Low-temperature alloys are important for a wide spectrum of modern technologies ranging from liquid hydrogen, superconductivity to the quantum technology. These applications push the limit of material performance into extreme coldness, often demanding a combination of strength and toughness to address various challenges.Steel is one of the most widely used materials in cryogenic applications. With the deployment in aerospace liquid hydrogen storage and transportation, aluminum and titanium alloys are also gaining increasing attention. Emerging medium-entropy alloys (MEAs) and high-entropy alloys (HEAs) demonstrate excellent low-temperature mechanical performance with a much-expanded space of material design. A database of low-temperature metallic alloys is reported here by collecting the literature data published from 1991 to 2023, which is hosted in an open repository. The workflow of data collection includes automated extraction based on machine learning and natural language processing, supplemented by manual inspection and correction, to enhance data extraction efficiency and database quality. The product datasets cover key performance parameters including yield strength, tensile strength, elongation at fracture, Charpy impact energy, as well as detailed information on materials such as their types, chemical compositions, processing and testing conditions. Data statistics are analyzed to elucidate the research and development patterns and clarify the challenges in both scientific exploration and engineering deployment.

Dataset Overview

This dataset provides the Young's Modulus values (in GPa) for 50 metals, covering a wide range of categories such as alkali metals, alkaline earth metals, transition metals, and rare earth elements. Young's Modulus is a fundamental mechanical property that measures a material's stiffness under tensile or compressive stress. It is critical for applications in materials science, physics, and engineering.

The dataset includes: - 50 metals with their chemical symbols and Young's Modulus values. - A wide range of stiffness values, from soft metals like cesium (1.7 GPa) to very stiff metals like ruthenium (447 GPa). - Clean and complete data with no missing or duplicate entries.

Data Science Applications

This dataset can be utilized in various data science and engineering applications, such as: 1. Material Property Prediction: Train machine learning models to predict mechanical properties based on elemental features. 2. Cluster Analysis: Group metals based on their mechanical properties or periodic trends. 3. Correlation Studies: Explore relationships between Young's Modulus and other physical/chemical properties (e.g., density, atomic radius). 4. Engineering Simulations: Use the data for simulations in structural analysis or material selection for design purposes. 5. Visualization and Education: Create visualizations to teach periodic trends and material property variations.

Column Descriptors

Ethically Mined Data

The dataset was ethically sourced from publicly available scientific references and academic resources. The data was verified for accuracy using multiple authoritative sources, ensuring reliability for research and educational purposes. No proprietary or sensitive information was included.

Key checks performed: - No missing values: The dataset contains complete entries for all 50 metals. - No duplicates: Each metal appears only once in the dataset. - Statistical analysis: The mean Young's Modulus is ~98.93 GPa, with a wide range from 1.7 GPa to 447 GPa.

Acknowledgments

We would like to thank the following sources for their contributions to this dataset: - Academic references such as WebElements, Byju's Chemistry Resources, and Wikipedia for cross-verifying the data. - Scientific databases like MatWeb and ASM International for providing accurate material property data. - Special thanks to DALL·E 3 for generating the accompanying dataset image.

This Database has been created from the NLP code delivered during the project, which has extracted compositions and properties from different journal papers. The headings in the database are the doi of the paper, the line the data is extracted from, the composition of the material, the units of the property and then the value of the property

Zones in this data set represent spatially contiguous areas that influence ground-water flow in the Death Valley regional ground-water flow system (DVRFS), an approximately 45,000 square-kilometer region of southern Nevada and California. The zones typically represent areas of differing material properties; however, they may also represent spatially similar areas, such as areas of similar infiltration rates. Material properties, such as horizontal hydraulic conductivity, vertical anisotropy, or storage characteristics may vary within a single hydrogeologic unit and be represented by numerous zones; or they may be the same for multiple hydrogeologic units and be represented by a single zone. The DVRFS zones were typically derived from geological or hydrological analyses by Sweetkind and others (2004) and Faunt and others (2004) and were used as input in the DVRFS transient ground-water flow model, a regional-scale model developed by the U.S. Geological Survey (USGS) for the U.S. Department of Energy (DOE) to support investigations at the Nevada Test Site (NTS) and at Yucca Mountain, Nevada (see "Larger Work Citation", Chapter A, page 8, for details).

Property Data Summaries are collections of property values derived from surveys of published data. These collections typically focus on either one material or one particular property. Studies of specific materials typically include thermal, mechanical, structural, and chemical properties, while studies of particular properties survey one property across many materials. The property values may be typical, evaluated, or validated. Values described as typical are derived from values for nominally similar materials.

MADB is a bibliographic database on physico-chemical properties of selected Minor Actinide compounds and alloys. The materials and properties are selected based on their importance in the advanced nuclear fuel cycle options. This list is updated up to 2008.

A complete copy of the Materials Project database as of 10/18/2018. Mp_all files contain structure data for each material while mp_nostruct does not.Available as Monty Encoder encoded JSON and as CSV. Recommended access method for these particular files is with the matminer Python package using the datasets module. Access to the current Materials Project is recommended through their API (good), pymatgen (better), or matminer (best).Note on citations: If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page.Dataset discussed in:A. Jain*, S.P. Ong*, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson (*=equal contributions) The Materials Project: A materials genome approach to accelerating materials innovation APL Materials, 2013, 1(1), 011002.Dataset sourced from:https://materialsproject.org/Citations for specific material properties available here:https://materialsproject.org/citing

The NIST WWW High Temperature Superconductors database (WebHTS) provides evaluated thermal, mechanical, and superconducting property data for oxide superconductors. The range of materials covers the major series of compounds derived from the Y-Ba-Cu-O, Bi-Sr-Ca-Cu-O, Tl-Sr-Ca-Cu-O, and La-Cu-O chemical families, along with numerous other variants of the cuprate and bismuthate materials that are known to have superconducting phases. The materials are described by specification and characterization information that includes processing details and chemical compositions. Physical characteristics such as density and crystal structure are given in numeric tables. All measured values are evaluated and supported by descriptions of the measurement methods, procedures, and conditions. In all cases, the sources of the data are fully documented in a comprehensive bibliography.

The NIST Clathrate Hydrate Physical Property Database contains data from two resources: the Thermophysical Properties of Gas Hydrates Database maintained by NIST and the Web-based Mallik Database from the Geological Survey of Canada . The Thermophysical Properties of Gas Hydrates Database contains thermophysical property data for gas hydrates taken from the archival literature. The data has all been evaluated for consistency by an expert in the field and is stored with complete reference information. This archive is intended to store ALL of this type of data available in published form. Maintenance of this database is an ongoing process and the archive grows continuously as new data becomes available. The Mallik Database contains observations from wells at the Mallik gas hydrate field in Canada.

U.S. National Property Data includes:

• Current Ownership (including Mailing Address)
• Physical Property Address
• Last Sale and Last Mortgage Data
• Assessment Values, Fiscal Year, and Tax Amounts
• Property Usage
• Over 35 Property and Building Characteristics
• Number of Bedrooms and Bathrooms
• Square Footage
• Construction Material
• Lot Size
• Type of Heating and Cooling System
• Roof Type
• Historical Tax Amount

Petrophysical properties are key to populate numerical models of subsurface process simulations and for the interpretation of many geophysical exploration methods. They are characteristic for specific rock types and may vary considerably as a response to subsurface conditions (e.g. temperature and pressure). Hence, the quality of process simulations and geophysical data interpretation critically depend on the knowledge of in-situ physical properties that have been measured for a specific rock unit. Inquiries for rock property values for a specific site might become a very time-consuming challenge given that such data are (1) spread across diverse publications and compilations, (2) heterogeneous in quality and (3) continuously being acquired in different laboratories worldwide. One important quality factor for the usability of measured petrophysical properties is the availability of corresponding metadata such as the sample location, petrography, stratigraphy, or the measuring method, conditions and authorship. The open-access database presented here aims at providing easily accessible, peer-reviewed information on physical rock properties in one single compilation. As it has been developed within the scope of the EC funded project IMAGE (Integrated Methods for Advanced Geothermal Exploration, EU grant agreement No. 608553), the database mainly contains information relevant for geothermal exploration and reservoir characterization, namely hydraulic, thermophysical and mechanical properties and, in addition, electrical resistivity and magnetic susceptibility. The uniqueness of this database emerges from its coverage and metadata structure. Each measured value is complemented by the corresponding sample location, petrographic description, chronostratigraphic age and original citation. The original stratigraphic and petrographic descriptions are transferred to standardized catalogues following a hierarchical structure ensuring intercomparability for statistical analysis. In addition, information on the experimental set-up (methods) and the measurement conditions are given for quality control. Thus, rock properties can directly be related to in-situ conditions to derive specific parameters relevant for modelling the subsurface or interpreting geophysical data.

The graph representation of complex materials plays a crucial role in the field of inorganic and organic materials investigations for developing data-centric materials science, such as those using graph neural networks (GNNs). However, the currently prevalent GNN models are primarily employed for investigating periodic crystals and organic small molecule data, yet they still encounter challenges in terms of interpretability and computational efficiency when applied to polymer monomers and organic macromolecules data. There is still a lack of graph representation of organic polymers and macromolecules specifically tailored for GNN models to explore the structural characteristics. The Polymer-unit Graph, a novel coarse-grained graph representation method introduced in study, is dedicated to expressing and analyzing polymers and macromolecules. By incorporating the Polymer-unit Graph into the GNN models and analyzing the organic semiconductor (OSC) materials database, it becomes possible to uncover intricate structure–property relationships involving branched-chain engineering, fluoridation substitution, and donor–acceptor combination effects on the elementary structure of OSC polymers. Furthermore, the Polymer-unit Graph enables visualizing the relationship between target properties and polymer units while reducing training time by an impressive 98% and minimizing molecular graph representation models. In conclusion, the Polymer-unit Graph successfully integrates the concept of Polymer-unit into the field of GNNs, enabling more accurate analysis and understanding of organic polymers and macromolecules.

NIST has accumulated a valuable and comprehensive collection of thermal conductivity data from measurements performed with a 200-mm square guarded-hot-plate apparatus. The guarded-hot-plate test method is arguably the most accurate and popular method for determination of thermal transmission properties of flat, homogeneous specimens under steady state conditions. Several organizations, including ASTM and ISO, have standardized the method. Version 1.0 of the database includes data for over 2000 measurements, covering several categories of materials including concrete, fiberboard, plastics, thermal insulation, and rubber. The data cover a temperature range corresponding to most building applications; however, the majority of the measurements were conducted at 24° C (75° F). Web version 1.0

Extract of Cambridge Assessing Department on-line property database file for the most recently released fiscal year. Contains residential, condo, commercial and exempt data. Please refer to Cambridge's property database website for official assessment data: https://www.cambridgema.gov/propertydatabaseThis Feature Service will be updated annually with the latest fiscal year's data.The following fields are contained in the property database table.Field NameDescriptionPIDInternal Unique Parcel IDGISIDLink to ML in GIS Parcels layerBldgNumBuilding Number on ParcelAddressParcel AddressUnitUnit NumberStateClassCodeState Classification CodePropertyClassClassification Code descriptionZoningZoning (Unofficial)Map/LotAssessor's Map and Lot IDLandAreaLand area in square feetYearOfAssessmentFiscal Year of Assessment for this recordTaxDistrictDistrict for valuation groupingResidentialExemptionReceiving Residential exemption for fiscal yearBuildingValueAssessed value of building improvements on the parcelLandValueAssessed value of land on the parcelAssessedValueTotal assessed valueSalePricePrice listed for last deed transfer for the parcelBook/PageBook and Page number from the registry of deeds for last deed transactionSaleDateDate of last deed transactionPreviousAssessedValueTotal assessed value for the prior fiscal yearOwner_NameName of owner of record for the date of assessmentOwner_CoOwnerNameName of co-owner of record for the date of assessmentOwner_AddressAddress of owner of record for the date of assessmentOwner_Address2Second line of address of owner of record for the date of assessmentOwner_CityCity of owner of record for the date of assessmentOwner_StateState of owner of record for the date of assessmentOwner_ZipZip code of owner of record for the date of assessmentExterior_StyleBuilding style descriptionExterior_occupancyBuilding occupany, or use, type descriptionExterior_NumStoriesNumber of stories for the buildingExterior_WallTypeExterior wall material descriptionExterior_WallHeightAverage height of floors in a commercial or apartment buildingExterior_RoofTypeRoof structure descriptionExterior_RoofMaterialRoof material descriptionExterior_FloorLocationFloor level for condominium unitsExterior_ViewView quality rating for condominiumsInterior_LivingAreaFinished area of buildingInterior_NumUnitsNumber of units in a commercial or apartment buildingInterior_TotalRoomsTotal number of rooms in a condominium or residential buildingInterior_BedroomsTotal number of bedrooms in a condominium or residetential buildingInterior_KitchensKitchen description in condominium unitInterior_FullBathsCount of full bathrooms in a condominium unit or residential buildingInterior_HalfBathsCount of half bathrooms in a condominium unit or residential buildingInterior_FireplacesCount of fireplaces in residential buildingsInterior_FlooringDescription of primary floor cover materialInterior_LayoutLayout description for condominium unitInterior_LaundryInUnitYes or No flag for in unit laundry for condominiumSystems_HeatTypeHeat system type descriptionSystems_HeatFuelHeat fuel type descriptionSystems_CentralAirCentral air conditioning system indicatorSystems_PlumbingRating of plumbing system for commercial buildingCondition_YearBuiltActual year built of buildingCondition_InteriorConditionDescription of interior condition of residential buildingCondition_OverallConditionDescription of overall condition of buildingCondition_OverallGradeDescription of overall grade of buildingParking_OpenNumber of open parking spaces for residential building or condominium unitParking_CoveredNumber of covered parking spaces for residential building or condominium unitParking_GarageNumber of garage parking spaces for residential building or condominium unitUnfinishedBasementGrossUnfinished basement areaFinishedBasementGrossFinished basement area

Raw data (processed) presented from Fig. 2 to Fig. 5 in "Melt crystallization of PLA/Talc in fused filament fabrication" submitted to Materials & Design

The NIST WWW Structural Ceramics Database (WebSCD) provides evaluated materials property data for a wide range of advanced ceramics known variously as structural ceramics, engineering ceramics, and fine ceramics. These materials tend to have low mass densities and high strengths and tend to be resistant to corrosion. These characteristics form the basis for applications of these materials in high-temperature, energy-efficient heat exchangers, advanced engine designs, bearings, wear resistant parts, and stable electronic substrates and electronic packaging. The range of materials covers the major series of compounds derived from the ceramic oxide, carbide, nitride, boride, and oxynitride chemical families. The materials are described by specification and characterization information that includes processing details and chemical compositions. Physical characteristics such as density and crystal structure are given in numeric tables. All measured values are evaluated and supported by descriptions of the measurement methods, procedures, and conditions. In all cases, the sources of the data are fully documented in a detailed bibliography.

Brazil Construction Industry: Cost of Real Estate Development: Building Materials: 250 to 499 Persons: Total Construction of Buildings data was reported at 4,864.000 BRL th in 2017. This records a decrease from the previous number of 26,918.000 BRL th for 2016. Brazil Construction Industry: Cost of Real Estate Development: Building Materials: 250 to 499 Persons: Total Construction of Buildings data is updated yearly, averaging 123,384.000 BRL th from Dec 2007 (Median) to 2017, with 11 observations. The data reached an all-time high of 483,403.000 BRL th in 2012 and a record low of 4,864.000 BRL th in 2017. Brazil Construction Industry: Cost of Real Estate Development: Building Materials: 250 to 499 Persons: Total Construction of Buildings data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Construction and Properties Sector – Table BR.EH018: Construction Industry: CNAE 2.0: Cost of Real Estate Development: Building Materials: by Activity.

Construction Industry: Cost of Real Estate Development: Building Materials: 5 Persons or More: South data was reported at 419,361.000 BRL th in 2017. This records a decrease from the previous number of 541,243.000 BRL th for 2016. Construction Industry: Cost of Real Estate Development: Building Materials: 5 Persons or More: South data is updated yearly, averaging 324,986.000 BRL th from Dec 2007 (Median) to 2017, with 11 observations. The data reached an all-time high of 657,465.000 BRL th in 2015 and a record low of 69,041.000 BRL th in 2009. Construction Industry: Cost of Real Estate Development: Building Materials: 5 Persons or More: South data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Construction and Properties Sector – Table BR.EH017: Construction Industry: CNAE 2.0: Cost of Real Estate Development: Building Materials: by Region and State.

This dataset provides rheometric data of silicone (Polydimethylsiloxane, PDMS SGM36)-corundum sand mixtures used for analogue modelling in Zwaan et al. (2016, 2017), Zwaan and Schreurs (2017) and in the Tectonic Modelling Lab of the Institute of Geological Sciences at the University of Bern (CH). The PDMS is produced by Dow Corning and its characteristics have been described by e.g. Rudolf et al. (2016a,b). The corundum sand (Normalkorund Braun 95.5% F120 by Carlo Bernasconi AG: https://www.carloag.ch/shop/catalog/product/view/id/643), has a grainsize of 0.088-0.125 mm and a specific density of 3.96 g cm^-3. Further rheological characteristics are described by Panien et al. (2006). The density of the tested materials ranges between 1 (pure PDMS) and 1.6 g cm^-3 (increasing corundum sand content in mixture). The material samples have been analysed in the Helmholtz Laboratory for Tectonic Modelling (HelTec) at GFZ German Research Centre for Geosciences in Potsdam using an Anton Paar Physica MCR 301 rheometer in a plate-plate configuration at room temperature. Rotational (controlled shear rate) tests with shear rates varying from 10^-4 to 10^-1 s^-1 were performed. According to our rheometric analysis, the material is quasi Newtonian at strain rates below 10^-3*s^-1 and weakly shear rate thinning above. Viscosity and stress exponent increase systematically with density from ~4*10^4 to ~1*10^5 Pa*s and from 1.06 to 1.10, respectively. A first application of the materials tested can be found in Zwaan et al. (2016). Detailed information about the data, methodology and a list of files and formats is given in the "data description" and "list of files" that are included in the zip folder and also available via the DOI landing page.

Extruded finite element models: a case study using early mammal jaws/Sensitivity analyses