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2,322 results for “precipitations”
SSU rRNA Gene Analysis of the Environmental and Biological Influences on Carbonate Precipitation within Hot Spring Microbial Mats in Little Hot Creek, CA
<p>R Markdown notebook and all data required to recreate the 16S rRNA Gene analysis figures found in Wilmeth et. al (In Submission), including mapping files, BIOM files, and R markdown notebook. </p>
Data from: Translocation precipitates natural hybridisation and pervasive introgression between marine gastropods with divergent developmental modes
<div> <div> <div> <p>Assisted colonisation, the introduction of species beyond their historical range, is increasingly necessary for conserving species. However, empirical evidence of the long-term genetic outcomes of assisted colonisation is grossly lacking. A risk associated with moving species beyond their native range is the possibility of interspecific hybridisation with a closely related species, potentially resulting in outbreeding depression or the genetic swamping of a parental species. Here, we use a combination of genome-wide Single Nucleotide Polymorphism (SNP) markers and mitochondrial DNA sequencing to determine the long-term genetic consequences of introducing the intertidal periwinkle <em>Bembicium vittatum</em> (a direct developer) beyond its native range and into the native range of its congener <em>Bembicium auratum</em> (a species with planktotrophic larval dispersal). We found novel evidence of natural, multigenerational hybridisation between marine invertebrates with different modes of development. Intriguingly, introgression was highly asymmetrical initially, but became more evenly bidirectional as the population became more admixed. There was a significant decline in the frequency of alleles from the introduced <em>B. vittatum</em> over time, providing evidence of genetic swamping. The present study also provides potential evidence of outbreeding depression, in the form of cytonuclear incompatibilities, leading to the observed pattern of asymmetrical introgression. This study reveals the potential for unexpected mixing between species when reproductive barriers are not well understood, resulting in failure of pure <em>B. vittatum</em> to persist at the translocation site, a major concern associated with assisted colonisation. Without long-term genetic monitoring interspecific hybridisation between <em>B. vittatum</em> and <em>B. auratum</em> would have gone undetected, highlighting the importance of long-term monitoring to detect unintentional negative consequences of conservation translocations. Successful assisted colonisation requires an understanding of the potential for interspecific hybridisation between the threatened species and closely related native species, to reduce the risk of adverse outcomes.</p> </div> </div> </div>
Improving the recovery for dissolved eDNA State: A comparative analysis of isopropanol precipitation, magnetic bead extraction, and centrifugal dialysis
<p>Environmental DNA consists of multiple states including but not limited to membrane-bound, adsorbed, and dissolved states. Because of their chemical and physical properties, each of these states may have different degradation and transport potential. Essential to the study of eDNA states is being able to isolate them from an environmental sample. Here we focus on improving the DNA recovery of the dissolved state of eDNA from water. We compared three recovery methods, namely isopropanol precipitation, magnetic bead extraction, and centrifugal dialysis. We evaluated the effectiveness of these methods based on the measured DNA recovery of two different species' DNA of different sizes. DNA recovery was assessed with qPCR. We also checked for the effect of inhibitor removal, and lastly estimated the cost of reagents and consumables (not labor). The DNA recovery varied among extraction methods, with isopropanol precipitation yielding the highest recovery (52.47 ± 19.69%), followed by centrifugal dialysis (12.58 ± 7.15%) and magnetic bead extraction (9.92 ± 3.89%). However, isopropanol precipitation's efficacy was influenced by humic acid concentration in the water matrix. The presence of humic acids significantly affected isopropanol precipitation, with higher humic acid concentrations leading to increased DNA recovery. This suggests that humic acids act as co-precipitators during isopropanol precipitation. We observed that longer DNA fragments (chicken) had lower recovery compared to shorter fragments (salmon) across all extraction methods. Magnetic bead extraction effectively removed inhibitors, while centrifugal dialysis and isopropanol extraction required an additional inhibitor removal step. Isopropanol, magnetic bead extraction, and centrifugal dialysis methods have estimated costs of 7.74, 8.53, and 23.62 USD in 2023 respectively. Overall, isopropanol precipitation was the least expensive and most effective with the highest measured recoveries, but it was dependent on humic acid concentration in the environmental sample.</p>
GHCN-daily: Western United States precipitation dataset
<p>This is a dataset of daily precipitation totals in the western United States (US) from January 1 1950 – February 29 2024. The dataset is based on daily precipitation measurements from 642 gauges across the western US, with periodic data gap-filling perfomed using estimates based on nearby gauges. From the daily records at the 642 gauge sites we produced a gridded record of daily precipitation totals at 0.25-degree resolution across the western US. The 642 gauges used as the basis for this dataset were selected for their uniquely long records and continuous coverage from the early 1950s through 2023. The purpose of producing this dataset was for evaluation of trends in sub-monthly cool-season (November–March) precipitation characteristics.</p>
An evaluation of cloud-precipitation structures in mixed-phase stratocumuli over the southern ocean in kilometer-scale ICON simulations during CAPRICORN
<p>The repository contains ICON simulated outputs (Cntrl_ICON.nc.gz, No_Gr_ICON.nc.gz, No_Ice_ICON.nc.gz, 100m_Vert_ICON.nc.gz, and 50m_Vert_ICON.nc.gz) and two km mean and ensemble forward simulated results (Cntrl_PAMTRA.gz, No_Gr_PAMTRA.gz, No_Ice_PAMTRA.gz, 100m_Vert_PAMTRA.gz, and 50m_Vert_PAMTRA.gz) for the two day period (00:00 UTC on March 26, 2016 to 00:00 UTC on March 28, 2016). The prefix in the filenames corresponds to the experiments described in the manuscript.</p>
data of precipitation cloud system information for April-June 2018-2022 in South China
<p>The three-dimensional structural information of precipitation cloud systems in South China for April-June 2018-2022 is recorded in this data, including the rain cells': id, latitude, longitude, DSD, precipitation contour, radar reflectivity factor, rainstorm top height, melting layer height and so on.</p>
Dataset on the effects of mineral grain size and seawater salinity on Mg(OH)2 dissolution and CaCO3 precipitation kinetics.
<p>Dataset from the manuscript "Effects of grain size and seawater salinity on magnesium hydroxide dissolution and secondary calcium carbonate precipitation kinetics: implications for ocean alkalinity enhancement" from Moras et al., 2024 (https://doi.org/10.5194/egusphere-2024-645). The dataset compiles all data used in the manuscript. The manuscript covers Mg(OH)2 dissoluton and CaCO3 precipitation kinetics for Ocean Alkalinity Enhancement. These kinetics are reported under different conditions, such as varying grain size and seawater salinity.</p>
Critical importance of tree and non-tree vegetation for African precipitation
<div>Supporting data and material with the following manuscript:</div> <div>"Critical importance of tree and non-tree vegetation for African precipitation"</div> <div> </div> <div>Authors:</div> <div>Te Wierik, S.A. (1,2,4) <br>Keune, J. (3) <br>Miralles, D. (3),<br>Gupta, J. (2), <br>Artzy-Randrup, Y.A (1), <br>Cammeraat, L.H. (1), </div> <div>van Loon, E.E. (1)</div> <div> </div> <div>1 Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, the Netherlands </div> <div>2 Governance and Inclusive Development, University of Amsterdam, Amsterdam, the Netherlands </div> <div>3 Hydro-Climate Extremes Lab, Ghent University, Ghent, Belgium</div> <div>4 Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, PO Box 60 12 03, 14412 Potsdam, Germany</div> <div> </div> <div>Last update: 03-07-2024</div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div>DATA </div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>1. FLEXPART_output_all Folder containing the precipitation source regions, differentiating between short and tall vegetation, for individual watersheds (NetCDF-file)</div> <div>Output files (<iwatershed>_ALLPBL_biascor-attr_linear_hybrid_monthly_1981-2019_E2P_and_T2P_all.nc) correspond with individual watershed numbers (see below).</div> <div>Files represent output of moisture tracking framework from Keune et al., 2022. </div> <div> </div> <div>2. major_watersheds Folder containing shapefile of the major African watersheds from hydroSHEDS (major_watersheds.shp)</div> <div> </div> <div>3. masks Folder containing:</div> <div>- Mask for all major African watersheds (1 degree) (mask_all_1x1_sum.nc) </div> <div>- Mask representing 1) forest loss hotspot region and 2) all cells with > 30% forest loss (1 degree) (mask_forest_loss.nc)</div> <div>- Mask representing 1) grass,- range- and cropland loss hotspot regions and 2) all cells with >30% grass-, range- and cropland loss (1 degree) (mask_croppasture_loss.nc).</div> <div> </div> <div>4. grid_area.nc Surface area (m2) of individual gridcells from a 1x1 degree global grid</div> <div> </div> <div>5. T_tall_short_mean_annual_1981-2019_1deg.nc GLEAM-Hybrid monthly total T and E data, global, 1 degree.</div> <div> </div> <div>6. vegetation-cover_1deg_monthly_1980-2019.nc MODIS derived vegetation cover fractions of short and tall vegetation and barren soil from MeASURES.</div> <div>Original data (0.05 degree) is resampled to 1 degree. </div> <div> </div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div>CODE AND ENVIRONMENT</div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>Short_tall_Tp_Africa.py Python code to analyze the moisture sources over individual watersheds</div> <div> </div> <div>Short_tall.yml Necessary environment to run Short-tall_Tp_Africa.py. Make sure the environment is imported and activated before running the script.</div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div>OUTPUT</div> <div>--------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>1. Figure 1</div> <div>t_volumetric.png 1d. Mean annual TP flow from trees (km3 year-1)</div> <div>t_normalized.png 1f. Mean annual TP flux from trees (mm year-1)</div> <div>h_volumetric.png 1e. Mean annual TP flow from non-tree vegetation (km3 year-1)</div> <div>h_volumetric.png 1g. Mean annual TP flux from non-tree vegetation (mm year-1)</div> <div> </div> <div>2. Supplementary Figure 2</div> <div>extent_t Contributing source regions of P from trees over the African continent using Tmin (mean annual TP > 0.5 mm yr-1).</div> <div>extent_h Contributing source regions of P from non-tree vegetation over the African continent using Tmin (mean annual TP > 0.5 mm yr-1)</div> <div> </div> <div>3. Supporting data</div> <div>0.5pshed_output_allwatersheds CSV file containing output data from all individual watersheds: mean annual contribution of short and tall vegetation (km3/year and mm/year); associated surface area (km2); relative contribution to precipitation from short and tall vegetation (%); relative contribution to dry and wet season precipitation from tall and short vegeation (%) </div> <div>0.5pshed_continental_sum_output.csv CSV file containing output data of aggregated means at continental level: mean annual contribution of short and tall vegetation to precipitation over Africa (mm/year and km3/year); associated surface area (km2).</div> <div>0.5pshed_hotspot_contributions.csv CSV file containing output data of LULC hotspot contributions to precipitation from all individual watersheds: volumetric contribution from hotspot regions (km3); surface area of the hotspot regions (km2); mean annual precipitation (km3/year) and mean annual precipitation from T (km3/year)</div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------</div> <div>WATERSHED NUMBER REFERENCES:</div> <div> </div> <div>1 Lake Chad</div> <div>2 Nile</div> <div>3 Rift Valley</div> <div>4 Congo</div> <div>5 Niger</div> <div>6 Volta</div> <div>7 Africa, West Coast</div> <div>8 Africa, North West Coast</div> <div>9 Mediterranean South Coast</div> <div>10 Africa, Red Sea - Gulf of Aden Coast</div> <div>11 Shebelli - Juba</div> <div>12 Africa, East Central Coast</div> <div>13 Madasgacar</div> <div>14 Zambezi</div> <div>15 Africa, Indian Ocean Coast</div> <div>16 Limpopo</div> <div>17 South Africa, South Coast</div> <div>18 South Africa, West Coast</div> <div>19 Orange</div> <div>20 Africa, South Interior</div> <div>21 Namibia, Coast</div> <div>22 Angola, Coast</div> <div>23 Gulf of Guinea</div> <div>24 Senegal</div> <div>25 Africa, North Interior</div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------</div> <div>REFERENCES & DATA SOURCES</div> <div> </div> <div>- Keune, J., Schumacher, D.L., and Miralles, D.G. (2022) A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models. Geosci. Model Dev., 15, 1875–1898, 2022 https://doi.org/10.5194/gmd-15-1875-2022</div> <div>- te Wierik, S.A., Keune, J., Miralles, D.G., Gupta, J., Artzy-Randrup, Y.A., Gimeno, L., Nieto, R., Cammeraat L.H. (2022) The Contribution of Transpiration to Precipitation Over African Watersheds. Water Resources Research, 58, e2021WR031721. https://doi. org/10.1029/2021WR031721</div> <div> </div> <div>• MEaSUREs vegetation continuous fields (VCF), Yearly, Global, 0.05 degree (https://cmr.earthdata.nasa.gov/search/concepts/C1452975608-LPDAAC_ECS.html; https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1#bands)</div> <div>• HydroSHEDS data is available via https://data.apps.fao.org/catalog/dataset/7707086d-af3c-41cc-8aa5-323d8609b2d1;</div> <div>• ERA-Interim data is available via https://apps.ecmwf.int/datasets/.</div> <div>• GLEAM data is available via https://www.gleam.eu/; </div> <div>• MSWEP data is available via http://www.gloh2o.org/. </div> <div>• OAFlux data is available from https://oaflux.whoi.edu/data-access/. </div> <div> </div>
Disturbance amplifies sensitivity of dryland productivity to precipitation variability
<p>Variability of the terrestrial global carbon sink is largely determined by the response of dryland productivity to annual precipitation. Despite extensive disturbance in drylands, how disturbance alters productivity-precipitation relationships remains poorly understood. Using remote-sensing to pair over 5600 km of natural gas pipeline corridors with neighboring undisturbed areas in North American drylands, we found that disturbance reduced average annual production 6-29% and caused up to a five-fold increase in the sensitivity of net primary productivity (NPP) to interannual variation in precipitation. Disturbance impacts were larger and longer-lasting at locations with higher precipitation (>450 mm mean annual precipitation). Disturbance effects on NPP dynamics were mostly explained by shifts from woody to herbaceous vegetation. Severe disturbance will amplify effects of increasing precipitation variability on NPP in drylands.</p>
Seasonal precipitation distribution determines ecosystem CO₂ and H₂O exchange by regulating spring soil water-salt dynamics in a brackish wetland
<p>The intensification of the global hydrological cycle is anticipated to increase the variability of precipitation patterns. Brackish wetlands respond to changes in precipitation patterns by regulating the absorption and release of CO<sub>2</sub> and H<sub>2</sub>O to maintain the stability of ecosystem functions. However, there is limited understanding of how the inter-seasonal precipitation distribution affects ecosystem CO<sub>2</sub> and H<sub>2</sub>O exchange compared to annual precipitation totals. Here, we conducted four consecutive years of field experiments in a brackish wetland, manipulating the proportion of precipitation across different seasons while maintaining a constant annual precipitation total. We utilized five inter-seasonal precipitation distribution proportions (+73%, +56%, control (CK), -56%, and -73%) to examine the effects of seasonal precipitation distribution (SPD) on ecosystem CO<sub>2</sub> and H<sub>2</sub>O exchange. Our findings revealed that the ecosystem CO<sub>2</sub> and H<sub>2</sub>O fluxes showed a trend of decreasing with the decrease of spring precipitation distribution. Among them, the annual net ecosystem CO<sub>2 </sub>exchange (NEE), evapotranspiration (ET), carbon use efficiency (CUE), and water use efficiency (WUE) were shown to be more sensitive to decrease in spring precipitation distribution and increase in summer and autumn precipitation distribution. This negative asymmetric response pattern suggests that annual ecosystem CO<sub>2</sub> and H<sub>2</sub>O exchange is primarily governed by seasonal precipitation variability, with spring soil water-salt dynamics identified as the key driver. Therefore, this association can be explained by the fact that drought of the early growth stage exacerbates soil salinization and inhibits vegetation colonization and growth, thereby greatly impairing the annual CO<sub>2</sub>-H<sub>2</sub>O exchange capacity of brackish wetlands. Our results emphasized that the spring's extreme precipitation-induced soil water-salt conditions will greatly influence CO<sub>2</sub> and H<sub>2</sub>O exchange in brackish wetlands in the future. These findings are crucial for improving predictions of the carbon sequestration and water-holding capacity of brackish wetlands.</p>
Changes in productivity partitioning induced by precipitation extremes increase inaccuracy of grassland carbon estimation
<p><span>The fraction of net primary productivity (NPP) allocated to belowground organs (<em>f</em><sub>BNPP</sub>) in grasslands is a critical parameter in global carbon cycle models; moreover, understanding the effect of precipitation changes on this parameter is vital to accurately estimating carbon sequestration in grassland ecosystems. Here, by conducting both a precipitation gradient experiment (100–500 mm) and a long-term observational study (34 years) in the Inner Mongolia grassland, we showed that <em>f</em><sub>BNPP</sub> decreased linearly along the precipitation gradient from extreme drought to extreme wetness due to stronger responses in aboveground NPP (ANPP) to drought and wet conditions than those of BNPP. The linearly decreasing response pattern in <em>f</em><sub>BNPP</sub> should be incorporated into models that forecast carbon sequestration in grassland ecosystems; failure to do so will lead to underestimation of the carbon </span><span>stock</span><span> in drought years and overestimation of the carbon </span><span>stock</span><span> in wet years in grasslands.</span></p>
Multi-Source Precipitation Data Fusion Across Continental United States
<p><strong><span>Dataset Description</span></strong><span>: This dataset supports our research presented in the paper "<em>A deep learning-based framework for multi-source precipitation fusion</em>" by Gavahi et al., (2023) published in <em>Remote Sensing of Environment</em>. The study introduces a novel deep learning architecture for merging and downscaling multiple precipitation products, aiming to enhance quantitative precipitation estimation (QPE) accuracy. The developed model, the Precipitation Data Fusion Network (PDFN), integrates 3D-CNN and ConvLSTM layers to capture the inherent spatiotemporal dependencies of precipitation data. The results indicate significant improvements in error statistics.</span></p> <p><span>The dataset includes merged daily precipitation estimations using the PDFN model. The data cover the Continental United States (CONUS) and are provided at a spatial resolution of 0.05 degrees. Temporal coverage spans from January 1, 2015, to April 30, 2024. The coordinate reference system used is WGS1984.</span></p> <p><strong><span>Note</span></strong><span>: Since the PERSIANN-CDR dataset is only available until the end of 2023, in this dataset, we used PDIR-Now instead to ensure the dataset's continuity and completeness. In the original paper, we used PERSIANN-CDR, but here we used PDIR-Now to extend the dataset to cover the period until April 30, 2024.</span></p> <p><strong><span>Usage Notes</span></strong><span>: This dataset is intended for use in applications such as land surface modeling, flood forecasting, drought monitoring and prediction. Users are requested to cite the associated paper when utilizing the dataset for academic or research purposes. </span></p> <p><strong><span>Related Publications</span></strong><span>: For further details on the methodology and applications of this dataset, refer to the paper "Gavahi, K., E. Foroumandi, and H. Moradkhani (2023), A deep learning-based framework for multi-source precipitation fusion, Remote Sensing of Environment, doi:10.1016/j.rse.2023.113723"</span></p>
Modelling Amorphization and Dissolution of Zr(Fe,Cr)2 Secondary Phase Precipitates in Zircaloys
<p>Data and accompanying paper "Modelling Amorphization and Dissolution of Zr(Fe,Cr)2 Secondary Phase Precipitates in Zircaloys" J. D. Robson, Journal of Nuclear Materials</p>
Greenland Ice Sheet precipitation and surface temperature from CloudSat and ECMWF
<p>This dataset contains code, data, and instructions for recreating the figures and analysis in Thompson-Munson et al. (submitted), "An Observational Constraint for Future Greenland Rainfall in a Warmer Atmosphere". Please see the readme for instructions and descriptions of the data and code.</p>
Preprocessed EUMETSAT H-SAF h61 Satellite and ECMWF HRES 24h Forecast Precipitation Datasets for Hydrometeorological Applications over Central Europe
<p>This dataset contains preprocessed precipitation data from the ECMWF's high resolution HRES forecast (24h) and EUMETSAT's blended infrared and microwave remotely sensed data for use in hydrological and meteorological research. <br><br><em>* Preprocessing procedure and codes are accessible <a href="https://gitlab.jsc.fz-juelich.de/kiste/atmoscorrect/-/blob/master/HRES_PP.ipynb?ref_type=heads">here for HRES</a>, and <a href="https://gitlab.jsc.fz-juelich.de/kiste/atmoscorrect/-/blob/master/HSAF_PP.ipynb?ref_type=heads">here for H-SAF</a> datasets.</em></p> <p><strong>HRES Data (HRES_pr.nc):</strong></p> <ul> <li><strong>Source:</strong> <a href="https://confluence.ecmwf.int/display/FUG/Section+2.1.2.4+HRES+-+High+Resolution+Forecasts">ECMWF's high-resolution, deterministic HRES 24-hour precipitation forecast at 12UTC.</a></li> <li><strong>Resolution and domain:</strong> 0.1° × 0.1° grid in (longmin: -1.1, longmax: 18.4, latmin: 44.1, latmax: 56.5)</li> <li><strong>Preprocessing Steps:</strong> <ol> <li>Extracted the precipitation variable (tp) out of variables.</li> <li>Converted precipitation units from meters (m) to millimeters (mm).</li> <li>Changed cumulative precipitation to instantaneous.</li> <li>Selected the first 24 hours of forecast data from the available 90-hour forecasts.</li> <li>Merged all processed files into a single NetCDF file.</li> </ol> </li> </ul> <p><strong>H-SAF Data (HSAF_pr.nc):</strong></p> <ul> <li><strong>Source:</strong> <a href="https://hsaf.meteoam.it/Products/Detail?prod=H61B">EUMETSAT's H-SAF h61B</a></li> <li><strong>Resolution and domain:</strong> Original product: ~4.8 km at nadir, ~8km in Europe; preprocessed product: resampled to 0.1° × 0.1° (~10 km) grid in (longmin: -1.1, longmax: 18.4, latmin: 44.1, latmax: 56.5).</li> <li><strong>Preprocessing Steps:</strong> <ol> <li>Trimmed the MSG coverage data to cover the study domain.</li> <li>Calculated the grid correspondance using lat/lon information from MSG grid using a <a href="https://www-cdn.eumetsat.int/files/2020-04/pdf_conf_2018_s1_mueller_p.pdf">reference method</a>.</li> <li>Merged all processed files into a single NetCDF file.</li> <li>Regridded the data to the 0.1° × 0.1° resolution using bilinear function in <a href="https://code.mpimet.mpg.de/projects/cdo">CDO</a></li> </ol> </li> </ul> <p><strong>Data Period:</strong> 01/07/2020-25/04/2023</p>
Precipitating electron fluxes of the magnetosphere-ionosphere coupling processes
<p>Numerical data for he precipitating electron fluxes, at altitude of 800 km, of the magnetosphere-ionosphere coupling processes, calculated for both Maxwell and Kappa distributions - used in Khazanov et al. (2018), in review.</p>
Data for the paper "Small-scale variabilities in the electric field and the particle precipitation and their impacts on Joule heating"
<p>This dataset works specifically for the paper Zhu et al., (2018), Small-scale variabilities in the electric field and the particle precipitation and their impacts on Joule heating. </p> <p>Notes:</p> <p>1) fig1 file for data generating fig 1</p> <p>2) fig2 zip for data generating fig 2 </p> <p>3) fig3 file for data generating fig 3</p> <p>4) fig4 zip for data generating fig 4 (In that zip file, Run 1 for fig 4a, Run2 for fig 4b, Run3 for fig 4c)</p> <p> </p>
Role of Troposphere-Convection-Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model version 1 (CESM1)
<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>
Steel, fcc precipitates in a bcc grain, EBSD
<p>The EBSD map is taken from a steel sample, from a region within a single bcc grain containing fcc precipitates after heat treatment. The sample is provided from Wiesław Świątnicki (Warsaw University of Technology). </p> <p> </p>
Tree rings as a proxy for seasonal precipitation variability and Early Neolithic settlement dynamics in Bavaria, Germany
<p>The file contains original measurements of total ring width (TRW) of 540 subfossil oak trees from the Main region (southern Germany) spanning the period from 6228 to 4057 BCE (2172 years).</p> <p>Latitude: 49.6-50.2°N</p> <p>Longitude: 10.2-11.2°E</p> <p>Resolution of measurement: 0.01 mm</p> <p>File format: Tucson (.tuc)</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.