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Blue Crab Densities in a Landscape Context on the Virginia Coast, Summer 2019
Table 1 Crab Data : Blue crab (Callinectes sapidus) abundance was sampled at twenty-four sites in Spider Crab Bay, Cobb Bay, and South Bay (comprising two large seagrass meadows: "South Bay" for the northern portion and "Ship Shoal" for the southern portion) as part of a study to evaluate the drivers of blue crab spatial patterns across restored seagrass meadows in the coastal bays of Virginia, USA. The four meadows are representative of seagrass meadows in the region that have recovered after experiencing large declines due to wasting disease and storms (Orth et al., 2006). Each meadow contained six fixed sampling sites. Each captured crab's morphological and demographic characteristics were evaluated; as such, data are presented at the individual level. Table 2 Landscape Data: Landscape measurements were taken for twenty-four sites in Spider Crab Bay, Cobb Bay, and South Bay (comprising two large seagrass meadows: "South Bay" for the northern portion and "Ship Shoal" for the southern portion) as part of a study to evaluate the drivers of blue crab spatial patterns across restored seagrass meadows in the coastal bays of Virginia, USA. The four meadows are representative of seagrass meadows in the region that have recovered after experiencing large declines due to wasting disease and storms (Orth et al. 2006). Each meadow contained six fixed sampling sites. All landscape measurements were made using QGIS 3.14.0 (QGIS.org 2020). Seagrass landscape measurements were calculated using maps developed from aerial imagery by Orth et al. (2019). Oyster reefs polygons were mapped by Ross and Luckenbach (2009). Salt marsh polygons were mapped by Berman et al. (2011) and modified based on imagery from Planet Team (2019). Mean seagrass shoot density measurements were based on a VCR-wide 2019 sampling effort led by McGlathery (2018).
Simulation data of barrier system cross-landscape interactions from BarrierBMFT
BarrierBMFT (version 1.0) simulation results for change in ecosystem extent (i.e., profile width) across a range of input conditions. Simulations vary by the relative sea-level rise rate (3-15 mm/yr), external suspended sediment concentration (40-80 mg/L), mainland slope (0.001-0.01), characteristic dune growth rate (0.45-0.75 1/yr), flow infiltration and drag parameter (1-2 dam^3/yr), and the coefficient for sediment transport entering back-barrier bay (0.5-0.85). Each row in the spreadsheet represents a unique model simulation; to account for storm stochasticity in the model, each unique combination of parameter values was simulated 25 or 50 times. Simulations run for 400 model years.
Fig. 2 in Autumn habitat selection of the harvest mouse (Micromys minutus Pallas, 1771) in a rural and fragmented landscape
Fig. 2. Map representing the study area and the different habitat types present in it. The Caricteum acutiformis patch in the North is the main tall sedge meadow, where most of the study was conducted. The transects (red lines) shown on this map were the ones used for trapping (i.e., for the Capture, Mark and Release event).
Fig. 7 in Autumn habitat selection of the harvest mouse (Micromys minutus Pallas, 1771) in a rural and fragmented landscape
Fig. 7. Relocations of the four male individuals M1, M2, M3 and M4. M1 was tracked from the 19th to the 21th of September 2017. M2 was tracked from the 06th to the 10th of October 2017. M3 was tracked from the 15th to the 19th of October 2017. M4 was tracked from the 15th to the 17th of October 2017.
Phenological time lapse images from landscape camera MC117-1 in Paljakka Spruce stand
<p>This record contains phenological time lapse images from camera Paljakka Spruce stand. Camera was mounted at landscape view level at location 64.677381;28.114014(N;E, WGS84).</p> <p>First set of images were taken between 02.11.2016--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact Mikko Peltoniemi (mikko.peltoniemi@luke.fi)</p>
Top: puffs of cornstarch reveal dense and varied tiny cryptic webs in the Gaoligongshan. Shown here upper left to lower right are a symphytognathid Patu jidanweishi sp. n., a mysmenid Gaoligonga changya gen. n., sp. n., and an unidentified linyphiid. Bottom: this misty mountain landscape at QiQi is typical of the Gaoligongshan in The symphytognathoid spiders of the Gaoligongshan, Yunnan, China (Araneae: Araneoidea): Systematics and diversity of micro-orbweavers
Top: puffs of cornstarch reveal dense and varied tiny cryptic webs in the Gaoligongshan. Shown here upper left to lower right are a symphytognathid Patu jidanweishi sp. n., a mysmenid Gaoligonga changya gen. n., sp. n., and an unidentified linyphiid. Bottom: this misty mountain landscape at QiQi is typical of the Gaoligongshan
Research4Life Landscape and Situation Analysis - Visualisation of Analysis Levels
<p>Visualisation of the analysis levels adopted in the report 'Research4Life: Landscape and Situation Analysis' prepared by Research Consulting for the Research4Life partnership.</p>
Research4Life Landscape and Situation Analysis - Global Megatrends PEST Analysis
<p>A PEST infographic summarising the key global megatrends relevant to research and scholarly communication, as identified in the report 'Research4Life Landscape and Situation Analysis' prepared by Research Consulting for the Research4Life partnership.</p>
Research4Life Landscape and Situation Analysis - Trends in Scholarly Communication PEST Analysis
<p>A PEST infographic summarising the key trends in scholarly communication, as identified in the report 'Research4Life Landscape and Situation Analysis' prepared by Research Consulting for the Research4Life partnership.</p>
Research4Life Landscape and Situation Analysis - Trends in the funding of research in and for LMICs PEST Analysis
<p>A PEST infographic summarising the key trends in the funding of research in and for low and middle-income countries, as identified in the report 'Research4Life Landscape and Situation Analysis' prepared by Research Consulting for the Research4Life partnership.</p>
Abiotic stress mediated modulation of chromatin landscape in Arabidopsis thaliana
<p>This dataset include figures and supplementary material for the manuscript entitled<strong> </strong>"Abiotic stress mediated modulation of chromatin landscape in <em>Arabidopsis thaliana" </em>to be published in Journal of Experimental Botany special issue focused on Chromatin.</p> <p><strong>Supplementary File 1:</strong> Table describing read count, mapping percentage and genome coverage from each sample in FAIRE-seq and DNase-seq.</p> <p><strong>Supplementary File 2:</strong> List of DHSs obtained from control and stress subjected samples.</p> <p><strong>Supplementary File 3:</strong> List of FIRs obtained from control and stress subjected samples.</p> <p><strong>Supplementary File 4:</strong> List of uniquely merged OCRs with respective chromatin accessibility score in cold, heat, salt and drought stress.</p> <p><strong>Supplementary File 5:</strong> List of GO terms enriched in nrOCRs, SRCRs, and SACRs.</p> <p><strong>Supplementary File 6:</strong> List of GO terms enriched in overlapping nrOCRs, SRCRs, and SACRs.</p> <p><strong>Supplementary File 7:</strong> List of digital footprints (DFPs) obtained from nrOCRs regions of control-cold, control-heat, control-salt and control-drought pairs.</p> <p><strong>Supplementary File 8: </strong>Annotation details of the chromatin regions which were either found to be in state of accessible (CAS > 0.2) or inaccessible (CAS < -0.2) upon exposure to all of the stresses studied (heat, cold, salt and drought stress).</p> <p><strong>Supplementary Fig S1: Overlap of DHSs in control sample of present study with previously published studies.</strong></p> <p>A Venn diagram showing overlap of DNase hypersensitive sites (DHSs) found in control sample of present study and Zhang et al 2010 (<strong>A</strong>) and Sullivan et al 2014 (<strong>B</strong>). The statistical significance of overlap is calculate using hypergeometric Fischer`s exact test.</p> <p><strong>Supplementary Fig S2: Genomic locations of DHSs and FIRs</strong></p> <p>A line diagram representing the genomic location of unique DHSs and FIRs over each chromosome. DHSs/FIRs identified from each sample were merged to generate unique non-redundant subset of DHSs/FIRs before plotting over genome.</p> <p><strong>Supplementary Fig S3: Validation of correlation between OCRs and gene expression using microarray.</strong></p> <p>Box plot representing expression of genes (log10(normalised expression)) whose various structual elements fall in OCRs.</p> <p><strong>Supplementary Fig S4: First exons are highly enriched in both DHSs and FIRs</strong></p> <p> A bar plot showing presence of uFIRs, uDHSs, and ovOCRs in various positions of exon in Arabidopsis genes. The X-axis represent the exon number whereas Y-axis represent the fraction of OCRs found in each exon number.</p> <p><strong>Supplementary Fig S5: Validation of correlation between Ha-SACRs/Ha-SRCRs and gene expression using microarray.</strong></p> <p>Relative expression of genes (log2 fold change) corresponding to Ha-SACRs (Top) and (Ha-SRCRs (bottom) in cold (A), heat (B), salt (C) and drought (D) stress are plotted as box plot (p- value from Mann-Whitney test). To further compare RNA-seq data of salt stress with microarray, RNA-seq data was down-sampled to include genes which were also present in microarray data (E).</p> <p><strong>Supplementary Fig S6: Genomic location of SACRs and SRCRs found in Drought sample.</strong></p> <p>A snapshot of Integrative Genome Viewer (IGV) showing genomic location of stress activated chromatin regions (SACRs) and stress repressed chromatin region (SRCRs) in drought sample. The location of the centromere on each chromosome is shown as green bar IGV track.</p>
Figure 7 in Diversity and life-history traits of wild bees (Insecta: Hymenoptera) in intensive agricultural landscapes in the Rolling Pampa, Argentina
Figure 7. Mean number of (a) above-ground nesting bee individuals, (b) floral specialist bee individuals, (c) oligolectic bee individuals and (d) oil-collecting bee individuals in cropped area (n = 28 points) and semi-natural area (n = 11 points). ns indicates a non-significant result. Asterisks indicate that means are significantly different (Wilcoxon rank sum test, ** = P <0.01). Bars show SEs.
Figure 2 in Diversity and life-history traits of wild bees (Insecta: Hymenoptera) in intensive agricultural landscapes in the Rolling Pampa, Argentina
Figure 2. Semi-natural area of the study site: (a) semi-natural grassland; (b) the stream 'Arroyo Dulce' and its banks (Photos: Violette Le Féon).
Figure 1 in Conservation in a changing landscape: habitat occupancy of the critically endangered Tennent's leaf-nosed lizard (Ceratophora tennentii) in Sri Lanka
Figure 1. Location of Knuckles forest reserve within Kandy and Matale Districts (left) and the four study sites [two at Riverston (1 and 2), Hunasgiriya (3) and Deanston (4)] within the reserve (right).
Figure 3 in Conservation in a changing landscape: habitat occupancy of the critically endangered Tennent's leaf-nosed lizard (Ceratophora tennentii) in Sri Lanka
Figure 3. Comparison of climatic and structural parameters among the four habitat types during the dry (dashed line) and wet (solid line) seasons. Data from both locations with lizards and random locations are considered in combination. (C = Cardamom plantations, M = Mixed cardamom forests, N = Natural forests, P = Pine plantations.)
Figure 2 in Conservation in a changing landscape: habitat occupancy of the critically endangered Tennent's leaf-nosed lizard (Ceratophora tennentii) in Sri Lanka
Figure 2. Mean number of sightings of Ceratophora tennentii within three habitat types at Knuckles Range, Sri Lanka.
Data from: A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes
1. A high level of variation of biodiversity recovery within a landscape during forest restoration presents obstacles to ensure large scale, cost-effective, and long-lasting ecological restoration. There is an urgent need to predict landscape variation in forest restoration success at a global scale. 2. We conducted a meta-analysis comprising 135 study landscapes to predict and map landscape variation in forest restoration success in tropical and temperate forest biomes. Our analysis was based on the amount of forest cover within a landscape – a key driver of forest restoration success. We contrasted 17 generalized linear models measuring forest cover at different landscape sizes (with buffers varying from 5 to 200 km radii). We identified the most plausible model to predict and map landscape variation in forest restoration success. We then weighted landscape variation by the amount of potentially restorable areas (agriculture and pasture land areas) within the same landscape. Finally, we estimated restoration costs of implementing Bonn Challenge commitments in three specific temperate and tropical forest biome types in USA, Brazil and Uganda. 3. Landscape variation decreased exponentially as the amount of forest cover increased in the landscape, with stronger effects within a 5 km radius. Thirty-eight percent of forest biomes have landscapes with more than 27% of forest cover and showed levels of landscape variation below 10%. Landscapes with less than 6% of forest cover showed levels of variation in forest restoration success above 50%. 4. At the biome level, Tropical and Subtropical Moist Broadleaf Forests had the lowest (12.6%), while Tropical and Subtropical Dry Broadleaf Forests had the highest (22.9%) average of weighted landscape variation in forest restoration success. Our approach can lead to a reduction in implementation costs for each Bonn Challenge commitment between US$ 973 Mi and 9.9 Bi. 5. Policy implications. Our approach identifies landscape characteristics that increase the likelihood of biodiversity recovery during forest restoration – and potentially the chances of natural regeneration and long-term ecological sustainability and functionality. Identifying areas with low levels of landscape variation can help to reduce the risks and financial costs associated with implementing ambitious restoration commitments.
Convolutional Neural Net (CNN) models for epigenomic landscapes in epidermal differentiation - Basset architecture, classification and regression
<p>Deep learning models trained on epigenomic landscapes in keratinocyte differentiation. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4062509. The file `nn.ggr.models.basset.clf.tar.gz` contains 10 cross-validated models that were pretrained using ENCODE-Roadmap trained model weights as initialization weights and also 10 cross-validated models that were initialized with random weights. Similarly, the file `nn.ggr.models.basset.regr.tar.gz` contains 10 cross-validated models that were pretrained using the classification model weights as initialization weights and also 10 cross-validated models that were initialized with random weights.</p>
The spatial landscape of lung pathology during COVID-19 progression - raw IMC data
<p>Recent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19). However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry9 targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.</p>
A novel phosphoproteomic landscape evoked in response to type I interferon in the brain and in glial cells
<p>Type I interferons (IFN-I) are key responders to central nervous system infection and injury. They mediate their effects primarily via transcriptional regulation of several hundred interferon-regulated genes. Using a mouse model for IFN-I-induced neurodegeneration, we identified widespread protein phosphorylation as a new mechanism by which IFN-I mediate their effects. Protein phosphorylation aligned with the clinical hallmarks and pathological outcome, including impaired development, motor dysfunction and seizures. <em>In vitro</em> experiments revealed extensive and rapid IFN-I-induced protein phosphorylation in microglia and astrocytes, the brain’s primary IFN-I-responding cells. Response to acute IFN-I stimulation was independent of gene expression and mediated by a small number of kinase families. The changes in the phosphoproteome affected a diverse range of cellular processes and functional analysis suggested that this response induced an immediate reactive state and prepared cells for subsequent transcriptional responses. Our studies reveal a hitherto unappreciated role for changes in the protein phosphorylation landscape in cellular responses to IFN-I and thus provide insights for novel diagnostic and therapeutic strategies for neurological diseases caused by IFN-I.</p>
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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.