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FIGURE 3 in Estimated richness and environmental correlates of miniature fish assemblages in the rio Jacundá, Brazil
FIGURE 3 | Non-metric multidimensional scaling (NMDS) plot illustrating 20 sampling locations (points) at upstream (blue boxes) and downstream (red circles) sampling sites. Miniature fish scores are shown as green text, including Aama (Ammoglanis amapaensis), Amin (Ammocryptocharax minutus), Epul (Elachocharax pulcher), Fpal (Fluviphylax palikur), Haft (Hemigrammus aff. tridens), Mwei (Microcharacidium weitzmani), Pana (Physopyxis ananas), Phas (Potamoglanis hasemani), and Tsp (Tyttobrycon sp.). Environmental and spatial correlates with miniature fish assemblage structure are shown as gray vectors and text, including water overhead canopy cover (%), water temperature (C), pH, total suspended solids (TSS, ppm), conductivity (µs/cm), and the spatial relationship among sites from the first axis of an asymmetric eigenvector map (AEM1).
FIGURE 2 in Estimated richness and environmental correlates of miniature fish assemblages in the rio Jacundá, Brazil
FIGURE 2 | Accumulation (solid lines), extrapolation (dashed lines), and 95% confidence intervals (shaded areas) for diversity of miniature (red) and non-miniature (blue) freshwater fishes collected from the rio Jacundá, rio Amazonas basin, Brazil.
Estimating future climate change impacts on human mortality and crop yields via air pollution: supplemental files
<p>Atmospheric chemistry model output and other gridded data sets necessary to estimate human mortality and crop yield losses associated with future climate change, as used in Murray et al. [PNAS, 2024] doi:10.1073/pnas.2400117121.</p>
Estimated reflectance hyperspectral libraries for Vigo sediment sample, seafloor sand samples and marine organism
<h2>Abstract</h2> <p>Estimated reflectance hyperspectral libraries created for sand samples from seafloor at Vigo sea zone 1, 2 and 3, sediment samples from Vigo fieldwork Sept. 2023 and some Vigo marine organisms such as sea cucumber, sea pens, sea stars, coral, seaweed.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Estimated reflectance hyperspectral libraries for Vigo sediment sample, seafloor sand samples and marine organism</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Estimated reflectance hyperspectral libraries created for sand samples from seafloor at Vigo sea zone 1, 2 and 3, sediment samples from Vigo fieldwork Sept. 2023 and some Vigo marine organisms such as sea cucumber, sea pens, sea stars, coral, seaweed.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Reflectance hyperspectral signature, library, sediment, sand, sea cucumber, sea star, coral, seaweed</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>25.05.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>25.05.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>CSV</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.25m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 3035</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone AS</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Figure 5 in Growth estimation of Merluccius merluccius off the northern coast of Tunisia
Figure 5. - Von Bertalanffy model for hake combined sex off the northern coast in Tunisia fitted to the data, compared to those observed in the Bay of Biscay and in the Gulf of Lion.
Figure 1 in Histological distinction between immature and regenerating females and its effect on maturity ogive estimation in three tropical hermaphroditic groupers
Figure 1. - Histological sections of ovaries in female Epinephelus morio (a), Mycteroperca bonaci (b) and Mycteroperca microlepis (c) in the regenerating phase (1), immature phase (2) and uncertain maturity phase (3). a1: E. morio, 72 cm FL, collected Jun. 1990; a2: E. morio, 68 cm FL, collected Jun. 1989; a3: E. morio, 51 cm FL, collected Aug. 1989. b1: M. bonaci, 107 cm FL, collected Jul. 1997; b2: M. bonaci, 62 cm FL, collected Oct. 1997; b3: M. bonaci, 65 cm FL, collected Jun. 1998. c1: M. microlepis, 97 cm FL, collected Jul. 1998; c2: M. microlepis, 82 cm FL, collected Aug. 1997; c3: M. microlepis, 89 cm FL, collected Jul. 1998. C, cord; BV, blood vessel; GW, gonad wall; MB, muscle bundle; OL, ovarian lamellae; PG, primary growth oocyte. Gabe and Martoja one-step trichrome stain. Scale bars = 300 microns.
Figure 4 in Histological distinction between immature and regenerating females and its effect on maturity ogive estimation in three tropical hermaphroditic groupers
Figure 4. - Size-frequency distributions of Mycteroperca microlepis females in immature, uncertain maturity, regenerating and mature active (developing, spawning capable, actively spawning and regressing) phases, collected in inshore and offshore waters of Campeche Bank, southern Gulf of Mexico.
Figure 2 in Histological distinction between immature and regenerating females and its effect on maturity ogive estimation in three tropical hermaphroditic groupers
Figure 2. - Size-frequency distributions of Epinephelus morio females in immature, uncertain maturity, regenerating and mature active (developing, spawning capable, actively spawning and regressing) phases, collected in inshore and offshore waters of Campeche Bank, southern Gulf of Mexico.
Figure 3 in Histological distinction between immature and regenerating females and its effect on maturity ogive estimation in three tropical hermaphroditic groupers
Figure 3.- Size-frequency distributions of Mycteroperca bonaci females in immature, uncertain maturity, regenerating and mature active (developing, spawning capable, actively spawning and regressing) phases, collected in inshore, offshore waters and coral reefs of Campeche Bank, southern Gulf of Mexico.
Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications". </p>
Intermediate-mass black hole binary parameter estimation with next-generation ground-based detector networks
<p><strong>Reading the</strong> <strong>data</strong></p> <p>Each of the <em>alldetectors</em> and <em>CE40CE20ET</em> .zip file contains a folder with .pickle files. Each of these .pickle files corresponds to a point on the respective grid. The files contain dictionaries structured as follows:</p> <ul> <li>In the <em>skyareas </em>and <em>m1m2grid</em> folders, each dictionary stores the source-frame component masses, redshift, angular parameters, network SNR, network covariance matrix and 90% sky area.</li> <li>In the <em>Mzgrid</em> folders, each dictionary stores source-frame total mass, redshift, angular parameters, individual SNR and Fisher matrix for each detector in the network.</li> </ul> <p>The grids, detector network and parameters used to perform the Fisher calculations are described in the companion paper. The <em>converted_errors</em> folder contains network SNR, 1-sigma errors on source-frame masses, redshift, 90% sky areas for all the networks considered in the companion paper. These quantities are all angle-averaged.</p> <p>The <em>full_pe</em> .zip files cointain data comparing our Fisher results with full Bayesian PE runs for a few selected cases, as well as Jupyter notebooks to read those results (see Appendix B of the companion paper).</p>
GRM: A Novel Stochastic Model for Real-time GNSS Tropospheric Delay Estimation
<p>The dataset includes the proposed RWPN model (Cal_rwpn_new.m) and related files. The model is built based on ERA5 ZWD products from 2010 to 2019, which can be accessed at (<a>ftp://ftp.gfz-potsdam.de/pub/home/GNSS/products/gfz-vmf1/</a>). The proposed GRM model can contribute greatly by providing an efficient RWPN value to real-time GNSS ZTD estimation with an accuracy improvement of over 10% compared to fixed RWPN results. In addition, GRM also shows the superiorities of saving computation cost significantly since a large volume of the ERA5-derived RWPN values is modeled with only several parameters.</p>
Data for: Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning
<p>The dataset accompanies the Journal of Energy Storage publication by Shuquan Wang et al. (2024), Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning, DOI 10.1016/j.est.2024.112571. </p> <h2><strong>Experimental Description:</strong></h2> <p>The dataset comprises results from two experimental tests: pulse testing and driving cycle testing. These tests were conducted on two types of sodium-ion batteries—one with a capacity of 3.2 Ah (battery numbers: 1, 2, and 5) and another with a capacity of 10 Ah (battery numbers: 3, 4, and 6).</p> <h3><strong>Pulse Testing:</strong></h3> <p>The pulse tests were carried out using a battery test platform, consisting of an Arbin battery testing system, a temperature-controlled chamber, and a computer. The tests were performed on two 3.2 Ah and two 10 Ah sodium-ion batteries from Transimage and HiNa, respectively, with a nominal voltage of 3.0 V. The upper and lower cut-off voltages were set at 3.9 V and 1.5 V.</p> <p>Enhanced pulse tests were conducted at six different temperatures: -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. The state-of-charge (SOC) was varied in 10% intervals, with pulse currents escalating incrementally from 0.25C to 3C at 0.25C intervals. Each pulse lasted for 5 seconds, followed by a 15-second rest. After completing each set of pulses, the current was increased, and the process was repeated with a two-minute pause between sets of pulses.</p> <h3><strong>Driving Cycle Testing:</strong></h3> <p>The driving cycle tests were designed to simulate real-world driving conditions using various standard test methods, including the Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), and Dynamic Stress Test (DST). These tests were performed in a temperature-controlled chamber using both the 3.2 Ah and 10 Ah sodium-ion batteries.</p> <p>As with the pulse tests, driving cycle tests were carried out at temperatures of -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. Before each test, the batteries were charged with a 0.5C constant current-constant voltage (CC-CV) charging protocol up to 3.9 V, with a cut-off current of 0.02C. After a 30-minute rest, the driving cycle protocol was performed for seven iterations.</p> <h2><strong>File Naming Conventions:</strong></h2> <p>The dataset files are named based on the experimental conditions, as follows:</p> <ul> <li><strong>Pulse_data_tempX_batY</strong>: Data from the pulse tests, where X represents the testing temperature and Y denotes the battery number.</li> <li><strong>Driving_cycle_data_tempX_batY</strong>: Data from the driving cycle tests, where X represents the testing temperature and Y denotes the battery number.</li> </ul>
Supplementary material to the publication entitled "Digital transformation at what cost? A case study from Germany estimating the adoption potential of precision farming technologies under different scenarios" in Smart Agricultural Technology, https://doi.org/10.1016/j.atech.2024.100585
<p>The file '<em>PAT_Descriptions_Assumptions_Supplementary Material.pdf</em>' contains descriptions of the selected Precision Agricultural Technologies (PATs) and detailed explanations of the assumptions made in the calculation model.</p> <p> </p> <p>The file '<em>Calculation Model_NUTS3_BW.xlsx</em>' includes the calculation model created for the publication.</p>
Coccolithophore Abundance, Size, Carbon And Distribution Estimates (CASCADE)
<div>CASCADE is a global dataset for 139 extant coccolithophore taxonomic units. CASCADE includes a trait database (size and cellular organic and inorganic carbon contents) and taxonomic-specific global spatiotemporal distributions (Lat/Lon/Depth/Month/Year) of coccolithophore abundance and organic and inorganic carbon stocks. CASCADE covers all ocean basins over the upper 275 meters, spans the years 1964-2019 and includes 33,119 taxonomic-specific abundance observations. Within CASCADE, we characterise the underlying uncertainties due to measurement errors by propagating error estimates between the different studies.</div> <div> </div> <div>Full details of the data set are provided in the associated Scientific Data manuscript. The repository contains five main folders: 1) "Classification", which contains YAML files with synonyms, family-level classifications, and life cycle phase associations and definitions; 2) "Concatenated literature", which contains the merged datasets of size, PIC and POC and which were corrected for taxonomic unit synonyms; 3) "Resampled cellular datasets", which contains the resampled datasets of size, PIC and POC in long format as well as a summary table; 4) "Gridded data sets", which contains gridded datasets of abundance, PIC and POC; 5) "Species lists", which contains spreadsheets of the "common" (>20 obs) and "rare" (<20 obs) species and their number of observations.</div> <div> </div> <div>The CASCADE data set can be easily reproduced using the scripts and data provided in the associated github repository: <a title="Opens in new tab" href="https://github.com/nanophyto/CASCADE/tree/v0.1.1" target="_blank" rel="noopener">https://github.com/nanophyto/CASCADE/</a> (<a href="../doi/10.5281/zenodo.12797197">zenodo.12797197</a>)</div> <div> <p>Correspondence to: Joost de Vries, joost.devries@bristol.ac.uk</p> <p>v.3 fixes:</p> <ol> <li><em>actually</em> fixes Guerreiro et al., 2023 longitude issue</li> <li>replaces negative PIC predictions from GLM with NA (an issue with some small HOL species)</li> </ol> <p>v.2 fixes:</p> <p>1. The wrongly specified <em>S. neapolitana</em> was removed from synonyms.yml (this species is now<em> S. nana</em>)<br>2. Longitudes were corrected for Guerreiro et al., 2023<br>3. A double entry for Dimizia et al., 2015 was fixed<br>4. Units in Sal et al., 2013 were correct to cells/L (previously cells/ml)<br>5. Data from Sal et al., 2013 was re-done, as some species were missing<br>6. Duplicate entries from Baumann et al., 2000 were dropped</p> </div>
Relative Random Errors in the Convective Atmospheric Boundary Layer Estimated by the Relaxed Filtering Method from Large Eddy Simulations
<p>Data supporting the paper "How representative are uncrewed aircraft system measurements of the convective boundary layer?" by Brian R. Greene, Leia M. Otterstatter, and Scott T. Salesky, submitted to Geophysical Research Letters in 2024. Data are postprocessed from large-eddy simulations of the convective atmospheric boundary layer that are used to produce the figures within the paper. Details on the production of these files are included in the supplementary informatin of this paper.</p>
AMT29 optical underway properties and estimates of chlorophyll-a concentration
<p>Dataset of inherent optical properties determined as described in https://doi.org/10.5194/essd-2024-267 and https://doi.org/10.1364/OE.25.0A1079</p>
Dataset for the article 'Estimating countries' additional carbon accountability for closing the mitigation gap based on past and future emissions'.
<p>Dataset for the article 'Estimating countries’ additional carbon accountability for closing the mitigation gap based on past and future emissions', published in Nature Communications. DOI: <a href="https://doi.org/10.1038/s41467-024-54039-x">10.1038/s41467-024-54039-x</a></p> <p>TablesInManuscriptandCalculations.xlsx includes a calculations sheet where the main results can be estimated using only Excel, and each respective table found in the article.</p> <p>PlannedEmissions.xlsx includes estimated pathways for all analyzed countries during 2023-2070. Results are given in million tonnes of carbon dioxide (MtCO₂).</p> <p>DataForSensitivityAnalysis.xlsx is a full database with all the results used in the article, both the main approach and sensitivity cases.</p> <p>These files are generated using R-code available at:</p> <p><a href="https://github.com/morfeldt/AdditionalCarbonAccountability">https://github.com/morfeldt/AdditionalCarbonAccountability</a></p> <p> </p>
Fig. 4. Maximum clade credibility and divergence time estimations from BEAST reconstruction using 89 specimens and 20 in One in, one out: Generic circumscription within subtribe Manilkarinae (Sapotaceae)
Fig. 4. Maximum clade credibility and divergence time estimations from BEAST reconstruction using 89 specimens and 20 genes. Background colors represent the four Manilkarinae clades. Node labels are given as the mean of node age estimates for the main clades, including their 95% HPD and posterior probability (PP). The latter are only shown when PP <1. Epoch and ages in million years ago are represented at the bottom. Primary calibration points from fossils data are indicated with a red star, whereas clades constrained as monophyletic are labeled with an asterisk. RN: Réserves Naturelles; SF: Service Forestier.
Archive for regression and estimation of the total freeboard of the Arctic using AMSR2 data
<p>This fileset contains data used for generating Tables and Figures in Kim et al. (2021). All file has csv format separated by a comma. In the first line of the individual file, we provide the variable name and unit of each column.</p> <p> </p> <p>The fileset has 6 sub-directories. The name of each directory indicates the figures corresponding to its contents.</p> <p>• Figure 3 (and Table 1-3)</p> <p>• Figure 4</p> <p>• Figure 5, 8</p> <p>• Figure 5_ICESat2</p> <p>• Figure 6,7</p> <p>• Figure A1</p>
ScienceDex guides
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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.