Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,838
datasets available to search
ShareScore release 0.9.0
Dataset results
1,838 results for “location”
Figure 1. Location and stratigraphy. A in Before the freeze: otoliths from the Eocene of Seymour Island, Antarctica, reveal dominance of gadiform fishes (Teleostei)
Figure 1. Location and stratigraphy. A, map of Antarctica showing the position of the Antarctic Peninsula; B, map of the Antarctic Peninsula showing Seymour Island; C, geological map of Seymour Island showing the outcrop of Telm 4-5 and localities IAA 1/90 and 2/ 95; D, composite measured section through the La Meseta Formation showing the stratigraphical position of the sampled 'Natica horizon' (IAA 1/90 and 2/95). Modified from Reguero et al. (2013). Strontium date values from Dutton et al. (2002), Ivany et al. (2008), Dingle & Lavelle (1998) and Reguero et al. (2002).
Figs 32–37. Head tubercles located and form. 32–34 in ON SPLITTING OF THE GENUS NOTOCUPES (COLEOPTERA: ARCHOSTEMATA): NEW DATA ON MORPHOLOGY AND TAXONOMY
Figs 32–37. Head tubercles located and form. 32–34 – linedrawings: 32 – Rhabdocupes laticella; 33 – Rhabdocupes tenuis; 34 – Notocupes caudatus; 35–37 – head photographs: 35 – Rhabdocupes laticella; 36 – Rhabdocupes tenuis; 37 – Notocupes caudatus. Abbreviations: Р1 – supraantennal protuberance; Р2 – supraocular protuberance; Р3 – posteromesal protuberance. Scale bar = 1 mm.
Location and caller familiarity influence mobbing behaviour and the likely ecological impact of noisy miners around colony edges
<p>Mobbing is a widespread, vocally coordinated behaviour where species approach and harass a threat. The noisy miner (<em>Manorina melanocephala</em>) is a notorious native Australian honeyeater, well-known for its hyperaggressive mobbing. Numerous studies have identified negative impacts of their mobbing behaviour, highlighting the exclusion of competitors from colony areas and the resulting loss of woodland-bird biodiversity. Despite this, few studies have investigated mobbing itself, and our understanding of the factors which influence its expression remains limited. Here, we use a field-based playback experiment to investigate whether mobbing responses vary in relation to colony borders and caller familiarity. Noisy miners were more likely to respond, reacted more quickly, and responded more strongly to mobbing calls broadcast inside as opposed to outside the colony. These behavioural differences likely arise from variation in the relative costs and benefits of responding. When noisy miners did mob outside the colony, more individuals joined in response to unfamiliar as opposed to familiar callers. Our results reveal that noisy miner mobbing may not be as indiscriminate as often assumed, with caller familiarity and location influencing this behaviour. We suggest there are benefits to greater consideration of the factors impacting noisy miner mobbing behaviour.</p>
FIGURE 1. Location map showing the locality where the AMU-CURS 184 in A new Megatheriinae skull (Xenarthra, Tardigrada) from the Pliocene of Northern Venezuela - implications for a giant sloth dispersal to Central and North America
FIGURE 1. Location map showing the locality where the AMU-CURS 184 specimen was recovered from San Gregorio Fm. outcrops.
Data for "Broadband thulium fiber amplifier for spectral region located beyond the L-band"
<p>Includes data for absorption and emission measurements, profiles of refractive index, and data for spectral dependence of amplifier output, as well as typical output spectra presented in the graphs.</p> <p> </p>
РИС. 4. Места находок Amuranodonta kijaensis в бассейне р. Амур: черные точки – ранее иЗвестные местонахождениЯ, белые квадраты – впервые обнаруженные колонии. Номера локалитетов соответствуют таковым в таблице 1. FIG. 4. Localitions of finds of Amuranodonta kijaensis in the Amur River basin: black dots are previously known locations, white squares are newly discovered colonies. The locality numbers correspond to those in Table 1. in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 4. Места находок Amuranodonta kijaensis в бассейне р. Амур: черные точки – ранее иЗвестные местонахождениЯ, белые квадраты – впервые обнаруженные колонии. Номера локалитетов соответствуют таковым в таблице 1. FIG. 4. Localitions of finds of Amuranodonta kijaensis in the Amur River basin: black dots are previously known locations, white squares are newly discovered colonies. The locality numbers correspond to those in Table 1.
РИС. 5. ИЗвестные находки Monacha cartusiana на Западе Украины. A. Анатомически проверенные авторами статьи. B. Определенные только по раковинам или беЗ учета анатомических раЗличий между M. cartusiana и M. claustralis. ИЗЗа масШтаба картосхем находки в блиЗко расположенных населенных пунктах объединены в одну точку. FIG. 5. Known records of Monacha cartusiana in Western Ukraine. A. Anatomically examined by the authors of this paper. B. Identifed only by shell or without regard to anatomical differences between M. cartusiana and M. claustralis. Due to the scale of the schematic maps, the findings in closely located settlements are combined into one point. in Monacha claustralis и M. cartusiana (Gastropoda, Hygromiidae) - два криптических вида антропохорных наЗемных моллюсков на Западе Украины
РИС. 5. ИЗвестные находки Monacha cartusiana на Западе Украины. A. Анатомически проверенные авторами статьи. B. Определенные только по раковинам или беЗ учета анатомических раЗличий между M. cartusiana и M. claustralis. ИЗЗа масШтаба картосхем находки в блиЗко расположенных населенных пунктах объединены в одну точку. FIG. 5. Known records of Monacha cartusiana in Western Ukraine. A. Anatomically examined by the authors of this paper. B. Identifed only by shell or without regard to anatomical differences between M. cartusiana and M. claustralis. Due to the scale of the schematic maps, the findings in closely located settlements are combined into one point.
Рис. 1. Карты Приморского края (А) и юЖного Приморья (В) с укаЗанием располоЖения стоянки Теляковского 2 и фотография побереЖья б. Теляковского (С); оранЖевая стрелка укаЗывает на располоЖение стоянки). Fig. 1. Maps of Primorsky Krai (Territory) (A) and its southern area (south Primorye) (B) showing location of Telyakobskogo 2 site and a photograph of the coast of Telyakovskogo Bay (C); orange arrow shows location of the site). in Mollusks from the shell-midden of the Telyakovskogo 2 site in southern Primorye (Yankovskaya culture), their paleoecology and role in paleoeconomy
Рис. 1. Карты Приморского края (А) и юЖного Приморья (В) с укаЗанием располоЖения стоянки Теляковского 2 и фотография побереЖья б. Теляковского (С); оранЖевая стрелка укаЗывает на располоЖение стоянки). Fig. 1. Maps of Primorsky Krai (Territory) (A) and its southern area (south Primorye) (B) showing location of Telyakobskogo 2 site and a photograph of the coast of Telyakovskogo Bay (C); orange arrow shows location of the site).
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>
Variation in the location and timing of experimental severing demonstrates that the persistent rhizome serves multiple functions in a clonal forest understory herb
<p>1. In clonal plants, persistent rhizomes can serve multiple purposes, including resource storage, modulation of heterogenous resource distributions, maintenance of bud banks and promotion of recovery from disturbance. Clonal plants are commonly long-lived and, in temperate zones, often exhibit organ preformation. Thus, investigations of how the timing of disturbance to the rhizome affects plant performance must occur over multiple growing seasons, but these types of studies are rare.</p> <p>2. We conducted a field experiment to examine how the persistent rhizome supports the existing shoot, new ramet production, and recovery from damage using mayapple (<i>Podophyllum peltatum</i>; Berberidaceae), a common herbaceous perennial of low-light forest understories in Eastern North America. Mayapple maintains a long-lived rhizome and exhibits a developmentally-programmed seasonal pattern of resource transport and new ramet initiation. We varied both the position and timing of rhizome severing in rhizome systems with terminal sexual or vegetative shoots, and tracked plants for two years following severing.</p> <p>3. The location and timing of severing affected both plant persistence (production of new shoots) and performance (leaf area), with effects differing for new shoots at the front vs. the back of the rhizome system. Across years, severing location and past years' shoot size influenced plant persistence and performance, while the effect of timing of severing diminished; initial sexual status had little effect on rhizome system response that was not accounted for by initial leaf area. Severing generally led to the establishment of two independent rhizome systems. Relative to unmanipulated control systems, these two systems had more total leaf area, but less average leaf area per system.</p> <p>4. Synthesis. Our results point to the rhizome as a resource integrator that affects plant responses to disturbance immediately following damage and in subsequent growing seasons. Rhizome bud age and/or subtending rhizome size, and developmental program influence responses to disturbance. While the effects of experimental disturbance on plant performance decreased two years after disturbance, further long-term investigation is needed to fully understand the demographic consequences of damage to persistent rhizomes. </p>
Seismic source location with a match field processing approach during the RESOLVE dense seismic array experiment on the Glacier d'Argentiere
<p>This deposit contains the data set we used in our paper ‘<em>Dynamic imaging of glacier structures at high-resolution using source localization with a dense seismic array</em>’. The paper is in review for GRL and a preprint can be found here: <a href="http://dx.doi.org/10.1002/essoar.10507953.1">10.1002/essoar.10507953.1</a>.</p> <p>The dataset present here contains 34 files named ‘<strong>beam_15423_jd***.h5</strong>’. These files correspond to the output of the matched field processing for each day. They are in .h5 format and we provide a matlab code (<strong>read_MFP_data.m</strong>) to read these files. These files can be read with any other language since they are in . h5.</p> <p>In linux you can use <strong>h5dump –A filename.h5</strong> and you can see the content of each files.</p> <p> </p> <p>More information on how the MFP process is conducted can be found in on the <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>dedicated to this aspect or on our paper. The whole procedure and associated codes is provided on the <a href="http://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/</a>.</p> <p>We also deliver with this deposit one day of seismic data <strong><a href="https://zenodo.org/api/files/873ccbe8-814d-4202-90ae-e115aab1942d/ZO_2018_121.h5?versionId=c59d2014-a6e1-43c5-96a6-91ee9a6b89ce">ZO_2018_121.h5 </a></strong>that can be used to test our MFP process. The data corresponds to the signal measured for 24 hours at each of the 98 sensors with a sampling rate of 500 Hz. More information on these seimsic signals can be found on our <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>and the whole seimsic dataset can be found here <a href="https://seismology.resif.fr/networks/#/ZO__2018">https://seismology.resif.fr/networks/#/ZO__2018</a>. Detailed for downloading the dataset should be search on our website.</p> <p> </p> <p>Other dataset linked to this project are:</p> <ul> <li>Nanni, Ugo, Gimbert, Florent, Roux, Phillipe, & Lecointre, Albanne. (2020). DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations." [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4024660">https://doi.org/10.5281/zenodo.4024660 </a></li> <li>Nanni, Gimbert, Roux, Helmstetter, Garambois, Lecointre, Walpersdorf, Jourdain, Langlais, Laarman, Lindner, Sergenat, Vincent, & Walter. (2020). DATA of the RESOLVE Project (https://resolve.osug.fr/) [Data set]. In Seismological Research Letters (Version v0). Zenodo. <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815 </a></li> </ul> <p>This dataset is also linked to two other study:</p> <p><em>Observing the subglacial hydrology network and its dynamics with a dense seismic array: </em></p> <p><a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a></p> <p><em>A Multi‐Physics Experiment with a Temporary Dense Seismic Array on the Argentière Glacier, French Alps: The RESOLVE Project</em></p> <p><a href="https://doi.org/10.1785/0220200280">https://doi.org/10.1785/0220200280</a></p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Do not hesitate to contact us if you would like to try this approach an another dataset.</p>
Lesser Yellowlegs location data describing the occurrence of birds within harvest zones in the Caribbean and South America
<p>Shorebirds have experienced a precipitous reduction in abundance over the past four decades. While some threats to shorebirds are widespread (e.g. habitat alteration), others are regional and may affect specific populations. Lesser Yellowlegs (<i>Tringa flavipes</i>) are long-distance migrants that breed across the North American boreal biome and have declined in abundance by 60-80% since the 1970s. The documented harvest of Lesser Yellowlegs in the Caribbean and northeastern South America during southward migration is a possible limiting factor for the species, but it is unknown to what extent birds from different breeding origins may be affected. To address the question of differential occurrence in harvest zones during southward migration, we used PinPoint GPS Argos transmitters to track the southward migrations of 85 adult Lesser Yellowlegs from across the species' breeding range and 80° of longitude from Anchorage, Alaska, USA to the Mingan Archipelago, Quebec, Canada. We classified migratory locations as inside or outside three zones with high levels of harvest (Caribbean, coastal Guianas, and coastal Brazil) and then fit generalized additive mixed models to estimate the probability of occurrence of Lesser Yellowlegs in harvest zones according to their breeding origin. Individuals from the Eastern Canada population had a higher probability of occurrence within one or more harvest zones and remained in those zones longer than individuals breeding in Alaska and western Canada. Linear regressions also suggested that longitude of the breeding origin is an important predictor of occurrence in harvest zones during southward migration. Lastly, our findings, combined with other sources of evidence, suggest that current estimated harvest rates may exceed sustainable limits for Lesser Yellowlegs, which warrants further investigation.</p>
December 2021 Eclipse data from location QG61mh by VK2ARL using SDRplay RSP2 and FLDIGI
<p>December 2021 Eclipse Festival data gathered by VK2ARL from the signal from WWV on 15.00MHz using a SDRplay RSP2 receiver connected to a horizontal loop antenna at location QG61mh (-28.678 ,153.027) and analysed by FLDIGI. Data droputs on some days were due to unexpected power failures during electrical storms.</p>
The Lagrangian particle trajectory output and the metadata of the Southern Ocean Biogeochemical Divide location for 'Localizing the Southern Ocean Biogeochemical Divide'
<p>1. Trajectory files<br> <br> The Lagrangian particle trajectory output files from virtual particle release experiments at the surface and 500m depth using Connectivity Modeling System (Paris et al. 2013, https://github.com/beatrixparis/connectivity-modeling-system) run offline in the ACCESS-OM2-01 model (Kiss et al., 2020), a global 0.1° ocean sea-ice model, with a JRA55-do repeat year neutral state atmospheric forcing (Stewart et al., 2020).<br> <br> These trajectory datasets are compressed to two .rar format files for 2 depths, which were outputted from the Connectivity Modeling System v2.0 (CMS) in NetCDF format. Trajectory files include latitude, longitude, depth, interpolated along-track salinity and interpolated along-track temperature for each particle which are outputted every five days in the virtual particle tracking experiment. In addition, these datasets also contain the "exitcode" and release date information of each particle. More information please see in the CMS user guide. Other experiment setup files included in each release directory are "nest_1.nml", "runconf.list" and "ibm.list".<br> <br> These datasets are the original data output by the CMS. Limited by multiple nodes and maximum running time on the supercomputer, the surface release experiment is composed of 5 consecutive sub-experiments, and the 500m release experiment is composed of 3 consecutive sub-experiments. Each sub-experiment contains 48 independent output NetCDF files. <br> <br> 2. SOBD files<br> <br> These two .rar SOBD files are original arrays of the percentage of the upper cell minus the lower cell (i.e., the SOBD percentage) at surface and 500m depth (as presented in Fig.3 in Localizing the Southern Ocean Biogeochemical Divide).<br> <br> We provide original arrays in both .csv and .npz formats. More information can be found in the "Readme.txt" file in each .rar file.<br> <br> Citation of associated paper: Y. Xie, V. Tamsitt, L. T. Bach Localizing the Southern Ocean Biogeochemical Divide. <strong><em>to be submitted to Geophysical Research Letters</em></strong></p> <p><br> References:</p> <p>Kiss, A. E., Hogg, A. M., Hannah, N., Dias, F. B., Brassington, G. B., Chamberlain, A., . . . Stewart, K. D. (2020). ACCESS-OM2 v1 . 0 : a global ocean – sea ice model at three resolutions. <strong><em>Geoscientific Model Development</em></strong>,13, 401–442. doi: https://doi.org/10.5194/gmd-13-401-2020</p> <p>Paris, A. C. B., Vaz, A. C., Helgers, J., & Wood, S.(2017).Connectivity Modeling System User 's Guide CMS v 2 . 0. Retrieved from https://github.com/beatrixparis/connectivity-modeling-system</p> <p>Stewart, K. D., Hogg, A. M. C., England, M. H., & Waugh, D. W.(2020).Response of the Southern Ocean Overturning Circulation to Extreme Southern408Annular Mode Conditions. <strong><em>Geophysical Research Letters</em></strong>,47(22), 1–10. doi:10.1029/2020GL091103</p>
The location and vegetation physiognomy of ecological infrastructures determine bat activity in Mediterranean floodplain landscapes
<p>Ecological infrastructures (EI), defined as natural or semi-natural structural elements, are important to support biodiversity and could play a crucial role in counteracting the well-known impacts of intensive agriculture. Yet, the importance of EI remains largely unexplored in Mediterranean agricultural landscapes and for species providing essential ecosystem services such as bats. Here, we evaluated the role of different EI types – in terms of location (riparian vs terrestrial) and vegetation physiognomy (woody vs non-woody) – in shaping bat guild activity in crop fields located in the floodplains of the Iberian Peninsula. We recorded 60,732 bat sequences in 96 crop fields and characterized 106 EI patches via an adaptation of the Biodiversity Potential Index (BPI). We found that the activity of mid-range echolocators (MRE) and long-range echolocators (LRE) was twofold higher when the nearest EI patch was riparian (i.e., contiguous to a watercourse) than when it was terrestrial. When assessing changes in bat activity in crop fields in relation to a gradient distance from EI types, our results revealed both distinct and similar effects of the location and vegetation physiognomy of the EI on bat guilds. For instance, while only the LRE guild positively responded to the proximity of woody EI, both MRE and LRE showed a marked increase of activity when increasing distances to non-woody EI, thus suggesting low bat activity levels near these features. Our habitat quality assessment also revealed that woody EI and riparian EI had higher biodiversity potential and related habitat quality, thus contributing to our understanding of bat responses to EI type in crop fields. As riparian areas are rarely targeted in biodiversity-friendly measures in farmland, we strongly recommend including riparian EI (especially the woody type) in conservation planning as they are crucial for both biodiversity conservation and ecosystem functioning.</p>
Model profiles at satellite locations, 09 - 21 UTC, 7th September, 2017
<p><strong>Summary</strong></p> <p>This dataset contains atmospheric profiles generated from an experimental run of the ECMWF integrated forecast system (IFS), interpolated to the observation locations and times of various satellite observations using the internal IFS observation operator (known as 'the GOM arrays'). The date and time range gives a snapshot of Hurricane Irma, 2017.</p> <p><strong>Satellite observations</strong></p> <p>Two satellite instruments are included:</p> <ul> <li>The Global Precipitation Mission (GPM) Microwave Imager (GMI): The profile locations are at the centres of boxes of roughly 40 km by 40 km. These boxes are used for superobbing the data, combining a minimum 15 raw GMI observations. The process of superobbing is used at ECMWF for a number of reasons including to colocate the various channels of GMI level 1b data, which are at different locations. Note that the 40 km superobbing resolution used here is different from the operational ECMWF superobbing resolution for this data (which is 80 km).</li> <li>The Advanced Technology Microwave Sounder (ATMS) on the Suomi NPP satellite. All locations in the raw level 1B dataset are included (note that the 3 x 3 averaging that is normally applied at ECMWF was switched off)</li> </ul> <p>The satellite observations themselves are not included in this dataset and must be obtained from the relevant data providers. The profiles are provided in a semi-random order and must be colocated to the relevant observations by the user.</p> <p><strong>Model details</strong></p> <p>IFS cycle 47r1 has been used; further information is at https://www.ecmwf.int/en/publications/ifs-documentation. The model profiles are generated from a run of the atmospheric forecast model that is initialised from the operational analysis at 00 UTC 7th September 2017. The profiles are hence based on a short forecast of 9 - 21 hours duration that is equivalent to the 'background forecast' in the data assimilation cycle. The model uses a horizontal resolution of T1279co (equivalent to 8 - 9 km) and 137 hybrid pressure levels in the vertical. Profiles represent slabs of atmosphere that are centred on the 'full' pressure levels and are bounded by 'half' pressure levels. Hence, half pressure level 1 is 0 hPa and half pressure at level 138 is the surface pressure. </p> <p>The dataset contains most of the necessary inputs for driving a radiative transfer model to simulate satellite-observed radiances. One important input that is not included is the surface emissivity, which may be estimated using a physical model integrated into the radiative transfer model, or it may be obtained from an emissivity atlas (in the context of the IFS data assimilation is in some cases estimated with a dynamic emissivity retrieval, which is not supplied in this dataset).</p> <p>There are 6 hydrometeors represented by the IFS. The large-scale condensation scheme represents cloud water, cloud ice, rain and snow as prognostic variables. Relating to these are a sub-grid cloud fraction which applies to cloud water and ice, and a precipitation fraction that applies to the large-scale rain and snow. Additionally, the mass flux convection scheme represents rain and snow generated by convection. The sub-grid fraction occupied by convection is not given in the file, since it is assumed to be a constant 0.05. The convection scheme does not represent convective cloud (e.g. non-precipitating particles) but it does represent the convective anvils via detrainment into the large-scale condensation scheme (hence convective anvils are represented in the large-scale cloud ice). Rain and snow were originally obtained as fluxes and converted to mixing ratios using assumptions of fall-speed and particle size distribution, as is standard within the all-sky satellite data processing at ECMWF.</p> <p>All mixing ratios [kg kg-1] are given relative to the moist atmosphere.</p> <p>The profile times are those of a model timestep close in time to when the observation was made. The model timestep is 7.5 minutes, but only every fourth timestep is available to the IFS observation operator, meaning that profile times are quantised at 0, 30 60 minutes (and so on) through the forecast range. Essentially, there should be no more than a 15 minute time mismatch between the validity time of the profile and the satellite observation time.</p> <p><strong>Licensing and copyright</strong></p> <ul> <li>Copyright statement: Copyright "© 2022 European Centre for Medium-Range Weather Forecasts (ECMWF)".</li> <li>Source: www.ecmwf.int</li> <li>Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/</li> <li>Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.</li> <li>Where applicable, when redistributing the data, give an indication if the material has been modified and an indication of previous modifications.</li> <li>Full ECMWF licence terms are given at https://apps.ecmwf.int/datasets/licences/general/ </li> </ul>
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-
The site of an experimental oil spill located at a freshwater wetland along the St. Lawrence River re-visited after 21 years
<p>In 1999 a wetland close to Ste. Croix de Lotibiniere (Quebec, Eastern Canada) and located along the St. Lawrence River was the site of a simulated oil spill. Test plots were set up, contaminated with crude oil and subsequently used to test natural attenuation, nutrient amendment or vegetation cropping as remediation treatments. In 2020, this study revisited the former test plots to investigate any lingering effects of the original Ste. Croix study. Test plot sediments and control sediments featured detectable quantities (75 - 165 g/kg) of mid-chain n-alkanes (C10-C36), but no other kinds of hydrocarbons. Differences in hydrocarbon, total organic carbon, nitrogen and phosphorous content were not significantly different between test plot sediments and control sediments. A microbial analysis did not detect significant differences in microbial load, microbial diversity or microbial community composition between test plot sediments and control sediments. Key genes for the aerobic and anaerobic degradation of n-alkanes as well as for the aerobic degradation of polycyclic aromatic hydrocarbons were detected in all sediment samples. Abundances of these genes did not differ significantly between formerly oil-contaminated sediments and control sediments. This study shows that after 21 years, previously oil-contaminated sediments of the Ste. Croix wetland can be considered completely remediated irrespective of the performed remediation treatment.</p> <p>In this file archive are included, metadata, metagenome co-assembly, functional and taxonomic annotations, contigs and genes abundance matrices and MAGs files.</p> <p>Raw Illumina sequence data was deposited in the NCBI SRA portal under accession PRJNA818909.</p>
Location List for IPCC AR6 Sea Level Projections
<p>This data set contains the location list file for the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. It can be used to cross-reference location IDs with names of the locations.</p> <p>Column 1 – Location name (string with spaces having been replaced with underscores)<br> Column 2 – Location ID (integer value)<br> Column 3 – Latitude (-90 to 90 degrees)<br> Column 4 – Longitude (-180 to 180 degrees)</p> <p>See <a href="https://zenodo.org/communities/ipcc-ar6-sea-level-projections">https://zenodo.org/communities/ipcc-ar6-sea-level-projections</a> for additional related data sets.</p>
Input for Bayesloc calculations for locating the 27 February 2022 Lop Nor earthquake
<p>Steven J Gibbons, NGI<br> 2022-04-04</p> <p>The directories contained within this tar file contain all the files needed to calculate the location estimates<br> of the 2022-02-27 Lop Nor earthquake using the Bayesloc program with various sets of<br> inputs.<br> No output is included, only the input files:</p> <p>bayesloc.cfg<br> arrival.dat<br> station.dat<br> origin_prior.dat<br> and any traveltime tables needed.</p> <p>In each directory, the Bayesloc program is run by typing</p> <p>bayesloc bayesloc.cfg</p> <p>The directories are as follows:</p> <p>(a) USGS_P1only_singleevent<br> The following 5 files needed to locate using only the first P arrivals<br> in the NEIC solution (see Data and resources)</p> <p> ak135_P1.dat arrival.dat bayesloc.cfg origin_prior.dat station.dat</p> <p>(b) superset_singleevent<br> The following 6 files needed to locate using a set of arrivals based upon<br> the USGS arrivals, selected arrivals from the file ISC_info_20220402.txt,<br> and manual picks made from open stations available from IRIS.</p> <p> ak135_P1.dat ak135_S1.dat arrival.dat bayesloc.cfg origin_prior.dat station.dat</p> <p>(c) 11 different directories<br> fisk_900526<br> fisk_900816<br> fisk_920521<br> fisk_920925<br> fisk_931005<br> fisk_940610<br> fisk_941007<br> fisk_950515<br> fisk_950817<br> fisk_960608<br> fisk_960729<br> <br> In each of these directories, there are the files<br> ak135_P1.dat arrival.dat bayesloc.cfg origin_prior.dat station.dat<br> needed to locate one of the 11 nuclear explosions described in Fisk (2002)<br> on an event by event basis.</p> <p>(d) all_events_joint_USGSonly_fixedGT</p> <p> This solves for the location of the 20220227 event simultaneously with the<br> locations of the 11 GT nuclear tests, using only those arrivals chosen from<br> the USGS solution for the 20220227 event<br> (i.e. the arrivals in the directory USGS_P1only_singleevent )</p> <p> This directory contains five files<br> ak135_P1.dat arrival.dat bayesloc.cfg origin_prior.dat station.dat</p> <p>(e) all_events_joint_superset_fixedGT</p> <p> This solves for the location of the 20220227 event simultaneously with the<br> locations of the 11 GT nuclear tests, using the arrivals from the 20220227<br> event "superset" (i.e. the arrivals in the directory superset_singleevent )</p> <p> This directory contains six files<br> ak135_P1.dat ak135_S1.dat arrival.dat bayesloc.cfg origin_prior.dat station.dat</p> <p>The image "locations_panel.png" contains the output from each of the Bayesloc<br> calculations in these directories, plus a few others using the same input files but with<br> changes to the origin_prior.dat files such that the GT events are not fixed to the locations<br> specified by Fisk (2002).</p> <p>Panel (a) displays the bulletin location estimates listed in the file<br> "ISC_info_20220402.txt" together with the GT locations of the nuclear tests provided by<br> Fisk (2002).</p> <p>Panel (b) displays the results from the single event location runs: i.e. the outputs<br> from all of the directories</p> <p>fisk_900526<br> fisk_900816<br> fisk_920521<br> fisk_920925<br> fisk_931005<br> fisk_940610<br> fisk_941007<br> fisk_950515<br> fisk_950817<br> fisk_960608<br> fisk_960729<br> superset_singleevent<br> USGS_P1only_singleevent</p> <p>Panel (c) displays the locations when the files in the directory<br> all_events_joint_superset_fixedGT are run but with modifications<br> to the origin_prior.dat file to remove the 1 km lateral constraint on the GT<br> events. In addition, the brown points on this map are the output from<br> the directory all_events_joint_USGSonly_fixedGT but with the origin_prior.dat<br> modified to remove the constraints of the GT events.</p> <p>Panel (d) displays the locations when the files in the directory<br> all_events_joint_superset_fixedGT are run as they are.<br> In addition, the brown points on this map are the output from<br> the directory all_events_joint_USGSonly_fixedGT.</p> <p>In panels (c) and (d) the nuclear explosion locations are only displayed<br> for the calculations in the all_events_joint_superset_fixedGT directory.<br> The locations for these events obtained in the all_events_joint_USGSonly_fixedGT<br> directory are very similar.</p> <p>Data and resources<br> ------------------</p> <p>The Bayesloc probabilistic multiple seismic event location software was obtained from<br> <a href="https://www-gs.llnl.gov/nuclear-threat-reduction/nuclear-explosion-monitoring/bayesloc">https://www-gs.llnl.gov/nuclear-threat-reduction/nuclear-explosion-monitoring/bayesloc</a><br> (last accessed April 2022).</p> <p>The file ISC_info_20220402.txt is the output from a search of<br> <a href="http://www.isc.ac.uk/iscbulletin/">http://www.isc.ac.uk/iscbulletin/</a><br> performed on April 2, 2022. (ISC, 2022)</p> <p>The National Earthquake Information Center earthquake report for the 27 February 2022 event is found on<br> <a href="https://earthquake.usgs.gov/earthquakes/eventpage/us6000h0k8/executive">https://earthquake.usgs.gov/earthquakes/eventpage/us6000h0k8/executive</a><br> (last accessed April 2022).</p> <p>References<br> ----------</p> <p>Fisk, M. D. (2002). Accurate Locations of Nuclear Explosions at the Lop Nor Test Site Using Alignment of Seismograms and IKONOS Satellite Imagery. Bull. Seismol. Soc. Am. 92, 29112925. doi:<a href="http://dx.doi.org/10.1785/0120010268">10.1785/0120010268</a>.</p> <p>International Seismological Centre (2022), On-line Bulletin, <a href="https://doi.org/10.31905/D808B830">https://doi.org/10.31905/D808B830</a></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.