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FIGURE 1 in A revision of European Plesiosminthus (Rodentia, Dipodidae), and new material from the upper Oligocene of Teruel (Spain)
FIGURE 1. Terminology of molars. Figures represent left-hand molars.
ForestPaths: European canopy cover map
<p>This repository contains a 10 m resolution canopy cover (at 5m height) map of the year 2020 over Europe derived from Sentinel-1 and Sentinel-2 data. The map is available as COGs over a 100 km grid in the spatial reference system EPSG 3035 (ETRS89 / LAEA Europe). </p> <p><strong>Known issues</strong></p> <p>- Due to the lack of GEDI data over the north of Europe, the accuracy of the model is expected to be lower above 52 degrees latitude. </p>
ForestPaths: European canopy height map
<p>This repository contains a 10 m resolution canopy height map of the year 2020 over Europe derived from Sentinel-1 and Sentinel-2 data. The canopy height model was trained using both GEDI and ICESat-2 data.</p> <p>The map is available as COGs over a 100 km grid in the spatial reference system EPSG 3035 (ETRS89 / LAEA Europe). </p> <p> </p>
ForestPaths: European FHD map
<p>This repository contains a 10 m resolution FHD map of the year 2020 over Europe derived from Sentinel-1 and Sentinel-2 data. The map is available as COGs over a 100 km grid in the spatial reference system EPSG 3035 (ETRS89 / LAEA Europe). </p> <p><strong>Known issues</strong></p> <p>- Due to the lack of GEDI data in the north of Europe, the accuracy of the model is expected to be lower above 52 degrees latitude.</p>
Code and Data for "A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery"
<p>Here we share the data and code for “A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery”</p> <p>Corresponding author: Devon Dunmire devon.dunmire@kuleuven.be</p> <p>‘model_training’ - contains script to train the ML model, and training data sets from (1) in-situ snow measurement sites (training_data.p) and (2) photogrammetry snow depth maps (map_training_data.p)</p> <p>‘Cross_val_predictions’ contains model predictions for our cross-validation of all the in-situ snow measurement sites</p> <p>‘run_model’ contains the trained model (final_model_xg.pkl) and scripts to retrieve snow depth with our ML model.</p> <p>‘SD_*’ zip folders contains daily ML snow depth output over the European Alps for each snow year from Sept. 1 2015 - Apr. 30 2023. Data from multiple orbits is averaged.</p> <p>Naming convention: ‘S1_ml_SD_{yyyymmdd}_.nc’</p>
Data from "Exploring Gut Microbiota Profile Induced by Antipsychotics in Schizophrenic Patients: Insights from an Eastern European Pilot Study", Nita (Ilie) et al. 2025
<p>Dataset containing raw demultiplexed FASTQ files of the sequenced samples, generated by the Illumina MiSeq platform. </p> <p>MiSeq_demultiplexed-V3_V4-HC_SCZ.zip - MiSeq raw sequences of the V3-V4 region 16S rRNA gene from subject fecal material. This ZIP file contains the FASTQ files of the paired-end reads (R1: forward reads; R2: reverse reads) produced for each sample using the MiSeq platform.</p> <p>metadata-HC_SCZ.csv - The list of sequenced samples and associated metadata.</p>
Network files and Python code used in "Designing a sector-coupled European energy system robust to 60 years of historical weather data"
<p><strong>Description</strong></p> <p>This repository contains data presented in the paper <a href="https://www.nature.com/articles/s41467-024-54853-3" target="_blank" rel="noopener">Designing a sector-coupled European energy system robust to 60 years of historical weather data</a>. It contains the derived metrics (.csv) files from a:</p> <ol> <li>joint capacity and dispatch optimization with weather years (design years) from 1960 to 2021 as input</li> <li>dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year.</li> </ol> <p>All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".</p> <p>The resulting network files (both from the capacity and dispatch optimization) are located <a href="https://anon.erda.au.dk/cgi-sid/ls.py?share_id=DuGvDWlkeI">here</a>.</p> <p>We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located <a href="https://github.com/ebbekyhl/multi-weather-year-assessment">here</a>.</p> <p><strong>Revisions:</strong></p> <p>This version includes the following additions compared to the previous versions: </p> <ul> <li>Timeseries of nodal loads for all years</li> <li>Timeseries of nodal heat pump Coefficient of Performance (COP) </li> <li>Nodal capacity and hourly capacity factors </li> </ul>
Fig. 2 in Faunal and facies changes at the Early-Middle Frasnian boundary in the north-western East European Platform
Fig. 2. Local stratigraphical chart of the north−central part of the Main Devonian Field.
Compilation of mean monthly water table depth data (2015-2023) and linkages to further published sources of water table data, from European peatlands
<p>This dataset (WH_D1_4_meanmonthly.csv) contains mean monthly water table depth data for 211 point locations, for which the data were originally captured at a higher temporal resolution and were additionally clipped to the temporal window (2015 onwards) of the available Earth Observations in the Sentinel-1 and Sentinel-2 archive. Links to higher resolution/longer time series of these source data, where these are already in the public domain, have been identified in the data submission in case future data users require more detailed water table datasets.Information on site co-ordinates, data period, condition class, and other details, are provided in the associated metadata file (WH_D1_4_metadata.csv). Further links to 165 additional water table dynamics data have been provided for future users, but were not summarised as monthly means in this data submission in case the source data are updated in future. Please refer to the README file for methodological details and important disclaimers.</p>
Data for EUA Trends 2024 - European higher education institutions in times of transition
<p>Since 1999, the EUA Trends reports have consistently mapped developments in the European higher education landscape, by presenting comparative data from the perspective of higher education institutions. In the ninth edition of the European University Association’s long-running series, the Trends 2024 report provides an overview of how European higher education institutions have experienced changes over the past five years, due to higher education reforms, and in the wider context of societal, political, economic and technological changes, marked among others by the implications of Covid-19 pandemic and Russia’s war against Ukraine.</p> <p>Trends 2024 is based on survey data collected in April to July 2023.</p> <p>Responses were gathered from 489 higher education institutions in 46 European higher education systems. The survey was open to all higher education institutions in the European Higher Education Area (EHEA) that provide study programmes in at least one of the three degree cycles (bachelor’s, master’s, doctoral). One response per institution was collected.</p> <p>The survey addressed the higher education institutions’ perspectives and strategies regarding:</p> <p>· The institution and its context</p> <p>· The student life cycle and experience</p> <p>· Learning, teaching and teachers </p> <p>· Inclusion, equity and diversity</p> <p>· Engagement and outreach with society and community </p> <p>· Internationalisation</p> <p>Results of the survey are published in “<a href="https://www.eua.eu/publications/reports/trends-2024.html"><strong>Trends 2024 - European higher education institutions in times of transition</strong></a>”.</p> <p>The following files are available:</p> <ul> <li>Codebook including original questionnaire</li> <li>Dataset</li> </ul>
The European Language Social Science Thesaurus (ELSST)
<p>The European Language Social Science Thesaurus (ELSST) is a broad-based, multilingual thesaurus for the social sciences. It is owned and published by the Consortium of European Social Science Data Archives (CESSDA) and its national Service Providers. The thesaurus consists of over 3,400 concepts and covers the core social science disciplines: politics, sociology, economics, education, law, crime, demography, health, employment, information and communication technology, and environmental science.</p> <p>ELSST is used for data discovery within CESSDA and facilitates access to data resources across Europe, independent of domain, resource, language or vocabulary.</p> <p><strong><em>Recommended Citation</em></strong>: CESSDA and Service Providers (2024) The European Language Social Science Thesaurus (ELSST) (Version 5), <a href="https://elsst.cessda.eu">https://elsst.cessda.eu</a>. DOI: 10.5281/zenodo.13843400</p>
Data for "Effects of forest dieback on deadwood patterns: large scale trends from a cross-analysis of European databases"
<p><strong><span>Aims</span></strong></p> <p><span>We carried out an opportunistic correlative study between past crown conditions and current deadwood volumes.</span></p> <p><span>Our aim was to mobilise available data on site factors and long-term monitoring of crown vitality indicators in Europe to investigate the influence of current and recent local defoliation levels on plot-level deadwood volume.</span></p> <p><span>For a subset of level I, 16*16-km monitoring plots located throughout Europe, we benefitted from data on both (i) deadwood measurements carried out within the framework of the Forest Focus Biosoil Project </span><span>(Galluzzi et al., 2019)</span><span>, pre-processed into a consistent and harmonized deadwood dataset by </span><span>Puletti et al. (2019)</span><span>, and (ii) defoliation assessments provided yearly since 1989 by the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests), the most comprehensive European monitoring network for the large-scale assessment of forest ecosystem health </span><span>(Vitale et al., 2014)</span><span>. </span></p> <p><span>Biosoil data on deadwood and ICP data on defoliation have never been crossed before.</span></p> <p><span>We used defoliation level as a proxy for the severity of stand dieback. Deadwood patterns can be addressed through deadwood profiles, which subdivide local deadwood stocks into classes based on size, position and decay stage.</span></p> <p><a name="_Toc175840512"></a><a name="_Toc116027761"></a><span><strong><span>ICP database and defoliation protocol</span></strong></span></p> <p><span>The International Cooperative Program to assess and monitor air pollution effects on the forest (ICP Forests) is responsible for an extensive level I monitoring system of forest sites </span><span>(Hauβmann & Fischer, 2004)</span><span>, which has been in operation since 1986. This large-scale level I network is made up of dense, spatially representative sampling points placed throughout European forests on a 16 × 16 km virtual grid, and is dedicated to monitoring forest conditions. The sampling points cover most European forested areas and encompasses ca. 6000 monitoring plots in 42 countries. In each plot, a visual evaluation of defoliation and discoloration of tree crowns is performed annually to survey forest health status (<a href="http://icp-forests.net/page/largescale-forest-condition">http://icp-forests.net/page/largescale-forest-condition</a>). Data management is presently carried out at the Programme Co-ordinating Centre (PCC) of ICP Forests in Eberswalde, Germany, and all data are available upon request. Since 1989, a standardized procedure for “annual surveys of crown condition’’ has been applied to 24 selected dominant and co-dominant trees with a minimum height of 60 cm and showing no significant mechanical damage. The defoliation and discoloration level of each tree crown is visually assessed on a sliding scale of 5% increments as the percentage of needle/leaf loss in the assessable crown as compared to a reference tree with full foliage. Mean defoliation at the plot scale was defined as the proportion of “damaged” trees i.e., with a defoliation rate of more than 25%, and used as a proxy for plot decline level. In the ICP database, the factors associated with observed defoliation related to natural disturbances or management (i.e., vertebrate or insect herbivory, fungal or fire damage, drought impacts, signs of removal of coarse woody debris, past landscape) were not recorded in a sufficiently standardized way to be used as covariates in our models. Similarly, plot-level living tree density and above-ground biomass for standing living trees (expressed in kg.ha<sup>−1</sup>), presumably surveyed in subplot 2, were not available.</span></p> <p><a name="_Toc175840513"></a><a name="_Toc116027762"></a><span><strong><span>Biosoil database and deadwood protocol</span></strong></span></p> <p><a name="_Toc116027763"></a><span>In the framework of the large collaborative European Forest Focus BioSoil-Biodiversity project</span><span>, a system of circular concentric subplots was built around certain ICP level I plots to collect additional data on stand structure and biodiversity between 2005 and 2008 (Figure 1). </span><span><span>The individual countries were responsible for selecting the ICP level I plots to be included in the BioSoil project </span></span><span><span>(Galluzzi et al., 2019)</span></span><span><span>. Overall, a total of 3243 geocoded Level I plots were considered in 19 European countries </span></span><span><span>(Puletti et al., 2017)</span></span><span><span>: Austria, Belgium (Flanders only), Cyprus, the Czech Republic, Denmark, Finland, France, Germany (eight federal states only), Hungary, Ireland, Italy, Latvia, Lithuania, Poland, Slovakia, Slovenia, Spain, Sweden and the United Kingdom (Figure 1). BioSoil project results are recorded in the multi-dimensional LI-BioDiv geodatabase that contains raw data on forest structure and vegetation records used to calculate simple plot-level structural and compositional forest variables (i.e., biomass, deadwood volume, plant alpha-diversity; </span></span><span><span>Bastrup-Birk et al. 2007; Hiederer & Durant 2010)</span></span><span><span>. At each plot, deadwood was quantified on an area of 400 m<sup>2</sup> (BioSoil subplots 1 and 2, radius of 11.28 m; </span></span><span><span>Puletti et al., 2017)</span></span><span><span>. The deadwood survey included coarse woody debris (including lying dead trees), snags (including standing dead trees) and stumps more than 10 cm in diameter. Only snags and stumps more than 130 cm in height were considered. Diameter, length or height, tree species and decay stage (5 classes) were recorded for each deadwood piece. The raw ICP deadwood data were processed by </span></span><span><span>Puletti et al. (2017, 2019)</span></span><span><span> into a consistent and harmonized pan-European deadwood dataset, which we used in this study. The dataset provides total deadwood volume and the volume of several deadwood types for each plot. Further details can be found in the ICP Forests manual (</span></span><a href="http://icp-forests.net/page/icp-forests-manual"><span><span>http://icp-forests.net/page/icp-forests-manual</span></span></a><span><span>), </span></span><span><span>Puletti et al. (2019)</span></span><span><span> and </span></span><span><span>Augustynczik et al. (2024)</span></span><span><span>.</span></span></p> <p><span><span>In our study, we considered the following response variables</span></span><span>: (i) total deadwood volume, (ii) </span><span>standing deadwood (snags) volume, (iii) volume of ground-lying deadwood, (iv) </span><span>fresh deadwood volume </span><span>(= Vm3_dec1_Biosoil + Vm3_dec2_Biosoil), and (v) decayed deadwood volume = (= Vm3_dec4_Biosoil + Vm3_dec5_Biosoil).</span></p> <p><span>A few environmental covariates were collected from the Biosoil data: (i) management intensity (grouped into two classes: recently harvested, i.e., with management evidence within the last 10 years; and not recently harvested, i.e., unmanaged (no management evidence) or managed a long time ago (management evidence but more than 10 years previously), (ii) average stand age (separated into 3 classes: mature [>100 yrs], mid-aged [41-100 yrs], young [1-40 yrs]), (iii) elevation (above sea level, a.s.l.), a continuous quantitative variable, (iv) dominant tree genus, and (v) forest type, depending on the dominant tree species: coniferous, deciduous or mixed.</span></p> <p><a name="_Toc175840514"></a><a name="_Toc116027764"></a><span><strong><span>Database joint</span></strong></span><span><strong><span>: <a name="_Toc116027765"></a>plot matching in time series</span></strong></span></p> <p><span>After harmonizing plot names and coordinates in the two datasets (ICP-defoliation and Biosoil-deadwood), only plots with matched data in both datasets were selected. Plots with a maximum of one year’s discontinuity in the data were retained, and the missing values were reconstructed from the average values in contiguous years. Plots with discontinuities in defoliation measurements of more than 2 years were deleted. We matched defoliation measurements for the Biosoil-ICP datasets from 1989 to 2007 and finally obtained 2,070 five-year, 1,804 ten-year and 1,399 fifteen-year time series. This approach made it possible to define three 10-year time series [1995-2005, 1996-2006, 1997-2007] with plots in 17 countries, from five plots in Ireland and nine in the United Kingdom, to 337 plots in Finland and 461 in France.</span></p> <p><a name="_Toc175840515"></a><a name="_Toc116027766"></a><span><strong><span>Calculation of global defoliation metrics</span></strong></span></p> <p><span>We calculated 16 univariate metrics to summarize changes in defoliation throughout the 10-year period prior to the Biosoil deadwood measurements. Some of the selected parameters describe the immediate possible effects of defoliation severity in the recent past on a given year: (i) defoliation level of the previous year (n-1), (ii) defoliation level of the year before the previous year (n-2), (iii) defoliation level of the year two years before the previous year (n-3). Other defoliation metrics relate to the cumulative effects of defoliation levels in the near or the distant past: (i) average defoliation level over the last two years, (ii) average defoliation level over the last three years, (iii) average defoliation level over the last five years, (iv) average defoliation level over the first five years of the 10-year time series, and (v) time elapsed since last peak defoliation. Several other parameters depict general trends in the level of defoliation over the 10-year time series: for cumulative metrics: (i) arithmetic mean of annual defoliation level; (ii) geometric mean of annual defoliation level; (iii) Area Under the defoliation time Curve (AUC), i.e., the cumulative sum of defoliation levels; and for the overall trend: (iv) the estimated slope of the linear regression line for defoliation level over time. Finally, some of the metrics reflect defoliation severity and repetition along the 10-year time series, and their potentially time-lagged effects: (i) maximum defoliation level; (ii) total number of years elapsed after the dieback peak level, whether successive or not; (iii) the number of peaks, consecutive or discontinuous, i.e., the number of severe defoliation events and defoliation frequency; and (iv) duration of the longest peak, i.e., the longest continuous time during which the level of defoliation was greater than the relative threshold.</span></p> <p><span>A peak in defoliation was defined as a year in which the level of defoliation exceeded a relative threshold, i.e., the third quartile value. In our 10-year time series, the peak value was 25% and above. <span><span> </span></span></span></p>
The reconstructed three-dimensional nitrate field dataset for the pan-European ocean using a continual learning-based multilayer perceptron
<p>Based on a newly developed continual learning-based multilayer perceptron model and environmental features, we reconstructed the pan-European 3D ocean nitrate field from 2010 to 2023. The reconstructed field features a monthly temporal resolution, a horizontal spatial resolution of 0.25 degrees, and 63 depth levels, with vertical intervals ranging from 5 to 50 m.</p>
Alarming decline in the carbon sink of European forests driven by disturbances
<p>This repository includes the dataset and figures associated with the following paper:</p> <p><strong>Ritter, F., Ciais, P., Senf, C., Santoro, M., Xu, Y., Pelissier-Tanon, A., Schwartz, M., Fayad, I., Carvalhais, N., Brandt, M., Fensholt, R., Besnard, S. & Avitabile, V.</strong>. Alarming decline in the carbon sink of European forests driven by disturbances</p>
Table 1 in Demodex bialoviensis sp. nov. (Acariformes, Demodecidae) a new, specific parasite of the European bison Bison bonasus (Artiodactyla, Bovidae)
<p><b>Table 1</b> Body size (micrometers) for adults and deutonymphs of <i>Demodex bialoviensis</i> sp. nov.</p><table><tbody><tr><th>Morphologic features</th><th>Males (n =12)</th><th>Females (n =</th><th>Deutonymph (n</th></tr></tbody><tbody><tr><th></th><td></td><td>34)</td><td>=5)</td></tr><tr><th>Length of</th><td>17 (15–20), SD 1</td><td>22 (20–26), SD 2</td><td>16 (13–20), SD 3</td></tr><tr><th>gnathosoma</th><td></td><td></td><td></td></tr><tr><th>Width of gnathosoma</th><td>15 (13–20), SD 2</td><td>21 (17–25), SD 2</td><td>14 (11–17), SD 3</td></tr><tr><th>(at base)</th><td></td><td></td><td></td></tr><tr><th>Length of podosoma</th><td>50 (45–58), SD 4</td><td>62 (55–68), SD 4</td><td>53 (50–58), SD 3</td></tr><tr><th>Width of podosoma</th><td>31 (30–35), SD 2</td><td>35 (30–40), SD 2</td><td>36 (35–38), SD 1</td></tr><tr><th>Length of</th><td>109 (93–125),</td><td>154 (125–178),</td><td>133 (125–145),</td></tr><tr><th>opisthosoma</th><td>SD 11</td><td>SD 13</td><td>SD 9</td></tr><tr><th>Width of opisthosoma</th><td>29 (25–33), SD 2</td><td>33 (30–39), SD 3</td><td>31 (30–33), SD 1</td></tr><tr><th>Aedeagus</th><td>21 (18–29), SD 3</td><td>–</td><td>–</td></tr><tr><th>Vulva</th><td>–</td><td>12 (10–17), SD 2</td><td>–</td></tr><tr><th>Total length of body</th><td>176 (158–198),</td><td>239 (200–268),</td><td>202 (188–218),</td></tr><tr><th></th><td>SD 13</td><td>SD 15</td><td>SD 12</td></tr></tbody></table>
VeLePor: European Portuguese Verbal Paradigms in Phonemic Notation
<p>This is a collection of European Portuguese verbal paradigms, in phonemic notation. They are suited for both computational and manual analysis.</p>
Data from: Natural history constrains the macroevolution of foot morphology in European plethodontid salamanders
The natural history of organisms can have major effects on the tempo and mode of evolution, but few examples show how unique natural histories affect rates of evolution at macroevolutionary scales. European plethodontid salamanders (Plethodontidae: Hydromantes) display a particular natural history relative to other members of the family. Hydromantes commonly occupy caves and small crevices, where they cling to the walls and ceilings. On the basis of this unique and strongly selected behavior, we test the prediction that rates of phenotypic evolution will be lower in traits associated with climbing. We find that, within Hydromantes, foot morphological traits evolve at significantly lower rates than do other phenotypic traits. Additionally, Hydromantes displays a lower rate of foot morphology evolution than does a nonclimbing genus, Plethodon. Our findings suggest that macroevolutionary trends of phenotypic diversification can be mediated by the unique behavioral responses in taxa related to particular attributes of their natural history.
Data from: The European Paromomyidae (Primates, Mammalia): taxonomy, phylogeny, and biogeographic implications
Plesiadapiforms represent the first radiation of Primates, appearing near the Cretaceous-Paleogene boundary. Eleven families of plesiadapiforms are recognized, including the Paromomyidae. Four species of paromomyids from the early Eocene have been reported from Europe: Arcius fuscus, Arcius lapparenti, and Arcius rougieri from France, and Arcius zbyszewskii from Portugal. Other Arcius specimens from the early Eocene are known from Masia de l'Hereuet (Spain), Abbey Wood (England), and Sotteville-sur-Mer (Normandy, France). A cladistic analysis of the European paromomyids has never previously been published. A total of 53 dental characters were analyzed for the four Arcius species and the specimens from Spain, England, and Normandy. The results of a parsimony analysis using TNT agree with previous conceptions of A. zbyszewskii as the most primitive member of the genus. Also consistent with existing hypotheses, Arcius rougieri is positioned as the sister taxon of A. fuscus and A. lapparenti, and the results suggest that the fossil from Normandy is A. zbyszewskii. However, the English fossil pertains to a primitive lineage, rather than grouping with A. lapparenti as had been suggested; as such it is recognized here as a distinct species (Arcius hookeri). The Spanish fossils cluster together with the French species, but do not show the previously proposed special relationship with A. lapparenti, and are sufficiently distinct to be placed in a new species (Arcius ilerdensis). Arcius is recovered as monophyletic, which is consistent with a single migration event from North America to Europe around the earliest Eocene though the Greenland land bridge.
Tracking the near Eastern origins and European dispersal of the Western house mouse
<p>The house mouse (<em>Mus musculus</em>) represents the extreme of globalization of invasive mammals. However, the timing and basis of its origin and early phases of dispersal remain poorly documented. To track its synanthropisation and subsequent invasive spread during the develoment of complex human societies, we analyzed 829 Mus specimens from 43 archaeological contexts in Southwestern Asia and Southeastern Europe, between 40,000 and 3,000 cal. BP, combining geometric morphometrics numerical taxonomy, ancient mitochondrial DNA and direct radiocarbon dating. We found that large late hunter-gatherer sedentary settlements in the Levant, c. 14,500 cal. BP, promoted the commensal behaviour of the house mouse, which probably led the commensal pathway to cat domestication. House mouse invasive spread was then fostered through the emergence of agriculture throughout the Near East 12,000 years ago. Stowaway transport of house mice to Cyprus can be inferred as early as 10,800 years ago. However, the house mouse invasion of Europe did not happen until the development of proto urbanism and exchange networks — 6,500 years ago in Eastern Europe and 4000 years ago in Southern Europe — which in turn may have driven the first human mediated dispersal of cats in Europe.</p>
Development of tools to rapidly identify cryptic species and characterize their genetic diversity in different European kelp species
<p>Marine ecosystems formed by kelp forests are severely threatened by global change and local coastline disturbances in many regions. In order to take appropriate conservation, mitigation and restoration actions, it is crucial to identify the most diverse populations which could serve as a "reservoir" of genetic diversity. This requires the development of specific tools, such as microsatellite markers to investigate the level and spatial distribution of genetic diversity. Here, we tested new polymorphic microsatellite loci from the genome of the kelp, <i>Lamina</i><i>ria digitata,</i> and tested them for cross-amplification and polymorphism in four closely related congeneric species (<i>Laminaria hyperborea, Laminaria ochroleuca, Laminaria rodriguezii and Laminaria pallida</i>). Adding these 20 new microsatellite loci to the ten <i>L. digitata</i> loci previously developed by Billot et al. (1998) and Brenan et al. (2014) and to the ten <i>L. ochroleuca</i> loci previously developed by Coelho et al. (2014), we retained a total of 30 polymorphic loci for <i>L. digitata</i>, 19 for <i>L. hyperborea</i>, 16 for <i>L ochroleuca</i>, 19 for<i> L. rodriguezii</i> and 12 for<i> L. pallida</i>. These markers have been tested for the first time in the last two species. As predicted, the proportion of markers that cross-amplified between species decreased with increasing genetic distance. In addition, as problems of species identification were reported in this genus, mainly between <i>L. digitata </i>and <i>Hedophyllum nigripes</i>,<i> </i>but also between <i>L. digitata, L. hyperborea </i>and<i> L. ochroleuca </i>in areas where their range distributions overlap, we report a rapid PCR identification method based on species-specific cox1 mitochondrial primers that allows these four species of kelp to be rapidly distinguished.</p>
ScienceDex guides
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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.