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1,936 results for “environmental data”
Benthic macroinvertebrate, water temperature, and stream environmental data for Green Lakes Valley, 2021.
This dataset contains stream benthic macroinvertebrate community structure data from nine sites in the Green Lakes Valley ranging from below treeline near Albion Camp to the outflow of the Arikaree Glacier, all sampled in summer of 2021. The dataset also contains files with stream environmental data related to substrate stability, periphyton chlorophyll a concentrations, and water temperatures.
Data for - The environmental footprint of transport by car using renewable energy
<p>Replacing fossil fuels in the transport sector by renewable energy will help combat climate change. However, lowering greenhouse gas emissions by switching to alternative fuels or electricity can come at the expense of land and water resources. To understand the scale of this possible tradeoff we compare and contrast carbon, land and water footprints per driven km in midsize cars utilizing conventional gasoline, biofuels, bioelectricity, solar electricity and solar-based hydrogen. Results show that solar-powered electric cars have the smallest environmental footprints per km, followed by solar-based hydrogen cars, and that biofuel-driven cars have the largest footprints.</p>
Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment
<p>Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment<br> Prepared by Natalie Ceperley, February 2020. </p> <p><br> All methods associated with this data are available in the manuscript: Elvira Mächler, Anham Salyani, Jean-Claude Walser, Annegret Larsen, Bettina Schaefli, Florian Altermatt, and Natalie Ceperley. 2019. Water tracing with environmental DNA in a high-Alpine catchment, Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-2019-551. <br> Related data sets are and will be published in the Vallon de Nant Community on Zenodo. Associated sequencing data are publicly available on European Nucleotide Archive (Mächler et al., 2020). </p> <p>All isotope data analyzed in the laboratory of Torsten W. Vennemann at the University of Lausanne. </p> <p> </p> <p><br> All Files:<br> ▪ NaN - No measurement or sample<br> ▪ Details regarding measurement are available in paper or supplement. </p> <p>Files: <br> 1) climate_hydro_2017_daily.csv <br> ⁃ 16 columns: <br> ⁃ 1. day of year with January 1, 2017 = 1<br> ⁃ 2-5. Q: daily mean, min, max, and baseflow discharge as measured at outlet (location ER/MR), in liters / day <br> ⁃ 6. P: mean mm of rain across catchment per day<br> ⁃ 7. SR: total solar radiation per day in W/hr/m2 as median of 4 meteorological stations<br> ⁃ 8-10. SCA: mean, min, and max snow covered area on days with satellite imagery available for whole catchment area, in %<br> ⁃ 11-13. water temperature, mean, min, and max, at outlet (location ER/MR), in degrees C<br> ⁃ 14-16. air temperature, mean, min, and max at 4 meteorological stations, in degrees C</p> <p>2) delta-18-O_permil.csv <br> ⁃ stable isotopes of water (delta 18-O) in per mil<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>3) delta-2-H_permil.csv <br> ⁃ stable isotopes of water (delta 2-H) in per mil<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>4) dqdt_outlet_prev48hrs.csv<br> - dq/dt determined at the outlet for the previous 48 hours at sampling moment (TimeOfSamples_HR.csv) for each sampling site<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p><br> 5) ednasamplecount.csv <br> - this is the tally of samples (1 sample includes 4 replicates)<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>6) electricalconductivity_instrument.csv <br> ⁃ Code: <br> 108 - post-analyzed using a glass bodied 6 mm probe in the laboratory (Jenway 4510, Staffordshire, UK). <br> 102 - hand measurement with WTW (multi-3510 with a IDS-tetracon-925, Xylem Analytics, Germany)<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p><br> 7) electricalconductivity_uScm.csv <br> - this is the electrical conductivity in micro siemens per cm, according to the instruments coded in electricalconductivity_instrument.csv<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>8) LC-excess.csv <br> - this is the line control execss from the meteoric water line as determined by the samples in the file: precipitationistopemetadata.csv<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>9) locations.csv <br> ⁃ Location codes used in other files. <br> - Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl. </p> <p>10) precipitationisotopemetadata.csv <br> - This is the sampling information for the isotope data that was used to calculate the meteoric water line. <br> - The full data set will become available in a subsequent publication on Zenodo linked to the same community. <br> - 4 columns: <br> - 1. code: rain (1) or snow (2)<br> - 2. collection date and time<br> - 3. elevation in m. asl. <br> - 4. in the case of rain, this is the depth of collection in mm (area normalized volume), in the case of snow, this is the mean depth below the surface that the sample was taken from in cm. <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>11) sampledates.csv <br> - These are the sample dates in day, month, year and day of year corresponding to the rows in other files</p> <p>12) stationlocations.csv<br> - These are the locations of four meteorological stations and discharge measurement station. <br> - Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl. </p> <p>13) TimeOfSamples_HR.csv <br> - This is the time of the sample in hours and decimals correspond to minutes past hour<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>14) watertemperature_degC.csv <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)<br> - measure in degrees C<br> - instrument in watertemperature_instrument.csv</p> <p>15) watertemperature_instrument.csv <br> ⁃ Code: <br> 1 = hand measurement with WTW (multi-3510 with a IDS-tetracon-925, Xylem Analytics, Germany)<br> 2 = HOBO Pendant Temperature/Light Data Logger 64K - UA-002-64", Onset (Bourne, MA, USA)<br> 3 = Continually logging WTW (IDS-tetracon-325, Xylem Analytics, Germany)<br> 4 = Continually logging (10min) HOBO U24-001 Conductivity, Onset (Bourne, MA, USA) <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p>
Supporting data for 'Hourly prediction of phytoplankton biomass and its environmental controls in lowland rivers'
<p>This dataset is used in the manuscript 'Hourly prediction of phytoplankton biomass and its environmental controls in lowland rivers' published in Water Resources Research. The dataset contains hourly observation of water quality in the lower Thames catchment, UK and were made available by the Environment Agency, UK. </p>
Exposure to pesticides data for residents and bystanders, and for environmental risk assessment
<p>In 2014, EFSA has commissioned a study to review and evaluate all published data related to the exposure to pesticides for residents and bystanders and for environmental risk assessment. The aim was to conduct a literature review and to produce a database containing all published data (predominately peer-reviewed publications supplemented by grey-literature) for the last 25-years, which will support the non-dietary exposure assessment to pesticides for bystanders and residents, as well as daily air concentration (vapours and aerosols) of pesticides, drift values from spray, seed and granular applications, and dislodgeable foliar residues.</p> <p>The data has been collated via a systematic and extensive literature review defined and managed according to a pre-defined 'review protocol'. The data was also exported in a format that meets the requirements of the EFSA Data Collection Framework (DCF).</p> <p>Based on quality and relevance criteria, articles and related studies have been selected. For dislodgeable foliar residues the assessment includes 27 articles (containing 49 discrete studies); for air concentrations, 26 articles (containing 84 discrete studies); for resident and bystander exposure, 5 articles (containing 8 discrete studies); and for drift values 55 articles (containing 275 discrete studies). </p> <p>For dislodgeable foliar residues the data retained covered 17 crops (including grass, glasshouse crops, lucerne, and citrus) and 29 pesticides; for air concentrations the data retained covered 21 crops (including fruit, glasshouse crops, ornamentals, grass, vegetables and cereals) and 39 pesticides. For drift values, the data covers a range of crops and landscapes from cereals, grass and turf, orchards, vineyards and regenerated forestry. The vast majority of the data retrieved applies to field studies for liquid spray drift, measured either as ground deposits or collected at various heights and were conducted using fluorescent tracers rather than pesticides. No data was found for microbials (biopesticides). For resident and bystander exposure, many articles were rejected due to the applied inclusion/exclusion criteria.</p>
Environmental proxy data from the Chap archaeological site (Kyrgyzstan)
<p>Sediment particle size, magnetic susceptibility, loss-on-ignition (LOI) and pollen data from the Chap archaeological site (described in Motuzaite Matuzeviciute et al. 2019, 2021, 2022).</p> <p>Particle size analysis using Malvern Mastersizer 2000 after treatment with HCl and H2O2.</p> <p>Particle size end members (EM 1 & 2) modelled using Analysize package (Paterson & Heslop 2015) for Matlab.</p> <p>Dimensionless magnetic susceptibility (Κ) readings were taken using a Bartington MS2B at low (0.46 kHz) and high (4.6 kHz) frequencies. Mass magnetic susceptibility (χ) was calculated by dividing Κ by sample bulk density. The percentage of frequency dependent components (χ­­<sub>fd%</sub>) was calculated as 100[(χ<sub>lf </sub>– χ<sub>hf</sub>)/χ<sub>lf</sub>].</p> <p>For LOI calculations, samples were weighed, fired at 550 degrees C for 4 hours, re-weighed then fired at 950 degrees C for 4 hours. </p> <p>Pollen was extracted following heavy liquid methods described in Leipe et al. (2019).</p> <p>Units in dataset:</p> <p>Centimetre (cm)</p> <p>Relative abundance (%)</p> <p>Absolute abundance (#)</p> <p>Sorting (σg) – after Folk & Ward (1957)</p> <p>Mass magnetic susceptibility (SI/g)</p>
The data behind the ApJ article "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters"
<p>Data from "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters", <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230601088L/abstract">NASA ADS</a></p><p>inner_cluster_data.csv and outer_cluster_data.csv include the SALT3 mB, x1, and c parameter values, distance moduli and Hubble residuals (with _1 referring to Figure 9 and _2 referring to Figure 10), outlier designation from MCMC procedure, host cluster, host cluster redshift (with Hubble diagram version converted to frame of CMB), host cluster r500, projected separation from cluster center, NED Host galaxy name, photometrically-derived estimate for host mass, host or SN redshift used in analysis, and the Host SFR category (Q: quiescent, SF: star-forming, GV: green valley) for our cluster SNe Ia.</p><p>sf_field.csv and quiescent_field.csv contain SALT parameter values, distance moduli and Hubble residuals (from Figure 10), host galaxy sSFR and mass measurements, and host redshifts (all spectroscopic, also with Hubble diagram converted values) for SNe Ia in our field samples.</p><p>full_cluster.csv contains the data from the table in the appendix of the paper.</p><p>The inner_cluster_/outer_cluster_mcmc_samples.csv files contain the samples needed to reproduce the corner plot for Figure 10.</p><p>The Python scripts recreate the figures from the paper given the above data. The details for which columns and constraints needed to reproduce the figures are included in these files.</p>
Shorelines, elevation transects and environmental data for Drew Point, Ak study area, 2019-2022
<p>This record consists of the shorelines and their derived products, elevation transects and environmental data used for the Cryosphere preprint: <em>Multiple modes of shoreline change along the Alaskan Beaufort Sea observed using ICESat-2 altimetry and satellite imagery</em>. There are three primary datasets:</p> <p><em>ERA-5 </em>contains hourly output ERA5 reanalysis dataset (Hersbach et al., 2020, https://doi.org/10.24381/cds.adbb2d47) from a single pixel (-153.83, 70.87) from May 1st through November 30th for 2019, 2020, 2021, and 2022. It also contians a list of derived open water days for each year.</p> <p><em>ICESat-2 </em>contains the subsetted ATL03 photons (Neuman et. al, 2023, https://doi.org/10.5067/ATLAS/ATL03.006) for RGT 137 ground tracks 1r, 2r and 3r on 07 April 2019, 04 January 2020, 02 July 2021, and 31 December 2021, elevation profiles derived from SlideRule (Shean et al., 2023), and shoreline boundaries picked from the elevation profiles and Planet imagery. It also includes cross-shore transects used to project ICESat-2-derived changes into the local shoreline-perpindicular direction.</p> <p><em>Planet_shorelines</em> contains shorelines drived from 3m multispectral imagery from Planet Labs (Planet Team, 2024) as well as their derivatiev products. This included shorelines used for year-to-year change (<em>annual_coastlines</em>) and for uncertainty analysis (<em>uncertainty_coastlines</em>). It also includes the derived year-to year-change <em>(shoreline_change),</em> the baseline and cross-shore transects used to derived these change estiamtes, and a table with our derived annual shoreline change estimates compared with previously published estimates in this region.</p> <p>More detailed information on each datasets can be found in each folder's README file.</p>
CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000
<p><strong>CitiesGOER</strong> is a database that provides environmental data for 52,602 cities and 48 environmental variables, including 38 bioclimatic variables, 8 soil variables and 2 topographic variables. Data were extracted from the same 30 arc-seconds global grid layers that were prepared when making the <strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> database that is available from <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>. Details on the preparations of these layers are provided by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. CitiesGOER was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site.</p> <p>The identities and coordinates of cities were sourced from a data set with information for cities with a population size larger than 1000 that was created by <a href="https://public.opendatasoft.com/explore/?sort=modified">Opendatasoft</a> and made available from <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name</a>. The data was downloaded on 22-JULY-2023 and afterwards filtered for cities with a population of 5000 or above. Cities where information on the country was missing were removed. The coordinates of cities were used to extract the environmental data via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.6-47) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>.</p> <p>Version 2023.08 provided median values from 23 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 and from 18 GCMs for SSP 3-7.0, both for the 2050s (2041-2060). Similar methods were used to calculate these median values as in the case studies for the TreeGOER manuscript (calculations were partially done via the <a href="https://rdrr.io/cran/BiodiversityR/man/ensemble.envirem.html">BiodiversityR::ensemble.envirem.run</a> function and with downscaled bioclimatic and monthly climate 2.5 arc-minutes <a href="https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html">future grid layers available from WorldClim 2.1</a>).</p> <p>Version 2023.09 used similar methods as for previous versions to provide median values from 13 GCMs for the 2090s (2081-2100) for SSP 5-8.5.</p> <p>The locations of the 52,602 cities are mapped in one of the series available from the <strong>TreeGOER Global Zones</strong> atlas that can be obtained from <a href="https://doi.org/10.5281/zenodo.8252756">https://doi.org/10.5281/zenodo.8252756</a>.</p> <p>Version 2024.10 includes a new data set that documents the location of the city locations in <strong>Holdridge Life Zones</strong>. Information is given for historical (1901-1920), contemporary (1979-2013) and future (2061-2080; separately for RCP 4.5 and RCP 8.5) climates inferred from global raster layers that are <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.41ns1rnff">available for download from DRYAD</a> and were created for the following article: Elsen et al. 2022. <strong>Accelerated shifts in terrestrial life zones under rapid climate change.</strong> <em>Global Change Biology</em>, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a>. Version 2024.10 further includes Holdridge Life Zones for the climates that were available from the previous versions, calculating biotemperatures and life zones with similar methods as used by Holdridge (<a href="https://www.jstor.org/stable/1675393?seq=1">1947</a>; <a href="https://app.ingemmet.gob.pe/biblioteca/pdf/Amb-56.pdf">1967</a>) and Elsen et al. (<a href="https://doi.org/10.1111/gcb.15962">2022</a>) (for future climates, median values were determined first for monthly maximum and minimum temperatures across GCMs ). The distributions of the 48,129 species documented in TreeGOER across the Holdridge Life Zones are given in this Zenodo archive: <a href="https://zenodo.org/records/14020914">https://zenodo.org/records/14020914</a>.</p> <p>Version 2024.11 includes a new data set that documents the location of the city locations in <strong>Köppen-Geiger climate zones</strong>. Information is given for historical (1901-1930, 1931-1960, 1961-1990) and future (2041-2070 and 2071-2099) climates, with for the future climates seven scenarios each (SSP 1-1.9, SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5). This data set was created from 30 arc-second raster layers available via: Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724 (2023). <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a> </p> <p>Version 2025.03 includes extra columns for the baseline, 2050s and 2090s datasets that partially correspond to climate zones used in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database. One of these zones are the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">Whittaker biome types</a>, available as a polygon from the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">plotbiomes</a> package (see also <a href="https://www.davidzeleny.net/wiki/lib/exe/fetch.php/vegecol:materials:ricklefs_bioms_chapter_5.pdf">here</a>). Whittaker biome types were extracted with similar R scripts as described by <a href="https://rpubs.com/Roeland-KINDT/1275232">Kindt 2025</a> (these were also used to calculate environmental ranges of TreeGOER species, as archived <a href="https://zenodo.org/records/14908944">here</a>).</p> <p>Version 2025.03 further includes information for the baseline climate on the steady state water table depth, obtained from a 30 arc-seconds raster layer calculated by the GLOBGM v1.0 model (Verkaik et al. <a href="https://gmd.copernicus.org/articles/17/275/2024/">2024</a>). Also included was the elevation, obtained from the same WorldClim 2.1 raster layer used to prepare TreeGOER.</p> <p> </p> <p>As an alternative to CitiesGOER, the <strong>ClimateForecasts</strong> database (<a href="https://zenodo.org/records/10776414">https://zenodo.org/records/10776414</a>) documents the environmental conditions at the locations of 15,504 weather stations. ClimateForecasts was integrated in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees"><strong>GlobalUsefulNativeTrees</strong> database</a> (see <a href="https://doi.org/10.1038/s41598-023-39552-1">Kindt et al. 2023</a>).</p> <p> </p> <p>When using CitiesGOER in your work, cite this depository and the following:</p> <ul> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. <a href="https://doi.org/10.5194/soil-7-217-2021">https://doi.org/10.5194/soil-7-217-2021</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> </ul> <p>When using information from the Holdridge Life Zones, also cite:</p> <ul> <li>Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K.-V., Reside, A. E., & Watson, J. E. M. (2022). Accelerated shifts in terrestrial life zones under rapid climate change. <em>Global Change Biology</em>, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a></li> </ul> <p>When using information from Köppen-Geiger climate zones, also cite:</p> <ul> <li>Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N.J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A.I. and Miralles, D.G. 2023. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724. <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a></li> </ul> <p>When using information on the Whittaker biome types, also cite:</p> <ul> <li>Ricklefs, R. E., Relyea, R. (2018). Ecology: The Economy of Nature. United States: W.H. Freeman.</li> <li>Whittaker, R. H. (1970). Communities and ecosystems.</li> <li>Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7145245">https://doi.org/10.5281/zenodo.7145245</a></li> </ul> <p>When using information on the steady state water table depth, also cite:</p> <ul> <li>Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X., & Bierkens, M. F. (2024). GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development, 17(1), 275-300. <a href="https://gmd.copernicus.org/articles/17/275/2024/">https://gmd.copernicus.org/articles/17/275/2024/</a></li> </ul> <p> </p> <p>The development of <strong>CitiesGOER</strong> was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative</strong> through the Royal Norwegian Embassy in Ethiopia to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, and by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project. Development of version 2024.10 was further supported by the <strong>Green Climate Fund</strong> through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em> project, by the <strong>Bezos Earth Fund</strong> to the <em>Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p>
Phase characteristic optimization of resonant MEMS environmental sensors (Data)
<p>Origin projects and figures used for the article "Phase characteristic optimization of resonant MEMS environmental sensors", published in the proceedings of Sensoren und Messsysteme 2018, 19. ITG/GMA-Fachtagung; 26.06.2018 to 27.06.2018; Nürnberg, Germany.</p>
Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>cutouts </strong>are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the <strong>year 2013</strong>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a></p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Supplemental data from: "From lake to river: Documenting an environmental transition across the Jura/Knockfarril Hill members boundary in the Glen Torridon region of Gale crater (Mars)."
<p>This document, uploaded on the FAIR repository Zenodo, contains large data tables pertaining to the Supplementary Online Material of the above-mentioned article.</p> <p>These tables contain the complete list of individual MAHLI and ChemCam targets investigated, detailed laminae measurements and complete ChemCam compositional data.</p>
Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea
<p>Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea</p>
Data from: Development of Single Nucleotide Polymorphism (SNP) Panel for determination of environmental influence on genome for wild Columbia River redband trout (Oncorhynchus mykiss gairdnerii) in Southwest Idaho streams
<p>DNA were derived from fin tissue samples taken from individual trout captured from Little Jacks Creek, Big Jacks Creek , and Duncan Creek of the Owyhee mountains and Keithly Creek and Upper Mann Creek in the Hitt mountains of Western Idaho, United States. Fin tissues were collected from individual trout from each stream during monthly sampling events in June through October 2020. </p> <p><em>DNA Extraction:</em> Extraction of DNA from caudal fin tissues were performed using Quick-DNA Miniprep Plus purification kits (Zymo Research Inc.©). Small sections of fin tissue (≤ 25 mg) were collected from each sample. This was mixed with a digesting solution comprised of ultra-pure water, solid tissue buffer (Zymo Research Inc.©) and proteinase K. All tissues were digested in sealed microcentrifuge tubes for at minimum 3 h at 55°C in a water bath. We then aliquoted 100 µL of digestion supernatant and combined with 200 µL of genomic binding buffer (Zymo Research Inc.©). DNA was eluted in 50, 75, and 100 µL of elution buffer to determine which volume provided sufficient DNA concentration for genotyping. After it was determined all quantities produced suitable concentrations, going forward, 50 µL of elution buffer used.</p> <p><em>Genotyping:</em> Following extraction, genotyping-in-thousands sequencing took place at the Hagerman National Fish Hatchery’s genetics research facility with the assistance of the Columbia River Intertribal Fish Commission (CRTFC). Genotyping protocols were as described in Campbell et al. (2015) and summarized below. First, samples were prepared for amplification via PCR by combining DNA extracts with a Qiagen Plus multiplex master mix and a species-specific pooled primer mix. This step added the Illumina sequencing primer sites to amplicons. Following the creation of the PCR cocktail, thermocycling was conducted for amplification. Amplified samples were then diluted 20-fold. Diluted samples were transferred to new 96-well PCR plates where two genetic indexes and barcodes provides a unique set of tagging primers to each well and plate. Tagged plates then underwent a second PCR step. After the second PCR, all DNA were transferred to Charm Biotech normalization plates where DNA was bound to wells, washed, and finally eluted. After normalization, all DNA was pooled together and a purification step using magnetized beads in two steps to selectively remove fragments of DNA that are both too large and too small for sequencing. Following purification, each plate was quantified via qPCR using Life Technologies QuantStudio 6 Flex Instrument (Life Technologies). Finally, sequencing was performed using an Illumina HiSeq 1500 instrument.</p> <p><strong>Ancillary peer-reviewed manuscripts:</strong><br> <em>Genotyping protocols</em><br> Campbell NR, Harmon SA, Narum SR. 2015. Genotyping-in-Thousands by sequencing (GT-seq): A cost effective SNP genotyping method based on custom amplicon sequencing. Mol Ecol Resour, 15: 855-867. https://doi.org/10.1111/1755-0998.12357<br> <em>SNP loci reference</em><br> Collins EE, Hargrove JS, Delomas TA, Narum SR. 2020. Distribution of genetic variation underlying adult migration timing in steelhead of the Columbia River basin. Ecology and Evolution, 10(17): 9486-9502. https://doi.org/10.1002/ece3.6641 </p> <p><strong>Data Use</strong>:<br> <em>License</em>: <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> <br> <em>Recommended Citation</em>: Wooding AP, Narum SR, Pradhan DS. 2022. Data from: Development of Single Nucleotide Polymorphism (SNP) Panel for determination of environmental influence on genome for wild Columbia River redband trout (Oncorhynchus mykiss gairdnerii) in Southwest Idaho streams (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7055582</p> <p>Funding for this project is provided by US National Science Foundation and Idaho EPSCoR through award: OIA-1757324 </p>
Environmental Condition Data
<p>List of {temperature, humidity, luminance} data from 2 building zones for a month period (summer period)</p>
Supplemental data for "Inequitable spatial and temporal patterns in the distribution of multiple environmental risks and benefits in Metro Vancouver"
<p><strong>DemoEnPoC2016.csv/DemoEnPoC2006.csv:</strong></p> <p>This is a table including environmental and demographic (Census variables) data at postal code level for Metro Vancouver in the year 2006 and 2016. The environmental data (SO2 metrics, PM2.5 metrics, Calculated ozone metrics, NO2 data, NDVI metrics, and Canadian Active Living Environments Index (Can-ALE) indexed to DMTI Spatial Inc. postal codes) were extracted from CANUE (Canadian Urban Environmental Health Research Consortium). The demographic data is extracted from Canadian Census analyzer (https://datacentre.chass.utoronto.ca/), the deprivation index is downloaded from from the Institut national de santé publique du Québec (INSPQ). </p> <p><strong>DGRwithLable:</strong></p> <p>This is the Dissemination Geographies Relationship File for the 2021 census year (Statistics Canada, 2021) with the lable of urban or rural, indicating which dissemination area (DA) is identified as urban and included in this study. The urban area is named as population certer. </p> <p><strong>Aggregation and SS Determination:</strong></p> <p>This script contains code for:</p> <ul> <li>Aggregating postal code level data to the Dissemination Area (DA) level.</li> <li>Eliminating rural DAs.</li> <li>Converting environmental data into ordinal categories using quartile and even break methods.</li> <li>Identifying sweet and sour spots for each DA based on these methods.</li> </ul> <p><strong>SSEJ Analysis:</strong></p> <p>This script includes code for:</p> <ul> <li>Creating violin and box plots to illustrate descriptive statistics of demographic groups across different environmental categories (sweet, sour, risky, and medium).</li> <li>Performing linear regression analyses between environmental categories and demographic variables.</li> </ul> <p><strong>SS Heatmap:</strong></p> <p>This script comprises code for:</p> <ul> <li>Summarizing the results of the linear regression analyses.</li> <li>Assessing changes in inequities among demographic groups between 2006 and 2016.</li> <li>Visualizing regression coefficients through heatmaps.</li> </ul> <p> </p>
Derived data and analysis code accompanying Deines et al. 2019, Environmental Research Letters
<p>This codebase accompanies the paper:</p> <p>Deines, JM, AD Kendall, JJ Butler, Jr., & DW Hyndman. 2019. Quantifying irrigation adaptation strategies in response to stakeholder-driven groundwater management in the US High Plains Aquifer. Environmental Research Letters. DOI: <a href="https://doi.org/10.1088/1748-9326/aafe39">https://doi.org/10.1088/1748-9326/aafe39</a></p> <p>Data and code at time of publication.</p>
A Multi-Year Data Set of Beach-Foredune Topography and Environmental Forcing Conditions at Egmond aan Zee, the Netherlands
<p>The data set contains 39 digital elevation models and 11 orthophotos of a beach-foredune system near Egmond aan Zee, the Netherlands, a high-wave storm-dominated site with an approximately 25 m high foredune. The elevation data set combines a long duration (six years; January 2013 - January 2019) with a high temporal resolution (typically 2-4 months) and is spatially extensive (1.4 km alongshore) with a high spatial (1 m) resolution. To facilitate the testing and further development of coastal dune evolution models, the data set is supplemented with high-frequency time series of offshore wave, water level and wind characteristics as well as several subtidal bathymetries.</p><p>The data set is described in detail in the following open-access, peer-reviewed paper:</p><p>Ruessink, G.; Schwarz, C.S.; Price, T.D.; Donker, J.J.A. A Multi-Year Data Set of Beach-Foredune Topography and Environmental Forcing Conditions at Egmond aan Zee, The Netherlands. <i>Data</i> <strong>2019</strong>, <i>4</i>, 73. <a href="https://doi.org/10.3390/data4020073">https://doi.org/10.3390/data4020073</a></p><p>Update December 7, 2023: The data descriptor paper contains a typo related to the rotation of the RD and local coordinate schemes. On page 4/15 it is said that this rotation angle is 177 degrees, it should be 172.8 degrees. A big thank-you to Haoyang Peng (UNSW, Australia) for pointing out that the 177 degrees is incorrect. </p><p> </p><p> </p>
caseysaenger/ForamMgCa_PSM: files and scripts for revised version of manuscript "Calibration and validation of environmental controls on planktic foraminifera Mg/Ca using global core-top data".
<p>files and scripts for revised version of manuscript "Calibration and validation of environmental controls on planktic foraminifera Mg/Ca using global core-top data". Saenger, C. and M. N. Evans. Resubmitted to Paleoceanography and Paleoclimatology, May 3, 2019.</p>
The terrestrial carnivorous plant Utricularia reniformis sheds light on environmental and life-form genome plasticity: Annotation, Gene Ontology and raw data
<p><strong>Description:</strong> In this work, we deeply sequenced (genome and transcriptome of different organs), assembled, and analyzed the 311-Mbp genome of the terrestrial carnivorous plant <em>U. reniformis</em> (Lentibulariaceae). This project presents great importance to the understanding of genomic, evolutive and functional aspects of<em> U. reniformis</em>, which may, with the next-generation sequencing and computational biology approaches shed light to a better understanding not only for the biology and evolution of <em>Utricularia</em> genus, but also for other genera and lineages of the Lentibulariaceae family. Here we present all the raw data generated, including annotation and gene ontology files.</p> <p><strong>External Information</strong></p> <p><a href="https://genomevolution.org/coge/GenomeInfo.pl?gid=54799">Genome Browser</a> avaliable at CoGe Portal (https://genomevolution.org/coge/GenomeInfo.pl?gid=54799)</p> <p><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">GenBank </a><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">Bioproject</a> (https://www.ncbi.nlm.nih.gov/bioproject/290588) for raw genomic and transcriptomic reads</p> <p><a href="https://bv.fapesp.br/en/auxilios/84264/genomics-and-transcriptomics-of-utricularia-reniformis-lentibulariaceae-an-evolutive-and-function/">FAPESP grant website</a> contaning the project abstract and other information.</p> <p><strong>Papers published related to <em>Utricularia reniformis</em> genome</strong></p> <pre><strong>[1]</strong> Silva SR, Diaz YC, Penha HA, Pinheiro DG, Fernandes CC, Miranda VF, MichaelTP, Varani AM. <strong>The Chloroplast Genome of Utricularia reniformis Sheds Light on the Evolution of the ndh Gene Complex of Terrestrial Carnivorous Plants from the Lentibulariaceae Family</strong>. PLoS One. 2016 Oct 20;11(10):e0165176. doi:<strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/27764252">10.1371/journal.pone.0165176</a></strong>. </pre> <pre><strong>[2] </strong>Silva SR, Alvarenga DO, Aranguren Y, Penha HA, Fernandes CC, Pinheiro DG, Oliveira MT, Michael TP, Miranda VFO, Varani AM. <strong>The mitochondrial genome of the terrestrial carnivorous plant Utricularia reniformis (Lentibulariaceae): Structure, comparative analysis and evolutionary landmarks.</strong> PLoS One. 2017 Jul19;12(7):e0180484. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/28723946">10.1371/journal.pone.0180484</a></strong>.</pre> <pre><strong>[3] </strong>Silva SR, Moraes AP, Penha HA, Julião MHM, Domingues DS, Michael TP, Miranda VFO, Varani AM. <strong>The Terrestrial Carnivorous Plant Utricularia reniformis Sheds Light on Environmental and Life-Form Genome Plasticity.</strong> Int J Mol Sci. 2019 Dec 18;21(1). pii: E3. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/31861318">10.3390/ijms21010003</a></strong>.</pre> <p><strong>Acknowledgements</strong></p> <p>This work was supported by Sao Paulo Research Foundation FAPESP, Grant ID: [1325164-6]</p> <p> </p> <p><strong>---------------------------------------------------------</strong><br> <strong>FILES DESCRIPTION</strong><br> <strong>---------------------------------------------------------</strong><br> <br> ----------------<br> <strong>ANNOT-vFinal.sql: </strong>MySQL database containing all integrated annotation information of Urenif and Ugibba<br> ----------------<br> <strong>TABLE fields description</strong><br> gene_name gene name generated by EVidence Modeler + PASA<br> length gene lenght<br> status duplicate_gene_classifier status (0:singleton, 1:dispersed, 2:proximal, 3: tandem, 4:WGD)<br> product gene product <br> GOterms Blast2GO/OmicsBox GOterms<br> GO_mapping Blast2GO/OmicsBox GOterms derived from direct mapping (UniProt)<br> GO_annotation Blast2GO/OmicsBox annotated GOterms<br> GO_interpro Blast2GO/OmicsBox derived from InterProScan<br> EC Blast2GO/OmicsBox EC number<br> EC_name Blast2GO/OmicsBox enzyme name<br> NOG_annot EggNOG annotation description<br> NOG_EC EggNOG EC number<br> NOG_GO EggNOG GOterms<br> NOG_class EggNOG COG/KOG classfication<br> KEGG_Pathway EggNOG KEGG pathyways<br> KEGG_ko EggNOG KEGG ko<br> CAZy EggNOG CAZy enzymes<br> TAIR_gene Closest A. thaliana gene name (homologous) TAIR database lasted version<br> TAIR_annot Closest A. thaliana gene product (homologous) TAIR database lasted version <br> ortho MCL clustering among Vvinifera, Athaliana, and Slycopersicum (S:singleton, C: clustered, Y: shared)<br> ortho_two MCL clustering among Urenif and Ugibba (S:singleton, C: clustered, Y: shared)<br> -<br> -<br> ----------------<br> <strong>CEGs.zip </strong> 336 shared and concatenated CEGs from Urenif, U. gibba, Genlisea nigrocaulis, G. hispidula, G. aurea, G. pygmaea, and G. repens.<br> ----------------</p> <p><strong>ProcessRepeats_mod</strong> Modified version of RepeatMasker, ProcessRepeats script for detection of plant evolutionary lineages<br> ----------------</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia gibba</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Ugibba</strong><strong>-no-masked.fa </strong> Ugibba genome excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ugibba-softmasked.fa</strong> Ugibba genome RepeatMasker softmasked and excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ug.collinearity </strong> MCScanX collinearity file<br> <strong>Ug-duplicates.txt</strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ug.gene_type </strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ug.tandem </strong> Ugibba tandem genes generated by MCScanX tool<br> <strong>Ugibba_annot.annot </strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim) <strong>Ugibba_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong><strong> </strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Ugibba</strong><strong>.cDNA</strong> Ugibba cDNAs fasta file<br> <strong>Ugibba</strong><strong>.CDS </strong> Ugibba CDSs fasta file<br> <strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Ugibba GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Ugibba GFF3 file fully annotated (genes only)<br> <strong>Ugibba_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Ugibba_fasta.fasta</strong> Blast2GO/OmicsBox Ugibba fasta proteins containg annotation (product and GO terms)<br> <strong>ugibba_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>ugibba_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>ugibba_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)</p> <p><strong>Ugibba_GAF.txt</strong> GAF file<br> <strong>Ugibba</strong><strong>.gene</strong> Ugibba gene fasta file<br> <strong>Ugibba_GOstat.txt </strong> GOstat file<br> <strong>Ugibba</strong><strong>-PASA-assemblies.fasta </strong> Ugibba PASA assemblies<br> <strong>Ugibba</strong><strong>-PASA.stats </strong> Ugibba annotation STATS<br> <strong>Ugibba</strong><strong>.</strong><strong>prot</strong><strong> </strong> Ugibba protein fasta file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff </strong> Ugibba RepeatMasker gff file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff3 </strong> Ugibba RepeatMasker gff3 file<br> <strong>Ugibba</strong><strong>-RepeatMasker.tbl </strong> Ugibba RepeatMasker results<br> <strong>Ugibba</strong><strong>-RepeatMasker-v2.gff3</strong> Ugibba RepeatMasker gff3 second version file<br> <strong>Ugibba</strong><strong>-RNAseq-assembled.fasta </strong> Ugibba RNAseq assembled transcriptome (Trinity)<br> <strong>Ugibba_TEs_DANTE_2019.fa </strong> Ugibba TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Ugibba_WEGO.txt </strong> WEGO file</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia reniformis</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Urenif</strong><strong>-no-masked.fa </strong> Urenif genome excluding organellar genomes<br> <strong>Urenif</strong><strong>-</strong><strong>softmasked</strong><strong>.fa</strong> Urenif genome RepeatMasker softmasked and excluding organellar genomes<br> <strong>Ur.collinearity </strong> MCScanX collinearity file<br> <strong>Ur-duplicates.txt </strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ur.gene_type</strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ur.tandem</strong> Urenif tandem genes generated by MCScanX tool<br> <strong>Urenif_annot.annot</strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim)<br> <strong>Urenif_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Urenif</strong><strong>.cDNA</strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>.CDS </strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Urenif GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Urenif GFF3 file fully annotated (genes only)<br> <strong>Urenif_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Urenif_fasta.fasta</strong> Blast2GO/OmicsBox Urenif fasta proteins containg annotation (product and GO terms)<br> <strong>urenif_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>urenif_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>urenif_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)<br> <strong>Urenif_GAF.txt </strong> GAF file<br> <strong>Urenif</strong><strong>.gene</strong> Urenif gene fasta file<br> <strong>Urenif_GOStat.txt </strong> GOstat file<br> <strong>Urenif</strong><strong>-PASA-assemblies.fasta</strong> Urenif PASA assemblies<br> <strong>Urenif</strong><strong>-PASA.stats </strong> Urenif annotation STATS<br> <strong>Urenif</strong><strong>.</strong><strong>prot</strong><strong> </strong> Urenif protein fasta file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff </strong> Urenif RepeatMasker gff file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff3 </strong> Urenif RepeatMasker gff3 file<br> <strong>Urenif</strong><strong>-RepeatMasker.tbl </strong> Urenif RepeatMasker results<br> <strong>Urenif</strong><strong>-RepeatMasker-v2.gff3 </strong> Urenif RepeatMasker gff3 second version file<br> <strong>Urenif</strong><strong>-RNAseq-assembled.fasta </strong> Urenif RNAseq assembled transcriptome (Trinity)<br> <strong>Urenif_TEs_DANTE_2019.fa </strong> Urenif TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Urenif_WEGO.txt </strong> WEGO file<br> <strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong></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.