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1,868 results for “Spatial Data”
Data from: Contrasting patterns of female house mouse spatial organisation among outbreaking and stable populations
<p>The size and distribution of home ranges reflects how individuals within a population use, defend, and share space and resources, and may thus be an important predictor of population-level dynamics. In eruptive species like the house mouse in Australian grain growing regions, contrasting space use between stable and outbreaking populations allows us to test predictions regarding social or life history strategies that may contribute to an outbreak. In this study we use spatially explicit capture-recapture models to compare home range overlap (as a proxy for territoriality) in female mice from populations showing different outbreak trajectories. We found that female space use in spring varied between outbreaking and stable populations. Our analysis indicated greater home range overlap in populations with stable trajectories compared to those that would later experience an outbreak, suggesting females in these stable populations may have had greater potential for cooperative group formation as indicated by shared space use. We discuss the results with respect to intrinsic factors, such as kin structure, with potential implications for better predicting mouse outbreaks at a microgeographic scale.</p>
Data from: Distances and their visualization in studies of spatial-temporal genetic variation using single nucleotide polymorphisms (SNPs)
<p>Distance measures are widely used for examining genetic structure in datasets that comprise many individuals scored for a very large number of attributes. Genotype datasets composed of single nucleotide polymorphisms (SNPs) typically contain bi-allelic scores for tens of thousands if not hundreds of thousands of loci.</p> <p>We examine the application of distance measures to SNP genotypes and sequence tag presence-absences (SilicoDArT) and use real datasets and simulated data to illustrate pitfalls in the application of genetic distances and their visualization.</p> <p>The datasets used to illustrate points in the associated review are provided here together with the R script used to analyse the data. Data are either simulated internal to this script or are SNP data generated as part of other studies and included as compressed binary files readily accessable by reading into R using R base function readRDS(). Refer to the analysis script for examples.</p>
The global distribution of plants used by humans datasets: list of utilised species, occurrence data and model outputs at 10 arc-minutes spatial resolution
<p>Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders <em>raw_data</em> and <em>processed_data</em> containing respectively the list of utilised plant species modelled -<em>utilised_plants_species_list.csv</em>-, and their occurrence data -<em>occurrence_data.zip-</em> and predicted distribution -<em>species_proba_per_cell.rds-.</em></p> <p> </p> <ul> <li>The file <em>utilised_plants_species_list.csv</em> in the <em>raw_data</em> folder contains a<strong> </strong>list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:</li> </ul> <p><strong>plant_ID:<em> </em></strong>plant identifier number ranging from between 1-35687</p> <p><strong>binomial_acc_name:</strong> binomial accepted name of the plant species</p> <p><strong>author_acc_name</strong>: name of the author(s)</p> <p><strong>is_hybrid:</strong> logical TRUE or FALSE indicating whether the species is an hybrid or not.</p> <p><strong>AnimalFood:</strong> forage and fodder for vertebrate animals only.</p> <p><strong>EnvironmentalUses:</strong> examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water.</p> <p><strong>Fuels:</strong> charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials.</p> <p><strong>GeneSources:</strong> wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs.</p> <p><strong>HumanFood:</strong> food for humans only, including beverages and food additives.</p> <p><strong>InvertebrateFood:</strong> plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs.</p> <p><strong>Materials:</strong> woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products.</p> <p><strong>Medicines:</strong> both human and veterinary.</p> <p><strong>Poisons:</strong> plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides.</p> <p><strong>SocialsUses:</strong> plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance.</p> <p><strong>Totals:</strong> total number of uses recorded for a species</p> <p> </p> <ul> <li>The zipfile <em>occurrence_data.zip</em> in the <em>processed_data</em> folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to build species distribution models with the following 14 fields:</li> </ul> <p><strong>Species:</strong> the binomial accepted name of the species</p> <p><strong>Fullname:</strong> same as species</p> <p><strong>decimalLongitude:</strong> the geographic longitude of the occurrence records of the species in decimal degrees</p> <p><strong>decimalLatitude:</strong> the geographic latitude of the occurrence records of the species in decimal degrees</p> <p><strong>countryCode:</strong> a three-letter standard abbreviation for the country of the occurrence locality</p> <p><strong>coordinateUncertaintyinMeters</strong>: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location</p> <p><strong>year:</strong> year of the observation of the occurrence record of the species</p> <p><strong>individualCount:</strong> the number of individuals present at the time of the observation</p> <p><strong>gbifID:</strong> unique identifier number for the occurrence from the original database</p> <p><strong>basisOfRecords:</strong> the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen</p> <p><strong>institutionCode</strong>: the name of the institution or organization listed as the data publisher on GBIF</p> <p><strong>establishmentMeans:</strong> statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans</p> <p><strong>is_cultivated_observation:</strong> whether or not an organism is cultivated</p> <p><strong>sourceID:</strong> name of the source database</p> <p> </p> <ul> <li>The file <em>species_proba_per_cell.rds</em> in the <em>processed_data</em> folder is<em> a R Data Serialization </em>(RDS) file containing a data.table object with the following 3 fields:</li> </ul> <p><strong>plant_ID:</strong><em> </em>plant identifier number ranging from between 1-35687</p> <p><strong>proba:</strong> species occurrence probability</p> <p><strong>cell:</strong><em> </em>raster grid cell number between 1-2251762</p> <p>This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.</p>
Data for: Spatial prey availability and pulsed reproductive tactics: encounter risk in a canid-ungulate system
<ol> <li>Predation risk is a function of spatiotemporal overlap between predator and prey, as well as behavioral responses during encounters. Dynamic factors (e.g., group size, prey availability, and animal movement or state) affect risk, but rarely are integrated in risk assessments. Our work targets a system where predation risk is fundamentally linked to temporal patterns in prey abundance and behavior. For neonatal ungulate prey, risk is defined within a short temporal window during which the pulse in parturition, increasing movement capacity with age, and anti-predation tactics have the potential to mediate risk.</li> <li>In our coyote – mule deer (<em>Canis</em> <em>latrans</em> – <em>Odocoileus</em> <em>hemionus</em>) system, leveraging GPS data collected from both predator and prey, we tested expectations of the shared enemy and reproductive risk hypotheses. We asked two questions regarding risk: (A) how do primary and alternative prey habitat, predator and prey activity, and reproductive tactics (e.g., birth synchrony, maternal defense) influence vulnerability of a neonate encountering a predator? (B) How do the same factors affect behavior by predators relative to time before and after an encounter?</li> <li>Despite increased selection for mule deer and intensified search behavior by coyotes during the peak in mule deer parturition, mule deer were afforded protection from predation via predator swamping, experiencing reduced per-capita encounter risk when most neonates were born. Mule deer occupying rabbit habitat (<em>Sylvilagus</em> spp.; coyote's primary prey) experienced the greatest risk of encounter but the availability of rabbit habitat did not affect predator behavior during encounters. Encounter risk increased in areas with greater availability of mule deer habitat, coyotes shifted their behavior relative to deer habitat, and the pulse in mule deer parturition and movement of neonatal deer during encounters elicited increased speed and tortuosity by coyotes.</li> <li>In addition to the spatial distribution of prey, temporal patterns in prey availability, and animal behavioral state were fundamental in defining risk. Our work reveals the nuanced consequences of pulsed availability on predation risk for alternative prey, whereby responses by predators to sudden resource availability, the lasting effects of diversionary prey, and inherent antipredation tactics ultimately dictate risk.</li> </ol>
Data and code for the paper "Geolocating Bees by Translating the Waggle Dance Into Spatial Coordinates"
<p>The dataset contains the database storing the visual decoding of 10 videos of bees in an observation hive (database.zip). The folder "derived data.zip" contains several files derived with code and GIS tools to obtain the results presented in the paper. The videos are available in the "videos.zip" file. Finally, the Python script "waggle_dance_annulus.py" is the code that generates the box-plot like annulus geometry for dances.</p>
STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping
<p>Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at <a href="https://github.com/JEFworks-Lab/STalign">https://github.com/JEFworks-Lab/STalign</a> and as supplementary software with additional documentation and tutorials available at <a href="https://jef.works/STalign">https://jef.works/STalign</a>.</p> <p>Here we have included alignment results that were used in performance analysis of STalign:</p> <p>We aligned Slice 2 Replicate 3 to Slice 2 Replicate 2 of the MERFISH mouse coronal brain sections available from Vizgen Data Release V1.0. May 2021 (<a href="https://info.vizgen.com/mouse-brain-map">https://info.vizgen.com/mouse-brain-map</a>).</p> <ul> <li>STalign_S2R3_to_S2R2.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to S2R2 with STalign, cell positions of S2R3 after supervised affine alignment to S2R2, and counts for genes and blanks.</li> <li>STalign_S2R2.csv.gz contains cell ids, cell centroid positions of S2R2 and counts for genes and blanks.</li> </ul> <p>Additionally, we aligned Slice 2 Replicate 3 to a Visium dataset of an FFPE preserved adult mouse brain were obtained from the 10X Datasets website for <em>Spatial Gene Expression Dataset by Space Ranger 1.3.0</em> (<a href="https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0">https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0</a>).</p> <ul> <li>STalign_S2R3_to_Visium.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to Visium H&E staining with STalign, and counts for genes and blanks.</li> </ul> <p>Furthermore, we performed alignments with the 50um resolution 3D Allen Reference Atlas Nissl common coordinate framework, CCF (<a href="https://help.brain-map.org/display/mouseconnectivity/API">https://help.brain-map.org/display/mouseconnectivity/API</a>). We applied STalign to align the Allen CCF to each of the 9 MERFISH slices (3 slice locations with 3 biological replicates) provided by Vizgen. Because the Allen CCF has annotated brain regions, we were able to lift over those brain region annotations to label all cells in the MERFISH datasets.</p> <p>Also, since the STalign mappings from the Allen CCF to the MERFISH slices are invertible, for each slice we can apply the inverse of the mapping to get cell positions in the Allen CCF coordinates.</p> <ul> <li>STalign_SXRX_with_structure_id_name.csv.gz contains cell ids for Slice X Replicate X, original cell centroid positions, cell xyz-coordinates in Allen CCF, brain structure id per cell, brain structure acronym</li> </ul> <p>To evaluate the 3D CCF alignment, we performed unified transcriptional clustering analysis and cell-type annotation. All MERFISH datasets were combined. Transcriptional clustering analysis and cell type annotation was performed using the SCANPY package [version 1.9.1]. Data were normalized to counts per million (scanpy: normalize_total) and log transformed (scanpy: log1p). PCA (scanpy: pca) was computed on the cell by gene matrix. A neighborhood graph of cells using the top 10 PCs and 10 nearest neighbors was created (scanpy: neighbors), and Leiden clustering was performed on this graph (scanpy: leiden) to identify 29 clusters. Differentially expressed genes were extracted from each cluster (scanpy: rank_genes_groups), and cell-types were annotated based on marker genes in each cluster.</p> <ul> <li>STalign_celltypeannotations_merfishslices_v2.csv.gz contains for all nine slices cell ids and cell type annotations</li> </ul> <p>This updated (v2) cell-type annotation file contains a new column with simplified cell-types. Briefly, we fixed typos, standardized lower case/upper case formats, merged subclasses of each cell-types. For example, subclasses of astrocytes­­, which are originally labeled as “Astrocytes”, “Astrocytes(1)”, “Astrocytes(2)”, “Astrocytes(3)”, are all labeled as “Astrocytes” in the added column.</p> <p>Note: Cell ids may have been mutated from original string of numbers through reading and writing across programming languages that handle numbers with different precision. If using R to read the files shared here, one can find the cells in STalign_celltypeannotations_merfishslices_v2.csv.gz that correspond with STalign_SXRX_with_structure_id_name.csv.gz when cell ids are formatted as a double in scientific notation, which is how R will read the file automatically.</p>
Spatial patterns and effects of invasive plants on soil microbial activity and diversity along river corridors - raw data
<p>Dataset for the study</p> <p><strong><span>Spatial patterns and effects of invasive plants on soil microbial activity and diversity along river corridors</span></strong></p> <ol> <li>environmental variables of the research plots</li> <li>vascular plant species composition of the research plots</li> <li>mcirobial activity on the research plots</li> <li>CLPP profiles of the research plots</li> </ol>
Data from: Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation
<p>Spatial assessments of Ecosystem Services (ES) are increasingly used in environmental management and spatial planning, but rarely provide information on the accuracy of predictions. Uncertainty estimates are essential to allow for confidence in the quality and credibility of ES assessments to enable informed decision-making. In marine environments, the need for uncertainty assessments for ES is unparalleled as they are data scarce, poorly (spatially) defined, with complex interconnectivity of seascapes. This study illustrates the uncertainty associated with a principle-based method for ES modelling by accounting for model variability, data coverage, and uncertainty in thresholds and parameters. A sensitivity analysis was applied on ES models for marine bivalves (<em>Austrovenus stutchburyi</em> and <em>Paphies australis</em>) and their contribution to <em>Food provision, Water quality regulation, Nitrogen removal,</em> and <em>Sediment stabilisation</em>.<em> </em>ES estimates from the sensitivity analysis were compared against baseline ES predictions. Spatial uncertainty patterns were analysed for individual ES through bi-plots and multiple ES through spatial prioritisation using Zonation. Results showed spatially explicit differences in uncertainty patterns for ES and between species. <em>Food</em><em> provision</em> had highest maximum uncertainty (>5 points) but also the largest area of high ES and high certainty conditions. Zonation analysis conducted on baseline and conservative ES values showed overall robust outcomes of top 30% area, but important nuances through shifts in top 10% and top 5% area that allowed for a consistently better representation of ES when accounting for uncertainty. The spatial prioritisation in combination with the ES uncertainty biplots provide tools for spatial planning of individual and multiple ES to focus on area of highest value with highest certainty and can thereby help reduce risk and aid informed decision-making at acceptable confidence levels. This type of information is urgently needed in marine ES assessments and their management, but likewise extends to other environments to improve transparency. </p>
Data from: Drivers of intra-individual spatial variability in methane emissions from tree trunks in upland forest
<p><span>CH</span><sub><span>4</span></sub><span> emissions from </span><span>tree </span><span>trunks in upland forests should be scaled accurately</span><span> in order</span><span> to assess the role of tree trunk</span><span>s</span><span> in </span><span>the </span><span>forest CH<sub>4</sub></span><span> budget. As </span><span>the </span><span>chambers used to measure emission</span><span>s</span><span> cover</span><span> only</span><span> a small part of the tree trunks, </span><span>it is necessary to </span><span>understand the intra-individual spatial variability </span><span>in</span><span> trunk CH<sub>4</sub></span><span> emission</span><span>s.</span> <span>W</span><span>e measured trunk CH<sub>4</sub></span><span> flux at nine locations per individual </span><span>on</span><span> four trees in a cool-temperate upland forest. To </span><span>appreciate</span> <span>the origin of th</span><span>is</span><span> variability and </span><span>the underlying</span><span> processes, we also measured</span><span> the</span><span> potential</span><span> rate of </span><span>CH</span><span>4</span><span> production and CH</span><sub><span>4</span></sub><span> concentration </span><span>at</span><span> sapwood and </span><span>characterized </span><span>wood and bark. Up to 15-fold spatial </span><span>variation in CH<sub>4</sub></span><span> fluxes were observed</span><span> at</span><span> the</span><span> individual</span><span> level</span><span>. This variability </span><span>can be highlighted </span><span>by the variation </span><span>in </span><span>the sapwood CH<sub>4</sub></span><span> concentration which </span><span>was</span><span> further </span><span>explained</span> <span>by </span><span>the variation in CH<sub>4</sub></span><span> production rate. The radial CH<sub>4</sub></span><span> diffusivity calculated from concentration gradient</span><span>s</span><span> and emission</span><span>s</span><span> was not related to the </span><span>measured </span><span>characteristics of </span><span>either </span><span>wood </span><span>or</span><span> bark, </span><span>raising the question of</span><span> the diffusion pathway. We emphasize</span><span>d</span><span> the importance </span><span>of</span><span> sampl</span><span>ing</span><span> trunk CH<sub>4</sub></span><span> flux at multiple locations on the surface of a tree trunk</span> <span>to capture spatial variability, a prerequisite for estimating tree-level CH<sub>4</sub></span><span> emissions.</span></p>
Data and scripts for: Airborne DNA reveals predictable spatial and seasonal dynamics of fungi
<p><span>Fungi are among the most diverse and ecologically important kingdoms of life. However, the distributional ranges of fungi remain largely unknown, as do the ecological mechanisms that shape their distributions. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardised aerial sampling of fungal spores. The vast majority of OTUs were detected only within one climatic zone, and the spatio-temporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal, and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating only for a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with microbial lifestyles follow the global biodiversity paradigms known for macro-organisms.</span></p> <p>The analyses presented in the paper can be reproduced with the R-script pipeline provided here. The starting point for the scripts is the datafile allData.RData that was published originally by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). The datafile allData.RData is provided also here for convenience, and it includes the following three objects: metadata, taxonomy, and otu.table (see Ovaskainen et al. for details). The script pipeline consists of the following elements (for deltails, see the Methods of the paper):</p> <ul> <li>Scripts S01: data preparation<br> <ul> <li>S01.1_download_clim_data.R. This script downloads daily climatic data for the entire world.</li> <li>S01.2_select_and_preprocess_clim_data.R. This script selects the data relevant for the study locations and preprocesses it.</li> <li>S01.3_add_climatic_data_to_metadata.R. This script adds the preprocessed climatic data to the metadata.</li> <li>S01.4_otu_guild_assignment.R. This script performs the guild assignment to the OTUs. It utilizes the datafiles Fung_LifeStyle_Data.RDS and funguild_db.rds provided here, and it utilizes the taxonomy of ProtaxFungi provided by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). Note that while the paper presents analyses and results only for the trait database of Aguilar-Trugueros et al., the scipts repeat the trait analyses also for the FunGuild database. The reason for not showing the results for the FunGuild database in the paper was that the database of Aguilar-Trugueros et al. contains FunGuild as one of the data sources, and that the results were highly coherent between the two databases.</li> <li>S01.5_add_trait_data_to_taxonomy_and_metadata.R. This script adds the guild data and spore size data to taxonomy (taxon-specific traits) as well as to metadata (community-weighted mean traits). It utilizes the datafile Spore_data_12Nov21.RDS provided here. This script can also be used to generated simulated contamination to the OTU table by setting contaminate=TRUE.</li> </ul> </li> <li>Scripts S02: exploratory analyses <ul> <li>S02.1_show_descriptive_statistics.R. This script outputs basic desriptive statistics from the data.</li> <li>S02.2_make_study_design_maps.R. This script plots the study design map shown in the paper.</li> <li>S02.3_compute_site_and_biome_profiles.R. This script computes site_profiles (needed in ordinations) and biome_profiles (needed to create Venn diagrams).</li> <li>S02.4_make_venns.R. This script produces Venn diagrams.</li> </ul> </li> <li>Scripts S03: ordination analyses <ul> <li>S03.1_make_ordination_maps.R. This script makes the ordination analyses.</li> </ul> </li> <li>Scripts S04: univariate analyses <ul> <li>S04.1_conceptualize_univariate_models.R. This script produces a figure that illustrates conceptually the differenent model variants. </li> <li>S04.2_make_univariate_analysis.R. This script implements the univariate analyses.</li> <li>S04.3_show_univariate_results.R. This script summarizes the results of the univariate analyses by producing tables of AIC and R2.</li> <li>S04.4_plot_univariate_results.R. This script plots the univariate models.</li> <li>S04.5_compute_temporal_turnover.R. This script computes site-specific indices of temporal turnover.</li> <li>S04.6_show_temporal_turnover.R. This script generates a plot illustrating temporal turnover.</li> </ul> </li> <li>Scripts S05: Hmsc analyses <ul> <li>S05.1_define_Hmsc_models.R. This script defines the Hmsc models. It utilizes the R-function as.phylo.formula provided here.</li> <li>S05.2_export_Hmsc_models_for_fitting.R. This script exports the unfitted Hmsc-models for fitting with Hmsc-HPC that operates on python/tensorflow.</li> <li>S05.3_import_fitted_Hmsc_models.R. This script imports the fitted Hmsc-models back to Hmsc-R.</li> <li>S05.4_postprocess_Hmsc_results.R. This script postprocesses the results of the fitted Hmsc model.</li> <li>S05.5_show_Hmsc_results.R. This script generates a plot that illustrates the postprocessed results.</li> </ul> </li> </ul>
Data for the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'
<p>The deposited data were employed to generate the figures concerning single-cell RNA (scRNA) and spatial transcriptomic analyses in the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'.</p>
Spatial Data Recovery and Resilience of Oyster Reefs in the Big Bend of Florida
<p><strong>Project:</strong> Recovery and Resilience of Oyster Reefs in the Big Bend of Florida</p> <p><a href="https://wec.ifas.ufl.edu/oysterproject/">https://wec.ifas.ufl.edu/oysterproject/</a></p> <p><strong>Lone Cabbage Reef Restoration Spatial Data (2017-2023) Repository:</strong></p> <p><a href="../communities/lonecabbagereef">https://zenodo.org/communities/lonecabbagereef</a></p> <p><strong>Contact:</strong> Joe Aufmuth, University of Florida, George A. Smathers Libraries, Academic Research and Consulting Services Department, <a href="mailto:mapper@ufl.edu">mapper@ufl.edu</a>, (352) 273-0371.</p> <p><strong>Clarifying Publication: </strong>Aufmuth, Moore, Pine, and Ennis (2024 in progress), An Oyster’s Pearl: Restoring the Elevation of Lone Cabbage Reef, Florida.</p> <p>The repository contains ArcGIS Map Packages (v3.2.0) that are listed in the repository file Descriptions_Lone_Cabbage_Reef_map_package_list_xls. </p> <p><strong>Purpose:</strong> Data collected varies in scale as well as positional and attribute accuracy. It is the responsibility of the user to verify that the data are appropriate for their project. No warranties or guarantees are made that the data are appropriate for uses other than the Recovery and Resilience of Oyster Reefs in the Big Bend of Florida project.</p> <p><strong>Data Collection:</strong> Elevation data was collected through professional certified surveyors (Lone Cabbage Reef 2017, 2018, and 2021) as well as through field data collection efforts using Trimble survey grade GPS equipment (University of Florida 2019). Oyster count data locations were collected through field efforts and mapped to field transects using Juniper GPS survey equipment (2018, 2019, 2020, 2021, 2022, 2023). Other spatial data layers included in this data set are credited in the layouts that produce the individual maps in the map packages.</p>
Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data
<p>This repository contains raw and processed data of the original datasets generated for Lee et al. "Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data".</p> <p>The files Dilution_3_ROI02.ome.tiff, Dilution_3_ROI02_mask.ome.tiff, st_reclustered_expr_data.h5ad contain raw data (as ome tiff), a mesmer-generated segmentation mask, and single-cell quantification in anndata format of the cell pellet data generated for the study.</p> <p>The file tonsil-for-zenodo.tar.gz contains all raw and processed data of the IMC data of the 16 human tonsil ROIs:</p> <ul> <li>imc contains raw tiff stacks of the acquisition</li> <li>mask contains mesmer-generated masks</li> <li>channel.yml contains channel info</li> <li>exp_mat/dc/ contains single cell quantifications using mesmer segmentation</li> </ul>
MetaVision3D: Automated Framework for the Generation of Spatial Metabolome Atlas in 3D | MALDI Data
<p>This repository contains MALDI data related to the Ma et al. study "<strong>MetaVision3D: Automated Framework for the Generation of Spatial Metabolome Atlas in 3D</strong>". Processed MALDI pixel-by-pixel .csv files for both metabolomics and lipidomics for two Wild-type samples, one 5xFAD sample and one GAA sample. If you use this dataset in your research, please consider citing the above study.</p> <p>The content of the files are:<br>wt.zip - pixel-by-pixel .csv files of metabolomics and lipidomics for wild-type sample.</p> <p>5x.zip - pixel-by-pixel .csv files of metabolomics and lipidomics for 5xFAD sample.</p> <p>gaa.zip - pixel-by-pixel .csv files of metabolomics and lipidomics for GAA sample.</p> <p>wt2.zip - pixel-by-pixel .csv files of metabolomics and lipidomics for wild-type2 sample.</p>
Spatial distribution of naming patterns in Russian law firms through text embeddings: data and code
<p>Data and code for a paper about spatial analysis of law firms naming in Russia. See also the attached paper's DOI and URL as well as the link to the GitHub repository.</p>
Mitigating autocorrelation during spatially resolved transcriptomics data analysis
<p>Here we include the marmoset brain and mouse gut STARmap data introduced in the corresponding manuscript, "Mitigating autocorrelation during spatially resolved transcriptomics data analysis". We also include the mouse brain STARmap PLUS data that was used to demonstrate cross-species spatial integration and was previously published in Shi, He, Zhou et al. 2022.</p>
Data for Characterizing the spatial signal of environmental DNA in river systems using a community ecology approach
<p>Environmental DNA (eDNA) is gaining a growing popularity among scientists but its applicability to biodiversity research and management remains limited in river systems by the lack of knowledge about the spatial extent of the downstream transport of eDNA.</p> <p>Here, we assessed the ability of eDNA inventories to retrieve spatial patterns of fish assemblages along two large and species rich Neotropical rivers. We first examined overall community variation with distance through the distance decay of similarity and compared this pattern to capture-based samples. We then considered previous knowledge on individual species distributions, and compared it to the eDNA inventories for a set of 53 species.</p> <p>eDNA collected from 28 sites in the Maroni and 25 sites in the Oyapock rivers permitted to retrieve a decline of species similarity with distance between sites. The distance decay of similarity derived from eDN<span>A </span>was similar, and even more pronounced, than that obtained with capture-based methods (gil-nets). In addition, the species upstream-downstream distribution range derived from eDNA matched to the known distribution of most species.</p> <p>Our results demonstrate that environmental DNA does not represent an integrative measure of biodiversity across the whole upstream river basin but provide a relevant picture of local fish assemblages. Importantly, the spatial signal gathered from eDNA was therefore comparable to that gathered with local capture based methods, which describes fish fauna over a few hundred metres.</p>
Supporting data for boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy
<p>This dataset includes the properties necessary to reproduce the analysis of water vapour statistics derived from imaging spectroscopy as in:</p> <p>Richardson et al. (2021a) DOI: 10.5194/amt-14-5555-2021<br> Richardson et al. (2021b) DOI: 10.5194/amt-2021-163 (pre-acceptance DOI, follow links to published version)</p> <p>Files include the retrieval emulator parameters, atmospheric profiles used in the emulator development, column-mean water vapour and cloud water both for the total column water vapour (TCWV) and "effective" TCWV, which accounts for the water vapour integrated along the direct solar path at a range of solar zenith angles, see Richardson 2021b, Eq. (7).</p>
Spatially explicit genetic capture-recapture data from black bears in Ontario, Canada, 2017-2019
<p>The Ontario Ministry of Northern Development, Mines, Natural Resources and Forestry sampled black bear (<em>Ursus americanus</em>) DNA at baited barbed wire hair corrals on 77 independent study areas in Ontario Canada, 2017-2019. Spatially explicit capture-recapture data from these surveys (>12 000 independent, spatially referenced detections of nearly 4000 individual bears) are archived here. This data set will be cited in manuscripts presenting different analyses of the entire data set or subsets thereof.</p>
Data from: The effects of human-altered habitat spatial pattern on frugivory and seed dispersal: a global meta-analysis
<p>Seed dispersal by frugivorous animals is important for plant mobility, regeneration, and persistence. Human-caused landscape change is thought to disrupt seed dispersal, but evidence is scarce. We performed a comprehensive meta-analysis on the effects of habitat spatial pattern on frugivory and seed dispersal. We found 233 effects from 71 studies. At a patch or local scale, altered habitat spatial pattern was measured as declining patch size, increasing patch isolation, or habitat edge (vs. interior). At a landscape scale it was measured as declining amount of habitat, increasing mean patch isolation, increasing number of patches, or increasing habitat edge in the landscape.</p> <p>We found overall negative effects of altered habitat spatial pattern on: (i) the quantity of frugivory or seed dispersal, (ii) the number of species involved in a plant-frugivore interaction, and (iii) seed dispersal distance. Moderator variable analysis was only possible for the first of these. It revealed negative responses of the quantity of frugivory or seed dispersal to habitat loss at both the local scale (declining patch size), and the landscape scale (declining habitat amount), but little evidence for a response to habitat edge at either scale. In addition, altered habitat spatial pattern reduced the quantity of frugivory or seed dispersal more strongly in temperate than tropical areas. Finally, the few-recorded effects of landscape-scale fragmentation per se (increasing patch density or edge density) on the quantity of frugivory or seed dispersal were mixed and weak. Our meta-analysis reinforces the notion that habitat loss is a major threat to frugivory and seed dispersal by animals, and reveals an insufficiency of studies of the effects of habitat fragmentation per se. Thus, based on the current literature, we conclude that maintaining and increasing habitat amount is vital for maintaining seed dispersal by frugivorous animals.</p>
ScienceDex guides
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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.