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Data from: "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat"
<p><em><strong>Data and code used for the study : "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat".</strong></em></p> <p>The script "Manuscript_Analyses.R" contains all code for the statistical analysis presented in the manuscript (main text & supplementary information). This script uses files produced in the folder "Locus-by-locus analysis" as inputs, and "manhattan_custom.R" as a source function ("manhattan_custom.R" is used to highlight SNPs in a given interval and to write specified SNPs name on manhattan plots). The file "Traits_monocultures.csv" contains the 20 functional traits measured on the 179 monoculture plots (see Supplementary Methods for more information on trait measurement). This file is used as an input in the script "Manuscript_Analyses.R".</p> <p>The "Locus-by-locus analysis" folder contains all analyses conducted to test the effect of allelic richness on the four variables of interest: Grain Yield (GY, g/m²), Spike Number per m² (SNb, nb spikes/m²), Thousand Kernel Weight (TKW, g), and Septoria tritici blotch (STB) severity. The locus-by-locus analysis is performed with the script "Allelic_richness_locus_by_locus_analysis.R". This analysis generates a list of .csv files with one file per chromosome. Each file contains the pvalues and estimated effect sizes of the tested SNPs for the given chromosome. These output files are stored in folders named after the variables for which the effect of allelic richness was tested ("RAW_GY", "RAW_SNb", "RAW_TKW", and "RAW_severity"). The script "Allelic_richness_locus_by_locus_output_processing.R" combines all .csv files into a single dataframe and produces three diagnostic plots: Manahattan plots, histograms of p-value distributions, and p-value q-q plots. p-value thresholds were computed based on a Family-Wise Error Rate of 5% using the Galwey correction. This is done in the "pvalue_thresholds" folder with the "Meff_computation.R" script. "Meff_computation.R" uses the "Meff_function.R" as a source function and generates "GY_thresholds.csv" and "STB_thresholds.csv" as outputs (these files contains different thresholds computed according to different methods but we only retained the Galwey method (most recent) for the analyses. Since GY, SNb, and TKW were analyzed with the same number of SNPs (~19K), we used the same significance threshold for the three variables ("GY_thresholds.csv"), whereas we computed a different thresholds for STB ("STB_thresholds.csv") for which we could only include ~6K SNPs in the analysis. The "geno_pos.csv" file contains the physical positions of the SNPs.</p> <p>Upstream the locus-by-locus analysis, phenotypic and genotypic files are prepared in the "Phenoytpic file preparation" and "Genotypic file preparation" folders, respecively.</p> <p>The phenotypic file preparation includes the correction of yield-related variables (GY, SNb, and TKW) for spatial auto-correlation in the "Spatial_analyses_YLD_variables" folder, and the computation of plot-level variables from individual-level variables with the "Allelic_richness_phenotypic_file_prep.R" script. In this script, we compute both absolute plot values (termed "RAW_...) and relative plot values (termed "RYT_..., only for mixture plots). All phenotypic files have the same structure with the same first 6 columns: "focal" = identity of the focal genotype (the one for which the variable is measured, only relevant for variables measured at the individual-level), "neighbor" = identity of the neighbor genotype (the neighbor of the genotype for which the variable is measured, only relevant for variables measured at the individual-level), "pair" = identity of the genotypic pair (combines the identity of the focal and the neighbor genotypes), "assoc" = type of plot ("M" = monoculture or pure stand plot, "P" = mixture plot), "row" = position of the plot along the smallest dimension of the grid (see Figure 1), "column" = position of the plot along the largest dimension of the grid (see Figure 1).</p> <p>The genotypic file preparation is done with the "Allelic_richness_genotypic_file_prep.R" script and includes SNP filtering, computation of matrices of allelic richness, and computation of matrices of genetic similarity between genotypic pairs. The analysis is done separatly for yield-related variables and for STB severity since the two types of variable were not measured on the same set of plots.</p>
Data for "Two-phase mixture of iron-nickel-silicon alloys in the Earth's inner core"
<p>This file is the dataset used in the article "Two-phase mixture of iron-nickel-silicon alloys in the Earth's inner core", <em>Commun. Earth Environ.</em> <strong>2</strong>, 225 (2021). https://doi.org/10.1038/s43247-021-00298-1</p>
Dataset for: Photoreactivity of an Exemplary Anthracene Mixture Revealed by NMR Studies, Including a Kinetic Approach
<p>Anthracenes are an important class of acenes. They are being utilized more and more often in chemistry and materials science, due to their unique rigid molecular structure and photoreactivity. In particular, photodimerization can be harnessed for the fabrication of novel photoresponsive materials. Photoreactions between the same anthracenes have been investigated and utilized in various fields, while reactions between varying anthracenes have barely been investigated. Here, Nuclear Magnetic Resonance (NMR) spectroscopy is employed for the investigation of the photodimerization of two exemplary anthracenes: anthracene (A) and 9-bromoanthracene (B), in the solutions with only A or B, and in the mixture of A and B. Estimated k values, derived from the presented kinetic model, showed that the dimerization of A was 10 times faster in comparison with B when compounds were investigated in separate samples, and 2 times faster when compounds were prepared in the mixture. Notably, the photoreaction in the mixture, apart from AA and BB, also yielded a large amount of the AB mixdimer. The investigation of a mixture with different anthracenes can deliver relative reactivity under the same experimental conditions. This results in a better understanding of the photoisomerization processes, which is essential for the practical utilization of the photodimerization of anthracenes.</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The study employs 400 MHz 1H-NMR spectroscopy for the investigation of the photodimerization of two exemplary anthracenes: anthracene (A) and 9-bromoanthracene (B) in the solutions with only A or B and in the mixture of A and B. Experiments were performed using different concentrations and oxygenation level of the sample. CD2Cl2 was used as a solvent. </p> <p> </p> <p><strong>Given data include eight 1H-NMR experiments processed by TopSpin 4.0.7 software of:</strong></p> <ol> <li> 4.5 mM of A (File: 1_A_concentration I.fid.zip)</li> <li> 2.25 mM of A (File: 2_A_concentration II.fid.zip)</li> <li> 2.25 mM of A with oxygen (File: 3_A_concentration II_Oxygen.fid.zip)</li> <li> 4.5 mM of B (File: 4_B_concentration I.fid.zip)</li> <li> 2.25 mM of B (File: 5_B_concentration II.fid.zip)</li> <li> 2.25 mM of B with oxygen (File: 6_B_concentration II_Oxygen.fid.zip)</li> <li> Mixture of A and B with molar mixing ratio 1:1.3 (File: 7_Mixture_ratio I.fid.zip)</li> <li> Mixture of A and B with molar mixing ratio 1:2.3 (File: 8_Mixture_ratio II.fid.zip)</li> </ol> <p>-------------------------------------------------------------</p>
Agronomic performance of cultivar mixtures and pure stands of 8 winter wheat varieties, obtained from mixture field trials in Switzerland from 2021 to 2023, together with associated functional traits measurements
<p>This dataset contains agronomic performance data for 8 Swiss winter wheat cultivars, grown in pure stands and in mixtures at 3 locations in Switzerland during 3 growing seasons (2021-2023). The dataset has been used to analyse the effects of cultivar mixtures on agronomic performance and stability, which is published in <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.eja.2024.127504" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.eja.2024.127504</a>. </p> <p>The dataset contains notably grain yield, protein content, thousand kernel weight, specific weight, and Zeleny sedimentation value, as well as functional traits measured at flowering for each mixture and pure stand plot. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h3>Methods </h3> <p> <em>Field trials </em></p> <div>Field trials were set up over the course of three growing seasons – 2020/2021, 2021/2022 and 2022/2023 – in three sites across the Swiss Central Plateau. The experimental sites were located in Changins (46°19′ N 6°14′ E, 455m a.s.l), Delley (46°55′ N 6°58′ E, 494m a.s.l) and Utzenstorf (47°97′ N 7°33′ E, 483m a.s.l.). </div> <div>Experimental communities consisted of pure stand plots, 2-cultivars mixtures, and one plot with the 8 cultivars mixed. We sowed every possible combination of 2-cultivar mixtures, amounting to a total of 28 2-cultivar mixtures treatments, to which we added the 8-cultivar mixture. Each community was grown in a plot of 7.1 m<sup>2</sup> (1.5m∗4.7m). We used a complete randomized block design, with 3 replicates, the plots being randomized at each site within each block. Sowing was performed with a small plot drill (Wintersteiger plotseed TC). Density of sowing was 350 viable seeds/m<sup>2</sup>. For the mixtures, seeds were mixed beforehand at a 2 × 50 % mass ratio for 2-cultivars mixtures and 8 × 12.5 % for the 8-cultivar mixture. We chose this method of mixing as this is what is commonly done by farmers in Switzerland. Plots were sowed mechanically each autumn and fertilized with ammonium nitrate at a rate of 140 N/ha in 3 applications (40 N/ha at tillering stage/BBCH 22–29; 60 N/ha at the beginning of stem elongation/BBCH 30–31; 40 N/ha at booting stage/BBCH 45–47). The trials were grown according to the Swiss <em>Extenso</em> scheme, i.e. without any fungicide, insecticide, and growth regulator. Weeds were regulated twice or thrice per season with the application of herbicides commonly used in Switzerland.</div> <div> </div> <div><em>Ear density</em></div> <div> </div> <div>Before harvest, we manually harvested horizontal bands of 1.5 × 0.3 square meters per plot. The location of the band was randomly chosen but we avoided plot edges (i.e. the band was located at more than 0.5 m from the lower and upper edge of each plot). We counted the heads, and obtained ear density from the head counts.</div> <div> </div> <div><em>Trait measurements </em></div> <div> </div> <div>At flowering time, we randomly sampled 6 healthy leaves per plot. We immediately wrapped this leaf in moist cotton; this was stored overnight at room temperature in open plastic bags. The following day, we removed excess surface water on the leaf and weighted it to obtain its water saturated weight. This leaf was then scanned with a flatbed scanner (Perfection V39II, Epson), oven-dried in a paper envelope at 80°C for 72 hours, and subsequently weighed again to obtain its dry weight. Leaf Dry Matter Content (LDMC) was calculated as the ratio of leaf dry mass (g) to water saturated leaf mass (g). Using the leaf scans, we measured leaf area with the image processing software ImageJ. Specific Leaf Area (SLA) was calculated as the ratio of leaf area (cm2) to leaf dry mass (g).</div> <div> </div> <div><em>Phenology and height </em></div> <div> </div> <div>For each plot, we recorded the heading date as the day of the year, in which 50 % of the ears of the plot had fully emerged from the flag leaf. Plant height was measured in each plot at BBCH 59–75, by taking the average height in centimeters from the ground to the top of five random ears, excluding awns.</div> <div> </div> <p><em>Harvest and post harvest measurements</em></p> <p>At maturity, we harvested each plot with a combine harvester (Zürn 150, Schontal-Westernhausen, Switzerland). The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured hectoliter weight (test weight, HLW, kg/hl) and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15 % of humidity. Protein content (% of dry matter) was measured at the site level with a near-infrared instrument (ProxiMate™, Büchi instruments). Thousand kernel weight (TKW, g) was measured at the plot level with a Marvin seed analyzer (GTA Sensorik, Neubrandenburg, Germany). Zeleny sedimentation value was measured by the laboratory of Delley Seeds and Plants. </p> <p> </p>
Agronomic performance of cultivar mixtures of winter wheat varieties, obtained from mixture field trials at 5 locations in Switzerland from 2019 to 2020, together with yield data from the varieties in pure stand obtained from the national variety testing trial network
<p>This dataset contains agronomic parameters of 32 winter wheat variety mixtures tested during 2 growing seasons (2019-2020) at 5 locations in Switzerland, as well as yield data of these varieties in pure stands originating from the Swiss national variety testing network. The dataset has been used to investigate the links between asynchrony and yield stability, published in <a href="https://doi.org/10.1002/csc2.21151">https://doi.org/10.1002/csc2.21151</a>. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h2>Methods </h2> <p><em>Field trials </em></p> <p>The experiment took place in five sites across Switzerland, in 2019 and 2020. The sites were located in Nyon (1260), Delley (1567), Utzenstorf (3428), Zurich (8046), and Ellighausen (8566).</p> <p>Experimental communities consisted of 32 different two-variety mixtures grown in 7.1-m<sup>2</sup> plots (1.5 × 4.7 m). We replicated the mixture experiment three times per site with the exact same variety composition. We used a randomized block design, with plots being randomized at each site within each block. Density of sowing was 350 seeds/m<sup>2</sup>, and seeds were mixed beforehand at a 50:50 ratio in terms of mass. We used the 50:50 mass ratio as this is what is generally done in practice by farmers and seed suppliers. Plots were sown mechanically each autumn. The plots were mechanically fertilized according to the Principles of Agricultural Crop Fertilisation in Switzerland (Federal Office for Agriculture) with an average of 140 kg N/ha (ammonium nitrate), applied in three splits (40 at the tillering stage—60 at stem elongation stage—40 when the flag leaf is visible). The experimental trials were conducted following the extenso Swiss scheme, which means that there was no application of any fungicide, insecticide, or plant growth regulator. </p> <p>The performances of single varieties were obtained by going through the trials of the national variety testing program. We gathered the data for the years 2018/2019 and 2019/2020. The data regarding single varieties could be obtained for three out of the five sites used for the mixtures: 1260, 1567, and 8566. Because there were no national variety trials at the two other sites (8046, 3428), we could not get any data for single varieties in these sites. Thus, all further analyses including single variety data were only done for the three sites mentioned above. At each of these sites, the variety trials were located on the same plot as the mixture trials, even though a little further apart. Therefore, soil parameters and crop precedents were the same between the mixture and variety testing trials. Furthermore, we only selected the national variety testing trials that respected the <em>extenso</em> conditions, that is, no fungicide, pesticide, or growth regulator application, and that received the same amount of fertilization as the mixture trials. In 8566 and 1567, sowing and harvesting dates were identical between the two trials; in 1260, sowing and harvesting dates could vary but remained within a week of each other.</p> <p> </p> <p><em>Data collection </em></p> <p>For each plot, heading dates were monitored, and average height at BBCH 59–75 was measured.</p> <p>The prevalence of diseases was scored twice in the growing season. Specifically, the severity of brown rust, yellow rust, powdery mildew, and Septoria tritici blotch was assessed. This was performed by grading each individual plot from 1 to 9 for each disease, with 1 representing no disease and 9 a complete infection. The scoring scale follows a logistic progression based on the symptoms of the top three leaves. We used the data from the final scoring for statistical analysis, as the disease severity was usually more important then.</p> <p>At maturity, we harvested each plot with a combine harvester. The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured specific weight and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content was measured at the site level with a near-infrared instrument (ProxiMate; Büchi instruments).</p>
Dimensionality reduction of local structure in glassy binary mixtures
<p>This dataset is associated with "<em>Dimensionality reduction of local structure in glassy binary mixtures</em>", by D. Coslovich, R. L. Jack, J. Paret. It includes data and workflow to allow for the replication of the analysis and figures of the manuscript.</p> <p>To reproduce the workflow and the figures, download and extract the package sprouts.tar.gz, then execute</p> <pre><code class="language-bash">./make all</code></pre> <p>This will create the figures under plots/paper and recompile project.pdf. If the workflow fails because of missing dependencies, read the project.pdf file below and check the requirements, or download the <a href="http://www.docker.com">docker</a> image sprouts-docker.tar.gz, load the image and execute the same command within the container.</p> <p><em>Make sure you have at least 20 Gb of free space on your disk if you use the package, and 22 Gb if you use the docker image.</em></p> <p>To speed up the execution of the workflow, download the data cache (cache.tar) and extract it at the root of the project folder.</p> <p>See project.pdf below for full details about the workflow.</p> <p><strong>Changelog</strong>:</p> <ul> <li>1.0.2: use more portable she-bang in scripts</li> <li>1.0.1: fix typos, remove some dead code</li> <li>1.0.0: initial submission</li> </ul>
FDTD simulation of stack of 320 nm PAAO and various thickness DLC:Ag mixture in different mediums (AoI 45 deg., s-polarization)
<p>FDTD software: Lumerical (Ansys, version 2021 R2.3).</p> <p>Structure: aluminum (Palik) substrate; 320 nm thickness (<em>h</em>e) aluminum oxide (Palik) layer with 35 nm diameter (<em>RPo</em>) cylindrical pores with 100 nm distance (<em>D</em>) between the pore centers (representing porous anodized aluminum oxide - PAAO); 35/50/65 nm thickness (<em>DLC</em>) layer with experimentally obtained optical properties of diamond-like carbon and silver nanocomposite (DLC:Ag). The pores extend through both PAAO and DLC:Ag layers as it was observed in SEM images. DLC:Ag optical properties are averaged result of the layer properties in DLC:Ag nanocomposite obtained by fitting spectroscopic ellipsometry data, which is available here: <a href="https://doi.org/10.5281/zenodo.7341684">https://doi.org/10.5281/zenodo.7341684</a> The file used in the simulations is provided in this data set. Here DLC:Ag is considered as homogeneous materials without separating DLC and Ag phases.</p> <p>Refractive index of the surrounding medium (<em>n</em>): 1.0; 1.1; 1.2; 1.3.</p> <p>Simulation region: from 300 nm below the substrate/PAAO interface to 1.3 µm above PAAO surface; x and y spans are equal to one period of the structure.</p> <p>Mesh override region: from 50 nm below the PAAO to 50 nm above DLC:Ag; 2 nm step size in each direction.</p> <p>Light source: BFAST plane wave light source; 500 nm above PAAO; 45° angle of incidence (<em>ang</em>); 300 nm – 1000 nm wavelength range; s-polarization (<em>pol</em>).</p> <p>Monitor (frequency domain field and power): 2D Z-normal; 1 µm above PAAO; results are in "<em>_reflection.txt</em>" files.</p> <p>Information in the file name: <em>he</em> - thickness of PAAO; <em>DLC</em> - thickness of DLC:Ag; <em>pol</em> - polarization; <em>RPo</em> - diameter of pores; <em>D</em> - distance between pore centers; <em>ang</em> - angle of incidence; <em>n</em> - refractive index of surrounding medium.</p> <p>Files: (1) "<em>_reflection.txt</em>" - lambda(nm) (first column) - wavelength in nanometers; Y (second column) - T data from the monitor above the structure. (2) "<em>_p0.log</em>" - log file produced by the software while running the simulation. (3) "<em>.fsp</em>" - Lumerical software file containing the simulation project (license required to open these files). (4) "<em>Lumerical_Screenshots.pdf</em>" - shows software screenshots for every object and its every property; red text is added to show which values are different for different simulations. (5) "<em>Structure_Illustration.png</em>" - a schematic of modeled structure. (6) "PAAO320nm<em>.jpg</em>" - a preview of data from "<em>_reflection.txt</em>" files. (7) "<em>DLC_Ag_SE_nk_average.txt</em>" - contains DLC:Ag optical properties (first column - wavelength in nanometers; second column - refractive index; third column - extinction coefficient).</p>
FDTD simulation of stack of 290 nm PAAO and various thickness DLC:Ag mixture in different mediums (AoI 45 deg., s-polarization)
<p>FDTD software: Lumerical (Ansys, version 2021 R2.3).</p> <p>Structure: aluminum (Palik) substrate; 290 nm thickness (<em>h</em>e) aluminum oxide (Palik) layer with 35 nm diameter (<em>RPo</em>) cylindrical pores with 100 nm distance (<em>D</em>) between the pore centers (representing porous anodized aluminum oxide - PAAO); 35/50/65 nm thickness (<em>DLC</em>) layer with experimentally obtained optical properties of diamond-like carbon and silver nanocomposite (DLC:Ag). The pores extend through both PAAO and DLC:Ag layers as it was observed in SEM images. DLC:Ag optical properties are averaged result of the layer properties in DLC:Ag nanocomposite obtained by fitting spectroscopic ellipsometry data, which is available here: <a href="https://doi.org/10.5281/zenodo.7341684">https://doi.org/10.5281/zenodo.7341684</a> The file used in the simulations is provided in this data set. Here DLC:Ag is considered as homogeneous materials without separating DLC and Ag phases.</p> <p>Refractive index of the surrounding medium (<em>n</em>): 1.0; 1.1; 1.2; 1.3.</p> <p>Simulation region: from 300 nm below the substrate/PAAO interface to 1.3 µm above PAAO surface; x and y spans are equal to one period of the structure.</p> <p>Mesh override region: from 50 nm below the PAAO to 50 nm above DLC:Ag; 2 nm step size in each direction.</p> <p>Light source: BFAST plane wave light source; 500 nm above PAAO; 45° angle of incidence (<em>ang</em>); 300 nm – 1000 nm wavelength range; s-polarization (<em>pol</em>).</p> <p>Monitor (frequency domain field and power): 2D Z-normal; 1 µm above PAAO; results are in "<em>_reflection.txt</em>" files.</p> <p>Information in the file name: <em>he</em> - thickness of PAAO; <em>DLC</em> - thickness of DLC:Ag; <em>pol</em> - polarization; <em>RPo</em> - diameter of pores; <em>D</em> - distance between pore centers; <em>ang</em> - angle of incidence; <em>n</em> - refractive index of surrounding medium.</p> <p>Files: (1) "<em>_reflection.txt</em>" - lambda(nm) (first column) - wavelength in nanometers; Y (second column) - T data from the monitor above the structure. (2) "<em>_p0.log</em>" - log file produced by the software while running the simulation. (3) "<em>.fsp</em>" - Lumerical software file containing the simulation project (license required to open these files). (4) "<em>Lumerical_Screenshots.pdf</em>" - shows software screenshots for every object and its every property; red text is added to show which values are different for different simulations. (5) "<em>Structure_Illustration.png</em>" - a schematic of modeled structure. (6) "<em>PAAO290nm.jpg</em>" - a preview of data from "<em>_reflection.txt</em>" files. (7) "<em>DLC_Ag_SE_nk_average.txt</em>" - contains DLC:Ag optical properties (first column - wavelength in nanometers; second column - refractive index; third column - extinction coefficient).</p>
FDTD simulation of stack of 260 nm PAAO and various thickness DLC:Ag mixture in different mediums (AoI 45 deg., s-polarization)
<p>FDTD software: Lumerical (Ansys, version 2021 R2.3).</p> <p>Structure: aluminum (Palik) substrate; 260 nm thickness (<em>h</em>e) aluminum oxide (Palik) layer with 35 nm diameter (<em>RPo</em>) cylindrical pores with 100 nm distance (<em>D</em>) between the pore centers (representing porous anodized aluminum oxide - PAAO); 35/50/65 nm thickness (<em>DLC</em>) layer with experimentally obtained optical properties of diamond-like carbon and silver nanocomposite (DLC:Ag). The pores extend through both PAAO and DLC:Ag layers as it was observed in SEM images. DLC:Ag optical properties are averaged result of the layer properties in DLC:Ag nanocomposite obtained by fitting spectroscopic ellipsometry data, which is available here: <a href="https://doi.org/10.5281/zenodo.7341684">https://doi.org/10.5281/zenodo.7341684</a> The file used in the simulations is provided in this data set. Here DLC:Ag is considered as homogeneous materials without separating DLC and Ag phases.</p> <p>Refractive index of the surrounding medium (<em>n</em>): 1.0; 1.1; 1.2; 1.3.</p> <p>Simulation region: from 300 nm below the substrate/PAAO interface to 1.3 µm above PAAO surface; x and y spans are equal to one period of the structure.</p> <p>Mesh override region: from 50 nm below the PAAO to 50 nm above DLC:Ag; 2 nm step size in each direction.</p> <p>Light source: BFAST plane wave light source; 500 nm above PAAO; 45° angle of incidence (<em>ang</em>); 300 nm – 1000 nm wavelength range; s-polarization (<em>pol</em>).</p> <p>Monitor (frequency domain field and power): 2D Z-normal; 1 µm above PAAO; results are in "<em>_reflection.txt</em>" files.</p> <p>Information in the file name: <em>he</em> - thickness of PAAO; <em>DLC</em> - thickness of DLC:Ag; <em>pol</em> - polarization; <em>RPo</em> - diameter of pores; <em>D</em> - distance between pore centers; <em>ang</em> - angle of incidence; <em>n</em> - refractive index of surrounding medium.</p> <p>Files: (1) "<em>_reflection.txt</em>" - lambda(nm) (first column) - wavelength in nanometers; Y (second column) - T data from the monitor above the structure. (2) "<em>_p0.log</em>" - log file produced by the software while running the simulation. (3) "<em>.fsp</em>" - Lumerical software file containing the simulation project (license required to open these files). (4) "<em>Lumerical_Screenshots.pdf</em>" - shows software screenshots for every object and its every property; red text is added to show which values are different for different simulations. (5) "<em>Structure_Illustration.jpg</em>" - a schematic of modeled structure. (6) "<em>PAAO260nm.jpg</em>" - a preview of data from "<em>_reflection.txt</em>" files. (7) "<em>DLC_Ag_SE_nk_average.txt</em>" - contains DLC:Ag optical properties (first column - wavelength in nanometers; second column - refractive index; third column - extinction coefficient).</p>
Supporting information for "Cosolvent effects on the structure and thermoresponse of a polymer brush: PNIPAM in DMSO-water mixtures"
<p>This deposition contains the data and analysis (Jupyter notebooks) detailed in "Cosolvent effects on the structure and thermoresponse of a PNIPAM brush”. All Jupyter notebooks have also been converted into PDF files for ease of viewing.</p> <p>All data and code (notebooks) required to reproduce the analysis can be found within the “supporting_data_analysis.zip” archive. This archive contains three sub-directories:</p> <ul> <li>FTIR <ul> <li>FTIR transmission data of binary DMSO-water mixtures as a function of solvent composition.</li> <li>FTIR deconvolution was performed using software readily available at <a href="https://github.com/haydenrob/spec_deconv">https://github.com/haydenrob/spec_deconv</a>.</li> </ul> </li> <li>Ellipsometry <ul> <li>Data directory containing all raw ellipsometry data.</li> <li>“refellips_Spectroscopic_SL.ipynb” notebooks to reproduce the analysis of a hydrated (solid-liquid) polymer brush. Relevant plotting tools can be found in the <a href="https://github.com/refnx/refellips">refellips</a> repo.</li> <li>A spatial map of the polymer brush used for spectroscopic ellipsometry data analysis: “surface_map.png”.</li> <li>“Ellipsometry_logistical_fitting.ipynb” notebook and “DMSO_6mol_results.csv” file for the demonstration of the extraction of a thermotransition temperature from an ellipsometry dataset.</li> </ul> </li> <li>Neutron_reflectometry <ul> <li>Data directory containing all relevant reduced reflectivity profiles from the Platypus reflectometry at ANSTO.</li> <li>“refnx_dry.ipynb” and “refnx_solvent.ipynb” notebooks required to reproduce the analysis pertaining to a dry polymer brush and a solvated brush, respectively.</li> <li>Additional code required to model the hydrated polymer brush and various plotting tools can be in the <a href="https://github.com/igresh/refnxtoolbox">refnxtoolbox</a> repo.</li> </ul> </li> </ul>
FDTD simulation of various thickness DLC:Ag mixture on quartz substrate in different mediums (AoI 45 deg., s-polarization)
<p>FDTD software: Lumerical (Ansys, version 2021 R2.3).</p> <p>Structure: SiO<sub>2</sub> (Palik) substrate; 35/50/65 nm thickness (<em>DLC</em>) layer with experimentally obtained optical properties of diamond-like carbon and silver nanocomposite (DLC:Ag). DLC:Ag optical properties are averaged result of the layer properties in DLC:Ag nanocomposite obtained by fitting spectroscopic ellipsometry data, which is available here: <a href="https://doi.org/10.5281/zenodo.7341684">https://doi.org/10.5281/zenodo.7341684</a> The file used in the simulations is provided in this data set. Here DLC:Ag is considered as homogeneous materials without separating DLC and Ag phases.</p> <p>Refractive index of the surrounding medium (<em>n</em>): 1.0; 1.1; 1.2; 1.3.</p> <p>Simulation region: from 300 nm below the substrate/DLC:Ag interface to 1.3 µm above it.</p> <p>Mesh override region: from 50 nm below the substrate/DLC:Ag interface to 50 nm above DLC:Ag; 2 nm step size in each direction.</p> <p>Light source: BFAST plane wave light source; 500 nm above the substrate/DLC:Ag interface; 45° angle of incidence (<em>ang</em>); 300 nm – 1000 nm wavelength range; s-polarization (<em>pol</em>). The model structure is not periodic, however, BFAST light source was used for easier comparison with other structures with the same material, which are periodic.</p> <p>Monitor (frequency domain field and power): 2D Z-normal; 1 µm above the substrate/DLC:Ag interface; results are in "<em>_reflection.txt</em>" files.</p> <p>Information in the file name: <em>DLC</em> - thickness of DLC:Ag; <em>pol</em> - polarization; <em>onQ</em> - indicates quartz substrate; <em>ang</em> - angle of incidence; <em>n</em> - refractive index of surrounding medium.</p> <p>Files: (1) "<em>_reflection.txt</em>" - lambda(nm) (first column) - wavelength in nanometers; Y (second column) - T data from the monitor above the structure. (2) "<em>_p0.log</em>" - log file produced by the software while running the simulation. (3) "<em>.fsp</em>" - Lumerical software file containing the simulation project (license required to open these files). (4) "<em>Lumerical_Screenshots.pdf</em>" - shows software screenshots for every object and its every property; red text is added to show which values are different for different simulations. (5) "<em>.jpg</em>" - a preview of data from "<em>_reflection.txt</em>" files. (7) "<em>DLC_Ag_SE_nk_average.txt</em>" - contains DLC:Ag optical properties (first column - wavelength in nanometers; second column - refractive index; third column - extinction coefficient).</p>
Computational Chromatography: A Machine Learning Strategy for Demixing Individual Chemical Components in Complex Mixtures
<p>This repository contains data for "Computational Chromatography: A Machine Learning Strategy for Demixing Individual Chemical Components in Complex Mixtures". </p>
Experimental investigation on hydrogen-rich fuel mixtures (H2/CH4/CO) doped with C6H6 in a 20 kW semi-industrial scale furnace
<p>The effects of benzene doping H2-rich fuel mixtures have been investigated in a semi-industrial furnace integrated with a recuperative burner of 20 kW of nominal power. The tested fuels consist of an H2/ CH4/CO blend, doped with a progressive addition of C6H6 (up to 5% v/v). This fuel blend represents a surrogate of a more complex Coke Oven Gas (COG) industrial mixture, an attractive by-product of coal carbonization. The relative ratios of H<sub>2</sub>, CH<sub>4,</sub> and CO correspond to the ones of a typical COG mixture. The emissions, along with the OH* and CH* chemiluminescence emissions and the flame temperatures were monitored under a wide range of equivalence ratios, i.e. Φ=0.71, 0.80, 0.91, 1.00, 1.05, 1.10, 1.20. The thermal input was kept constant at 20 kW for all the investigated cases, hence the flow rate of the fuel was decreased when C<sub>6</sub>H<sub>6</sub> was added to the reference mixture due to the increase of the lower calorific value. The exhaust gas composition was monitored by means of a Fourier Transform Infrared Spectroscopy (FTIR) analyzer from HORIBA® (HORIBA MEXA-ONE), equipped with a paramagnetic analyzer (MPA) for O2 measurements. On the other hand, OH* and CH* chemiluminescence imaging was carried out by means of an IRO (Intensified Relay Optics) and a CCD (Charge-Coupled Device) camera 1.4 M (La Vision 1392 x 1040 pixels) coupled with UV 78mm f/3.8 lens and two interferential filters to collect the chemiluminescence emitted by OH* (310 ± 10 nm) and CH* (438 ± 24 nm). Finally, in-situ flame temperature measurements were also performed by using an air-cooled suction pyrometer probe equipped with a B-type thermocouple.</p> <p> </p>
Excess Entropy Scaling in Supercooled Ternary Metallic Mixtures
<p>The folder contains the results from the simulations;<br> for some of the mixtures the python start file is included.</p> <p>The Jupyter notebook files were used to make the plots in our report.</p> <p>The csv files in the main folder contain the most important results.</p> <p>The files "binary_results.csv" and "single_results.csv" were taken from the research paper:<br> "Excess-entropy scaling in supercooled binary mixtures" I. H. Bell, J. C. Dyre, and T. S. Ingebrigtsen <br> (Nature communications)</p> <p>The dataset includes the results organized the following way:<br> Mixture -> density -> "simfiles" -> temperature -> run-number -> results of the simulation</p> <p>In order to use the Jupyter notebooks, the file paths may have to be updated.</p> <p>Let us know if you find the results useful or have suggestions for us.</p>
Machine Learning Models for Surface Wave Dispersion Curve Inversion using Mixture Density Networks
<p>Machine learning (ML) approach for dispersion curve inversion using mixture density networks (MDN) based on Keil and Wassermann (2023).</p> <p>The ML approach presented here allows the simultaneous estimation of layer numbers, layer depth and a complete probability distribution of the S-wave velocity structure in the upper 100 m. This is achieved by a two-step ML approach, where 1) a regular NN classifies the number of layers within the upper 100 m of the subsurface and 2) individual trained mixture density networks output the depth estimates together with a fully probabilistic solution of the S-wave velocity structure. We trained the model to distinguish structures with 2 - 7 subsurface layers.</p> <p>The trained classification NN and the individual MDNs are located in the folder ./trained_models.<br> With the jupyter notebook Prediction.ipynb the dispersion curve inversion can be performed using the already trained ML models.<br> With the jupyter notebooks Training-MDN.ipynb and Training-classification.ipynb the models can be trained on new data.<br> The code for the set-up of the MDN is based on Earp et al. (2020).</p> <p> </p> <p>More details and updates on the code can be found on: <a href="https://github.com/SabrinaKeil/MDN_Inversion">https://github.com/SabrinaKeil/MDN_Inversion</a> </p>
HETEAC – The Hybrid End-To-End Aerosol Classification model for EarthCARE: Look-Up Table (LUT) for aerosol mixtures
<p>The dataset contains the look-up table (LUT) of EarthCARE’s Hybrid End-To-End Aerosol Classification (HETEAC) model. The LUT contains optical and radiative parameters for four pure aerosol components (fine mode weakly absorbing, fine mode strongly absorbing, coarse mode spherical and coarse mode non-spherical) and their mixtures. In total, 314 aerosol mixtures are considered. The LUT returns the mixing state of an aerosol mixture based on the lidar ratio and the particle linear depolarization ratio at 355 nm. The mixing state is expressed in terms of relative volume contribution of the four pure aerosol components. Additionally, the LUT returns the effective radius, the asymmetry parameter, the single scattering albedo (at 355, 532, 550, 670, 865, 1064, 1650 and 2210 nm) and the Angstrom exponent (at 28 wavelength combinations) of the aerosol mixture. The lidar ratio and the particle linear depolarization ratio is also provided at 532, 550, 670, 865, 1064, 1650 and 2210 nm.</p> <p>The datafile contains two top-level groups: the HeaderData, which contains the header variables, and the ScienceData with the variables. The latter contains two groups, the AerosolComponents, which includes the aerosol-component-related optical and microphysical variables, and the LookUpTable, which contains the HETEAC LUT variables.</p> <p>The variables included in the datafile are listed below. For each variable, a full description is provided in the long_name attribute.</p> <ul> <li>HeaderData <ul> <li>angstrom_exponent_header</li> </ul> </li> <li>ScienceData <ul> <li>AerosolComponents <ul> <li>backscatter</li> <li>effective_radius</li> <li>extinction</li> <li>logarithmic_width</li> <li>mode_radius_number</li> <li>mode_radius_volume</li> <li>particle_linear_depolarization_ratio</li> <li>refractive_index_imaginary</li> <li>refractive_index_real</li> <li>scattering</li> </ul> </li> <li>LookUpTable <ul> <li>angstrom_exponent</li> <li>asymmetry_parameter</li> <li>effective_radius</li> <li>lidar_ratio</li> <li>particle_linear_depolarization_ratio</li> <li>relative_volume_contribution</li> <li>single_scattering_albedo</li> </ul> </li> <li>radiation_wavelength</li> </ul> </li> </ul> <p>Contact</p> <p>For any further clarifications or expression of interest with respect to the EarthCARE LUT, please contact Ulla Wandinger (ulla.wandinger@tropos.de) and/or Athena Augusta Floutsi (floutsi@tropos.de).</p> <ul> </ul>
Data set for figure 2-4 from publication "Missed Evaporation from Atmospherically Relevant Inorganic Mixtures Confounds Experimental Aerosol Studies",
<p>Data set for figure 2-4 from publication "Missed Evaporation from Atmospherically Relevant Inorganic Mixtures Confounds Experimental Aerosol Studies".</p>
Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval
<p>This dataset provides the steps of the image analysis techniques used to soil texture classes retrieval related to the paper 'Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval' in wich the principles of the spectral mixture analyses are used.</p>
Data for: Single-gene resolution of diversity-driven overyielding in plant genotype mixtures
<p>In plant communities, diversity often increases productivity and functioning, but the specific underlying drivers are difficult to identify. Most ecological theories attribute positive diversity effects to complementary niches occupied by different species or genotypes. However, the specific nature of niche complementarity often remains unclear, including how it is expressed in terms of trait differences between plants. Here, we use a gene-centred approach to study positive diversity effects in mixtures of natural <em>Arabidopsis </em><em>thaliana</em> genotypes. Using two orthogonal genetic mapping approaches, we find that between-plant allelic differences at the <em>AtSUC8</em> locus are strongly associated with mixture overyielding. <em>AtSUC8</em> encodes a proton-sucrose symporter and is expressed in root tissues. Genetic variation in <em>AtSUC8</em> affects the biochemical activities of protein variants and natural variation at this locus is associated with different sensitivities of root growth to changes in substrate pH. We thus speculate that - in the particular case studied here - evolutionary divergence along an edaphic gradient resulted in the niche complementarity between genotypes that now drives overyielding in mixtures. Identifying such genes important for ecosystem functioning may ultimately allow linking ecological processes to evolutionary drivers, help identify traits underlying positive diversity effects, and facilitate the development of high-performing crop variety mixtures.</p>
Additive and dose-dependent mixture effects of Flumite 200 (flufenzin, acaricide) and Quadris (azoxystrobin, fungicide) on the reproduction and survival of Folsomia candida (Collembola)
<p> Our model organism was Folsomia candida (Collembola). We aimed to gain information on the toxicity of Quadris (azoxystrobin) and Flumite 200 (flufenzine aka. diflovidazine) on survival and reproduction and whether the animals can mitigate the toxicity with soil and/or food avoidance behaviour. Also, we aimed to test the effect of the mixture of these two pesticides. We used the OECD 232 reproduction test, a soil avoidance test, and a food choice test for both single pesticides and their mixture. We prepared the mixtures based on the concentration addition model, so the 50% effective concentrations (EC50) of the single materials were used as one toxic unit with a constant ratio of the two materials in the mixture. In the end, the measured mixture EC and LC (lethal concentration) values were compared to the estimate of the concentration addition model.</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.