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12,393 results for “materiality”
Total organic carbon, total nitrogen, and iron-bound organic carbon in surficial sediment and settling particulate material from Falling Creek and Beaverdam Reservoirs in 2019 and 2021
This dataset includes measurements of sediment properties (total organic carbon, total nitrogen, and iron-bound organic carbon) in surficial sediment and sedimenting material from two reservoirs: Falling Creek and Beaverdam Reservoirs, both located in Vinton, VA, USA. To measure surficial sediment properties, sediment cores were collected at the deepest site in each reservoir using a gravity corer, and the top 1 cm was frozen then lyophilized. Sediment cores were collected approximately once per month in both reservoirs throughout the stratified period (May–November) in 2019 and 2021, though sampling frequency and duration varied by reservoir and year. Sedimenting material was sampled using sediment traps suspended approximately 1 m above the sediment in both reservoirs. Iron-bound organic carbon was measured using the citrate-bicarbonate-dithionite method, and we used a CN analyzer (Elementar VarioMax, Ronkonkoma, NY, USA) to determine the amount of OC per unit mass of sediment.
Major ion and nutrient concentrations of water-soluble salts leached from aeolian material, McMurdo Dry Valleys, Antarctica (2013-2016)
To evaluate the role of Antarctic aeolian transport in surface chemistry homogenization, fifty-three samples of aeolian material from the McMurdo Dry Valleys region of Antarctica were collected and analyzed for water-soluble major ions and nutrients at a 1:5 sediment:water ratio. Samples were collected seasonally from Alatna Valley, Victoria Valley, Miers Valley, and Taylor Valley (Taylor Glacier, East Lake Bonney, F6 (Lake Fryxell), and Explorer’s Cove) at five heights (~5, 10, 20, 50, 100 cm) above the surface between 2013 and 2015. This data package is associated with Aeolian Material Transport and Its Role in Landscape Connectivity in the McMurdo Dry Valleys, Antarctica, published by Diaz et al. in 2018 (DOI: 10.1029/2017JF004589).
SBC LTER: Beach: Distribution of terrestrial organic material in intertidal and nearshore marine sediment due to debris flow response efforts
These data describe the distribution and processing of terrestrial organic material observed in the Santa Barbara Channel (Goleta Bay) during the spring of 2018. Specifically, beach, slough, and marine sediments were sampled during and after the debris disposal event that took place between January and February 2018 following the Thomas Fire and the subsequent Montecito Debris Flow. Data are contained in one table, including organic carbon, carbon isotope, lignin phenol, and pyrogenic carbon measurements analyzed from sediment cores collected at different depths and locations in the nearshore region.
Supplementary Material for "General Principles for Yield Optimization of Nucleoside Phosphorylase-Catalyzed Transglycosylations"
<p>This is the supplementary material for our publication "General Principles for Yield Optimization of Nucleoside Phosphorylase-Catalyzed Transglycosylations".</p> <p>The .pdf file contains the supplementary information: Author Contributions, Conflict of Interest, Sample analysis by HPLC, Table S1 and S1, Figure S1 and a suggested workflow for NPase-catalyzed nucleoside synthesis.</p> <p>The .xlsx file contains an implementation of the simplified formula for yield prediction (equation (4) ), using previously reported thermodynamic data (10.5281/zenodo.3459298).</p> <p>The Python code and all data calculated from numerical solutions of the system of equilibrium constraints is available elsewhere (10.5281/zenodo.3522588).</p>
Supplementary Material for "Thermodynamic Reaction Control of Nucleoside Phosphorolysis"
<p>This is the supplementary material for our publication "Thermodynamic Reaction Control of Nucleoside Phosphorolysis".</p> <p>The .pdf file contains the supplementary information: Author Contributions, Figure S1 and Tables S1-S3.</p> <p>The .zip file contains the raw data, metadata and results from Figure 1, 2, 4 and 5.</p> <p>The .xlsx file contains</p> <ol> <li>the apparent transformed values of the reaction enthalpy and entropy of nucleosides <strong>1</strong>-<strong>24</strong></li> <li>the calculated apparent Gibbs free energies of nucleosides <strong>1</strong>-<strong>24</strong> and</li> <li>the implementation of these values for the calculation of equilibrium conversions of nucleosides <strong>1</strong>-<strong>24</strong> with variable (adjustable) reaction conditions (concentrations of the nucleoside, phosphate and reaction temperature).</li> </ol> <p>For the software employed for spectra unmixing, please see doi: 10.5281/zenodo.3243376 and our previous work (doi: 10.3390/mps2030060) as well as its supporting material (doi: 10.5281/zenodo.3333469) for clarification.</p>
ribosomeprofiling/ribo_manuscript_supplemental: Supplementary material for the Ribo ecosystem manuscript
<p>Supplementary material for the Manuscript RiboFlow, RiboR and RiboPy: An ecosystem for analyzing ribosome profiling data at read length resolution</p>
Supplementary materials (Allometry and fighting behaviour of a dimorphic stag beetle Cyclommatus miniszechi (Coleoptera: Lucanidae))
<p><strong>Supplementary Materials:</strong></p> <p><strong>Table S1.</strong> The morphological measurements of males of <em>Cyclommatus mniszechi</em> used for allometry analyses.</p> <p><strong>Table S2. </strong>The behavioural sequence data used for sequential analyses of size-matched contests in major males of <em>Cyclommatus mniszechi</em>.</p> <p><strong>Table S3. </strong>The behavioural sequence data used for sequential analyses of size-matched contests in minor males of <em>Cyclommatus mniszechi</em>.</p> <p><strong>Video S1.</strong> The behavioural sequence of males of <em>Cyclommatus mniszechi</em> in fighting contests under the laboratory setups. One of the opponents walked to the other and touched it (00:07), and then both of them displayed ‘defensive posture’ (00:08) after ‘touch’. Once both individuals approached each other, they accelerated antennation and raised their mandibles and prothoracic parts. The contest then progressed into ‘body raising’ (00:14). They then performed ‘attack’ and ‘push’ to each other several times and then escalated to ‘tussle’ (00:20) and interlocked their mandibles until one of the contestants was clamped (‘clamp1’) in the air by the other for a second and flipped (00:50). The winner dropped the loser and kept attacking and pushing the loser while the loser retreated and moved backwards (00:51).</p>
Thermal Comfort and Perception of Different Materials for Tabletops: Datasets, Processing Code, and Supplementary Tables.
<p>In this data repository, data related to the study <em>Thermal Comfort and Perception of Different Materials for Tabletops </em> is deposited, and includes datasets, processing code, and supplementary tables.</p> <p> </p>
Table S2. List of museum specimen material inspected for each of the 10 new taxa. Over 300 specimens were examined in total for plumage comparisons.
<p>Supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
Additional material for "Sharp Congruences Adequate with Temporal Logics Combining Weak and Strong Modalities"
<p>Complementary material including the proofs of all theorems presented at TACAS 2020, a detailed description of the RERS 2019 experiment, and the actual data used for the RERS 2018,RERS 2019, and TFTP experiments.</p>
"Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments" video material
<p>"Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments" video material</p>
Supplementary Materials for "Simultaneous single-shot radiographic imaging using a laser-driven x-ray and proton micro-source"
<p>Simulation Data Repository, please read the contained README file in the contained simulation/ directory.</p> <p>This directory contains a copy of the used PIConGPU source code, version 0.5.0-dev-60ad9eb85 and analysis scripts.</p> <p>The PIConGPU source code is archived including its complete git history (git version 2.17.1) in source/picongpu.tar.gz with the input parameter template inside in share/picongpu/examples/Wneedle .</p> <p>Generally, PIConGPU source code is available via <a href="https://doi.org/10.5281/zenodo.591746">https://doi.org/10.5281/zenodo.591746</a> with its public git repository being maintained on <a href="https://github.com/ComputationalRadiationPhysics/picongpu">https://github.com/ComputationalRadiationPhysics/picongpu</a> .</p> <p>The two simulations’ exact input is modified accordingly in the directory input/ inside: 2D_a0-45_Z-10_ppc-20_002_light.tar.gz (p-polarized; along X) 2D_a0-45_Z-10_ppc-20_003_light.tar.gz (s-polarized; along Z).</p> <p>“Heavy” simulation data (checkpoints in simOutput/checkpoints/, full-resolution field and particle output in simOutput/bp/ ) has been stripped from this archive and are archived on NERSC’s HPSS tape archive.</p> <p>Analysis scripts are provided as Jupyter notebooks (DensityPlot_polX.ipynb and DensityPlot_polZ.ipynb) and depend on the following software:</p> <p>- adios 1.13.1 python bindings with enabled c-blosc transformations<br> - numpy 1.17.1<br> - matplotlib 3.1.1<br> - PIConGPU post-processing helper modules located in each simulation root directory under “input/lib/python/”<br> <br> The detailed conda environment can be found in the README.</p>
Bio‐derived Carbon Nanofibres from Lignin as High‐Performance Li‐Ion Anode Materials
<p>Development of cost‐effective and increasingly efficient sustainable materials for energy‐storage devices, such Li‐ion batteries, is of crucial future importance. Herein, the preparation of carbon nanofibres from biopolymer blends of lignin (byproduct from the paper and pulp industry) and polylactic acid (PLA) or a thermoplastic elastomeric polyurethane (TPU) is described. SEM analysis shows the evolving microstructural morphology after each processing step (electrospinning, stabilisation and carbonisation). Importantly, it is possible to tailor the nanofibre porosity by utilising miscibility/immiscibility rules between lignin and the polymer additive (PLA/TPU). PLA blends (immiscible) generate porous structures whereas miscible lignin/TPU blends are solid when carbonised. Electrodes produced from 50 % PLA blends have capacity values of 611 mAh g<sup>−1</sup> after 500 charge/discharge cycles, the highest reported to date for sustainable electrodes for Li‐ion batteries. Thus, this work will promote the development of lignocellulose waste materials as high‐performance energy‐storage materials.</p>
Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms (supplemental material: brain wave loops movies)
<p>This is a collection of videos supplementing the paper "Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms"</p> <p>Examples of wave trajectories and emergent persistent loop patterns for the spherical shell cortex model with<br> varying amounts of tensor anisotropy and inhomogeneous shell layer thickness.</p> <p><br> Examples of brain wave trajectories and emergent persistent loop patterns for cortical fold geometry with different<br> approaches used for estimation of inhomogeneity and anisotropy. Among those examples are several simple cases with variable inhomogeneity and fixed anisotropy (similar to the above spherical shell cortex model) as well as with more complex estimates of anisotropy based on multiple diffusion gradients MRI (dMRI) acquisitions.</p>
Vector-LabPics dataset for images of materials in vessels in the chemistry lab
<p><strong>LabPics 2: A newer and larger a version (But harder to use) can be found here: <a href="../record/4736111"> https://zenodo.org/record/4736111</a></strong></p> <p>The Vector-LabPics V1 dataset contains 2187 images of chemical experiments with materials within mostly transparent vessels in various laboratory settings and in everyday conditions such as beverage handling. Each image in the dataset has an annotation of the region of each material phase and its type. In addition, the region of each vessel and its labels, parts, and corks are also marked.</p> <p>For more details see:</p> <p><a href="https://pubs.acs.org/doi/10.1021/acscentsci.0c00460">https://pubs.acs.org/doi/10.1021/acscentsci.0c00460</a></p> <p> </p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support.</p> <p>Images of the dataset were taken from images and videos shared on Youtube and Instagram, Twitter and Tumblr channels and other contributors; we do not have copyright for the images. Any commercial or none academic use of the images depends on acquiring permission from the owner of the images. Note that the name of each image contains the image source. For any non-academic use of the images, please contact their sources for permission. We like to thank the following channels for sharing the images used in this dataset.</p> <p>We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support. Images from C&EN's Chemistry in Pictures (<a href="http://cen.chempics.org/">cen.chempics.org</a>) used here with permission from C&EN and ACS. All rights reserved. Please contact cenchempics@acs.org to inquire about republishing.</p>
The Middle Chalcolithic to Middle Bronze Age Chronology of Cyprus: Refinements and Reconstructions - Supplementary Material
<p>Supplementary material for the article The Middle Chalcolithic to Middle Bronze Age Chronology of Cyprus: Refinements and Reconstructions - Supplementary Material. The material consists of the CQL code for OxCal used to run the models in the article.</p> <p>Article citation: Paraskeva, C. 2019. The Middle Chalcolithic to Middle Bronze Age Chronology of Cyprus: Refinements and Reconstructions, in: Kearns, C., and S. Manning (eds.), New Direction in Cypriot Archaeology, Ithaca-London: Cornell University Press, 45-74.</p>
Notified bodies, horizontal specifications and harmonised standards for construction materials third party assessment according to the CPR - EU 305/2011
<p>This dataset was extracted from NANDO, the website of the European Commission for notified bodies. It contains the list of notified bodies, horizontal specifications and harmonised standards for construction materials third party assessment according to the CPR - EU 305/2011</p>
MMORS Final Year Project: Supplementary Materials
<p>The two files uploaded here are data sets obtained from a final year project completed at Cardiff University, School of Mathematics. For more information regarding this project and the data sets, please see the corresponding GitHub Repository. < <a href="https://github.com/shapperzsm/final-project">https://github.com/shapperzsm/final-project</a> ></p> <p>- The `main.db' file contains results obtained from a large Iterated Prisoner's Dilemma experiment focusing on the probability of defection for varying game-ending probabilities. </p> <p>- The `main.csv' file was created from the database. It contains the game-ending probabilities at which playing `defector' was no longer a rational action.</p>
Indo-Aryan – a house divided? Evidence for the East-West Indo-Aryan divide and its significance for the study of northern South Asia - Supporting materials
<p>The dataset includes raw data and images and plots used for the paper ``Indo-Aryan – a house divided? Evidence for the East-West Indo-Aryan divide and its significance for the study of northern South Asia'' (Ivani et al., in press)</p>
ScienceDex guides
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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.