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Text-fig. 16. Photomicrographs of thin sections of specimen BP/16/1734, Terminalioxylon mozambicense sp. nov. from Mhengere Hill, Gorongosa, Mozambique. a: TS with round vessels, scanty paratracheal to vasicentric parenchyma, diffuse and narrow terminal or initial bands; b: TS at higher magnification, note the very narrow rays; c: TLS, vessels with small alternate pits, and partly tylosed; d–g: TLS with crystals (small white arrows) in the parenchyma cells, medium to thick-walled fibres and uniseriate, low rays; h: TLS, rays up to 20 cells high; i: RLS rays with procumbent body cells and 1–2 rows of marginal upright cells. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 16. Photomicrographs of thin sections of specimen BP/16/1734, Terminalioxylon mozambicense sp. nov. from Mhengere Hill, Gorongosa, Mozambique. a: TS with round vessels, scanty paratracheal to vasicentric parenchyma, diffuse and narrow terminal or initial bands; b: TS at higher magnification, note the very narrow rays; c: TLS, vessels with small alternate pits, and partly tylosed; d–g: TLS with crystals (small white arrows) in the parenchyma cells, medium to thick-walled fibres and uniseriate, low rays; h: TLS, rays up to 20 cells high; i: RLS rays with procumbent body cells and 1–2 rows of marginal upright cells.
Dataset for "High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO2 diffusion and efficient light use"
<p>Dataset used in the paper</p> <p>Retta MA, Van Doorselaer L, Driever SM, Yin X, de Ruijter NCA, Verboven P, Nicolaï BM, Struik PC. High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO<sub>2</sub> diffusion and efficient light use. New Phytol. 2024 Sep 18. doi: 10.1111/nph.20136. PMID: 39294895.</p> <p>Please cite the paper presenting this datase.</p> <h1><strong>Plant Species and Inbred Lines:</strong></h1> <ul> <li><em>Hirschfeldia incana L. (7th generation inbred line 190003 HIN-NIJ-07-B) </em></li> <li><em>Brassica nigra L. (3rd generation inbred line 210093 BNI-DG1-03-B)</em></li> <li><em>Brassica rapa L. (inbred line ‘R-o-18’)</em></li> <li><em>Arabidopsis thaliana (accession Columbia)</em></li> </ul> <h1><strong>Growth Conditions:</strong></h1> <ul> <li><em>Media:</em> Rock-wool blocks (Grodan Plantop, Roermond, Netherlands, 10×10×7.5 cm)</li> <li><em>Fertigation:</em> Nitrogen-rich nutrient solution via automated dripping system.</li> <li><em>Light Conditions:</em> 12 h day/12 h night, light intensity of 200 µmol m-2 s-1 and 1800 µmol m-2 s-1</li> <li><em>Temperature:</em> Day/Night temperatures of 23 °C and 20 °C, respectively.</li> <li><em>Relative Humidity:</em> 70%</li> </ul> <h1><strong>Codes</strong></h1> <p><strong>Species:</strong></p> <ul> <li><em>Hirschfeldia incana L. - H. incana</em></li> <li><em>Brassica nigra L. - B. nigra</em></li> <li><em>Brassica rapa L. - B. rapa</em></li> <li><em>Arabidopsis thaliana - A. thaliana</em></li> </ul> <p><strong>Light conditions:</strong></p> <ul> <li><em>High light - HL</em></li> <li><em>Low light - LL</em></li> </ul> <p><strong>Replicates:</strong></p> <ul> <li><em>Biological replicates were labeled with numbers, e.g. replicate one from high light grown Hirschfeldia incana is referred to as HiHL1</em></li> </ul> <h1><strong>Measurements</strong></h1> <h2><strong>Leaf Gas Exchange and Chlorophyll Fluorescence Measurements (GasExchangeData.zip):</strong></h2> <ul> <li>Four leaves per species per treatment.</li> <li>Conducted using a LI-6800 (LI-COR, Lincoln, NE, USA) on the mid-position of the youngest fully expanded leaf.</li> <li>The resoponse of photosynthesis to irradiance and external CO2 concentrations augumneted with multi-phase flash fluorescence were made</li> </ul> <h2><strong>Optical Properties Measurement </strong>(<strong>Absorbance & chlorophyll.zip):</strong></h2> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li>Leaf transmittance and reflectance measured using a dual channel spectrophotometer (absorptance_reflac_data_355_750.xlsx)</li> <li>Chlorophyll content measured using a spectrophotometer (Chlorophyll.xlsx).</li> </ul> <h2><strong>Stomatal Density and Size Analysis </strong>(<strong>Stomata.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from three different plants.</li> <li><em>Leaf-side:</em> abaxial and adxial leaf side.</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li>Stomatal imprints made using clear nail polish, imaged using a light microscope at 20x.</li> </ul> <p><strong>Data Output:</strong></p> <ul> <li><em>Imaging Results:</em> .jpg files organised under folders for species e.g. AtHL\R1 T+B.zip contains images ofimprints of top (T) and bottom (B) leaf sides from replicate plant 1 (R1) of A. thaliana grown under high light (AtHL). The images are named as for example, AT_HL_BOTTOM_R1_A_stacked_minimum.jpg, The leters A to E label various imges made from one imprint.</li> </ul> <h2><strong>Light and Electron Microscopy of Leaf Sections (</strong><strong>CellwallChloroplast.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from four different plants.</li> </ul> <p><strong>Sample preparation</strong></p> <ul> <li>Leaf samples fixed, dehydrated, embedded in Araldite, and sectioned for imaging.</li> <li>1 µm think sections were made for light microscopy</li> <li>Sections of 70 nm were double stained for TEM</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li>Mesophyll cells imaged at 400x and 700x to measure chloroplast coverage.</li> <li>Electron microscopy performed with Zeiss EM900 electron microscope.</li> </ul> <h2><strong>Mesophyll Chlorophyll (ConfocalData.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Three leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from three different plants.</li> <li><em>Thickness:</em> 200 ± 10 µm sections prepared using a sliding microtome</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li><em>Microscope:</em> Leica DM8 inverted scope equipped with a Stellaris 5 confocal microscope (Leica Microsystems, Wetzlar, Germany).</li> <li><em>Excitation:</em> 490 nm excitation laser line</li> <li><em>Fluorescence Recording:</em> Chlorophyll autofluorescence recorded in a spectral range of 660−700 nm.</li> <li><em>Objective:</em> Leica objective ×10/0.4 NA.</li> <li><em>Z-Stacks:</em> 85–112 µm depth, two random positions per sample</li> </ul> <p><strong>Data Output:</strong></p> <ul> <li><em>Imaging Results:</em> Z-stacks of chlorophyll autofluorescence in mesophyll cells.</li> <li><em>Spectral Information:</em> Chlorophyll autofluorescence recorded in the 660−700 nm range.</li> </ul> <p><strong>Analysis:</strong></p> <ul> <li><em>Software:</em> The confocal files are in .lif format and can be viewed using Leica application suite (LASx), ImageJ</li> </ul>
DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model
<p>Here, we store the modeled structures generated by DiffModeler for its 4 benchmark datasets: CryoREAD dataset(0-5A resolution, protein-DNA/RNA complex), ModelAngelo dataset(0-5A resolution, most protein complexes, a few protein-RNA complex), intermediate resolution dataset (5-10A resolution, protein complex), low resolution dataset (10-20A resolution, protein complex). For all protein-DNA/RNA complex, the map will be modeled by CryoREAD+DiffModeler.</p> <p>For each dataset, we keep the modeled structures by DiffModeler, named as [EMD-ID]_DiffModeler.cif; and their corressponding native structures from RCSB are saved as [EMD-ID]_[PDB_ID]_native.cif.</p> <p>For CryoREAD dataset, it includes 61 targets. For ModelAngelo dataset, it includes 28 targets. For intermediate resolution dataset , it includes 71 targets. For low resolution dataset, it incldues 6 targets.</p> <p>For intermediate resolution dataset, many maps were run with inaccurate AF2 predicted single-chain structures. We also benchmarked DiffModeler's performance by using native single-chain structures as input. They are saved under "dataset_5_10A_nativechain" folder.</p> <p>Additionally, we have stored the traced backbone map of the intermediate resolution dataset in the "dataset_5_10A_diffusion_traced_backbone_map" folder. The traced maps are saved as [EMD-ID]_diffusion.mrc. The intermediate reverse diffusion maps of the intermediate resolution dataset are saved in the "dataset_5_10A_reverse_diffusion_maps" folder. Each sub-folder is named according to the corresponding map's [EMD-ID] and contains three intermediate reverse diffusion maps: 20percentile_reverse_diffusion.mrc, 50percentile_reverse_diffusion.mrc, and 80percentile_reverse_diffusion.mrc. A higher percentile indicates a map closer to the end of the reverse diffusion steps.</p> <p>If you used DiffModeler, please cite: "Wang, Xiao, Han Zhu, Genki Terashi, Manav Taluja, and Daisuke Kihara. "DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model." bioRxiv (2024): 2024-01.".</p> <p>If you used CryoREAD, please cite: "Xiao Wang, Genki Terashi & Daisuke Kihara. De novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nature Methods, 2023."</p>
Lattice Boltzmann simulation of liquid water transport in gas diffusion layers of proton exchange membrane fuel cells: Impact of gas diffusion layer and microporous layer degradation on effective transport properties
<p><span>Underlying data to publication Sarkezi-Selsky et al., <em>J. Pow. Sour.</em> 556 (2023) 232415,<span> https://doi.org/10.1016/j.jpowsour.2022.232415</span> <br><br>Polymer Electrolyte Membrane Fuel Cells (PEMFCs) represent a promising technology for clean drivetrain solutions, in particular for heavy-duty applications. However, lifetime requirements demand high durability of each cell component.<br></span><span>In this work, transport of liquid water through pristine and degraded gas diffusion layers (GDL) was simulated with a 3D Color-Gradient Lattice Boltzmann model. The GDL microstructure was reconstructed </span><span>from high-resolution X-ray micro-computed tomography (</span><span>μ</span><span>-CT) of an impregnated Freudenberg H14. The </span><span>effect of a microporous layer (MPL) was considered by reconstruction of an impregnated and MPL-coated H14. Aged microstructures were generated artificially, assuming loss of polytetrafluoroethylene (PTFE) within the GDL and increase of MPL macroporosity as main degradation mechanisms. Liquid water transport within aged microstructures was simulated by imposing a liquid phase flow rate until breakthrough was reached. Subsequently, the GDL microstructures were analyzed for their breakthrough characteristics by means of saturation and effective gas transport properties. When the MPL was pristine, no distinct GDL degradation effect was observable, this was attributed to the MPL dominating capillary transport. MPL aging, however, led to increased saturations and thus to a deterioration of the effective gas transport. With a partially degraded MPL, aging of the GDL then appeared to affect the breakthrough characteristics.</span></p>
Dataset for "The Ithildin library for efficient numerical solution of anisotropic reaction-diffusion problems in excitable media"
<p>This archive contains the full source code of Ithildin as well as the data generated by the simulations used in the paper introducing the Ithildin software.</p>
ULF Wave Radial Diffusion Coefficients
<p>Hourly radial diffusion coefficients, DLL, derived from ground-based magnetometer measurements from March 16 to March 20, 2015 in units of days<sup>-1</sup>. The DLL values are plotted out at 100 equally spaced L* values from L*=1 to L*=5 derived from the TS05 magnetic field model at K=0 G<sup>1/2</sup>Re. These DLL values are used to reproduce the the evolution of the electron phase space density profiles in the Earth's outer radiation belt during the March 2015 geomagnetic storm presented in:</p> <p>Ozeke et al., The March 2015 Superstorm Revisited: Phase Space Density Profiles and Fast ULF Wave Diffusive Transport, Journal of Geophysical Research, 2019, submitted.</p> <p>The attached plot shows a comparison of these event specific diffusion coefficient with those obtained from the empirical models of:</p> <p>Brautigam, D. H., and J. M. Albert (2000), Radial diffusion analysis of outer radiation belt electrons during the October 9, 1990, magnetic storm, <em>J. Geophys. Res.</em>, 105(A1), 291–309, doi:10.1029/1999JA900344.</p> <p>and</p> <p>Ozeke, L. G., I. R. Mann, K. R. Murphy, I. Jonathan Rae, and D. K. Milling (2014), Analytic expressions for ULF wave radiation belt radial diffusion coefficients, <em>J. Geophys. Res. Space Physics</em>, 119, 1587–1605, doi:10.1002/2013JA019204.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Datasets for 'Reducing variability in along-tract analysis with diffusion profile realignment'
<p>Datasets we used in the manuscript 'Reducing variability in along-tract analysis with diffusion profile realignment'.</p> <p>Additional code available at https://github.com/samuelstjean/dpr</p>
Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration
<p>Interactive plots showing the correlation between histological parameters of optic nerves and chiasm, and metrics derived from diffusion MRI in a rat model of unilateral retinal ischemia.</p> <p> </p> <p>There are two .html files, each containing an interactive figure, one for data pertaining to the optic nerve, the other for the chiasm. The left panel shows the correlation matrix. Click on any cell to see the corresponding scatter plot on the right panel. </p>
Axial diffusion of respired CO2 confounds stem respiration estimates during the dormant season
<p>Efflux-based estimates of stem respiration in oak trees during the dormant season were biased by axial diffusion of locally respired CO<sub>2</sub>. Light-induced axial CO<sub>2 </sub>diffusion along the stem due to woody tissue photosynthesis may lead to equivocal estimates of stem respiratory coefficients during the dormant season, which are generally used to estimate maintenance respiration throughout the year.</p>
Datasets for 'Automated characterization of noise distributions in diffusion MRI data'
<p>Datasets we used for the manuscript 'Automated characterization of noise distributions in diffusion MRI data'.</p>
Online Appendix - Scented Since the Beginning: On the Diffuseness of Test Smells in Automatically Generated Test Code
<p>Online appendix for the paper "Scented Since the Beginning: On the Diffuseness of Test Smells in Automatically Generated Test Code".</p> <p>The full description of the content of this appendix can be found in the README file.</p>
Data Supporting The role of dopant segregation on the oxygen vacancy distribution and oxygen diffusion in CeO2 grain boundaries
<p>Data supporting the article "The role of dopant segregation on the oxygen vacancy distribution and oxygen diffusion in CeO<sub>2</sub> grain boundaries, accepted for publication in the Journal of Physics: Energy (<a href="https://doi.org/10.1088/2515-7655/ab28b5">doi.org/10.1088/2515-7655/ab28b5</a>). Data includes inputs for molecular dynamics simulations using the DL_POLY classic code.Simulations can be rerun from the inputs provide. </p> <p>Input data required for the generation of the grain boundary structures is included and uses the METADISE code. </p>
Supplementary Data - Rectification of Bacterial Diffusion in Microfluidic Labyrinths
<p>Maze5um_ecoli: <em>E. coli </em>trajectories (x- and y-positions, time, index) in a microfluidic maze (height 5µm).<br> Maze8um_ecoli: <em>E. coli</em> trajectories (x- and y-positions, time, index) in a microfluidic maze (height 8µm).<br> OutofMaze5um_ecoli:<em> E. coli</em> trajectories (x- and y-positions, time, index) in a microfluidic channel in absence of a maze (height 5µm).</p> <p>For experimental conditions and details of the tracking algorithm see Weber et al. <em>Frontiers in Physics </em>(2019).</p>
Tracer gas (SF6 and Xe) migration data collected in 2018 as a part of the Diffusion experiment
<p>The dataset contains the results of the gas sampling performed following two injections of a mixture of the tracer gases (SF<sub>6</sub> and Xe). Two tracer gas injections were conducted in 2018 in Carroll, New Hampshire. Tracer gas injections were performed beneath the water table in two chemical-explosion generated cavities to compare the migration of sulfur hexafluoride (SF<sub>6</sub>) and xenon (Xe) through an explosion-generated fracture network and to study the influence of ground water on gas transport. A mixture of tracer gases (50% of SF<sub>6</sub> and 50% of Xe) was injected into each cavity. The first gas injection took place on September 8, 2018 and gas sampling continued until September 20, 2018. The second injection was performed on October 31, 2018 and gas sampling continued until November 8, 2018. The data were collected using an automatic sampling system. The data analysis was performed by using a gas chromatograph (Shimadzu GC-8A) with a Thermal Conductivity Detector (TCD). The gas concentrations measured at the surface from 4 sampling locations are provided.</p> <p>In addition, we provide measurements of the barometric pressure and temperature recorded using an Onset HOBO pressure transducer placed in the vicinity of the injection site approximately 1 m above ground and collecting pressure and temperature samples every 15 minutes for the duration of the experiment. The pressure and temperature data is provided in Tables S2 and S4.</p> <p> </p>
Figure 4 in Іnfluence of some heavy metals to the pulmonary and direct diffusive respiration of the great ramshorn Planorbarius corneus allospecies (Mollusca: Gastropoda: Planorbidae) from the Ukrainian river system
Figure 4. Photo of habitat Planorbarius corneus from Sula River (Romny, Sumy region) in 2021 (Photos: Yuliia V. Babych).
Figure 2 in Іnfluence of some heavy metals to the pulmonary and direct diffusive respiration of the great ramshorn Planorbarius corneus allospecies (Mollusca: Gastropoda: Planorbidae) from the Ukrainian river system
Figure 2. Map showing the type localities of Planorbarius corneus s. lato allospecies: black triangle – «western»; black square – «eastern».
Figure 3 in Іnfluence of some heavy metals to the pulmonary and direct diffusive respiration of the great ramshorn Planorbarius corneus allospecies (Mollusca: Gastropoda: Planorbidae) from the Ukrainian river system
Figure 3. Photo of habitat Planorbarius corneus from Hnyla River (Horodnytsia village, Ternopil region) in 2021 (Photos: Yuliia V. Babych).
Figure 1 in Іnfluence of some heavy metals to the pulmonary and direct diffusive respiration of the great ramshorn Planorbarius corneus allospecies (Mollusca: Gastropoda: Planorbidae) from the Ukrainian river system
Figure 1. Shells of Planorbarius corneus s. lato. (A – allospecies "western", B – allospecies "eastern"): 1 – top view; 2 – bottom view; 3 – side view. Scale bars: 10 mm. (Photos: Yuliia V. Babych).
Dataset: Use of bioresorbable fibers for short-wave infrared spectroscopy using time-domain diffuse optics
Open the record for dataset details and reuse information.
Surrogate Modeling Benchmark - Two-dimensional heat diffusion model
<p>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>.</p> <p>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.</p> <h2>Description of the dataset file</h2> <p>The dataset file includes two variables:</p> <ul> <li><em>ExpDesigns</em>, and</li> <li><em>ValidationSet</em>.</li> </ul> <p>Both variables are Matlab structures with fields <em>X</em>, <em>Y</em>, and <em>nSamples</em>. Variable <em>ExpDesigns</em> is a non-scalar structure sized according to the number of experimental design groups. Each field of <em>X</em> 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 <em>ValidationSet</em> variable, except it contains only one dataset per benchmark case.</p> <p>The structure can be summarized as follows:</p> <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> <p>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.</p> <p>For <strong>Polynomial Chaos Expansions (PCE)</strong>, 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).</p> <p><strong>Polynomial Chaos Kriging (PCK)</strong> 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.</p> <p>We benchmarked both linear and ordinary <strong>Kriging</strong>, 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.</p> <p>For further details on the settings, please refer to the competitors.m file and UQLab user manuals:</p> <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> <p>The results file contains one variable: <em>Metrics</em>. 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:</p> <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l)<br> <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> <p>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>.</p> <h2>Additional files</h2> <p>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 <em>working_with_datafiles.*</em> (the extension depends on the selected language).</p> <h2>Acknowledgment</h2> <p>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.</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.