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12,393 results for “materiality”
Supplementary Material for Nine tips for ecologists using machine learning
<p>R code and data to reproduce the analysis in "Nine tips for ecologists using machine learning" (https://arxiv.org/abs/2305.10472)</p>
Supplementary materials to: Nano-Strainer: a workflow for identification of single-copy nuclear loci for plant systematic studies, using target capture kits and Oxford Nanopore long reads
<p>In the paper associated with this dataset, a workflow is presented which enables the identification of single-/low-copy nuclear molecular markers for a plant group of interest, by mining data from a small representative target capture experiment done using a commercial probe kit and Oxford Nanopore long-read sequencing. The proposed pipeline first assesses sequence variability contained in the data from targeted loci and assigns reads to their respective genes, via a combined BLAST/clustering procedure. Cluster consensus sequences are then examined based on four pre-defined criteria presumably indicative for absence of paralogy. This is done by calculating four specialized indices; loci are ranked according to their performance in these indices, and top-scoring loci are considered putatively single- or low-copy. The approach can be applied to any probe set. As it relies on long reads, the contribution also provides template workflows for processing Nanopore-based target capture data. Identified loci can be used for NGS amplicon sequencing. For detection of possibly remaining paralogy in these data, which might occur in groups with rampant paralogy, the long-read assembly tool CANU is employed. The presented workflow can be useful for researchers dealing with reticulate or polyploidization phylogenetic histories in plants.</p> <p>The present dataset contains several documents supplementing the original paper. Its most important elements are a detailed description (alongside two graphical workflow figures) of all methods employed in the study, suitable for reproducing the steps of the workflow and also the wet-lab work. The workflow employs a collection of BASH, Python and R scripts which is available here, together with a detailed account on command line use in Linux. Also, reference sequences for the identified markers can be found as well as sequence alignments derived from the amplicon sequencing.</p>
Dataset and Model for "Understanding the limits to short-range order suppression in many-component disordered rock salt lithium-ion cathode materials"
<p>Data and Model For "Understanding the limits to Short-range order Suppression in Many-Component Disordered Rock Salt Lithium-ion Cathode Materials"</p> <p>Paper DOI: 10.1039/D3TA02088F</p> <p>Raw data (VASP calculations) and production model for the associated paper.</p> <p>- `cluster_expansions` contains the final model used for the analysis in the<br> paper. The model can be loaded using the `icet` cluster expansion package<br> using </p> <p> ```<br> from icet import ClusterExpansion # requires the icet python package</p> <p> ce = ClusterExpansion.read("cluster_expansion.ce")<br> ```<br> <br> see the icet docs for how to manipulate this object: https://icet.materialsmodeling.org/</p> <p>- `raw_data` contains the data used to fit the model. The calculation data<br> contains the raw vasp calculations in tar.gz files, and each training<br> generation has an associated `.json` files. The files contain Pymatgen<br> ComputedStructureEntry objects. Probably the easiest way to load these <br> into a python script is </p> <p> ```<br> from monty serialization import loadfn # requires the monty package<br> <br> training_data = loadfn("calculation_data.json")<br> ```</p> <p>- element references contains VASP calculations (stored as above) for <br> the elemental reference calculations used to determine the formation<br> energies of the training structures</p>
Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"
<p>Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"</p> <p>Contents of this material</p> <ul> <li>Supplemental Text S1 and Text S2.</li> <li>Figures S1, S2, S2, S4, S5.</li> <li>Tables S1, S2</li> </ul> <p>For any questions email JHF (john.h.fairweaher@gmail.com).</p>
Supplementary materials for: Multiple ruptures on the North Sofia fault at Gorni Bogrov from scarp profile and shallow geophysics
<p>The data set contains resistivity data of two profiles that were measured at Gorno Bogrov site in the Sofia basin in Bulgaria. The resistivity survey was performed to study the North Sofia fault.</p> <p>The resistivity data in files r1ohm.tx and r2ohm.txt should be inverted using the BERT software (http://resistivity.net/) with configuration files r1cfg.txt and r2icfg.txt, respectively. The file bog2.txt contains fault coordinates for constraining the inversion mesh of the shorter profile (files r2ohm.txt, r2icfg.txt).</p> <p>The data set was collected within the project Assessment of Earthquake Ground Motion Amplification in the Sofia Basin (<a href="http://sofiabasin.atwebpages.com/">http://sofiabasin.atwebpages.com/</a>), funded by the Bulgarian National Science Fund, contract number KP-06-N64/1 from 15.12.2022.</p>
Low-cost test rig for characterization of photocatalytic two dimensional materials using photonically sized UV-A LED light sources
<p>In the presented studies, a system for the characterization of two-dimensional photocatalysts was developed and tested. In the system, reference substances can be studied online with regard to their degradability and adsorption on photocatalytic materials. In order to perform accurate calculations of the quantum and photon efficiency of the catalysts, the LED arrays used were adjusted in their spacing by simulations so that a homogeneous light field is imaged on the catalysts. The system was tested with respect to measurement accuracy and reproducibility and the photocatalytic degradation of methylene blue, methyl orange and rhodamine B was investigated. Exemplarily, the reaction kinetics, photolysis and adsorption on the tested photocatalysts were determined for these compounds and the calculation was presented in detail. The exact construction plans and circuits as well as the sensors and their programming are presented in detail and should encourage other scientists to replicate the experimental setup, since especially in the field of photocatalysis research, often the results of publications cannot be compared with each other.</p>
Supplementary material S3. Henoticus groehni sp. nov., holotype, No. 5087 [GPIH], X-ray micro-CT volume rendering of the left antenna.
<p>Supplementary material S3 in paper: Bukejs A., Lyubarsky G.Yu., Alekseev V.I. The oldest fossil species of the genus Henoticus Thomson (Coleoptera: Cryptophagidae) from Eocene Baltic amber. Historical Biology.</p> <p> </p>
Dataset for manuscript "Plants as inspiration for material‑based sensing and actuation in soft robots and machines"
<p>The dataset includes data for Figure 2 in the article "Plants as inspiration for material-based sensing and actuation in soft robots and machines<em>" MRS Bulletin</em> (2023). https://doi.org/10.1557/s43577-022-00470-8</p>
A Spectroscopic Study of Mars-Analog Materials with Amorphous Sulfate and Chloride Phases: Implications for Detecting Amorphous Materials on the Martian Surface
<p>This repository contains the raw and baseline or continuum corrected data associated with the manuscript entitled:</p> <p> A Spectroscopic Study of Mars-Analog Materials with Amorphous Sulfate and Chloride Phases: Implications for Detecting Amorphous Materials on the Martian Surface</p> <p>This work is being submitted to The Planetary Science Journal</p> <p>ABSTRACT</p> <p>The Chemistry and Mineralogy X-ray diffraction (XRD) instrument aboard the Curiosity rover has consistently identified substantial amorphous material at Gale Crater. The amorphous component is compositionally variable, but often includes elevated sulfur and iron, suggesting that amorphous ferric sulfate (AFS) may be present. Understanding the spectral changes of common Martian materials exposed to ferric sulfate brines as they desiccate to AFS is a key step in bridging the gap between simple mixing studies and analyses of complex/realistic reaction assemblages. Visible and near-infrared reflectance (VNIR), mid-infrared attenuated total reflectance (MIR, FTIR-ATR), and Raman spectra, along with XRD data are presented for basaltic glass, hematite, gypsum, nontronite, and magnesite, each at three grain sizes (<25, 25-63, and 63-180 μm), mixed with ferric sulfate alone or also with NaCl, hydrated through deliquescence, and then rapidly desiccated in 11% relative humidity or via vacuum. All desiccated products are partially or completely XRD amorphous; crystalline phases include starting materials and trace precipitates, leaving the bulk of the ferric sulfate in the amorphous fraction. Due to considerable spectral masking, the detectability of AFS is highly dependent on spectroscopic technique and the observed mineral assemblage. This has strong implications for remote and in-situ observations of Martian samples which include an amorphous component. AFS is only identifiable in VNIR spectra for magnesite, nontronite, and gypsum samples; hematite and basaltic glass samples appear similar to pure materials. Sulfate features dominate Raman spectra for nontronite and basaltic glass samples; the analog material dominates Raman spectra of hematite and gypsum samples. MIR spectra reveal all end members most clearly except for basaltic glass samples, where the analog material is almost completely masked. NaCl leads to similar FTIR-ATR and Raman features, regardless of analog material.</p> <p>Associated photographs of samples in this project can be found at:</p> <p>https://www.lionsandlamms.com/</p> <p>This research was supported by NSF award #1819209.</p>
Data and supplementary materials in support of "Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator"
<p>Data and supplementary materials in support of "Evgenios Vlachos, Regine Ejstrup, Thea Marie Drachen, Bertil Fabricius Dorch (2023) Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator, Frontiers in Research Metrics and Analytics, doi: 10.3389/frma.2023.1218213" </p> <p>It includes the anonymized dataset with the OADO values for all researchers from 10 departments (2 per faculty), the R code to perform the analysis and the graphs, the Scopus search string for the background search on Open Access and metrics, and the documentation for pulling data out from Pure.</p>
Supplementary materials for: Lost Loops: 19th century thermosiphon ventilation and its potential for heat recovery in buildings today
<p>We investigate ventilation heat recovery with buoyancy flow, arranging interior spaces in an open thermal loop with heat exchange through partition walls. We draw inspiration from a Victorian-era thermosiphon scheme after finding it in Montreal's former Royal Victoria Hospital and tracing the concept back to the original Centre Block of Canada's Parliament Hill. We corroborate the archival evidence with analog models to visualize how the original hospital's ventilation system worked. We use saline baths to verify how the siphon functioned and digital Schlieren to confirm how the full flow cycle worked with heat recovery. Then, to generalize these observations, we develop a mathematical model showing how heat recovery with buoyancy ventilation can work in simple room arrangements. We define a criterion for stable, unidirectional flow and a heat recovery efficiency limit of 50%. Finally, we validate the mathematical model experimentally, demonstrating stable, one-way flow close to the heat recovery limit.</p>
A database of vacancy formation enthalpies for materials discovery
<p><strong>A database of vacancy formation enthalpies for materials discovery</strong></p> <p>Matthew Witman<sup>a</sup>, Anuj Goyal<sup>b</sup>, Tadashi Ogitsu<sup>c</sup>, Anthony McDaniel<sup>a</sup>, Stephan Lany<sup>b</sup></p> <p><sup>a</sup> Sandia National Laboratories, <sup>b</sup> National Renewable Energy Laboratory, <sup>c</sup> Lawrence Livermore National Laboratories</p> <p><strong>Abstract</strong></p> <p>This dataset provides DFT calculations of cation and oxygen vacancy defects in oxides which can be used to derive efficient data-driven models for vacancy formation enthalpy. DFT calculations were performed as described in <DOI: 10.26434/chemrxiv-2022-frcns>, where a graph neural network surrogate model was trained and used to screen the Materials Project for promising solar thermochemical water splitting materials. The data, models, scripts and code needed to reproduce the results in <DOI: 10.26434/chemrxiv-2022-frcns> are described below.</p> <p><strong>Data & Models</strong></p> <p>1) data_01_03_22/* corresponds to oxide compounds used in model training</p> <p>2) known_cmpds/* corresponds to known STCH compounds</p> <p>3) screeningMP/* corresponds to the screening related data</p> <ul> <li>screening_inelements/* stores only Materials Project oxides whose composition is a subset of the training elements and contains all the vacancy defect predictions</li> <li>MP_O_PDs/* stores offline PDs from Materials Project so that adjusting oxide stability metrics can be done somewhat rapidly</li> <li>MP_O_Compounds/* stores possible MP oxide compounds to screen</li> </ul> <p>In general, the above folders contain:</p> <ul> <li>DFT data/structures are included in sub-directories: poscars, magnetic moments, oxidation states, and csvs (containing the vacancy enthalpy for each unique site)</li> <li>cgcnn/* contains the processed DFT data for use in the CGCNN code (see <strong>Scripts</strong> for how to prepare this) <ul> <li>id_prop.csv.* contains [cif name, defect formation enthalpy] pairs <ul> <li>Different id_prop.csv.* files correspond to different K-fold stratifications</li> <li>in the screening directory, defect formation enthalpy is omitted since it has not been computed with DFT</li> </ul> </li> <li>model-(X1)k(X2)_(X3)_(X4) corresponds to different CV models for <ul> <li>X1 different training set sizes (i.e., try to train with only 10%, 40%, or 100% of the data)</li> <li>X2 different k folds</li> <li>X3 = "struct" or "" for "structure-wise validation" or "defect-wise validation", respectively</li> <li>X4 for different encoding strategies</li> </ul> </li> <li>structure X-Yz.cif indicates structure X, defect element Y, symmetry site z, where one instance of that site has been re-ordered to be the first atom in the cif file</li> <li>*.locals contains a one-hot encoding of oxidation states of all sites in that crystal</li> <li>*.locals_continuous contains a continuous encoding of oxidation state in that crystal</li> <li>*.globals contains global properties of the host structure</li> </ul> </li> </ul> <p><strong>Scripts</strong></p> <ul> <li>scripts/*.sh scripts to rerun the screenings for different k-folds, encodings, etc.</li> <li>scripts/*.ipynb to analyze results</li> <li>scripts/prepare_cgcnn.py for translating the data in (poscars/*, csvs/*, oxstate/*, mags/*) to the ML input needed in cgcnn/*</li> </ul> <p><strong>Code</strong></p> <ul> <li>Install CGCNN and its defect modifications from https://github.com/mwitman1/cgcnndefect</li> </ul> <p><strong>Questions/Collaborations</strong></p> <ul> <li>Please contact <a href="mailto:mwitman@sandia.gov">mwitman@sandia.gov</a></li> </ul> <p><strong>Acknowledgements</strong></p> <ul> <li>This material is based upon work supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), specifically the Hydrogen and Fuel Cell Technologies Office. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. Part of the work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under contract No.~DE- AC52-07NA27344. The National Renewable Energy Laboratory (NREL) is operated by the Alliance for Sustainable Energy, LLC, for the DOE under Contract No.~DE-AC36-08GO28308. This work used High-Performance Computing resources at NREL, sponsored by DOE-EERE. The views expressed in this article do not necessarily represent the views of the U.S. Department of Energy or the United States Government.</li> </ul>
Audiovisual Materials in Research and Teaching – an Overview of Copyright Aspects
<p>Das <i>Rechtsgutachten</i> (Neuauflage der Fassung von 2015) erläutert, wie <i>audiovisuelle Materialien</i> in Forschung und Lehre genutzt werden dürfen. Es stellt dar, in welchen Fällen Forschende eine Erlaubnis einholen müssen und in welchen die Nutzung gesetzlich erlaubt ist.</p><p>Dies ist die <strong>Datenpublikation</strong> (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung "Audiovisuelle Materialien in Forschung und Lehre – eine Übersicht zu urheberrechtlichen Aspekte".</p><p><strong>Die Online-Handreichung ist verfügbar unter </strong><a href="https://nfdi4culture.de/go/E4105"><strong>https://nfdi4culture.de/go/E4105</strong></a><strong>.</strong></p>
Understanding the robustness of spectral-temporal metrics across the global Landsat archive from 1984-2019 – a quantitative evaluation: extended material
<p>This dataset contains extended material for the paper:</p> <p>Frantz, D., Rufin, P., Janz, A., Ernst, S., Pflugmacher, D., Schug, F., Hostert, P.<strong>: Understanding the robustness of spectral-temporal metrics across the global Landsat archive from 1984-2019 – a quantitative evaluation. </strong><em>In revision.</em></p>
Supporting material of "A nonequilibrium alchemical method for drug-receptor absolute binding free energy calculations: the role of restraints"
<p>Supporting material of the paper "A nonequilibrium alchemical method for drug-receptor absolute binding free energy calculations: the role of restraints".</p> <p>The directory is fully documented with README files.</p> <p>Differences of V2.0 with V1.0:</p> <ul> <li>Due to a software bug we had to re-parametrize the ligands whose torsions were parametized with ANI-2.X (ligand 6 and 7).</li> <li>During the peer reviewing process we also parametrized with ANI-2.X and docked ligand 8.</li> </ul>
Material factors for buildings, roads and rail-based infrastructure in CONUS
<p>Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. The data presented hereinafter constitute that basis for quantifying material stocks in a country that exhibits one of the highest material stocks in the world, the United States.</p><p><strong>Data</strong><br>This dataset includes the following material intensities:</p><ul><li>material intensity in mass per volume of above-ground building (kg/m³) per building type</li><li>material intensity in mass per area of road (kg/m²) per road type</li><li>material intensity in mass per area of railway track (kg/m²) per railway type</li><li>material intensity in mass per area (kg/m²) per other infrastructure type</li></ul><p>Material intensity factors are split into the following 15 material categories: metals (iron/steel, copper, aluminum, all other metals), non-metallic minerals (concrete, bricks, glass, aggregate except for concrete, all other minerals), biomass-based materials (timber, other biomass-based materials), petrochemical-based materials (bitumen, other petrochemical-based materials), insulation, and other materials. </p><p>Material intensity factors are available for each of the following 19 aggregated stock type categories: </p><ul><li><i>Buildings</i>: low-rise residential (RES-LR), mid-rise residential (RES-MR), low/mid-rise residential / commercial mixed use (RCMU), high-rise residential / commercial mixed use (RCMU-HR), residential / commercial mixed use skyscrapers (RCMU-SKY), commercial / industrial (C/I), and mobile homes and light-weight buildings (MLB)</li><li><i>Roads</i>: motorway, primary roads, secondary roads, tertiary roads, local roads, rural roads</li><li><i>Rail-based infrastructure</i>: railway, subway, tram</li><li><i>Other</i>: airport runways, parking lots, other remaining impervious</li></ul><p>Since construction standards for residential buildings and gravel roads vary between different climate zones across the conterminous United States, material intensities for low-rise residential buildings (RES-LR), local roads and tracks were further differentiated according to climate zones.</p><p>The dataset provides three sets of material intensity factors for sensitivity purposes: mean, low, and high. Low and high material intensity factors represent 25th and 75th percentiles of data points per material category and stock type. </p><p>In addition, the following building volume conversion factors required for deriving material intensity factors for buildings are included in the dataset:</p><ul><li>floor-to-floor height per building type</li><li>roof volume factors (m³/m² footprint) per building type</li><li>share of useable area (SUA) in gross floor area per building type</li></ul><p>Building volume conversion factors are based on Haberl et al. (2021) and were used in the calculation of the above-ground volume for those case studies where either the floor-to-floor height or information regarding the roof volume were unavailable, or where only the UA, but not the GFA necessary for the calculation of the above-ground volume were specified.</p><p><strong>Further information</strong><br>The dataset complements a scientific article which includes further information and an in-depth dataset description. For further information, please see the publication below or contact Dominik Wiedenhofer (dominik.wiedenhofer@boku.ac.at). <br><br>D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl, Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. <i>Nat Commun</i>. <strong>14</strong>, 8014 (2023), doi: <a href="https://doi.org/10.1038/s41467-023-43755-5">10.1038/s41467-023-43755-5</a><br><br>Check out this peer-reviewed article detailing the overall approach and novel method:</p><p>H. Haberl, D. Wiedenhofer, F. Schug, D. Frantz, D. Virág, C. Plutzar, K. Gruhler, J. Lederer, G. Schiller, T. Fishman, M. Lanau, A. Gattringer, T. Kemper, G. Liu, H. Tanikawa, S. van der Linden, P. Hostert, High-Resolution Maps of Material Stocks in Buildings and Infrastructures in Austria and Germany. <i>Environ Sci Technol</i>. <strong>55</strong>, 3368–3379 (2021), doi:<a href="https://doi.org/10.1021/acs.est.0c05642">10.1021/acs.est.0c05642</a></p><p>Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
Supplementary Material for "Entropic barrier of water permeation through single-file channels"
<p>Python code for a global activation energy (E<sub>a</sub>) fit to temperature dependent stopped-flow data (*.bka file):</p> <p>As shown in our manuscript, the accuracy of E<sub>a</sub> estimation from stopped-flow data is greatly increased by a global fit to the data. Thereby, the ensemble of curves of a sample at different temperatures is fitted globally using the analytical solution (<strong>Eqn. </strong><strong>10</strong>), instead of evaluating permeabilities for different temperatures individually and independently. In our python code, osmotic water flux measurements of a sample (mixture of vesicles with and without water channels) recorded at different temperatures are simultaneously fitted using <strong>Eqn. </strong><strong>7</strong>, <strong>Eqn. </strong><strong>10</strong>,<strong> Eqn. </strong><strong>11</strong> and <strong>Eqn. </strong><strong>14</strong>. Here, the only free fit parameter, which is evaluated for the different temperatures independently is coefficient <em>b</em> from <strong>Eqn. </strong><strong>7</strong>. The accuracy of the results increases with the span of the temperature values, number of performed measurements and a decreased empty vesicle fraction.</p> <p> </p>
Linking up Bell beakers in the Iberian Peninsula: Supplementary Material
<p>Many studies in complexity theory employ agent-based models whose interactions can be expressed as networks. In such models, the pattern of interactions between actors is crucial, and the network topology that emerges from the raw data can be characterized through many metrics. There is one tool that has been previously employed in Archaeology studies and has the potential to deal with networks in social contexts at different scales of analysis: social network analysis (SNA). This discipline has been applied successfully in wide range of archaeological problems, providing valuable insights and a different perspective. It also could be useful to provide quantification to concepts associated with social complexity, such as robustness, resilience, and edge-of-chaos threshold. In this work, we will propose some methodologic possibilities to approximate some of these concepts through SNA. In order to illustrate the process, we will present a case study from the Copper Age in the Iberian Peninsula: Bell beaker phase.</p>
Crystal Structure Solution and High Temperature Thermal Expansions of NaZr2(PO4)3-type Materials
<p>The NaZr<sub>2</sub>P<sub>3</sub>O<sub>12 </sub>(NZP) family<sub> </sub>of materials have shown low and tailorable thermal expansion properties. This dataset includes SrZr<sub>4</sub>P<sub>6</sub>O<sub>24</sub>, CaZr<sub>4</sub>P<sub>6</sub>O<sub>24</sub>, NaTi<sub>2</sub>P<sub>3</sub>O<sub>12</sub>, NaZr<sub>2</sub>P<sub>3</sub>O<sub>12</sub>, MgZr<sub>4</sub>P<sub>6</sub>O<sub>24</sub>, and related solid solutions. These materials were synthesized using the organic-inorganic steric entrapment method. The samples were characterized with in-situ high-temperature x-ray diffraction at the Advanced Photon Source and National Synchrotron Light Source II from 25 to 1500 ℃. The average linear thermal expansion of SrZr<sub>4</sub>P<sub>6</sub>O<sub>24</sub> and CaZr<sub>4</sub>P<sub>6</sub>O<sub>24</sub> was between -1 x10<sup>-6</sup>/℃ and 6 x10<sup>-6</sup>/℃ from 25 to 1500 ℃. High temperature polymorphs of CaZr<sub>4</sub>P<sub>6</sub>O<sub>24</sub> and SrZr<sub>4</sub>P<sub>6</sub>O<sub>24</sub> were solved by Fourier difference mapping and Rietveld refinement. This polymorph is present above about 1250 ℃. This work measured thermal expansion coefficients to 1500 ℃ for all samples and investigated the differences in thermal expansion mechanisms between polymorphs and between compositions. </p>
[Supplementary material] IDLGen: Automated Code Generation for Inter-parameter Dependencies in Web APIs
<p>This is the supplementary material of the paper entitled IDLGen: Automated Code Generation for Inter-parameter Dependencies in Web APIs.</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.