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FIGURE 5 in Revising the taxonomy of Darevskia valentini (Boettger, 1892) and Darevskia rudis (Bedriaga, 1886) (Squamata, Lacertidae): a Morpho-Phylogenetic integrated study in a complex Anatolian scenario
FIGURE 5. Canonical Discriminant Analysis (CDA) plot for FEMALES. Specimens, sample centroids, and group perimeters are represented. Green circle: D. v. spitzenbergerae; Clear blue triangle: "Clade A" from Candan et al. 2021; Inverted violet triangle "Clade B" from Candan et al. 2021; Cross: D. v. valentini; Blade: D. v. lantzicyreni; Asterisk: D. b. bithynica; Diamond: D. b. tristis; Minute dot: D. r. rudis; Side inclined clear gray triangle: D. r. bischoffi; Side inclined dark gray triangle: D. r. obscura; Clear gray square: D. r. macromaculata; Gray circle: D. r. mirabilis; Yellow triangle: D. r. bolkardaghica. These two first axes explain together 79.8 % of the total variability.
FIGURE 7 in Revising the taxonomy of Darevskia valentini (Boettger, 1892) and Darevskia rudis (Bedriaga, 1886) (Squamata, Lacertidae): a Morpho-Phylogenetic integrated study in a complex Anatolian scenario
FIGURE 7. UPGMA tree derived from the matrix of distances (Table 1) among FEMALE samples, as in the males one, shows three groups: a basal one, well different, with D. bithynica (inc. ssp. tristis), and two more closer groups that include the former rudis and valentini-complexes. See the text for an explanation of the results. The tree, derived from the calculation of ultrametric distances calculated in UPGMA, reflects very well the relationships in respect to the original distanced matrix (see Table 1). Its Cophenetic Correlation Index, r = 0.94, shows that the obtained dendrogram has a very good fit (r> 0.9; Rohlf 2000).
FIGURE 2 in Revising the taxonomy of Darevskia valentini (Boettger, 1892) and Darevskia rudis (Bedriaga, 1886) (Squamata, Lacertidae): a Morpho-Phylogenetic integrated study in a complex Anatolian scenario
FIGURE 2. Canonical Discriminant Analysis (CDA) plot for MALES. Specimens, sample centroids, and group perimeters are represented. Green circle: D. v. spitzenbergerae; Clear blue triangle: "Clade A" from Candan et al. 2021; Inverted violet triangle: "Clade B" from Candan et al. 2021; Cross: D. v. valentini; Blade: D. v. lantzicyreni; Asterisk: D. b. bithynica; Diamond: D. b. tristis; Minute dot: D. r. rudis; Side inclined clear gray triangle: D. r. bischoffi; Side inclined dark gray triangle: D. r. obscura; Clear gray square: D. r. macromaculata; Gray circle: D. r. mirabilis; Yellow triangle: D. r. bolkardaghica. These two first axes explain together 80.3 % of the total variability.
FIGURE 12. a in Revising the taxonomy of Darevskia valentini (Boettger, 1892) and Darevskia rudis (Bedriaga, 1886) (Squamata, Lacertidae): a Morpho-Phylogenetic integrated study in a complex Anatolian scenario
FIGURE 12. a) Darevskia spitzenbergerae wernermayeri ssp. nov. (Paratype; nº 12, Male; Başeğmez Village, Çaldıran, Turkey); b) Darevskia mirabilis stat. nov. (Paratype; nº 5, Female; Ovit Pass, Kaçkar Mountains, Rize, Turkey); c) Darevskia rudis bolkardaghica (Paratype; nº 1, Male; Karagöl, Ulukışla, Niğde, Central Anatolia, Turkey); d) Darevskia rudis lantzicyreni comb. nov. (nº 23, male; Kümbet Village, Zara, Turkey); e) Darevskia josefschmidtleri sp. nov. (Paratype; nº 20, Male; Yukarınarlıca Village, Çatak, Van, Turkey); f) Darevskia valentini (nº 9, Male; Tepeler Village, Ardahan, Turkey) and temporal area of an Armenian specimen (Karvansaray, Martuni District, Armenia); g) Darevskia spitzenbergerae spitzenbergerae stat. et comb. nov. (nº 1, Male; Cilo Sat Mountains, Hakkari, Turkey)- Also, temporal area of other specimen from the same locality. The new nomenclature proposed in the text is used.
Can Forest Management Practices Counteract Species Loss Arising from Increasing European Demand for Forest Biomass under Climate Mitigation Scenarios?
<p>Here are stored two datasets belonging to the model build for the following research article:</p> <p><strong>Can Forest Management Practices Counteract Species Loss Arising from Increasing European Demand for Forest Biomass under Climate Mitigation Scenarios?</strong></p> <p>Francesca Rosa, Fulvio Di Fulvio, Pekka Lauri, Adam Felton, Nicklas Forsell, Stephan Pfister, Stefanie Hellweg</p> <p><em>Environmental Science and Technology</em>, <strong>2023</strong></p> <p><a href="https://doi.org/10.1021/acs.est.2c07867">https://doi.org/10.1021/acs.est.2c07867</a></p> <p>The rest of the data, the code and further documentation are provided in this github repository: <a href="https://github.com/francesca-git/EU28-ForestMng-Climate">https://github.com/francesca-git/EU28-ForestMng-Climate</a> </p> <p> </p>
OSEMOSYS CAMEROON SCENARIOS
<p>OSEMOSYS CAMEROON SCENARIOS </p>
Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.</p>
Projected changes in forest biomass to 2100 by county and species for 20 future scenarios
<p>Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) are important drivers of forest demography. Here we apply previously-derived growth and survival responses for 94 tree species, representing >90% of the contiguous U.S. forest basal area, to project how changes in mean annual temperature, precipitation, and N and S deposition from 20 different future scenarios may affect forest composition to 2100. We find that under the low climate change scenario (RCP 4.5), reductions in aboveground tree biomass from higher temperatures are roughly offset by increases in aboveground tree biomass from reductions in N and S deposition. However, under the higher climate change scenario (RCP 8.5) the decreases from climate change overwhelm increases from reductions in N and S deposition. These broad trends underlie wide variation among species. We found that averaged across temperature scenarios, the relative abundance of 60 species was projected to decrease by more than 5%, 20 species were projected to increase by more than 5%, and reductions of N and S deposition led to a decrease for 13 species and an increase for 40 species. This suggests large shifts in the composition of U.S. forests in the future. Negative climate effects were mostly from elevated temperature and were not offset by scenarios with wetter conditions. We found that by 2100 an estimated 1 billion trees under the RCP 4.5 scenario and 20 billion trees under the RCP 8.5 scenario may be pushed outside the temperature record upon which these relationships were derived. These results may not fully capture future changes in forest composition as several other factors were not included. Overall efforts to reduce atmospheric deposition of N and S will likely be insufficient to overcome climate change impacts on forest demography across much of the United States unless we adhere to the low climate change scenario.</p>
Shared Earthquake Scenarios for SeisSol
<p>Shared Earthquake Scenarios for SeisSol</p>
SCTrans Curated Scenarios
<p>Scenarios curated by SCTrans. The structure of the zip file is:</p> <pre><code class="language-bash">SCTrans-data ├── LGSVL-environment ├── LGSVL-VSE └── OpenSCENARIO</code></pre> <p>And each directory contains:</p> <ul> <li>LGSVL-environment: LGSVL map assets</li> <li>LGSVL-VSE: LGSVL VSE scenarios in JSON format</li> <li>OpenSCENARIO: OpenSCENARIO and OpenDRIVE files, i.e., *.xosc and *.xodr </li> </ul> <p> </p> <p>Hello</p>
MOON: Assisting Students in Completing Educational Notebook Scenarios (Artifacts)
<p>This repository contains the artifacts supporting the paper "<em>MOON: Assisting Students in Completing Educational Notebook Scenarios</em>" published in the <em>IEEE Symposium on Visual Languages and Human-Centric Computing</em> (VL/HCC) 2023.</p> <ul> <li><strong>moon.zip</strong> contains the source code of MOON at the time of publication</li> <li><strong>notebooks.zip</strong> contains the example Jupyter notebook used as illustration in the paper as well as the notebook manipulated by students in the evaluation</li> <li><strong>analysis.zip</strong> contains the Jupyter notebooks used to analyze the raw data extracted in the evaluation as well as the results of the user study conducted with students</li> </ul>
Scenario Data: Annual Demand and Supply per Subregion (NUTS3 regions)
<p>4 scenarios:</p> <p>DE, GA, DEE, DEG</p> <p>Regions included: NUTS3</p> <p>Scenario years: 2030, 2035, 2040, 2045, 2050</p> <p>Energetic scope: Electricity, hydrogen demand and supply per sector/subsector/application/technology</p> <p>Units: annual volumes in TWh, installed capacities in GW.</p>
Scenario Data: Annual Demand and Supply per Country
<p>4 scenarios:</p> <p>DE, GA, DEE, DEG</p> <p>Countries included: BE, DE etc.</p> <p>Scenario years: 2030, 2035, 2040, 2045, 2050</p> <p>Energetic scope: Electricity, hydrogen demand and supply per sector/subsector/application/technology</p> <p>Units: annual volumes in TWh, installed capacities in GW.</p>
Scenario Data: Annual Demand and Supply per Subregion (NSWPH regions)
<p>4 scenarios:</p> <p>DE, GA, DEE, DEG</p> <p>Regions included: BEXX, DEXX etc.</p> <p>Scenario years: 2030, 2035, 2040, 2045, 2050</p> <p>Energetic scope: Electricity, hydrogen demand and supply per sector/subsector/application/technology</p> <p>Units: annual volumes in TWh, installed capacities in GW.</p>
Estuarine Hypoxia – Identifying High Risk Catchments Now and Under Future Climate Scenarios - Water Level Dataset
<p>Historic water level data used in determining the inundation characteristics for each of the catchments within the study area. The locations of each water level gauge, a summary of the distribution of water levels and the distribution of data to each catchment is detailed in the Supporting Information accompanying the manuscript "Estuarine Hypoxia – Identifying High Risk Catchments Now and Under Future Climate Scenarios".</p>
Extended reference scenarios (ERS) files (including NLTE input and uncertainty perturbations)
<p>Extended reference scenarios (ERS) files (including NLTE input and uncertainty perturbations) for CAIRT studies.</p> <div> <p>The dataset includes essentially all the atmospheric input (based on ERS <span><a href="../records/10022129"><span>https://zenodo.org/records/10022129</span></a></span>) required to perform the GRANADA NLTE and KOPRA radiance simulations used in the CAIRT tomographic linear error analysis. This includes the input for unperturbed and perturbed NLTE and radiance simulations, the latter referring to perturbations of atmospheric parameters (CO2, O, T>120 km) and NLTE kinetic parameters. </p> </div> <div> <p>The data files are ascii files with the following naming <month>_<latitude>_<parameters>[_ngt][_hs][_hv][_<perturbation>].dat with parameters = pT, vmr, npar, ratio; _ngt for night (day has no suffix); _hs for ‘high solar’ (low solar has no suffix); _hv for high volcanic aerosol load (low volcanic has no suffix); <perturbations> refers to all perturbed atmospheric and NLTE parameters assessed in the error calculations (unperturbed has no suffix). </p> </div> <div> <p>parameter=ratio are the NLTE population files produced by GRANADA and read by KOPRA</p> <p>parameter=pT are pressure and temperature as in ERS (<span><a href="../records/10022129"><span>https://zenodo.org/records/10022129</span></a></span>) </p> </div> <div> <p>parameter=vmr includes vmr profiles of the ERS, but has in addition vmr profiles that are required for NLTE modeling (i.e., OH, HO2, N4S, N2D, H, O1D). It also includes some additional minor species not provided in the ERS which have been considered in the KOPRA radiative transfer calculations. Data sources for these profiles are specified within the files.</p> </div> <div> <p>parameter=npar are photolysis rates needed for the NLTE calculations, computed with the MIPAS preprocessor (invoking TUV).</p> </div>
Data from: Four scenarios in which shadow competition should be prominent and factors affecting its strength
<p>Shadow competition is the interception of moving prey by a predator closer to its arrival source, preventing its availability to predators downstream. Shadow competition is likely common in nature, and unlike some other competition types, has a strong spatial component (with the exception of competition for space, which clearly also has a spatial component). We used an individual-based spatially-explicit simulation model to examine whether shadow competition takes place and which factors affect it in four scenarios considering ambush predators and active prey. First, when prey capture is uncertain ('the ricochet effect'). Here, the strength of shadow competition increases when it is harder to capture prey after the first unsuccessful capture attempt, whereas shadow competition is moderated if capture success is higher in successive attempts. Second, shadow competition becomes stronger when predators can capture prey arriving only from certain directions. Third, when prey tend to move along a barrier after encountering it. Here, predators located along this barrier may be more successful than those at random positions, but shadow competition in this scenario drastically decreases the capture success of predators in central positions along a barrier (i.e., having more than a single neighbour). Finally, in three-level systems of plants in clusters, herbivores searching for plants, and predators ambushing herbivores inside plant patches, predators with ambush locations in the periphery of plant patches are more successful than those at the patch center, especially at high predator densities. Our simulation indicates that shadow competition is plausibly relevant in various scenarios of ambush predators and prey, and that it varies based on the habitat structure and capture probability of prey by predators as well as the change in capture probability with successive encounters.</p>
GeaVR-tailored Immersive Virtual Scenario for dykes affecting Tertiary lava units in Eastern Iceland
<p>GeaVR-tailored Immersive Virtual Scenario for dykes affecting Tertiary lava units in Eastern Iceland</p><p> The dataset regards a virtual scenario designed to work with GeaVR software (https://geavr.eu/).</p><p><i><strong>Area of interest:</strong></i> A portion of the eastern and older rift in Iceland, where dykes affect Tertiary lava units.</p><p><i><strong>Aerial extent:</strong></i> 270 x 220 m.</p><p><i><strong>Texture resolution:</strong></i> 1.05 cm/pixel. </p><p><i><strong>Type of scenario:</strong></i> derived from photogrammetry processing – UAV-collected pictures.</p>
GeaVR-tailored Immersive Virtual Scenario for the 1809 CE volcanic system, Mt Etna, Italy
<p>The dataset regards a virtual scenario designed to work with GeaVR software (https://geavr.eu/).</p><p><i><strong>Area of interest:</strong></i> N20°E-aligned craters related to the historical eruption occurred in 1809 CE along the NE rift of Mt Etna.</p><p><i><strong>Aerial extent:</strong></i> 250 x 150 m.</p><p><i><strong>Texture resolution:</strong></i> 0.6 cm/pixel. </p><p><i>Type of scenario:</i> derived from photogrammetry-processing – field-camera collected pictures.</p>
GeaVR-tailored Immersive Virtual Scenario for the Theistareykir Fissure Swarm, Northern Iceland
<p>The dataset regards a virtual scenario designed to work with GeaVR software (https://geavr.eu/).</p><p><i><strong>Area of interest</strong></i>: the western portion of the emerging mid-ocean ridge in Northern Iceland, especially in the so-called Theistareykir Fissure Swarm.</p><p><i><strong>Aerial extent</strong></i>: 12.6 x 10.2 km.</p><p><i><strong>Texture resolution</strong></i>: 67.4 cm/pixel. </p><p><i><strong>Type of scenario</strong></i>: derived from photogrammetry-processing – historical aerial photos acquired in 1982 A.D..</p><p><i><strong>Please cite:</strong></i> Tibaldi, A., Bonali, F.L., Vitello, F. <i>et al.</i> Real world–based immersive Virtual Reality for research, teaching and communication in volcanology. <i>Bull Volcanol</i> <strong>82</strong>, 38 (2020). https://doi.org/10.1007/s00445-020-01376-6</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.