Find research datasets worth reusing

Fig. 8 in Three-dimensional micro- and nanostructural characteristics of the scleractinian coral skeleton: A biocalcification proxy

Fig. 8. "Ceratotrochus" magnaghii Cecchini, 1914. A. ZPALH.23/4 in oblique view (A 1); distal septal edge enlarged in A2. A3. "Patches of microcrystals" at growing septal edge (RAF). B. ZPALH.23/5. Polished and etched septum sectioned transversely with dRAF that appear to be com − posed of homogenous "microcrystalline" material, SEM micrograph. C. TL M (C1, C2) and MFM (C3) micrographs of longitudinally sectioned septum ZPALH.23/6 in RAF plane. Organic and mineral phases regularly alternate (grayscale enlargement in C 2); brownish organic dRAF components (C1) stained with acridine orange, fluoresce (C2) with bright−green light. Seemingly homogenous dRAF in transverse section (B), sectioned longitudinally is composed of elongated units spaced ca. 5–7 µm (red arrows in C1). Growth direction of septum indicates black arrow. A. Recent, deep−water specimen, SEAMOUNT 2 cruise, Stat. DW 279, 33°55.60'N, 28°23.70'W, 805 m. B, C. Recent, shallow−water specimen from Mediterranean (Marseille, Riou−Grand Conglu submarine cave), 50 m.

Fig. 1. Stephanocyathuspaliferus Cairns, 1977. ZPALH.23 in Three-dimensional micro- and nanostructural characteristics of the scleractinian coral skeleton: A biocalcification proxy

Fig. 1. Stephanocyathuspaliferus Cairns, 1977. ZPALH.23/1 (originally NMNH 46443 lot). Recent, south of Bonaire, 11°18.8'N, 68°22'W, 384–607 m. Pills sta. P−753. July 26, 1968. A–C. Distal (A), lateral (B), and proximal (C) views of corallum. D, E. SEM of septa and paliform lobes. D. Arrow indicates portion of septum enlarged on E. E. "Patches of microcrystals" (fasciculi of Wise 1972) at the growing septal edge here called the Rapid Acretion Front (RAF).

Fig. 11 in Three-dimensional micro- and nanostructural characteristics of the scleractinian coral skeleton: A biocalcification proxy

Fig. 11. Platygyra daedalea (Ellis and Solander, 1786); ZPALH.23/8 (originally NMNH A.G. Humes collection. Acc. number 274378). Recent, 25 m, north of Ankazo−beravina, near Nosy Be, Madagaskar, August 24, 1967. A. Transverse polished section of septum in TLM; note brownish "calcification centers" (dRAF), regular growth increments of fibers (e.g., in encircled area), and dark brown regions (red arrows) of filaments of endolithic organisms (most likely algae, fungi). B. The same septal fragment as A, stained with acridine orange in MFM view; dRAF exhibit very light, green−yellow fluorescence; higher magnifications (C) show delicate greenish bands matching organic components trapped between successive growth increments of fibers (white arrows). D. Transverse polished section of septum in TLM; dRAF in lower right corner; regular growth increments of fibers (TD) are emphasized by levels of brownish bands. E. SEM micrograph of transverse polished and etched section of septum; fibers adjacent to row of dRAF ("calcification centers") taper regularly at ca. 3 µm matching brownish bands in D.

Fig. 7 in Three-dimensional micro- and nanostructural characteristics of the scleractinian coral skeleton: A biocalcification proxy

Fig. 7. Desmophyllum dianthus (Esper, 1794). ZPALH.23/3. Recent, southern Indian Ocean (NE St. Paul Island), MD50 cruise, Stat. 32/CP 145, 38°40.66'S, 77°35.47'E, 825–1020 m. A, B. Polished and etched septum sectioned transversely (A) and longitudinally in RAF plane (B). dRAF form chain of dCRA ("centers of calcification") (A), which sectioned longitudinally exhibit alternating etching relief (B). dCRA (A) are composed of apparently non−crystalline, "blurry" material. C–E. Transverse polished section of septum in TLM (C, D) and MFM (E) micrographs. In TLM (C, D) organic components differentiate into dark−brown zone of wall and septal dRAF and light−brown, banded zone enclosing dRAF; in MFM (E) they exhibit lighter, green−yellow, and darker, greenish fluorescence, respectively. Contact zone between bundles of fibers (also with apparent fractures) contain organic matter that forms a fluorescent network (E); examples highlighted with red arrows. F, G. Septum longitudinally sectioned in RAF plane in TLM (F) and MFM (G). Minute growth increments (less than 5 µm) appear as "denticulations" on fluorescent bars (G). Growth direction within septum indicated by black arrow.

Fig. 6. Flabellumchunii Marenzeller, 1904. ZPALH.23 in Three-dimensional micro- and nanostructural characteristics of the scleractinian coral skeleton: A biocalcification proxy

Fig. 6. Flabellumchunii Marenzeller, 1904. ZPALH.23/2/4. Locality data as in Fig. 5. A–C. Complementary regions of longitudinally sectioned and polished septum (RAF plane): TLM (A), Back−Scattered Electron mode (BSE) (B), and pseudocolor carbon mapping images acquired on the electron microprobe by wavelength−dispersive techniques (C); black/dark blue equals lower concentration (<150 counts per second) whereas yellow−white equals higher concentrations (>150 c/s). Brownish structures exposed at section surface (A), appear darker in BSE mode (B) as it enhances atomic number contrast; elements with lower atomic numbers appear darker, those with higher atomic numbers appear lighter. BSE darker regions (arrows) match exactly to carbon−enriched regions in WDS x−ray mapping image (C, arrows). D–F. TL M (D, E) and MFM (F) micrographs of longitudinally sectioned septum. Organic and mineral phase of dRAF regularly alternate (grayscale enlargement in E); brownish organic components (D) exposed and stained with acridine orangedye, fluoresce (F) with bright−green light. Growth direction within septum indicated by black arrow.

New advance in land controlled-source audio-frenquency magnetotellurics exploration: measurement at a shortened transmitter-receiver offset and three-dimensional inversion

Ambient noise significantly affects the quality of land controlled-source audio-frequency magnetotellurics (CSAMT) data. Shortening the transmitter–receiver offset can enhance the raw data signal-to-noise ratios (S/N). However, most of CSAMT explorations still collect field data using a large separation. To enhance the S/N of the raw electric and magnetic field and the corresponding Cagniard apparent resistivity, we propose shortening offset to 2–4 km during CSAMT fieldwork and inverting apparent resistivity rather than only electric field. We tested this approach with three experimental models with varying offsets, simulating 3D responses. Result showed that longer offsets predominantly produce data anomalies in the far-field zone, whereas shorter offsets shift these anomalies toward the transition zone with some in the far-field or near-field zones. Shortened-offset responses exhibit stronger electric and magnetic fields. Synthetic datasets with different noise levels were generated for inversion analysis. The results indicate that shortening the offset, particularly in noisy environments, improves data S/N. Inversion results reveal that a shortened CSAMT transmitter–receiver offset allows for data collection with most anomalies in the transition-field zone, facilitating accurate prediction of the true model in the 3D inversion. In low-noise environments, inversion results from data with a shortened offset are comparable to those with a long offset. However, in high-noise environments, the shortened-offset approach yields more accurate results due to improved data S/N. Field data inversion from Yanqing, China, further confirms the effectiveness of our scheme. The shortened-offset approach achieves higher S/N datasets and inversion results that align with actual geological structures.

Datasets used in a Transformer network for image inversion of multi-dimensional nonuniform aperture synthesis radiometers

该数据集于 2023 年 11 月在中南大学生成,并通过 matlab 仿真软件进行仿真和收集。主要用于图像重建网络的训练和测试。该数据集由原始场景亮度数据、能见度数据和一维、二维和三维非均匀天线阵列图像重建的能见度函数对应的频域采样点位置数据,以及使用其他一些常规方法进行图像重建获得的亮度数据组成。此外,为了验证所提方法的有效性,在工作频率为 33.5 Ghz 的原型 8 元一维非均匀天线阵列上进行了室内实验,并生成了测量数据集。 具体来说,名为 Tb_in、V2_noise、T3_AAF 和 T2_idft 的四个仿真数据集存储在名为 1d 的 zip 包中。 Tb_IN_1d存储了一维非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感影像场景分类公共数据集。 V2_noise存储了包含各种误差的能见度函数值,主要是通过将原始场景亮温图像输入到运行在接收频率为 33.5 GHz 的非均匀积分孔径辐射计模拟程序中得到的。 T2_idft 和 T3_AAF 分别是使用逆离散傅里叶变换和阵列因子形成方法进行图像重建获得的明亮温度数据。这两组数据都可用于后续的比较实验。 名为 Tb_IN_2d、Tb_out_2d、VS_2d 和 VS_P_2d 的四个数据集存储在名为 2d 的 zip 包中。 Tb_IN_2d内部存储的是二维非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感影像场景分类公共数据集。 VS_2d为二维非均匀天线阵列对应的能见度函数值,主要是将原始场景亮温图像输入到接收频率为 33.5 GHz 的非均匀集成孔径辐射计模拟程序中得到的。 VS_P_2d存储了二维非均匀天线阵列的能见度函数对应的频域采样点位置,该值主要通过计算能见度函数值得到。 Tb_out_2d文件存储了使用传统方法进行图像重建得到的亮温值,该数据也用于后续与所提方法获得的数据的比较实验。 名为 Tb_IN_3d、Tb_out_3d、VS_3d 和 VS_P_3d 的四个数据集存储在名为 3d 的 zip 包中。 Tb_IN_3d内部存储的是 3D 非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感图像场景分类公共数据集。 VS_3d是三维非均匀天线阵列对应的能见度函数值,是将原始场景亮温图像输入到接收频率为 33.5 GHz 的非均匀积分孔径辐射计仿真程序中得到的。 VS_P_3d存储了三维非均匀天线阵列的能见度函数对应的频域采样点位置,该位置是通过计算能见度函数值得到的。 Tb_out_3d文件存储了使用常规方法进行图像重建得到的亮温值,该数据也用于后续与所提方法获得的数据的比较实验。 名为 Array2_R、Array3_R、V2_noise 和 Tb_out 的四个数据集存储在名为 8mm8 channel 的 zip 包中。 存储在 Array2_R 和 Array3_R 中的是系统在不同位置测量的目标点源的相关矩阵,矩阵中元素的值反映了测试点对目标点源的检测能力。根据此相关矩阵,可以计算可见性值。 存储在 V2_noise 内部的是包含各种误差的测量可见性函数的样本。对这些数据进行实验主要是为了验证所提方法的有效性。 存储在 Tb_out 中的是使用测试数据集获得的亮温结果数据,用于测试训练的网络。

Surrogate Modeling Benchmark - Two-dimensional heat diffusion model

This dataset is related to the Two-dimensional heat diffusion model benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: <a href="https://uqworld.org/t/benchmark-case-two-dimensional-heat-diffusion-model/" target="_blank" rel="noopener">https://uqworld.org/t/benchmark-case-two-dimensional-heat-diffusion-model/</a>. The experimental designs include datasets with 400, 800, 1200, 1600, and 2000 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses. <h2>Description of the dataset file</h2> The dataset file includes two variables: <ul> <li>ExpDesigns, and</li> <li>ValidationSet.</li> </ul> Both variables are Matlab structures with fields X, Y, and nSamples. Variable ExpDesigns is a non-scalar structure sized according to the number of experimental design groups. Each field of X for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the ValidationSet variable, except it contains only one dataset per benchmark case. The structure can be summarized as follows: <ul> <li>ExpDesigns(i).X(j,k,l) <ul> <li>i: dataset group,</li> <li>j: sample index,</li> <li>k: variable index, and</li> <li>l: replication index.</li> </ul> </li> </ul> <ul> <li>ExpDesigns(i).Y(j,m,l) <ul> <li>i, j, l: same as above,</li> <li>m: computational model output index.</li> </ul> </li> </ul> <ul> <li>ValidationSet.X(j,k) <ul> <li>j, k: same as above.</li> </ul> </li> </ul> <ul> <li>ValidationSet.Y(j,m) <ul> <li>j, m: same as above.</li> </ul> </li> </ul> <h2>Description of benchmarked metamodel competitors</h2> The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity. For Polynomial Chaos Expansions (PCE), we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP). Polynomial Chaos Kriging (PCK) employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters. We benchmarked both linear and ordinary Kriging, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization. For further details on the settings, please refer to the competitors.m file and UQLab user manuals: <ul> <li>S. Marelli, N. Luethen, B. Sudret, <a href="https://www.uqlab.com/pce-user-manual">UQLab User Manual – Polynomial Chaos Expansions</a>, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/kriging-user-manual">UQLab User Manual – Kriging (Gaussian Process Modeling)</a>, Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>R. Schoebi, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/pck-user-manual">UQLab User Manual – Polynomial Chaos Kriging</a>, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.</li> </ul> <h2>Description of the results file</h2> The results file contains one variable: Metrics. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows: <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l) <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> The description of the performance measures (metrics) can be found here: <a href="https://uqworld.org/t/metamodel-performance-measures/" target="_blank" rel="noopener">https://uqworld.org/t/metamodel-performance-measures/</a>. <h2>Additional files</h2> We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called working_with_datafiles.* (the extension depends on the selected language). <h2>Acknowledgment</h2> This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.

Surrogate Modeling Benchmark - One-dimensional diffusion model

This dataset is related to the One-dimensional diffusion model benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: <a href="https://uqworld.org/t/benchmark-case-one-dimensional-diffusion-model" target="_blank" rel="noopener">https://uqworld.org/t/benchmark-case-one-dimensional-diffusion-model</a>. The experimental designs include datasets with 200, 400, 600, 800, and 1000 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses. <h2>Description of the dataset file</h2> The dataset file includes two variables: <ul> <li>ExpDesigns, and</li> <li>ValidationSet.</li> </ul> Both variables are Matlab structures with fields X, Y, and nSamples. Variable ExpDesigns is a non-scalar structure sized according to the number of experimental design groups. Each field of X for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the ValidationSet variable, except it contains only one dataset per benchmark case. The structure can be summarized as follows: <ul> <li>ExpDesigns(i).X(j,k,l) <ul> <li>i: dataset group,</li> <li>j: sample index,</li> <li>k: variable index, and</li> <li>l: replication index.</li> </ul> </li> </ul> <ul> <li>ExpDesigns(i).Y(j,m,l) <ul> <li>i, j, l: same as above,</li> <li>m: computational model output index.</li> </ul> </li> </ul> <ul> <li>ValidationSet.X(j,k) <ul> <li>j, k: same as above.</li> </ul> </li> </ul> <ul> <li>ValidationSet.Y(j,m) <ul> <li>j, m: same as above.</li> </ul> </li> </ul> <h2>Description of benchmarked metamodel competitors</h2> The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity. For Polynomial Chaos Expansions (PCE), we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP). Polynomial Chaos Kriging (PCK) employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters. We benchmarked both linear and ordinary Kriging, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization. For further details on the settings, please refer to the competitors.m file and UQLab user manuals: <ul> <li>S. Marelli, N. Luethen, B. Sudret, <a href="https://www.uqlab.com/pce-user-manual">UQLab User Manual – Polynomial Chaos Expansions</a>, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/kriging-user-manual">UQLab User Manual – Kriging (Gaussian Process Modeling)</a>, Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>R. Schoebi, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/pck-user-manual">UQLab User Manual – Polynomial Chaos Kriging</a>, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.</li> </ul> <h2>Description of the results file</h2> The results file contains one variable: Metrics. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows: <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l) <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> The description of the performance measures (metrics) can be found here: <a href="https://uqworld.org/t/metamodel-performance-measures/" target="_blank" rel="noopener">https://uqworld.org/t/metamodel-performance-measures/</a>. <h2>Additional files</h2> We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called working_with_datafiles.* (the extension depends on the selected language). <h2>Acknowledgment</h2> This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.

opencc-by-4.0Oct 2024View details →

Nonlinear methods for dimensionality reduction and clustering of bacterial single-cell sequencing data - intermediate data and figures (MSc thesis)

Data, intermediate results and figures for analyses of my master's thesis in biostatistics at LMU Munich. I took a look on how to use Nonlinear Matrix Decomposition (NMD) (<a href="https://doi.org/10.1137/21M1405769">Saul, L., 2022</a>) in the context of bacterial scRNA-seq analysis (Heumos, L., et. al. 2023), replacing Principal Component Analysis in the optimized workflow, as outlined in Ostner, J. (2024). My thesis was structured along the following objectives: <ul> <li>implement the algorithms from <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> in the Python module <a href="https://github.com/flatironinstitute/nomad/">nomad</a> in cooperation with <a href="https://www.simonsfoundation.org/flatiron/" rel="nofollow">Flatiron Institute</a></li> <li>code for the simulation study of the algorithms in <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> with varying sparsity can be found in <code>/simulation</code></li> <li>apply NMD in the context of the BacSC workflow (<a href="https://www.biorxiv.org/content/10.1101/2024.06.22.600071v1">Ostner, J., et. al. (2024)</a>) on raw and normalized counts (found in <code>/application/analysis</code>), also for manually set number of latent dimensions</li> <li>explore NMD's potential for imputation of <a href="https://www.nature.com/articles/s41467-021-27729-z" rel="nofollow">sampling zeros</a> (check <code>/application/NMD_zero_imputation /</code>)</li> <li>potential of Poisson-Hurdle model-based clustering (<a href="https://academic.oup.com/bioinformatics/article/39/1/btac782/6873739">Qiao, Z., et. al. (2023)</a>) for scRNA-seq (<code>/application/poisson_hurdle</code>).</li> </ul>

opencc-by-4.0Oct 2024View details →

Effect of Organic Cation Size on Structural, Thermochromic, Dielectric and Photoluminescence Properties of Two-Dimensional Lead Iodide Perovskites with Extremally Reduced Dielectric Confinement

Dataset for scientific publication entitled Effect of Organic Cation Size on Structural, Thermochromic, Dielectric and Photoluminescence Properties of Two-Dimensional Lead Iodide Perovskites with Extremally Reduced Dielectric Confinement.  This research was supported by the National Science Center (Narodowe Centrum Nauki) in Poland under project No. 2020/38/A/ST3/00214. JKZ acknowledges support from Academia Iuvenum, Wroclaw University of Science and Technology.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Applying n-dimensional hypervolumes for species delimitation: unexpected molecular, morphological, and ecological diversity in the Leaf-Toed Gecko Phyllodactylus reissii Peters, 1862 (Squamata: Phyllodactylidae) from northern Peru.

Natural history specimen data linked to collectors and determiners held within, "Applying n-dimensional hypervolumes for species delimitation: unexpected molecular, morphological, and ecological diversity in the Leaf-Toed Gecko Phyllodactylus reissii Peters, 1862 (Squamata: Phyllodactylidae) from northern Peru". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4de18441-9329-474b-a4a0-dea3aaf1e1ee">https://bionomia.net/dataset/4de18441-9329-474b-a4a0-dea3aaf1e1ee</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4de18441-9329-474b-a4a0-dea3aaf1e1ee">https://gbif.org/dataset/4de18441-9329-474b-a4a0-dea3aaf1e1ee</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Jul 2021View details →

Four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018)

These data were four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018). They were also the output results of the findings of Ma et al. (2021).

opencc-by-4.0Jun 2021View details →

Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse

Data products from ApJ article Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse, 2021. Four 3D core-collapse supernova progenitor models. Works that utilize these progenitor models are required to cite article. The models were evolved to times listed in Table 1 of the article, the collapse time according to the 1D MESA model. All 3D data are in FLASH4 format using the HDF5 data structure.

opencc-by-4.0Aug 2021View details →

Naphthalenediimide/Formamidinium-Based Low-Dimensional Perovskites

Structural, optoelectronic, photovoltaic, and supplementary characterization data for “Naphthalenediimide/Formamidinium-Based Low-Dimensional Perovskites”, DOI:10.1021/acs.chemmater.1c01635 <ul> <li>DFT.zip: Data (DFT calculated structures and properties) described in Figures 5c–d and S4–S5, as well as Tables S1–S2, in *.cif and Excel (*.xlsx) file format.</li> <li>MD.zip: Data (MD simulated structures) described in Figures 2 and S3 as *.pdb and *mpg files.</li> <li>NMR.zip: Data described in Figure 4 (13C, 14N and 15N solid-state NMR data) in JCAMP (*dx) format and Figures S1–S2 (1H and 13C solution NMR data) in MestReNova (*mnova) file format.</li> <li>Optical.zip: Data (UV-vis and TA spectra) described in Figures 5b, 5e–h, S7, and S8 as Excel (*.xlsx) files.</li> <li>PV.zip: Data (photovoltaic characteristics) described in the Supporting Information Section S8 and Figure S10 in Origin (*opj) file format.</li> <li>XRD_GIWAXS.zip: Data (X-ray diffraction and GIWAXS) described in Figures 3c and S6 as Excel (*.xlsx) files. </li> </ul>

opencc-by-4.0Aug 2021View details →

Instances for the two-dimensional heterogeneous vector bin packing problem

The dataset consist of 56 instances for the two-dimensional heterogeneous vector bin packing problem. Six small instances with 10, 11, 12, 13, 15 and 20 items, as well as 50 randomly generated large instances were considered. Weights and volumes of items were randomly uniformly chosen integer values from [1;15] tons and [1;25]m3, respectively. The set of large instances had 5 instances with each of the following numbers of items: 50, 70, 100, 120, 150, 200, 350, 500, 750 and 1000. File structure: - number of items - weights of items - volumes of items

Figure 4 in The circulatory system and its spatial relations to other major organ systems in Spelaeogriphacea and Mictacea (Malacostraca, Crustacea) - a three-dimensional analysis

Figure 4. Three-dimensional reconstructions of a semithin cross section series of Mictocaris halope. A–E, surface renderings of the main organ systems in the cephalothorax. A, three aspects of the digestive system. B, three aspects of the central nervous system. C, D, The haemolymph vascular system (HVS) in the cephalothorax. C, frontal aspect. D, lateral aspect. E, antero-dorsal aspect. F, three virtual sections of the whole image stack. Same colour codes as in the surface renderings. See the Appendix for a list of abbreviations used.

Figure 6 in The circulatory system and its spatial relations to other major organ systems in Spelaeogriphacea and Mictacea (Malacostraca, Crustacea) - a three-dimensional analysis

Figure 6. Schematic diagrams of the pumping function of the 'myoarterial formation a' in Mictocaris halope. A, diastole. B, systole. Arrows depict flow directions of the haemolymph. See the Appendix for a list of abbreviations used.

Figure 3 in The circulatory system and its spatial relations to other major organ systems in Spelaeogriphacea and Mictacea (Malacostraca, Crustacea) - a three-dimensional analysis

Figure 3. Major organ systems in Mictocaris halope. A, Schematic diagram of the haemolymph vascular system (HVS) and digestive system in M. halope. Arteries running into the antennae are depicted by broken lines. B, C, photographs of live animals showing the digestive system and the gonads (courtesy of Peter Parks, Image Quest 3D). B, dorsal aspect. C, lateral aspect. See the Appendix for a list of abbreviations used.

Figure 1 in The circulatory system and its spatial relations to other major organ systems in Spelaeogriphacea and Mictacea (Malacostraca, Crustacea) - a three-dimensional analysis

Figure 1. Reconstructions of the major organ systems in Spelaeogriphus lepidops. A, Schematic diagram of the haemolymph vascular system (HVS) and digestive system. Arteries running into the antennae are depicted by broken lines. B, C, three-dimensional reconstructions (surface rendering) of a horizontal semithin section series showing the HVS in the cephalothorax. B, antero-lateral aspect. C, Lateral aspect. D, E, three-dimensional reconstructions (surface rendering) of a horizontal semithin section series showing the central nervous system (CNS) in the cephalothorax. D, dorsal aspect. E, frontal aspect. F, three-dimensional reconstructions (surface rendering) of a horizontal semithin section series showing the major organ systems in the cephalothorax. Green, digestive system; red, HVS; yellow, CNS. The CNS is shown as slightly transparent. See the Appendix for a list of abbreviations used.