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Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 1. - Mitotic metaphases, karyograms and idiogram of Euterpe species with 2n=36 chromosomes. Euterpeedulis (A–B), Euterpeoleracea (C-D) and Euterpeprecatoria (E–F). Arrows indicate secondary constrictions. Semi-reticulate interphase nuclei of Euterpeedulis (G), Euterpeoleracea (H) and Euterpeprecatoria (I). Bar: 10 µm.
Figure 1. - Mitotic metaphases, karyograms and idiogram of Euterpe species with 2n=36 chromosomes. Euterpeedulis (A–B), Euterpeoleracea (C-D) and Euterpeprecatoria (E–F). Arrows indicate secondary constrictions. Semi-reticulate interphase nuclei of Euterpeedulis (G), Euterpeoleracea (H) and Euterpeprecatoria (I). Bar: 10 µm.
Figure 1. - Paracreptotremarosenthali sp. n. A Ventral view of holotype; arrows indicate fragments of eyespot pigment B Cirrus sac; arrow indicates anteriormost margin of acetabulum. Scale bars: A = 250 µm; B = 25 µm.
Figure 1. - Paracreptotremarosenthali sp. n. A Ventral view of holotype; arrows indicate fragments of eyespot pigment B Cirrus sac; arrow indicates anteriormost margin of acetabulum. Scale bars: A = 250 µm; B = 25 µm.
Figure 11. - Fruits and galls produced by Epicephala species on Glochidionobovatum. A Fruit produced after pollination by Epicephalaobovatella (Tomogashima, Wakayama) B Gall induced on female flower by Epicephalacorruptrix (Takae, Okinawa) C Cross section of the gall induced by Epicephalacorruptrix. Arrow indicates the galled locule with feeding trace of Epicephala larva. Note that the irregularly developed ovules of the galled locule have merged indistinguishablly to septa. Scale bar 2 mm.
Figure 11. - Fruits and galls produced by Epicephala species on Glochidionobovatum. A Fruit produced after pollination by Epicephalaobovatella (Tomogashima, Wakayama) B Gall induced on female flower by Epicephalacorruptrix (Takae, Okinawa) C Cross section of the gall induced by Epicephalacorruptrix. Arrow indicates the galled locule with feeding trace of Epicephala larva. Note that the irregularly developed ovules of the galled locule have merged indistinguishablly to septa. Scale bar 2 mm.
Figure 7. - Section of the female proboscis of the Japanese Epicephala species. All photographs were taken from non-type specimens. A Epicephalaanthophilia (slide No. AK303) B Epicephalabipollenella (slide No. AK298) C Epicephalalanceolatella (slide No. AK300) D Epicephalaperplexa (slide No. AK301) E Epicephalaobovatella (slide No. AK307) F Epicephalacorruptrix (slide No. AK304) G Epicephalavitisidaea (slide No. AK297) H Epicephalaparasitica (slide No. AK308), arrows indicate rudimentary sensilla I Epicephalanudilingua (slide No. AK309). lp, labial palp. Scale bar: 0.1 mm.
Figure 7. - Section of the female proboscis of the Japanese Epicephala species. All photographs were taken from non-type specimens. A Epicephalaanthophilia (slide No. AK303) B Epicephalabipollenella (slide No. AK298) C Epicephalalanceolatella (slide No. AK300) D Epicephalaperplexa (slide No. AK301) E Epicephalaobovatella (slide No. AK307) F Epicephalacorruptrix (slide No. AK304) G Epicephalavitisidaea (slide No. AK297) H Epicephalaparasitica (slide No. AK308), arrows indicate rudimentary sensilla I Epicephalanudilingua (slide No. AK309). lp, labial palp. Scale bar: 0.1 mm.
Data supplementing the article "Assessing ecological status with diatoms DNA metabarcoding : scaling-up on a WFD monitoring network (Mayotte island, France)" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal
<p>These data supplement the article"Assessing ecological status with DNA metabarcoding or microscopy? Comparison using benthic diatoms in tropical rivers" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal.</p> <p>The directory contains the following files:</p> <p><strong>80 PGM sequencing libraries (raw data, fastq files).rar </strong>- contains the 80 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>80 fastq files information.xlsx</strong> - contains the information relative to the 80 samples including: the ID used in Mothur analyses (corresponding to the name of the fastq files), the sample name, the sampling site code, the name of the river, the monitoring network to which rivers belong, the year of sampling and the GPS coordinates of sampling sites.</p> <p><strong>OTU (95 percent of similarity) list of 80 Mayotte samples.xlsx</strong> - contains the final OTU list obtained after applying all the bioinformatics treatments (trimming, clustering,...): OTUs created at 95% of similarity, the number of DNA reads per sample was normalized at 5710 reads (the smallest values obtained in one sample). A DNA representative sequence and the taxonomic assignment determined using Mothur (using classify.otu command) are also provided for each OTU.</p>
The morphologically glossed Rigveda - The Zurich annotation corpus revised and extended. Hosted by VedaWeb - Online Research Platform for Old Indic Texts.
<p>This file contains morphological and lexicographic annotations for the Rigveda. It was created in the DFG-funded research project Vedaweb and used as source data for the linguistic research platform <a href="https://vedaweb.uni-koeln.de">vedaweb.uni-koeln.de</a>.</p> <p>Prof. Dr. Paul Widmer and Dr. Salvatore Scarlata from the "Institut für Vergleichende Sprachwissenschaft" (Universität Zürich) provided the VedaWeb project a Filemaker file that was later transformed in Cologne into an Excel file. This data contained a version of the Rigveda by Prof. Dr. A. Lubotsky ("Indo-European Linguistics", Leiden University) that had been morphosytactically annotated over the course of more than 10 years at the University of Zurich. It also contained for each token, if available, a reference to an entry in Grassmann's dictionary for the Rigveda.</p> <p> </p> <p><strong>Modifications made by Jakob Halfmann and Natalie Korobzow to the data in 2020:</strong></p> <p>Disambiguation of the relevant categories, if unspecified in Zurich data, according to the Grassmann dictionary (updates from 6th edition partially included up to page 274):</p> <ul> <li>case, gender and number for nouns, pronouns (columns G–I)</li> <li>number, person, mood, tense and voice for verbs (columns I–M) up to line 109216</li> <li>case, gender, number, tense and voice for participles (columns G–I, L–M) up to line 109216</li> <li>absolutives are marked as Abs. in columns N and V</li> <li>Inconsistencies between the original file from Zurich and the Grassmann dictionary as well as internal inconsistencies in Grassmann are noted in column AE, whenever they were noticed.</li> <li>Zurich data was overwritten by conflicting Grassmann data in columns G–M but retained elsewhere.</li> <li>Verb classes according to Whitney (1885) and Jamison (1983) for class 10 in column Y, differences in root spelling between Whitney and Grassmann are noted in column Z. All potential verb classes provided by Whitney are given for every occurrence of the root.</li> <li>Local particles and verbal forms containing them are marked as LP in column AF.</li> <li>Comparatives and superlatives are marked as such in column X and desideratives as Des. in column Y.</li> </ul> <p> </p> <p><strong>Modifications made by Anna Fischer (data transformation, technical realisation) to the data:</strong></p> <p>New structure of data table for linguistic annotations with new column titles:</p> <ul> <li>A - "VERS_NR": renamed column (from "belege::stelleMMSSSRR")</li> <li>B - "PADA_NR": renamed column (from "belege::pada")</li> <li>C - "PADA_TEXT_LUBOTSKY": renamed column (from "belege::lubotskypada")</li> <li>D - "TOKEN_NR_VERS": renamed column (from "belege::wortnummer rc")</li> <li>E - "TOKEN_NR_PADA": renamed column (from "belege::wortnummer pada")</li> <li>F - "FORM": renamed column (from "belege::form")</li> <li>G - "KASUS": renamed column (from "belege::kasus")</li> <li>H - "GENUS": renamed column (from "belege::genus")</li> <li>I - "NUMERUS": renamed column (from "belege::numerus")</li> <li>J - "PERSON": renamed column (from "belege::person")</li> <li>K - "TEMPUS": moved and renamed column (from L "belege::tempus")</li> <li>L - "PRAESENSKLASSE": created new column for present stem class for each form</li> <li>M - "LEMMA_PRAESENSKLASSEN": created column for present stem classes of respective lemma: Moved and renamed column (from Y "formen::zusätzliche merkmale verb"), moved values "Abs." and "Inf." to column P "INFINIT", moved values "Prek." and "si-Ipv." to column N "MOOD", moved value "Des." to column Q "ABGELEITETE_KONJUGATION": moved value "se-Form" to column W "WEITERE_WERTE"</li> <li>N - "MODUS": moved and renamed column (from K "belege::modus")</li> <li>O - "DIATHESE": moved and renamed column (from M "belege::diathese")</li> <li>P - "INFINIT": created new column for infinite forms "Abs.", "Inf.", "Ptz.", "ta-Ptz.", "na-Ptz."</li> <li>Q - "ABGELEITETE_KONJUGATION": created new column for secondary conjugation "Des.", "Int.", "Kaus."</li> <li>R - "GRADUS": created new column for degree: "Comp.", "Sup."</li> <li>S - "LOKALPARTIKEL": moved and renamed column (from AF "LP")</li> <li>T - "LEMMA_ZÜRICH": moved and renamed column (from AA "lemmata klassisch::lemma")</li> <li>U - "LEMMA_ZÜRICH_LEMMATYP": moved and renamed column (from AB "lemmata klassisch::lemmatyp")</li> <li>V - "LEMMA_ZÜRICH_BEDEUTUNG": moved and renamed column (from AC "lemmata klassisch::bedeutung")</li> <li>W - "WEITERE_WERTE": created new column for all miscellaneous values: e.g. "Hyperchar.", "n-haltig", "se-Form"</li> <li>X - "KOMMENTAR": created new column merging former columns Z "formen::HELPformbestimmung", AD "lemmata klassisch::HELPbedeutung" and AE "anmerkungen abweichungen"</li> </ul> <p>Columns that were removed due to redundant information:</p> <ul> <li>"formen::zusätzliche merkmale nomen": values "superlative" And "comparative" were renamed "sup." and "comp." and moved to new column R "GRADUS", all other values were moved to new column for miscellaneous W "WEITERE_WERTE"</li> <li>"belege::belegbestimmung summe simpel": values "Ptz.", "ta-Ptz." and "na-Ptz." were moved to new new column P "INFINIT"</li> <li>"belege::kasus bestof"</li> <li>"belege::genus bestof"</li> <li>"belege::numerus bestof"</li> <li>"belege::person bestof"</li> <li>"belege::modus bestof"</li> <li>"belege::tempus bestof"</li> <li>"belege::diathese bestof"</li> <li>"belege::belegbestimmung bestof summe sophistiziert"</li> </ul> <p> </p> <p><strong>Revisions and additions made by Antje Casaretto to the data in 2023:</strong></p> <ul> <li>F-T: - revision and correction (wherever necessary) of all annotations (books 1-7)</li> <li>G,H,I - disambiguation of case forms, reg. pronouns and nominal forms, if unspecified in Zurich data (books 1-7)</li> <li>L - disambiguation of present stem classes (book 7 and book 1 up to line 21050 vers 01.125.01)</li> <li>M - disambiguation of denominal verbs from primary verbs of the 10th class (books 1-10)</li> <li>N - disambiguation of precative and optative forms wherever possible (books 1-7)</li> <li>Q - new annotations for "Int." (intensives) and "Kaus." (causatives) (books 1-7)</li> </ul> <p> </p> <p dir="ltr"><strong>Revisions and additions made by Antje Casaretto to the data in 2024 with support in data modeling and automation by Anna Fischer:</strong></p> <ul> <li>F-T: revision and correction (wherever necessary) of all annotations (books 8-10)</li> <li>G,H,I: disambiguation of case and gender forms in nominal and pronominal forms, if unspecified in Zurich data (books 8-10)</li> <li>L, M: disambiguation of present stem classes (books 1-10)</li> <li>N: disambiguation of precative and optative forms wherever possible (books 8-10)</li> <li>P: new annotations for "Gdv." (gerundives)</li> <li>Q: new annotations for "Den." (denominatives) (books 1-10) and further annotations of “Kaus.” (causatives) and “Int.” (intensives) (books 8-10)</li> <li>T: revision of lemmatization (books 1-10)</li> <li>V: update of meanings according to revised lemmatization; minimal revision</li> <li>W: revised annotation of ending -se (“se-Form”) (books 1-10); no systematic revision</li> <li>X: no systematic revision</li> <li>A-U: general revision of formal inconsistencies and typing errors (book 1-10)</li> </ul> <p> </p> <p dir="ltr"><strong>Revisions made by Natalie Korobzov and Pascal Coenen to the data in 2024 with computational support by Anna Fischer:</strong></p> <ul> <li>Y - "LEMMA_GRASSMANN_ID": new column for references to Grassmann dictionary (books 1-10) and revision of Grassmann references</li> </ul>
Refractive Indices For Virga Exoplanet Cloud Model
<p><strong>Difference between v1 and v2? </strong></p> <p>The optical constants were updated based on Batalha et al (in prep). The default values and references are located in the <a href="https://github.com/natashabatalha/virga/blob/master/virga/ior_factory.py">IOR Factory</a>. Additionally the specific radii for which the mieff parameters were computed have also changed. Therefore if using Virga V1 we recommend using the Mieff's computed in V2. </p> <p><strong>Additional Details</strong></p> <p>These files are used to compute parameterized cloud models with the <a href="http://github.com/natashabatalha/virga">Virga Exoplanet Cloud Model</a>. The <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#">documentation is available</a> here for specifically how these files are included into virga.</p> <p><strong>1. REFRACTIVE INDICES: (.refrind): </strong>contain the refractive indices of each condensate species. If you are running cloud models utilizing this data, please cite the corresponding source for each species listed below. All files are 4 columns structured as : </p> <blockquote> <p>index, wavelength (micron), real part, imaginary part. </p> </blockquote> <p>Virga reads in these files <a href="https://github.com/natashabatalha/virga/blob/32665150fc83769cae46fd3290f5a237f959712c/virga/calc_mie.py#L452">in this routine</a> but you can simply use this python code:</p> <pre><code>filename = "H2O.refrind" idummy, wave, nn, kk = np.loadtxt(open(filename,'rt').readlines(), unpack=True, usecols=[0,1,2,3])#[:-1] </code></pre> <p> </p> <p><strong>2. MIE PARAMETERS: (.mieff): </strong>There are specific tutorials and functions in virga that will guide you through computing these on your own. However, we provide them here for completeness. The program that calculates these is <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#Creating-the-Mie-scattering-Database">demonstrated here</a>. Note that each Mie parameters are averaged 6 points within the wavelength bin. You can use this function here to <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#Analyzing-Mie-Parameters">parse the data</a>. Or, you can simply read the mieff files with this code: </p> <pre><code>import pandas as pd gas = "H2O" df = pd.read_csv(gas+".mieff",names=['wave','qscat','qext','cos_qscat'], delim_whitespace=True) </code></pre> <p> </p> <p><strong>CITATIONS TO REFERENCE FOR EACH SPECIES (See Table 1 Batalha et al. submitted):</strong></p> <p> </p>
Indicative distribution map for Ecosystem Functional Group F1.3 Freeze-thaw rivers and streams
<p>This archive contains indicative distribution maps and profiles for <strong>F1.3 Freeze-thaw rivers and streams</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Slow slip as an indicator of fault stress criticality
<p>This dataset contains simulated data used in Lambert (submitted): Slow slip as an indicator of fault stress criticality.</p>
Data and code from "Facial blushing and feather fluffing are indicators of emotions in domestic fowl"
<p>Data to produce the manuscript and R codes use to analyse the data</p>
Dataset for the paper "The magnetopause deformation indicated by fast cold ion motion"
<p>This dataset is used for the paper "The magnetopause deformation indicated by fast cold ion motion" and contains pubilicly available data of MMS dayside magnetopause crossing events with fast cold ion motion from 2015 to 2021, which includes crossing time and position, solar wind parameters (dynamic pressure, IMF, solar wind speed) and data source, the normals from the model (Shue et al., 1997 and Liu et al., 2015) and observations (Timing and MVA methods), the normal speed of magnetopause (VMP) from Timing method, the component speeds of cold ion along normal and tangential directions, the deflection angle between the observed magnetopause normal and the prediction, and the magnetopause deformation amplitude calculated by integrating cold ion speed (Positive or negative values indicate whether the magnetopause is moving outward or inward) of all 30 crossings in geocentric solar magnetospheric (GSM) coordinates.</p>
Data of monthly climate variables and drought indices within continental Chile for 1981-2023
<p>The dataset contains derived climatic data from ERA-5 at monthly frequency for continental Chile. The variables are:</p><ul><li>Precipitation (pre)</li><li>Minimum temperature (tas_min)</li><li>Mean temperature (tas)</li><li>Maximum temperature (tas_max)</li><li>Reference evapotranspiration (pet)</li><li>Snow water eqivalent (swe)</li><li>Soil volumetric water content at 1m depth (sm)</li></ul><p>Besides, the dataset contains the follwoing derived drough indices:</p><ul><li>Standardized Precipitation Index (SPI) for 1, 3, 6, 12, 24, and 36 months (spi_1<i> to </i>spi<i>_</i>36)</li><li>Standardized Precipitation Evapotranspiration Index (SPEI) for 1, 3, 6, 12, 24, and 36 months (spei_1<i> to </i>spei<i>_</i>36)</li><li>Evaporative Demand Drought Index (EDDI) for 1, 3, 6, 12, 24, and 36 months (eddi_1<i> to eddi_</i>36)</li><li>Anomaly of cumulative soil moisture at 1m depth (zcSM) for 1, 3, 6, 12, 24, and 36 months (zcsm_1<i> to zcsm_</i>36)</li><li>Anomaly of cumulative NDVI (zcNDVI) for 1, 3, and 6 months (zcndvi<i>1 to zcndvi</i>6)</li><li>Snow Water Equivalent Index (SWEI)</li></ul><p> </p>
Indicators used to calculate the Vulnerability of European wine PDOs to climate change
<p>Dataset of the indicators used to define the Vulnerability of European wine PDO to climate change. The database includes the values of Exposure, Sensitivity and Adaptive Capacity for each PDO region. In the case of Exposure we also included the values for the indicators of Cool Night Iindex, Dryness Index and Huglin index. In the case of Adaptive Capacity we also included all the fifteen indicators used for the assessment of financial, natural, physical, social and human capacity. The classification in one of the Vulnerability classes is included in the related field.</p> <p>Please refer to the following article when citing the dataset:</p> <p>Tscholl, S., Candiago, S., Marsoner, T., Fraga, H., Giupponi, C., & Egarter Vigl, L.Climate resilience of European wine regions. Nat Commun 15, 6254 (2024). https://doi.org/10.1038/s41467-024-50549-w</p>
Data for: Assessing above and belowground recovery from ammonium sulphate addition and wildfire in a lowland heath: mycorrhizal fungi as potential indicators.
<p>Atmospheric pollution containing soil-nitrifying ammonium sulphate ((NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>) affects semi-natural ecosystems worldwide. Long-term additions of (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> to nitrogen(N)-limited habitats, including heathlands, increase climate stress affecting recovery from wildfires. Although heathland vegetation largely depends on ericoid mycorrhizal fungi (ErM) to access soil N, we lack a detailed understanding of how prolonged exposure to (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> may alter ErM community composition and host plants' reliance on fungal partners following wildfire and affect recovery. Simulation of atmospheric pollution ((NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>) occurred bi-weekly for 5 years after a 2006 wildfire in a UK heathland. Ten years after treatments ceased, we measured vegetation structure, lichen and lichen photobiont composition, soil characteristics, ErM colonisation, ErM diversity in roots and soil, and assessed ErM potential as novel recovery indicators. Heather height and density, and <a>moss </a>groundcover, were greater in N-enriched plots. Lichen community indices showed significant treatment effects but without differences in photobionts. Soil pH and Mg were significantly lower in treated plots while soil cation exchange capacity was significantly higher. There were no detectable differences in ErM composition and keystone ErM taxa between control and treated plots. Soil carbon stock measures were variable. Our results indicate atmospheric pollution following fire can have significant lingering effects above- and belowground. ErM diversity and root colonization were not assessed in the original N-addition experiment; we advocate for their inclusion in future studies as an integral part of the recovery assessment toolkit. We show that mycorrhizal fungi diversity is a viable ecological tool and summarise key steps for ErM identification.</p>
Monitoring genome-wide diversity over contemporary time with new indicators applied to Arctic charr populations
<p>Genetic diversity is fundamental to the adaptive potential and survival of species. Although its importance has long been recognized in science, it has a history of neglect within policy – until now. The new Global Biodiversity Framework recently adopted by the Convention on Biological Diversity, states that genetic diversity must be maintained at levels assuring adaptive potential of populations, and includes metrics for systematic monitoring of genetic diversity in so called indicators. Similarly, indicators for genetic diversity are being developed at national levels. Here, we apply new indicators for Swedish national use to one of the northernmost salmonid fishes, the Arctic charr (<em>Salvelinus alpinus</em>). We sequence whole genomes to monitor genetic diversity over four decades in three landlocked populations inhabiting protected alpine lakes in central Sweden. We find levels of genetic diversity, inbreeding and load to differ among lakes but remain stable over time. Effective population sizes are generally small (<500), suggesting a limited ability to maintain adaptive variability if genetic exchange with nearby populations became eliminated. We identify genomic regions potentially shaped by selection; SNPs exhibiting population divergence exceeding expectations under drift and a putative selective sweep acting within one lake to which the competitive brown trout was introduced during the sampling period. Identified genes appear involved in immunity and salinity tolerance. Present results suggest that genetically vulnerable populations of Arctic charr have maintained neutral and putatively adaptive genetic diversity despite small effective sizes, attesting the importance of continued protection and assurance of gene flow among populations.</p>
Replication data for: Sputnik Planitia as an impactor remnant indicative of an ancient rocky mascon in an oceanless Pluto
<p>Data used to produce all figures presented in "Sputnik Planitia as an impactor remnant that conceals an ancient rocky mascon in an oceanless Pluto" (Ballantyne et al. 2024). "simulations.zip" contains all SPH simulation data for the presented impacts. "grids.zip" contains the spherical grids (derived from the SPH simulations) used to create the impactor distribution maps.</p>
Carotenoid skin ornaments as flexible indicators of male foraging behavior in a marine predator: Variation among Mexican colonies of Brown Booby (Sula leucogaster)
<p>Carotenoid-dependent ornaments can reflect animals' diet and foraging behaviors. However, this association should be spatially flexible and variable among populations to account for geographic variation in optimal foraging behaviors. We tested this hypothesis using populations of a marine predator (the brown booby, <em>Sula leucogaster</em>) that forage across a gradient in ocean depth in and near the Gulf of California. Specifically, we quantified green chroma for two skin traits (foot and gular color) and their relationship to foraging location and diet of males, as measured via GPS tracking and stable carbon isotope analysis of blood plasma. Our three focal colonies varied in which foraging attributes were linked to carotenoid-rich ornaments. For gular skin, our data showed a shift from a benthic prey-green skin association in the shallow waters in the north to a pelagic prey-green skin association in the deepest waters to the south. Mean foraging trip duration and distance of the foraging site from the coast also predicted skin coloration in some colonies. Finally, brown booby colonies varied in which trait (foot vs. gular skin color) was associated with foraging metrics. Overall, our results indicate that male ornaments reflect the quality of diet and foraging – information that may help females select mates who are adapted to local foraging conditions and therefore, are likely to provide better parental care. More broadly, our results stress that diet-dependent ornaments are intimately linked to animals' environments and that we cannot assume ornaments or ornament signal content are ubiquitous within species, even when ornaments appear similar among populations. </p>
Plate 1 in Reproduction Performance, Serum Biochemical and Growth Indices of Grower Rabbits (Oryctolagus cuniculus) fed Sheabutter (Vitellaria paradoxa C.F. Gaertn.) Nut Meal
Plate 1. Uterus of the dead doe showing four embryos
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.