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2,118 results for “Metal”
Research data for "Intermediates of Forming Transition Metal Dichalcogenides Heterostructures Revealed by Machine Learning Simulations"
<p>This dataset supports the paper "Intermediates of Forming Transition Metal Dichalcogenides Heterostructures Revealed by Machine Learning Simulations". </p> <p><strong>Included Files:</strong></p> <ul> <li><strong>ocp_active.zip</strong>: Modified version of ocp (https://github.com/Open-Catalyst-Project/ocp) tailored for active learning applications.</li> <li><strong>deployed.pth</strong>: Pre-trained model used in the experiments.</li> <li><strong>chemiscopy_run.py</strong>: Script integrating the chemiscopy and nequip modules, designed for dataset visualization.</li> <li><strong>new_energy.py</strong>: Modified version of the nequip module, featuring a repulsive potential function.</li> <li><strong>test_datasets.extxyz</strong> & <strong>train_datasets.extxyz</strong>: The test and training datasets in extxyz format.</li> </ul> <p>How to use the modified version of the nequip module:</p> <p>To train this version of the potential function, it is recommended to use nequip<=0.5.6 (on Linux). The NequIP training files need to be updated as follows:</p> <pre><code>model_builders: - new_energy.EnergyModel - StressForceOutput min_bond_len: 1.8</code></pre> <p>Then run:</p> <p><code>export PYTHONPATH=${PYTHONPATH}:$PWD</code><br><code>nequip-train config.yml # Train the potential function</code><br><code>nequip-deploy build --train-dir nequipresultsdir build.pth # Deploy the trained model</code></p>
RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)
<p>This is an RDFied version of the dataset published in Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. <em>Nanomaterials</em> <strong>2020</strong>, <em>10</em>, 2017.</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.3390/nano10102017">https://doi.org/10.3390/nano10102017</a></p> <p>The Original publication authors: Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A.</p>
Metal Additive Manufacturing Open Repository
<p><strong>Metal Additive Manufacturing Open Repository</strong></p> <p>This dataset gathers data from different parts of Additive manufacturing processes (Laser metal deposition - LMD, and Wire-arc additive manufacturing - WAAM). The dataset covers not only the process data, but also the design, NDT (Non-Destructive Testing) and dimensional inspection.</p> <p><br> <strong>Motivation</strong></p> <p>The industrialisation of Additive Manufacturing (AM) requires a holistic data management and integrated automation. The presented dataset is part of an end-to-end Digital Manufacturing solution, enabling a cybersecured bidirectional dataflow for a seamless integration across the entire AM chain.</p> <p>The goal is to develop a new manufacturing methodology capable of ensuring the manufacturability, reliability and quality of a target metal component from initial product design via Direct Energy Deposition (DED) technologies, implementing a zero-defect manufacturing approach ensuring robustness, stability and repeatibility of the process.</p> <p>To that end, we present the Metal Additive Manufacturing Open Dataset, the first holistic dataset for AM manufacturing, covering all engineering stages from desing to validation. We hope that this dataset will be the first step for the development of new data pipelines aimed to optimize and improve the AM processes and to speed up their digital transformation.</p> <p><br> <strong>Authors</strong></p> <ul> <li>Carlos Gonzalez-Val: Main contact (carlos.gonzalez@aimen.es)</li> <li>Baltasar Lodeiro</li> <li>Marcos Diez</li> </ul> <p> </p> <p><strong>Entities</strong></p> <p>This dataset was collected under the INTEGRADDE project. Attributions:</p> <ul> <li>AIMEN: Process data collection and manufacturing of T-Coupons, CC-Coupons-AIMEN and Jet Engine.</li> <li>MX3D: Process data collection and manufacturing of CC-Coupons-MX3D and Plates.</li> <li>University of West: Process data collection and manufacturing of CC-Coupons-WEST.</li> <li>IREPA: Process data collection and manufacturing of CC-Coupons-IREPA.</li> <li>CEA: Tomography analysis.</li> <li>DATAPIXEL: Dimensional inspection.</li> </ul> <p><br> <strong>Structure</strong></p> <p>The dataset follows this structure:</p> <ul> <li>Dataset <ul> <li>[SAMPLE 1 NAME] <ul> <li>README: metadata and information about the sample. Format: txt.</li> <li>Photo: a photo of the manufactured sample. Format: jpg.</li> <li>Design: a 3D design file of the piece before manufacturing (original design). Format: stl.</li> <li>Trajectories: the trajectories followed for the manufacturing. Format: gcode.</li> <li>Process data: data recorded from the process. Format hdf5.</li> <li>Tomography: data from a 3D tomographic reconstruction. Format: raw.</li> <li>Dimensional inspection: A comparison</li> </ul> </li> <li>[SAMPLE 2 NAME] <ul> <li>...</li> </ul> </li> </ul> </li> </ul> <p>Further information and metadata is contained in each stage's subdirectory.</p> <p>Note that not all the samples contain all the stages.</p> <p><br> <strong>Software</strong></p> <p>To open the different files that conform the dataset, we recommend the following Open softwares:</p> <ul> <li> hdf5 -> HDF5 Viewer: https://www.hdfgroup.org/downloads/hdfview/</li> <li> stl/amf -> Slic3r: https://slic3r.org / OpenJScad: https://openjscad.org/</li> <li> stp -> ShareCad: https://beta.sharecad.org/</li> <li> gcode -> Text editor / Slic3r: https://slic3r.org/</li> <li> raw -> ImageJ: https://imagej.net/</li> </ul> <p>More information on how to open the files of the dataset can be found in the README.</p>
Electrochemical and Spectroscopic Data supported by Computational Models for Exploring the Metal- and Ligand-Based Oxidation of Mackinawite Nanoparticles
<p>Supporting information to our study, where under anaerobic conditions, ferrous iron reacts with sulfide producing FeS precipitate, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases such as gregite or pyrite. The dataset provide information about the chemical speciation of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Nanoparticulate FeS was found to get oxidized to a Fe<sup>3+</sup> containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7) and in a concomitant oxidation step, polysulfides are proposed to give a material described as Fe<sup>2+</sup><sub>(1−3x)</sub>Fe<sup>3+</sup><sub>(2x)</sub>S<sup>2-</sup><sub>(1-y)</sub>(S<sub>n</sub><sup>2-</sup>)<sub>y</sub>. The thermodynamic differences between ligand- and metal-based oxidation processes from density functional theory can be used to describe one- and two-electron electronic and structural transformations. These findings together point to the existence of a previously unknown, metastable FeS phase located between FeS and greigite (Fe<sup>2+</sup>Fe<sup>3+</sup><sub>2</sub>S<sup>2-</sup><sub>4</sub>) along a metal oxidation path, and Fe<sup>2+</sup>S<sup>2-</sup> and pyrite (Fe<sup>2+</sup>S<sub>2</sub><sup>2-</sup>) along a ligand oxidation path, respectively.</p>
The metal content of the hot atmospheres of galaxy groups - supporting data
<p>Supporting data used to generate the figures included in the review chapter "The metal content of the hot atmospheres of galaxy groups", to appear in the MDPI journal "Universe". These values were collected and compiled from existing literature; each text file lists the relevant references to the original articles where various sets of results were initially published. </p>
Dataset from the paper: "RR Lyrae From Binary Evolution: Abundant, Young and Metal-Rich"
<p>The two files contain the Tables presented in Appendix B of Bobrick & Iorio et al. (2024, MNRAS, 527, 12196–12218)<br>(https://ui.adsabs.harvard.edu/abs/2024MNRAS.52712196B/abstract) in CSV format.</p> <p># V3 updates</p> <p>- New columns added: GRRL, Gcomp, RRRL, Rcomp, and Teffcomp. These columns are not included in the published paper tables.<br>- The columns G and BP_RP were previously described as the Gaia G magnitude of the RRL, but they actually represent the G magnitude and BP–RP color of the entire system.<br>- The previous description of the columns was missing the PorbRRL entry.<br>- A new file, TableB2_SingleMadeBinaryRRL_V3.csv, has been added to replace the previous version, which had mismatched columns and some empty fields.<br>- The README now reports the format for both tables.</p> <p># Tables</p> <p>There are two tables included:</p> <p>## TableB1_BinaryMadeRRL_V3.csv</p> <p>This table contains the systems reported in Table B1 of the paper, with additional columns (marked with a +).</p> <p>### Columns:</p> <p>- **Age**: Age of the system since the zero-age main sequence [Myr]<br>- **GBin**: Galactic bin from the Galactic model (Table 1 in the paper)<br> - TD1: Thin Disc – Bin 1 <br> - TD2: Thin Disc – Bin 2 <br> - TD3: Thin Disc – Bin 3 <br> - TD4: Thin Disc – Bin 4 <br> - TD5: Thin Disc – Bin 5 <br> - TD6: Thin Disc – Bin 6 <br> - TD7: Thin Disc – Bin 7 <br> - B: Bulge <br> - TKD: Thick Disc <br> - H: Halo <br>- **Mproj**: Progenitor ZAMS mass of the RRL [Msun]<br>- **Mcomp**: Progenitor ZAMS mass of the RRL companion [Msun]<br>- **Porb_init**: Initial orbital period [days]<br>- **feh**: [Fe/H] metallicity<br>- **MRRL**: RRL mass [Msun]<br>- **McompRRL**: Mass of the RRL companion [Msun]<br>- **PorbRRL**: Current orbital period [days]<br>- **McRRL**: Core mass of the RRL [Msun]<br>- **LRRL**: Bolometric luminosity of the RRL [Lsun]<br>- **Teff**: Effective temperature of the RRL [K]<br>- **G**: Gaia G-band magnitude of the system as a whole [mag]<br>- **BP_RP**: Gaia BP–RP color of the system as a whole [mag]<br>- **GRRL**: Gaia G-band magnitude of the RRL [mag] +<br>- **Gcomp**: Gaia G-band magnitude of the companion [mag] +<br>- **RRRL**: Radius of the RRL [Rsun] +<br>- **Rcomp**: Radius of the companion [Rsun] +<br>- **Teffcomp**: Effective temperature of the companion [K] +</p> <p>---</p> <p>## TableB2_SingleMadeBinaryRRL_V3.csv</p> <p>This table contains the systems reported in Table B2 of the paper, with additional columns (marked with a +).</p> <p>### Columns:</p> <p>- **Age**: Age of the system since the zero-age main sequence [Myr]<br>- **GBin**: Galactic bin from the Galactic model (Table 1 in the paper)<br> - TD1: Thin Disc – Bin 1 <br> - TD2: Thin Disc – Bin 2 <br> - TD3: Thin Disc – Bin 3 <br> - TD4: Thin Disc – Bin 4 <br> - TD5: Thin Disc – Bin 5 <br> - TD6: Thin Disc – Bin 6 <br> - TD7: Thin Disc – Bin 7 <br> - B: Bulge <br> - TKD: Thick Disc <br> - H: Halo <br>- **Mproj**: Progenitor ZAMS mass of the RRL [Msun]<br>- **MRRL**: RRL mass [Msun]<br>- **McompRRL**: Mass of the RRL companion [Msun]<br>- **PorbRRL**: Current orbital period [days]<br>- **feh**: [Fe/H] metallicity<br>- **McRRL**: Core mass of the RRL [Msun]<br>- **LRRL**: Bolometric luminosity of the RRL [Lsun]<br>- **Teff**: Effective temperature of the RRL [K]</p>
Computational data for "On the role of metal cations in CO2 electrocatalytic reduction"
<p>This dataset is used for the analysis published in D. Le and T.S. Rahman, "On the role of metal cations in CO<sub>2</sub> electroreduction reduction," Nature Catalysis (2022). DOI:10.1038/s41929-022-00876-2</p>
Laboratory toxicity incubation experiments on phytoplankton using trace metals (Cu, Cd, Zn)
<p>This data compilation contains previously published toxicity threshold concentrations of copper, cadmium and zinc for different phytoplankton, as determined by incubation experiments. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper, cadmium and zinc as 63.546, 112.411 and 65.380 g/mol, respectively, and salinity as 1.025 kg/L. The growth medium is included in the dataset, as well as the environment where the phytoplankton in question may commonly occur (open or coastal ocean). </p>
Potentially toxic trace metal (Cu, Cd) threshold concentrations for phytoplankton at given open and coastal locations
<p>This data compilation contains previously published threshold concentrations of copper and cadmium of phytoplankton in open and coastal oceans. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper and cadmium as 63.546 and 112.411, respectively, and salinity as 1.025 kg/L. The temperature and salinity provided by the authors were also included, in case there is a desire for future users to utilise different conversion methods to recalculate original data. Only data with information on whether the authors measured the trace metal concentrations in open or coastal marine environments were included, along with the name of the phytoplankton. The oceans were divided into geographical sections, namely the Atlantic Ocean, Indian Ocean, Pacific Ocean and Southern Ocean, and subsequently further subdivided according to the information authors have given in their publications. In this context, several chemically diverse seas were included in geographical regions in order to limit the number of broad ocean regimes. Chemically diverse sub-regimens were, however, considered within each geographical grouping.</p>
Concentrations of trace metals (Cu, Cd, Zn) in the ocean at given open and coastal locations
<p>This data compilation contains previously published concentrations of copper, cadmium and zinc in open and coastal oceans of the world. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper, cadmium and zinc as 63.546, 112.411 and 65.380 g/mol, respectively, and salinity as 1.025 kg/L. The temperature and salinity provided by the authors were also included, in case there is a desire for future users to utilize different conversion methods to recalculate original data. Only data with information on whether the authors measured the trace metal concentrations in open or coastal marine environments were included. The oceans were divided into different geographical regions, namely the Atlantic Ocean, Pacific Ocean, Indian Ocean and Southern Ocean, and subsequently subdivided according to the information authors have given in their publications. In this context, several chemically diverse seas were included in geographical regions in order to limit the number of broad ocean regimes. Chemically diverse sub-regimens were, however, considered within each geographical grouping.</p>
Dataset for "Universality of grain boundary phases in fcc metals: Case study on high-angle [111] symmetric tilt grain boundaries"
<p>This repository contains the input files, scripts, and raw data of the simulations for the paper "Universality of grain boundary phases in fcc metals: Case study on high-angle [111] symmetric tilt grain boundaries".</p> <p>This data comprises structures of grain boundaries in high-angle [111] symmetric tilt grain boundaries in fcc metals as modeled by various (M)EAM potentials. Investigations include ground state thermodynamic excess properties, excess free energy calculations under stress or strain and with varying temperature, and related molecular dynamics simulations.</p> <p>See README.md for details.</p>
Open database on global coal and metal mine production
<p>See also the associated Data Descriptor published in Nature Scientific Data: <a href="https://www.nature.com/articles/s41597-023-01965-y">www.nature.com/articles/s41597-023-01965-y</a></p> <p>This data set covers global extraction of coal and metal ores on an individual mine level. It covers<br> 1171 individual mines in 80 different countries, reporting mine-level production for 80 different materials in the period 2000-2021. Furthermore, also data on mining coordinates, ownership, mineral reserves, mining waste, transportation of mining products, as well as mineral processing capacities (smelters and mineral refineries) and production is included. The data was gathered manually from more than 1900 openly available sources, such as annual or sustainability reports of mining companies. All datapoints are linked to their respective source documents. After manual screening and entry of the data, automatic cleaning, harmonization and data checking was conducted. Geoinformation was obtained either from coordinates available in company reports, or by retrieving the coordinates via Google Maps API and subsequent manual checking. For mines where no coordinates could be found, other geospatial attributes such as province, region, district or municipality were recorded, and linked to the GADM data set, available at <a href="https://www.gadm.org">www.gadm.org</a>.</p> <p>The data set, found in the "data" sub-folder, consists of 12 tables. The table “facilities” contains descriptive and spatial information of mines and processing facilities, and is available as a GeoPackage (GPKG) file. All other tables are available in comma-separated values (CSV) format. If you are working in Excel or have problems handling the GeoPackage file, it can be converted to Excel with an online tool, such as <a href="https://mygeodata.cloud/converter/gpkg-to-xlsx">https://mygeodata.cloud/converter/gpkg-to-xlsx</a>.</p> <p>A schematic depiction of the database is provided in the file database_model.pdf. A description of all variables of all tables is provided in the Excel file variables_descriptions.xlsx, and all materials for which production is reported in the database are listed in the file materials_covered.xlsx.</p> <p>For convenience, global and national coverage shares for every material and country with recorded production in the database is provided in the file coverage_table.pdf. These coverage shares were calculated by comparing the production values of this database to official production statistics reported in the UNEP IRP Global Material Flows Database, to be found under <a href="https://www.resourcepanel.org/global-material-flows-database">https://www.resourcepanel.org/global-material-flows-database</a>. For significant raw material producing countries, these coverage shares are also visualised in the file coverage_national_area_charts.pdf.</p>
Supplementary material for "Increased sensitivity of marine invertebrates to metal toxicity in the past two decades linked to Climate Change and Ocean Acidification: revelations from a natural population of sea urchins in the Mediterranean Sea." by "Davide Sartori, Guido Scatena, Cristina Vrinceanu, Andrea Gaion".
<p>Satellite observations of environmental factors and effect concentration 50 for copper to sea urchin, from 2003 to 2022.</p>
Metal-organic frameworks as regeneration optimized sorbents for atmospheric water harvesting
<p>Dataset for 'Metal-organic frameworks as regeneration optimized sorbents for atmospheric water harvesting' article published at <em>Cell Reports Physical Science, </em><a href="https://doi.org/10.1016/j.xcrp.2023.101252">https://doi.org/10.1016/j.xcrp.2023.101252</a>.</p> <p>Code used for data analysis, visualization and kinetics modelling can be found at <a href="https://github.com/AndreyBezrukov/Water_Sorption_Kinetics">AndreyBezrukov/Water_Sorption_Kinetics (github.com)</a></p>
Supplementary Information and EBSD data for 'Intermetallic phase layers in cold metal transfer aluminium-steel welds with an Al-Si-Mn filler alloy'
<p>Supplementary information and electron backscatter diffraction (EBSD) data for the article entitled 'Intermetallic phase layers in cold metal transfer aluminium-steel joints with an Al-Si-Mn filler alloy'. There are three EBSD datasets, I-III, named "I_EBSD.dat" - "III_EBSD.dat", each with corresponding calibration and background patterns, as well as secondary electron scanning electron microscopy images showing the scanned area and text files containing the acquisition parameters. The data analysis workflow has been published on GitHub, see References.</p>
Supplemental data for "Computational screening of chemically active metal center in coordinated dipyridyl tetrazine network"
<p>Atomic coordinates of structures used in N. Ud Din, D. Le, T. S. Rahman "Computational screening of chemically active metal center in coordinated dipyridyl tetrazine network", J. Phys.: Condens. Matter .(2023). DOI: 10.1088/1361-648X/acb8f3</p>
Transition metal-free approach for late-stage benzylic C(sp3)–H etherifications and esterifications
<p><strong>Description of the dataset: </strong></p> <p><strong>Origin of the data: </strong>Experimental spectroscopic measurements<br> <strong>Data Type: </strong>experimental measurements, open access supporting information<br> </p> <p>The data are in CSV, XLSX and FBSW. Supporting information are supplied in PDF format.</p> <p>Data <strong>generated </strong>by instruments: </p> <p>Varian Cary 5000 UV-Vis-NIR spectrophotometer for UV-Vis measurements,<br> Varian Cary Eclipse fluorescence spectrophotomer for fluorescence quenching measurements.</p> <p><strong>Analytical and procedural information: </strong>Stern-Volmer fluorescence quenching experiments and UV-Vis measurements.</p> <p><strong>Definition of variables: </strong>Wavelength, Absorbance, Concentration<br> <strong>Units of measurement: </strong>nanometers (nm), moles-per-litre (mol/l)</p> <p><strong>Abbreviations: </strong><br> File names and data headers use the following abbreviations:</p> <ul> <li><strong>SVQuench </strong>refers to Stern-Volmer quenching experiments</li> <li><strong>MesAcrMe xx </strong>refers to data related to the catalyst 9-mesityl-10-methylacridinium. <strong>Xx </strong>is the amount of catalyst in mol/l (10-4 should be intended as 0.1 mmol/l and so on).</li> <li><strong>EtAn xx </strong>refer to measurements related to 8-ethylanisol. <strong>Xx </strong>is the amount of quencher in mol/l as above.</li> <li><strong>BenzAc xx </strong>refer to measurements related to benzoic acid. <strong>Xx </strong>is the amount of quencher in mol/l as above.</li> <li><strong>Pyrz xx </strong>refer to measurements related to pyrazole. <strong>Xx </strong>is the amount of quencher in mol/l as above.</li> </ul>
Photoinduced Electron Transfer in Multicomponent Truxene- Quinoxaline Metal−Organic Frameworks
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>ARACAT_WP4_20200825_ULEI_03_60min_MUF77_OME_100K </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>MUF7_OME – </strong>NC-MUF-7_dbc-dpq-OMe MOF, <strong>MUF7_OME – </strong>MUF-7_dbc-dpq-OMe MOF, <strong>MUF7_dpq – </strong>MUF-7_dbc-dpq MOF<strong> MUF77_paq – </strong>MUF-7_dbc-paq MOF</li> <li>_100K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Magnetic coupling of divalent metal centers in postsynthetic metal exchanged bimetallic DUT-49 MOFs by EPR spectroscopy
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20201111_ULEI_21_DUT49Mn@7K </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20201111_ULEI_00_DUT49Mn@simulation </strong>folder includes computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>DUT49Cu – </strong>DUT-49(Cu) MOF, <strong>DUT49Mn – </strong>DUT-49(Mn) MOF, <strong>DUT49CuZn – </strong>DUT-49(CuZn) MOF, <strong>DUT49MnCu – </strong>DUT-49(MnCu) MOF.</li> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> </ul> </li> </ul> <p>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</p>
Dataset for visualizing the atomic-scale origin of metallic behavior in Kondo insulators
<p>This dataset contains the raw data files and analysis steps used to produce the figures in the manuscript "Visualizing the atomic-scale origin of metallic behavior in Kondo insulators" Science 379, 1214–1218 (2023)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.