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165 results for “MAT”
Macrophyte and microbial mat biomass co-variation along a hydrologic gradient and response to a removal experiment in temporary wetlands Everglades, FL, USA, February 2003 – November 2006
This data package encompasses hydrologic variables, soil depth, hydrologically-regulated macrophyte community types, macrophyte biomass and community structure, and microbial mat biomass that was collected in two observational surveys and one in-situ experimental manipulation in six temporary wetland regions located in the Everglades, FL, USA. The goal of this project was to examine the co-variation in macrophyte and microbial mat biomass along the hydrologic gradient present across wetland regions and to determine the type and strength of interactions occurring between the two communities, which was tested using a biomass (macrophyte or microbial mat) removal experiment. The census observational survey took place at 140 sites from 2003-04-09 to 2004-05-26, which were randomly distributed across the hydrologic gradient present across the six temporary wetland regions. The transect observational survey occurred along six transects and each was deliberately established along the present hydrologic gradient within each region; a total of 254 sites were sampled from 2003-02-19 to 2005-03-04. The experiment took place at three temporary wetland sites with contrasting hydroperiods (3 – 6 months), and four transects were established per site with 24 pairs of control and treatment plots per transect. The removal treatment occurred one year before data collection, and data collection occurred from 2004-06-20 to 2006-11-25. The package includes six datasets, one R code file, and two shape files associated with the R code. Data collection for all datasets is complete. FCE1274_Census_Survey includes hydrologically-regulated macrophyte community type classifications, macrophyte biomass, microbial mat ash-free dry mass, mean soil depth, water depth, mean annual hydroperiod, and vegetation-inferred hydroperiod; each site was sampled once during the survey period and a subset of sites were sampled each year. FCE1274_Transect_Survey includes macrophyte community type classifications
The MAT_STOCKS database: economy-wide material flows and material stock dynamics around the world
<p>Material stocks of buildings, infrastructure, machinery and other short-lived products form the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy, and for climate change mitigation. Here, we provide a global, country-level database of national-level material stocks differentiated by four end-uses and four summary material groups, for 177 countries from 1900 to 2016.</p> <p>This MAT_STOCKS database is derived from the economy-wide, dynamic, inflow-driven stock-flow model of Material Inputs, Stocks and Outputs (<em>MISO2) </em>(Wiedenhofer et al. 2024)<em>. </em>MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling and waste management, as well as 13 end-use types of stocks. Further information on the model and its system definition, as well as the model input data and assumptions and data processing procedures can be found in the accompanying peer-reviewed publication. The model code and exemplary input data can be found in the GitHub repository. </p> <p><strong>The MAT_STOCKS database version 1.0 </strong>provided here is summarized from the more detailed modeling presented in (Wiedenhofer et al. 2024). The dataset here gives:</p> <ul> <li>Material stocks by 4 main end-uses: buildings, infrastructure, machinery and other short-lived products (summarized from 13 detailed end-uses modeled) (S_10)</li> <li>Material stocks and flows by 4 main material groupings: biomass, non-metallic minerals, metals, as well as fossil-fuels derived materials (summarized from 23 raw materials and 20 stock-building materials modeled)</li> <li>Flows: Gross Additions to Stocks (F_9_10) and End-of-Life/Waste potentials (F_10_11)</li> <li>177 countries</li> <li>1900 to 2016 </li> </ul> <p>All units in kilotons. Paramter names are in accordance with the system definition given in the publication.</p> <p>Additionally, this repository includes all data presented in the figures of the related journal article.</p> <p><strong>Further information</strong></p> <p>This dataset complements the following scientific article:</p> <p>Wiedenhofer, Dominik and Streeck, Jan and Wieland, Hanspeter and Grammer, Benedikt and Baumgart, Andre and Plank, Barbara and Helbig, Christoph and Pauliuk, Stefan and Haberl, Helmut and Krausmann, Fridolin, From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World (2024). Journal of Industrial Ecology, <a href="https://doi.org/10.1111/jiec.13575">https://doi.org/10.1111/jiec.13575</a></p> <p>The model code and its documentation are available on Github and Zenodo (see links below). For further information please see the publications. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <em>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</em></p> <p><strong>Funding</strong></p> <p>This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950), and the European Union's Horizon Europe programme (CircEUlar, grant agreement No 101056810). Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or granting authorities.<br><br></p>
Fungal litter mat cover in Cannopy Trimming Experiment (CTE) plots responses to canopy opening, hurricanes and drought
Fungi that bind leaf litter into mats and produce white-rot via degradation of lignin and other aromatic compounds influence forest nutrient cycling and soil fertility. Over three and a half years beginning in June 2014, 6 months before the second iteration of the Canopy Trimming Experiment (CTE), we measured quarterly the extent of white-rot litter mats formed by basidiomycete fungi in the Luquillo Mountains of Puerto Rico in response to disturbances – a simulated hurricane treatment executed by canopy trimming and debris addition in December 2014 (CTE0, a mid-year drought in 2015, and two hurricanes 10 days apart in September 2017. Percent fungal litter mat cover ranged from 0.4% after hurricanes Irma and Maria to a high of 53% in forest with undisturbed canopy prior to the 2017 hurricanes, with means mostly between 10 - 45% of fungal litter mat cover in undisturbed forest. Drought decreased litter mat cover in both treatments, except in one undisturbed plot dominated by a drought-resistant fungus, Marasmius crinis-equi. Percent fungal litter mat cover sharply declined after real hurricanes and the simulated hurricane treatment (CTE). We found that solar radiation had a significant treatment effect and was strongly negatively correlated with percent litter mat cover within each of the four climatic seasons. Solar radiation was also strongly negatively correlated with relative humidity, throughfall, rain and litter wetness. However, rainfall was negatively correlated with litter mat cover, possibly due to erosion or saturation during high rainfall events. Canopy opening reduced leaf litterfall rates but did not affect litter mat cover. The main negative effect on basidiomycete fungi that bind leaf litter into mats was lower litter moisture associated with increased solar radiation from canopy opening and high leaf fall during drought. Variation in drought tolerance among basidiomycete fungal litter mat formers provided some resilience to drought. \<para\> Support f
Metacommunity simulations for diatom assemblages residing in benthic cyanobacterial mats in Fryxell Basin in Taylor Valley in the McMurdo Dry Valleys, Antarctica
Here, we use MCSim, a spatially explicit metacommunity simulation package for R, to test alternative hypotheses about the roles of dispersal and species sorting in maintaining the biodiversity of diatom assemblages residing in black and orange mats in Fryxell Basin in Taylor Valley in the McMurdo Dry Valleys of Antarctica. The spatial distribution and patchiness of cyanobacterial mat habitats was characterized by remote imagery of the Lake Fryxell sub-catchment in Taylor Valley collected in January 2015. The available species pool for diatom metacommunity simulation scenarios was informed by the Antarctic Freshwater Diatoms Database, maintained by the McMurdo Dry Valleys Long Term Ecological Research program, representing samples collected between January 1994 and January 2013. We used simulation outcomes to test the plausibility of alternative community assembly hypotheses to explain empirically observed patterns of freshwater diatom biodiversity in the long-term record. The most plausible simulation scenarios suggest species sorting by environmental filters, alone, was not sufficient to maintain biodiversity in the Fryxell Basin diatom metacommunity. The most plausible scenarios included either (1) neutral models with different immigration rates for diatoms in orange and black mats or (2) species sorting by a relatively weak environmental filter, such that dispersal dynamics also influenced diatom community assembly, but there was not such a strong disparity in immigration rates between mat types. The results point to the importance of dispersal for understanding current and future biodiversity patterns for diatoms in this ecosystem, and more generally, provide further evidence that metacommunity theory is a useful framework for testing hypotheses about microbial community assembly. This dataset supports the paper: Sokol, E. Et al, 2020. Evaluating Alternative Metacommunity Hypotheses for Diatoms in the McMurdo Dry Valleys Using Simulations and Remote Sensing Data
Spectral and biological characteristics of microbial mats and mosses across Fryxell Basin, Taylor Valley, Antarctica (2018-2019)
An intensive field campaign was conducted during the 2018-2019 austral summer to assess spectral and biological characteristics of multiple microbial mat types, as well as mosses, across nine ephemeral glacial meltwater streams in the Fryxell Basin of Taylor Valley, located in the McMurdo Dry Valleys region of Antarctica. In addition to biological sample collection, hyperspectral visible/near-infrared (VNIR) measurements were conducted using an ASD FieldSpec4 spectrometer, corrected and averaged across each mat, moss, and soil type, and downsampled to the multispectral resolution of the WorldView-2 satellite. This package contains a detailed sample archive, associated images, biological characteristics that include ash free dry mass (AFDM), chlorophyll-a, and pigment concentrations, as well as hyperspectral and multispectral reflectance spectra.
Metagenomics of a pustular microbial mat from Shark Bay, Australia: Raw sequences and assembled MAGs
<p>This data accompanies the paper, "<a href="https://www.nature.com/articles/s43705-022-00128-1">Metagenomic, (bio)chemical, and microscopic analyses reveal the potential for the cycling of sulfated EPS in Shark Bay pustular mats</a>" which looks at the cycling of sulfated polysaccharides in peritidal pustular mats from Shark Bay, Australia. The microbial community was sequenced, assembled, and binned. The raw sequencing reads used in this analysis are the following:</p> <ul> <li>SB_forward_paired_copy.fastq.gz </li> <li>SB_reverse_paired_copy.fastq.gz </li> </ul> <p>The resulting metagenome-assembled genomes (MAGs) are presented in the following folder:</p> <ul> <li>MAGs.zip</li> </ul> <p> </p> <ul> </ul> <p> </p>
A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances (MAT-Version)
<p>Head related impulse response measurements with the KEMAR dummy head performed in an anechoic chamber with a resolution of 1°. The impulse responses are provided for different distances and are accompanied by headphone compensation filters.</p> <p>This entry stores the measurements in the MAT format for use in Matlab/Octave. The measurements are identical to the once stored in the SOFA format available at <a href="https://doi.org/10.5281/zenodo.55418">https://doi.org/10.5281/zenodo.55418</a></p>
Data from: Validating a practical methodology for thatch - mat - soil distinction in turfgrass soils
<p><span>We described a <a name="_Hlk163223211"></a>practical method for the distinction of thatch, mat and soil layers in turfgrass soils, being a combination of visually and manually observable characteristics. For two widely different turfgrass species, we analyzed total organic matter (TOM) and dry bulk density (ρd) in thin slices of 6 mm (between 0 and 10 cm soil depth), resulting in clear patterns for both soil properties with increasing depth. Statistical analysis of TOM patterns resulted in similar boundary depths between calculated and observed layers, validating our practical method for the distinction of thatch, mat and soil layers as a reliable method. </span><span>Furthermore, we characterized thatch, mat and soil layer by different TOM fractions. <span>TOM was fractionalized into three distinctive and functional pools of organic matter: (1) visible organic matter (VOM), consisting of mainly non-decomposed plant structures, (2) decomposed organic matter (DOM), consisting of mainly decomposed plant structures with its associated microbial biomass, and (3) soil organic matter (SOM), being the background value or recalcitrant native organic matter in a soil and its local microbial biomass.</span></span></p> <p><span><span>We distinguished thatch, mat and soil layer based on visual and manual observable characteristics of the layers and a protocol as described in Evers et al. (2024) https://doi.org/10.1002/its2.148. This study was conducted on a well-established turfgrass demonstration field with monoculture plots of turfgrass varieties (turfgrass seed company DLF; Moerstraten, the Netherlands; 51°32'27'' N 4°20'54" E) 3.5 years after sowing, reflecting the result of organic matter accumulation over these initial years of turfgrass establishment. The climate regime was marine with cool to medium summer temperatures and mild winters (Cfb/Cfa according to the Köppen-Geiger climate classification system (Peel, et al., 2007)). The field was built on a sandy soil (Hortic Anthrasol as described in the FAO/UNESCO soil map of the world (2006)). Sampling of the soil took place in June 2016 before the field was sown. We tested our methodology with two turfgrass species, slender creeping red fescue (<em>Festuca rubra trichophylla </em>(Frt), variety Beudin of DLF) <span>as an example of a spreading turfgrass</span><em> </em><span>and perennial ryegrass (<em>Lolium perenne </em></span>(Lp)<em>,</em> variety Duparc of DLF) as an example of a bunch-type grass, as these two species were expected to differ widely in thatch and mat depth. The individual plot size for each variety was approximately 1 m<sup>2</sup> (0.8 x 1.2 m). Plots of each species were sampled at the end of February 2020. A subplot of 0.25 m<sup>2</sup> (0.5 m x 0.5 m) in the center of one plot per species was selected, to avoid contamination with other varieties (at least 90% pure monoculture), and it was marked with a metal frame. From the 25 cells of 5 x 5 cm in this frame, nine evenly dispersed cells were chosen to take a set of soil samples of 10 cm depth, using a core sampler with 2.8 cm diameter, for thatch-mat and mat-deeper soil boundary observation and TOM and <em>ρ</em><sub>d</sub> analyses. A<span>nother set of nine samples was taken next to the previous cells for VOM analyses. </span>Every fresh 10 cm soil core was first photographed (Canon Powershot S5 camera, 24 megapixels; Canon Europe, Amstelveen, the Netherlands), judged on thatch-mat and mat-deeper soil boundaries following the method described in supplementary information, and then sliced into 14 subsamples of 6 mm each plus a 16 mm subsample at the bottom, starting to measure just below the green canopy with an accurate ruler (Sola HK ¼ W12, EU-accuracy class 3), for analyzing TOM and <em>ρ</em><sub>d</sub>.in every subsample. TOM and <em>ρ</em><sub>d</sub> were analyzed after samples were dried at 105°C for 24 h. VOM was analyzed after a<span>ll sediment per slice was carefully washed off with tap water in a fine sieve (approximately 600 µm (27 mesh)), after which the remaining (dead and living) plant biomass, mainly roots and rhizomes, was dried at 65 °C for at least 48 h. SOM was determined separately in the bulk soil of the study site and is 2.6% of the dry matter. DOM was calculated via subtraction of VOM and SOM from TOM</span></span></span></p> <p><span>Based on the analyzed TOM content of the 14 slices per nine replicates, the boundaries of distinctive layers were calculated. For this, we rescaled the TOM results per replicate via the normalized function <em>F </em>(χ) = (χ-χ<sub>min</sub>)/(χ<sub>max</sub>-χ<sub>min</sub>) into values between 0 and 1 to overcome scale differences between replicates but keeping distributions the same. Per turfgrass species, the best fitted line, i.e., the smallest <em>rse</em>, and its 95%-confidence interval through all 135 points was iteratively calculated by non-linear least square regression with the nls function of R (version 3.5.2; 2018-12-20). For the fitting of the statistical models, either a logistic function (<em>F </em>(χ) = α/(1+e<sup>-(βχ+γ)</sup>) or a bell-shaped Gaussian function (<em>G </em>(χ) = αe<sup>-((χ-β)^2/2γ^2)</sup>) was used, based on the best fit for the respective turfgrass species. The characteristics of these mathematical functions were used to explain the TOM-dynamics in the soil. To this end, the turning points of the curves were calculated by finding where the first derivative of both functions equals zero, i.e., solving <em>F’</em> (χ) = 0 and <em>G’ </em>(χ) = 0 respectively. The inflection points of each curve were determined by analyzing the second derivative, i.e., identifying the points where the second derivative <em>F’’</em> (χ) or <em>G’’</em> (χ) = 0, indicating changes in concavity. The inflection points and turning points indicated changes in TOM content in the turfgrass soil profile as likely boundaries between distinctive soil layers. </span><span>Differences in parameters between distinctive soil layers and turfgrass species were determined based on the calculated means of parameters per nine replicates of soil slices Normality of residuals and the equality of variances was checked with diagnostic plots and Levene’s test, respectively. Normally distributed means were compared with one-way ANOVA for a 3-layered soil system, followed by either Tukey post hoc tests in case of equality of variance, or by the Games-Howell post hoc test in case of no equality of variance, or with a one sample t-test for a 2-layered soil system. </span></p>
Role of vegetation and coarse wood debris on soil processes and mycorrhizal mat distribution patterns at the Hi-15, Andrews Experimental Forest, 1994-1995
The main objective of this study was to determine if there were relationships between forest floor attributes such as the location of: (1) individual trees, (2) clusters of undergrowth vegetation, (3) coarse woody debris, (4) rocks and (5) topography and both soil characteristics and distribution patterns of ectomycorrhizal fungal mats. This data set includes mat, rock, wood, and moss distribution patterns (as presence or absence at each sampling node) as well as basic soil date taken at the same locations. The forest floor attributes were digitized using Esri ArcGIS. These GIS data layers are available as separate files in FSDB Database code SP029.
Soil respiration associated with ectomycorrhizal mats in an old-growth stand along lower Lookout Creek, HJ Andrews Experimental Forest (2008-2009)
Comparisons of respiration rate and environmental variables for mat and non-mat soil were conducted between July 2008 to Nov 2009 in a 0.1ha plot adjacent lower Lookout Creek, approximately 700m downstream from Lookout Camp (44 deg 13”25’N, 122 deg 15”30’W, 484m above sea level). The predominant overstory species are Psuedotsuga menziesii, Tsuga heterophylla, and Thuja Plicata. Associated ectomycorrhizal communities were measured over the 1.5 year period and data collection for the study is complete. Soil respiration was measured using LiCOR instrumentation, and analyses were performed computationally by correlating soil respiration with known environmental metrics (moisture, temperature, etc.) measured in other studies (TW006, MV001, etc.).
Biophysicochemical properties, pigment concentrations, and nif gene counts of microbial mats and soils from Taylor and Beacon Valleys, McMurdo Dry Valleys, Antarctica (2019-2020)
This data package includes biophysicochemical properties, pigment concentrations, and nif gene counts from microbial mat and soil samples collected from the McMurdo Dry Valleys of Antarctica during the 2019-20 austral summer. Specifically, data include physicochemical properties of the collected soils (gravimetric water content, pH, electrical conductivity, inorganic nitrogen, inorganic phosphorous, sulfate, chloride, soil organic carbon, and total nitrogen), biological properties of the microbial mats (ash-free dry mass and pigment concentrations – scytonemin, scytonemin-red, myxoxanthophyll, zeaxanthin, chlorophyll-a, chlorophyll-b, β-carotene, canthaxanthin, echinenone), and nif gene counts (overall nif and nifH) of the soils and microbial mats. These data were collected to infer the functioning and nitrogen cycling abilities of microbial mat and soil communities from areas of differing landscape histories and geochemical legacies (Ross and Taylor tills in the Taylor Valley, and Beacon Cirque in Beacon Valley).
Distribution models of microbial mats and mosses across Fryxell Basin, Taylor Valley, Antarctica (2018-2019)
Long-term ecological field surveys from the McMurdo Dry Valleys Long Term Ecological Research program (MCM LTER) have documented the abundance and diversity of microbial mat types across ephemeral glacial meltwater streams in the McMurdo Dry Valleys region of Antarctica. However, field surveys are limited and are incapable of being performed across the entirety of streams within a field season. Therefore, we used remote sensing to examine the distribution of these diverse communities across streams in order to determine whether large scale distribution patterns are similar to those the MCM LTER has already thoroughly studied in situ. As part of our 2018-2019 field campaign, we established two to three 20 x 20 m plots within five different streams (Bowles Creek, McKnight Creek, a relict channel, Canada Stream, and Crescent Stream) in the Fryxell Basin of Taylor Valley. We performed point transect and quadrat field surveys of microbial mat and moss cover within each 20 x 20 m plot. We then used hyperspectral measurements of mat and moss collected in the field, previously archived in “Spectral and biological characteristics of microbial mats and mosses across Fryxell Basin, Taylor Valley, Antarctica (2018-2019),” in linear spectral mixing models to determine mat and moss coverage in the same 20 x 20 m plots within an atmospherically corrected WorldView-2 satellite image from Dec. 12, 2018. We ground truthed our modeled mat and moss abundances with our field survey coverages and determined the limitations of our methods. We then modeled mat and moss coverage across Huey Creek and Von Guerard Stream to apply our methods to streams without ground truthing measurements. Our results demonstrate the spatial distribution of moss and black, orange, red, and green microbial mat across Fryxell Basin streams. Observations of mat and moss coverage at the basin-wide scale are similar to those seen in localized stream areas.
Algal microbial mat biomass measurements from the McMurdo Dry Valleys and Cape Royds, Antarctica (1994-2025, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This dataset contains microbial biomass concentrations found in algal mats located in streams throughout the McMurdo Dry Valleys as well as in ponds of the nearby Cape Royds. Microbial mat biomass has been collected as part of the McMurdo LTER since the 1993-1994 field season and measured as ash-free dry mass (AFDM) and chlorophyll-a (Chl-a).
Biomass, stoichiometry, and isotopic signatures of stream microbial mats, McMurdo Dry Valleys, Antarctica (2012-2013)
We conducted a field survey to quantify the biomass (chlorophyll-a and ash-free dry mass), nutrient ratios (molar C:N:P), and isotopic signatures (δ13C and δ15N) of four microbial mat types (green, orange, black, and red) in the glacial meltwater streams of the McMurdo Dry Valleys, Antarctica. All samples were taken from late December to late January during the 2011-2012 and 2012-2013 austral summers, and included sites from Taylor, Miers, Garwood, and Wright valleys. Most collection sites were located at the lake outlet of streams, but for a subset (e.g. Delta, Von Guerard, Onyx, Miers, and Canada) more than one sample site is included per stream system.
SCM-CNN: A Robust Deep Learning Matting Model for Cloud Removal in Optical Imagery
<p>This is a dataset that can be used for cloud detection and cloud opacity estimation. The data is saved in python-numpy form, and stored in dictionary :dict_keys(['OriginImage', 'Gimage', 'Alpha', 'Trimap', 'CloudMaxDN']) represents the cloud-free remote sensing image, cloud remote sensing image, cloud opacity, trilateration information and cloud brightness respectively. The command {np.load("Path",allow_pickle=True).item()} is used to read, where "Path" is the corresponding path to the file.</p>
Raw GNSS data divided into observation data in CRX, RINEX and mat format and navigation data in SP3 format and both group of the data in mat format
Open the record for dataset details and reuse information.
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.
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.
Dataset: Mattel, Inc. (MAT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
The data on the optical properties of kombucha and kombucha proteinoids, as well as chaos calculations are provided for the paper titled "Kombucha Mats as Responsive Materials."
<p>The data on the optical properties of kombucha and kombucha proteinoids, as well as chaos calculations are provided for the paper titled "Kombucha Mats as Responsive Materials."</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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OpenNeuro
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