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199 results for “Uralic”
Collection of spatial information and maps of human past and environment in the Uralic languages speaker area
<p>The collection of spatial information and maps of the past and environment in the Uralic languages speaker area consists excessive amount of multidisciplinary data related to the vast region extending from Eastern Europe to Siberia, encompassing countries like Russia, Finland, and parts of Scandinavia. Uralic speakers are predominantly found in this region, with historical roots in areas around the Ural Mountains and adjacent territories. These datasets can be integrated for multidisciplinary purposes, allowing to explore human-environment interactions, migration patterns, and cultural evolution over time. Datasets are collected initially by the BEDLAN team <a href="https://bedlan.net/">https://bedlan.net/</a> - a research group specialized in various disciplines - linguists, archaeologists, geneticists, and geographers. The data collection and mapmaking have grown beyond the initial stages (publications, applications, exhibitions), hence collaborative effort for data publishing is now crucial. As the data collections and mapmaking continue to evolve dynamically together with ongoing projects, the current repository will be updated accordingly.</p>
Phlorest phylogeny derived from Honkola et al. 2013 'Cultural and climatic changes shape the evolutionary history of the Uralic languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Honkola T, Vesakoski O, Korhonen K, Lehtinen J, Syrjänen K & Wahlberg N. 2013. Cultural and climatic changes shape the evolutionary history of the Uralic languages. Journal of Evolutionary Biology, 26(6):1244–1253.</p> </blockquote>
Uralic Typological database - UraTyp
<blockquote><p>Miina Norvik, Yingqi Jing, Michael Dunn, Robert Forkel, Terhi Honkola, Gerson Klumpp, Richard Kowalik, Helle Metslang, Karl Pajusalu, Minerva Piha, Eva Saar, Sirkka Saarinen and Outi Vesakoski (ms. 2021) Uralic typology in the light of new comprehensive data sets (submitted to Journal of Uralic Linguistics)</p></blockquote><p>The status of the manuscript will be updated here and at <a href="https://bedlan.net/">https://bedlan.net/</a>.</p>
Dataset of CONTAINMENT and SUPPORT in the Uralic languages of the Volga-Kama area
<p>This open access dataset contains examples of the expressions of CONTAINMENT and SUPPORT in the Uralic languages of Volga-Kama area. The exact languages and the sources of data are given in Table 1. The dataset contains data on the relational nouns (RN) and plain spatial cases expressing prototypical CONTAINMENT and SUPPORT in the languages. The RN included in the dataset are listed in Table 2, and case forms in Table 3.</p> <p> </p> <table> <tbody> <tr> <td> <p>language</p> </td> <td> <p>corpora</p> </td> </tr> <tr> <td> <p>Erzya (MdE)</p> </td> <td> <p>Syatko-subcorpus of the MokshEr corpus (MokshEr 2010)</p> </td> </tr> <tr> <td> <p>Moksha (MdM)</p> </td> <td> <p>Subcorpora in Moksha of the MokshEr corpus (MokshEr 2010)</p> </td> </tr> <tr> <td> <p>Meadow Mari (MaM)</p> </td> <td> <p>Marko East (Marko [no year]), Oncyko (Oncyko 2000), Meadow Mari corpus (Arkhangelskiy 2019b), Wanca (Meadow Mari) (Helsingin yliopisto et al. 2019)</p> </td> </tr> <tr> <td> <p>Hill Mari (MaH)</p> </td> <td> <p>Marko West (Marko [no year]), Wanca (Hill Mari) (Helsingin yliopisto et al. 2019)</p> </td> </tr> <tr> <td> <p>Udmurt (Udm)</p> </td> <td> <p>Pilot version of Udmurt corpus (relational nouns; presently included into [Arkhangelskiy 2018])</p> <p>Udmurt corpus (content nouns) (Arkhangelskiy 2018)</p> </td> </tr> <tr> <td> <p>Komi Zyrian (KoZ)</p> </td> <td> <p>Komi Zyrian Web Corpus (Arkhangelskiy 2019a), Коми корпус (Fu-Lab team 2021)</p> </td> </tr> <tr> <td> <p>Komi Permyak (KoP)</p> </td> <td> <p>Komi Permyak text collection from the University of Turku (Permyak 2009)</p> </td> </tr> </tbody> </table> <p><strong>Table 1.</strong> Languages included into the dataset and the sources of the data for each language.</p> <p> </p> <table> <tbody> <tr> <td> <p> </p> </td> <td> <p>MdE</p> </td> <td> <p>MdM</p> </td> <td> <p>MaM</p> </td> <td> <p>MaH</p> </td> <td> <p>Udm</p> </td> <td> <p>KoZ</p> </td> <td> <p>KoP</p> </td> </tr> <tr> <td> <p>containment</p> </td> <td> <p><em>pot</em>(<em>mo</em>)-</p> </td> <td> <p><em>potmə-</em></p> </td> <td> <p><em>kørgø</em>,<em> kørgə-</em></p> </td> <td> <p><em>kørgə̈-</em></p> </td> <td> <p><em>puʃk-</em></p> </td> <td> <p><em>pɨt͡ʃk-</em></p> </td> <td> <p><em>pɨt͡ʃk-</em></p> </td> </tr> <tr> <td> <p>support</p> </td> <td> <p><em>lang-</em></p> </td> <td> <p><em>lang-</em></p> </td> <td> <p><em>ymba-</em></p> </td> <td> <p><em>βə̈(l)-</em></p> </td> <td> <p><em>vɨl-</em></p> </td> <td> <p><em>vɨl-/vɨv-</em></p> </td> <td> <p><em>vɨl-/vɨv-</em></p> </td> </tr> </tbody> </table> <p><strong>Table 2.</strong> RN included in the dataset.</p> <p> </p> <table> <tbody> <tr> <td> <p> </p> </td> <td> <p>MdE</p> </td> <td> <p>MdM</p> </td> <td> <p>MaM</p> </td> <td> <p>MaH</p> </td> <td> <p>Udm</p> </td> <td> <p>KoZ</p> </td> <td> <p>KoP</p> </td> </tr> <tr> <td> <p>location</p> </td> <td> <p><em>-so</em>/<em>-se</em> (inessive)</p> </td> <td> <p><em>-sa</em> (inessive)</p> </td> <td> <p><em>-ʃte/-ʃto/-ʃtø</em> (inessive)</p> </td> <td> <p><em>-ʃtə/-ʃtə̈</em> (inessive)</p> </td> <td> <p><em>-ɨn</em> (inessive)</p> </td> <td> <p><em>-ɨn</em> (inessive)</p> </td> <td> <p><em>-ɨn</em> (inessive)</p> </td> </tr> <tr> <td> <p>source</p> </td> <td> <p><em>-sto</em>/<em>-ste</em> (elative)</p> </td> <td> <p><em>-sta</em> (elative)</p> </td> <td> <p><em>gət͡ɕ</em> (source postposition)</p> </td> <td> <p><em>gə̈t͡s</em> (source postposition)</p> </td> <td> <p><em>-ɨɕ</em> (elative)</p> </td> <td> <p><em>-ɨɕ</em> (elative)</p> </td> <td> <p><em>-iɕ</em> (elative)</p> </td> </tr> <tr> <td> <p>goal</p> </td> <td> <p><em>-s</em> (illative)</p> </td> <td> <p><em>-s</em>/<em>-t͜s</em> (illative)</p> </td> <td> <p><em>-ʃke/-ʃko/-ʃkø/-ʃ</em>, (illative)</p> </td> <td> <p><em>-ʃkə/-ʃkə̈/-ʃ</em>, (illative)</p> </td> <td> <p><em>-e</em>/<em>-ɨ </em>(illative)</p> </td> <td> <p><em>-ɘ </em>(illative)</p> </td> <td> <p><em>-ɘ </em>(illative)</p> </td> </tr> <tr> <td> <p>path</p> </td> <td> <p><em>-ka</em>/<em>-ga</em>/<em>-va</em> (prolative)</p> </td> <td> <p><em>-ka</em>/<em>-ga</em>/<em>-va</em>/<em>-gæ</em> (prolative)</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> <td> <p><em>-ti/-eti</em>/<em>-jeti/</em><em>-ɨti</em> (prolative)</p> </td> <td> <p><em>-ɘd</em> (prolative); <em>-ti</em> (transitive)</p> </td> <td> <p>-<em>ɘt </em>(prolative); <em>-ti</em> (transitive)</p> </td> </tr> </tbody> </table> <p><strong>Table 3.</strong> Cases that have been included into the dataset. All cases do not necessary show in every set, as for some combinations of RN and case there is no data.</p> <p> </p> <p>The main purpose of the dataset is to enable the study of variation between a plain case and RN inflected in case when expressing CONTAINMENT or SUPPORT. To facilitate this each expression of relation has been given a prototypicality score 4 = most prototypical, 1 = non-prototypical, which tells if the relation between landmark and trajector expressed in the sentence is typical for the entities participating in it. The prototypicality scores are based on the pre-linguistic concepts of containment and support, which are robustly attested and therefore should be independent of any single language. The scoring is based on the authors understanding of the language external relations, and no native consultants are used to verify the results. Therefore, some caution is in order when using the dataset.</p> <p> </p> <p>The dataset contains files with data of CONTAINMENT RN, SUPPORT RN, and plain case on all the included languages. The files are named according to the scheme element_languge (e. g. Containment_Erzya for the containment data on Erzya). In addition, files named element_frequencies show the number of examples divided by case and prototypicality score for each language, and element_summary shows the total number of prototypicality scores for each language. For plain case there are also summary files for the scores of CONTAINMENT and SUPPORT separately.</p> <p> </p> <p>The dataset is annotated for following information:</p> <ol> <li>The case in which the content noun or RN is inflected.</li> <li>The predicate as inflected in the data.</li> <li>The content noun as given in the data.</li> <li>Translations of both (mainly in citation form, but in predicate sometimes with some grammatical information, cf. abbreviations below).</li> <li>The prototypicality score. In the data on plain cases the prototypicality score is given only for the clauses where the relation is either CONTAINMENT or SUPPORT (i. e. the prototypicality score indicates the prototypicality of the relation as CONTAINMENT or SUPPORT according to the type of relation expressed).</li> <li>In the data on plain cases, the relation expressed by the case is marked (CONT = CONTAINMENT, SUP = SUPPORT, N/A = some other relation).</li> <li>The original sentence context.</li> <li>Free translation. Some of the translations are done following the lexical meanings and syntactic structures of the languages, so the English is unidiomatic from time to time.</li> <li>The file name with which the original sentence can be located in the corpus.</li> </ol> <p> </p> <p>The three final columns are partly lacking at the moment from the Mari and Komi languages. The translations in the data are intended only as guidelines, and anyone using the dataset should refer to the original language data in the analysis. The data in the columns is presented according to the following conventions :</p> <ul> <li>If the predicate is in square brackets, it means that the predicate is not present in the clause with the target LM. This can be because of two reasons: 1) The predicate is given in a previous clause, and is elliptically omitted, 2) the “predicate” is copula, which is not obligatory in the present tense in the languages studied.</li> <li>The following abbreviations are used to specify the meaning of the predicate when the English translation is ambiguous (note that the use is not checked, and the abbreviations might be lacking from some predicates):</li> </ul> <p>CAUS causative</p> <p>CONT continuative</p> <p>CVB converb</p> <p>FRQ frequentative</p> <p>INCH inchoative</p> <p>INF infinitive</p> <p>ITR intransitive</p> <p>MOM momentaneous</p> <p>NEG negative</p> <p>NMLZ nominalization</p> <p>PASS passive</p> <p>PTCP participle</p> <p>REFL reflexive</p> <p>TRA transitive</p> <p>The authors of this dataset are Tomi Koivunen and Riku Erkkilä and it is published under CC-BY-NC-ND licence. If used in a publication, please refer to this publication as well as mention the original source(s):</p> <p>This dataset has been used in following publications:</p> <p> </p> <p>References to used corpora:</p> <p>Arkhangelskiy, Timofey. 2018. <em>Udmurt corpus</em>. http://udmurt.web-corpora.net/index.html.</p> <p>Arkhangelskiy, Timofey. 2019a. <em>Komi-Zyrian corpus</em>. http://komi-zyrian.web-corpora.net/index.html.</p> <p>Arkhangelskiy, Timofey. 2019b. <em>Meadow Mari corpus</em>. http://meadow-mari.web-corpora.net/index_en.html.</p> <p>Fu-Lab team. 2021. <em>Корпус коми языка</em>. http://komicorpora.ru/.</p> <p>Helsingin yliopisto, FIN-CLARIN, H. Jauhiainen, T. Jauhiainen & K. Lindén. 2019. <em>Wanca 2016, Korp Version</em>. Kielipankki. http://urn.fi/urn:nbn:fi:lb-2019052401.</p> <p>Marko. (no year). <em>MARKO - Corpus of Mari language</em>. University of Turku.</p> <p>MokshEr, V.3. 2010. <em>Mokšan ja ersän sähköinen korpus</em>. Turun yliopisto.</p> <p>Oncyko. 2000. <em>Oncyko corpus</em>. University of Turku.</p> <p>Permyak. 2009. <em>Turku Komi-Permyak Corpus</em>. University of Turku.</p>
SemUr - Semantic Databases for Uralic Languages
<p>These databases are translated from <a href="http://mikakalevi.com/semfi">SemFi</a> by using Giellatekno XML dictionaries. The included python script can be used to update these databases or to create new ones for other languages.</p> <p>Currently, SemUr has the following languages</p> <ul> <li>SemSms - Skolt Sami</li> <li>SemKpv - Komi Zyrian</li> <li>SemMyv - Erzya</li> <li>SemMdf - Moksha</li> </ul> <p> </p> <p><strong>Cite as</strong></p> <p>Hämäläinen, Mika. (2018). <a href="https://helda.helsinki.fi//bitstream/handle/10138/282733/paper9.pdf?sequence=1">Extracting a Semantic Database with Syntactic Relations for Finnish to Boost Resources for Endangered Uralic Languages</a>. In The Proceedings of Logic and Engineering of Natural Language Semantics 15 (LENLS15)</p> <p> </p>
Figs 2–12 in A new species of Cryptomonas (Cryptophyceae) from the Western Urals (Russia)
Figs 2–12. Light micrographs of Cryptomonas uralensis Martynenko, Gusev, Kulizin & Guseva sp. nov. (strain UR168). 2–3. Lateral view (left side). 4–5. Lateral view (right side). 6–8. Ventral view. 9. Dorsal view. 10–11. Apical view. 12. Cells of the strain UR168 in mucilage. Scale bars: 10 µm.
Fig. 14 in A new species of Cryptomonas (Cryptophyceae) from the Western Urals (Russia)
Fig. 14. Predicted secondary structure of the nuclear internal transcribed spacer 2 of the strain UR168 (from 5' to 3' terminus in clockwise direction).
Fig. 13 in A new species of Cryptomonas (Cryptophyceae) from the Western Urals (Russia)
Fig. 13. Bayesian phylogenetic tree of the partial small subunit rDNA (SSU rDNA) and large subunit ribosomal DNA (LSU rDNA) combined data set. The Bayesian posterior probability (left) and maximum likelihood bootstrap value (right) are shown. Scale bar represents estimated number of substitution per site.
Interpolated data on bioavailable strontium in the southern Trans-Urals, 2020-2022 version 3.1 (current)
<p><strong>Description</strong></p> <p><strong>The Interpolated Strontium Values dataset Ver. 3.1 </strong>presents the interpolated data of strontium isotopes for the southern Trans-Urals, based on the data gathered in 2020-2022. The current dataset consists of five sets of files for five various interpolations: based on grass, mollusks, soil, and water samples, as well as the average of three (excluding the mollusk dataset). Each of the five sets consists of a CSV file and a KML file where the interpolated values are presented to use with a GIS software (ordinary kriging, 5000 m x 5000 m grid). In addition, two GeoTIFF files are provided for each set for a visual reference. </p> <p><a href="../records/10253264/files/Averaged%205000%20m%20interpolated%20points.kml?download=1">Average 5000 m interpolated points.kml</a> / <a href="../records/10253264/files/Averaged%205000%20m%20interpolation.csv?download=1">csv</a>: these files contain averaged values of all three sample types.</p> <p><a href="../records/10253264/files/Grass%205000%20m%20interpolated%20points.kml?download=1">Grass 5000 m interpolated points.kml</a> / <a href="../records/10253264/files/Grass%205000%20m%20interpolation.csv?download=1">csv</a>: these files contain data interpolated from the grass sample dataset.</p> <p><a href="../records/10253264/files/Mollusks%205000%20m%20interpolation%20raster.tif?download=1">Mollusks 5000 m interpolated points.kml</a> / <a href="../records/10253264/files/Mollusks%205000%20m%20interpolation.csv?download=1">csv</a>: these files contain data interpolated from the mollusk sample dataset.</p> <p><a href="../records/10253264/files/Soil%205000%20m%20interpolated%20points.kml?download=1">Soil 5000 m interpolated points.kml </a>/ <a href="../records/10253264/files/Soil%205000%20m%20interpolation.csv?download=1">csv</a>: these files contain data interpolated from the soil sample dataset.</p> <p><a href="../records/10253264/files/Water%205000%20m%20interpolated%20points.kml?download=1">Water 5000 m interpolated points.km</a>l / <a href="../records/10253264/files/Water%205000%20m%20interpolation.csv?download=1">csv</a>: these files contain data interpolated from the water sample dataset.</p> <p>The current version is also supplemented with GeoTiff raster files where the same interpolated values are color-coded. These files can be added to Google Earth or any GIS software together with KML files for better interpretation and comparison.</p> <p><a href="../records/10253264/files/Averaged%205000%20m%20interpolation%20raster.tif?download=1">Averaged 5000 m interpolation raster.tif</a>: this file contains a raster representing the averaged values of all three sample types.</p> <p><a href="../records/10253264/files/Grass%205000%20m%20interpolation%20raster.tif?download=1">Grass 5000 m interpolation raster.tif</a>: this file contains a raster representing the data interpolated from the grass sample dataset.</p> <p><a href="../records/10253264/files/Mollusks%205000%20m%20interpolation%20raster.tif?download=1">Mollusks 5000 m interpolation raster.tif</a>: this file contains a raster representing the data interpolated from the mollusk sample dataset.</p> <p><a href="../records/10253264/files/Soil%205000%20m%20interpolation%20raster.tif?download=1">Soil 5000 m interpolation raster.tif</a>: this file contains a raster representing the data interpolated from the soil sample dataset.</p> <p><a href="../records/10253264/files/Water%205000%20m%20interpolation%20raster.tif?download=1">Water 5000 m interpolation raster.tif</a>: this file contains a raster representing the data interpolated from the water sample dataset</p> <p>In addition, the cross-validation rasters created during the interpolation process are also provided. They can be used as a visual reference of the interpolation reliability. The grey areas on the raster represent the areas where expected values do not differ from interpolated values for more than 0.001. The red areas represent the areas where the error exceeded 0.001 and, thus, the interpolation is not reliable. </p> <p> </p> <p><strong>How to use it?</strong></p> <p>The data provided can be used to access interpolated background values of bioavailable strontium in the area of interest. Note that a single value is not a good enough predictor and should never be used as a proxy. Always calculate a mean of 4-6 (or more) nearby values to achieve the best guess possible. Never calculate averages from a single dataset, always rely on cross-validation by comparing data from all five datasets. Check the cross-validation rasters to make sure that the interpolation is reliable for the area of interest. </p> <p> </p> <p><strong>References</strong></p> <p>The interpolated datasets are based upon the actual measured values published as follows:</p> <p>Epimakhov, Andrey; Kisileva, Daria; Chechushkov, Igor; Ankushev, Maksim; Ankusheva, Polina (2022): Strontium isotope ratios (87Sr/86Sr) analysis from various sources the southern Trans-Urals. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.950380</p> <p> </p> <p><strong>Description of the original dataset of measured strontium isotopic values</strong></p> <p>The present dataset contains measurements of bioavailable strontium isotopes (87Sr/86Sr) gathered in the southern Trans-Urals. There are four sample types, such as wormwood (n = 103), leached soil (n = 103), water (n = 101), and freshwater mollusks (n = 80), collected to measure bioavailable strontium isotopes. The analysis of Sr isotopic composition was carried out in the cleanrooms (6 and 7 ISO classes) of the Geoanalitik shared research facilities of the Institute of Geology and Geochemistry, the Ural Branch of the Russian Academy of Sciences (Ekaterinburg). Mollusk shell samples preliminarily cleaned with acetic acid, as well as vegetation samples rinsed with deionized water and ashed, were dissolved by open digestion in concentrated HNO 3 with the addition of H 2 O 2 on a hotplate at 150°C. Water samples were acidified with concentrated nitric acid and filtered. To obtain aqueous leachates, pre-ground soil samples weighing 1 g were taken into polypropylene containers, 10 ml of ultrapure water was added and shaken in for 1 hour, after which they were filtered through membrane cellulose acetate filters with a pore diameter of 0.2 μm. In all samples, the strontium content was determined by ICP-MS (NexION 300S). Then the sample volume corresponding to the Sr content of 600 ng was evaporated on a hotplate at 120°C, and the precipitate was dissolved in 7M HNO 3. Sample solutions were centrifuged at 6000 rpm, and strontium was chromatographically isolated using SR resin (Triskem). The strontium isotopic composition was measured on a Neptune Plus multicollector mass spectrometer with inductively coupled plasma (MC-ICP-MS). To correct mass bias, a combination of bracketing and internal normalization according to the exponential law 88 Sr/ 86 Sr = 8.375209 was used. The results were additionally bracketed using the NIST SRM 987 strontium carbonate reference material using an average deviation from the reference value of 0.710245 for every two samples bracketed between NIST SRM 987 measurements. The long-term reproducibility of the strontium isotopic analysis was evaluated using repeated measurements of NIST SRM 987 during 2020-2022 and yielded 87 Sr/ 86 Sr = 0.71025, 2SD = 0.00012 (104 measurements in two replicates). The within-laboratory standard uncertainty (2σ) obtained for SRM-987 was ± 0.003 %. </p>
Interpolated data on bioavailable strontium in the southern Trans-Urals, 2020-2023, version 1.2. (current)
<p><strong>The Interpolated Strontium Values dataset Ver. 1.2 </strong>presents the interpolated data of strontium isotopes for the southern Trans-Urals, based on the data gathered in 2020-2023. The current dataset consists of five sets of files for two interpolations: based on grass, mollusks, soil, and water samples, as well as the average of three (excluding the mollusk dataset). Each of the five sets consists of a CSV file and a KML file where the interpolated values are presented to use with a GIS software (ordinary kriging, 5000 m x 5000 m grid). In addition, GeoTIFF and JPEG files are provided for each set for a visual reference. </p> <p>Version 1.2 fixes bugs in GeoTIFF files. They can now be accessed in Google Earth (choose "Scale" if prompted that an imported image is too large). </p> <p><strong>How to use it?</strong></p> <p>The data provided can be used to access interpolated background values of bioavailable strontium in the area of interest. Note that a single value is not a good enough predictor and should never be used as a proxy. Always calculate a mean of 4-6 (or more) nearby values to achieve the best guess possible. Never calculate averages from a single dataset, always rely on cross-validation by comparing data from all five datasets. Check the cross-validation rasters to make sure that the interpolation is reliable for the area of interest. </p> <p> </p> <p><strong>References</strong></p> <p>The interpolated datasets are based upon the actual measured values, partially (2020-2022) published as follows:</p> <p>Epimakhov, Andrey; Kisileva, Daria; Chechushkov, Igor; Ankushev, Maksim; Ankusheva, Polina (2022): Strontium isotope ratios (87Sr/86Sr) analysis from various sources the southern Trans-Urals. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.950380</p> <p>Kiseleva, D., Ankusheva, P., Maksim, A., Chechushkov, I., & Epimakhov, A. (2024). Strontium isotopes (87Sr/86Sr) data from southern Trans-Urals, 2023 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14257258</p> <p> </p> <p><strong>Description of the original dataset of measured strontium isotopic values</strong></p> <p>The present dataset contains measurements of bioavailable strontium isotopes (87Sr/86Sr) gathered in the southern Trans-Urals. There are two sample types, such as leached soil (n = 56) and water (n = 56), collected to measure bioavailable strontium isotopes. The analysis of Sr isotopic composition was carried out in the cleanrooms (6 and 7 ISO classes) of the Geoanalitik shared research facilities of the Institute of Geology and Geochemistry, the Ural Branch of the Russian Academy of Sciences (Ekaterinburg). Mollusk shell samples preliminarily cleaned with acetic acid, as well as vegetation samples rinsed with deionized water and ashed, were dissolved by open digestion in concentrated HNO 3 with the addition of H 2 O 2 on a hotplate at 150°C. Water samples were acidified with concentrated nitric acid and filtered. To obtain aqueous leachates, pre-ground soil samples weighing 1 g were taken into polypropylene containers, 10 ml of ultrapure water was added and shaken in for 1 hour, after which they were filtered through membrane cellulose acetate filters with a pore diameter of 0.2 μm. In all samples, the strontium content was determined by ICP-MS (NexION 300S). Then the sample volume corresponding to the Sr content of 600 ng was evaporated on a hotplate at 120°C, and the precipitate was dissolved in 7M HNO 3. Sample solutions were centrifuged at 6000 rpm, and strontium was chromatographically isolated using SR resin (Triskem). The strontium isotopic composition was measured on a Neptune Plus multicollector mass spectrometer with inductively coupled plasma (MC-ICP-MS). To correct mass bias, a combination of bracketing and internal normalization according to the exponential law 88 Sr/ 86 Sr = 8.375209 was used. The results were additionally bracketed using the NIST SRM 987 strontium carbonate reference material using an average deviation from the reference value of 0.710245 for every two samples bracketed between NIST SRM 987 measurements. The long-term reproducibility of the strontium isotopic analysis was evaluated using repeated measurements of NIST SRM 987 during 2020-2022 and yielded 87 Sr/ 86 Sr = 0.71025, 2SD = 0.00012 (104 measurements in two replicates). The within-laboratory standard uncertainty (2σ) obtained for SRM-987 was ± 0.003 %. </p>
Fig. 2 in Accumulation Of Heavy Metals By Small Mammals The Background And Polluted Territories Of The Urals
Fig. 2. The dendrogram is obtained for element analysis (Cu+Zn+Cd) in small mammals from natural populations in the background zone (Bcg) and polluted territories (Imp). The results of cluster analysis confirmed the statistically significant differences in heavy metals total accumulation in three species of small mammals.
Biodiversity of urban floras of the Urals and Volga region
<p>The dataset, “Biodiversity of urban floras of the Urals and Volga region”, includes data on the composition of 19 urban floras located in the Sverdlovsk, Samara and Ulyanovsk regions, the Republic of Bashkortostan and the Udmurt Republic. The studied cities, according to the classification adopted in the Russian Federation, differ in population size into: small, with a population of less than 50,000 people (Kambarka, Krasnoufimsk, Mozhga, Novoulyanovsk, Sengiley, Turinsk), medium, with a population of 50,000-100,000 people (Votkinsk, Zhigulevsk, Ishimbai, Kumertau, Meleuz), large, with a population of 100,000-250,000 people (Dimitrovgrad, Kamensk-Uralsky, Salavat, Sterlitamak), very large, with a population of 250,000-1,000,000 people (Izhevsk, Tolyatti, Ulyanovsk) and a city with a population of over 1 million people – Yekaterinburg.</p> <p>In the urban flora 2050 plant species were recorded, and synonymy was aligned with The Plant List (http://www.theplantlist.org). The dataset provides information about the distribution of each species in studied urban floras as well as grouping of species into native plants, neophytes and archaeophytes.</p> <p> </p> <p>The general list of vascular plants of the analyzed urban flora is compiled on the basis of the authors' own field research. All types of habitats (natural, semi-natural and artificial) were examined. Our direct observations were supplemented with information from herbarium collections: the Museum of the Institute of Plant and Animal Ecology of the Ural Branch of the Russian Academy of Sciences (SVER), Ural Federal University (UFU), Kurgan State University, Udmurt State University (UDU), South Ural Botanical Garden-Institute, Institute of Ecology of the Volga River Basin of the Russian Academy of Sciences (PVB RAS). Published sources were also consulted (Ilminskikh et al., 1998; Rakov, 2003; Rakov, Saxonov, 2008; Kornilov et al., 2012; Mogutova Mountain..., 2013; Rakov et al., 2013; Golovanov, Abramova, 2014a; 2014b; Baranova, Bralgina, 2015; Golovanov et al., 2015; Golovanov et al., 2017; Golovanov, 2018).</p> <p> </p> <p>In the general list of vascular plants, native and alien species were identified. Alien species are understood as plant species either unintentionally introduced into our region as a result of human economic activity, or as ornamental or purposefully introduced species found outside their cultivation areas (Tretyakova, Shurova, 2013; Baranova et al., 2018). Alien species, depending on the time of their appearance in the flora, are traditionally divided into two groups: archaeophytes and neophytes (Pysek et al., 2004). Archaeophytes are alien species that appeared in the study area before 1800, neophytes appeared after this date. The main sources for classifying archaeophytes and neophytes into groups were complete lists of flora of the Sverdlovsk Region (Knyazev et al., 2016; 2017; 2018; 2019a; b; 2020; 2021) and the Udmurt Republic (Baranova, Puzyrev, 2012), as well as lists of alien plants of the Samara and Ulyanovsk regions (Senator, Vasyukov, 2019), the Republic of Bashkortostan (Muldashev et al., 2017). The analysis also uses data from K. F. Ledebour (von Ledebour, 1842-1853), K. K. Klaus (Klaus, 1852), p. Korzhinsky (Korzhinsky, 1898), H. F. Lessing (Lessing, 1835), A. A. Bunge (Bunge, 1851), Y. K. Schell (Schell, 1880; 1883), O. and B. Fedchenko (Fedchenko, Fedchenko, 1894), which summarized information about the flora of the Urals and the Volga region, accumulated by the beginning of the XIX century.</p> <p> </p> <p>In order to provide a single classification scheme in which each species is assigned to only one category, we used the approach described by La Sorte and co-authors (La Sorte et al., 2008). Species that were not identified exclusively as native were classified as archaeophytes if they were identified as archaeophytes in at least one urban flora. Similarly, species have been classified as neophytes if they have not been identified as archaeophytes in any urban flora and have been identified as neophytes in at least one urban flora. At the same time, preference is given to assigning an alien status to a species, because such species show the ability to settle in a secondary area. Among alien plants, preference has been given to the status of archaeophytes, due to their earlier appearance in new regions outside the primary range. Accordingly, species have been universally identified as archaeophytes if they are classified as archaeophytes in at least one urban flora (La Sorte et al., 2008).</p>
Figure 1 in The first record of Promyialges uncus (Acariformes: Epidermoptidae) on the louse fly Ornithomya chloropus (Diptera: Hippoboscidae) in the subpolar Ural
Figure 1. Promyialges uncus (female) – A. Non-engorged mite, ventral view of body; B. Engorged mite, dorsal view of body; C. Tibia and tarsus I; D. Genu, tibia and tarsus II.
Figure 2 in The first record of Promyialges uncus (Acariformes: Epidermoptidae) on the louse fly Ornithomya chloropus (Diptera: Hippoboscidae) in the subpolar Ural
Figure 2. Promyialges uncus (female) – A. Mites attached to the basal part of the fly's wing; B. Female and eggs attached to the basal part of the fly's wing.
Fig. 3. Microdrile oligochaete PIN 5640 in A probable oligochaete from an Early Triassic Lagerstätte of the southern Cis-Urals and its evolutionary implications
Fig. 3. Microdrile oligochaete PIN 5640/212 (A) from the Petropavlovka Formation, Olenekian (Lower Triassic), Petropavlovka III section, Russia and extant Tubifex tubifex (Müller, 1774) (B) from the Khripan' River, Moscow region, Russia, SEM. A. Anterior part with possible prostomium (arrowed) A1). Posterior part of specimen with W-shaped depression (arrowed) and possible genital region (A2). B. Anterior part showing prostomium (arrowed) and arrangement of chaetae (B1). Genital region depicting male pores (arrowed) (B2).
Fig. 1. A in A probable oligochaete from an Early Triassic Lagerstätte of the southern Cis-Urals and its evolutionary implications
Fig. 1. A. Map showing the Lower Triassic locality Petropavlovka III (asterisk) on the Sakmara River valley bank near the village of Petropavlovka ca. 45 km north-east of the town of Orenburg, Russia, in the tectonic context (dashed lines, boundaries of tectonic regions; modified from Minikh and Minikh 1997). B. Combined stratigraphic log of Petropavlovka II–IV sections (modified from Tverdokhlebov 1967).
Fig. 2. Microdrile oligochaete PIN 5640 in A probable oligochaete from an Early Triassic Lagerstätte of the southern Cis-Urals and its evolutionary implications
Fig. 2. Microdrile oligochaete PIN 5640/212 (A) from Petropavlovka Formation, Olenekian (Lower Triassic), Petropavlovka III section, Russia and extant Tubifex tubifex (Müller, 1774) (B) from Khripan' River, Moscow region, Russia. A. Photograph under polarised light (A1) and SEM image depicting main features of the specimen (А2): general outlines (continuous line), W-shaped depression (long dashed line), prominent annuli (dashed line), dissepiments (dotted line), and post-mortem fractures (dash-and-dot line). B. SEM image. Dissepiments (asterisks), segments are numbered, depression in posterior part of genital region (arrow).
Figure 7 in A new troglobiont species of Rhagidiidae (Acari: Eupodoidea) from cave sites in the Ural Mountains (Russia)
Figure 7 DIC photomicrographs of Foveacheles uralensisn. sp., female: A – distal part of fixed digit with setaechaandchb, posterolateral
Figure 6 in A new troglobiont species of Rhagidiidae (Acari: Eupodoidea) from cave sites in the Ural Mountains (Russia)
Figure 6 Foveacheles uralensisn. sp., female: A – specimen with an abnormal number of setae on coxisternal fields I and III (arrows point at additional setae); B – variability in the location of solenidia on tibia III.
Figure 10 in A new troglobiont species of Rhagidiidae (Acari: Eupodoidea) from cave sites in the Ural Mountains (Russia)
Figure 10 Foveacheles uralensisn. sp., deutonymph: A – right leg I, dorsolateral aspect; B – right leg II, posterolateral aspect; C – right leg III, anterolateral aspect; D – right leg IV, dorsal-posterolateral aspect.
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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
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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
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