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LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe – files
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (<a href="https://doi.org/10.5194/essd-13-4529-2021">https://doi.org/10.5194/essd-13-4529-2021</a>).</p> <p>LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution.<br> LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (<a href="https://doi.org/10.5194/hess-21-5293-2017">https://doi.org/10.5194/hess-21-5293-2017</a>), Chile (<a href="https://doi.org/10.5194/hess-22-5817-2018">https://doi.org/10.5194/hess-22-5817-2018</a>), Brazil (<a href="https://doi.org/10.5194/essd-12-2075-2020">https://doi.org/10.5194/essd-12-2075-2020</a>), Great Britain (<a href="https://doi.org/10.5194/essd-12-2459-2020">https://doi.org/10.5194/essd-12-2459-2020</a>) and Australia (<a href="https://doi.org/10.5194/essd-13-3847-2021">https://doi.org/10.5194/essd-13-3847-2021</a>), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.</p> <p>We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.</p> <p><strong>Note: </strong>It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that <strong>NAs</strong> are indicated by the number<strong> -999</strong> in the <strong>runoff time series</strong>.</p> <p><strong>Disclaimer:</strong> We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (<a href="https://www.gkd.bayern.de/en/rivers/discharge/tables">https://www.gkd.bayern.de/en/rivers/discharge/tables</a>) and Baden-Württemberg (<a href="https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer">https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer</a>). Runoff data from the Czech Republic may not be used to set up operational warning systems (<a href="https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf">https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf</a>).</p> <p><strong>License: </strong>This work is licensed with CC BY-SA 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.</p> <p><strong>Additional references: </strong>We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying <a href="https://doi.org/10.5194/essd-13-4529-2021">paper</a>.</p> <p><strong>Supplements: </strong>We have created additional files after publication (therefore non peer-reviewed):<br> 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge.<br> 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes.<br> If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at</p>
Dataset of the Marital and Parental Relationships of Western- and Central Europe between 1350-1550
<p>All the data is collected from WikiData manually, allowing for a critically reflection of the available data. In case of missing data this has been reconstructed when possible. The dataset includes the rulers and consorts of the following territories:</p><p>the Duchy of Anjou, the Kingdom of Aragon, the Archduchy of Austria, the Duchy of Auvergne, the Duchy of Bar, the Duchy/Electorate of Bavaria, the Duchy of Berg, the Kingdom of Bohemia, the Duchy of Bouillon, the Duchy of Bourbon, the Duchy of Brabant, the Margraviate of Brandenburg, the Duchy of Brittany, the Duchy of Burgundy, the Kingdom of Castile, the Duchy of Cleves, the Kingdom of Denmark, the Kingdom of England, the Duchy of Florence / Grand Duchy of Tuscany, the Kingdom of France, the Duchy/Archduchy of Further Austria, the Duchy of Guelders, the Duchy of Holstein-Gottorp, the Holy Roman Empire, the Kingdom of Hungary, the Duchy/Archduchy of Inner Austria, the Duchy of Jülich (Cleves-Berg), the Duchy of Limburg, the Grand Duchy of Lithuania, the Duchy of Lorraine, the Duchy/Archduchy of Lower Austria, the Duchy of Luxembourg, the Kingdom of Majorca, the Duchy of Milan, the Kingdom of Naples, the Kingdom of Navarre, the Kingdom of Norway, the Principality of Orange, the Electorate Palatinate, the Kingdom of Poland, the Duchy of Pomerania, the Kingdom of Portugal, the Kingdom of Sardinia, the Duchy of Savoy, the Electorate of Saxony, the Kingdom of Scotland, the Principality of Sedan, the Kingdom of Sicily, the Kingdom of Spain, the Kingdom of Sweden, the Duchy/Archduchy of Tyrol and the Duchy of Württemberg.</p><p> </p><p>1. Node and Edge lists of the <strong>marital</strong> relationships between rulers and consorts of Western- and Central Europe. This dataset also includes biographical and spatial information suitable for GIS.</p><p>2. Node and Edge list of the <strong>parental </strong>relationships to be used for Network Analysis.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</p>
Neolithic Settlements in Central Europe: Data from the Project 'Lifestyle as an Unintentional Identity in the Neolithic'
<p>This repository contains data set submitted to <strong>Journal of Open Archaeology Data</strong>. The data set originated in course of the Czech Science Foundation project n. 19-16304S entitled <strong>Lifestyle as an unintentional identity in the Neolithic</strong>.</p> <p>The data set comprises of over 2100 Neolithic settlement sites from Central Europe (mainly Czech Republic with small parts of Slovakia and Austria). Each site is is defined by spatial coordinates and information about relative chronology phase. The time span is 4900 BCE to 3300 BCE.</p> <p>The data set consists of the following files:</p> <ul> <li> <p><strong>sites.csv</strong> – is a main list of sites with unique identifiers in the id field, id starting with B means the site is from the eastern part of the Bohemia section of the study area and in case of id starting with M the site is from the Morava river catchment. Field orig_id contains identifier by which the site is referenced in cited works and field site contains the site name;</p> </li> <li> <p><strong>pot_traditions.csv</strong> – contains site ids, field chrono giving the general pottery tradition and field period listing occurence of the site in one of the nine time slices;</p> </li> <li> <p><strong>pot_groups.csv</strong> – has same fields as the previous file with the difference in chrono field that contains information on detailed pottery groups;</p> </li> <li> <p><strong>references.csv</strong> – list of references, where possible, the excavation reports are linked to their source in the Digital Archive of the Archaeological Map of the Czech Republic (<a href="https://digiarchiv.aiscr.cz/">https://digiarchiv.aiscr.cz/</a>). Column ref_id is linked through file references_sites.csv to the database of sites in sites.csv file.</p> </li> <li> <p><strong>references_sites.csv</strong> – connects files references.csv and sites.csv</p> </li> </ul> <p>Geodata (in S-JTSK / Krovak East North coordinate reference system):</p> <ul> <li> <p><strong>site_locations.gml</strong> and site_locations.xsd – settlement sites locations. The id field gives a unique identifier for each site, column accuracy gives how accurately the site location is defined, value 1 meaning accurate location (instrumentally measured), value 2 is precision in hundreds of meters, i.e. the site location is known by the local name, street name or so and value 3 means the location is not very accurate, in approx. 1 km range. The field surface is TRUE if the site is defined based on surface survey only and field altitude gives altitude in meters;</p> </li> <li> <p><strong>study_area.gml</strong> and study_area.xsd – polygon giving the borders of the area where data was initially collected;</p> </li> <li> <p><strong>regions.gml</strong> and regions.xsd – polygons giving the extent of the two studied regions, (1) eastern part of Bohemia and (2) Morava river drainage basin;</p> </li> <li> <p><strong>raw_material_sources.gml</strong> and raw_material_sources.xsd – locations of raw material sources as points or lines. Points are based on places where prehistoric procurement activities are known or the outcrops of the given raw materials are present. Lines give the border of the raw material occurrence in case of erratic flint or river courses, in which the raw materials can be procured. The rm column gives an abbreviated name of the raw material and type field is either l for chipped stone tools or p for polished stone tools.</p> </li> </ul> <p>Vocabularies:</p> <ul> <li> <p><strong>voc_periods.csv</strong> – contains period labels;</p> </li> <li> <p><strong>voc_pot_traditions.csv</strong> – contains pottery traditions labels, where possible, field periodo_link maps the period to AMCR Periods Vocabulary at Periodo (<a href="http://n2t.net/ark:/99152/p0wctqt">http://n2t.net/ark:/99152/p0wctqt</a>);</p> </li> <li> <p><strong>voc_pot_groups.csv</strong> – contains pottery groups labels, same fields as previous file;</p> </li> <li> <p><strong>voc_pot_groups_facets.csv</strong> – general labels for pottery groups;</p> </li> <li><strong>voc_raw_materials.csv</strong> – list of raw material abbreviations in the rm column of raw_material_sources.gml file with full names.</li> </ul>
A new Geo-Lithological Map (Geo-LiM) for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy)
<p><strong>We introduce a new geo-lithological map of Central Europe (Geo-LiM) elaborated adopting a lithological classification compliant to the methods more used in the litterature for estimating the consumption of atmospheric CO2 due by chemical weathering. <br> Geo-LiM represents a novelty if compared with published global geo-lithological maps. The first novelty is due by the attention paid in discriminating metamorphic rocks that were classified according to the chemistry of protoliths. The second novelty is that the procedure used for the definition of the map is made available on the web to allow the replicability and reproducibility of the product.</strong></p>
Comparison of occlusal dental wear and degenerative alterations of the temporomandibular joint in two medieval populations from central Europe
<p>This repository contains the radiographs of the mandibular condyles published in the paper titled "Comparison of dental status parameters and degenerative alterations of the temporomandibular joint in central European medieval skeletal finds". The radiographs were obtained with a portable X-ray unit (Kavo Nomad-Pro).</p> <p>Selected CT reconstructions of the condyles are available for a sub-group of individuals.</p> <p>Files naming convention:</p> <p>- Files name start with the burial number (ex. B10; Z98);</p> <p>- R and L stand for "right" and "left";</p> <p>- AP: anteroposterior view; LM: lateromedial view.</p>
Simulated discharge statistics in Central and Southwestern Europe considering water use under 2K global warming
<p>We provide a novel, high-resolution hydrological modelling dataset using pseudo-global warming climate data as forcing to the Community Water Model (CWatM). CWatM is a state-of-the-art large-scale rainfall-runoff and channel routing water resources model that is process-based and used to quantify water supply, as well as human water withdrawals from different sectors (industry, domestic, agriculture) and multiple sources representing the effects of water infrastructure, including reservoirs, groundwater pumping and irrigation canals. CWatM is forced by a pseudo-global warming (PGW) experiment from 1981 to 2010. PGW simulations resemble historical weather patterns and events under globally warmer conditions (here, 2 K global warming) by perturbing historical, reanalysis-driven regional climate simulations. We performed simulations considering regular incremental adjustments of the historical water withdrawals (ranging between +/- 50% of historic water withdrawals) under PGW conditions. That range represents an ad hoc and simplified representation of multiple possible future water management scenarios across Southwestern and Central Europe. The approach allows us to investigate the effects of changing water withdrawals under 2 K global warming. Especially in Western and Central Europe, the projected impacts on low flows highly depend on the chosen water withdrawal assumption. The data highlights the importance of accounting for future water withdrawals in discharge projections.</p> <p> </p> <p><strong>Discharge statistics</strong> based on daily output from CWatM within 1981-2010:</p> <ul> <li><strong>Q1</strong> - 1st percentile</li> <li><strong>Q5</strong> - 5th percentile</li> <li><strong>Q10</strong> - 10th percentile</li> <li><strong>Qavg</strong> - average discharge</li> <li><strong>Q90</strong> - 90th percentile</li> <li><strong>Q95</strong> - 95th percentile</li> <li><strong>Q99</strong> - 99th percentile</li> </ul> <p><strong>Files:</strong></p> <ul> <li><strong>Qxx_reference</strong>: CWatM considering historical water use forced by RACMO-ERA5</li> <li><strong>Qxx_PGW:</strong> CWatM considering historical water use forced by RACMO-ERA5 + climate pertubations under 2 K global warming. In the reference experiment, RACMO is forced at the lateral and sea surface boundaries of the model domain by unperturbed ERA5 reanalysis data, while in the pseudo-global warming experiment, the forcing data consist of perturbed reanalysis data. Perturbations are added to the ERA5 reference data corresponding to climate change patterns of surface pressure and sea surface temperature, and atmospheric profiles of temperature, relative humidity, and wind speed components that are retrieved from a 16-member single model initial condition ensemble of EC-EARTH global climate simulations.</li> <li><strong>Qxx_PGW_adjusted_demand:</strong> We have performed 11 additional hydrological simulations adjusting the historical water demand (ranging between +/- 50% of historic water withdrawals) to enable sensitivity assessments of low and high flows under 2 K global warming.</li> </ul> <p>An upcoming publication will be made available and linked to this research very soon.</p> <p> </p>
MODIS-adjusted NDVI3g for Central Europe v1.1
<p>The noise filtered and MODIS-adjusted NDVI3g dataset was created from the original NDVI3g (Pinzon and Tucker, 2014) to support vegetation related research in Central Europe. Details about the method used for the creation of this dataset are available in Kern et al. (2016). Further information and datasets can be found at http://nimbus.elte.hu/NDVI_CE/.</p> <p> </p> <p>References</p> <p>Kern, A., Marjanović, H., Barcza, Z., 2016. Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013. Remote Sensing 8, 955. doi:10.3390/rs8110955</p> <p>Pinzon, J.E.; Tucker, C.J., 2014. A Non-Stationary 1981-2012 AVHRR NDVI<sub>3g</sub> Time Series. Remote Sensing 6, 6929-6960. doi:10.3390/rs6086929</p> <p> </p>
Text-fig. 1. Situation of Early Miocene plant localities. a: Central Europe with Brno area and other fossil sites mentioned in text (1 – Znojmo and Přímětice, 2 – Oberdorf, 3 – Modrý Kameň Basin, 4 – Lipovany, 5 – Ipolytarnóc; CZ – the Czech Republic, PL – Poland, SK – Slovakia, H – Hungary, A – Austria, D – Germany). b: Brno area with Líšeň municipal district indicated. c: Líšeň municipal district with fossil sites indicated by asterisk. in A New Early Miocene (Ottnangian) Flora Of The "Rzehakia Beds" From Brno-Líšeň
Text-fig. 1. Situation of Early Miocene plant localities. a: Central Europe with Brno area and other fossil sites mentioned in text (1 – Znojmo and Přímětice, 2 – Oberdorf, 3 – Modrý Kameň Basin, 4 – Lipovany, 5 – Ipolytarnóc; CZ – the Czech Republic, PL – Poland, SK – Slovakia, H – Hungary, A – Austria, D – Germany). b: Brno area with Líšeň municipal district indicated. c: Líšeň municipal district with fossil sites indicated by asterisk.
Fig. 6 in La Piquera in central Iberian Peninsula: A new key vertebrate locality for the Early Pliocene of western Europe
Fig. 6. ESEM images of insectivores from La Piquera site, Lower Pliocene, Duero Basin, Spain. A. Soricid Myosorex meini Jammot, 1977, UCM-LPQEUL-1, right M1 in occlusal view. B. Soricid Neomyini indet., UCM-LPQ-EUL-2, left mandibular fragment with p4–m2 in lateral view. C. Neomyini indet., UCM-LPQ-EUL-3, left mandibular fragment with m2 in lateral view. D. Erinaceid Parasorex ibericus (Mein and Martín-Suárez, 1993), UCM-LPQ-EUL-4, right M1 in occlusal view.
Fig. 7 in La Piquera in central Iberian Peninsula: A new key vertebrate locality for the Early Pliocene of western Europe
Fig. 7. ESEM images of rodents from La Piquera site, Lower Pliocene, Duero Basin, Spain, all in occlusal view. A. Cricetid Ruscinomys lasallei Adrover, 1969, UCM-LPQ-ROD-1, left M1. B. Cricetid Blancomys aff. sanzi Adrover, Mein, and Moissenet, 1993, UCM-LPQ-ROD-2, right M1. C. Cricetid Apocricetus cf. barrierei (Mein and Michaux, 1970), UCM-LPQ-ROD-3, left M1. D, E. Murid Stephanomys dubari Aguilar, Michaux, Bachelet, Calvet, and Faillat, 1991. D. UCM-LPQ-ROD-4, right M1. E. UCM-LPQ-ROD-5, right m1 and m2. F. Murid Occitanomys alcalai Adrover, Mein, and Moissenet, 1988, UCM-LPQ-ROD-6, left M1. G. Murid Apodemus gorafensis Ruiz Bustos, Sesé, Dabrio, Peña, and Padial, 1984, UCM-LPQ-ROD-7, right M1. H. Murid Castillomys gracilis van de Weerd, 1976, UCM-LPQ-ROD-8, left M1. I, J. Murid Paraethomys meini (Michaux, 1969). I. UCM-LPQ-ROD-9, right M2. J. UCM-LPQ-ROD-10, right m1, m2, and m3. K. Gerbillid Debruijnimys sp., UCM-LPQ-ROD-11, left m1. L. Glirid Eliomys truci Mein and Michaux, 1970, UCM-LPQ-ROD-12, left M1/2. M. Glirid Glis cf. minor, UCM-LPQ-ROD-13, left m1. N, O. Sciurid Atlantoxerus sp. N. UCM-LPQROD-14, left M1/2. O. UCM-LPQ-ROD-15, right m1/2.
Prediction of runoff characteristics in ungauged basins in Central Europe with machine learning – files
<p><em><strong>English</strong></em></p> <p>This are the shapefiles accompanying the paper: Klingler et al. (2022), Prediction of runoff characteristics in ungauged basins with machine learning, published in the journal Österreichische Wasser- und Abfallwirtschaft: <a href="https://doi.org/10.1007/s00506-022-00891-4">https://doi.org/10.1007/s00506-022-00891-4</a></p> <p>The basic idea was to train a machine learning model with observed runoff characteristics of the hydrological years 2003 - 2017 (LamaH_observations, 859 features) and 90 different catchment characteristics to be able to predict runoff characteristics in unobserved catchments (OWK_predictions, 9533 features).</p> <p>We provide two shapefiles to download:<br> <strong>1) LamaH_observations</strong>, which contains attributes for 6 different runoff characteristics calculated from observed runoff timeseries from the LamaH-CE dataset (https://doi.org/10.5194/essd-13-4529-2021).<br> <strong>2)</strong> <strong>OWK_predictions</strong>, which includes additionally to the predicted 6 runoff characteristics also attributes for uncertainty quantification.<br> All attributes of the shapefiles are described in the associated metadata (.qmd files).</p> <p><strong>Disclaimer:</strong> We have created the shapefiles with care and checked the outputs for plausibility. By downloading the data, you agree that we nor the provider of the used source datasets (e.g. observed runoff time series) cannot be liable for the data provided.</p> <p><strong>License:</strong> This work is licensed with CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ÖWAV paper, version of dataset), indicate if and what changes were made and distribute your work under the same public license as the original.</p> <p><strong>Contact:</strong> If you find any errors in the dataset or have any further questions, feel free to send us an email: info@baseflow.ai</p> <p>-------------</p> <p><strong><em>Deutsch</em></strong></p> <p>Dies sind die beiden Shapefiles, welche dem folgenden Fachartikel zugehörig sind: Klingler et al. (2022), Vorhersage von hydrologischen Abflusskennwerten in unbeobachteten Einzugsgebieten mit Machine Learning, veröffentlicht im Journal Österreichische Wasser- und Abfallwirtschaft: <a href="https://doi.org/10.1007/s00506-022-00891-4">https://doi.org/10.1007/s00506-022-00891-4</a></p> <p>Der Ansatz hinter dieser Arbeit war ein Machine Learning Modell mit beobachteten Abflusskennwerten der hydrologischen Jahre 2003 - 2017 (LamaH_observations, 859 Features) und 90 verschiedenen Einzugsgebietseigenschaften zu trainieren, um anschließend diese Abflusskennwerte in unbeobachteten Einzugsgebieten vorherzusagen (OWK_predictions, 9533 Features).</p> <p>Wir bieten zwei Shapefiles zum Download an:<br> <strong>1)</strong> <strong>LamaH_observations</strong>, welche Attribute für 6 verschiedene Abflusskennwerten (MJHQ, MQ, MJNQ, MJNQ7, Q95, Q98) enthält, die aus beobachteten Abflusszeitreihen aus dem LamaH-CE-Datensatz berechnet wurden (https://doi.org/10.5194/essd-13-4529-2021).<br> <strong>2)</strong> <strong>OWK_predictions</strong>, welche zusätzlich zu den vorhergesagten 6 Abflusskennwerten auch Attribute zur Quantifizierung der Unsicherheit enthält.<br> Alle Attribute der Shapefiles sind in den zugehörigen Metadaten (.qmd-Dateien) beschrieben.</p> <p><strong>Haftungsausschluss:</strong> Wir haben die Shapefiles mit Sorgfalt erstellt und die Ergebnisse auf Plausibilität geprüft. Mit dem Herunterladen der Daten erklären Sie sich damit einverstanden, dass weder wir noch der Anbieter der verwendeten Quelldatensätze (zB. beobachtete Abflusszeitreihen) für die bereitgestellten Daten haften.</p> <p><strong>Lizenz:</strong> Diese Arbeit ist lizenziert mit CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). Dies bedeutet, dass Sie die Daten frei verwenden und verändern dürfen (auch für kommerzielle Zwecke). Sie müssen jedoch eine entsprechende Quellenangabe machen (zugehöriger ÖWAV-Artikel, Version des Datensatzes), angeben ob und welche Änderungen vorgenommen wurden, und Ihre Arbeit unter der gleichen Lizenz wie das Original veröffentlichen.</p> <p><strong>Kontakt:</strong> Wenn Sie Fehler im Datensatz finden oder weitere Fragen haben, können Sie uns gerne eine E-Mail schicken: info@baseflow.ai</p>
Data for: Holocene history of the landscape at the biogeographical and cultural crossroads between Central and Eastern Europe (Western Podillia, Ukraine)
<p><strong>Here, we publish the working data sheets for individual proxies (pollen, plant macrofossils, mollusc and geochemical composition), which was used for paleoecological diagrams in the paper Hájková et al. in Quaternary Science Reviews. </strong></p>
Fig. 1 in Ladislavella Occulta (Jackiewicz, 1959) - A Species Of Aquatic Snails New For Hungary With Remarks On Its Distribution In Central And Eastern Europe
Fig. 1. Shells of Ladislavella occulta from Hungary and Poland. Upper row – Hungary, Bátorliget (HNHM), lower row – three paratypes of this species from Rawicz, Poland (ZIN). Scale bars: 2 mm
Fig. 2 in Ladislavella Occulta (Jackiewicz, 1959) - A Species Of Aquatic Snails New For Hungary With Remarks On Its Distribution In Central And Eastern Europe
Fig. 2. Shells of L. occulta (A-G) and L. terebra (H) from various countries of Central and Eastern Europe. A. Ukraine, Zhitomir Region, Dzerzhinsk (ZMB); B. Germany, Halle District, Salziger Lake (NMG); C. Ukraine, Transcarpathian Region, a pool in Khust District (LMBI); D. Russia, Moscow Region, Oka River basin, without exact locality (ZMMU); E. Russia, Republic of Mordovia, Alatyr' River near Obrochnoye station (ZIN); F. Russia, Kursk Region, Seym River near L'gov Town (ZIN); G. Ukraine, Poltava Region, Lubny District, Pleistocene deposits (ZIN); H. Russia, Barents Sea, Kolguev Island (ZMMU). Scale
Fig. 3. A in Ladislavella Occulta (Jackiewicz, 1959) - A Species Of Aquatic Snails New For Hungary With Remarks On Its Distribution In Central And Eastern Europe
Fig. 3. A map of Europe showing known findings of L. occulta. The Pleistocene records are not shown, This map is based on data published in literature (JACKIEWICZ 1998, 2000; BERAN 2008, STADNICHENKO 2004, ANISTRATENKO et al. 2018) and own data. Squares indicate findings identified by anatomical data; circles – findings based on empty shells. The location of Bátorliget is indicated by a star
Figure 12 in New Leptogamasus mite species (Parasitiformes: Parasitidae) from Europe. III. Northern and Central Italy
Figure 12 Leptogamasus(L.) silvestris n. sp., female: A – ventral side of idiosoma, holotype. B –perianal region of idiosoma in other specimen. Abbreviations as in Figure 7.
Figure 24 in New Leptogamasus mite species (Parasitiformes: Parasitidae) from Europe. III. Northern and Central Italy
Figure 24 Leptogamasus(L.) monteamiatusn. sp., female: A – paragynium; B – epigynium; C–E – endogynium; F – stipule; G – gnathotectum, two aspects; H – chelicera, antiaxially. D – holotype.
Figure 20 in New Leptogamasus mite species (Parasitiformes: Parasitidae) from Europe. III. Northern and Central Italy
Figure 20 Leptogamasus(L.) parasilvestrisn. sp., male: A – presternal plates, genital lamina and sternogenital shield; B – gnathotectum; C
Figure 17 in New Leptogamasus mite species (Parasitiformes: Parasitidae) from Europe. III. Northern and Central Italy
Figure 17 Leptogamasus(L.) parasilvestrisn. sp., female: ventral side of idiosoma, holotype. Abbreviations as in Figure 2.
Figure 10 in New Leptogamasus mite species (Parasitiformes: Parasitidae) from Europe. III. Northern and Central Italy
Figure 10 Leptogamasus(L.) cortinis n. sp., male: A – Fe II, Ge II and Ti II, ventrally; B – Fe II, Ge II and Ti II in the anterolateral perspective. Some setae are marked. C – Ti IV ventrally. Arrows at the anterolateral side.
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.