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4,694 results for “data analysis”
Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) III: Introducing the KEN; Data for CH4
<p>We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.8-1.5 micron, with 25 evenly spaced 20%, 30%, and 40% bandpasses in this range. The SNR ranges from 3-20. We present the lower and upper wavelength per bandpass, the input CH4 value (abundance case), the retrieved CH4 value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for CH4. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) III: Introducing the KEN, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input CH4': str})</p>
Data for phylogenomic analysis of chelicerate gene family evolution
<p>We used phylogenomics to investigate patterns of gene family evolution across ticks and other chelicerates, which include a diverse array of parasites. We used phylogenetic profiling and trait-association tests to predict gene families that may enable parasitic species to feed on hosts undetected for prolonged periods (>1 day). This release accompanies the pub, “<a href="https://doi.org/10.57844/arcadia-4e3b-bbea">Comparative phylogenomic analysis of Chelicerates points to gene families associated with long-term suppression of host detection</a>." Please see the pub for more information.</p> <ul> <li>chelicerata-v1-10062023.zip contains the outputs from NovelTree that are needed as inputs for phylogenetic profiling.</li> <li>annotated.zip contains gene annotations used to do orthogroup filtering.</li> <li>tx2gene.tsv has presence/absence of expression for each Amblyomma americanum transcript. </li> <li>chelicerate_proteome_preprocessing_outputs.zip contains the outputs of chelicerate protein data curation.</li> <li>chelicerata-v1-parameterfile.json & chelicerata-v1-samplesheet.csv were inputs for setting up the initial NovelTree run.</li> <li>2024-06-24-all-chelicerate-noveltree-proteins.fasta has the full set of chelicerate protein sequences.</li> <li>summary_of_noveltree_results.zip contains summary figures from the outputs of the NovelTree run.</li> <li>chelicerate-samples.tsv is the sample sheet used in proteome curation upstream of NovelTree.</li> </ul>
Data for: Mapping the Limits of Passive Samplers in Water: Chemical Space Coverage Using Nontargeted LC-HRMS Analysis
<p>This dataset provides files for passive samplers nad blanks analyzed by LC-HRMS fullscan DIA MS2.</p> <p>Excel file provides information about passive samplers, sampling site and sample files.</p>
The macroeconomic determinants of trade openness in Latin American countries: A panel data analysis
<p><strong><span>Background:</span></strong><span> Trade openness shows a positive impact on economic growth, supported by economic theory, and export diversification and economic complexity show a positive dynamic in trade openness in the world; however, a specificity is generated in South American countries. Therefore, the objective of the research is to analyse the macroeconomic determinants of trade openness in Latin American countries.</span></p> <p><strong><span>Methods: </span></strong><span>The research approach was quantitative and explanatory using panel data methodology from the databases of the World Bank, Harvard University and the Economic Commission for Latin America and the Caribbean for the period 2000-2020.</span></p> <p><strong><span>Results: </span></strong><span>The fixed effects panel data model showed that the variables that had a negative impact on trade openness were GDP, the economic complexity index and the logistic performance index, while the variables that had a positive impact were exports of high-tech products (a proxy for innovation), exports, imports, research and development expenditure and interregional trade in goods.</span></p> <p><strong><span>Conclusions: </span></strong><span>Therefore, during the analysis period of 2000-2020 in South America, based on the panel data analysis under fixed effects, a total of 8 countries had a negative impact on trade openness, and only the economies of Chile, French Guiana, and Brazil had a positive impact on trade openness; these economies are characterized by their better performance in the economic complexity index, their higher percentage of budget for research and development expenses, and their trade policies oriented towards the industrialization of their value-added products.</span></p>
scmcclelland/joint-mediation-study: Data, Analysis, and Figure Scripts for "Soil organic carbon sequestration jointly-mediated by plants and microbes after compost application"
<p>This repository contains data, analysis, and figure scripts to create findings from the manuscript "Soil organic carbon sequestration jointly-mediated by plants and microbes after compost application" currently under minor revisions.</p> <p>This release includes updated code, primarily improvements to figures, and a new script for a supplementary map figure.</p>
PSM-AP Comparative document analysis data: Priorities and challenges in the policy and digital strategies of ten PSM
<p>The document consists of a list of 61 key policy and strategy documents analysed as part of the comparative work conducted in WP1 of the project Public Service Media in the Age of Platforms (PSM-AP). It also contains a series of selected quotes supporting the three key areas prioritised by the policies and PSM digital strategies: People (reaching audiences), Personalisation (developing the video-on-demand portal), and Prominence (of PSM services and content). The data was collected and analysed in 2023, from documents concerning 10 PSM organisations in seven media markets: Belgium-Flanders (VRT), Belgium-Wallonia Brussels (RTBF), Canada (CBC/Radio-Canada), Denmark (DR, TV 2) Italy (RAI), Poland (TVP), and the UK (BBC, Channel 4, ITV). All quotes were translated to English by the authors.</p>
Data set from: Can laboratory-based XAFS compete with XRD and Mössbauer spectroscopy as a tool for quantitative species analysis?
<p><strong>Abstract:</strong> This work investigated the capability of quantitative laboratory X-ray Absorption Fine Structure Spectroscopy (lab-XAFS) via Linear Combination Fitting (LCF) of reference spectra in comparison with quantitative X-ray diffraction (XRD) and Mössbauer spectroscopy. While lab-XAFS already show good results when performing LCF with significant different spectra of the species to be identified, the method is challenging when the reference spectra and possibly species in the sample are very similar as it is the case for α-Fe<sub>2</sub>O<sub>3</sub>, γ- Fe<sub>2</sub>O<sub>3</sub> and Fe<sub>3</sub>O<sub>4</sub>. For this investigation an iron oxide mineral with origin from Mexico (here named Mexican Magnetite) with different iron oxide phases was used and measured using all three methods.</p> <p> </p> <p>This data set contains the raw data of the work “<em>Can laboratory-based XAFS compete with XRD and Mössbauer spectroscopy as a tool for quantitative species analysis? Critical evaluation using the example of a natural iron ore</em>” of XAFS, XRD and Mössbauer measurements. This includes XAFS, Mössbauer and XRD spectra of the reference materials α-Fe<sub>2</sub>O<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub> and the sample Mexican magnetite, the XAFS spectra of the reference material γ- Fe<sub>2</sub>O<sub>3</sub> and the XAFS, XRD and Mössbauer spectra of three different α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures.</p> <p> </p> <p><u>Sample information/sample list</u></p> <p><strong>sample/references:</strong> The sample and the corresponding short cut name used in the data files is listed. Furthermore the method the sample was measured with is also listed.</p> <table> <tbody> <tr> <td> <p><strong>Short cut name</strong></p> </td> <td> <p><strong> Sample/reference</strong></p> </td> <td> <p><strong>Measured with</strong></p> </td> </tr> <tr> <td> <p>MexicanMagnetite</p> </td> <td> <p> Iron oxide mineral with origin in Mexico</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe2O3</p> </td> <td> <p>Fe2O3-alpha / Hematite</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe3O4</p> </td> <td> <p>Fe3O4 / Magnetite</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe</p> </td> <td> <p>Iron powder</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>Fe2O3-alpha</p> </td> <td> <p>Fe2O3-alpha / Hematite</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>Fe2O3-gamma</p> </td> <td> <p>Fe2O3-gamma / Maghemite</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>30-70</p> </td> <td> <p>Mixture of 30 % Fe2O3-alpha/ 70 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>50-50</p> </td> <td> <p>Mixture of 50 % Fe2O3-alpha/ 50 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>70-30</p> </td> <td> <p>Mixture of 70 % Fe2O3-alpha/ 30 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Mixtures ratios:</strong> The prepared Fe2O3-Fe3O4 model mixtures with the weight-in ratios and the actual achieved mass percentage ratio between the two iron species, taken impurities of the used materials into account, are listed below. The short cut name is the name used in the data files (see table above).</p> <table> <tbody> <tr> <td> <p><strong>Short cut name</strong></p> </td> <td> <p><strong>Actual achieved weigh-in ratios</strong></p> <p><strong>m(Fe2O3)/m(Fe3O4)*</strong></p> </td> <td> <p><strong>Actual achieved mass percentage ratios ωrel(Fe2O3) / ωrel(Fe3O4)</strong></p> </td> </tr> <tr> <td> <p>30-70</p> </td> <td> <p>0.31380 g / 0.7059 g</p> </td> <td> <p>31.8 / 68.2</p> </td> </tr> <tr> <td> <p>50-50</p> </td> <td> <p>0.5140 g / 0.5174 g</p> </td> <td> <p>50.6 / 49.4</p> </td> </tr> <tr> <td> <p>70-30</p> </td> <td> <p>0.7037 g / 0.3041 g</p> </td> <td> <p>70.5 / 29.5</p> </td> </tr> </tbody> </table> <p>*the given masses here, ar the masses of the materials of the mixtures before sampel prepration. For the sample prepration the mass applied on the tape or mixed with wax is about 5-10 mg.</p> <p><u>Spectrometer Specifications</u></p> <p><strong>XAFS:</strong> The experimental setup for the laboratory XAFS measurement is based on the Highly Annealed Pyrolytic Graphite (HAPG) von Hámos spectrometer with the use of a cylindrically shaped crystal.</p> <p>As detector unit the pixelated X-ray hybrid-CMOS detector Dectris Eiger2 R 500k was used. The area of detection is 77.3 mm x 38.6 mm with a pixel size of 75 µm x 75 µm. The X-ray source was a water-cooled micro focus X-ray tube with molybdenum as anode material, a power of 30 Watt optimised at 15 kV and a spot size of 70 µm.</p> <p><strong>Sample preparation</strong>: α-Fe<sub>2</sub>O<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub>, the three α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures and the sample Mexican magnetite were applied on adhesive tape, sliced in 1cm x 1cm pieces characterized with XRF to determine the iron content as [<em>Q</em>] = mg/cm² and then stacked by taking the iron content of each slice into account to achieve an absorption of <em>µ*Q</em> of about 1 at the edge.</p> <p>The γ- Fe<sub>2</sub>O<sub>3</sub> and also the three α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures were prepared as Pellet. Here the sample material was mixed with Hoechst Wax C in a ratio of 1:6, mixed in a vortex shaker and then pressed with a hydraulic press with a Pellet diameter of 13 mm. The amount of the wax/sample powder material was weight before inserting in the press to the amount of <em>Q</em> to achieve a <em>µ*Q</em> of about 1 with a 13 mm Pellet.</p> <p>Shifts of the energy axis as well as a widening or compression of this axis could be present when comparing the data with other data sets of other spectrometer or synchrotron radiation facilities, since no precise energy calibration was carried out due to the reason that the samples were compared to the measured references and would have the same shift, widening or compression.</p> <p> </p> <p><strong>XRD:</strong> Two different commercial XRD set ups have been used. For the Mexican magnetite the Benchtop XRD spectrometer Bruker D2Phaser with a Cobalt X-ray source and a SSD160 detector (active length = 12 mm) was used. The measurement range was 10°- 90° 2theta with 0.014° step size and 4.8 s/step, resulting in a total measurement time of 8h. During the measurement the sample was rotated with 10 rpm. The sample was filled in PMMA-holders (Ø 2.5 mm) using the top-loading technique. The analysis was carried out using a 1-mm fixed divergence slit, a 2.5° primary and a 4° secondary soller collimator, a fixed knife edge (3 mm above the sample surface), and an Fe Kβ filter (2.5).</p> <p>For the X-ray diffraction measurements of the α-Fe2O3/Fe3O4 mixtures and the pure references a Panalytical X’Pert PRO diffractometer with a Bragg-Brentano setup was used. The diffractometer operates with a Cu anode and without a monochromator (Cu-Kalpha radiation) at 40 kV and 30 mA. The diffraction data were obtained over a measurement range of 10–120° 2theta. Samples were applied flat on a cut-off Si wafer attached to the sample holder.</p> <p><em> </em></p> <p><strong>Mössbauer:</strong> Mössbauer spectroscopy was performed at a MIMOS II type spectrometer with a <sup>57</sup>Co source (in rhodium matrix). For the analyses the <sup>57</sup>Fe-γ-line E = 14.4 keV was used and α-iron (α-Fe foil) was applied for the velocity calibration before the samples were analyzed. The samples were prepared in plastic powder sample holders and measured in transmission mode at room temperature. The measurement time varied between 12 h and 120 h depending on the sample.</p> <p> </p> <p><strong>Information on data sets</strong></p> <p>XAFS - this folder contains the XAFS spectra as intensity file with I0 (without the sample) and the It (transmission signal through the sample) for each sample. Multiple samples (It) share the same I0 and are therefore in the same data set. The Number in the filename between “XAFS“ and “data-set..” is the date of the measurement in the following format: YYYY_MM_DD. The first column in each file is the energy in unit eV. The abbreviation “WP” after each sample name in the header means “<strong>W</strong>ax <strong>P</strong>ellet” and indicates that the measurement was performed on a sample prepared as a wax pellet, the number (WP<strong>1</strong>) indicates the number of the pellet. Two pellets of each mixture were prepared to investigate the influence of the sample preparation. If the sample name is missing “WP#” the sample was prepared on adhesive tape as described above. The information on the contents of each data set as well as the measurement time (t = #h) for each It of the sample/reference can be found in data_dictionary_v2.txt.</p> <p>The intensity is normalized to counts per 1800 seconds in a 0.25 eV (for data-set-1) and 1 eV (for data-set-2, data-set-3 and data-set-4) energy interval with the indicated central bin energy.</p> <p> </p> <p>XRD - this folder contains the raw intensity files over 2theta (ASC-file). Each sample has its own file with the first column for the 2theta in unit degree and the second column for the measured intensity.</p> <p>The Number in the file name between XRD and sample name (e. g. Fe2O3, 30-70) is the date of the measurement in the following format: YYYY_MM_DD.</p> <p> </p> <p>MOESSBAUER - this folder contains the recoil Lorentz site analysis fit data of the samples. The files consist of the observed intensity (Iobs) over the velocity (v (mm/s)), including the calcucalted intensity (Icalc) and the fits of the subspectra (Sextet Site 1, etc. ). Each sample has it owns file. While the references substances <br>Fe2O3 and Fe3O4 were measured between 2016 and 2019, the MexicanMagnetite was measured 2020. An exact measurement date can’t be determined anymore.</p> <p> </p> <p>The corresponding sample to the short cut name (e. g. Fe2O3, 30-70,..) in the files can be found above and is listed in the <em>data_dictionary.txt</em> file as well.</p> <p> </p>
Data supporting "A comprehensive analysis of air-sea CO2 flux uncertainties constructed from surface ocean data products"
<p>Changelog</p> <p>v2: Fixes an identified issue in FluxEngine v4.0.7 that affects the calculation of fCO2atm. Fluxes have been recalculated using FluxEngine v4.0.9.1, and the analysis regenerated. The intergrated air-sea CO2 flux (or ocean sink) has reduced by ~0.2-0.3Pg C yr-1 but uncertainties are unchanged. </p> <p>v1: Initial dataset released along with the supporting manuscript</p> <p> </p> <p>Data included in this repository supports the manuscript "A comprehensive analysis of air-sea CO<sub>2</sub> flux uncertainties constructed from surface ocean data products".</p> <p>Two files are present:</p> <ol> <li>A Python config file used to run the software developed for the analysis (Ford et al., 2024)</li> <li>A ZIP file containing the input, neural network, and output files for the analysis.</li> </ol> <p>Within the ZIP file, multiple folders are present:</p> <ol> <li>Decorrelation contains .csv files that contain the annual estimates of the decorrelation lengths for the parameters requiring these (SST, sea ice, wind, fCO<sub>2</sub> and fCO<sub>2</sub> network).</li> <li>Flux contains the individual FluxEngine output files that provide all the flux calculations, and auxillary data to the flux calculations.</li> <li>Fluxengine_input contains the input files to FluxEngine, which specifies the fCO<sub>2 (sw), </sub>xCO<sub>2 (atm)</sub> and the temperature, salinities for the skin and subskin layers.</li> <li>Inputs contains all the monthly 1 degree input data used. Many of the data used are not native monthly 1 deg, and so these are generated from the higher resolution data. These are all combined into the neural_network_input.nc file, so a single file can be distributed with all the inputs used.</li> <li>Networks contains the TensorFlow neural network (FNN) files, where each province has 10 folders (one for each ensemble).</li> <li>Plots contains output plots for debugging and final plots of uncertainties</li> <li>Scalars contains the scalars used to normalise the data before input into the neural network. These are saved as Python pickle files, as they are needed if the neural network is used on other data.</li> <li>Unc_lut contains the look up tables to generate the parameter uncertainty as described in the manuscript. These are Python pickle files.</li> <li>Validation contains a csv file with the independent test RMSD, along with Python Pickle files of the validation data.</li> </ol> <p>In the main folder, three files are present:</p> <ol> <li>Annual_flux.csv contains the annual air-sea CO<sub>2</sub> flux (or ocean sink estimate) estimated from the fCO<sub>2 (sw)</sub> fields. This also contains the annual integrated uncertainties for each component in the uncertainty flow chart in the manuscript.</li> <li>Output.nc contrains the gridded global fields of the fCO<sub>2 (sw)</sub>, the air-sea CO<sub>2</sub> flux, and the uncertainties for all the individual components. Metadata within the file should provide all the information required.</li> <li>Training.tsv contains the training/validation data alongside the input parameters for neural network training</li> </ol> <p> </p> <p>Please contact Daniel J. Ford (<a href="mailto:d.ford@exeter.ac.uk">d.ford@exeter.ac.uk</a>) if you have any questions.</p> <p><strong>Acknowledgements</strong></p> <p>This work was funded by the Convex Seascape Survey (https://convexseascapesurvey.com/) and the European Union under grant agreement no. 101083922 (OceanICU; https://ocean-icu.eu/) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p> </p> <p><strong>References</strong></p> <p>Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschutzer, P., Jersild, A., & Shutler, J. D. (2024, June 30). OceanICU Neural Network Framework with per pixel uncertainty propagation (v1.1) (Version v1.1). Zenodo. https://doi.org/10.5281/ZENODO.12597803</p>
Data from: Molecular Dating of Phylogeny of Sturgeons (Acipenseridae) Based on Total Evidence Analysis
<p>Bayesian chronograms (original and updated 08.10.2022) of cladogenesis of fossil and recent Acipenseriformes reconstructed on the basis of combined (mtDNA, morphological characters) data.</p>
DATA SET USED IN THE PHYLOGENETIC ANALYSIS ?, condition not preserved. Coding for Paraortygoides based on BMNH PAL A 6217 (holotype of P. radagasti) and SMRME 1303 (holotype of P. messelensis) in The Fossil Galliform Bird Paraortygoides from the Lower Eocene of the United Kingdom
DATA SET USED IN THE PHYLOGENETIC ANALYSIS ?, condition not preserved. Coding for Paraortygoides based on BMNH PAL A 6217 (holotype of P. radagasti) and SMRME 1303 (holotype of P. messelensis)
Supplementary datasets, data analysis code, and R tutorials for: Phylogenetic analysis of adaptation in comparative physiology and biomechanics: overview and a case study of thermal physiology in treefrogs
<p>Comparative phylogenetic studies of adaptation are uncommon in biomechanics and physiology. Such studies require collecting data from many species, a challenge when data collection is experimentally intensive. Moreover, researchers struggle to employ the most biologically appropriate phylogenetic tools for identifying adaptive evolution. Here, we detail an established but greatly underutilized phylogenetic comparative framework—the Ornstein-Uhlenbeck process—that explicitly models long-term adaptation. We discuss challenges in implementing and interpreting the model, and we outline potential solutions. We demonstrate use of the model through studying the evolution of thermal physiology in treefrogs. Frogs of the family Hylidae have twice colonized the temperate zone from the tropics, and such colonization likely involved a fundamental change in physiology due to colder and more seasonal temperatures. However, which traits changed to allow colonization is unclear. We measured cold-temperature tolerance and characterized thermal performance curves in jumping for twelve species of treefrogs distributed from the Neotropics to temperate North America. We then conducted phylogenetic comparative analyses to examine how tolerances and performance curves evolved and to test whether that evolution was adaptive. We found that tolerance to low temperatures increased with the transition to the temperate zone. In contrast, jumping well at colder temperatures was unrelated to biogeography and thus did not adapt during dispersal. Overall, our paper shows how comparative phylogenetic methods can be leveraged in biomechanics and physiology to test the evolutionary drivers of variation among species.</p>
Mining and Extractivism Records Data for Bibliometric Analysis (Scopus database 1992-2020)
<p>The dataset file export from scopus database and the dataset file export as bibliometrix file on excel format from biblioshiny.</p>
Analysis of international funder data polices
<p>In October 2021, STM commissioned a research on funder data policies. The top 100 funders based on number of Crossref records were selected, and analyzed for the availability of data polices. These data policies were analyzed according to the elements of the journal data policy framework as developed by Hrynaszkiewicz et al. (https://datascience.codata.org/article/10.5334/dsj-2020-005/). This research will be used as input for more alignment between funder and data policies.</p>
Data and analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in Vanessa cardui"
<p>Butterfly scale data and data analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in <em>Vanessa cardui</em>."</p> <p> </p> <p>It is recommended to download all files and folders into a single root folder for use in MATLAB.<br> This code was prepared for use in MATLAB R2019b, and some scripts or functions require the Image Processing Toolbox.</p>
Data from: Using model analysis to unveil hidden patterns in tropical forest structures
<p>Data set of the article entitled: <strong>Using model analysis to unveil hidden patterns in tropical forest structures</strong></p> <p>This data set gives the following structural attributes for 133 forest plots at 9 sites in the tropics:</p> <ul> <li>tree density (ha<sup>-1</sup>)</li> <li>basal area (m<sup>2</sup> ha<sup>-1</sup>)</li> <li>mean diametere (cm)</li> <li>equivalent diameter (cm)</li> <li>density of trees in the dbh class 10-30 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 30-60 cm (ha<sup>-1</sup>)</li> <li>density of trees with dbh ≥ 60 cm (ha<sup>-1</sup>)</li> <li>aboveground dry biomass (Mg ha<sup>-1</sup>)</li> <li>fraction of the biomass of trees with dbh ≥ 60 cm</li> <li>weighted mean wood density (g cm<sup>-3</sup>)</li> <li>density of trees in the dbh class 10-20 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 20-30 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 30-40 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 40-50 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 50-60 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 60-70 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 70-80 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 80-90 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 90-100 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 100-110 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 110-120 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 120-130 cm (ha<sup>-1</sup>)</li> <li>density of trees with dbh ≥ 130 cm (ha<sup>-1</sup>)</li> </ul>
AutoDock and CB-Dock data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands
<p>AutoDock 4.2 and CB-Dock data for (NPA)<sub>6</sub>Zn<sub>3</sub>(H<sub>2</sub>O)<sub>2</sub> with M<sup>pro</sup> from SARS-CoV-2 from PDB Id: 6LU7. </p>
NMR data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands
<p><sup>1</sup>H, <sup>13</sup>C, COSY, HMBC, and HSQC NMR data in fid format for (NPA)<sub>6</sub>Zn<sub>3</sub>(H<sub>2</sub>O)<sub>2</sub> (NPA = 2-(phenylamino) benzoate) in DMSO-<em>d</em><sub>6.</sub></p>
Reference data and analysis software for "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane"
<p>Reference data set for the single molecule co-tracking analysis presented in "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane". Corresponding author for further inquiries:</p> <p>Prof. Dr. Jacob Piehler</p> <p>University of Osnabrück, Department of Biology/Chemistry, Division of Biophysics, Barbarastr. 11, 49076 Osnabrück, Germany</p> <p>https://www.biophysik.uni-osnabrueck.de/</p>
Data for figures in Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_
<p>Tar files containing gridded metrics, domain-wide metric means and confidence intervals, and rain-gauge reports used to generate figures in Kemp et al (2021).<br> <br> Citation:<br> </p> <p>Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_.</p>
Code to generate figures 3 and 4 of: "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."
<p>Code to generate figures 3 and 4 of the manuscript titled "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."</p> <p> </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.