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64 results for “site characteristics”
Soil Characteristics in the Clearcut Site at Harvard Forest 2012
Soil properties (C:N ratio, pH, soil moisture, bulk density, litter layer thickness) were measured at the clear cut site in December 2012. Measurements were taken inside the large PVC collars that were used to measure soil respiration at the sites during the 2012 growing season. The collars were removed for storage thereafter. 15 locations in total were sampled: 3 collars were in trenched plots, and 12 on untrenched soil. Soil properties were collected in order to help interpret spatial variability in the corresponding soil CO2 fluxes measured at the site.
Urban forest canopy cover, vegetation, and site characteristics, Twin Cities Metro Area, 2022 and 2023.
This data was primarily collected to assess forest quality within the Minneapolis-St. Paul (MSP) Metropolitan Area and to link above-ground and below-ground properties as part of the goals of the MSP-LTER Urban Tree Canopy research group. Here, we sampled vegetation on 48 circular plots with a 12.5 m radius distributed across 18 parks, registering the date of sampling, park and management agency names, the plot number, and geolocation (latitude, longitude, and elevation). The plots were randomly selected based on GEDI (Global Ecosystem Dynamics Investigation instrument) 2021 footprints in the MSP Metropolitan Area along accessible forested areas inside public parks, where the management agency allowed sampling. In each plot, we measured forest structure and diversity metrics, species names and abundance, DBH, height, distance from the plot center, the height where each individual canopy starts, and the relative position, exposure, and density of each canopy. We also measured understory plant structure and diversity in 4 subplots per plot, totaling 192 subplots. In these subplots, we surveyed all individual plants with heights over 20 cm, recording species names and abundance, plant basal diameter, plant height, and the total number of branches. Furthermore, we assessed the canopy openness above each subplot by calculating percent DIFN (diffuse non-interceptance) from fish eye pictures of the canopy at 1.3 meters over the subplot.
Toklat River Fire in Denali National Park and Preserve: Site level environmental, soil, tree, vegetation, and fire characteristics measured in 2016
This dataset contains site-level average estimated of environmental, soil, tree, vegetation, and fire characteristics measured in 2016, three years after the Toklat River Fire in Denali National Park and Preserve. Measured parameters include latitude, longitude, slope, aspect, elevation, moisture classification, bulk density of the surface soil, residual organic soil depth, thaw depth, burn depth, density and basal area of all tree species pre-fire, the density of all tree species post-fire, estimates of above- and below-ground carbon combustion, and understory vegetation turnover from pre-fire to post-fire. There is also data on seed trap collection and experimental regeneration of seedlings collected in 2017 and 2018 at a subset of sites.
Site Location and Environmental Characteristics for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Accoss Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains the GPS location, slope, orientation, elevation, forest type based on dominant tree biomass, Age, sampling year, total vascular cover, total lchen cover, deciduous index (Alexander et al. 2012, Ecosphere), basal area of black spruce, Alaska paper birch, trembling aspen, large deciduous shrubs and trembling aspen, organic layer depth, pH, heatload, approximated moisture class for all sites (Johnstone et al. 2008), gravimetric moisture content, and volumetric moisture content. This dataset was part of the site-level covariates used in a study of bryophyte post-fire succession in deciduous and coniferous successional trajectories. NOTE: there is some overlap between the >20 years old sites that were sampled by Heather Alexander with some of the data she submitted in relation to her 2012 Ecosphere paper.
Bisley 40 X 40 grid vegetation and site characteristics
Relationships between landforms, soil nutrients, forest structure, and the relative importance of different disturbances were quantified in two subtropical wet steepland watersheds in Pueno Rico. Ridges had fewer landslides and treefall gaps, more above-ground biomass, older aged stands, and greater species richness than other landscape positions. Ridge soils had relatively low quantities of exchangeable bases but high soil organic matter, acidity and exchangeable iron. Valley sites had higher frequencies of disturbance, less biomass, younger aged stands, lower species richness and soils with more exchangeable bases. Soil N, P, and K were distributed relatively independently of geomorphic setting, but were significantly related to the composition and age of vegetation. On a watershed basis, hurricanes were the dominant natural disturbance in the turnover of individuals, biomass, and forest canopy. However. turnover by the mortality of individuals that die without creating canopy openings was faster than the turnover by any natural disturbance. Only in riparian areas was forest turnover by treefall gaps faster than turnover by hurricanes. The same downslope mass transfer that links soil forming processes across the landscape also influences the distribution of landslides, treefall gaps, and the structure and composition of the forest. One consequence of these interactions is that the greatest aboveground biomass occurs on ridges where the soil nutrient pools are the smallest. Geomorphic stability, edaphic conditions, and biotic adaptations apparently override the importance of spatial variations in soil nutrients in the accumulation of above-ground biomass at this site. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. A
Characteristics of human and viral RNA binding sites and site clusters recognized by SRSF1 and RNPS1
<p>This dataset was developed for the following article:</p> <p> Rogan PK, Mucaki EJ and Shirley BC. A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections [version 1; peer review: awaiting peer review]. <em>F1000Research</em> 2020, <strong>9</strong>:943 (<a href="https://doi.org/10.12688/f1000research.25390.1">https://doi.org/10.12688/f1000research.25390.1</a>)</p> <p><strong>Section 1. Extended Data Tables</strong></p> <p>This archive contains the extended data tables for the research article "A proposed mechanism for molecular pathogenesis of severe RNA-viral pulmonary infections". These tables provide SRSF1, RNPS1 and hnRNP A1 binding site and information-dense cluster counts across various RNA viral genomes [including multiple SARS-CoV-2 and influenza strains] and the human transcriptome, the estimated SARS-CoV-2 doubling time necessary for viral genome SRSF1 binding site availability to exceed sites within the host transcriptome, and an analysis of influenza, dengue, and aplastic anemia patients misdiagnosed as irradiated by established radiation gene signatures.These tables are:</p> <p><strong>Section 1 - Table 1.</strong> RNPS1 and hnRNPA1 binding sites and Information-Dense Clusters for RNPS1 and<br> hnRNPA1 in RNA Virus Genomes<br> <strong>Section 1 - Table 2A.</strong> Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 1) in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 2B.</strong> Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 2) in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 2C.</strong> Detailed Analysis of Information-Dense Clusters for RNPS1 in RNA Virus Genomes<br> <strong>Section 1 - Table 2D.</strong> Detailed Analysis of Information-Dense Clusters for hnRNP A1 in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 3.</strong> Binding Site Analysis of Multiple Coronavirus Strains (Both Strands)<br> <strong>Section 1 - Table 4A.</strong> Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Negative Strand Only)<br> <strong>Section 1 - Table 4B.</strong> Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Both Strands)<br> <strong>Section 1 - Table 5.</strong> SRSF1, RNPS1 and hnRNPA1 Binding Sites and Information-Dense Clusters by Gene<br> <strong>Section 1 - Table 6A.</strong> Transcriptome-Wide Information Dense Clusters Intersecting DRIP- and DRIPc-seq<br> Intervals<br> <strong>Section 1 - Table 6B. </strong>Exome-Wide Information Dense Clusters within DRIP- and DRIPc-seq Intervals<br> <strong>Section 1 - Table 6C.</strong> Transcriptome-Wide Scan of Strong Binding Sites Intersecting DRIP- and DRIPc-seq<br> Intervals<br> <strong>Section 1 - Table 6D. </strong>Exome-Wide Scan of Strong Binding Sites within DRIP- and DRIPc-seq Intervals<br> <strong>Section 1 - Table 7.</strong> Rate of False Positives for Influenza, Dengue Virus and Aplastic Anemia Using<br> Radiation Signatures<br> <strong>Section 1 - Table 8.</strong> Radiation Model Genes Contributing to False Positives for Patients with Influenza A,<br> Dengue Virus, and Aplastic Anemia<br> <strong>Section 1 - Table 9A.</strong> Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding<br> Sites (Positive-Strand Sites Only)<br> <strong>Section 1 - Table 9B.</strong> Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding<br> Sites (Both Strands Considered)</p> <p><strong>Section 2. All SRSF1, hnRNPA1 and RNPS1 binding site tracks for human and viral genomes</strong></p> <p>We provide bedgraph tracks which provide the location and strength of binding sites (and binding site clusters) for SRSF1, RNPS1 and hnRNPA1 across the human transcriptome (GRCh37), the human exome (including +/-300nt surrounding the exon; non-intergenic only), and for all viral genome investigated in this study (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [two strains]). Note that if no clusters were found for a particular viral genome, a file for said genome will not be present in the Zenodo archive.</p> <p>Folder “Cluster-to-DRIPseq-Intersection-Tracks” contain tracks which indicate where binding site clusters have been identified, intersected with DRIP-seq and DRIPc-seq intervals which indicate where there is evidence of R-Loop formation in the human genome. The DRIP-seq dataset (GSE68845) is not strand specific. DRIPc-seq (GSE70189) is strand specific, and has been taken into account in the intersection (e.g. tracks only list positive strand clusters found in positive-strand DRIPc-seq intervals).</p> <p>Due to sheer size, the human transcriptome and exome tracks which indicate the location of individual binding sites are split into two separate files (separated by strand). While the custom tracks containing human binding site information are designed to be uploaded to the UCSC Genome Browser, files containing transcriptome-wide binding site information may be too large to be uploaded and may require further filtering (i.e. by chromosome).</p> <p>To be classified as a cluster, binding sites on the same strand must have <em>Ri</em> values which sum to >50 bits, each binding site must have a neighboring site within 25nt, and all binding sites in the cluster must have <em>R<sub>i</sub></em> greater than a minimum bit threshold. For human transcriptomes and exomes, this bit minimum was set to <em>R<sub>sequence</sub></em>. The bit minimum for viral binding sites was set to 0.1 * <em>R<sub>sequence</sub></em>. The information density-based clustering algorithm utilized in this work is described in Lu and Rogan 2018 (<a href="https://f1000research.com/articles/7-1933/v2">https://f1000research.com/articles/7-1933/v2</a>) and archived source code is available through Zenodo (<a href="https://dx.doi.org/10.5281/zenodo.1892051">https://dx.doi.org/10.5281/zenodo.1892051</a>).</p> <p><strong>Section 3. Binding site clusters - lollipop plots</strong></p> <p>Lollipop plots present the genomic coordinates and information densities of clusters across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). The height of the "lollipop" corresponds to the information density of a cluster. Labels above "lollipops" present the start and end genomic coordinate (GRCh37) of the cluster followed by the number of sites in the cluster enclosed in brackets. Lollipop plots associated with human transcriptomes/exomes each contain a single gene. Influenza has 8 segments and each segment requires its own plot, other viral genomes examined are presented in a single plot.</p> <p>File naming convention for human plots:</p> <ul> <li>RBP_Gene.png</li> <li>e.g. RNPS1_ADK.png</li> </ul> <p>File naming convention for viral plots (elements in square brackets do not always appear):</p> <ul> <li>Virus[.InfluenzaSegment].RiThreshold.Strand.RBP.png</li> <li>e.g. Wuhan-Hu-1.complete-genome.4.2-bits.PosStrand.hnRNPA1.png</li> </ul> <p>The specified Ri threshold indicates all binding sites which comprise a cluster have <em>R<sub>i</sub></em> greater-than or equal to the threshold.</p> <p><strong>Section 4. Ri(b,l) matrices for all binding sites scanned</strong></p> <p>The information theory-based position weight matrices for the following RNA binding proteins (RBP) used in this study: SRSF1, hnRNPA1 and RNPS1. We investigated binding using two different RNPS1 binding models. While similar, these two models contained binding site information on opposing sides of the binding site motif which is why we found it prudent to scan with both models.</p> <p>Structure of each file:</p> <p>Line #1: Start position, End position and<em> R<sub>sequence</sub></em> [average strength of sequences used to generate the model]</p> <p>Subsequent lines describe the information on each position of the binding site:</p> <ul> <li>First four columns: <em>R<sub>i</sub></em> contribution of nucleotide at this position of the matrix [A, C, G, T]</li> <li>Row #5: Position of the matrix</li> <li>Last four columns: Number of binding sites used to generate model with a particular nucleotide at this position of the matrix [A, C, G, T]</li> </ul> <p>Example:</p> <p>-2.965775 1.282153 0.034225 -4.906891 0 1 19 8 0</p> <p>At zero position of the matrix (first nucleotide), a ‘C’ would have a positive contribution to binding site strength, a ‘G’ would be relatively neutral, and an ‘A’ or ‘T’ would negatively contribute to binding site strength.</p> <p>Generation of R<sub>i</sub>(b,l) matrices and computation of <em>R<sub>i</sub></em> values and can be accomplished by utilizing the Delila package (<a href="https://alum.mit.edu/www/toms/delila/delilaprograms.html">https://alum.mit.edu/www/toms/delila/delilaprograms.html</a>).</p> <p><strong>Section 5. Ri and intersite distance - histograms</strong></p> <p>Two sets of histograms present <em>R<sub>i</sub></em> distribution and intersite distance distribution across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). </p> <p>File naming convention for human plots (elements in square brackets do not always appear):</p> <ul> <li>[IntersiteDistancesThreshold-]Human-[DRIPc]-AllChrs-RBP[-RiThreshold].png</li> <li>e.g. IntersiteDistances500-Human-AllChrs-hnRNPA1-4.6-bits.png</li> </ul> <p>File naming convention for viral plots (elements in square brackets do not always appear):</p> <ul> <li>[IntersiteDistancesThreshold-]Strand-RBP-Virus[.InfluenzaSegment][-RiThreshold].png</li> <li>e.g. IntersideDistances1000-PosStrandOnly-SRSF1-top50000sitesReplicate1-HIV-1-Strain-B.png</li> </ul> <p>Intersite distance thresholds of 500 or 1000 were assigned for all intersite distance histograms. Any distances above the corresponding threshold were excluded from the plot. Plots presenting <em>R<sub>i</sub></em> distributions contain a dashed line indicating <em>R<sub>sequence</sub></em> if it is visible within the scope of the plot.</p> <p><strong>Section 6. Perl Scripts and Descriptions</strong></p> <p>This archive contains all Perl scripts discussed in this archive's associated manuscript and a document file which describes them ("Perl-Script-Descriptions-Page.docx"). The programs and their general functions are as follows:</p> <p>“ClusterToDRIPseqAnalysisProgram.pl” – reports which information-dense clusters are located within DRIPc- and/or DRIP-seq intervals (individually and by gene)</p> <p>“ClusterToDRIPseqAnalysisProgram.GeneDensityFinder.pl” – uses the output from script “ClusterToDRIPseqAnalysisProgram.pl” to determine the number and the density of information-dense clusters within a gene (total clusters within the gene and those within DRIPc-seq intervals)</p> <p>“calculateIntersiteDistance.pl” – determines the distance between all binding sites in the same gene from a list of genomic coordinates</p> <p>“removeOutliersHigherThanN.pl” – discards intersite distances computed by script “calculateIntersiteDistance.pl” that are greater than a specified threshold</p> <p>“getStatisticsOnCol.pl” – calculates the count, geometric mean, median, arithmetic mean, and standard deviation of values from the output of script “removeOutliersHigherThanN.pl”</p> <p>“ScanDataSummaryProgram.pl” – determines the number of binding sites (above a specified <em>R<sub>i</sub></em> threshold) found within known genes (the program also reports the total expression of those genes using external A549 and pneumocyte expression datasets) from binding site coordinate data</p> <p>“TotalBindingSitePerCellCalculator.pl” – estimates the number of binding sites expressed in a single A549 or pneumocyte cell at any given time.</p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Chemical and physical characteristics of soil samples collected from a chronosequence of reforested urban sites in Lexington, KY USA.
This dataset contains information on soil physical and chemical characteristics across twenty urban reforestation sites planted as part of the Reforest the Bluegrass program in Lexington, Kentucky, USA. At least three plots (and up to nine plots) were established in each site, with additional plots added if forested patches were sufficiently large. Soil samples were collected from each plot in summer 2020. Samples were composited from five subsamples collected from plot center and approximately 1 m from plot center in the four cardinal directions. Subsamples were collected to a depth of approximately 10 cm using a sampling spade. Composite samples were air-dried and passed through a 2cm sieve, and then sent to the University of Kentucky Regulatory Services soils lab for analysis for pH, P, K, Ca, Mg, Zn, particle size distribution, total C, and total N.
Summary of environmental and stand characteristics of thinleaf alder sites along the Tanana River floodplains from 2006-2007.
This dataset contains a summary of environmental and stand characteristics of thinleaf alder dominated along the Tanana River floodplains. Environmental data for each site include terrace height, total thickness of organic layers, depth of uppermost organic layer, soil pH, soil C, N, and P, and soil N/P ratio. Also included are mean thinleaf alder foliar C, N, and P concentrations and foliar N/P ratio for a sumbsample of sites. Estimates of stand age, based on the age of the oldest alder, are available for a subsample of sites. Other stand characteristics include canopy cover, stem density and basal area of thinleaf alder, willows, balsam poplar, and white spruce, and % of alder stems infected with a fungal stem canker at various degrees of disease severity.
Marsh soil characteristics at nine GCE-LTER sampling sites in May 2001
Soil cores were collected from two tidal marsh locations at nine GCE-LTER sampling sites in May 2001. Bulk density and organic Carbon, total Nitrogen, total Phosphorous, Cesium-137 and Lead-210 content were measured in 15 2cm sections from each 30cm core. The sampling locations were chosen to represent three estuaries of the Georgia coast (USA) that vary in delivery of freshwater and sediment. This study was conducted to evaluate the effects of freshwater input on soil properties, accretion and accumulation.
Dataset for 'Atmospheric VOC measurements at a High Arctic site: characteristics and source apportionment'
<p>This dataset includes the VOCs (and expanded uncertainties) as well as VOCs (and expanded uncertainties) used in the PMF model as described in 'Pernov, J. B., Bossi, R., Lebourgeois, T., Nøjgaard, J. K., Holzinger, R., Hjorth, J. L., and Skov, H.: Atmospheric VOC measurements at a High Arctic site: characteristics and source apportionment, Atmos. Chem. Phys. Discuss., 2020, 1-36, 10.5194/acp-2020-528, 2020.' </p>
Hyytiälä SMEAR II site characteristics
<p>This dataset contains description of site history and the essential characteristics of the forest stand surrounding SMEAR II research station located in Hyytiälä, Finland. The original tree inventory and increment core data are also provided.</p> <p>The dataset consists of following files:</p> <p>1. Description of site history and essential stand and soil characteristics (Hyytiala site description.pdf)</p> <p>2. Stand characteristics, tree biomasses and leaf area index calculated for all biomass inventory plots and for ICOS target stand (Hyytiala stand characteristics.xlsx)</p> <p>3. Table (line_plots.csv) that indicates which biomass sample plots are<br> a) located in the area that was burned in 1962<br> b) thinned in 2002<br> c) inside ICOS target stand</p> <p>4. Cleaned-up tree-wise inventory data collected at the line sampling plots in 2001, 2008, 2012 and 2015 (Biomass inventories.xlsx)</p> <p>5. Cleaned-up core drilling data collected at the line sampling plots in 2012, 2014 and 2016 for pine growth calculations (Core Drilling YYYY.txt, Core Drilling readme.txt)</p>
Non-crop vegetation characteristics and vocalizing bird richness across 44 sites in Iowa, USA in June 2019
<p>This data was derived from field work conducted in June 2019 where sixty AudioMoth passive acoustic monitors were placed along agricultural field margins in Iowa, USA. Twenty-five of the monitoring location were established by farmer and landowner collaborators, and the remaining (35) sites were established by the author (A.P.D.). Unique vocalizing bird species were counted in ninety-five recordings from 6 to 8 days during dawn hours per site. High resolution mapping identified non-crop vegetation and texture at spatial extents ranging from 100 to 1000 meters. Pesticide and fertilizer application were collected via a survey with collaborators. Site location names are included when the research site was an Iowa State Research and Demonstration Farm (ISRF). When the site was a collaborator, the site name was anonymized to "Collaborator" to respect the privacy of participants.</p>
Fig. 3 in Assessing the distribution, roosting site characteristics, and population of Pteropus lylei in Thailand
Fig. 3. Roosting sites responded (total number: 34) according to responses from community-based questionnaire, and result of a field survey to validate survey data.
Fig. 2 in Assessing the distribution, roosting site characteristics, and population of Pteropus lylei in Thailand
Fig. 2. Pteropus lylei roosting sites and foraging zones; questionnaire respondents and bat hunting zones are marked in each sub-district.
Fig. 1 in Sleeping site selection in two Asian viverrids: effects of predation risk, resource access and habitat characteristics
Fig. 1. Home ranges, core areas and sleeping sites of binturongs (Arctictis binturong) and masked palm civets (Paguma larvata). Home ranges (minimum convex polygon [MCP] 95%) and core areas (MCP 50%) with sleeping sites overlaid of (a) three masked palm civets and a female binturong at Tikong, (b) a male binturong at Sesawo, and (c) location of study sites (Sesawo and Tikong) within the study area (Thung Yai Naresuan Wildlife Sanctuary – West). Different gray shades within home ranges represent core areas of each animal.
Fig. 4 in Sleeping site selection in two Asian viverrids: effects of predation risk, resource access and habitat characteristics
Fig. 4. Use and reuse of sleeping sites. Cumulative number of unique sleeping sites in relation to the total number of sites observed for two binturongs (Arctictis binturong) and three masked palm civets (Paguma larvata). Numbers of unique sleeping sites (sites that are not re-used) versus total sleeping sites observed and study areas are indicated in parenthesis.
Fig. 3 in Sleeping site selection in two Asian viverrids: effects of predation risk, resource access and habitat characteristics
Fig. 3. Use of sleeping sites within different forest types. Percentage of different forest types used (denoted as U) for sleeping sites versus forest types available (A) for two binturongs (Arctictis binturong) and three masked palm civets (Paguma larvata). Forest types are: semi-evergreen forest (SEF), mixed deciduous forest (MDF), and dry dipterocarp forest (DDF). Numbers in parenthesis after individual animals represent the number of sleeping sites used in the analysis, excluding reused sites.
Fig. 2 in Sleeping site selection in two Asian viverrids: effects of predation risk, resource access and habitat characteristics
Fig. 2. Use of vertical strata for sleeping sites. Percentage use of different vertical strata of sleeping sites by five radio-collared viverrids (two binturongs Arctictis binturong and three masked palm civets Paguma larvata). Strata are: Above canopy, Canopy, and Sub-canopy. Numbers in parenthesis represent number of sleeping sites where animals were directly observed, excluding reused sites.
Site characterization, regeneration attributes, and juvenile conifer growth characteristics for 25 forest and woodland sites in the southwestern United States
<p>This dataset contains biotic and abiotic site characterization data, juvenile conifer regeneration and growth data, and detailed juvenile growth characteristic data for destructively sampled juvenile conifers. Data are included for 25 forest and woodland sites in the southwestern United States, and were collected in summer 2019. A detailed sampling and analysis methodology is available from the following publication (in press as of 10-2021):</p> <p>Pirtel NL, Bradford JB, Hubbard RM, Abella SR, Kolb TE, Litvak ME, Porter SL and Petrie MD. 2021. The aboveground and belowground growth characteristics of juvenile conifers in the southwestern United States, Ecosphere: in press.</p> <p>Please contact the corresponding author (MD Petrie) with any questions or requests.</p> <p> </p>
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.