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Data From: Population-specific responses in eastern oysters exposed to low salinity in the northern Gulf of Mexico
<p>Eastern oysters, <em>Crassostrea virginica</em>, are facing rapid environmental changes in the northern Gulf of Mexico and can respond to these changes via plasticity or evolution. Plastic responses can immediately buffer against environmental changes, although this buffering may impact the organism's ability to evolve in subsequent generations. While plasticity and evolution are not mutually exclusive, the relative contribution and interaction between them remain unclear. In this study, we investigate the roles of plastic and evolved responses to low salinity in <em>C. virginica</em> using a common garden experiment with four populations exposed to two salinities. We use three transcriptomic analyses (edgeR, PERMANOVA, and WGCNA) combined with physiology data to identify the effect of genotype (population), environment (salinity), and genotype-by-environment interaction on both whole organism and molecular phenotypes. We demonstrate that variation in gene expression is mainly driven by population, with relatively small changes in response to salinity. In contrast, the morphology and physiology data reveal that salinity has a larger influence on oyster performance than the population of origin. All analyses lacked signatures of genotype-by-environment interaction, and in contrast to previous studies, we find no evidence for population-specific responses to low salinity. However, individuals from the highest salinity estuary displayed highly divergent gene expression from other populations, which could potentially drive population-specific responses to other stressors. Our findings suggest that C. virginica largely rely on plasticity in physiology to buffer the effects of low salinity, but that these changes in physiology do not rely on large persistent changes in gene expression.</p>
Additively Manufactured Titanium 'Alloy-Alloy Composites' and Site-Specific Property Design
<p>Paper links: Journal of Materials Characterization: https://doi.org/10.1016/j.matchar.2021.111577</p> <p>Research Gate (free preprint): https://bit.ly/3xy1Ldm</p> <p>Video written and produced by Alec Davis and Jacob Kennedy. Research was conducted at the University of Manchester and Cranfield University, UK, by Jacob Kennedy, Alec Davis, Armando Caballero, Michael White, Jon Fellowes, Ed Pickering, and Phil Prangnell. This work was supported by grants: NEWAM (EPSRC EP/R027218/1), Lightform (EPSRC EP/R001715/1), and Henry Royce Institute for Advanced Materials (EPSRC EP/R00661X/1, EP/S019367/1, EP/P025021/1, and EP/P025498/1).</p>
Respiration regimes in rivers: Partitioning source‐specific respiration from metabolism time series
<p>This repository hosts preprocessed input data and model codes of the paper:</p> <p>"Respiration regimes in rivers: Partitioning source‐specific respiration from metabolism time series" <br> Limonology and Oceanography, 2022.<br> DOI: 10.1002/lno.12207</p>
Data and code for Barrett, Martin, and Shepherd (2022) "Manipulation-specific cortical activity as mice handle food"
<p>This dataset contains the tracking data, spiking data, and ethograms necessary to reproduce the results of Barrett, Martin, and Shepherd (2022) "Manipulation-specific cortical activity as mice handle food". Also included is Matlab and R code that generates all of the figures, tables, and stats in the aforementioned paper.</p>
Gene expression and splicing counts from the Yepez, Gusic et al study - non strand-specific
<p><strong>File description:</strong></p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p> </p> <p>The gene counts were originated using the GTF file from release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a>, and the split and non-split counts contain only the annotated junctions from the same release.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the DROP can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Number of samples: </strong>154<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34<br> <strong>Disease (ICD-10: N):</strong> E75: 1, E79: 13, E88: 118, G31: 9, K72: 3, NONE: 10<br> <strong>Strand specific: </strong>FALSE<br> <strong>Paired end: </strong>TRUE<br> <strong>Protocol: </strong>poly(A) enrichment<br> <strong>Dataset contact: </strong>Vicente Yepez, yepez@in.tum.de; Julien Gagneur, gagneur@in.tum.de; Holger Prokisch, prokisch@helmholtz-muenchen.de</p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p>
Data from: Domain-specific neural networks improve automated bird sound recognition already with small amount of local data
<p><span><span>An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. <br></span></span></p> <p><span><span>In this article we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentation and a combination of global and local data can be used to train a convolutional neural network to classify vocalizations of 101 bird species. We construct a model and train it with a global data set to obtain a base model. The base model is then fine-tuned with local data from Southern Finland in order to adapt it to the sound environment of a specific location and tested with two data sets: one originating from the same Southern Finnish region and another originating from a different region in German Alps.<br></span></span></p> <p><span><span>Our results suggest that fine-tuning with local data significantly improves the network performance. Classification accuracy was improved for test recordings from the same area as the local training data (Southern Finland) but not for recordings from a different region (German Alps). Data augmentation enables training with a limited number of training data and even with few local data samples significant improvement over the base model can be achieved. Our model outperforms the current state-of-the-art tool for automatic bird sound classification.<br></span></span></p> <p><span><span>Using local data to adjust the recognition model for the target domain leads to improvement over general non-tailored solutions. The process introduced in this article can be applied to build a fine-tuned bird sound classification model for a specific environment.</span></span></p>
VZV-specific T cell clonotypes following recovery from herpes zoster
<p>These are TRB sequences, obtained from Adaptive Biotechnology v4b Immunoseq assay, for PBMC and skin biopsy (rash site and uninvolved contralateral site) samples over a time-course (day 45, day 90, day 360) following the onset of herpes zoster in one human subject. Additionally, HLA-A*02:01- tetramer-sorted repertoires were obtained representing varicella-zoster virus specific CD8 T cell clonotypes. </p>
CN-N (version 1): a crop-specific, 1 km-resolution nitrogen rate data product in China over 2004–2016
<p>CN-N version 1: A dataset of crop-specific, 1 km resolution nitrogen rate for rice, wheat and maize in China over 2004-2016 </p>
Species-specific effects and the ecological role of Programmed cell Death in the microalgae Ankistrodesmus (Sphaeropleales, Selenastraceae)
<p>Reports of programmed cell death (PCD) in phytoplankton raise questions about the ecological evolutionary role of cell death in these organisms. We induced PCD by nitrogen deprivation and unregulated cell death (non-PCD) in one strain of the green microalga <em>Ankistrodesmus densus</em> and investigated the effects of the cell death supernatants on phylogenetically related co-occurring organisms using growth rates and maximum biomass as proxies of fitness. PCD-released materials from <em>A. densus</em> CCMA-UFSCar-3 significantly increased growth rates of two conspecific strains compared to healthy culture (HC) supernatants and improved the maximum biomass of all <em>A. densus</em> strains compared to related species. Although growth rates of non-<em>A. densus</em> with PCD supernatants were not statistically different from HC treatment, biomass gain was significantly reduced. Thus, the organic substances released by PCD, possibly nitrogenous compounds, could promote conspecific growth. These results support the argument that PCD may differentiate species or subtypes and increases inclusive fitness in this model unicellular chlorophyte. Further research, however, is needed to identify the responsible molecules and how they interact with cells to provide the PCD benefits.</p>
Mitochondrial influence on sex-specific phenotypes within natural populations
<p>Data and scripts for the manuscript entitled "Mitochondrial influence on sex-specific phenotypes within natural populations" submitted to <em>Evolution</em><em>. </em></p>
Supplementary Tables for "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort"
<p>Supplementary Tables from "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort".</p>
Figure 3. The graphic representation of the frequencies of the specifications for the technological dimension in curricular documents--Educational Research on the Technological Dimension of Private Life
<p>There are also major differences between schooling levels (Figure 3). In the<br> gymnasium educational system (12) and high school educational system (15) there are more<br> themes concerned with the technological dimension at the level of school curricula, as<br> compared to the primary educational system (2). Also, in alternative textbooks, these themes<br> are predominant in the high school educational system (56) and the gymnasium educational<br> system (27), as compared to the primary educational system (1).</p>
PSSdb-Taxa: A Pelagic Size Structure database taxa-specific product to support biogeochemical modeling: Update to first release of taxa-specific products
<p>This dataset represents the first update to the first release of taxa-specific products from the Pelagic Size Structure database (PSSdb,<a href="https://pssdb.net/"> </a><a href="https://pssdb.net">https://pssdb.net</a>), a scientific project investigating the global particle size distributions measured from multiple pelagic imaging systems. These devices include the Imaging Flow Cytobot (Olson and Sosik 2007), benchtop scanners like the ZooScan (Gorsky et al. 2010), and the Underwater Vision Profiler (Picheral et al. 2010). The data sources originate from Ecotaxa (<a href="https://ecotaxa.obs-vlfr.fr/">https://ecotaxa.obs-vlfr.fr/</a>), Ecopart (<a href="https://ecopart.obs-vlfr.fr/">https://ecopart.obs-vlfr.fr/</a>), and Imaging FlowCytobot dashboards (<a href="https://ifcb.caloos.org/dashboard">https://ifcb.caloos.org/dashboard</a> and<a href="https://ifcb-data.whoi.edu/dashboard"> </a><a href="https://ifcb-data.whoi.edu/dashboard">https://ifcb-data.whoi.edu/dashboard</a>).</p> <p>Taxa-specific products were generated after standardization of the automated or manual taxonomic annotations assigned to individual particles following the recent guidelines of Neeley et al. (2021). We used the World Register of Marine Species (WoRMS, <a href="https://www.marinespecies.org/">https://www.marinespecies.org/</a>) to assign each particle its final taxonomic annotation, and published group-specific relationships linking biovolume to carbon biomass or dry weight.</p> <p>The herein taxa-specific products include both taxonomic class-specific data, obtained after grouping all particles in a given taxonomic class<strong><sup>§</sup></strong>, and broad plankton functional type (PFT) data<strong><sup>Ɨ</sup></strong>. Detrital materials were also separated based on common categories (marine snow, aggregate, fecal pellet) and known biovolume-to-biomass conversion factors (Durkin et al. 2021). Links to the PSSdb code (including the taxonomic and allometric look-up tables) and documentation are available on the PSSdb webpage (<a href="https://pssdb.net">https://pssdb.net</a>).</p> <p> </p> <p>This <em>updated version</em> includes the following changes (modified in April, 2024):</p> <p>● An error in the size classes defined in the <a href="https://github.com/jessluo/PSSdb/blob/main/ancillary/ecopart_size_bins.tsv">ecopart_size_bins.tsv</a> used by the script generating taxa-specific products (<a href="https://github.com/jessluo/PSSdb/commit/b5725df2f32f0300ab64b0a630136c7f5a6857c7">scripts/5_compute_taxa_NBSS.py</a>) was corrected, the size ratio between consecutive size bins is the same across all the size range.</p> <p> </p> <p><em><strong> For additional information, please see the PDF documentation available below ...</strong></em></p>
FIGURE 4 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 4. Site-specific data for lamina length and lamina width plotted for each taxon. 4A: Lamina length for Platanus neptuni. 4B: Lamina width for P. neptuni. 4C: Lamina length for Eotrigonobalanus furcinervis. 4D: Lamina width for E. furcinervis. 4E: Lamina length for Daphnogene cinnamomifolia. 4F: Lamina width for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 1 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 1. Map showing the locations of the considered sites, which are numbered according to Table 1.
FIGURE 3 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 3. Site-specific data for lamina area and lamina perimeter plotted for each taxon. 3A: Lamina area for Platanus neptuni. 3B: Lamina perimeter for P. neptuni. 3C: Lamina area for Eotrigonobalanus furcinervis. 3D: Lamina perimeter for E. furcinervis. 3E: Lamina area for Daphnogene cinnamomifolia. 3F: Lamina perimeter for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 6 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 6. Site-specific data for lamina centroid and leaf length-to-width ratio (LWR) plotted for each taxon. 6A: Lamina centroid for Platanus neptuni. 6B: LWR for P. neptuni. 6C: Lamina centroid for Eotrigonobalanus furcinervis. 6D: LWR for E. furcinervis. 6E: Lamina centroid for Daphnogene cinnamomifolia. 6F: LWR for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 2 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 2. Plasticity index (PI) of various leaf traits, for the considered sites and taxa. 2A: PI for lamina area. 2B: PI for lamina length. 2C: PI for lamina perimeter. 2D: PI for lamina width. 2E: PI for lamina circularity. 2F: PI for lamina centroid. Squares: Platanus neptuni. Circles: Daphnogene cinnamomifolia. Triangles: Eotrigonobalanus furcinervis. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 5 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 5. Site-specific data for lamina circularity and lamina roundness plotted for each taxon. 5A: Lamina circularity for Platanus neptuni. 5B: Lamina roundness for P. neptuni. 5C: Lamina circularity for Eotrigonobalanus furcinervis. 5D: Lamina roundness for E. furcinervis. 5E: Lamina circularity for Daphnogene cinnamomifolia. 5F: Lamina roundness for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 7. Age-specific discriminant analysis using all morphometric parameters for all sites. 7A in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 7. Age-specific discriminant analysis using all morphometric parameters for all sites. 7A: Eocene. 7B: Oligocene. Triangles: Platanus neptuni. Squares: Eotrigonobalanus furcinervis. Circles: Daphnogene cinnamomifolia.
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.