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1,819 results for “Experimental data”
Data from: Experimental evidence of rapid heritable adaptation in the absence of initial standing genetic variation
<p>The success of genetically depauperate populations in the face of environmental change is contrary to the expectation that high genetic diversity is required for rapid adaptation. Alternative pathways such as environmentally induced genetic modifications and non-genetic heritable phenotypes have been proposed mechanisms for heritable adaptation within an ecologically relevant timeframe. However, experimental evidence is currently lacking to establish if, and to what extent, these sources of phenotypic variation can produce a response.<br> <br> To test if adaptation can rapidly occur in the absence of initial standing genetic variation and recombination in small populations, we (i) exposed replicate monoclonal populations of the microzooplankton <em>Brachionus calyciflorus</em> to a culturing regime that selected for phenotypic variants with elevated population growth with either high or low phosphorus food for a period of 55 days and (ii) examined population-level response in two fully factorial common garden experiments at day 15 and 35 of the exposure experiment.<br> <br> Within six generations, we observed heritable local adaptation to nutrient limitation. More specifically, populations with a history of exposure to P-limited food exhibited higher population growth rates under low P food conditions than populations with a high P exposure history. However, the capacity for such a response was found to vary among clones.<br> <br> Our study finds that although standing genetic variation is considered essential for rapid heritable adaptation, the rapid emergence of <em>de novo</em> genetic variation or alternative sources of phenotypic variation could aid in the establishment and persistence of low diversity populations.</p>
Data from: Are brood sex ratios adaptive? The effect of experimentally altered brood sex ratio on nestling growth, mortality, and recruitment
<p><span>Brood sex ratios (BSRs) have often been found to be non-random in respect of parental and environmental quality, and many hypotheses suggest that non-random sex ratios can be adaptive. To specifically test the adaptive value of biased BSRs, it is crucial to disentangle the consequences of BSR and maternal effects. In multiparous species, this requires cross-fostering experiments where foster parents rear offspring originating from multiple broods, and where the interactive effect of original and manipulated BSR on fitness components are tested. To our knowledge, our study on collared flycatchers (<em>Ficedula albicollis</em>) is the first that meets these requirements. In this species, where BSRs had previously been shown to be related to parental characteristics, we altered the original BSR of the parents shortly after hatching by cross-fostering nestlings among trios of broods, and examined the effects on growth, mortality, and recruitment of the nestlings. We found that original and experimental BSR, as well as the interaction of the two were unrelated to the fitness components considered. Nestling growth was related only to background variables, namely brood size and hatching rank. Nestling mortality was related only to hatching asynchrony. Our results therefore do not support that the observed BSRs are adaptive in our study population. However, we cannot exclude the possibility of direct effects of experimentally altered BSRs on parental fitness, which should be evaluated in the future. In addition, studies similar to ours are required on various species to get a clearer picture of the adaptive value of non-random BSRs.</span></p>
Data from: Voice efficiency for different voice qualities combining experimentally derived sound signals and numerical modeling of the vocal tract
<p>This dataset contains Stereo-Lithographic (STL) surface models of a human vocal tract, derived Finite-Element-Models, numerical results, and scripts for analyzing these results and (re-)running the computation.</p> <p> </p> <p><strong>In the main folder, this dataset contains:</strong></p> <p>1) Python files (*fig*.py) for the creation of figures and tables (*tab*.py)</p> <p>2) Python files (*.py) for analyzing Finite-Element (FE) calculations (x_resonances.py, x_libs.py, x_fem2excel.py)</p> <p>3) Python-files (*.py) for analyzing stl-data (x_analyzeSTL.py)</p> <p>4) Python files (*.py) for deriving Infinite-Impulse-Response (IIR) filter and their impulse responses (x_IIR.py)</p> <p>5) Excel files (*.xlsx) containing Volume-velocity-transfer-functions (Vlg.xlsx), Pressure-transfer-functions at the lips (Hlg.xlsx), and the glottis (Hgg.xlsx) based on FE, the sound spectra of audio signals (sound_spectra.xlsx), the polynomials describing the IIR (IIR_polynomial.xlsx) and their impulse responses (IIR_impulse_responses.xlsx), and glottal waveforms (glottal_waveform.xlsx) and spectra (glottal_spectra.xlsx)</p> <p>6) Several figures (*.pdf)</p> <p> </p> <p><strong>In folder „x_fenics/x_Subject-1“ (and sub-folders), this data set contains:</strong></p> <p>1) Surface models of the human vocal tract for different voice qualities (glottis.stl, wall.stl, lips.stl)</p> <p>2) Sub-volumes of the vocal tract cavities (*ET.stl, *HPl.stl, *HPu.stl, *OPf.stl, *OPr.stl, *SP.stl, *.VV.stl)</p> <p>3) Derived gmsh volume meshes (*.msh) (www.gmsh.info)</p> <p>3) Derived volume models applicable to FE-Solvers (*.h5, *.xdmf)</p> <p>4) Results of the FE-calculation (*pvtf*.txt, *vvtf*.txt, *pglottis*.txt)</p> <p>5) Formant frequencies computed by inverse filtering (*.for)</p> <p> </p> <p><strong>In folder „x_fenics/x_misc“ the data set contains:</strong></p> <p>1) Python-files (*.py) for (re-)running the calculations using the FE-Method</p>
Data for: Whole Animal Feed FLat (WAFFL): A complete and comprehensive validation of a novel high-throughput fly experimentation system
<p>Non-mammalian model organisms have been essential for our understanding of the mechanisms that control development, disease, and physiology, but they are underutilized in pharmacological and toxicological phenotypic screening assays due to their low throughput in comparison with cell-based screens. To increase the utility of using <em>Drosophila melanogaster</em> in screening, we designed the Whole Animal Feeding FLat (WAFFL), a novel, flexible, and complete system for feeding, monitoring, and assaying flies in a high-throughput format. Our 3-D printed system is compatible with inexpensive and readily available, commercial 96-well plate consumables and equipment. Experimenters can change the diet at will during the experiment and video record for behavior analysis, enabling precise dosing, measurement of feeding, and analysis of behavior in 96-well plate format. </p>
Data and code from: Experimental evolution of environmental tolerance, acclimation, and physiological plasticity in a randomly fluctuating environment
<p>Environmental tolerance curves, representing absolute fitness against the environment, are an empirical assessment of the fundamental niche, and emerge from the phenotypic plasticity of underlying phenotypic traits. Dynamic plastic responses of these traits can lead to acclimation effects, whereby recent past environments impact current fitness. Theory predicts that higher levels of phenotypic plasticity should evolve in environments that fluctuate more predictably, but there have been few experimental tests of these predictions. Specifically, will still lack experimental evidence for evolution of acclimation effects in response to environmental predictability. Here, we exposed 25 genetically diverse populations of the halotolerant microalgae Dunaliella salina to different constant salinities, or to randomly fluctuating salinities, for over 200 generations. The fluctuating treatments differed in their autocorrelation, which determines the similarity of subsequent values, and thus environmental predictability. We then measured acclimated tolerance surfaces, mapping population growth rate against past (acclimation) and current (assay) environments. We found that experimental mean and variance in salinity caused the evolution of niche position (optimal salinity) and breadth, with respect to not only current but also past (acclimation) salinity. We also detected weak but significant evidence for evolutionary changes in response to environmental predictability, with higher predictability leading notably to an upwards shift in optimal salinities and stronger acclimation effect of past environment on current fitness. We further showed that these responses are related to the evolution of plasticity for intracellular glycerol, the major osmoregulatory mechanism in this species. However the direction of plasticity evolution did not match simple theoretical predictions. Our results underline the need for a more explicit consideration of the dynamics of environmental tolerance and its underlying plastic traits to reach a better understanding of ecology and evolution in fluctuating environments.</p>
Data belonging to the paper "Experimental Investigations of Partially Valve-, Partially Displacement-Controlled Electrified Telehandler Implements"
<p>These are measured energy values of work cycle halves that are the basis for the analyses in the paper "Experimental Investigations of Partially Valve-, Partially Displacement-Controlled Electrified Telehandler Implements", which is currently under review at a journal.</p>
MicroFPGA experimental data
<p>Data used to produce the figures in the MicroFPGA paper (doi: ).</p>
Data from: Experimental evolution in the cyanobacterium <i>Trichormus variabilis</i>: increases in size and morphological diversity.
<p>Data deposited in this repository was collected during the investigation entitled; Experimental evolution in the cyanobacterium <em>Trichormus variabilis: </em>increases in size and morphological diversity.</p> <p>Using experimental evolution, we selected the filamentous cyanobacteria <em>T. viariabilis </em>larger size by means of settling selection. The experimental design consisted of 20 replicate populations, ten propagated without selection, and ten populations were transferred by means of settling selection. Settling selection consisted of centrifugation of a 1.5 mL sample of a 72h grown population at 100 g for 30 seconds and transferring the bottom 100 µL of the 1.5 mL microcentrifuge tube to fresh media. The other ten were propagated in batch culture transferring 100 µL of a mixed population to fresh media after 72h growth. The selection experiment was carried out for 45 transfer cycles. </p> <p>Data present the morphology, size, growth, and settling rate of the populations over time, and at the end of the experiment, transfer cycle 45. The population's growth cycle was assessed at transfer 45 over two transfer cycles. Populations were assessed by image analysis of populations' microphotographs. </p> <p>Morphological classification of the evolved morphologies was assessed by a double-blind volunteer panel and by objective analyses of the population's microphotographs. Canonical evolved <em>Cluster </em>and <em>Tangle </em>morphologies were compared using Principal Component Analysis. </p> <p>Lyticase and 2% β-glucuronidase/arylsulfatase treatment of the populations enables the assessment of heritability of the evolved morphologies starting with single cells.</p> <p> </p>
Mechanical behavior of C45 steel at high temperatures and high strain rates—experimental data set and numerical approach
<p>In this publication we provide experimental data of dynamic compression tests of four microstructural variants of the C45 steel alloy performed at high temperatures and high strain rates. The presented data evidences the presence of Dynamic Strain Aging (DSA) in the material. Moreover, we provided the MATLAB codes of a calibration approach to estimate the material parameters of a plasticity model that accounts for DSA.</p> <p>The file "Mechanical behavior of C45" contains:</p> <p>1) Folder EXP_DATA contains experimental data of dynamic compression tests of the C45 steel variants in .mat format. The data is organized in cell arrays, and every cell in the arrays contains the data of one experiment. The data of each experiment is arranged as four columns arrays, as: column 1: Plastic strain, column 2: Flow stress, column 3: absolute temperature, column 4: strain rate</p> <p>2) Matlab code named CALIBRATION.m which executes a calibration approach of a modified Johnson-Cook that accounts for DSA. The form of the model is the following:</p> <p><span class="math-tex">\(\sigma(\varepsilon ,T,\dot{\varepsilon}) = (A+B\varepsilon^n)\left(1+Cln\left(\frac{\dot{\varepsilon}}{\dot{\varepsilon}^{ref}}\right)\right)\left( 1-\left(\frac{T-T^{ref}}{T^{melt}-T^{ref}}\right)^m\right) +\sigma^{dsa}(\varepsilon ,T,\dot{\varepsilon}) \)</span></p> <p>with,</p> <p><span class="math-tex">\(σ^{dsa} (ε,T,\dotε )=\frac{B_1}{ν} \left(\frac{ε^β}{ ε ̇T} exp(-\frac{Q_m}{KT}) \right)^{\frac{2}{3}}\)</span>, <span class="math-tex">\(\frac{B_1}{\nu} = \frac{\psi \dot{\varepsilon}}{exp(\eta \frac{T}{\dot{\varepsilon}^\alpha})} \)</span></p> <p> </p> <p>The code CALIBRATION contains a data presentation section that can be used to generate plots to compare calibrated models with the experimental data. Further instructions are commented in the code CALIBRATION.m.</p>
Isothermal titration calorimetric study on the binding of iron to human serum transferrin - experimental data
<p>Experimental data for a paper entitled <em>Glycoproteomics meets thermodynamics: a calorimetric study of the effect of sialylation and synergistic anion on the binding of iron to human serum transferrin</em> (<a href="https://doi.org/10.1016/j.jinorgbio.2023.112207">https://doi.org/10.1016/j.jinorgbio.2023.112207</a>).<br> <br> Separate files are provided with the following information:<br> <br> 1. ITC titrations of native (Tf+s) and desialylated (Tf-s) apo-transferrin with FeNTA in the presence of carbonate and oxalate.<br> <br> a) Raw data with corresponding fit results are available in .apj file format: <a href="https://zenodo.org/api/files/188f4399-e839-4710-8a28-996af05961aa/Borko et al. ITC - Malvern Microcal format.zip?versionId=e1a08c09-9401-42fa-a626-683a2fcc1be5">Borko et al. ITC - Malvern Microcal format.zip </a><br> <br> The files can be accessed using MicroCal PEAQ-ITC Analysis software, which can be downloaded here:<br> <br> <a href="https://www.malvernpanalytical.com/en/support/product-support/microcal-range/microcal-itc-range/microcal-peaq-itc#software">https://www.malvernpanalytical.com/en/support/product-support/microcal-range/microcal-itc-range/microcal-peaq-itc#software</a><br> <br> b) The raw data including control experiments are also available in ASCII (.csv) format: <a href="https://zenodo.org/api/files/188f4399-e839-4710-8a28-996af05961aa/Borko et al. ITC - ASCII format.zip?versionId=20ecf17b-54d9-4b33-8de9-a6f256211cc6">Borko et al. ITC - ASCII format.zip</a></p> <p>2. Overview of titration results, including error propagation and inputs for statistical analysis: <a href="https://zenodo.org/api/files/188f4399-e839-4710-8a28-996af05961aa/Borko%20et%20al.%20-%20Results.xlsx">Borko et al. - Results.xlsx</a><br> <br> 3. Graph of the overall effect of desialylation and synergistic anion on the thermodynamic parameters for the reaction of native (Tf+s) and desialylated (Tf-s) apotransferrin with FeNTA, based on the values of ∆<sub>r</sub><em>H</em>° and -<em>T</em>∆<sub>r</sub><em>S</em>°: <a href="https://zenodo.org/api/files/188f4399-e839-4710-8a28-996af05961aa/Borko%20et%20al.%20-%20dH%20vs.%20-TdS%20plot.xlsx">Borko et al. - dH vs. -TdS plot.xlsx</a></p> <p>4. Complete statistical analysis of results: <a href="https://zenodo.org/api/files/188f4399-e839-4710-8a28-996af05961aa/Borko%20et%20al.%20-%20Statistics.xlsx">Borko et al. - Statistics.xlsx </a><br> <br> a) Shapiro-Wilk test for normality of data.<br> b) Brown-Forsythe test for homogeneity of variances.<br> c) One-way and two-way ANOVA for all parameters, including Welch-James one-way ANOVA with approximate degrees of freedom and an overview of the obtained results.<br> d) Hedges' d values for effect size.<br> <br> All calculations were performed using the built-in functions in Excel 365 or the Real Statistics Resource Pack, which can be downloaded here:<br> <br> <a href="https://www.real-statistics.com/free-download/real-statistics-resource-pack/">https://www.real-statistics.com/free-download/real-statistics-resource-pack/</a><br> <br> After installation, it may be necessary to change the source of the XRealStats add-in to automatically update the calculations using the Edit Links dialogue. The location of the installed XRealStats.xlam file may need to be added to the Trusted Locations in the Excel Trust Center settings.<br> <br> 5. Welch-James Two-way ANOVA with approximate degrees of freedom performed with Rstudio (version 2022.01.1 build554) using the welchADF package for R (version 4.2.1): <a href="https://zenodo.org/api/files/188f4399-e839-4710-8a28-996af05961aa/Borko%20et%20al.%20-%20WelchADF%202-way%20ANOVA%20%28Rstudio%29.zip">Borko et al. - WelchADF 2-way ANOVA (Rstudio).zip</a><br> <br> More details about the welchADF package can be found here: <a href="https://doi.org/10.32614/RJ-2017-049">https://doi.org/10.32614/RJ-2017-049</a><br> <br> 6. UHPLC chromatograms used to assign the structures and content of N-glycans in native and desialylated apo-transferrin, including the Python script for peak integration: <a href="https://zenodo.org/api/files/188f4399-e839-4710-8a28-996af05961aa/Borko%20et%20al.%20-%20UHPLC%20chromatograms.zip">Borko et al. - UHPLC chromatograms.zip</a></p> <p>For additional details and background, please visit: <a href="https://glymech.pharma.hr//GlyMech.html">https://glymech.pharma.hr//GlyMech.html</a>.</p> <p> </p>
Data from: Reproducing in hot water: experimental heatwaves deteriorate multiple reproductive traits in a freshwater ectotherm
<p>Heatwaves are occurring at an increasing frequency and intensity under ongoing climate change. With many reproductive traits – including mating behaviour and gamete traits– being sensitive even to small stressors, including short temperature changes, the impact of heatwaves on reproduction and sexual selection processes is likely to be vast. Also, understanding whether the sexes may differentially respond to these extreme events is crucial to understand the impact on fecundity and the consequence at the population level. Nonetheless, our knowledge of the effects of heatwaves on these key aspects of an animal life is still limited. Here, we expose recently mated male and female guppies (Poecilia reticulata) to an experimental heatwave (32°C, 6°C above the control, for 5 days) to determine its effects on several traits, including sexual behaviour, condition, ornamentation, and fertility. Using this design, in contrast to most other experimental set ups, we had the possibility to attribute the effects of the heatwave to males' and females' reproductive traits independently. Overall, our results indicate that heatwaves can drastically affect key reproductive traits and unravel sex-specific responses. In males, there was no effect of the heatwave on survival, but both pre- and post-copulatory reproductive traits were affected. After the heatwave, we detected a decrease in orange colouration (the most important ornament on which female choice is based) and the overall level of sexual activity, and a shift in the preferred mating tactic towards forced copulation attempts. The latter suggest implications in sexual conflict dynamics, as forced copulations override female mate choice. Also, after the heatwave, males had more sperm but of lower quality, and, in addition, an increased variance in sperm number. Overall, heatwaves may result in a compromised ability to secure mating and fertilization. In females, the heatwave significantly affected survival, with increased mortality in the short term, and impaired fecundity, with many females from the heatwave treatment not reproducing at all. The negative effects of heatwaves on key reproductive traits unravelled by our study could have major implications for population dynamics and persistence. It highlights the need for further studies on these extreme events on reproduction, to improve our understanding of the impacts of climate change.</p>
Data for: Butterfly foraging is remarkably synchronous in an experimental tropical macrocosm
<p><span>Diel patterns in foraging activity are dictated by a combination of abiotic, biotic, and endogenous limits. Understanding these limits is important for insects because ectotherm taxa will respond more pronouncedly to ongoing climatic change, potentially affecting crucial ecosystem services. We leverage an experimental macrocosm, the Montreal Insectarium Grand Vivarium, to test the importance of endogenous mechanisms in determining temporal patterns in foraging activity of butterflies. Specifically, we assessed the degree of temporal niche partitioning among 24 butterfly species originating from the Earth's tropics within controlled environmental conditions. We found strong niche overlap, with the frequency of foraging events peaking around solar noon for 96% of the species assessed. Our models suggest that this result was not due to the extent of cloud cover, which affects radiational heating and thus limits body temperature in butterflies. Together, these findings suggest that an endogenous mechanism evolved to regulate the timing of butterfly foraging activity within suitable environmental conditions. Understanding similar mechanisms will be crucial to forecast the effects of climate change on insects, and thus on the many ecosystem services they provide.</span></p>
A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data
<p> </p> <p>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data.</p> <p>Introduction</p> <p>This dataset is the Planet Labs PBC (VanderSat B.V.) contribution to the ESA 4DMED hydrology project (<a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org/</a>). It includes Soil Moisture, Land Surface Temperature and Vegetation Optical Depth for the 4DMED spatial domain and time period (2015-2021) at 1km pixel size. If you use the data please include the following reference:</p> <blockquote> <p>Jaap Schellekens, Tessa Kramer, Michel van Klink, Robin van der Schalie, Yoann Malbeteau, Arjan Geers, Richard de Jeu. (2022) <em>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data</em>. DOI: 10.5281/zenodo.7684993. Planet Labs PBC/VanderSat B.V., ESA Contract No. 4000136272/21/I-EF</p> </blockquote> <p> </p> <p><em>Figure 1: Average L-Band Soil moisture for 2020 over the 4dmed spatial domain</em></p> <p>Variables and files</p> <p>The dataset consists of the following files and products for the 4DMED domain. Detailed information about the products can also be found at <a href="https://docs.vandersat.com/data_products/soil_water_content/specification.html">docs.vandersat.com</a>:</p> <ul> <li><strong><code>planet-teff-4dmed-V4.0.zip</code></strong> - LST (TEFF) ascending (daytime) and descending (nighttime) <ul> <li><code>TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime (13:30 solar time) at 1 km</li> </ul> </li> <li><code>TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature nighttime (01:30 solar time) at 1 km</li> </ul> </li> </ul> </li> <li><strong><code>planet-teff-qf-4dmed-V4.0.zip</code></strong> - LST (TEFF) quality flags <ul> <li><code>QF-TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> <li><code>QF-TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> </ul> </li> <li><strong><code>planet-vod-4dmed-V4.1.zip</code></strong> - vegetation optical depth C and X band (interpolated from C3S passive soil moisture) <ul> <li><code>VOD_AMSR2_C1_DESC_V41_1000</code> <ul> <li>C1 band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> <li><code>VOD_AMSR2_X_DESC_V41_1000</code> <ul> <li>X band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-4dmed-V4.0.zip</code></strong> - All soil moisture products (C1, X and L-band) <ul> <li><code>SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-qf-4dmed-V4.0.zip</code></strong> - Soil moisture quality maps see <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python</a> <ul> <li><code>QF-SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture quality flags (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-cor-4dmed-V4.0.zip</code></strong> - Yearly correlation maps of soil moisture derived from the difference microwave bands. To be used as an extra quality indicator (for example undetected RFI) or for uncertainty estimation <ul> <li><code>SM-CORR-C1-X-DESC_V4.0_1000</code> - yearly C1 vs X band pearson's correlation maps</li> <li><code>SM-CORR-L-C1-DESC_V4.0_1000</code> - yearly L vs C1 band pearson's correlation maps</li> <li><code>SM-CORR-L-X-DESC_V4.0_1000</code> - yearly L vs X band pearson's correlation maps</li> </ul> </li> <li><strong><code>planet-aux-flags-4dmed-V4.0</code></strong> - Extra flags for frozen soil and bare soil. Determined at 0.25 degree and interpolated to the 4dmed grid <ul> <li><code>QF-SNOWFROZEN-AMSR2-ASC_1000::RD</code> - Frozen soil determined from dayttime data</li> <li><code>QF-SNOWFROZEN-AMSR2-DESC_1000::RD</code> - Frozen soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-DESC_1000::RD</code> - Bare soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-ASC_1000::RD</code> - Bare soil determined from daytime data</li> </ul> </li> </ul> <p>All files are archived into one zip file per product group. Each individual netcdf file in the zip file consists of one observation for the whole domain. If you need you can combine the files into one file using the cdo software <a href="https://code.mpimet.mpg.de/projects/cdo">https://code.mpimet.mpg.de/projects/cdo</a> (e.g. <code>cdo -f nc4c mergetime *.nc outfile.nc</code>).</p> <p>License</p> <p>The data for 4DMED is released under the Creative Commons license: CC BY-NC-SA 4.0 (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>)</p> <ul> <li>Contains modified Copernicus Sentinel data 2015-2021</li> <li>Contains modified JAXA GCOM-W1/AMSR2 data 2015-2021</li> <li>Contains modified SMAP L1B Radiometer data: Piepmeier, J. R., P. Mohammed, J. Peng, E. J. Kim, G. De Amici, J. Chaubell, and C. Ruf. 2020. SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures, Version 4,5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/ZHHBN1KQLI20">https://doi.org/10.5067/ZHHBN1KQLI20</a></li> </ul> <p>Contact</p> <p>Jaap Schellekens: <a href="mailto:jaap@planet.com">jaap@planet.com</a></p> <p>Versions</p> <ul> <li>1.0 Initial creation</li> <li>1.1 Adjusted 4DMED Mask. Data itself unchanged but more LST (TEFF) measurements added</li> <li>1.2 Removed VOD and replaced by 25km C3S VOD interpolated to 1km (V4.1)</li> </ul> <p>Further information</p> <p>More information on the data and the flags can be found at <a href="https://docs.vandersat.com/">https://docs.vandersat.com</a> and <a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org</a></p> <p>Background publications</p> <p>R.A.M. De Jeu, A.H.A. De Nijs, M.H.W. Van Klink (2016) <em>Method and system for improving the resolution of sensor data</em>, US10643098B2,EP3469516B1, WO2017216186A1</p> <p>De Jeu, R. A., Holmes, T. R., Parinussa, R. M., & Owe, M. (2014). <em>A spatially coherent global soil moisture product with improved temporal resolution</em>. Journal of hydrology, 516, 284-296.</p> <p>Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I. and Forkel, M., 2020. <em>The global long-term microwave vegetation optical depth climate archive (VODCA)</em>. Earth System Science Data, 12(1), pp.177-196.</p> <p>Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M.: <em>Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties, Biogeosciences Discuss.</em> [preprint], <a href="https://doi.org/10.5194/bg-2022-85">https://doi.org/10.5194/bg-2022-85</a>, in review, 2022</p> <p>Van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al- Yaari, A., Parinussa, R.M., Mecklenburg, S. and Drusch, M. (2017), <em>The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E</em>, Remote Sensing of Environment, 189, pp.180-193.</p> <p>van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., Dorigo, W., 2020. Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing 12, 3439. <a href="https://doi.org/10.3390/rs12203439">https://doi.org/10.3390/rs12203439</a></p>
Relative Phase Data to 'Experimental observation of curved light-cones in a quantum field simulator', arXiv:2209.09132
<p><strong>Relative phase profiles and averaged density profiles for the results shown in arXiv:2209.09132</strong></p> <p>Each file "phase_and_mean_density_scan_X.mat" contains data for a measurement presented in the manuscript, where "X" is the corresponding scan number.<br> The following table shows the relevant scan number to measurement descriptions mentioned in the manuscript (see Table S1 in the SI Appendix).</p> <table align="center"> <thead> <tr> <th scope="col">Measurement description</th> <th scope="col">Scan number</th> </tr> </thead> <tbody> <tr> <td> <p> Homogeneous (main text)</p> </td> <td> 9185</td> </tr> <tr> <td> <p> Inhomogeneous with sharp edges </p> </td> <td> 10419</td> </tr> <tr> <td> <p> Inhomogeneous with smoothed edges</p> </td> <td> 8935</td> </tr> <tr> <td> <p> Homogeneous 2 (SI Appendix)</p> </td> <td> 10455</td> </tr> </tbody> </table> <p> </p> <p><strong>File Contents</strong></p> <p>Each file contains the following variables:</p> <ul> <li>"phase": A MATLAB cell containing all the phase profiles for every time step. Thus, "phase{t_ind}" is a matrix where rows represent experimental realizations and columns the spatial grid points. For example, "phase{5}(1,:)" would be a one-dimensional phase profile, representing the first realization of the fifth time step. To learn more about the extraction of phase profiles, read Section 2 and see Fig. S5 in SI Appendix.</li> <li>"z_grid_phase_si": Vector. Grid points for phase profiles in SI units (m).</li> <li>"averaged_density_si": Vector. Averaged initial linear density in SI units (m^-1). See Fig. 1(a).</li> <li>"z_grid_density_si": Vector. Grid points for averaged density in SI units (m).</li> <li>"times_si": Vector. Time points in SI units (s).</li> </ul> <p> </p> <p><strong>Matlab script calculating the velocity field</strong></p> <p>In addition to the data, a MATLAB script (velocity_field_calculation.m) loads a data file and calculates the velocity field and its correlations following the equations in the manuscript:</p> <ul> <li>"u": MATLAB cell. Velocity field for every time step.</li> <li>"u_u_corr": MATLAB cell. Second-order correlations of the velocity field for every time step.</li> <li>"std_u_u_corr": MATLAB cell. Standard deviation of second-order correlations of the velocity field for every time-step.</li> </ul> <p>Finally, the script plots "u_u_corr" for all the time steps and plots the averaged linear density.</p>
Experimental materials for "Reanalysis of Empirical Data on Java Local Variables with Narrow and Broad Scope"
<p>Data, scripts, and graphs for the paper "Reanalysis of Empirical Data on Java Local Variables with Narrow and Broad Scope", published in ICPC 2023.</p><p>See README file for details on directories and usage.</p>
Experimental data set for the article entitled "Mathematical Model of Steam Reforming in the Anode Channel of a Molten Carbonate Fuel Cell"
<p>Experimental data for the article: Szablowski, L.; Dybinski, O.; Szczesniak, A.; Milewski, J. Mathematical Model of Steam Reforming in the Anode Channel of a Molten Carbonate Fuel Cell. Energies 2022, 15, 608. The experiments were performed by the first two authors.<br> These data set refer to experiments carried out on a stand used to test high-temperature fuel cells. The subject of the study was a molten carbonate fuel cell fueled with a mixture of methane and steam with steam to carbon ratio of 2.0, 2.5, 3.0 and 3.5 and at the cell operating temperature of 550°C and 650°C. Additionally, in the anode channel of the cell, there was a catalyst in the amount of 2 g. The active area of the cell was 20.25 cm<sup>2</sup>. The article that uses these research results is published in an open access journal with a CC-BY license. This research was funded by the National Science Center, Poland (Grant number 2020/39/D/ST8/02021).</p>
HAARP API experimental data
<p>Raw data corresponding to HF signals received during API experiments conducted at HAARP from Oct. 24--26, 2022.</p>
Data and codes for: Experimental evolution of evolutionary potential in fluctuating environments
<p>This supplementary data contains raw data for a publication: "Experimental evolution of evolutionary potential in fluctuating environments." 1) Raw data of optical density measurements (10 .csv files), 2) R-code for obtaining maximal growth rate and maximum biomass yield from measurements. 3) resulting data file 4) R-code for Bayesian analysis of the data.</p>
Stepwise design of pseudosymmetric protein hetero-oligomers; experimental data
<p>Experimental data presented in the manuscript "Stepwise design of pseudosymmetric protein hetero-oligomers"</p>
Dataset: Experimental vignette-based survey data on NPI acceptance during travel (SNSF NRP 78)
<p>The data set contains rating scales on the willingness of the Swiss resident population to take risks in connection with touristic travel during the coronavirus pandemic. The data includes a selection of items of the Domain-Specific Risk-Taking Scale (DOSPERT) (Weber et al., 2002), the health belief model (HBM; Rosenstock, 1974, see also Champion & Skinner, 2008), and the theory of planned behaviour (Ajzen, 1991) to predict tourists’ intentions to travel under implementation of specific NPIs and the vaccination passport. The dataset contains data of N = 2’018 participants that have been collected between March 29 and April 9, 2021, based on a representative quota sampling (language region, age, gender). The data were collected based on a computer assisted web interview (CAWI) of a leading Swiss market research company. The following travel-related protective measures were tested regarding the constructs of the above-mentioned theories:</p> <ul> <li>Vaccination passport</li> <li>Surgical masks</li> <li>Travel warnings</li> <li>Rapid Testing at points of entry</li> <li>FFP2 masks</li> <li>PCR tests taken 72h before travel</li> <li>10-day quarantine of returning travelers from high-risk areas</li> <li>14-day quarantine of inbound travelers</li> </ul> <p>Ajzen, I. (1991). The theory of planned behavior. <em>Organizational Behavior and Human Decision Processes, 50</em>, 179-211. <a href="https://doi.org/10.1016/0749-5978(91)90020-T">https://doi.org/10.1016/0749-5978(91)90020-T</a></p> <p>Rosenstock, I. M., (1974). The health belief model and preventive health behavior. <em>Health Education Monographs,</em> <em>2</em>, 354-386. <a href="https://doi.org/10.1177/109019817400200405">https://doi.org/10.1177/109019817400200405</a></p> <p>Weber, E. U., Blais, A.-R., & Betz, N. E. (2002). A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. <em>Journal of Behavior Decision Making, 15</em>, 263-290. <a href="https://doi.org/10.1002/bdm.414">https://doi.org/10.1002/bdm.414</a></p> <p>Champion, V. L., & Skinner, C. S. (2008). The health belief model. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), <em>Health Behavior and Health Education. Theory, Research, and Practice</em> (4 ed, pp. 45-65). San Francisco, CA: Jossey-Bass.</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.