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Рис. 2. A, B. Modiolus (Modiolus) kurilensis Bernard, 1983: леваЯ створка снаружи (A), иЗнутри (B). Длина раковины 26.4 мм, высота – 47.3 мм; C, D. Gari (Gobraeus) kazusensis (Yokoyama, 1922): леваЯ створка снаружи (C), иЗнутри (D). Длина раковины 71.0 мм, высота – 39.9 мм. E, F. Panomya norvegica (Spengler, 1973): леваЯ створка снаружи (E), праваЯ створка иЗнутри (F). Длина раковины 70.8 мм, высота – 44.5 мм, толЩина – 36.7 мм. Fig. 2. A, B. Modiolus (Modiolus) kurilensis Bernard, 1983: left valve (A) outside, (B) inside. Shell length 26.4 mm, height – 47.3 mm. C, D. Gari (Gobraeus) kazusensis (Yokoyama, 1922): left valve (C) outside, (D) inside. Shell length 71.0 mm, height – 39.9 mm. E, F. Panomya norvegica (Spengler, 1973): left valve (E) outside, right valve (F) inside. Shell length 70.8 mm, height – 44.5 mm, thickness – 36.7 mm. in On the species composition of marine bivalves of the Sikhote-Alin Reserve (northern Primorye, Japan/East Sea)
Рис. 2. A, B. Modiolus (Modiolus) kurilensis Bernard, 1983: леваЯ створка снаружи (A), иЗнутри (B). Длина раковины 26.4 мм, высота – 47.3 мм; C, D. Gari (Gobraeus) kazusensis (Yokoyama, 1922): леваЯ створка снаружи (C), иЗнутри (D). Длина раковины 71.0 мм, высота – 39.9 мм. E, F. Panomya norvegica (Spengler, 1973): леваЯ створка снаружи (E), праваЯ створка иЗнутри (F). Длина раковины 70.8 мм, высота – 44.5 мм, толЩина – 36.7 мм. Fig. 2. A, B. Modiolus (Modiolus) kurilensis Bernard, 1983: left valve (A) outside, (B) inside. Shell length 26.4 mm, height – 47.3 mm. C, D. Gari (Gobraeus) kazusensis (Yokoyama, 1922): left valve (C) outside, (D) inside. Shell length 71.0 mm, height – 39.9 mm. E, F. Panomya norvegica (Spengler, 1973): left valve (E) outside, right valve (F) inside. Shell length 70.8 mm, height – 44.5 mm, thickness – 36.7 mm.
Data from: Local environment and coral composition affect recovery and determine long-term coral responses to recurrent mass mortalities in the Lakshadweep Archipelago
<p>A quarter century after the first global coral bleaching event in 1998, reports differ on the relative importance of anthropogenic influences, local environment and bleaching recurrence in determining the resilience of coral reefs. While life history traits largely determine how corals respond to temperature anomalies, it is unclear if these traits also determine how corals fare over time. From 1998 to 2022, we tracked compositional changes in reefs across the Lakshadweep Archipelago to explore how global El Niño events, and local environment (wave climate and depth) influenced coral responses to repeated mass bleaching. From the 1998 to the 2016 bleaching event, the magnitude of coral mortality reduced overall, particularly at deeper reefs (shallow: -38% to -3%; deep: -18% to -0.45%). Post-bleaching recovery correlated positively with higher wave exposure, linked to the creation of stable structures for coral settlement and survival. Across bleaching phases, recovery was initially slow (6-7 years post-mortality), but, given time, showed a much steeper increase, led by space-occupying genera like <em>Acropora</em>. However, recurring mass bleaching maintained coral cover low (~15% across all sites). These broad trends mask dynamic compositional patterns. Genera such as <em>Porites</em>, <em>Pocillopora</em>, and <em>Favia</em> declined less through time compared to <em>Acanthastrea</em>, <em>Turbinaria</em>, <em>Psammocora</em> and <em>Plesiastrea </em>among others. We identified six community clusters that describe contrasting long-term responses to local and global factors, mediated by depth and wave exposure. Interestingly, genera with different functional traits cluster together indicating that bleaching susceptibility interacts with depth and exposure, creating a spatial mosaic of coral assemblages. These clusters serve as a predictive, site-specific framework to understand the dynamically shifting but declining assemblage of Lakshadweep reefs. While local management could help maintain this changing composition, urgent global action is needed to secure the long-term ecological integrity of tropical reefs.</p>
Data from: Fatty acid composition as a function of latitude in barnacle cyprid larvae
<p>In this study, we investigated the fatty acid composition of the non-feeding stage of barnacle larvae (cyprids) using an integrative (larvae–environment) and comparative (latitudinal) approach. We measured fatty acids in the pelagic particulate matter and cyprids from <em>Chthamalus bisinuatus</em>, <em>C. proteus</em>, and <em>Semibalanus</em> <em>balanoides</em> from tropical to polar (Arctic) latitudes to identify potential food sources during the feeding larval stages (nauplius) that precede the cyprids and to ascertain larval capacity to integrate neutral (energetic) and polar (structural) fatty acids. </p> <p>Here we provide the complete dataset of all fatty acids detected both in i) the total particulate matter (TPM) present in the water column, and ii) the cyprid larvae of the different barnacle species. This could be useful in comparing data with future studies investigating fatty acid trophic markers in nearshore habitats. Raw data of biophysical paramateres includes cyprid i) size, ii) supply and (iii) settlement, as well as temperature collected using waterproof loggers (HOBO Pendant® Temperature/Light 64K).</p> <p>Image provided corresponds to the cyprid of the acorn barnacle <em>Semibalanus balanoides</em>, showcasing multiple lipid droplets, ie., the cyprid's main lipid storage organelles that store fat in the form of neutral lipids.</p> <p> </p>
Data from: Costs of antibiotic resistance genes depend on host strain and environment and can influence community composition
<p>Antibiotic resistance genes (ARGs) benefit host bacteria in environments containing corresponding antibiotics, but it is less clear how they are maintained in environments where antibiotic selection is weak or sporadic. In particular, few studies have measured the effect of ARGs on host fitness in the absence of direct selection or determined if any costs are fixed or depend on the host strain, perhaps marking some ARG-host combinations as reservoirs that can maintain ARGs in the absence of antibiotic selection. We quantified the fitness effects of six ARGs in 11 diverse <em>Escherichia spp</em>. strains. Three ARGs (blaTEM-116, cat, and dfrA5, encoding resistance to β-lactams, chloramphenicol, and trimethoprim, respectively) imposed an overall cost but all ARGs had an effect in at least one host strain, reflecting a significant strain interaction effect. A simulation predicts these interactions cause the success of ARGs to depend on available host strains, and, to a lesser extent, for successful host strains to depend on the ARGs present in a community. These results indicate the importance of considering ARG effects over different host strains, especially the potential of reservoir strains that allow resistance to persist in the absence of direct selection, in efforts to understand resistance dynamics.</p>
Source Data for main text and supplemental figures for manuscript "Community assessment of methods to deconvolve cellular composition from bulk gene expression"
<p>Source Data for main text and supplemental figures for manuscript "Community assessment of methods to deconvolve cellular composition from bulk gene expression"</p>
Figure 1 in Biodiversity of epiphytic marine macroalgae in Mexico: composition and current status
Figure 1: Numbers of algal species in each of the families that are best represented in each biogeographical ecoregion around Mexico, as defined by Spalding et al. (2007).
Рис. 2. Река БоΛьшая Пёра, ниже устья реки Юхта Fig. 2. The Bolshaya Pyora River, below the mouth of the Yukhta River in The Taxonomic Composition And Quantitative Indicators Of Zoobenthos In The Downstream Of The Bolshaya Pyora River (Zeya River Basin, Amur Region)
Рис. 2. Река БоΛьшая Пёра, ниже устья реки Юхта Fig. 2. The Bolshaya Pyora River, below the mouth of the Yukhta River
Рис. 1. Карто-схема реки БоΛьшая Пёра с указанием мест отбора проб (обозначены кружками) Fig. 1. Maps of the Bolshaya Pyora River with sampling locations (marked by circles) in The Taxonomic Composition And Quantitative Indicators Of Zoobenthos In The Downstream Of The Bolshaya Pyora River (Zeya River Basin, Amur Region)
Рис. 1. Карто-схема реки БоΛьшая Пёра с указанием мест отбора проб (обозначены кружками) Fig. 1. Maps of the Bolshaya Pyora River with sampling locations (marked by circles)
Database of Static Buckling Experiments with Cylindrical Composite Shells under Axial Compression
<p>This document lists all freely available data on thin-walled axially loaded composite cylindrical shells. The list includes the material used, the laminate lay-up, the wall thickness, the radius, the length, the determined material parameters, the boundary conditions, the buckling load, the manufacturer and manufacturing process as well as the test rig used.</p> <p>If you have new test data you want to add in this database feel free to contact us via tobias.hartwich@tuhh.de or stefan.panek@tuhh.de</p>
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
Figure 1 in Composition and spatio-temporal dynamics of aquatic bird community in humid areas of Alto Parana Atlantic Forest
Figure 1. Map of the RPPN Foz do Rio Aguapeí and location of the six studied areas in the RPPN Foz do Rio Aguapeí. Legend: (1) Lagoa São Gabriel; (2) Lagoa das Piranhas; (3) Lagoa dos Porcos; (4) Constructed wetland; (5) Aguapei river –; and (6) Lagoa da sede. Sources: CESP (2013) and Google Earth (2021).
Figure 2 in Composition and spatio-temporal dynamics of aquatic bird community in humid areas of Alto Parana Atlantic Forest
Figure 2. Cumulative curve of the 52 waterfowl bird species in the RPPN Foz do Rio Aguapeí showing stability from sample 27 to 31.
Figure 3 in Composition and spatio-temporal dynamics of aquatic bird community in humid areas of Alto Parana Atlantic Forest
Figure 3. NMDS (stress of 0.097) of the spatial distribution of the aquatic bird community recorded by the transect method in the lagoons of the RPPN Foz do Aguapeí, during the dry (rounded symbols) and rainy seasons (square symbols). Legend: LS = Lagoa da Sede; LSG = Lagoa São Gabriel; LP = Lagoa da Piranha and LPO = Lagoa dos Porcos.
Fig. 3 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 3 | Response of tree species to climate change across biomes. The median absolute latitude and median elevation shift among species, fraction of lost and gained species, and change in taxonomic and phylogenetic composition under climate change were computed for each forest ecoregion. The boxplots show statistics for n = 239 ecoregions for Tropical Moist Broadleaf Forests, n = 14 ecoregions for Tropical Coniferous Forests, n = 55 ecoregions for Tropical Dry Broadleaf Forests, n = 26 ecoregions for Boreal Forests, n = 91 ecoregions for Temperate Broadleaf Forests, n = 49 ecoregions for Temperate Conifer Forests and n = 61 ecoregions for Mediterranean Forests. The center line of the boxplots shows the median, the box limits the quartiles, the whiskers 1.5 times the interquartile range, and the points the outliers.Changes are computed between predicted distributions with climate variables for 1981-2010 and climate projections for 2071-2100 under climate change scenario SSP 5.85. Changes in composition are computed as the Euclidean distance between scaled NMDS and evoPCA values computed at the ecoregion level. Source data are provided as a Source Data file.
Fig. 2 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 2 | Species occupancy range distribution and loss. a Distributions of species occupancy range sizes globally (gray) and constrained to forests (at least 10% tree cover, color) for species in each forest biome. b Boxplot of relative range reduction across species in each forest biome with the center line showing the median, the box limits the quartiles, the whiskers 1.5 times the interquartile range, and the points the outliers. The distributions and boxplots are computed for n = 6810 species for Tropical Moist Broadleaf Forests, n = 588 species for Tropical Coniferous Forests, n = 1101 species for Tropical Dry Broadleaf Forests, n = 54 species for Boreal Forests, n = 1744 species for Temperate Broadleaf Forests, n = 178 species for Temperate Conifer Forests and n = 580 species for Mediterranean Forests. c Global map of median species range size constrained to forests, created with QGIS110. The gray base map corresponds to all areas for which model predictors were available. d Plot of species' median latitude against range size constrained to forests, colored by point density, where red indicates the highest density. Source data are provided as a Source Data file.
Fig. 1 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 1 | Gradients in taxonomic and phylogenetic composition show a near- a, c. Scatter plot of taxonomic and phylogenetic ordinations in environmental unique biodiversity signature of every single location on the planet. Taxonomic space, a 2-dimensional space made up of the 2 first axes of a PCA of the environcomposition is represented by a 3-axis non-metric dimensional scaling (NMDS) and mental variables used for species distribution modeling: mean annual temperature phylogenetic beta-diversity is represented by the 3 first axes of a phylogenetic (MAT), temperature seasonality (T season), annual precipitation (Annual P), preordination (evoPCA). Both the taxonomic and phylogenetic ordinations are com- cipitation seasonality (P season), growing season length (GSL), net primary proputed on the global community matrix derived from the modeled distributions of ductivity (NPP),silt content (Silt),coarse fragments (CF),and soil pH (pH).b, d. Map n = 10,590 tree species sampled at a resolution of 100 km, resulting in n = 12,548 of taxonomic and phylogenetic ordinations in geographical space. Source data are sites. The 3 axes of each ordination are mapped to red, green, and blue with provided as a Source Data file. The maps were created with QGIS110 and the gray minimum and maximum values corresponding to the 10th and 90th percentiles. base map corresponds to all areas for which model predictors were available.
Translaminar Fracture in a Mini-Protruded Compact Tension Specimen: A Dataset of Micro-Scale Tomograms of a Thin-Ply Carbon Fibre-Epoxy Composite acquired via Synchrotron Radiation Computed Tomography During In-Situ Loading
<p>In this study, we developed a scaled-down “mini-protruded compact tension specimen” to facilitate in-situ tensile testing coupled with synchrotron radiation computed tomography (SRCT). This innovative design provides valuable insights into in-situ translaminar damage mechanisms, significantly enhancing the accuracy of data used in finite element models.</p> <p>The specimen is made of HS40 carbon fibres and ThinPreg<sup>TM </sup>736LT epoxy resin, with the layup of [90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>]. The translaminar fracture experiments were conducted under continuous loading and scanning using ultra-fast SRCT at the Swiss Light Source (SLS) TOMCAT beamline (Paul Scherrer Institut in Villigen, Switzerland). A polychromatic beam with an energy of 24 keV was used. The achieved voxel size was 800 <em>nm</em>, and 1000 projections per scan and 2 <em>ms</em> exposure time were acquired per scan. The GigaFRoST camera served as the detector. The scans were reconstructed into 3D volumes using the SLS’s in-house absorption-based algorithm (Gridrec) for critical loading steps during a test—both before and after a load drop (detailed in the accompanying Excel file). The tensile loading was exerted on the specimen at a rate of 0.2 <em>mm/min</em> until failure during scanning with the Deben CT500.</p>
Stable and radiocarbon isotope compositions of DOC and DIC in Southeast Asian drainage canals
<p>This dataset contains the stable and radiocarbon isotope compositions of dissolved organic carbon (DOC) and radiocarbon isotope compositions of dissolved inorganic carbon (DIC) produced during DOC microbial respiration and photomineralization in canal waters sampled across disturbed peatlands in West Kalimantan, Indonesia in 2022. Microbial respiration and photomineralization experiments were carried out during laboratory incubations and natural sunlight exposures of canal water samples, respectively. Significant changes in the radiocarbon isotope composition of DIC between treatment and control waters were detected using a headspace extraction technique followed by a small-carbon extraction line for radiocarbon samples. </p>
Composition-based estimates of the thermal properties of New Zealand basement rocks: data used for calculations and figures
<p>This archive contains four files with compositional data (mineralogical and geochemical) used to estimate thermal conductivity and heat production in New Zealand basement terranes (Kirkby et al., 2024).</p> <p><br>HPR_source_data.csv - contains K, Th, and U concentrations and calculated heat production rates.</p> <p>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>K_wtpct - K concentration in weight percent<br>Th_ppm - Th concentration in ppm<br>U_ppm - U concentration in ppm<br>Heat_production_uW/m3 - heat production calculated from K, Th and U concentrations, in microWatts per meter cubed<br>Terrane - name of basement terrane that sample has been assigned to<br>Source - source of data, either PetLAB (Strong et al. 2016), PMAP (Turnbull ref) or separate compilation for this study<br>Reference - Reference citation for data as listed in PetLAB/PMAP or added for this study</p> <p> </p> <p>TC_from_majors_PMAP.csv - contains thermal conductivity estimated from major element geochemistry (Kirkby et al., 2024) using method of, Jennings et al. (2019).<br>Columns are as follows:</p> <p>Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016)<br>Collection_Number - sample number within the collection above<br>Sample_ID - unique sample ID in PetLAB<br>Analysis_ID - unique analysis ID in PetLAB<br>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>**_wtpct - major oxide concentration in weight percent (normalised to non-volatile component)<br>Terrane - name of basement terrane that sample has been assigned to<br>Analysis_Method - analysis method for major oxide concentrations<br>Thermal_conductivity_pred - calculated estimate of thermal conductivity based on major oxide composition, W/mK<br>Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB<br>Reference - analysis source reference, direct copy of Reference field in PetLAB</p> <p><br>TC_from_modal_mineralogy.csv - contains thermal conductivity estimated from modal mineralogy (Kirkby et al., 2024).<br>Columns are as follows:</p> <p>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>Analysis_ID - unique analysis ID in PetLAB<br>Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016)<br>Collection_ID - sample number within the collection above<br>Subsample - in the case of thermal conductivity, sometimes two samples were measured. This column distinguishes the two measurements<br>Mineralogy_method - method of determining mineralogy, either point count or QEMSCAN<br>Quartz - percentage of quartz in sample<br>Olivine - percentage of olivine in sample<br>Pyroxene - percentage of pyroxene in sample<br>Other - percentage of other minerals in sample<br>Thermal_conductivity_grain_pred - estimated thermal conductivity (W/mK) from mineralogy (Kirkby et al, 2024), W/mK<br>Thermal_conductivity_dry_measured - measured dry thermal conductivity (W/mK) for samples reported by Sanders et al (2024), W/mK<br>Porosity_measured_pct - measured porosity (percent) for samples reported by Sanders et al (2024)<br>Thermal_conductivity_grain_measured - grain thermal conductivity (W/mK) calculated from measured dry thermal conductivity and porosity<br>Terrane - name of basement terrane that sample has been assigned to<br>Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB<br>Reference - analysis source reference, direct copy of Reference field in PetLAB</p> <p><br>TC_from_normative_mineralogy.csv</p> <p>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>Analysis_ID - unique analysis ID in PetLAB<br>Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016)<br>Collection_ID - sample number within the collection above<br>Mineralogy_method - method of determining mineralogy, either Mesonorm or CIPWnormhb (CIPW norm with hornblende)<br>Quartz - percentage of quartz in sample<br>Olivine - percentage of olivine in sample<br>Pyroxene - percentage of pyroxene in sample<br>Other - percentage of other minerals in sample<br>Thermal_conductivity_grain_pred - estimated thermal conductivity (W/mK) from mineralogy (Kirkby et al, 2024)<br>Thermal_conductivity_dry_measured - measured dry thermal conductivity (W/mK) for samples reported by Sagar et al (2022)<br>Porosity_measured_pct - measured porosity (percent) for samples reported by Sagar et al (2024)<br>Thermal_conductivity_grain_measured - grain thermal conductivity (W/mK) calculated from measured dry thermal conductivity and porosity<br>Terrane - name of basement terrane that sample has been assigned to<br>Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB<br>Reference - analysis source reference, direct copy of Reference field in PetLAB</p> <p> </p> <p>tc_by_terrane.csv</p> <p>Contains thermal conductivity, standard deviation, and standard error of thermal conductivity estimates by terrane as shown in Figure 7 of Kirkby et al (2024)</p> <p> </p> <p>hpr_by_terrane.csv</p> <p>Contains thermal conductivity, standard deviation, and standard error of thermal conductivity estimates by terrane as shown in Figure 7 of Kirkby et al (2024).</p> <p> </p> <p><br>References</p> <p>Jennings, S., Hasterok, D., & Payne, J. (2019). A new compositionally based thermal conductivity model for plutonic rocks. Geophysical Journal International, 219(2), 1377-1394. https://doi.org/10.1093/gji/ggz376<br>Kirkby, A., N. Mortimer, R. Funnell, M. Sagar, A. Seward, K. Faure and F. Sanders (2024). Composition-based estimates of the thermal properties of New Zealand basement rocks, New Zealand Journal of Geology and Geophysics.<br>Sagar, M. W., Funnell, R., Randell, K., Faure, K., Seward, A., Sanders, F., & Stratford, W. R. (2022). Physical properties of Te Riu-a-Māui / Zealandia crustal rocks: Reconnaissance study and future research. (GNS Science Internal Report 2022/05. <br>Sanders, F., Seward, A., Sagar, M., & Faure, K. (2024). Thermal Properties of Zealandia Basement Rocks: results of Thermal Conductivity Scanner measurements 2023. Lower Hutt. (GNS Science Report. <br>Strong, D. T., Turnbull, R. E., Haubrock, S., & Mortimer, N. (2016). Petlab: New Zealand’s national rock catalogue and geoanalytical database. New Zealand Journal of Geology and Geophysics, 59(3), 475-481. https://doi.org/10.1080/00288306.2016.1157086 </p>
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.
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.