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[Erina Fujita](https://orcid.org/0000-0002-0987-5597), Chang Liu, Asuka Ishikawa, [Tomoya Mato](https://orcid.org/0000-0002-0918-6468), Koichi Kitahara, Ryuji Tamura, [Kaoru Kimura](https://orcid.org/0000-0001-5050-4256), [Ryo Yoshida](https://orcid.org/0000-0001-8092-0162), [Yukari Katsura](https://orcid.org/0000-0002-8905-2995)

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[Comprehensive experimental datasets of quasicrystals and their approximants](https://mdr.nims.go.jp/datasets/26c8267a-0eb0-4daa-bb88-aa726d83e794)

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Comprehensive experimental datasets of quasicrystals and their approximants1Scientific Data |         (2024) 11:1211  | https://doi.org/10.1038/s41597-024-04043-zwww.nature.com/scientificdataComprehensive experimental datasets of quasicrystals and  their approximantsErina Fujita   1,2 ✉, Chang Liu   1, Asuka Ishikawa   3, Tomoya Mato   2, Koichi Kitahara4, Ryuji Tamura   3, Kaoru Kimura   1,2, Ryo Yoshida   1,2,5 ✉ & Yukari Katsura   2,6,7 ✉Quasicrystals are solid-state materials that typically exhibit unique symmetries, such as icosahedral or decagonal diffraction symmetry. They were first discovered in 1984. Over the past four decades of quasicrystal research, around 100 stable quasicrystals have been discovered. In recent years, machine learning has been employed to explore quasicrystals with unique properties inherent to quasiperiodic systems. However, the lack of open data on quasicrystal composition, structure, and physical properties has hindered the widespread use of machine learning in quasicrystal research. This study involves a comprehensive literature review and manual data extraction to develop open datasets consisting of composition, structure types, phase diagrams, and sample fabrication processes for a wide range of stable and metastable quasicrystals and approximant crystals, as well as the temperature-dependent thermal, electrical, and magnetic properties.Background & SummaryQuasicrystals (QCs) are a class of aperiodic materials that typically possess unique symmetries, such as ico-sahedral or decagonal diffraction symmetry, which are different from those of ordinary crystals, and exhibit highly ordered atomic arrangements. QC was discovered in the Al-Mn alloy system by Dan Shechtman1 and named by Paul J. Steinhardt2 in 1984, which was a metastable quasicrystal obtained by rapid cooling of a liq-uid alloy. The first thermodynamically stable QC was found in the Al-Li-Cu system in 19863. Following these discoveries, An-Pang Tsai discovered a stable and higher quality QC in the Al-Fe-Cu alloy system in 19874. Owing to the continuous developments with regard to unraveling new QCs, in 1992, the International Union of Crystallography revised the definition of crystals to include QCs as a new form of crystalline materials5.Since the first QC was unraveled by Dan Shechtman 40 years ago, more than 100 stable QCs have been found. Their quasiperiodic structures are classified into two-dimensional and three-dimensional categories6. The mate-rial discovered by Shechtman was an icosahedral three-dimensional quasicrystal (IQC). Three-dimensional quasicrystals are known to form an icosahedral structure. Two-dimensional quasicrystals are composed of dodecagonal QCs (DoQCs), decagonal QCs (DQCs), and octagonal QCs (OQCs). Crystalline systems where the same structural units as a particular QC type are periodically arranged are called approximant crystals (ACs). These are referred to by the structural type of the related quasicrystal, such as decagonal QC approxi-mants (DACs) and icosahedral QC approximants (IACs).The quasiperiodic materials exhibit distinct characteristics different from conventional periodic systems. For instance, conventional metals possess high electrical conductivity, with electrical resistivity tending to increase with temperature. Contrastingly, many QCs have low electrical conductivity, and their electrical resistivity decreases with increasing temperature7,8. Similarly, the temperature dependence of thermal conductivity in QCs and conventional metals shows opposite trends above room temperature9. The high-temperature specific heat of 1The Institute of Statistical Mathematics (ISM), Research Organization of Information and Systems, Tachikawa, Tokyo, 190-8562, Japan. 2National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, 305-0047, Japan. 3Department of Materials Science and Technology, Tokyo University of Science, Katsushika- ku, Tokyo, 125–8585, Japan. 4Department of Materials Science and Engineering, National Defense Academy, Yokosuka, Kanagawa, 239-8686, Japan. 5Graduate University for Advanced Studies, SOKENDAI, Tachikawa, Tokyo, 190-8562, Japan. 6Graduate School of Science and Technology, Tsukuba University, Tennodai, Tsukuba, Ibaraki, 305-8573, Japan. 7RIKEN Center for Advanced Intelligence Project, RIKEN, Chuo-ku, Tokyo, 103-0027, Japan. ✉e-mail: fujita-e@ism.ac.jp; yoshidar@ism.ac.jp; KATSURA.Yukari@nims.go.jpData DescriptorOPENhttps://doi.org/10.1038/s41597-024-04043-zhttp://orcid.org/0000-0002-0987-5597http://orcid.org/0000-0002-9511-4283http://orcid.org/0000-0003-4707-0703http://orcid.org/0000-0002-0918-6468http://orcid.org/0000-0001-8589-4311http://orcid.org/0000-0001-5050-4256http://orcid.org/0000-0001-8092-0162http://orcid.org/0000-0002-8905-2995mailto:fujita-e@ism.ac.jpmailto:yoshidar@ism.ac.jpmailto:yoshidar@ism.ac.jpmailto:KATSURA.Yukari@nims.go.jphttp://crossmark.crossref.org/dialog/?doi=10.1038/s41597-024-04043-z&domain=pdf2Scientific Data |         (2024) 11:1211  | https://doi.org/10.1038/s41597-024-04043-zwww.nature.com/scientificdatawww.nature.com/scientificdata/QCs is significantly higher than the Dulong–Petit value, which is the saturation value for conventional solids10. The physical mechanisms responsible for these temperature-dependent properties have been elucidated11–13. Recently, quantum critical14, superconducting15,16, and ferromagnetic17,18 QCs have been discovered. The variety of QCs unravels the essential differences in electronic states between QCs and conventional crystals. Further enhancements in QC variety, such as semiconducting, antiferromagnetic, ionic bonding, oxide QCs, and others, are expected to accelerate the understanding of QC characteristics.Recently, the application of machine learning to the realm of quasicrystals has shown notable progress. Liu et al.19 developed a machine learning classifier to predict whether a stable phase resulting from any given composition constitutes a QC or an AC. The model was trained using the chemical compositions of the available QCs and ACs. Subsequently, in Liu et al.20, this classifier was leveraged for high-throughput virtual screening across extensive compo-sition spaces, leading to the discovery of three QCs. Uryu et al.21 developed a binary classifier to determine the presence of IQC in a multi-phase sample based on its powder X-ray diffraction pattern, leading to a novel QC in the Al-Ru-Si system from data accumulated in their laboratory. These pioneering studies have demonstrated the potential of machine learning as a new tool for the exploration of novel QCs. Nonetheless, compared to other material systems, the application of machine learning in QC research is lacking. This is due to the absence of data resources. To date, there is no comprehensive repository of structural and property data for QCs and ACs comparable to those available for ordinary periodic crystalline materials such as ICSD22, Materials Project23, AFLOW24, OQMD25,26 and AtomWorks27.In this study, we systematically constructed an open dataset of QCs and ACs, called HYPOD-X (Hypermaterials Open Datasets for X, where X represents a wildcard for application targets, such as machine learning), through a comprehensive literature survey and data extraction. The atomic configurations of QC and AC can be described in a unified manner by projection from a higher-dimensional periodic lattice into three-dimensional space6. In our scientific project (https://www.rs.tus.ac.jp/hypermaterials/en/index.html), we refer to QCs and ACs as hypermaterials, a class of materials that can be regarded as “high-dimensional periodic crystals”. This category also includes incommensurately modulated structures and incommensurate composites. The composition dataset encompasses 915 QCs, 525 ACs, and 8 QCs or ACs synthesized to date, along with their structural types, sample preparation methods, and the corresponding bibliographic information. Additionally, we curated a dataset of phase diagrams by digitizing regional information from 43 ternary alloy phase diagrams extracted from images in journal articles. Furthermore, we compiled a dataset of temperature-dependent physical properties, including electrical resistivity, Seebeck coefficient, thermal conductivity, and magnetic susceptibility for 925 quasiperiodic materials. All these data are structured and distributed in machine-readable text formats.MethodsData were manually extracted from literature in a variety of formats, including text, tables, and figures. In-house software was used to facilitate the data collection process along with our web application, Starrydata2 (https://www.starrydata2.org/)28. Additionally, an open web application called WebPlotDigitizer29 was employed to dig-itize images (Fig. 1). These digitized data, consisting of composition, phase diagram, and properties datasets, are distributed on Figshare30.Fig. 1  Workflow for extracting data from journal articles.https://doi.org/10.1038/s41597-024-04043-zhttps://www.rs.tus.ac.jp/hypermaterials/en/index.htmlhttps://www.starrydata2.org/https://www.starrydata2.org/3Scientific Data |         (2024) 11:1211  | https://doi.org/10.1038/s41597-024-04043-zwww.nature.com/scientificdatawww.nature.com/scientificdata/Composition dataset.  The composition dataset was constructed by compiling data described in text or tables in 130 journal articles. The validity of the textual information was carefully and rigorously reviewed by three experts. Each compositional piece of information was recorded on a single line. The data were saved and provided in the form of a comma-separated values (CSV) file.QCs and ACs are classified as IQC, IAC, DQC, or DAC based on their structural categories. ACs are further classified according to the degree of approximation to the corresponding QC, such as 1/0, 1/1, 2/1, and 3/26. As another classification criterion, IQCs and their IACs are classified into the Mackay, Bergmann, or Tsai cluster according to their basic structural unit, consisting of a few dozen atoms, arranged to approximate an icosahe-dron cluster6. In an IQC, clusters are geometrically arranged into a simple cubic (P-type), body-centered cubic (I-type), or face-centered cubic (F-type) type quasi-lattice in six-dimensional space. Other structural informa-tion such as space groups and lattice parameters are also provided where indicated in the literature.The dataset provides labels indicating the stability of each material, denoted as “stable” or “metastable” in terms of thermodynamics. Detailed information on the heat treatment during the sample fabrication process was also described in “heat treatment” and “heat treatment condition” columns. In addition, each sample was labeled to distinguish whether it was observed in a single- or multi-phase in the “phase information” column. Samples with the same composition but different fabrication procedures were distinctly considered and recorded separately. Additionally, the compositions recorded in the “composition types” were categorized as “nominal”, “alloy”, and “analyzed”, respectively. For the “analyzed” compositions, details on analytic methods were also described, if available. If the composition was given as an interval in the original journal articles (e.g. A80B20-yCy, 0 < y < 20), the data were duplicated with equally spaced grid points within the interval. For the reference infor-mation, the composition that was described as a main result in the literature is referred to as “main”, and that only cited is referred to as “reference.”In summary, the dataset contains a wide variety of attribute information associated with the compositional information. The complete list of attributes is described in the Data Records section.Phase region dataset.  Currently, the phase region dataset records the phase diagrams of 43 aluminum (Al) ternary systems. Since the discovery of quasicrystal in the Al-Mn alloy system, Al alloys have been one of the most actively studied systems in QC research. Around 1990, Tsai et al. uncovered a series of stable QCs in Al-Cu-Fe and Al-Pd-Mn alloys. Subsequently, since the early 2000s, Benjamin Grushko determined the ternary phase dia-grams of numerous Al-based quasicystalline and approximant crystal alloys. We extracted regional information regarding the quasicrystalline, approximant, liquid, and ordinary crystalline phases from the phase diagrams by Grushko. WebPlotDigitizer26 and an Excel macro written in Visual Basic for Applications (VBA) were used to convert the coordinates of the boundary for each phase region extracted from the phase diagram. In-house tools created using Python were used to perform the post-processing of the extracted data (e.g., merging individually separated columns into one dict type variable).First, using WebPlotDigitizer (version 4.4), three vertices at the corners of a ternary phase diagram were captured, and their image coordinates, along with composition values, were defined as the reference point set. In addition, two orthogonal axes were specified in the image space for automated extraction of the coordinate value of any clicked point. Subsequently, the outer boundary of each phase region was traced by successively clicking on it. Consequently, the outer point set of each phase region was recorded as a set of x-y coordinates. These data were temporarily stored in WebPlotDigitizer and subsequently downloaded as a CSV file after tracing all phases in the diagram.Note that the extracted boundary points form a coordinate set in Euclidean space; therefore, it is necessary to further transform them into their composition values. To address this, we used a VBA macro that implements a Fig. 2  (a) Original image of an Al-Cu-Mn alloy system on the WebPlotDizitizer screen. B. Grushko, et al.28 was copied and pasted. (This figure is presented with permission for reuse by Elsevier.) Subsequently, phase boundaries were manually traced by clicking. Points are shown as red dots (b). Extracted coordinates were converted into element ratios via a VBA macro.https://doi.org/10.1038/s41597-024-04043-z4Scientific Data |         (2024) 11:1211  | https://doi.org/10.1038/s41597-024-04043-zwww.nature.com/scientificdatawww.nature.com/scientificdata/custom-made coordinate transformation algorithm. Let u u u, ,0 1 2 be the two-dimensional coordinate vectors in the reference set on the corners of the phase diagram image that were extracted as described above. Let c c,0 1, and c2 be their known composition values. The transformation from u to c is linearly described as follows:c Mu v= +Here, M and v must be determined to define the mapping from u to c. The difference between any two composi-tions, e.g. −c c1 0, is related to their extracted coordinates independent of the origin, as follows:− = −c c M u u( ) ( )1 0 1 0Expressing the two equations for c u( , )1 1  and c u( , )2 2  in matrix form yields the following expression:c c c c M u u u u[ , ] [ , ]2 0 1 0 2 0 1 0− − = − −The solution for the transformation matrix M is then given as:M c c c c u u u u[ , ] [ , ]2 0 1 0 2 0 1 01= − − − − −Finally, the intercept term can be estimated as:v c Mu0 0= −In our workflow, the extracted image coordinates in the phase diagram (Fig. 2a) were downloaded as a CSV file, and then transformed into the composition values by using the VBA macro (Fig. 2b).Properties dataset.  The properties dataset was constructed by extracting temperature-varying physical property values of QCs and ACs from 193 journal articles, including thermal conductivity, electrical conductivity, Seebeck coefficient, and magnetic susceptibility. (See the Data Record section for the full list of recorded proper-ties.) We facilitated the data extraction process using our web application Starrydata2. This application enables the extraction of data points from figures imaging temperature-dependent properties by simply clicking on the screen. This also allows for efficient conversion of the extracted image coordinates to their measurement values in the physical property space. The extracted data were managed with the compositional, sample preparation, and Item Data type DescriptionID string IDCombination of elements string Combination of elementsOriginal formula string Compositional formula described in the literatureAlphabetical formula string Compositional formula of elements in alphabetical orderNumber of elements integer Number of elementsCombination of elements string List of elementsComposition type string Composition type (e.g., nominal, alloy, analyzed)QC, AC type string Structure type of QC or AC (IQC, IAC, DQC, or DAC)Stability string StabilityCluster type string Cluster type of QC or AC (Mackay, Bergman or Tsai)Lattice type string Structure type of the latticeDegree of approximant string Approximation degree of ACCrystal system string Class of crystal structureAnalysis device string Devices used for compositional analysisPhase information string Phase informationSample shape string Shape of the sampleSpace group string Space groupPearson symbol string Pearson symbolLattice parameter string Lattice parameterHeat treatment string Types of heat treatment in the sample fabricationHeat treatment condition string Heat treatment conditionsAdditional fabrication information string Additional information on the sample fabrication processSolidification string Solidification processes and methodsMethod string Method of sample fabricationNote string Additional informationData source string DOI or bibliographic informationSource type string Main: main subject of research in the articleReference: cited from a different articleTable 1.  List of items in the composition dataset.https://doi.org/10.1038/s41597-024-04043-z5Scientific Data |         (2024) 11:1211  | https://doi.org/10.1038/s41597-024-04043-zwww.nature.com/scientificdatawww.nature.com/scientificdata/publication information in an easily accessible format. Once the extracted property values were registered, they could be visualized in the Starrydata2 system. The interactive visualization also aids human decision-making to improve the accuracy of data collection.Starrydata2 implements a work environment that interoperates with WebPlotDigitizer, which was applied to the process of extracting numerical values from given images. First, a screenshot of the figure image from the source was captured and pasted into a designated area of WebPlotDigitizer. Subsequently, the reference Item Data type DescriptionID string ID of the compositionElements list Set of elements in the original phase diagramComposition key value Composition ratio of the data pointPhase_type string Type of phases. IQC: icosahedral quasicrystal, IAC: icosahedral approximant, DQC: decagonal quasicrystal, DAC: decagonal approximant, CRY: crystal, LQD: liquidPhase_symbol string Symbols denoted in the original phase diagramElement1 string The first element in the ternary systemElm1_lower_range (atomic percent) integer Lower bound of the composition axis of Element1Elm1_upper_range (atomic percent) integer Upper bound of the composition axis of Element1Element2 string The second element in the ternary systemElm2_lower_range (atomic percent) integer Lower bound of the composition axis of Element2Elm2_upper_range (atomic percent) integer Upper bound of the composition axis of Element2Element3 string The third element in the ternary systemElm3_lower_range (atomic percent) integer Lower bound of the composition axis of Element3Elm3_upper_range (atomic percent) integer Upper bound of the composition axis of Element3DOI string DOI of the literatureFig No. string Figure No. in the original articleTable 2.  Details of the items in the phase region dataset.Item Data type DescriptionComposition string Composition of the sampleQC_or_AC_type string Type of QC or AC (IQC, IAC, DQC, DAC)Degree_of_approximant string Approximation degree of ACprop_x string Name of x axisprop_y string Name of y axisunit_x string Unit of x axisunit_y string Unit of y axisX float Value of xY float Value of ySID string Unit of y axisSample_id string Sample ID in Starrydata2Figure_id string Figure ID in Starrydata2DOI string DOI of the literatureTable 3.  List of items in the properties dataset.Property Number of curvesElectrical resistivity 736Seebeck coefficient 227Thermal conductivity 202Magnetic susceptibility 73ZT 72Hall coefficient 40Specific heat 32Power factor 30Specific heat capacity 19Thermal diffusivity 18Table 4.  Details of each property in the properties dataset.https://doi.org/10.1038/s41597-024-04043-z6Scientific Data |         (2024) 11:1211  | https://doi.org/10.1038/s41597-024-04043-zwww.nature.com/scientificdatawww.nature.com/scientificdata/points for each axis were specified by clicking on any two points within the x- and y-axes, and assigning the corresponding property values. Next, by clicking on each point from the property curves on the target graph, coordinate values were extracted and calculated with respect to the reference points for the x- and y-axes. This process was repeated for all samples in the graph, and the property coordinate data for each sample were stored and managed as a curve alongside sample information within Starrydata2.The web application allows for the arbitrary specification of units (e.g., μΩcm for electrical resistivity is automatically converted to Ωm). Moreover, it facilitates unit conversion through internal unit and multiplier conversion functions. This ensures that recorded data, which may have different units or scales, can be managed consistently, thereby facilitating comparative studies and simplifying data utilization on the same scale.Starrydata2 can also store sample information. Apart from chemical formulas, each sample is associated with the classification of QC and AC and experimental conditions.Data RecordsComposition dataset.  The composition dataset provides a list of 915, 525 and 8 instances of QC, AC, and QC or AC, respectively. These were collected from 130 journal articles, consisting of 656, 463 and 8 unique com-positions for QC, AC, and QC or AC, respectively. Each composition was linked to the literature information. The dataset is available in a CSV file named “composition_dataset.csv” on Figshare30. Table 1 lists the items present in the data table.Phase region dataset.  We used Scopus to narrow down the list of eligible articles to 218 papers authored by Benjamin Grushko obtained through the search query “TITLE-ABS-KEY (grushko)” as of May 26th 2021. Subsequently, 327 Al-based ternary phase diagrams were extracted and saved as image files. Considering the temperature ranges of the sample preparation process and the range of phase diagrams (which may not cover the entire composition values of the three elements), 49 phase diagrams with unique combinations of the three ele-ments were selected. If there were multiple phase diagrams for the same ternary system, preference was given to the one with a larger coverage area of the diagram or the one encompassing both QCs and Acs. From these phase diagram images, a total of 556 phase regions, comprising 21 QCs and 100 ACs, were extracted for a total of 16,208 reproduced compositions, including ordinary crystals and liquid phases. Table 2 presents the associated details. The phase region dataset named “phase_region_dataset.csv” is available on Figshare30.Properties dataset.  In the properties dataset, data were extracted from 490 figures in 193 papers, includ-ing temperature-dependent observations of thermal conductivity, electrical properties, and magnetic proper-ties, along with additional information on samples and bibliography. Table 3 provides the details for each item. Currently, a total of 1,449 temperature-dependent curves and 52,311 data points have been recorded. Table 4 pre-sents the breakdown of the recorded curves. Data for 96 curves are included in the private version of Starrydata. The SID, sample_id, and figure_id of the corresponding data are appended with “hmt_”. The properties dataset is available in a CSV file named “properties_dataset.csv” on Figshare30.Fig. 3  (a) Al-Mn-Cu ternary diagram sample32 (b). Image generated through processing the boundary point set of the phase region in the dataset. The black dots denote the extracted coordinate set, which is processed using a Python script for the visualization. The green, pink, and yellow regions indicate QC, AC, and other phases (ordinary periodic crystal or liquid phase), respectively.https://doi.org/10.1038/s41597-024-04043-z7Scientific Data |         (2024) 11:1211  | https://doi.org/10.1038/s41597-024-04043-zwww.nature.com/scientificdatawww.nature.com/scientificdata/Technical ValidationComposition dataset.  To reduce recording errors and ambiguous wording in papers, issues were individ-ually reviewed under the scrutiny of multiple experts when needed. To further ensure data accuracy, randomly sampled instances of the collected data underwent double-checking by the experts. If an error was found in a sampled record, the extracted information was reviewed and corrected as needed. A total of 330 compositions were revalidated during this exercise.Fig. 4  The left figure displays the extracted phase regions, where pink, navy, and light blue denote QC, AC, and crystalline phases, respectively. The corresponding original image is presented on the right side33–39.https://doi.org/10.1038/s41597-024-04043-z8Scientific Data |         (2024) 11:1211  | https://doi.org/10.1038/s41597-024-04043-zwww.nature.com/scientificdatawww.nature.com/scientificdata/Phase region dataset.  The data in the phase region dataset were visually inspected on the screen. The data were provided as a set of discrete coordinates on a bounding region. The enclosed area defined by the coordinate set could be filled and visualized via a Python script. All generated images were verified to maintain consistency with their original figures, as depicted in Fig. 3.As a part of validation, focusing on seven specific cases, the comparison between the extracted phase contour point sets and their original figures are shown in Fig. 4.Properties dataset.  Property data were visualized using the Bokeh31 library in Python to double-check temperature-dependent behavior through expert discussions (Fig. 5). Temperature units such as 1/T (K−1) and 1000/T (K−1), physical property units, and physical property values that are not automatically converted were interactively corrected using Python scripts. Furthermore, the capitalization and orthographical variants were standardized for consistency.Data extraction from images followed a standardized protocol in Starrydata2. We conducted a compre-hensive test to measure the reading error of semi-automatic data extraction for several data collectors using WebPlotDigitizer (version 4.018). The results showed that the detection accuracy of the temperature-dependent property values for the test images was confined to approximately 0.30% of the overall width of the graph area. Since the data collectors in this study are participants of the Starrydata project and follow the same protocol, the data extraction accuracy is assumed to be comparable.Code availabilityThe datasets of compositions, phase diagrams, and physical properties are available in Figshare30.Received: 26 April 2024; Accepted: 24 October 2024;Published: xx xx xxxxReferences  1.  Shechtman, D., Blech, I., Gratias, D. & Cahn, J. W. Metallic phase with long-range orientational order and no translational symmetry. Phys. Rev. 53, 1951–1953 (1984).  2.  Levine, D. & Steinhardt, P. J. Quasicrystals: A new class of ordered structures. Phys. Rev. Lett. 53, 2477–2480 (1984).  3.  Sainfort, P. & Dubost, B. The T2 compound: A stable quasi-crystal in the system Al-Li-Cu-(Mg)? J. Phys. Colloq. 47, 321–330 (1986).  4.  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Calphad 31, 217–232 (2007). with permission from Elsevier.AcknowledgementsThis work was supported by a MEXT KAKENHI Grant-in-Aid for Scientific Research in Innovative Areas (19H05820, 19H05818), JST CREST (JPMJCR22O3, JPMJCR19I3), and Grant-in-Aid for Scientific Research (A) (19H01132) from the Japan Society for the Promotion of Science (JSPS).Author contributionsThe data collection was carried out by E. Fujita, with support from Y. Katsura, T. Mato, A. Ishikawa and R. Tamura. K. Kitahara conceived the automated data conversion algorithm for the phase diagram data. C. Liu assisted in developing the phase diagram drawing program. E. Fujita, R. Yoshida, and K. Kimura wrote the manuscript. The research was supervised by Y. Katsura, R. Yoshida, and K. Kimura. All listed authors have read and approved the final manuscript.Competing interestsThe authors declare no competing interests.Additional informationCorrespondence and requests for materials should be addressed to E.F., R.Y. or Y.K.Reprints and permissions information is available at www.nature.com/reprints.Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-ative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not per-mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2024https://doi.org/10.1038/s41597-024-04043-zhttps://automeris.io/https://doi.org/10.6084/m9.figshare.25650705.v3https://doi.org/10.6084/m9.figshare.25650705.v3http://www.bokeh.pydata.orghttp://www.nature.com/reprintshttp://creativecommons.org/licenses/by/4.0/ Comprehensive experimental datasets of quasicrystals and their approximants Background & Summary Methods Composition dataset.  Phase region dataset.  Properties dataset.  Data Records Composition dataset.  Phase region dataset.  Properties dataset.  Technical Validation Composition dataset.  Phase region dataset.  Properties dataset.  Acknowledgements Fig. 1 Workflow for extracting data from journal articles. Fig. 2 (a) Original image of an Al-Cu-Mn alloy system on the WebPlotDizitizer screen. Fig. 3 (a) Al-Mn-Cu ternary diagram sample32 (b). Fig. 4 The left figure displays the extracted phase regions, where pink, navy, and light blue denote QC, AC, and crystalline phases, respectively. Fig. 5 Temperature-dependent behavior of electrical resistivity is exhaustively visualized. Table 1 List of items in the composition dataset. Table 2 Details of the items in the phase region dataset. Table 3 List of items in the properties dataset. Table 4 Details of each property in the properties dataset.