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.
236
datasets available to search
ShareScore release 0.9.0
Dataset results
236 results for “Eurasia”
Figure 2 in Integrative taxonomy of two morphologically similar species of the subgenus Cryobius Chaudoir, 1838 (Coleoptera: Carabidae: Pterostichus Bonelli, 1810) from northern Eurasia and North America
Figure 2. Median-joining haplotype network of the COI sequences of Pterostichus (Cryobius) mandibularoides dataset (n = 30). The red numbers near the branches indicate the number of nucleotide substitutions between haplotypes. The size of the circles corresponds to the number of available sequences for each haplotype (smallest circle = 1 sequence). See Table 1 for the list of sequences. The photo of a male specimen with genital structures (from left to right: median lobe of aedeagus in right lateral and ventral aspect, right and left parameres) is taken by N.A. Zubrii. Scale bars: 1 mm for the body and 0.5 mm for the structures of genitalia.
Fig. 6 in Xenoxylon synecology and palaeoclimatic implications for the Mesozoic of Eurasia
Fig. 6. Distribution and diversity of the fossil wood groups associated with Xenoxylon. A. Late Jurassic to Early Cretaceous (1, Japan; 2, Northeast China-Beijing, Liaoning, Jilin, Heilongjiang, and Inner Mongolia; 3, Southeast Mongolia; 4, Western China-Xinjiang; 5, Primorye; 6, Arctic; 7, Greenland; 8, Canada-Alberta; 9, Canadian Arctic Archipelago; 10, Alaska-Central North Slope). B. Late Cretaceous (1, Sakhalin; 2, Alaska-Central North Slope). Abbreviations: A, Protocedroxylon-group; B, Taxodioxylon-group; C, Phyllocladoxylon-group; D, Podocarpoxylon-group; E, Agathoxylon; F, Brachyoxylon-group; G, others (for the composition of each taxonomical group see Table 2).
Fig. 2 in Xenoxylon synecology and palaeoclimatic implications for the Mesozoic of Eurasia
Fig. 2. Fossil wood genera co-occurring with Xenoxylon from the Late Triassic to the Late Cretaceous in Northern Hemisphere.
Fig. 5 in Xenoxylon synecology and palaeoclimatic implications for the Mesozoic of Eurasia
Fig. 5. Distribution and diversity of the fossil wood groups associated with Xenoxylon. A. Late Triassic (modified from the DINODATA; www.dinodata. org; accessed Oct. 11. 2011) (1, Japan; 2, Korea; 3, Southern China-Guangdong; 4, Arctic. B. Early to Middle Jurassic (1, Japan; 2, Korea; 3, Northeast China-Beijing, Liaoning, Jilin, Heilongjiang, and Inner Mongolia; 4, Central China-Henan; 5, Vietnam; 6, Uzbekistan; 7, Iran; 8, Lithuania; 9, Poland; 10, Georgia; 11, Germany; 12, UK; 13, Greenland). Abbreviations: A, Protocedroxylon-group; B, Taxodioxylon-group; C, Phyllocladoxylon-group; D, Podocarpoxylon-group; E, Agathoxylon; F, Brachyoxylon-group; G, others (for the composition of each taxonomical group see Table 2).
Fig. 4 in Xenoxylon synecology and palaeoclimatic implications for the Mesozoic of Eurasia
Fig. 4. Generic diversity of Xenoxylon wood-assemblages during the Mesozoic times—global curve and curve for specific geographic areas. Note that the real diversity was probably much lower, because of taxonomical bias. Abbreviations: LT, Late Triassic; EMJ, Early to Middle Jurassic; LJEK, Late Jurassic to Early Cretaceous; LK, Late Cretaceous.
Fig. 3 in Xenoxylon synecology and palaeoclimatic implications for the Mesozoic of Eurasia
Fig. 3. Number of data for each taxonomical wood group (for the composition of each taxonomical group see Table 2); LT, Late Triassic; EMJ, Early to Middle Jurassic; LJEK, Late Jurassic to Early Cretaceous; LK, Late Cretaceous.
Fig. 1. A in Xenoxylon synecology and palaeoclimatic implications for the Mesozoic of Eurasia
Fig. 1. A. In situ erect stump of Mesozoic Xenoxylon from Bayakodan, near Kuwajima in Shiraminemura, Japan. Used with the permission of Kazuo Terada (specimen no. 53901 in Suzuki and Terada 1992). B. Typical xenoxylean radial pitting and window-like cross-field pits in radial section of Xenoxylon (slide CBNU 73112).
Fig. 7 in Xenoxylon synecology and palaeoclimatic implications for the Mesozoic of Eurasia
Fig. 7. Diversity index (DI = GD/N, where GD is the number of genera associated with Xenoxylon, and N the number of occurrences) for each time period. Abbreviations: LT, Late Triassic; EMJ, Early to Middle Jurassic; LJEK, Late Jurassic to Early Cretaceous; LK, Late Cretaceous.
Text-fig. 1. Map of northern Eurasia with the most important sites discussed in the text: 1: Předmostí, 2: Pavlov, 3: Dolní Věstonice, 4: Krems Wachtberg, 5: Braives, 6: Trou des Nutons, 7: Jaurens, 8: Maldidier, 9: Lokomotiv, 10: Geographical Society cave. in Consumption Of Canid Meat At The Gravettian Předmostí Site, The Czech Republic
Text-fig. 1. Map of northern Eurasia with the most important sites discussed in the text: 1: Předmostí, 2: Pavlov, 3: Dolní Věstonice, 4: Krems Wachtberg, 5: Braives, 6: Trou des Nutons, 7: Jaurens, 8: Maldidier, 9: Lokomotiv, 10: Geographical Society cave.
Text-fig. 1. Palaeogeographical scheme (distribution of land and sea basins) in part of Eurasia at the beginning of the Late Cretaceous (modified from Spicer et al. 2008). The green leaf symbol indicates the site of the Arman Flora. Asterisks indicate the Okhotsk-Chukotka volcanogenic belt. Dashdotted line indicates the boundary between the Siberian- Canadian and Euro-Sinian palaeofloristic regions (modified from Vakhrameev 1991). in On The Likely Palaeoelevation Of The Turonian - Coniacian Arman Flora Site (North-Eastern Asia)
Text-fig. 1. Palaeogeographical scheme (distribution of land and sea basins) in part of Eurasia at the beginning of the Late Cretaceous (modified from Spicer et al. 2008). The green leaf symbol indicates the site of the Arman Flora. Asterisks indicate the Okhotsk-Chukotka volcanogenic belt. Dashdotted line indicates the boundary between the Siberian- Canadian and Euro-Sinian palaeofloristic regions (modified from Vakhrameev 1991).
Fig. 17 in Rhagasostoma (Bryozoa) from the Late Cretaceous of Eurasia: taxonomic revision, stratigraphy and palaeobiogeography
Fig. 17. Rhagasostoma rowei (Brydone, 1906). A–B. SMF 29924, late Campanian, Harmignies near Mons, Belgium. A. Overview of erect bifoliate colony. B. Autozooids and avicularia. C. TsNIGR Museum 26/12939, late Campanian, Cibicidoides voltzianus Zone, Uzbekistan, southern Aral Sea Region, erect bifoliate colony showing ovicellate (ooecia arrowed) and non-ovicellate autozooids and avicularia. D–E. TsNIGR Museum 35/9757, late Campanian, western Kopetdag, Turkmenistan. D. Erect bifoliate colony. E. Ovicellate autozooids (some ooecia arrowed) and avicularia. F–G. PIN 5502/3051, late Campanian (Belemnitella lanceolata Zone), Kazakhstan, Emba River. F. Overview of erect bifoliate stem-like then dichotomously branching colony. G. Ovicellate and non-ovicellate autozooids and avicularia. H–I. SMF 29931, early Maastrichtian, Rügen, Germany. H. Overview of erect bifoliate stem-like colony. I. Opesia and opesiules of avicularium. Scale bars: A, C–E, G = 1 mm; F = 2 mm; B, H = 300 µm; I = 100 µm.
Fig. 12 in Rhagasostoma (Bryozoa) from the Late Cretaceous of Eurasia: taxonomic revision, stratigraphy and palaeobiogeography
Fig. 12. Rhagasostoma gibbosum (Marsson, 1887). A–F. Early Maastrichtian, Saturn quarry near Kronsmoor, Schleswig-Holstein, Germany. A–E. SMF 29906. A. Overview of erect bifoliate colony. B. Ovicellate and non-ovicellate autozooids and avicularia. C. Autozooid, avicularium with wedge-like structure and kenozooid. D. Ovicellate autozooids and avicularia with wedge-like structure. E.Avicularium with broken wedge-like structure. F. SMF 29907, autozooids and avicularia. G. SM B36679, Upper Chalk, early Maastrichtian, Ostrea lunata Zone, Trimingham, Norfolk, England, UK. Ovicellate and non-ovicellate autozooids, some with gymnocyst (arrowed), avicularia and kenozooids. Scale bars: A, F = 1 mm; B–D = 300 µm; E = 100 µm; G = 200 µm.
Figure 1. A in Evolutionary History of the Subgenus Mus in Eurasia with Special Emphasis on the House Mouse Mus musculus
Figure 1. A sketch of the evolutionary patterns of lineage differentiation among species in the genus Mus based on molecular phylogenetic analysis of nuclear gene sequences (Suzuki et al., 2004; Shimada et al., 2010). The tree shows the four subgenera of the genus Mus and the four species groups (SGs) of the subgenus Mus: M. musculus, M. booduga, M. lepidoides, and M. caroli (previously termed as M. cervicolor SG), representing four geographic regions of the Palaearctic region, Indian subcontinent, Myanmar, and Southeast Asia, respectively. The taxon previously regarded as "M. cervicolor" in Thailand is here referred to as "M. sp.", due to uncertainty regarding the taxonomic status of the sampled specimens (see main text). The estimated divergence times for the subgenera and species groups are approximately 5 and 2.5 million years ago, respectively (Shimada et al., 2010). Specific habitat transitions from grasslands to forests and arid areas are marked for the species lineages of M. cookii and M. lepidoides. Predicted dispersal events between geographic regions are indicated with dotted arrows.
Figures 2–4 in Evolutionary History of the Subgenus Mus in Eurasia with Special Emphasis on the House Mouse Mus musculus
Figures 2–4. Assessment of population genetic structure using concatenated sequences (4302 bp) of seven nuclear genes. (2) Positions of the analysed regions (open triangles) in seven genes on murine chromosome 8 (Nunome et al., 2010; Kodama et al., 2013). (3) Neighbour- Net network based on concatenated sequences from 98 Mus musculus, showing haplogroups representing the subspecies groups Mus musculus domesticus (DOM), Mus musculus castaneus (CAS), and Mus musculus musculus (MUS) as well as recombinant haplotypes (Re) (Kodama et al., 2013). In the network, the level of diversity of CAS is markedly higher than those of DOM and MUS, yielding five distinct phylogroups A–E. Scale bar indicates genetic divergence. (4) Approximate geographic ranges of the five subclusters of CAS. Localities where samples used in this analysis were collected are marked with open and filled circles, representing the mitochondrial haplogroup CAS-1 and all other types, respectively (Kodama et al., 2013). The phylogroups A–E of CAS showed rough geographical distributions and one of them, phylogroup D, comprised the haplotypes recovered from a large geographical area of Southeast Asia, south China, and Indonesia and can be characterized as the lineage dispersed with prehistoric human movement (arrow; Kodama et al., 2015). Note that subcluster D (arrow in Fig. 3) shows a broad distribution range in Southeast Asia and the southern part of East Asia. In the Neighbor-Net network, this subcluster exhibits limited divergence among haplotypes.
Genetic variation of Scots pine in Eurasia: Impact of postglacial recolonisation and human-mediated gene transfer
<p><strong>The dataset comprises nuclear microsatellite data (PCR products lengths) used in the paper "Genetic variation of Scots pine in Eurasia: Impact of postglacial recolonisation and human-mediated gene transfer".</strong></p> <p><strong>The pdf file includes the list of populations.</strong></p> <p>Abstract: Scots pine (<em>Pinus sylvestris</em> L.) seems to be a species of low conservation priority because it has a very wide Eurasian distribution and plays a leading role in many forest tree breeding programs. Nevertheless, considering its economic value, long breeding history, range fragmentation, and increased mortality, which is also projected in the future, it requires a more detailed description of its genetic resources. Our goal was to compare patterns of genetic variation found in biparentally inherited nuclear DNA with previous research carried out with mitochondrial and chloroplast DNA due to their different modes of transmission. We analysed the genetic variation and relationships of 60 populations across the distribution of Scots pine in Eurasia (1,262 individuals) using a set of nuclear DNA markers. We confirmed the high genetic variation and low genetic differentiation of Scots pine spanning large geographical areas. Nevertheless, there was a clear division between European and Asian gene pools. The genetic variation of Asian populations was lower than in Europe. Spain, Turkey, and the Apennines constituted separate gene pools, the latter showing the lowest values of all genetic variation parameters. The analyses showed that most populations experienced genetic bottlenecks in the distant past. Ongoing admixture was found in Fennoscandia. Our results suggest a much simpler recolonization history of the Asian than European part of the Scots pine distribution, with migration from limited sources and possible founder effects. Eastern European stands seem to have descended from the Urals refugium. It appears that Central Europe and Fennoscandia share at least one glacial refugium in the Balkans and migrants from higher latitudes, as well as from southeastern regions. The low genetic structure between Central Europe and Fennoscandia, along with their high genetic admixture, may result at least partially from past human activities related to the transfer of germplasm in the 19<sup>th</sup> and early 20<sup>th</sup> centuries. In light of ongoing climate changes and projected range shifts of Scots pine, conservation strategies are especially needed for marginal and isolated stands of this species. Genetic research should also be complemented in parts of the species distribution that have thus far been poorly studied.</p>
Literary Authors from Europe and Eurasia Web Archive collection derivatives
<p>Web archive derivatives of the <a href="https://archive-it.org/collections/12172">Literary Authors from Europe and Eurasia Web Archive</a> collection from the <a href="https://archive-it.org/home/IvyPlus">Ivy Plus Libraries Confederation</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a> and <a href="https://cloud.archivesunleashed.org/">Archives Unleashed Cloud</a>.</p> <p>The <strong>ivy-12172-parquet.tar.gz</strong> derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/datathon-nyc/parquet_pandas_stonewall.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Word processor files<br> </li> </ul> <p>The <strong>ivy-12172-auk.tar.gz </strong>derivatives<strong> </strong>are the <a href="https://cloud.archivesunleashed.org/derivatives">standard set of web archive derivatives</a> produced by the Archives Unleashed Cloud.</p> <ul> <li><strong>Gephi </strong>file, which can be loaded into <a href="https://gephi.org/">Gephi</a>. It will have basic characteristics already computed and a basic layout.</li> <li><strong>Raw Network</strong> file, which can also be loaded into <a href="https://gephi.org/">Gephi</a>. You will have to use that network program to lay it out yourself.</li> <li><strong>Full text</strong> file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content.</li> <li><strong>Domains count</strong> file. A text file containing the frequency count of domains captured within your web archive.</li> </ul>
Data from: Phylogenetic conservatism and biogeographic affinity influence woody plant species richness-climate relationships in eastern Eurasia
<p>Mechanisms underlying species richness patterns remain a central yet controversial issue in biology. Climate has been regarded as a major determinant of species richness. However, the relative influences of different evolutionary processes, (i.e. niche conservatism, diversification rate, and time for speciation) on species richness-climate relationships remain to be tested. Here, using newly compiled distribution maps for 11,422 woody plant species in eastern Eurasia, we estimated species richness patterns for all species and for families with tropical and temperate affinities separately, and explored the phylogenetic signals in species richness patterns of different families and their relationships with contemporary climate and climate change since the Last Glacial Maximum (LGM). We further compared the effects of niche conservatism (represented by contemporary-ancestral climate niches differences), diversification rate and time for speciation (represented by family age) on variation in the slopes of species richness-climate relationships. We found that winter coldness was the best predictor for species richness patterns of most tropical families while Quaternary climate change was the best predictor for those of most temperate families. Species richness patterns of closely-related families were more similar than those of distantly-related families within eudicots, and significant phylogenetic signals characterized the slopes of species richness-climate relationships across all angiosperm families. Contemporary-ancestral climate niche differences dominated variation in the relationships between family-level species richness and most climate variables. Our results indicate significant phylogenetic conservatism in family-level species richness patterns and their relationships with contemporary climate within eudicots. These findings shed light on the mechanisms underlying large-scale species richness patterns and suggest that ancestral climatic niche may influence the evolution of species richness-climate relationships in plants through niche conservatism.</p>
Load and renewable generation time series for selected regions in Africa and Eurasia
<p>The data set consist of three groups of yearly time series for 2003-2012 used in grid modelling. All time series are presented in .csv format. The data covers wind and PV power generation and load of 12 regions. The regions have 2 letter identifier and include the following:</p> <p>EU - EU countries</p> <p>NA - Algeria, Egypt, Libya, Morocco, Tunisia</p> <p>WA - Cameroon, Ghana, Nigeria</p> <p>EA - Ethiopia, Kenya, Tanzania, Uganda</p> <p>SA - South Africa</p> <p>RU - European part of Russia, Belarus, Ukraine</p> <p>MS - Arabic countries, Israel, Turkey</p> <p>IP - Bangladesh, India, Pakistan, Sri Lanka</p> <p>SB - Asian part of Russia, Kazakhstan, Turkmenistan, Uzbekistan</p> <p>IC - Indonesia, Malaysia, Philippines, Thailand, Vietnam</p> <p>CN - China</p> <p>FE - South Korea, Japan</p> <p> </p> <p>Wind power time series are derived from MERRA wind speed data for Enercon E-126 wind turbine.</p> <p>PV power time series are derived from MERRA solar surface irradiance data.</p> <p>Load time series are generated with the periodical function.</p> <p> </p> <p>Data for EU was derived within the project RESTORE 2050 as described in</p> <p>Kies, A., Chattopadhyay, K., von Bremen, L., Lorenz, E., & Heinemann, D. (2016). Simulation of renewable feed-in for power system studies.</p> <p>Data for all other nodes was derived as described in</p> <p>Krutova, M. et al., The smoothing effect for renewable resources in an Afro-Eurasian power grid, Adv. Sci. Res., 2017</p> <p>Load for EU is obtained from ENTSO-E and is not included in this data set.</p>
Ecoregional patterns of protist communities in mineral and organic soils: assembly processes, functional traits and diversity of testate amoebae in Northern Eurasia
<p>TA_Traits.csv file includes trait data for testate amoebae. TA_Abundance.csv file includes environmental variables (soil type and region) and relative abundance data of testate amoebae.</p>
The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities
<p>Velocity field for the India-Eurasia collision zone from Sentinel-1 InSAR and GNSS data</p> <p>Citations:</p> <p>[1] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2023). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10053499</p> <p>[2] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2024). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities, Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2023JB028571</p> <p>More details about the methodology to generate the velocity field can be found in Wright et al. (2023):</p> <p>[3] Tim J Wright, Greg Houseman, Jin Fang, Yasser Maghsoudi, Andy Hooper, John Elliott, Lynn Evans, Milan Lazecky, Qi Ou, Barry Parsons, Chris Rollins, Lin Shen, Hua Wang (2023). High-resolution geodetic strain rate field reveals dynamics of the India-Eurasia collision, submitted to Science, preprint available at https://doi.org/10.31223/X5G95R.</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.