relation', 'value': 'Fauser, Dominik; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis", <a href="https://doi.org/10.18419/darus-2021">https://doi.org/10.18419/darus-2021</a>, DaRUS.\n<p>\nFauser, Dominik; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Thermal Humid Mechanical Cycle", <a href="https://doi.org/10.18419/darus-2023">https://doi.org/10.18419/darus-2023</a>, DaRUS.'}, {'key': 'set_spec', 'value': 'all'}, {'key': 'harvest_object_id', 'value': '4266b7dc-b530-4566-9239-43ec5f7ac575'}, {'key': 'harvest_source_id', 'value': '8ba5ef26-d024-46cd-8099-94f1e74e7a36'}, {'key': 'harvest_source_title', 'value': 'Darus Test Harvest'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2023-05-08T19:13:27.526170', 'format': 'HTML', 'hash': '', 'id': '7f64bf07-4252-43cc-8e0f-7a50688d2124', 'last_modified': None, 'metadata_modified': '2023-05-08T19:13:27.503359', 'mimetype': None, 'mimetype_inner': None, 'name': 'Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements', 'package_id': 'doi-10-18419-darus-2024', 'position': 0, 'resource_type': 'HTML', 'size': None, 'state': 'active', 'url': 'https://doi.org/10.18419/darus-2024', 'url_type': None}], 'tags': [{'display_name': 'absorption', 'id': '2abba78c-ce01-4707-aa78-b29cd6f6c493', 'name': 'absorption', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'chemistry', 'id': '20e4e978-2a22-4286-a18b-4ae22d1ffca1', 'name': 'chemistry', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'computer-and-information-science', 'id': '75b28b6d-af69-4c24-9c9e-451d429aad9b', 'name': 'computer-and-information-science', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'desorption', 'id': '4d353493-0b7a-478e-b38f-076f78e62a5b', 'name': 'desorption', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'diffusion', 'id': 'e4bc6f26-2ca1-4af4-92f1-c95130fe6a01', 'name': 'diffusion', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'earth-and-environmental-sciences', 'id': '550b3d1c-2608-46b4-8e21-47340a0d61d3', 'name': 'earth-and-environmental-sciences', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'engineering', 'id': '3ff6cbc9-08ad-4fd1-aa1e-6676db9d1e1c', 'name': 'engineering', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'physics', 'id': '820fb04d-8f9c-45ca-9a54-9054d91e527b', 'name': 'physics', 'state': 'active', 'vocabulary_id': None}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Fauser, Dominik, Steeb, Holger', 'author_email': None, 'creator_user_id': '1be646ae-ab26-47b8-8835-e4b27f11961e', 'id': 'doi-10-18419-darus-2021', 'isopen': False, 'language': 'English', 'license_id': '', 'license_title': '', 'maintainer': 'DaRUS', 'maintainer_email': None, 'metadata_created': '2023-05-08T19:13:27.051481', 'metadata_modified': '2023-05-08T19:13:27.051487', 'name': 'doi-10-18419-darus-2021', 'notes': 'This data contains iso-thermal and iso-humid shear frequency-sweep measurements of Shape Memory Polymers (SMP). \nThe SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc.\nThe SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands).\n<p>\n<p>\nFrequency-sweeps were performed in a temperature range (5 °C - 85 °C) and at relative humidity 0 %, 30 %, 50 %, 70 % and 100 %. \nA preheating step was performed before each frequency measurement to relieve residual stresses stored by the manufacturing process. Subsequently, at a temperature T = 60 °C above the glass transition temperature (T_G), the relative humidity is set and held for 5 days to fully saturate the sample.\nAfter that, the sample is slowly cooled down to 10 °C in order not to store residual stresses in the sample. \nThe sample geometry is measured again and then the actual frequency measurement is performed.\n<p>\n<p>\nThe frequency measurements are performed in the frequency range from 0.01 Hz to 10 Hz at isothermal and isohumid conditions. The shear strain amplitude is set below T_G with gamma = 0.1 % and above T_G with gamma = 0.2 %. The sample is drawn below T_G with a normal force of F_N = - 0.4 N and above T_G with a normal force of F_N = - 0.2 N. So that the specimen is always drawn and does not bend, which affects the shear measurements.\n<p>\n<p>\nWith the results of the isothermal and isohumid frequency measurements, on the one hand, temperature shifts at different humidities can be presented. The temperature shifts are used to determine the humidity-dependent T_Gs. On the other hand, master curves can be determined at different humidities. With the humidity-dependent master curves, the viscoelastic material behavior is determined in a larger frequency range (<a href="https://en.wikipedia.org/wiki/Time%E2%80%93temperature_superposition">Time-Temperature Superpositon</a>).\nIn addition, the shift parameters (alpha_T) determined for the master curves are included.\n<p>\n<p>\nThe data is organized in the following folder structure, e.g.\ndynamic thermal humid analysis at dry condition = dmtha_dry.\n<p>\n<ul>\n<li><b>dmtha_dry</b>\n<ul>\n<li>frequency_sweep_SMP_dry\n<li>mastercurve_SMP_dry\n<li>alpha_T_dry\n</ul>\n<li><b>dmtha_redry</b>\n<ul>\n<li>frequency_sweep_SMP_redry\n<li>mastercurve_SMP_redry\n<li>alpha_T_redry\n</ul>\n<li><b>dmtha_30</b>\n<ul>\n<li>frequency_sweep_SMP_30\n<li>mastercurve_SMP_30\n<li>alpha_T_30\n</ul>\n<li><b>dmtha_50</b>\n<ul>\n<li>frequency_sweep_SMP_50,\n<li>mastercurve_SMP_50,\n<li>alpha_T_50\n</ul>\n<li><b>dmtha_70</b>\n<ul>\n<li>frequency_sweep_SMP_70\n<li>mastercurve_SMP_70\n<li>alpha_T_70\n</ul>\n<li><b>dmtha_wet</b>\n<ul>\n<li>frequency_sweep_SMP_wet\n<li>mastercurve_SMP_wet\n<li>alpha_T_wet\n</ul>', 'num_resources': 1, 'num_tags': 10, 'organization': {'id': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'name': 'darus', 'title': 'DaRUS', 'type': 'repository', 'description': 'Chemistry collection from DaRUS, the data repository of the University of Stuttgart.', 'image_url': 'logoDarusKreis.png', 'created': '2023-05-03T09:01:04.791551', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a7d2a53-21f6-412a-afb9-a15122df0640', 'private': False, 'related_molecule': [], 'state': 'active', 'title': 'Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis', 'type': 'dataset', 'extras': [{'key': 'contributor', 'value': 'Fauser, Dominik'}, {'key': 'creator', 'value': 'Fauser, Dominik'}, {'key': 'date', 'value': '2021-05-08T00:00:00'}, {'key': 'identifier', 'value': 'https://doi.org/10.18419/darus-2021'}, {'key': 'metadata_modified', 'value': '2022-11-29T01:00:04'}, {'key': 'relation', 'value': 'Fauser, Dominik; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Thermal Humid Mechanical Cycle", <a href="https://doi.org/10.18419/darus-2023">https://doi.org/10.18419/darus-2023</a>, DaRUS.\n<p>\nFauser, Dominik; Kuhn, Moritz; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements", <a href="https://doi.org/10.18419/darus-2024">https://doi.org/10.18419/darus-2024</a>, DaRUS.'}, {'key': 'set_spec', 'value': 'all'}, {'key': 'harvest_object_id', 'value': '814f0d75-6be1-4cd8-81ee-84403ec61fcb'}, {'key': 'harvest_source_id', 'value': '8ba5ef26-d024-46cd-8099-94f1e74e7a36'}, {'key': 'harvest_source_title', 'value': 'Darus Test Harvest'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2023-05-08T19:13:27.082489', 'format': 'HTML', 'hash': '', 'id': '7e0d60a0-9573-40ba-b60b-a52ece863543', 'last_modified': None, 'metadata_modified': '2023-05-08T19:13:27.025778', 'mimetype': None, 'mimetype_inner': None, 'name': 'Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis', 'package_id': 'doi-10-18419-darus-2021', 'position': 0, 'resource_type': 'HTML', 'size': None, 'state': 'active', 'url': 'https://doi.org/10.18419/darus-2021', 'url_type': None}], 'tags': [{'display_name': 'chemistry', 'id': '20e4e978-2a22-4286-a18b-4ae22d1ffca1', 'name': 'chemistry', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'computer-and-information-science', 'id': '75b28b6d-af69-4c24-9c9e-451d429aad9b', 'name': 'computer-and-information-science', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'dynamic-mechanical-thermal-analysis', 'id': '471cca6e-3e09-4188-bd69-320a75d96b40', 'name': 'dynamic-mechanical-thermal-analysis', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'earth-and-environmental-sciences', 'id': '550b3d1c-2608-46b4-8e21-47340a0d61d3', 'name': 'earth-and-environmental-sciences', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'engineering', 'id': '3ff6cbc9-08ad-4fd1-aa1e-6676db9d1e1c', 'name': 'engineering', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'physics', 'id': '820fb04d-8f9c-45ca-9a54-9054d91e527b', 'name': 'physics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'shape-memory-polymer', 'id': '6aa9d288-c639-4ce6-bf25-22accb1b2098', 'name': 'shape-memory-polymer', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'thermo-chemo-mechanics', 'id': 'e4a44b6c-ef0c-4e33-9765-b59de51feca1', 'name': 'thermo-chemo-mechanics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'thermo-viscoelasticity', 'id': 'cb5cf2e7-a77b-45f5-bb1d-954b42adbd52', 'name': 'thermo-viscoelasticity', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'time-temperature-superposition', 'id': '3dfdf8cd-df10-4db5-866a-5d2334828ce6', 'name': 'time-temperature-superposition', 'state': 'active', 'vocabulary_id': None}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Fauser, Dominik, Steeb, Holger', 'author_email': None, 'creator_user_id': '1be646ae-ab26-47b8-8835-e4b27f11961e', 'id': 'doi-10-18419-darus-2023', 'isopen': False, 'language': 'English', 'license_id': '', 'license_title': '', 'maintainer': 'DaRUS', 'maintainer_email': None, 'metadata_created': '2023-05-08T19:13:26.956085', 'metadata_modified': '2023-05-08T19:13:26.956091', 'name': 'doi-10-18419-darus-2023', 'notes': 'This data contains a Thermal Humid Mechanical Cycle (THMC) of Shape Memory Polymers (SMP). The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands).\n<p>\n<p>\nThe THMC is divided into a preheating step, a programming step and a stress-free recovery step.\nA preheating step was performed before the THMC, to relieve residual stresses stored by the manufacturing process.\nThen the temperature is set to T_trans = 55 °C and waited until the sample is in thermodynamic equilibrium.\n<p>\nA displacement of s_max = 2.5 mm is performed and fixed. \nIn the same step, the thermal chamber is set to cooling, by setting a lower temperature T_1 = 50 °C, to speed up the cooling process. \nIn the next step the displacement s_max is kept fixed and the temperature is decreased to T_low = 30 °C.\n<p>\nOnce the sample is in thermodynamic equilibrium, the fixation is released and the stress free recovery step by setting the normal force to F_N = 0 N is performed. \nHumidity-dependent recovery of the initial geometry is performed by increasing the relative humidity to RH = 70 %.\n<p>\n<p>\nBy performing a THMC, the thermal shape fixation and humidity-dependent shape recovery are captured. 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