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.
8,119
datasets available to search
ShareScore release 0.7.1
Dataset results
8,119 results for “species distribution”
Fig. 27 in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants
Fig. 27. Biotope, host plant, and adult of Melitaea shahvarica sp. nov. in nature. A. Shahvar Mt., E Elburs Ridge. B. Biotope with Phlomoides molucelloides (Bunge) Salmaki. C. Host plant Ph. molucelloides. D. Female on Ph. molucelloides.
Fig. 21 in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants
Fig. 21. Adults of Melitaea shahvarica sp. nov. A–H. UPS. I–P. UNS. A, I. Holotype, ♂ (SDM). B–H, J–P. Paratypes (EDMSU). A–C. ♂. Iran, Semnan Prov., Shahrud area, S macroslope of Shahvar Mt., alt. 2200–2400 m. D. ♂. Iran, Semnan Prov., Shahrud area, S macroslope of Shahvar Mt., Tohar v. vicinity, alt. 2200 m. E–H. ♀. Iran, Semnan Prov., Shahrud area, S macroslope of Shahvar Mt., alt. 2200– 2400 m. I–K. ♂. Iran, Semnan Prov., Shahrud area, S macroslope of Shahvar Mt., 2200–2400 m. L. ♂. Iran, Semnan Prov., Shahrud area, S macroslope of Shahvar Mts, Tohar v. vicinity, alt. 2200 m. M–P. ♀. Iran, Semnan Prov., Shahrud area, S macroslope of Shahvar Mt., alt. 2200–2400 m.
Fig. 25. I–IV in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants
Fig. 25. I–IV instar caterpillars of Melitaea shahvarica sp. nov. (a = view from above; b = lateral view). A. First instar caterpillar after hatching. B. First instar caterpillar before molting. C. Second instar caterpillar. D. Third instar caterpillar. E. Fourth instar caterpillar.
Fig. 26. V–VI in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants
Fig. 26. V–VI instar caterpillars of Melitaea shahvarica sp. nov. (a = view from above; b = lateral view). A. Fifth instar caterpillar. B. Sixth instar caterpillar. C. Sixth instar caterpillar, head capsule, front view. D. Sixth instar caterpillar, head capsule, lateral view.
Fig. 20 in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants
Fig. 20. Eggs of Melitaea timandra binaludica subsp. nov., Iran, Kuh-e-Binalud Mts. A–C. Lateral view. D–F. View from above. G–I. Micropile area.
Fig. 19. Male genitalia and harpe. A–C in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants
Fig. 19. Male genitalia and harpe. A–C. Melitaea timandra timandra Coutsis & van Oorschot, 2014. D–I. M. timandra binaludica subsp. nov. A–C. Turkmenistan, Sary-Yazy, alt. 300 m. D–F. Iran, Rezavi Khorassan Prov., Kuh-e-Binalud Mts, Dorrud v. vicinity, alt. 2430 m. G. Afghanistan, Bamian Prov., Band-e-Amir, alt. 3200 m. H. Afghanistan, Bamian Prov., Band-e-Amir, Dzhudoi-Kvak Gorge, alt. 3200 m. I. Afghanistan, Band-e-Amir, Hazarajat.
Data for: "Dynamic species distribution modeling reveals the pivotal role of human-mediated long-distance dispersal in plant invasion"
<p>All the data needed to reproduce the results and Figures of our article:</p> <p>Botella, C., Bonnet, P., Hui, C., Joly, A., & Richardson, D. M. (2022). Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion. <em>Biology</em>, <em>11</em>(9), 1293. <a href="https://doi.org/10.3390/biology11091293">https://doi.org/10.3390/biology11091293</a></p> <p>Please, find the R scripts and guidelines to reproduce our results on the article's Github repository :</p> <p><a href="https://github.com/ChrisBotella/plectranthus_barbatus/tree/main">https://github.com/ChrisBotella/plectranthus_barbatus/tree/main</a></p>
Figure 2. Mitotype tree and distribution maps for 98 in Integrative taxonomy reveals cryptic diversity in North American Lasius ants, and an overlooked introduced species
Figure 2. Mitotype tree and distribution maps for 98 DNA-barcodes belonging to 7 mitotypes of the ant Lasius niger (blue, n = 70) and 15 mitotypes of L. ponderosae sp. nov. (red, n = 28). The red dashed line delimits the expected natural range of L. ponderosae sp. nov.53 Maps have been created using the free R-package "ggmap" v3.0.0 (https://github.com/dkahle/ggmap) in R v4.1.1. Map tiles by Stamen Design, under CC BY 3.0.
Spatial confounding in Bayesian species distribution modeling
<ol> <li>Species distribution models (SDMs) are currently the main tools to derive species niche estimates and spatially explicit predictions for species geographical distribution. However, unobserved environmental conditions and ecological processes may confound the model estimates if they have a direct impact on the species and, at the same time, they are correlated with the observed environmental covariates. This, so-called spatial confounding, is a general property of spatial models but it has not been studied in the context of SDMs before.</li> <li>Here we examine how the estimation accuracy of SDMs depends on the type of spatial confounding. We construct two simulation studies where we alter spatial structures of the observed and unobserved covariates and the level of dependence between them. We fit generalized linear models with and without spatial random effects applying Bayesian inference and record the bias induced to model estimates by spatial confounding. After this, we examine spatial confounding also with real vegetation data from northern Norway.</li> <li>Our results show that model estimates for coarse-scale covariates, such as climate covariates, are likely to be biased if a species distribution depends also on an unobserved covariate operating on a finer spatial scale. Pushing higher probability for a relatively weak and spatially smoothly varying spatial random effect compared to the observed covariates improved estimation accuracy. The improvement was independent of the actual spatial structure of the unobserved covariate.</li> <li>Our study addresses the major factors of spatial confounding in SDMs and provides a list of recommendations for pre-inference assessment of spatial confounding and for inference-based methods to decrease the chance of biased model estimates.</li> </ol>
Fig. 36. Monomorium carbo Forel, 1910 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 36. Monomorium carbo Forel, 1910, syntype, worker (CASENT0249908, AntWeb.org (Shannon Hartman)). A. Body in profile. B. Head in full-face view. C. Distribution map.
Fig. 31. Crematogaster chiarinii Emery, 1881 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 31. Crematogaster chiarinii Emery, 1881, worker (CASENT0906369, AntWeb.org (Estella Ortega)). A. Body in profile. B. Head in full-face view. C. Distribution map.
Fig. 28 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 28. Cardiocondyla yemeni Collingwood & Agosti, 1996, worker (CASENT0922874, AntWeb.org (Michele Esposito)). A. Body in profile. B. Head in full-face view. C. Distribution map.
Fig. 51 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 51. Trichomyrmex mayri (Forel, 1902), worker (CASENT0922869, AntWeb.org (Michele Esposito)). A. Body in profile. B. Head in full-face view. C. Distribution map.
Fig. 27 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 27. Cardiocondyla wroughtonii (Forel, 1890), worker (CASENT0922871, AntWeb.org (Michele Esposito)). A. Body in profile. B. Head in full-face view. C. Distribution map.
Fig. 43 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 43. Monomorium venustum (Smith, 1858), syntype, worker (CASENT0902221, AntWeb.org (Will Ericson)). A. Body in profile. B. Head in full-face view. C. Distribution map.
Fig. 25. Leptanilla islamica Baroni Urbani, 1977 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 25. Leptanilla islamica Baroni Urbani, 1977, ♂ (CASENT0922880, AntWeb.org (Michele Esposito)). A. Body in profile. B. Head in full-face view. C. Distribution map.
Fig. 33. Meranoplus mosalahi Sharaf, 2019 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 33. Meranoplus mosalahi Sharaf, 2019, paratype, worker (CASENT0922861,AntWeb.org (Michele Esposito)). A. Body in profile. B. Head in full-face view. C. Distribution map.
Fig. 24. Plagiolepis barbara Santschi, 1911 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 24. Plagiolepis barbara Santschi, 1911, worker (CASENT0912424,AntWeb.org (Zach Lieberman)). A. Body in profile. B. Head in full-face view. C. Distribution map.
Fig. 23 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 23. Paratrechina longicornis (Latreille, 1802), worker (CASENT0922867, AntWeb.org (Michele Esposito)). A. Body in profile. B. Head in full-face view. C. Distribution map.
Fig. 32 in Faunal composition, diversity, and distribution of ants (Hymenoptera: Formicidae) of Dhofar Governorate, Oman, with updated list of the Omani species and remarks on zoogeography
Fig. 32. Crematogaster jacindae Sharaf & Hita Garcia, 2019, paratype, worker (CASENT0922856, AntWeb.org (Michele Esposito)). A. Body in profile. B. Head in full-face view. C. Distribution map.
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.