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10,016 results for “Observation”
Figures 107–112 in Revisiting Potter Cove, King George Island, Antarctica, 12 years later: new observations of marine benthic diatoms
Figures 107–112: SEM images of the marine benthic diatoms in Potter Cove, King George Island, Antarctica, summer 2015. (107, 108) Porosira glacialis, internal valve view showing the single marginal labiate process. (109–111) Entopyla ocellata, (109, 110) external valve view showing the wide and superficial apical porefield which occupies large area of valve face, (111) internal valve structure showing the narrow and crescent-like rimoportula openings (white arrows). (112) Navicula sp. 2.
Figures 27–38 in Revisiting Potter Cove, King George Island, Antarctica, 12 years later: new observations of marine benthic diatoms
Figures 27–38: Marine benthic diatoms in Potter Cove, King George Island, Antarctica, summer 2015. (27) Corethron pennatum. (28) Fragilaria striatula. (29) Synedropsis sp. (30) Licmophora antarctica. (31–32) Licmophora belgicae. (33) Licmophora gracilis. (34) Licmophora luxuriosa. (35) Thalassionema nitzschioides. (36) Rhabdonema arcuatum. (37, 38) Entopyla ocellata.
Figures 79–92 in Revisiting Potter Cove, King George Island, Antarctica, 12 years later: new observations of marine benthic diatoms
Figures 79–92: Marine benthic diatoms in Potter Cove, King George Island, Antarctica, summer 2015. (79) Gyrosigma arcuatum. (80) Gyrosigma fasciola. (81) Gyrosigma cf. obscurum. (82) Pleurosigma eudon var. kerguelensis. (83) Pleurosigm diversestriatum. (84) Pleurosigma strigosum. (85) Amphora gourdonii. (86) Amphora marina. (87, 88) Amphora sp. 1. (89) Amphora sp. 2. (90) Cylindrotheca closterium. (91, 92) Fragilariopsis cylindrus.
Figure 3 in Observations on the mating behaviour and related copulatory anatomy of Alaena margaritacea Eltringham, 1929 (Papilionoidea: Lycaenidae: Poritiinae)
Figure 3 – Mating pair, female on top and male below: A – red arrow indicates tip of female abdomen; B – images showing the titled angle of copulation: C – close-up of Fig. 3B.
Figure 1 in Observations on the mating behaviour and related copulatory anatomy of Alaena margaritacea Eltringham, 1929 (Papilionoidea: Lycaenidae: Poritiinae)
Figure 1 – Google Earth polygon (shaded area) of TL breeding area of Alaena margaritacea; an area of about 650 m2.
Figure 5 in Observations on the mating behaviour and related copulatory anatomy of Alaena margaritacea Eltringham, 1929 (Papilionoidea: Lycaenidae: Poritiinae)
Figure 5 – The red arrow points to the shiny black button ventrally at a distance away from the posterior tip of a living female abdomen.
Figure 6 in Observations on the mating behaviour and related copulatory anatomy of Alaena margaritacea Eltringham, 1929 (Papilionoidea: Lycaenidae: Poritiinae)
Figure 6 – SEM micrograph of ostium bursa of A. margaritacea female. It is situated ventrally towards the posterior end of the abdomen. Note the hardened cup and the substance covering the orifice (image: W. Landman).
Figure 2 in Observations on the mating behaviour and related copulatory anatomy of Alaena margaritacea Eltringham, 1929 (Papilionoidea: Lycaenidae: Poritiinae)
Figure 2 – Mating pair, female on top and male below: A – female wafting her wings; B – pair moved to a position where they are screened by an unidentified fern.
Figure 4 in Observations on the mating behaviour and related copulatory anatomy of Alaena margaritacea Eltringham, 1929 (Papilionoidea: Lycaenidae: Poritiinae)
Figure 4 – Abdomen of a female specimen. The red arrow points to the shiny black button-like structure situated at a relative distance away from the posterior tip (marked "p"). This button or sclerotized cup is part of the complex ostium bursa, which partially covers the vaginal orifice.
Data for publication: "Quantifying the relationship between observed variables that contain censored values using Bayesian error-in-variables regression"
<p>This archive contains the two datasets used in the publication: Vermeiren, Charles, Munoz: Quantifying the relationship between observed variables that contain censored values using Bayesian error-in-variables regression <br>Preprint: <a href="https://hal.science/hal-04764660" rel="nofollow">https://hal.science/hal-04764660</a><br><br>The first dataset is used to develop and test the model using cross-validation, the 2nd dataset is used as an independent, external dataset to test the model. For details, see the publication.</p> <p>The model code, combined with the data and outputs, are also available on GitHub: https://github.com/Peter-Vermeiren/EIVmodels </p>
TreeGOER Köppen-Geiger Zone Distributions: Observations for 48,129 tree species across the 30 climate zones for 1931-1960, 1961-1990 and 1991-2020 climates
<p><strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> is a database that documents the environmental ranges (minimum, maximum, median, mean and 5%, 25%, 75% and 95% quantiles) for 48,129 tree species and for 51 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. TreeGOER is available from the following Zenodo archives: <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a></p> <p>The TreeGOER ranges were calculated after cleaning occurrence records and standardizing species names with the <a href="https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.11388">WorldFlora</a> R package to <a href="https://onlinelibrary.wiley.com/doi/10.1002/tax.12373">World Flora Online</a> or the <a href="https://www.nature.com/articles/s41597-021-00997-6">World Checklist of Vascular Plants</a> for a global GBIF occurrence download of 44,267,164 occurrences (GBIF.org 2021 <strong>GBIF Occurrence Download</strong> <a href="https://doi.org/10.15468/dl.77gcvq">https://doi.org/10.15468/dl.77gcvq</a>). The process of compilation of TreeGOER with 30 arc-seconds global grid layers, two examples of BIOCLIM applications that investigated the effects of climate change on global tree diversity patterns and R scripts to repeat these analyses have been described 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>.</p> <p>This Zenodo archive documents the occurrence of the same previously compiled and cleaned observations for the TreeGOER across global raster layers that document the 1931-1960, 1961-1990 and 1991-2020 <strong>Köppen-Geiger climate zones</strong>. These global raster layers were created for the following article:</p> <ul> <li>Beck, H. E., T. R. McVicar, N. Vergopolan, A. Berg, N. J. Lutsko, A. Dufour, Z. Zeng, X. Jiang, A. I. J. M. van Dijk, and D. G. Miralles. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections, Scientific 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>. The Köppen-Geiger classifcation maps, associated confidence maps, and underpinning monthly near-surface air temperature and precipitation climatologies in netCDF format can all be downloaded <a href="https://doi.org/10.6084/m9.figshare.21789074.v1">here</a>.</li> </ul> <p> </p> <p>For each of the 48,129 tree species, the distribution is given for</p> <ul> <li>Historical climates: number of observations in the 1931-1960, 1961-1990 and 1991-2020 Köppen-Geiger Zone climate zone</li> <li>Mixed climate: number of observations in the 1931-1960 Köppen-Geiger Zone climate zones if observations were before 1961, in the 1961-1990 Köppen-Geiger Zone climate zones if observations were between 1961 and 1990, and in the 1991-2020 Köppen-Geiger Zone climate zones if observations were after 1990</li> <li>Static climate: number of observations if those observations remained in the same Köppen-Geiger Zone climate zones</li> </ul> <p> </p> <p>The development of this data set archive 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 through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em> projects, 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> <p> </p>
Replication Data for figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s
<p>Supporting data to reproduce figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s</p>
Basic Meteorological Parameters measured by surface observation
<p>This dataset is an example of various weather stations in europe which collect meteorological data from the earths surface. <br>The selected station is located in Thessaloniki city centre. The dataset is a json of different parameters measured with temporal resolution of 10min. The json includes a 24h time interval.<br>Measured parameters are: <br>2m Air Temperature, Dewpoint, Windspeed, Wind direction, Wind gust, Relative Humidity, Mean Sealevel Pressure, Pressure, Global Radiation</p> <p>The data is availlable via API at Meteologix.com .</p> <p>(curl --request GET --url '<a href="https://api.kachelmannwetter.com/v02/stations/BK0078/observations?endTime=2024-10-21&startTime=2024-10-20" target="_blank" rel="noopener noreferrer">https://api.kachelmannwetter.com/v02/stations/BK0078/observations?endTime=2024-10-21&startTime=2024-10-20</a>' <span>--header </span><span>'Accept: application/json' </span><span> --header </span><span>'Authorization: Basic undefined')</span></p>
Land surface temperature (heatmaps) derived from earth observation data to assess thermal behaviour of 3 European cities: Milano, Logroño and Athens.
<p>Next tables present the detail description of the datasets developed in REACHOUT to characterize heat phenomena at city level by providing an assessment of the <strong>land surface temperature (heatmaps)</strong> of three European cities: Milan, Logroño and Athens. TECNALIA is the responsible partner for these datasets.</p> <p>There is a wide range of methods that can be used to characterise the thermal behaviour of a city, each of them with its advantages and disadvantages. One of these methods uses the land surface temperature that is obtained from remote sensing observations. Although thermal indices are considered more suitable when characterising thermal comfort, still the LST can provide a useful information about the behaviour of a citiy’s surfaces and materials. This has implications for several applications such as urban energy efficiency or urban environmental health. </p> <p>The input data used by the current version of the dataset came from Landsat 8. All the images acquired since 2013 by this satellite for Milan, Logroño and Athens were downloaded and processed to characterise not only the current (2019-2023) thermal behaviour of the city, but also its evolution considering the last seven 5-year windows.</p> <p>- 2013-2017<br>- 2014-2018<br>- 2015-2019<br>- 2016-2020<br>- 2017-2021<br>- 2018-2022<br>- 2019-2023</p> <p>The input data used in this dataset come from Landsat 8 downloaded from <a href="https://earthexplorer.usgs.gov/">Earth Explorer (usgs.gov)</a>.</p> <p>The format of this dataset is organized in two ZIP format files:</p> <p>- LANDSAT_8_L2SP_000000-milan_LST_peak.zip</p> <p>- LANDSAT_8_L2SP_000000-logrono_LST_peak.zip</p> <p>- LANDSAT_8_L2SP_000000-athens_LST_peak.zip</p> <p>Each of these zip files contain seven TIF images that represent the peak LST map according to the images of the above mentioned seven periods. The peak LST is obtained after getting the Annual Cycle Parameters of each of the periods and selecting a 30-day window centred on the day that the city reaches the maximum LST.</p> <p>The values of the images are in degree Celsius and nodata value is -9999.</p> <p> </p>
Resolving Pleiades Binary Stars with Gaia and Speckle Interferometric Observations
<p>These supplementary data accompany the paper "Resolving Pleiades Binary Stars with Gaia and Speckle Interferometric Observations", <a href="https://iopscience.iop.org/article/10.3847/1538-3881/ada564" target="_blank" rel="noopener">published by the Astronomical Journal</a>. Table data are also available through the <a href="https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/AJ/169/145" target="_blank" rel="noopener">VizieR service</a>. <br><a href="https://ui.adsabs.harvard.edu/abs/2025AJ....169..145C/abstract" target="_blank" rel="noopener">ADS: 2025AJ....169..145C</a>, <a href="https://arxiv.org/abs/2412.20986" target="_blank" rel="noopener">arXiv: 2412.20986</a></p> <p>Observations were obtained with the Speckle Polarimeter (SPP) instrument of the 2.5-m telescope of the Caucasian Observatory of the SAI MSU.</p> <p>Full versions of Table 5 and Table 6, containing binarity information and detection limits, are stored in "table5.mrt" and "table6.mrt". Contrast curves and autocorrelation functions for all observed objects are stored in "acfs/" folder inside "SPP_contrastCurves_ACFs_Pleiades.zip". These plots can be accessed directly with filename search by Gaia DR3 source identifier. The csv-table "contrastCurves_ACFs_filenames_info.csv" contains additional information about observations - date of observation, passband, seeing, comments etc. We provide a small Jupyter notebook script "display_contrastCurves_ACFs.ipynb" to display available observations along with fragments of Table 5 and Table 6 for specified object.</p>
GOES-Observed Fire Event Representation (GOFER) product for 28 California wildfires from 2019-2021
<p>The GOES-Observed Fire Event Representation (GOFER) algorithm uses geostationary satellite observations of active fires from GOES-East and GOES-West to map the hourly progression of large wildfires (over 50,000 acres or 202 sq. km). GOES observes North and South America with a spatial resolution of 2 km at the equator and at a frequency of 10-15 minutes for the full disk view. Along with the fire perimeter, we derive the active fire lines and fire spread rates. We tested the GOFER algorithm on a set of 28 wildfires in California from 2019-2021 and produced three versions of the product: GOFER-Combined, GOFER-East, and GOFER-West. GOFER-Combined uses both GOES-East and GOES-West observations, while GOFER-East and GOFER-West use only GOES-East and only GOES-West observations, respectively. We find that GOFER performs reasonably well compared to final perimeters from California's Fire and Resource Assessment Program (FRAP) and 12-hourly perimeters from the Fire Event Data Suite (FEDS), derived from 375-m active fire observations. See our <a href="https://globalfires.earthengine.app/view/gofer">GOFER Visualization</a> app on Earth Engine Apps for an overview of the dataset, alongside other datasets, such as FEDS and FRAP perimeters and 30-m burn severity from Monitoring Trends in Burn Severity (MTBS). Please refer to the corresponding <a href="https://github.com/tianjialiu/GOFER">GitHub repository</a> for the code, detailed dataset description, and version history.</p>
Radio Science observations of Mars Express and Tianwen-1 spacecraft from the 2021 Maritan Solar Conjunction
<p>Phase scintillation dataset from the 2021 Martian solar conjunction is contained in the DATA.zip file. The Read Me pdf file contiants information useful to understanding the file naming conventions and their contents.</p>
AIMS - Earth Observation Satellite Data of Wave and Wind in the Tyrrhenian Sea
<p>This dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which has the vision to develop and validate novel Artificial Intelligence (AI) algorithms to unlock the true potential of remote monitoring and enable a faster transition to a climate neutral society and economy: the AI algorithms will leverage the advantages of usual monitoring methodologies of the features of waves and offshore wind, and eventually overcome their intrinsic limitations. The value and resolution of sparse measurements of satellites and unevenly-distributed in-situ instruments will be increased, hence leading to a significant reduction of the cost and execution time of data collection, ultimately making knowledge wider and more accessible.</p> <p>In particular, this dataset aggregates earth observation satellite data from 10 different satellites, measureing the significant wave height and the wind speed at 10 meters above sea leavel in the Tyrrhenian Sea, from January 2021 to May 2024.</p>
ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations
<p><strong>ClimateForecasts</strong> is a database that provides environmental data for 15,504 weather station locations and 49 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 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><a name="_Hlk141002106"></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>. A similar extraction process was used for the <strong>CitiesGOER</strong> database that is also available from Zenodo via <a href="../doi/10.5281/zenodo.8175429">https://zenodo.org/doi/10.5281/zenodo.8175429</a>.</p> <p><strong>ClimateForecasts</strong> (as the 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. One example of combining data from these different sets in the R statistical environment is available from this Rpub: <a href="https://rpubs.com/Roeland-KINDT/1114902">https://rpubs.com/Roeland-KINDT/1114902</a>.</p> <p>The identities including the geographical coordinates of weather stations were sourced from <a href="https://meteostat.net/en/">Meteostat</a>, specifically by downloading (17-FEB-2024) the <a href="https://dev.meteostat.net/bulk/stations.html">‘lite dump’ data set</a> with information for active weather stations only. Two weather stations where the country could not be determined from the ISO 3166-1 code of ‘XA’ were removed. If weather stations had the same name, but occurred in different ISO 3166-2 regions, this region code was added to the name of the weather station between square brackets. Afterwards duplicates (weather stations of the same name and region) were manually removed.</p> <p>Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s. Similar methods were used to calculate these median values as in the case studies for the <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">TreeGOER manuscript</a> (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>Maps were added in version 2024.03 where locations of weather stations were shown on a map of the Climatic Moisture Index (CMI). These maps were created by a similar process as in the <a href="../doi/10.5281/zenodo.8252756">TreeGOER Global Zones Atlas</a> from the environmental raster layers used to create the TreeGOER via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.7-46) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>. Added country boundaries were obtained from <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/">Natural Earth</a> as <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip">Admin 0 – countries vector layers</a> (version 5.1.1). Also added after obtaining them from Natural Earth were <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_boundary_lines_disputed_areas.zip">Admin 0 – Breakaway, Disputed areas</a> (version 5.1.0, coloured yellow in the atlas) and <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip">Roads</a> (version 5.0.0, coloured red in the atlas). For countries where the GlobalUsefulNativeTrees database included subnational levels, boundaries were added and depicted as dot-dash lines. These subnational levels correspond to level 3 boundaries in the World Geographical Scheme for Recording Plant Distributions. These were obtained from <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a>. Check <a href="https://github.com/tdwg/wgsrpd/blob/master/109-488-1-ED/2nd%20Edition/TDWG_geo2.pdf">Brummit 2001</a> for details such as the maps shown at the end of this document.</p> <p>Maps for version 2024.07 modified the dimensions of the sheets to those used in version 2024.06 of the <a href="../doi/10.5281/zenodo.8252756">TreeGOER Global Zones Atlas</a>. Another modification was the inclusion of Natural Earth boundaries for <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/physical/ne_10m_lakes.zip">Lakes</a> (version 5.0.0, coloured darkblue in the atlas).</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) 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. 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>. Version 2024.10 further includes Holdridge Life Zones for the climates available from the previously included climates, 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 weather stations 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 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>).</p> <p> </p> <p>When using <strong>ClimateForecasts</strong> 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, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</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>ClimateForecasts</strong> and its partial integration in version 2024.03 of the GlobalUsefulNativeTrees database 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 through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, 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>
dataset for "Non-LTE Synthetic Observables of a Multidimensional Model of Type Ia Supernovae"
<p>Included here are datafiles supplementary to the article "Non-LTE Synthetic Observables of a<br>Multidimensional Model of Type Ia Supernovae" (Boos, Dessart, Townsley, Shen), submitted <br>to ApJ/arxiv in October 2024. This is a revised version of the dataset using an improved<br>method to generate the 1D LTE observables (1D LTE and pseudo-2D non-LTE observables have<br>thus changed from the previous version).</p> <p>We include in this dataset the 2D double detonation ejecta model (produced in Boos et al. 2021),<br>as well as the 15 wedge profiles constructed from this model, which were used in the radiative<br>transfer calculations of this work. These profiles are provided in Sedona input format. The<br>headers/columns for the 1D profiles are as follows:<br>1: Sedona model type<br>2: num_cells, rmin, ejecta time, num_isos<br>3: isos<br>4-: v_r, rho, T, X_a, X_b, etc.</p> <p>We also include the synthetic spectra and photometry from this work. These synthetic observables<br>are given for each set of calculations (1D LTE, 2D LTE, 1D non-LTE, and pseudo-2D non-LTE) between<br> -3 and +15 d from maximum light. The photometry is given in absolute magnitude in the Vega system.<br>The spectra have been rebinned such that the bins are the same between the Sedona and CMFGEN<br>calculations. The flux in these spectral files is given for a distance of 1 kpc.</p> <p><br>-Samuel J. Boos (sjboos@crimson.ua.edu)</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.