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2,014 results for “Resolvers”
Source apportionment of highly time-resolved elements during a firework episode from a rural freeway site in Switzerland
<p>Data to accompany "Source apportionment of highly time-resolved elements during a firework episode from a rural freeway site in Switzerland" publication in Atmospheric Chemistry and Physics. This repository contains measurement data in Härkingen, Switzerland, a permanent station of the Swiss National Air Pollution Monitoring Network (NABEL). Sampling was performed from 23 July to 13 August 2015. This repository has excel file (all data.xlsx) for all the raw data measured during campaign. In addition, it has data corresponding to each figures presented in main text published version.</p>
Example Cloud-Resolving Model Output (Dec 2013 run)
<p>This dataset contains a selection of hourly, domain-mean quantities from the large-ensemble of realistic cloud-resolving model experiments described here: https://acp.copernicus.org/articles/20/6291/2020/</p>
Spatially Resolved Infrared Radiofluorescence (SR IR-RF) Image Data
<p>This dataset contains measurement sequences and data output <br> of spatially resolved infrared radiofluorescence (SR IR-RF) measurements<br> on K-feldspar samples carried out at the IRAMAT-CRP2A, UMR 5060, CNRS-Université Bordeaux Montaigne (France)<br> in 2019. The data analysis was performed in 2020. </p> <p>The data may serve as reference data and allow detailed inspection by others to <br> verify or advance the used analysis procedures. </p> <p>Along with the raw image data (TIF-files), the datasets also contain documented R scripts used for data processing and partly treated data as an example. To reproduce the full data analysis, additional software is needed; not part of this repository. </p> <p>Further details can be found in the README.md (README.html), which is part of the dataset.</p>
Time-resolved reconstruction of M87*
<p>The dataset contains 160 approximate posterior samples of the time-variable shadow of M87*</p> <p>Details can be found in readme.txt</p>
The Three Rs: Resolving Respiration Robotically in Shelf Seas
<p>Ocean gliders were deployed to conduct 'virtual mooring’ profiles at a study site in the seasonally stratified central Celtic Sea (station CCS, 49° 24’ N, 8° 36’ W) (see Fig. 1) during spring 2015 (6th April to- 28th April, decimal day 95 to 117) and summer 2015 (15<sup>th</sup> July and- 2nd August, decimal day 195 to 213). The integrated approach adopted in this study, combining ship based and glider measurements enabled estimates of spatial gradients while also minimising tidal aliasing that would likely be introduced by long spatial transects with the glider. A Slocum (Teledyne Webb Research, Falmouth, USA) Ocean Microstructure Glider (OMG, see Palmer et al., 2015 for full details) was equipped with a MicroRider microstructure package (Rockland Scientific International) to measure turbulentthe microstructure of velocity shear, a Seabird SBE42 CTD sensor to measure temperature, salinity and pressure, and an Aanderaa 4831 oxygen optode to measure O<sub>2</sub> (precision 0.2 µmol kg<sup>-1</sup>). Measurements were taken within 5 m of the bed and 2 m of the surface on most dives, with each yo-yo profile taking approximately 20 minutes. Glider salinity data was corrected for thermal inertia following Palmer et al. (2015). The glider AA4831 optode is known to experience severe lag across strong oxygen gradients, and therefore oxygen data was corrected where possible for optode membrane lag following Bittig et al. (2014). Where optode lag across the oxycline was too great and so not correctable using this method, it was omitted and oxygen data from coinciding CTDs was used. In comparison to other oxygen optodes, the AA4831 has been documented by various scientific studies as being an extremely stable optode with low detectable drift ( <0.5% yr<sup>-1</sup>) and high precision of <0.2 µmol kg<sup>-1</sup> (Kortzinger et al., 2004; Nicholson et al., 2008; Johnson et al., 2010; Champenois & Borges, 2012). Optode drift was calculated in this study by comparing discrete Winkler-analysed samples taken at deployment and recovery of the gliders, identifying a downward drift of 0.001% d<sup>-1</sup>, in close agreement with quoted manufacturer values.</p> <p>Glider sensors (temperature, salinity and ) were calibrated against nearby ship CTD profiles (CTD calibrated 1 month prior to cruise, SBE 43 precision = 2% of saturation) and discrete water samples collected within 3 hours and 2 km of glider deployment and recovery times and glider position, respectively, as part of the Shelf Sea Biogeochemistry programme (<em>RRS Discovery</em>, DY029 and DY033). Error estimates for the total change in (µmol kg<sup>-1</sup>) were calculated as the sum of the optode precision (0.2 µmol kg<sup>-1</sup>) and drift over the entire respective deployments (<0.1 µmol kg<sup>-1</sup>). Currents, tides, salinity and temperature were monitored throughout the glider deployments by a mooring at the CCS study site, which was equipped with an acoustic current profiler (ADCP), salinometer and thermistors that provided near-continuous data (Wihsgott et al., 2019; Ruiz-Castello et al., 2019).</p>
Spatially resolved metabolic composition in seeds of common bean: comparison of the low phytic acid mutant and the wild type
<p>Common bean (Phaseolus vulgaris L.) seeds are a good source of energy, are rich in proteins and carbohydrates, minerals and vitamins (such as Fe, Zn, B-vitamin), and bioactive compounds, such as polyphenols. However, the presence of some antinutritional compounds, such as phytic acid (PA), which decreases mineral bioavailability, can limit the nutritional value of common beans. Therefore, genotypes with low PA concentrations in common beans have been generated. The increased bioavailability of Fe from LPA mutant seeds compared to the wild type common beans was shown in a stable Fe-isotope absorption study in Swiss women, indicating that the seeds of LPA common bean could be used to help remedy the Fe malnutrition in women. Within this TNA project, we spatially resolved molecular composition in LPA mutant and wild-type common beans, particularly the distribution of PA. In total, three replicates of each genotype were analyzed with MeV-SIMS at RBI. Positive and negative modes were operated for analysis of the samples and of the standard (PA). Best spectra were obtained in negative mode, in which three distinct peaks were observed in the standard (PA): 63 m/z: PO2-, 79 m/z: PO3- and 97 m/z: H2PO4-.</p>
Data for: Systematic and highly resolved modelling of biodiversity in inherently rare groundwater amphipods (Journal of Biogeography, 2024)
<p>Original research article:</p> <p>Knüsel, M., Alther, R., Locher, N., Ozgul, A., Fišer, C. & Altermatt, F. (2024). Systematic and highly resolved modelling of biodiversity in inherently rare groundwater amphipods. <em>Journal of Biogeography</em>, https://doi.org/10.1111/jbi.14975.</p>
REsolved ALMA and SMA Observations of Nearby Stars (REASONS)
<p>This is the data release of the REASONS survey, a sample of planetesimal belts around nearby stars resolved interferometrically (see journal article for full details). Every tar file corresponds to a planetary system, and contains:<br>Data:<br>1 - the calibrated continuum visibility data in CASA .ms format<br>2 - a FITS file with the non-primary-beam-corrected image of the system. <br>3 - a PDF image of the system<br>Visibility modelling results:<br>4 - an ASCII file ('*_fitresults.txt') containing the results of the MCMC visibility fitting, as reported in Table X in the article but containing extra parameters fitted (such as background sources, extra astrometry for fits of multiple datasets/pointings, weight-rescaling factors)<br>5 - an ASCII notes ('*_fitnotes.txt') file, which should always be consulted when interpreting the fit results, as it typically points out peculiarities in the posterior probability distributions.<br>6 - a PDF of the triangle ('corner') plot of the N-dimensional posterior probability distribution of the fitted parameters, which should be consulted to get a better idea of the results reported in the ASCII files.<br>7 - a PDF image (targetstar_imagecombo.pdf) showing the data, model, residuals and visibility data+model curves, to visually evaluate the goodness of the visibility fit.<br>8 - a PDF image (targetstar.pdf) showing the multiwavelength photometry for the planetary system and a star+belt modified-blackbody fit, with parameters reported in the journal article.<br>Please refer to the journal article for more details on the methods used to obtain these data and modelling results.</p>
A plant biodiversity effect resolved to a single genetic locus - datasets
<p>Despite extensive evidence that biodiversity promotes plant community productivity, progress towards understanding the mechanistic basis of this effect remains slow, impeding the development of predictive ecological theory and agricultural applications<em>. </em>Here, we analysed non-additive interactions between genetically divergent Arabidopsis accessions in experimental plant communities. By combining methods from ecology and genetics, we identified a major effect locus that promotes complementarity amongst genotypes and above-ground productivity in mixed communities. In experiments with near-isogenic lines, we show that this diversity effect can act independently of other genomic regions and be resolved to a single locus representing less than 0.3% of the genome. Using plant-soil-feedback experiments, we demonstrate that allelic diversity also causes genotype-specific soil legacy responses in a subsequent growing period. Our work thus shows that positive diversity effects can be linked to single Mendelian factors, and that a range of complex community properties, some of which manifest themselves even after the original community has disappeared<strong>, </strong>can have a simple, single cause. This may pave the way to novel breeding strategies, focussing on phenotypic properties that manifest themselves beyond isolated individuals, i.e. at a higher level of biological organisation.</p>
Size-resolved cloud condensation nuclei data collected during the CalWater 2015 field campaign
<p>This repository contains raw and processed data for the size-resolved cloud condensation nuclei instrument deployed during the Calwater-2015 field campaign. It also contains the averaged cluster data presented in the paper "Classification of aerosol population type and cloud condensation nuclei properties in a coastal California littoral environment using an unsupervised cluster model" by Atwood et al. (2019). Details about the datafiles are provided in README.md file in markdown format.</p>
Time-resolved compound repositioning predictions on a text-mined knowledge network
<p><strong>gs_positives.csv</strong>: The re-processed version of DrugCentral indications, utilized as training and testing positives in the analysis.</p> <p><strong>top_5000_predictions.csv</strong>: The top 5000 drug-disease pairs, by probability, produced by this analysis pipeline.</p> <p><strong>file_info.txt</strong>: Information about the column headings in of the two files.</p> <p> </p>
Test Input and Output Files for Cloud Resolving Radar Simulator (CR-SIM) Version 4.0
<h2>Overview</h2> <p>The dataset includes input and output files for testing the Cloud-Resolving Radar Simulator (Oue et al. 2020) version 4.0. </p> <p>The following files are included:</p> <ul> <li>crsimtest1_inp_MP10.tar.gz includes input files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_inp_MP50.tar.gz includes input files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_inp_MP40.tar.gz includes input files for Test-3 with the microphysical option MP40</li> <li>crsimtest1_out_ref_MP10.tar.gz includes example output files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_out_ref _MP50.tar.gz includes example output files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_out_ref _MP40.tar.gz includes example output files for Test-3 with the microphysical option MP40</li> </ul> <p>Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v4.0).</p>
A proteome-wide quantitative platform for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs
<p><strong>EM Quantitation:</strong></p> <p>Raw data gathered from EM images taken to determine nanodisc population size distribution.</p> <p> </p> <p><strong>NNB TGN46 analysis:</strong></p> <p>Data analysis of the Native Nanobleach experiments of TGN46 in native nanodiscs to determine population distribution of oligomeric organizations.</p> <p> </p> <p><strong>Polymer conditions:</strong></p> <p>Physiochemical characteristic and extraction conditions for all polymers in the library both commercially available and in-house.</p> <p> </p> <p><strong>Protein groups polymer screen original file:</strong></p> <p>Original output of MaxQuant data processing of polymer screen data.</p> <p> </p> <p><strong>Organelle matching:</strong></p> <p>Code used for mathcing proteins identified in the proteomics output to organelle or residence for all organellar annotations.</p> <p> </p> <p><strong>Polymer code:</strong></p> <p>Code used to process and normalize the MaxQuant output and calulate extraction efficiency across all detected proteins.</p> <p> </p> <p><strong>MAP Library Details:</strong></p> <p>Graphic and table explaining chemical details of all polymer used in the screen, both commerically available and in-house synthesized.</p> <p> </p> <p><strong>NNB TGN46:</strong></p> <p>Raw scope files for the TIRF microscopy single molecule step photobleaching experiment with TGN46.</p> <p> </p> <p><strong>Organellar Breakdown Database:</strong></p> <p>Proteins detected in the polymer screen through proteomics experiments stratified into organelle of residence.</p> <p> </p> <p><strong>Human Proteome FASTA:</strong></p> <p>The FASTA file used for proteome searching in processing the proteomics data to build the screening database.</p> <p> </p> <p><strong>Hand Curated Organellar Proteomes:</strong></p> <p>Organellar proteomes used for organellar sorting and identification of proteins detected in the screen.</p> <p> </p> <p><strong>Polymer SEC Superdex75:</strong></p> <p>Size exculsion chromatography traces for chloroSMA series of polymers. Was used to characterize length and population polydispersity.</p> <p> </p> <p><strong>Negative Stain Raw:</strong></p> <p>RAW TEM scope images of purified synaptophysin-vamp2 containing nanodiscs. Populatoin size distribution was determined.</p> <p> </p> <p><strong>FSEC Polymer CS80:</strong></p> <p>Fluoresence size exclusion chromatogram for purified synaptophysin-vamp2 containing nanodiscs to ensure population homogeneity and purity.</p> <p><strong>NMR Raw data:</strong></p> <p>NMR raw files for characterizing the in-house synthesized Chloro-SMA series and AASTY series.</p> <p> </p>
Characterizing cell-type spatial relationships across length scales in spatially resolved omics data: data repository
<h1>CRAWDAD</h1> <p>Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we develop CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/.</p> <p>During CRAWDAD's development, we generated simulated datasets and new cell-type annotations for human spleen data, provided here. The external datasets such as the mouse cerebellum, mouse embryo, mouse brain, and human breast cancer data used in the paper can be found in their original publication. See more information in CRAWDAD's data availability statement.</p> <h2>Simulated Datasets</h2> <ul> <li>sim.csv: the simulated data. Used in Figure 1 b-g, Supplementary Figure 1 a-c, and Supplementary Figure 9 a-b.</li> <li>ext_sim.csv: the extended simulated data. Used in Supplementary Figure 1 d-f.</li> <li>null_sim_visualization.csv: the null simulated data. Used to generate the plots Supplementary Figure 2 a-d.</li> <li>null_sim_1.csv - null_sim_10.csv: the 10 null simulated datasets. Used to quantitatively compare CRAWDAD, Squidpy’s co-occurrence implementation, and Ripley’s K Cross.</li> </ul> <h2>HuBMAP Datasets</h2> <ul> <li>pkhl.csv: annotated cell types and positions of sample HBM389.PKHL.936 from donor HBM966.VNKN.965. Used in Figure 5 a-h, Supplementary Figure 5 a, Supplementary Figure 7 a-c, and Supplementary Figure 8 c. doi:10.35079/HBM389.PKHL.936</li> <li>xxcd.csv: annotated cell types and positions of sample HBM772.XXCD.697 from donor HBM966.VNKN.965. Used in Figure 5 d-h, Supplementary Figure 5 a-c, and Supplementary Figure 7 a-c. doi:10.35079/HBM772.XXCD.697</li> <li>fsld.csv: annotated cell types and positions of sample HBM342.FSLD.938 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM342.FSLD.938</li> <li>pbvn.csv: annotated cell types and positions of sample HBM825.PBVN.284 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM825.PBVN.284</li> <li>ksfb.csv: annotated cell types and positions of sample HBM556.KSFB.592 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM556.KSFB.592</li> <li>ngpl.csv: annotated cell types and positions of sample HBM568.NGPL.345 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM568.NGPL.345</li> </ul> <h2>External Datasets</h2> <ul> <li>Mouse cerebellum: Used in Figure 2 a-e, Supplementary Figure 3 a-b, Supplementary Figure 4 a-d, and Supplementary Figure 8 a.</li> <li>Mouse embryo: Used in Figure 2 f-j, Supplementary Figure 3 c-d, Supplementary Figure 4 e-h, and Supplementary Figure 8 b.</li> <li>Human breast cancer: Used in Figure 3 a-c.</li> <li>Mouse brains: Used in Figure 4 a-e.</li> </ul>
Large structural variations in the haplotype-resolved African cassava genome
<p>Cassava TME7 haplotype resolved assemblies and annotation</p> <p> </p> <p>ABSTRACT:</p> <p>Cassava (<em>Manihot esculenta</em> Crantz, 2n=36) is a global food security crop. Cassava has a highly heterozygous genome, high genetic load, and genotype-dependent asynchronous flowering. It is typically propagated by stem cuttings and any genetic variation between haplotypes, including large structural variations, is preserved by such clonal propagation. Traditional genome assembly approaches generate a collapsed haplotype representation of the genome. In highly heterozygous plants, this results in artifacts and an oversimplification of heterozygous regions. We used a combination of Pacific Biosciences (PacBio), Illumina, and Hi-C to resolve each haplotype of the genome of a farmer-preferred cassava line, TME7 (Oko-iyawo). PacBio reads were assembled using the FALCON suite. Phase switch errors were corrected using FALCON-Phase and Hi-C read data. The ultra-long-range information from Hi-C sequencing was also used for scaffolding. Comparison of the two phases revealed more than 5,000 large haplotype-specific structural variants affecting over 8 Mb, including insertions and deletions spanning thousands of base pairs. The potential of these variants to affect allele specific expression was further explored. RNA-seq data from 11 different tissue types were mapped against the scaffolded haploid assembly and gene expression data are incorporated into our existing easy-to-use web-based interface to facilitate use by the broader plant science community. These two assemblies provide an excellent means to study the effects of heterozygosity, haplotype-specific structural variation, gene hemizygosity, and allele specific gene expression contributing to important agricultural traits and further our understanding of the genetics and domestication of cassava.</p>
Vectra Polatis image of human colorectal cancer (CRC1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Two 4 µm thick serial sections were cut from CRC1 FFPE block using a microtome. The first slide was dewaxed and rehydrated before carrying out HIER with Antigen Retrieval Reagent-Basic (R&D Systems). The tissue was then blocked and incubated with the anti-CD3 antibody (Dako, Supplementary Table 2) followed by horseradish peroxidase (HRP) conjugated anti-rabbit antibody (Dako) and stained with 3,3' diaminobenzidine (DAB) substrate (Abcam) and haematoxylin. Areas with CD3<sup>+</sup> infiltration in the proximity of the tumour invasive margin were identified by a clinical pathologist (M. R-J.)</p> <p>The second slide was stained with a panel of six antibodies (CD8, PD1, Ki67, PDL1, CD68, GzB, Supplementary Table 2), Opal fluorophores and 4’,6-diamidino-2-phenylindole (DAPI) on a Ventana Discovery Ultra automated staining platform (Roche). Expected expression and cellular localisation of each marker as well as fluorophore brightness were used to minimise fluorescence spillage upon antibody-Opal pairing. Following a one-hour incubation at a 60°C, the slide was subjected to an automated staining protocol on an autostainer. The protocol involved deparaffinisation (EZ-Prep solution, Roche), HIER (DISC. CC1 solution, Roche) and seven sequential rounds of: one hour incubation with the primary antibody, 12 minutes incubation with the HRP-conjugated secondary antibody (DISC. Omnimap anti-Ms HRP RUO or DISC. Omnimap anti-Rb HRP RUO, Roche) and 16 minute incubation with the Opal reactive fluorophore (Akoya Biosciences). For the last round of staining, the slide was incubated with Opal TSA-DIG reagent (Akoya Biosciences) for 12 minutes followed by Opal 780 reactive fluorophore for our hour (Akoya Biosciences). A denaturation step (100°C for 8 minutes) was introduced between each staining round in order to remove the primary and secondary antibodies from the previous cycle without disrupting the fluorescent signal. The slide was counterstained with DAPI (Akoya Biosciences) and coverslipped using ProLong Gold antifade mounting media (Thermo Fisher Scientific). The Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences) was used to scan the labelled slide. Six fields of view, within the area selected by the pathologist, were scanned at 20x and 40x magnification using appropriate exposure times and loaded into inForm{Kramer, 2018 #23} for spectral unmixing and autofluorescence isolation using the spectral libraries. After spectral unmixing and merging of six 20x fields of view for a total of >5mm<sup>2</sup> ROI (Table 2), one single-tiff image was extracted for each marker and its intensity was rescaled from 0 to 1 with custom R scripts.</p>
Residual dynamics resolves recurrent contributions to neural computation
<p>This repository contains all the neural and simulated datasets used in the above publication. All the data are stored as .mat files.</p> <p>This repository is organized into three main folders. For ease of use, we recommend extracting the contents of each of the three folders into a single folder titled ‘data’.</p> <ol> <li>neuraldata –Raw and preprocessed neural data. Organized into the following folders: <ol> <li>array_TDR_dotsTask_data – contains the non-processed neural data of each session for both monkeys</li> <li>array_dotsTask_beh_xx – contains the behavioral data of each session for both monkeys (xx = monkey)</li> <li>array_reppasdotsTask_datasegmented_binsize=xxms – contains the pre-processed neural data (session wise) of both monkeys for a specified bin size (xx = bin size)<br> </li> </ol> </li> <li>simulations – Data from the simulated models. Organized into the following folders: <ol> <li>toymodels – Simulated data from all models of decisions/movement. Also includes simulations of the augmented line attractor and rotational dynamics models.</li> <li>twoarearnn – Simulated data from the two types of (feedback and nofeedback) two-area RNN models, along with the impulse response simulations.<br> </li> </ol> </li> <li>analyses – contains processed data files from various stages of the analysis pipeline. Only the most important files are listed below: <ol> <li>xx_aligned_reppasdotsTask_binsize=45ms.mat – session-aligned neural data for a specified monkey (xx = monkey)</li> <li>xx_ndim=8_lag=3_alpha=200_50_allconfigsresdynresults.mat – contains the residual dynamics for a specified monkey.</li> <li>xx_aligned_hankelagdimCV_reppasdotsTask_binsize=45ms.mat and xx_aligned_smoothnessCV_reppasdotsTask_binsize=45ms.mat – results of cross-validation of the residual dynamics pipeline for a specified monkey (xx = monkey)</li> </ol> </li> </ol>
Supplementary material to: Highly resolved topography and illumination at Mercury south pole from MESSENGER MDIS-NAC
<p>We produced a new higher-resolution topographic map of Mercury’s south polar region (75°-90° South, covering ~1.3 million km<sup>2</sup>) by using data collected by the NASA MESSENGER spacecraft’s Mercury Dual Imaging System (MDIS; Hawkins et al, 2007) over the years 2011-2015. This new map enables, <em>e.g.</em>, the first detailed modeling of illumination and thermal conditions in these southern radar-bright locations and the first constraints on the nature and history of volatiles residing there, but it is also intended as a resource for other geophysical analyses and for the preparation of the BepiColombo mission, currently en-route to the planet.</p> <p>For more details, please visit <a href="https://pgda.gsfc.nasa.gov/products/88">https://pgda.gsfc.nasa.gov/products/88</a>.</p> <p><strong>Products:</strong></p> <p>DEM (interpolated), DEM (filled), Slopes, PSR masks</p> <p>All these files (except the PSR masks shapefile) are 250 m/pix GeoTiffs with south polar stereographic X/Y coords in meters.</p> <p><br> <em>If using these products, please cite:</em><br> Bertone, S., E. Mazarico, M.K. Barker, M. Siegler, J. M. Martinez Camacho, C. Hamill, A. Glatzenberg, N. L. Chabot, 2022: <em>Highly resolved topography and illumination at Mercury south pole from MESSENGER MDIS-NAC</em>. The Planetary Science Journal, 02/2023, <a href="http://dx.doi.org/10.3847/PSJ/acaddb">doi:10.3847/PSJ/acaddb</a></p>
In situ Time-Resolved Spectroelectrochemistry Reveals Limitations of Biohybrid Photoelectrode Performance
<p>Unprocessed and Source Data related to the Publication 'In situ Time-Resolved Spectroelectrochemistry Reveals Limitations of Biohybrid Photoelectrode Performance' in <em>Joule.</em></p>
Time and momentum resolved characterization of hybrid plasmonic heterostructure Au/WSe2
<p>Dataset attached to paper titled "Observation of Multi-Directional Energy Transfer in a Hybrid Plasmonic-Excitonic Nanostructure" with time and momentum characterization of a 2D palsmonic heterostructure formed by Au nanoislands on bulk WSe2. It contains Angle-resolved photoemission spectroscopy (ARPES) and time-resolved ARPES data (trARPES.zip); femtosecond electron diffraction (FED) data (FED.zip); optical absorption spectroscopy data (Optical_absorbance.zip) and Transmission electron microscopy micrographs (TEM.zip).</p> <p>For <strong>trARPES.zip</strong>, the following table reports the grid of measurements and most important parameters:</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Sample temperature (K)</strong></td> <td> <p><strong>Pump Wavelength (nm)</strong></p> </td> <td><strong>Pump Duration (fs)</strong></td> <td><strong>Material</strong></td> </tr> <tr> <td>trARPES_Metis_002.mpes.nxs</td> <td>300</td> <td>800</td> <td>35</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2124.mpes.nxs</td> <td>300</td> <td>800</td> <td>35</td> <td>WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2146.mpes.nxs</td> <td>70</td> <td>800</td> <td>35</td> <td>WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2197.mpes.nxs*</td> <td>70</td> <td>800</td> <td>35</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2198.mpes.nxs*</td> <td>70</td> <td>800</td> <td>35</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2212.mpes.nxs*</td> <td>300</td> <td>800</td> <td>35</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2219.mpes.nxs*</td> <td>300</td> <td>800</td> <td>35</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan3159.mpes.nxs</td> <td>300</td> <td>1030</td> <td>200</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan3164.mpes.nxs**</td> <td>300</td> <td>1030</td> <td>200</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan3185.mpes.nxs</td> <td>300</td> <td>1030</td> <td>200</td> <td>WSe<sub>2</sub></td> </tr> </tbody> </table> <p>*These scans are acquired with higher angular dispersion requiring separate scans for K and Sigma valleys.</p> <p>**Fluence scan.</p> <p><strong>FED.zip</strong> contains the following subfolders:</p> <ul> <li><em>Manuscript_Figure</em>: Experimental data and fit parameters depicted in Figure 4 of the main article.</li> <li><em>Analysis</em>: Additional information for the FED data including: raw data descriptions (delay, power, filename and more), Matlab scripts with comments, masks and backgrounds for image processing. The <em>Static_patterns</em> subfolder contains electron diffraction patterns of pure WSe<sub>2</sub> flakes and Au-covered WSe<sub>2</sub> flakes.</li> </ul> <p><strong>Optical_absorbance.zip</strong> contains the following subfolders & subfiles:</p> <ul> <li><em>without Au</em> & <em>with Au</em> containing all the optical measurements of pristine and Au-covered WSe<sub>2</sub> flakes, respectively.</li> <li><em>comparison_with_and_without_Au.xlsx</em> contains the analysis of the difference curves</li> <li><em>manuscript_figure.txt </em>contains the data that were used in Figure 1 of the main article.</li> </ul> <p> </p>
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