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1,634 results for “Data integration”
Fig. 2 in Polyclinum constellatum (Tunicata, Ascidiacea), an emerging non-indigenous species of the Mediterranean Sea: integrated taxonomy and the importance of reliable DNA barcode data Abstract
Fig. 2: A) Orange colony of Polyclinum constellatum from Taranto harbour (colony P1); B) Magnification of the oral (arrow pointing put the oral tentacles of different size) and cloacal aperture (asterisk); C) P. constellatum collected in Heraklion (colony K19) with zooids arranged in systems around the cloacal apertures; D) Section of the colony showing the zooids located only around the outer edge (arrow).
Fig. 3 in Polyclinum constellatum (Tunicata, Ascidiacea), an emerging non-indigenous species of the Mediterranean Sea: integrated taxonomy and the importance of reliable DNA barcode data Abstract
Fig. 3: A) Whole zooid of Polyclinum constellatum, showing a clear division into thorax, abdomen and post-abdomen with a long vascular stolon. ab, abdomen; pa, post-abdomen; th, thorax; vs, vascular stolon; B) Zooid with evident pharynx, rectum, anus and four embryos incubated in the atrial cavity. The funnel-shaped oesophagus, the smooth stomach and the twisted gut loop are visible in the abdomen. The post-abdomen shows the heart at its terminal end, as well as several rounded testicular follicles and the ovary, with the gonoducts running parallel to the rectum. an, anus; e, embryos; gd, gonoducts; gl, gut loop; oe, oesophagus; ov, ovary; h, heart; r, rectum; st, stomach; tf, testicular follicles; C) Magnification of the oral siphon with six pointed lobes (arrows) and six longitudinal muscle bands (indicated with numbers 1-6); D) Branchial sac with 18 rows of stigmata and narrow languets of the dorsal lamina (arrows); E) Magnification of the pharynx, with minute papillae (arrows) at the level of the transverse vessels; F) Magnification of the six-lobed anus (lobes indicated with numbers 1-6).
Fig. 1 in Polyclinum constellatum (Tunicata, Ascidiacea), an emerging non-indigenous species of the Mediterranean Sea: integrated taxonomy and the importance of reliable DNA barcode data Abstract
Fig. 1: Map of the Mediterranean Sea showing the literature records (black rhombuses) of P. constellatum and the new findings (red dots). performed in a final reaction volume of 25 μl contain- nus was reconstructed with the online software PHYML ing: 1X reaction buffer with 1 mM final concentration of v3.0 (http://www.atgc-montpellier.fr/phyml-sms/) (Guin- MgCl 2 (Takara Bio Inc.), 0.2 mM of each dNTP, 0.3 μM don & Gascuel, 2003), which also includes the automatof each primer and 1.25 Units of PrimeStar HS (Takara ic model selection algorithm SMS (Smart Model Selec- Bio Inc.). Amplification conditions were: 30 cycles with tion). The best-fit substitution model was selected using denaturation for 10 s at 98°C, annealing for 15 s at 46°C the Akaike Information Criterion (AIC). Bootstrap val- or 50°C, extension for 1 min 30 s at 72°C; a final elonga- ues, indicating node reliability, were based on 100 reption step of 5 min at 72°C. licates. The sequence dataset used for this phylogenetic PCRs with the DreamTaq polymerase were performed reconstruction is reported in Supplementary Table S1 and in a final volume of 25 μl containing: 1X reaction buffer was extracted from the phylogenetic dataset published in with 2 mM final concentration of MgCl 2 (Thermo Fish- Tabudravu et al. (2019). It includes representative species er Scientific), 0.2 mM of each dNTP, 0.4 μM of each of of the Polyclinidae family plus Eudistoma and Pseudodithe two primers, and 1.25 Units of DreamTaq polymerase stoma species chosen as outgroups for their morphologi- (Thermo Fisher Scientific). The amplification conditions cal similarities with Polyclinidae. were as follows: an initial denaturation for 3 min at 95°C, then 34 amplification cycles (denaturation for 30 s at 95°C; annealing for 30 s at 46-50°C; extension for 1 min Results 30 s at 72°C) followed by a final elongation step of 5 min at 72°C. Morphological analyses The obtained amplicons were purified with the DNA Clean&Concentrator kit (Zymo Research) and directly The colonies collected in Taranto harbour and Hersequenced according to the Sanger method by Microsynth aklion marina were all morphologically identified as P. AG (Switzerland). The sequence quality check, compar- constellatum based on the following features: colonies isons and alignment were carried out with Geneious ver. without sand in/outside, zooids arranged in systems, 5.5.7.2 (Kearse et al., 2012). The sequences obtained post-abdomen (without vascular stolon) shorter than the were deposited in the GenBank database (see Accession thorax and abdomen combined, pharynx with 16-18 rows numbers MT873559 and OL597608). For comparative of stigmata, more than 15 stigmata per row, and a 6-lobed analyses, homologous sequences of the genus Polycli- anus. These characteristics are in accordance with the key num were searched for in the non-redundant nucleotide of Polyclinum species edited by Kott (1963) and they are database (nr-nt db, on 21st September 2021) of the NCBI also reported in the description of the species made by (National Center for Biotechnology Information) by En- Van Name (1945). trez text search, and by BLASTn (Altschul et al., 1990) using our P. constellatum sequences as the query. Uncorrected pairwise distances were calculated with PAUP 4.0a (Swofford, 2002), while a Maximum Likelihood (ML) phylogenetic tree of the genus Polyclinum ge-
Data underlying the paper titled "Integrating multimodal Raman and photoluminescence microscopy with enhanced insights through multivariate analysis"
<p>The folder includes Raman and Photoluminescence surface maps of microsamples from Cultural Heritage materials. The maps were obtained using a multimodal optical microscope that integrates Raman and Photoluminescence optical techniques to perform a raster scanning of microsample surface. </p> <p>Data refer to the publication: https://doi.org/10.1088/2515-7647/ad5773</p> <p> </p>
Fig. 1 in Polyclinum constellatum (Tunicata, Ascidiacea), an emerging non-indigenous species of the Mediterranean Sea: integrated taxonomy and the importance of reliable DNA barcode data Abstract
Fig. 1: Map of the Mediterranean Sea showing the literature records (black rhombuses) of P. constellatum and the new findings (red dots).
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Data set for "Bidirectional microwave-optical transduction based on integration of high-overtone bulk acoustic resonators and photonic circuits"
<p>The repository contains raw data, processing scripts, simulation and GDS files for the manuscript "Bidirectional microwave-optical transduction based on integration of high-overtone bulk acoustic resonators and photonic circuits". For detailed usage instructions, please take a look at the README.txt file. </p>
Fig. 11 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations
Fig. 11 Rates of chatter calls in different magpie populations and individuals. a Each mark represents average chattering rate for a single bird from five populations indicated by colours. Figures are numbers for the outliers: 1, 2—jankowskii from the mixed population of Argun'; 3, 4, 5—hybrid birds from the hybridogeneous population of Kerulen. b Each mark represents average chattering rate for a series of chatterings of one selected individual representing jankowskii, leucoptera, and hybrid birds, respectively. Green mark—pair #6 jankowskii from Vladivostok; gray—pair #43 leucoptera from Tsasuchei, Transbaikalia; blue—pair #24 hybrids from Kerulen, eastern Mongolia. X-axis—number of elements per second in a total series of chattering; Y-axis— number of elements per second in a series of 5 elements of chattering
Fig. 12 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations
Fig. 12 Violin plot diagram of the chatter call speed (elements per second) of Eurasian magpie populations across regions. X-axis presents a set of populations; Y-axis—elements per second. Box outlines the interquantile range (25%, 75%), whiskers represent range without outliers, central bar is the median, red dot is the mean, and figure shape is the probability density. The brackets on the top denote statistically significant pairwise differences (GamesHowell test, p<0.05)
Fig. 9 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations
Fig. 9 Population genetic structure based on unlinked SNP markers. Scatter plots of principal component analysis (PCA) show individual variation in components one and two (a) and three and four (b). The amount of variance explained by each PC is shown in parentheses. I—leucoptera,
Fig. 7 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations
Fig. 7 Bayesian skyline plots (BSPs) for effective female population sizes for haplogroups, subspecies, and populations of Pica pica. a Comparison of 6 haplogroups, depicted in the network Fig. 4. b Comparison of 6 subspecies. c Comparison of 4 populations of P. p. jankowskii. d Comparison of 3 populations of P. p. leucoptera.
Fig. 6 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations
Fig. 6 Mismatch distribution of nucleotide differences in populations representing different haplogroups as at Figs. 4 and 5. X-axis— number of nucleotide differences; Y-axis—proportion (frequency). Solid lines—expected distributions (under expectation of population growth); dashed lines—observed distributions. a Haplogroup 1:
Fig. 5 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations
Fig. 5 Time-calibrated Bayesian tree based on mitochondrial control region sequences of Pica pica. Numbers at the branches indicate Bayesian posterior probability values (left) and bootstrap values of the ML analysis (right, in percent). Triangle widths are proportional to specimen numbers. Blue bars next to nodes indicate 95% credibility intervals for their age estimates. The figures in bold and the time scale below are in million years (Ma) before present
Fig. 4 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations
Fig. 4 Phylogenetic medianjoining network based on 256 mitochondrial control region sequences. Sizes of circles correspond to the number of birds sharing this haplotype; branch lengths are proportional to the number of substitutions and those over 2 are shown at the branches. Haplogroups 1–6 are indicated by numbers
Fig. 2 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations
Fig. 2 Map of sampling localities for mitochondrial DNA analysis in the zone of contact between Pica pica leucoptera and Pica pica jankowskii. Distribution of haplotypes is indicated by colours: Pica
District heating modelling data for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems"
<p>Modelling data for a district heating system model which has been used for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems" on the 4th Generation District Heating (4GDH) conference 2018.</p>
Data from Churan et al. 2018 Eye movements during path integration
<p>Subjects</p> <p>Six human subjects (two male and four female, mean age 23 years) took part in the experiment. The subjects had normal or corrected‐to‐normal vision and normal hearing.</p> <p>Apparatus</p> <p>Experiments were conducted in a darkened (but not completely dark) sound attenuated room. Subjects were seated at a distance of 114 cm from a tangential screen (70° x 55° visual angle) and their head‐position was stabilized by a chin‐rest. Visual stimuli were generated on a windows PC using an in‐house built stimulus package and were back‐projected onto the screen by a CRT‐Projector (Electrohome Marquee 8000) at a resolution of 1152 x 864 pixels and a frame rate of 100 Hz. The auditory stimuli were also generated using MATLAB and presented to the subjects by head‐phones (Philips SHS390). The eye position was recorded by a video‐based eye‐tracker (EyeLink II, SR Research) at a sampling rate of 500 Hz and an average accuracy of ~0.5°. During the distance reproduction, the subjects controlled the speed of simulated self‐motion using an analog joystick (Logitech ATK3) that was placed on a desk in front of them. The speed of the simulated self‐motion was proportional to the inclination angle of the joystick. The data from the joystick were acquired at a rate of 100 Hz and minimal change in speed of simulated self‐motion that could be triggered by the joystick was 1/1000 of the maximum range of speeds used in the experiments.</p> <p>Stimuli</p> <p>The visual stimulus consisted of a horizontal plane of white (luminance: 90 cd/m<sup>2</sup>) randomly placed small squares on a dark (<0.1 cd/m<sup>2</sup>) background that filled the lower half of the screen. The size of the squares was scaled between 0.2° and 1.9° in order to simulate depth. The direction of the simulated self‐motion was always straight‐ahead.The distances are always quantified in arbitrary units (AU) and the speed of simulated self‐motion in AU/s.The auditory stimuli were sinusoidal tones (SPL approximately 80 dB) with a frequency proportional to the simulated speed. The frequencies were in a range between 220 and 440 Hz and changed linearly as a function of the speed of the simulated self‐motion, which was in the range of 0–20 AU/s.</p> <p>Procedure</p> <p>Each trial consisted of two phases. During the “Encoding phase” the subjects were presented with a simulated self‐motion at one of the three speeds (8, 12 or 16 AU/s). The presentation lasted 4 seconds each which resulted in three different traveled distances (32, 48, 64 AU). The presentation was always bimodal, i.e., visual motion was accompanied by a sound representing the respective speed. The sound frequencies corresponding to the three speeds used during the Encoding phase were 308, 354, and 396 Hz, respectively. The task of the subjects in this phase was to monitor the distance covered for later reproduction. After the Encoding phase, a dark screen was presented for 500 msec and then the subjects had to reproduce the previously observed distance using a joystick. In different conditions of this “Reproduction phase,” either only the visual display was presented (visual condition) or only the auditory stimulus was presented while the screen was dark (auditory condition) or both sources of information were available at the same time (bimodal condition). During reproduction, the subjects were able to change the simulated speed by changing the inclination of the joystick. After the subjects had reached the distance they perceived to be identical to that during the Encoding phase, they had to press a joystick button to complete the trial. The subjects were allowed to move their eyes freely during the Encoding and the Reproduction phases. There were thus nine different experimental conditions: three different speeds in three different modalities. In each experimental condition, 80 trials were recorded. All conditions were presented in a pseudo‐randomized order and the subjects were not informed in advance about the sensory modality of the Reproduction phase.</p> <p>Data</p> <p>Eye position as well as the speed of the simulated self‐motion were recorded at a sampling rate of 500 Hz.<br> The file 'all_data.mat' is a MATLAB data file that consists of the cell structure 'all_data' has the elements 'pas' that contains data from the Encoding phase and 'akt' that contains data from the Reproduction phase.<br> The sub-structure 'pas' consists of 6x9x80 elements. The first dimension represents single subjects (6)<br> The second dimension represents the nine different conditions: 1. 8AU/s auditory 2. 8AU/s visual 3. 8AU/s bimodal 4. 12AU/s auditory 5. 12AU/s visual 6. 12AU/s bimodal 7. 16AU/s auditory 8. 16AU/s visual 9. 16AU/s bimodal<br> The third dimension represents the number of (80) trials recorded for each subject and condition.<br> Each element of 'pas' is a matrix consisting of two rows, the first giving the horizontal eye position and the second row giving the vertical eye position. The sampling rate (columns) was 500 Hz. The simulated self-motion started at first sample and ended 4 sec (=2000 samples) later.</p> <p>The sub-structure 'akt' has the same general shape. The only difference is that each element consists of three rows; horizontal eye-position, vertical eye-position, and (the actively chosen) speed of simulated self-motion.</p>
Pol III modeling data and scripts using the Integrative Modeling Platform
<p>This repository contains the data obtained running the Integrative modeling platform with crosslinks and cryoem data, on the RNA Pol III system.</p>
Data for figures in the article "Thermal-integration in Photoelectrochemistry for Fuel and Heat Co-Generation"
<p>This repository contains the data used to generate figures in the article "<span>Thermal-integration in Photoelectrochemistry for Fuel a</span><span>nd Heat Co-Generation" in 2024 in Sustainable Energy and Fuels.</span></p> <p><span>Authors of this data and the article are Evan F Johnson and Sophia Haussener.</span></p>
High-Resolution Pan-European Forest Structure Maps: An Integration of Earth Observation and National Forest Inventory Data
<p>We developed Pan-European maps of timber volume (V), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10 x 10 m<sup>2</sup> for the reference year 2020 using a combination of a Sentinel 2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data.</p> <p>For mapping, we used the k-Nearest Neighbor (kNN, k=7) approach with a harmonized database of species-specific V and AGB from 14 NFIs across Europe. This database encompasses approximately 151,000 sample plots, which were intersected with the above-mentioned Earth observation data. The maps cover 40<a> European countries, </a>forming a continuous coverage of the western part of the European continent.</p> <p>A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to <a>73 % </a>the South-Eastern area.</p> <p>The created maps are the first of their kind as they are utilizing a huge amount of harmonized NFI observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high V and AGB values tend to be underestimated. Summarizing map values (pixel counting) over large regions such as countries or whole Europe will consequently result in biased estimates that need to be interpreted with care.</p> <p>The author list is sorted by last name except for the first and last authors who also serve as corresponding authors.</p> <p>Corresponding authors: <a href="mailto:Jukka.Miettinen@vtt.fi">Jukka.Miettinen@vtt.fi</a>, <a href="mailto:Johannes.Breidenbach@nibio.no">Johannes.Breidenbach@nibio.no</a></p>
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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.