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Figure 1. – A1 in The European catfish loves Asian clam soup
Figure 1. – A1: Deposited shells of Asian clams. A2: Clam dead soft tissues, without shell (black narrow). B: One catfish individual is coming nearer floating dead clams (black narrows); C1: Aggregation of European catfish individuals (white narrows) feeding on dead clams. C2: One catfish individual is swallowing one dead clam.
Figure 1 in Rivers in search of the European eel. Distribution and threats of the Critically Endangered Anguilla anguilla (Linnaeus, 1758) in Sicily: the province of Ragusa as a case study
Figure 1. – Distribution of the European eel Anguilla anguilla in the rivers of the Ragusa province. (D: Dirillo, A: Amerillo, Mz: Mazzarronello, Ip: Ippari, R: Rifriscolaro, I: Irminio, V: Volpe, M: Mastratto, C: Ciaramite, SL: San Leonardo, MS: Modica-Scicli, F: Favara, Tr: Tellaro, Tl: Tellesimo, Pr: Prainito; m: mortality; p: poaching).
Figure 5 in A third European species of grayling (Actinopterygii, Salmonidae), endemic to the Loire River basin (France), Thymallus ligericus n. sp.
Figure 5. – Average dot numbers on each scale row on the flank for populations of Thymallus ligericus n. sp. from the Upper Ance (47 specimens), Lower Ance (7 specimens) and Lignon Rivers (16 specimens) in the Loire drainage as well as for Thymallus thymallus populations from the Ain (176 specimens) and Loue rivers (35 specimens) in the Rhône drainage.
Fig. 1 in First insights into micromorphology of needle epicuticular waxes of south-eastern european Pinus nigra J. F. Arnold populations
Fig. 1. Location of analyzed south-eastern European populations of three P. nigra subspecies. P. nigra ssp. banatica (Populations I-IV), P. nigra ssp. pallasiana (Populations V-VII), and P. nigra ssp. nigra (Populations VIII and IX). ROU = Romania; SRB = Serbia; MKD = Macedonia; BIH = Bosnia and Herzegovina. For the description of the taxa, locations, and habitat conditions of the populations cf. Table 1.
Fig. 2 in First insights into micromorphology of needle epicuticular waxes of south-eastern european Pinus nigra J. F. Arnold populations
Fig. 2. SEM micrographs of P. nigra epicuticular waxes. A. adaxial surface of needles; B. marginal teeth with mucrones (MC); C. clusters of longitudinally aggregated rodlets (LAR); D. suprastomatal chambers surrounded by alone tubes lying on the surface (AT) as well as individual granules (GR); E. tubes slightly fused to each other (TSF) and tubes fused together (TF), which surround and fill the suprastomatal chambers; smooth layers (SL - amorphous wax that covers the largest area of needles).
Figure 6 in Populations of Microcondylaea bonellii (Férussac 1827), Unionidae - an european freshwater mussel at rapid decline - and Unio mancus in Istria, Croatia
Figure 6. Adult Microcondylaea bonellii from Butoniga (1.10.2011), note the arboriform siphonal papillae typical for this species.
Accompanying data to "Could an extremely cold central European winter such as 1963 happen again despite climate change?"
<h2>Accompanying data to "Could an extremely cold central European winter such as 1963 happen again despite climate change?"</h2> <div> </div> <div><strong>16.07.2024 This repository contains data that underlies the following publication:</strong></div> <div>Sippel, S., Barnes, C., Cadiou, C., Fischer, E., Kew, S., Kretschmer, M., Philip, S., Shepherd, T. G., Singh, J., Vautard, R., and Yiou, P.: Could an extremely cold central European winter such as 1963 happen again despite climate change? <em>Weather and Climate Dynamics</em> (accepted), 2024. Preprint: https://doi.org/10.5194/egusphere-2023-2523.</div> <div> </div> <div>This repository is a data collection, which contains simulated extremely cold Central European winter storylines. Climate model simulations use the technique of climate model boosting, and statistical generation using stochastic weather generators (SWG) empirical importance sampling. The repository contains the following data files:</div> <div> </div> <h3>(1) Climate model ensemble boosting for extremely cold winter storylines. </h3> <div> <ul> <li>Zip file BSSP370cmip6.0000013.zip: Contains all 750 files of the first-order boosting. First order boosting is based on ensemble member 21 in the CESM2-ETH ensemble, and with restart dates between 01.12 and 15.12.2022 (SSP3-70 scenario), with 50 members for each starting date. Example file: BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc</li> </ul> </div> <div>The boosting files follow a naming convention: </div> <div> <ul> <li> <ul> <li>BSSP370cmip6 all files based on CMIP6 SSP3-70 forcing.</li> <li><span>2022-12-06 starting date of the respective ensemble member.</span></li> <li><span>0000013 Ensemble member of CESM2-ETH that was used for boosting (i.e. member 13 of CESM2-ETH).</span></li> <li><span>ens023 Ensemble member of the boosted ensemble (i.e. member 23 with starting date 06.12.2022).</span></li> </ul> </li> </ul> </div> <div>The second-order boosting was branched off from first-order boosting file BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc.</div> <div> </div> <div> <ul> <li>Zip file BSSP370cmip6.0230013.zip: Contains all 750 files of the first set of second-order boosting simulations. All these simulations are based on first-order boosting file BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc. That is, the first-order boosting file started from ensemble member 13 of CESM2-ETH, starting date 06.12.2022 and ensemble member 23 of the first-order boosted ensemble. The second-order boosting file shown in Figs. 5-6 is the file BSSP370cmip6.0230013.2023-01-08.ens047.cam.h1.2023-01-09-00000.nc. That is, ensemble member 47 in second-order boosting ensemble from starting date 08.01.2023. </li> </ul> </div> <div> </div> <div> <ul> <li>Zip file BSSP370cmip6.0480013.zip: Contains all 750 files of the second set of second-order boosting simulations. All these simulations are based on first-order boosting file BSSP370cmip6.0000013.2022-12-15.ens048.cam.h1.2022-12-16-00000.nc. That is, the first-order boosting file started from ensemble member 13 of CESM2-ETH, starting date 15.12.2022 and ensemble member 48 of the first-order boosted ensemble. The second-order boosting file shown in Figs. 5-6 is the file BSSP370cmip6.0480013.2023-01-08.ens032.cam.h1.2023-01-09-00000.nc. That is, ensemble member 32 in second-order boosting ensemble from starting date 08.01.2023. </li> </ul> </div> <div> </div> <div> </div> <h3>(2) CESM2 maps of extremely cold winters (to generate Fig. 5)</h3> <div>* Zip file cesm2_maps.zip. Contains the following entries, all for DJF average anomalies (relative to the ensemble average climatology):</div> <div>- tas_ssp370_r2i1p1.2005-2035_anom.nc</div> <div>- tas_ssp370_r12i1p1.2005-2035_anom.nc</div> <div>Two members (r2i1p1 in 2008, r12i1p1 in 2007) from the CESM2-ETH ensemble, which produce very cold winters. Variables tas (surface air temperature), Z500 (geopotential height at 500 hPa), FSDS (surface downwelling shortwave radiation), and FSNS (surface net shortwave radiation) are available (FSDS and FSNS to calculate albedo). </div> <div>- tas_ssp370_0230013.2023-01-08.ens047_anom.nc</div> <div>- tas_ssp370_0480013.2023-01-08.ens032_anom.nc</div> <div>The two extremely cold boosted winters as described above, concatenated with their parent files from boosting. </div> <div> </div> <div> </div> <h3>(3) Storylines of extremely cold winters generated via Stochastic weather generator (SWG) empirical importance sampling</h3> <div>SWG-empirical-importance-sampling.zip Storylines of extremely cold winters generated via Stochastic weather generator (SWG) empirical importance sampling (Yiou and Jézéquel, 2020, https://doi.org/10.5194/gmd-13-763-2020). The available maps are seasonal average anomalies resampled from ERA5 (to generate Fig. 5):</div> <div> <ul> <li>Surface air temperature: t2m_WEGE_germany_1963_1972-2021_DJFmean.nc</li> <li><span>Albedo: fal_WEGE_germany_1963_1972-2021_DJFmean.nc</span></li> <li><span>z500: z500_WEGE_germany_1963_1972-2021_DJFmean.nc</span></li> </ul> </div> <div> </div>
Figure 4 in Ontogenetic development of the European basal aquatic turtle Pleurosternon bullockii (Paracryptodira, Pleurosternidae)
Figure 4. Partial shells and isolated plates of juvenile specimens of Pleurosternon bullockii (Paracryptodira, Pleurosternidae), from the Berriasian (Early Cretaceous) of Swanage (Dorset, England). (a) NHMUK 48343, in ventral view. (b) NHMUK 48344, in ventral view. (c) NHMUK 48347, in ventral view. (d) NHMUK 48252, hypoplastron in ventral view. Scale bars equal 2 cm.
Figure 6 in Ontogenetic development of the European basal aquatic turtle Pleurosternon bullockii (Paracryptodira, Pleurosternidae)
Figure 6. Shape differences of the third vertebral scute of Pleurosternon bullockii (Paracryptodira, Pleurosternidae), taking into account specimens from the Berriasian (Early Cretaceous) of Swanage (Dorset, England). (a–b) Principal component analysis (PCA). Wireframes indicate the main shape changes in each PC; the light blue concerns the average shape, whereas the dark blue is the shape variance related to the extreme of variation along the axis. (c) Transformation grids to visualize shape deformation relative to the first three principal components. Abbreviation: N, NHMUK.
Figure 9 in Ontogenetic development of the European basal aquatic turtle Pleurosternon bullockii (Paracryptodira, Pleurosternidae)
Figure 9. Linear regression between Procrustes coordinates (dependent variable) and logged-centroid size values (independent variable) of the shell elements of Pleurosternon bullockii (Paracryptodira, Pleurosternidae), from the Tithonian (Upper Jurassic) and Berriasian (Lower Cretaceous) of England. (a) Nuchal. (b) Third vertebral scute. (c) Anterior plastral lobe. (d) Entoplastron.
Figure 2 in Ontogenetic development of the European basal aquatic turtle Pleurosternon bullockii (Paracryptodira, Pleurosternidae)
Figure 2. Partial shells and isolated plates of juvenile specimens of Pleurosternon bullockii (Paracryptodira, Pleurosternidae), from the Berriasian (Early Cretaceous) of Swanage (Dorset, England). (a) NHMUK 48263a, in dorsal view. (b) NHMUK 48263c, in dorsal view. (c) NHMUK 48263e, in dorsal (carapace) and ventral (anterior plastral lobe) views. (d) NHMUK 48263, in dorsal view. (e) NHMUK 48351, in dorsal view. (f–g) NHMUK 48352, disarticulated plates of the carapace in dorsal view (f) and entoplastron in ventral view (g). Scale bars equal 2 cm.
CSREU-EUROPEAN UNION'S POLICY ON CORPORATE SOCIAL RESPONSIBILITY
<p>CSREU-EUROPEAN UNION'S POLICY ON CORPORATE SOCIAL RESPONSIBILITY </p>
Supplementary material 1 from: van Nieukerken EJ, Lees DC, Doorenweerd C, Koster S(JC), Bryner R, Schreurs A, Timmermans MJTN, Sattler K (2018) Two European Cornus L. feeding leafmining moths, Antispila petryi Martini, 1899, sp. rev. and A. treitschkiella (Fischer von Röslerstamm, 1843) (Lepidoptera, Heliozelidae): an unjustified synonymy and overlooked range expansion. Nota Lepidopterologica 41(1): 39-86. https://doi.org/10.3897/nl.41.22264
Specimen and Locality Data Antispila. : Explanation note: Specimen data.
FIGURE 2 in Description of the larva of Adicella cremisa Malicky 1972 and a larval key to Central European species of Adicella McLachlan 1877 (Trichoptera: Leptoceridae)
FIGURE 2. MauerbaCh River in Vienna, Austria, habitat of A. cremisa;
FIGURE 1 in Description of the larva of Adicella cremisa Malicky 1972 and a larval key to Central European species of Adicella McLachlan 1877 (Trichoptera: Leptoceridae)
FIGURE 1. Adicella cremisa adult, resting position; note the long whitish antennae.
FIGURE 6 in A new genus of nesticid spiders from western European Peninsulas (Araneae, Nesticidae)
FIGURE 6. Distribution of the genus Domitius n. gen. in the western Mediterranean.
Supplementary Data: Code, Input Data and Model data: PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p>Supplementary Data (preliminary version)</p> <p>PyPSA-Eur: An Open Optimisation Model of the European Transmission System</p> <p>Authors: J. Hörsch, F. Hofmann, D. Schlachtberger, T. Brown</p> <p>and</p> <p>The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios</p> <p>Authors: J. Hörsch, T. Brown</p> <p>The files in this record contain the scripts to build a <a href="http://pypsa.org/">PyPSA</a> model of the European Electricity System including renewable feed-in from wind, solar and hydro installations derived from reanalysis weather data satellite irradiation. The model PyPSA-Eur is described in the above publication.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>base_network.py creates the initial PyPSA network topology.</p> <p>add_electricity.py adds generators and storage units to the models, it generates the detailed resolved model described in the PyPSA-Eur paper.</p> <p>simplify_network.py removes stub ac-buses from network topology and simplifies long dc lines.</p> <p>cluster_network.py creates clustered representations of the electricity network for a given number of buses following the topology described in the "spatial scale" paper.</p> <p>prepare_network.py adds parameters like the CO2 limit and the transmission expansion volume relevant for the optimization to the model.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p><strong>Data</strong></p> <p>The input data include:</p> <ul> <li>Electricity sector data</li> <li>Topology derived from the analysis of an extract of the <a href="https://www.entsoe.eu/data/map/">ENTSO-E online map</a> using <a href="https://github.com/bdw/GridKit">GridKit</a> .</li> <li>A cost database with literature sources.</li> </ul> <p> </p>
FIGURE 11 in New data clarifying the taxonomy of European members of the Lepidocyrtus pallidus - serbicus group (Collembola, Entomobryidae)
FIGURE 11. Lepidocyrtus florae sp. nov.: a, habitus (lateral view); b, dorsal head pigmentation.
Supplementary material 2 from: Sieber I, Borges P, Burkhard B (2018) Hotspots of biodiversity and ecosystem services: the Outermost Regions and Overseas Countries and Territories of the European Union. One Ecosystem 3: e24719. https://doi.org/10.3897/oneeco.3.e24719
all studies were conducted between 1991 and 2017 .
Supplementary material 8 from: Sieber I, Borges P, Burkhard B (2018) Hotspots of biodiversity and ecosystem services: the Outermost Regions and Overseas Countries and Territories of the European Union. One Ecosystem 3: e24719. https://doi.org/10.3897/oneeco.3.e24719
Appendix of included literature
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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)
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DANDI Archive for NWB datasets
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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.