Data models, synthetic procedures, PXRD patterns, and analysis results for "Data-Model-Driven Management and Analysis of MOF Synthesis Data"

DOI

This dataset contains data models, synthesis procedures, PXRD patterns, and analysis results underlying the article “Data-Model-Driven Management and Analysis of MOF Synthesis Data”. It includes a data model for MOF synthesis based on XDL and two example MOF synthesis datasets (Fe–terephthalate MOFs and MOCOF-1) with procedures and experimental product PXRD patterns, as well as phase mole fractions obtained by PXRD analysis and decision tree models. The folders are structured according to the type of data. The dataset is intended to support replication of the published analyses and reuse for method development in MOF synthesis data management.

Identifier
DOI https://doi.org/10.18419/DARUS-5695
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-5695
Provenance
Creator Neubauer, Felix ORCID logo; Endo, Kenichi ORCID logo; Bender, Frederic ORCID logo; Çiftçi, Esengül ORCID logo; Hansen, Niels ORCID logo; Krause, Simon ORCID logo; Uekermann, Benjamin ORCID logo; Pleiss, Jürgen ORCID logo
Publisher DaRUS
Contributor Endo, Kenichi; Pleiss, Jürgen; Neubauer, Felix
Publication Year 2026
Funding Reference DFG 358283783 - SFB 1333
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Endo, Kenichi (University of Stuttgart); Pleiss, Jürgen (University of Stuttgart)
Representation
Resource Type Data models, experimental data, analysis data; Dataset
Format application/json; text/html; application/pdf; text/tab-separated-values; application/zip; text/markdown; text/xml
Size 4968; 118307; 2934; 246686; 34644; 22758; 7347; 1709; 3767; 2004; 92724; 2378194; 834; 767; 6183; 150037; 23990; 964107; 7361; 8329; 129789; 6211750; 677; 694; 1723; 696; 963; 1786609; 5395; 18511467; 8017; 446431
Version 1.0
Discipline Chemistry; Computer Science; Computer Science, Electrical and System Engineering; Engineering Sciences; Natural Sciences; Research Data Management