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Fig. 7 in Past, present and future of host‾parasite co-extinctions
Fig. 7. Schematic representation of the possible different parasitological consequences of a biological invasion. A: The invader loses its parasite and does not get local parasites; B: The invader loses its parasites and gets new ones from native hosts; C: The invader retains its parasites and these establish new symbioses with local species; D: The invader retains its parasites and acquire new parasites from local hosts; its parasites establish new symbioses with local host species; E: The invader does not lose its parasites, does not get new ones from native hosts, and its parasites do not expand their host range.
Fig. 6 in Past, present and future of host‾parasite co-extinctions
Fig. 6. Example of asymmetry of interactions as observed in all host parasite records available from FishPest dataset (Strona and Lafferty, 2012). The graph shows the relationship between the maximum specificity of the parasites using a certain host species, and the parasite richness on that host species. Boxplots correspond to different classes of hosts identified on the basis of the maximum specificity of their parasites. Thus, the first boxplot provides information on parasite species richness of all fish species whose most specific parasite uses just one host. It is apparent that specific parasites tend to use hosts harboring many parasites, while species-poor parasitofaunas are often composed by generalist parasites. Boxes indicate first and third quartiles, whiskers indicate range values, and horizontal lines indicate median values.
Fig. 5 in Past, present and future of host‾parasite co-extinctions
Fig. 5. Graph showing the relationship between fish parasite specificity and the corresponding average vulnerability of the hosts used by those parasites. Data were obtained using the same data and procedure as in Strona et al. (2013), computing mean host vulnerability values for different parasite host range classes. Differently from Strona et al. (2013), however, classes were defined using a logarithmic progression instead of a geometric one, resulting in an even tighter relationship between log(host range) and mean host vulnerability (rs = 0.93; p <0.05).
Fig. 3 in Past, present and future of host‾parasite co-extinctions
Fig. 3. Distribution of parasite specificity expressed as the logarithm of host range size in fish (A) and terrestrial vertebrates (B). Data for fish parasites (Acantocephala, Cestoda, Monogenea, Nematoda and Trematoda) were collected from FishPest (Strona and Lafferty, 2012). Data for parasites of terrestrial vertebrates (Acantocephala, Cestoda, Nematoda and Trematoda for amphibians, birds, mammals and reptiles) were collected from the Natural Museum History database (http://www.nhm.ac.uk). Since (as to June 11th 2015) all amphibians in the database are erroneously classified as reptiles, information was corrected using Catalogue of Life (http://www.catalogueoflife.org/). Y-axes indicate parasite species numbers.
Instantaneous, three-dimensional velocity fields past a bio-prosthetic aortic valve measured in-vitro with tomographic particle image velocimetry.
<p>Each folder contains instantaneous, three-dimensional velocity vector data obtained in a simplified model of the aortic root with a distinct size and geometry (small, medium, large, and sinus-less). The specific geometry of each aortic root model is contained in the corresponding folder.</p> <p>The velocity data is structured in the following way: Two separate folders for velocity data in the "ascending aorta" domain (AAo) and in the "sinus of Valsalva" domain (SOV). Each domain contains velocity datasets for instances t=0.00, 0.03, 0.06, ..., 0.39 s (t000, t003, t006, ..., t039). Each velocity dataset contains N=16 phase-locked instantaneous 3D velocity fields.</p> <p>The data was acquired using tomographic particle image velocimetry and a custom built hydraulic setup capable of replicating normal physiological flow conditions in the human aorta (heart rate = 72 bpm, cardiac output = 4.8 l)</p> <p>Data format:</p> <p>- aortic root geometry: STL (the geometry is provided with respect to the reference frame of the velocity data)</p> <p>- velocity data: NPY (NumPy), shape= (N_nodes, 6), columns contain X, Y, Z, U, V, W data, where U, V, W are the X, Y, Z components of the instantaneous vector field</p>
The Present Meets the Pasts: Our Mythical Childhood Workshops, 2018
<p>The workshops <strong><em>The Present Meets the Past</em></strong> within the project <em><strong>Our Mythical Childhood</strong></em> took place in May 14–20, 2018, in <strong>the European Year of Cultural Heritage</strong> at the Centre for Studies on the Classical Tradition (OBTA), Faculty of “Artes Liberales”, University of Warsaw. The workshops included a number of dissemination and society-oriented activities, mainly focused on the collaboration with high schools. They took place in the European Year of Cultural Heritage 2018. </p> <p>For more see: <a href="http://omc.obta.al.uw.edu.pl/present-past">http://www.omc.obta.al.uw.edu.pl/present-past</a>. The movie is also available here: <a href="https://www.youtube.com/watch?v=2RizUWYMW0Q">https://www.youtube.com/watch?v=2RizUWYMW0Q</a>. </p> <p>Video by Mirosław Kaźmierczak. Scholarly supervision - Katarzyna Marciniak</p>
Figure 3. The effective population size through recent time for 3 in Comparative analyses of past population dynamics between two subterranean zokor species and the response to climate changes
Figure 3. The effective population size through recent time for 3 clades of Gansu zokor (Eospalax cansus).
Past, present and future rainfall erosivity in Northwestern Europe
<p>Past, present and future rainfall erosivity in Northwestern Europe calculated from convection-permitting climate simulations in CNRM-AROME (Lucas-Picher et al., 2023; <a href="https://doi.org/10.1007/s00382-022-06637-y">https://doi.org/10.1007/s00382-022-06637-y</a>) using emission scenario RCP 8.5. A description of the methodology is given in the article "Past, present and future rainfall erosivity in central Europe based on convection-permitting climate simulations" by Magdalena Uber et al. (2024) in Hydrology and Earth System Sciences (<a href="https://doi.org/10.5194/hess-28-87-2024">https://doi.org/10.5194/hess-28-87-2024</a>). Please see the README-file for further information.</p> <p>This work was funded by the German Federal Ministry for Digital and Transport in the framework of the DAS-Basisdienst.</p>
Fig. 1 in Out of the Past: A new species of Tantilla of the calamarina group (Squamata: Colubridae) from southeastern coastal Guerrero, Mexico, with comments on relationships among members of the group
Fig. 1. Dorsal (A), lateral (B), and ventral (C) views of the head of the holotype of Tantilla carolina sp. nov. (BMNH 1906.6.1.241).
Fig. 2 in The dawn of phylogenetic research on Neotropical fishes: a commentary and introduction to Baskin (1973), with an overview of past progress on trichomycterid phylogenetics
Fig. 2. From left to right: Naércio Menezes, Jonathan Baskin, Paulo Vanzolini, Almenor Tacla and Hans Reichardt at entrance of Museu de Zoologia da Universidade de São Paulo, São Paulo, in 1963.
Figure 1 in The Upper Juruá Extractive Reserve in the Brazilian Amazon: past and present
Figure 1. Map of the Upper Juruá Extractive Reserve: a) its location in Brazil, Acre State; b) some of the rivers that flow within the reserve that had their riverine populations studied here.
Figure 3 in The Upper Juruá Extractive Reserve in the Brazilian Amazon: past and present
Figure 3. Percentage of citation about fish consumption in the winter (high water, rainy season, September-March) and in the summer (low water, dry season, April-August), by women (73 interviews) and men (46 interviews), at UJER. 1993/1994.
Figure 2 in The Upper Juruá Extractive Reserve in the Brazilian Amazon: past and present
Figure 2. Data on animal husbandry (1993/1994, average): A) Juruá and Tejo rivers, B) Bagé River, C) Igarapé São João and D) Breu River.
Past surface mass balance of the Juneau Icefield, Southeast Alaska (1980 to 2019)
<p>This dataset contains the modelled past surface mass balance of the Juneau Icefield in Southeast Alaska for three models. A reanalysis model, CFSR from 1980 to 2019. And two global climate models (GFDL-CM3 and NCAR-CCSM4) from 1980 to 2010. The simulated surface mass balance was produced using COSIPY - a coupled snowpack and ice surface energy and mass balance model. The input climate data was originally dynamically downscaled in Lader et al., (2020, <em>J. Appl. Meteor. Climatol.)</em></p> <p>This data supplements the manuscript "<em>Surface mass balance modelling of the Juneau Icefield highlights the potential for rapid ice loss by the mid-21st century</em>" submitted to the Journal of Glaciology. </p>
Linked collectors and determiners for: From the shadows of the past: Moricand senior and junior, two 19 th century naturalists from Geneva, with their newly described taxa and molluscan types.
Natural history specimen data linked to collectors and determiners held within, "From the shadows of the past: Moricand senior and junior, two 19 th century naturalists from Geneva, with their newly described taxa and molluscan types". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/59e37f50-05dc-43e2-9954-f33875cbb0c6">https://bionomia.net/dataset/59e37f50-05dc-43e2-9954-f33875cbb0c6</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/59e37f50-05dc-43e2-9954-f33875cbb0c6">https://gbif.org/dataset/59e37f50-05dc-43e2-9954-f33875cbb0c6</a>. Formatted as a Frictionless Data package.
Analyzing the Impact of Copying-and-Pasting Vulnerable Solidity Code Snippets from Question-and-Answer Websites
<p>This data comprises all input and output, including intermediate results for the evaluation of the tool cpg-contract-checker(CCC) and the crawled data and results for the study published under the name "Analyzing the Impact of Copying-and-Pasting Vulnerable Solidity Code Snippets from Question-and-Answer Websites".<br>We conducted a study on the impact of vulnerable code reuse from Q&A websites during the development of smart contracts and provided tools uniquely fit to detect vulnerable code patterns in complete and incomplete Smart Contract code. The paper proposes a pattern-based vulnerability detection tool that is able to analyze code snippets (i.e., incomplete code) as well as full smart contracts based on the concept of code property graphs. We also propose a methodology that leverages fuzzy hashing to quickly detect code clones of vulnerable snippets among deployed smart contracts. Our results show that our vulnerability search, as well as our code clone detection, are comparable to state-of-the-art while being applicable to code snippets. The tools are used to realize a study pipeline for which the dataset and (intermediate) results are contained in this archive.</p>
Dataset for the article 'Estimating countries' additional carbon accountability for closing the mitigation gap based on past and future emissions'.
<p>Dataset for the article 'Estimating countries’ additional carbon accountability for closing the mitigation gap based on past and future emissions', published in Nature Communications. DOI: <a href="https://doi.org/10.1038/s41467-024-54039-x">10.1038/s41467-024-54039-x</a></p> <p>TablesInManuscriptandCalculations.xlsx includes a calculations sheet where the main results can be estimated using only Excel, and each respective table found in the article.</p> <p>PlannedEmissions.xlsx includes estimated pathways for all analyzed countries during 2023-2070. Results are given in million tonnes of carbon dioxide (MtCO₂).</p> <p>DataForSensitivityAnalysis.xlsx is a full database with all the results used in the article, both the main approach and sensitivity cases.</p> <p>These files are generated using R-code available at:</p> <p><a href="https://github.com/morfeldt/AdditionalCarbonAccountability">https://github.com/morfeldt/AdditionalCarbonAccountability</a></p> <p> </p>
Table 1 in First insights into past biodiversity of giraffes based on mitochondrial sequences from museum specimens
<p><b>Table 1.</b> Currently accepted giraffe subspecies (Muller <i>et al.</i> 2018) with their synonyms (modified after Shorrocks 2016).</p><table><tbody><tr><th><b>Subspecies</b></th><th><b>Description</b></th><th><b>Type specimen</b></th><th><b>Type locality</b></th><th><b>Synonyms</b></th></tr></tbody><tbody><tr><th><i>camelopardalis</i></th><td>Linnaeus 1758</td><td>Living giraffe illustrated by Belon du Mans (1553), never deposited in a museum collection.</td><td>Sennar (Sudan) and Ethiopia</td><td><i>Camelopardalis biturigum</i> Duvernoy, 1844 <i>Camelopardalis aethiopica</i> Ogilby, 1837 <i>Giraffa camelopardalis typica</i> Bryden, 1899</td></tr><tr><th><i>giraffa</i></th><td>Boddaert 1784</td><td>Specimens of the Prince of Orange Museum (The Hague) and of Vosmaer (1787) (Museum Leiden), Netherlands.</td><td>Cape of Good Hope, South Africa</td><td><i>Camelopardalis</i> <i>capensis</i> Lesson, 1842 <i>Camelopardalis</i> <i>australis</i> Swainson, 1835 <i>Camelopardalis maculata</i> Weinland, 1863 <i>Giraffa camelopardalis wardi</i> Lydekker, 1904</td></tr><tr><th><i>antiquorum</i></th><td>Jardine 1835</td><td>SMF-498: unspec. type and SMF-497: paratype</td><td>South of Darfour, Sudan</td><td><i>Giraffa camelopardalis senaariensis</i> Trouessart, 1898 <i>Giraffa camelopardalis congoensis</i> Lydekker, 1903</td></tr><tr><th><i>peralta</i></th><td>Thomas 1898</td><td>NHMUK-1898.2.18.1</td><td>Lokoja junction Niger and Benue rivers, Nigeria</td><td>–</td></tr><tr><th><i>tippelskirchi</i></th><td>Matschie 1898</td><td>ZMB-084951 (syntype); second specimen might be considered lost</td><td>Lake Eyasi, Tanzania</td><td><i>Giraffa schillingsi</i> Matschie, 1898</td></tr><tr><th><i>reticulata</i></th><td>de Winton 1899</td><td>NHMUK-18971.30.1</td><td>Loroghi Mountains, Kenya</td><td><i>Giraffa hagenbecki</i> Knottnerus-Meyer, 1910 <i>Giraffa reticulata nigrescens</i> Lydekker, 1911 <i>Giraffa camelopardalis australis</i> Rhoads, 1896</td></tr><tr><th><i>rothschildi</i></th><td>Lydekker 1903</td><td>NHMUK-1903.4.15.1</td><td>Guasin-gisha Plateau east of Mount Elgon, Kenya</td><td><i>Giraffa camelopardalis cottoni</i> Lydekker, 1904</td></tr><tr><th><i>angolensis</i></th><td>Lydekker 1903</td><td>NHMUK- 1939.480</td><td>Cunene River, Angola</td><td><i>Giraffa camelopardalis infumata</i> Noack, 1908</td></tr><tr><th><i>thornicrofti</i></th><td>Lydekker 1911</td><td>NHMUK-1910.10.17.1</td><td>Petauke district, Zambia</td><td>–</td></tr></tbody></table>
FIG. 6 in Caribou hunting and utilization in West Greenland: Past and present variants
FIG. 6. — Caribou utilization in Angujâartorfiup Nunâ after 2000 AD. (Rangifer tarandus after Beauval & Coutureau © 2003, Archeozoo.org)
Fig. 1 in Zoantharia (Cnidaria: Anthozoa: Hexacorallia) of the South China Sea and Gulf of Thailand: a species list based on past reports and new photographic records
Fig. 1. Map of the South China Sea and Gulf of Thailand showing locations of past literature records of Zoantharia (black dots) and newly reported photographic records in this study (pink dots). Note that within each location (Table 1) there may be more than one locality (details in Table 1 and text).
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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)
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.