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Dynamics of SARS-CoV-2 spike protein in open and closed states and identification of key structural perturbations upon mutations
<p>The SARS-Cov-2 spike protein resides on the exterior surface of the coronavirus, and therefore, acts as the first point of contact that mediates cell attachment and fusion. During this process, it undergoes dramatic conformational changes upon host receptor binding. We are leveraging high-performance computing to identify these structural perturbations in wildtype and mutant spike protein models. The files contain structures from molecular dynamics simulations of closed SARS-Cov-2 spike protein embedded in POPC membrane.</p>
FT5 Schubert 13-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT5 Schubert 13-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video (formerly listed as "Anonymous 6").</p> <p> </p>
Future Ocean Warming May Threaten Key Photosynthetic Microbes
<h2>Description</h2> <p>The datasets supporting the conclusions of this article, including field measurements of <em>Prochlorococcus</em> division rates, are available in this repository. </p> <p>The R code performs the following tasks:</p> <ul> <li>Loads data from various sources, including lab experiments, dilution experiments, and in-situ measurements.</li> <li>Calculates thermal norm predictions using different models (Eppley, Hinshelwood, Eppley-Norberg) to predict division rates based on temperature.</li> <li>Generates figures to visualize the results, including latitudinal and temperature effects on division rates, model predictions compared with observed data, and changes in primary production under different emission scenarios.</li> <li>Fits the Hinshelwood model to culture data and extracts best-fit parameters.</li> <li>Performs bootstrapping to estimate uncertainty in the Hinshelwood model parameters.</li> <li>Calculates confidence intervals for the bootstrapped parameters.</li> </ul> <h2>R Scripts</h2> <ul> <li><strong>Ribalet_main.R:</strong> This script contains the main analysis code, including data loading, model fitting, figure generation, and bootstrapping.</li> <li><strong>Ribalet_fitting.R:</strong> This script defines functions for fitting different growth models to the data and estimating model parameters.</li> </ul> <h2>Requirements</h2> <ul> <li>R version 4.4.2 (2024-10-31)<br>Platform: aarch64-apple-darwin20<br>Running under: macOS Sequoia 15.1.1</li> <li>Matrix products: default<br>BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib <br>LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0</li> <li>attached base packages:<br>[1] parallel stats graphics grDevices utils datasets <br>[7] methods base </li> <li>other attached packages:<br> [1] DEoptim_2.2-8 arrow_15.0.1 ggpubr_0.6.0 lubridate_1.9.3<br> [5] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 <br> [9] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 <br>[13] tidyverse_2.0.0</li> </ul> <h2>Installation</h2> <p>Install the required R packages:</p> <div> <div>Code snippet</div> <div> <div> <pre><code>install.packages(c("tidyverse", "ggpubr", "arrow", "DEoptim")) </code></pre> </div> </div> </div> <h2>Usage</h2> <p>The scripts will generate figures and output files in the same directory.</p> <h2>Input Data</h2> <p>The code requires the following input data files:</p> <ul> <li>culture.csv</li> <li>dilution.csv</li> <li>abundance.csv</li> <li>mpm.csv</li> <li>model_results.parquet</li> <li>bootstrap_projections.csv</li> <li>modeled-thermal-traits.tsv</li> <li>sst.parquet</li> <li>culture_syn.csv</li> </ul> <p>Please ensure that these files are present in the same directory as the R script files.</p> <h2>Output Data</h2> <p>The code generates the following output files:</p> <ul> <li>Figures: Figure1.png, Figure2.png, Figure3.png, FigureS1.png, FigureS2.png, FigureS3.png, FigureS4.png, FigureS5.png, FigureS6.png, FigureS9.png, FigureS11.png, FigureS12.png, FigureS13.png, FigureS14.png, FigureS15.png</li> <li>CSV files: bootstrap_parameters.csv, cultures_thermal_reactions.csv</li> </ul> <h2>License</h2> <p>This code is licensed under the MIT License.</p>
What are the key tensions in educational technology (Edtech)?
<p>This video outlines the key challenges and tensions that have arisen in the higher education sector as it increasingly operates using digital technology, and how these can be used to direct future improvements of digital processes in higher education. The findings come from the ESRC-funded project 'Universities and Unicorns: building digital assets in the higher education industry'.</p>
Integrated Preservation of Water Activity as Key to Intensified Chemoenzymatic Synthesis of Bio-Based Styrene Derivatives
<p>The valorization of lignin-derived feedstocks by catalytic means enables their defunctionalization and upgrading to valuable products. However, the development of productive, safe, and low-waste processes remains challenging. This paper explores the industrial potential of a chemoenzymatic reaction performing the decarboxylation of bio-based phenolic acids in wet cyclopentyl methyl ether (CPME) by immobilized phenolic acid decarboxylase from <em>Bacillus subtilis</em>, followed by a base-catalyzed acylation. Key-to-success is the continuous control of water activity, which fluctuates along the reaction progress, particularly at high substrate loadings (triggered by different hydrophilicities of substrate and product). A combination of experimentation, thermodynamic equilibrium calculations, and MD simulations revealed the change in water activity which guided the integration of water reservoirs and allowed process intensification of the previously limiting enzymatic step. With this, the highly concentrated sequential two-step cascade (400 g·L–1) achieves full conversions and affords products in less than 3 h. The chemical step is versatile, accepting different acyl donors, leading to a range of industrially sound products. Importantly, the finding that water activity changes in intensified processes is an academic insight that might explain other deactivations of enzymes when used in non-conventional media.</p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the region of the Yucatán Peninsula
<p>The ensemble provides future projections of key marine variables under climate change for the region of the Yucatán Peninsula. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).<br> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the Chilean coast, see “Related identifiers”.</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Chilean coast
<p>The ensemble provides future projections of key marine variables under climate change for the Chilean coast. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and three different variables (potential temperature, dissolved oxygen, and pH) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Bay of Biscay
<p>The ensemble provides future projections of key marine variables under climate change for the Bay of Biscay region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the North Sea
<p>The ensemble provides future projections of key marine variables under climate change for the North Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Mediterranean Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Mediterranean region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the North Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p> <p> </p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Baltic Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Baltci Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
EXIT_Dataset_Survey on key policies and perceptions_2023_Survey data Serbia
<p>Survey data generated in WP2 of the EXIT project in the context of Serbia.The purpose of the survey was to collect data on the perceptions of policies used to address territorial inequalities in Serbia. </p>
Quick keys to the Bominae genera of South Africa (Araneae: Thomisidae)
<p>In this paper, keys are provided to identify the genera <em>Avelis</em> Simon, 1895, <em>Holopelus</em> Simon, 1886, <em>Parabomis,</em><br>1901 and <em>Thomisops</em> Karsch, 1879 and their species in the field and from photographs. With their small and round bodies they resemble seeds and may easily be overlooked in the field. The latest information on their distribution and conservation status in South Africa is provided.</p>
A geospatial dataset of lichen key attributes in the Earth's three poles
<p>To develop the geospatial dataset, we initially defined two lichen attributes: color type and growth form. These attributes were chosen due to their significant correlation with lichen physiological and biochemical characteristics, as well as their association with reflection spectra. Each record of this geospatial dataset consists of information such as scientific name, longitude, latitude, ecoregion name, biome name, color type, growth form, and the occurranceID belongs to the GBIF original dataset.</p> <p>This dataset serves as a foundational resource for extensive investigations into the intricate interplay between lichen physiology and the environment, addressing a significant knowledge gap in the field. Furthermore, our dataset holds the potential to address challenges associated with remote sensing monitoring of lichens, a longstanding issue in vegetation remote sensing. Precise in situ observation records, as provided by our dataset, can facilitate the development of remote sensing techniques tailored for lichen monitoring.</p> <p>"Program" is the code used in the process of establishing the geospatial dataset of lichen key attributes in the Earth’s three poles.</p> <p> </p>
Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures
<p>This Dataset comprises two sub-sets of information:</p> <ul> <li>Database and Results of the work present in the paper "Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures" published in The Journal of Physical Chemistry B (https://doi.org/10.1021/acs.jpcb.4c04432).</li> <li>Sample of the code used, in order to reproduce any of the results presented above. This can be found in the previous version of this Dataset (v1.0 https://zenodo.org/records/11216901)</li> </ul> <p> </p> <p>Regarding the sample code, an example for all ANN Models used in this work is provided. This includes the three models used:</p> <ol> <li>One based only on Critical Properties of Ionic Liquids (CRT Model)</li> <li>One based only on Structural Properties of Ionic Liquids (STR Model)</li> <li>One combination of the previous models, taking into account both Critical and Structural Properties (COMB Model)</li> </ol> <p>In this manner, it is possible to observe the differences between the performance of the different models, either through statiscal analysis or using graphical representation. This allows for the benchmarking to be done in a more concise way.</p>
Raw Data and Scripts for manuscript submitted to Oikos as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in a Subalpine Grassland Ecosystems'
<p>Raw Data and Scripts for manuscript submitted to PCI as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in Subalpine Grassland Ecosystems'</p>
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution; Supplementary Information"
<p>These files contains datasets from which the Figures appearing in the Supplementary Information have been calculated. </p> <p>Description of datasets:</p> <p>Datasets related to SupplFig1 contain two columns: Frequency in Hz and phase noise in rad^2/Hz</p> <p>Data_SupplFig1_stabilised_fringes: psd of the phase noise calculated from the interference fringes in a stabilised condition</p> <p>Data_SupplFig1_unstabilised_fringes: psd of the phase noise calculated from the interference fringes in an unstabilised condition</p> <p>Data_SupplFig1_roundtrip_sensing_laser: psd of the sensing laser signal after a round-trip in the interferometer, calculated from self-heterodyne beatnote</p> <p>Data_SupplFig1_differential_roundtrip_sensing_vs_reference_laser: psd of the difference between the round-trip self-heterodyne beatnotes at the sensing and reference laser wavelengths</p> <p>Datasets related to SupplFig2 contain two columns: time in seconds and normalised intensity (calculated as detailed in the main publication).</p> <p>Data_SupplFig2_High_power_PD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when no artificial phase drift was applied</p> <p>Data_SupplFig2_High_power_PD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_High_power_SPD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when no artificial phase drift was applied </p> <p>Data_SupplFig2_High_power_SPD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_Attenuated_SPD_free_evol: normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when no artificial phase drift was applied </p> <p>Data_SupplFig2_Attenuated_SPD_phase_drift: : normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when an artificial phase drift was applied (8pi/s)</p> <p> </p>
NMR Data for "Unveiling a Key Catalytic Pocket for the Ruthenium NHC-Catalysed Asymmetric Heteroarene Hydrogenation" (DOI: 10.1039/D1SC06409F)
<p># NMR Data for "Unveiling a Key Catalytic Pocket for the Ruthenium NHC-Catalysed Asymmetric Heteroarene Hydrogenation" (DOI: 10.1039/D1SC06409F)</p> <p>In the following, the original NMR Data for the publication "Unveiling a Key Catalytic Pocket for the Ruthenium NHC-Catalysed Asymmetric Heteroarene Hydrogenation" (DOI: 10.1039/D1SC06409F) is provided. </p> <p>## Experimental methodology and associated data</p> <p>### Dataset 200113.40a</p> <p>Inside an argon filled glovebox, 4.6 mg of **1-A** (5.4 µmol) was dissolved in 0.65 mL THF-d<sub>8</sub> (distilled over sodium/benzophenone and stored over 3 Å molecular sieves), yielding a clear, dark yellow solution. The solution was then transferred into a medium pressure J Young NMR tube and sealed. Subsequently, initial NMR spectra under argon atmosphere were recorded (t = 0 h).</p> <p>Afterwards, 2 bar of H2 pressure were applied to the J Young NMR tube, resulting in a partial H<sub>2</sub> pressure of 1 bar (due to the presence of 1 bar argon). After shaking to dissolve the added H<sub>2</sub>, a slow color change to orange could be observed. The reaction was monitored using 1H NMR (Bruker AV 400, 400 MHz), showing consumption of dissolved H<sub>2</sub> as evidenced by a decrease in intensity for the H<sub>2</sub> signal at δ(1H) = 4.55 ppm. Six hours after the first addition of H<sub>2</sub>, the J Young NMR tube was re-pressurized with 2 bar H<sub>2</sub>, and again after 36 h, shaking the NMR tube regularly to dissolve H<sub>2</sub>. After the third pressurization, no further decrease of dissolved H<sub>2</sub> could be observed.</p> <p>#### NMR experiments and timestamps</p> <p>File name | NMR Experiment | Time stamp / h (relative to H<sub>2</sub> addition)<br> --- | --- | ---<br> 200113.40a.1 | 1H | 0<br> 200113.40a.2 | 1H | 0<br> 200113.40a.3 | 1H | 0.5<br> 200113.40a.4 | 1H | 1<br> 200113.40a.5 | 1H | 2.5<br> 200113.40a.6 | 1H | 2.5<br> 200113.40a.7 | 1H | 5 <br> 200113.40a.8 | 1H COSY-45 | 5<br> 200113.40a.9 | 1H | 6.5<br> 200113.40a.10 | 1H | 22.5<br> 200113.40a.11 | 1H | 30<br> 200113.40a.12 | 1H | 31<br> 200113.40a.13 | 29Si-inept | 31<br> 200113.40a.14 | 1H-29Si HMBC | 31<br> 200113.40a.15 | 1H | 45.5<br> 200113.40a.16 | 1H-29Si HMBC | 45.5<br> 200113.40a.17 | 1H | 55<br> 200113.40a.18 | 1H | 69.5<br> 200113.40a.110 | 1H (larger measurement window) | 22.5<br> 200113.40a.111 | 1H (larger measurement window) | 30<br> 200113.40a.115 | 1H (larger measurement window) | 45.5<br> 200113.40a.117 | 1H (larger measurement window) | 55<br> 200113.40a.118 | 1H (larger measurement window) | 69.5</p> <p>### Dataset 200117.40a</p> <p>69.5 h after the first addition of H<sub>2</sub> no significant changes could be observed in the 1H NMR spectra anymore. At this point, 0.15 mL of a 0.052 M solution of benzofuran in THF-d<sub>8</sub> (7.8 µmol benzofuran, ca. 1.5 equivalents relative to **1-A**) were added to the NMR tube while applying 2 bar of H<sub>2</sub> pressure. No significant color change was observed upon addition of the substrate. Subsequently, the NMR tube was sealed and the reaction was monitored using 1H NMR for an additional 119 h, especially following the hydride signals at δ(1H) = −3.7 ppm and δ(1H) = −3.8 ppm as well as the signals of 2,3-dihydrobenzofuran at δ(1H) = 4.48 ppm and δ(1H) = 3.15 ppm. After 119 h of reaction time, the color of the reaction solution had changed to light orange.</p> <p>#### NMR experiments and timestamps</p> <p>File name | NMR Experiment | Time stamp / h (relative to substrate addition)<br> --- | --- | ---<br> 200117.40a.1 | 1H | 0.5<br> 200117.40a.2 | 1H | 1.5<br> 200117.40a.3 | 1H | 7<br> 200117.40a.4 | 1H | 24<br> 200117.40a.5 | 1H | 24.5 <br> 200117.40a.6 | 1H | 72 <br> 200117.40a.7 | 1H COSY-45 | 72<br> 200117.40a.101 | 1H (larger measurement window) | 0.5<br> 200117.40a.103 | 1H (larger measurement window) | 7<br> 200117.40a.104 | 1H (larger measurement window) | 24</p>
Eremohaplomydas, Haplomydas, and Lachnocorynus matrix-based key in SDD-format
<p>Matrix-based, multi-entry key to species of the genera Eremohaplomydas, Haplomydas, and Lachnocorynus (Diptera: Mydidae) developed with Lucid Builder v4 in XML Structure of Descriptive Data (SDD) format.</p>
Optimal elevated agrivoltaic system design and key performance indicators across Europe based on three crop light levels
<p>Optimal elevated (stilted) agrivoltaic system design (PV coverage ratio) is given on a European gridded level (25km grid and NUTS3 regions) based on three light levels: shade-loving crops (daily light integral (DLI) of 12 mol/m²day), shade-tolerant crops (DLI of 12 mol/m²day) and shade-intolerant crops (DLI of 25 mol/m²day)</p> <p>Estimations of other performance indicators are given: power capacity (kWp/ha), energy production (MWh/ha), levelized cost of electricity (€/MWh) and land equivalent ratio (LER -).</p> <p>The assumptions and methodology of this dataset can be found in the article "Geospatial assessment of elevated agrivoltaics on arable land in Europe to highlight the implications on design, land use and economic level."</p> <p>Interactive maps can be found on https://iiw.kuleuven.be/apps/agrivoltaics/maps.html</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.