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469 results for “Dimensions”
Point-Quarter Distance and Dimension Measurements to Calculate Shrub Density and Estimate Shrub ANPP in a Chihuahuan Desert Creosote Shrubland at the Sevilleta National Wildlife Refuge, New Mexico
In an effort to better quantify NPP of Creosotebush in the Five-Points region, it was decided to test the Point-Quarter method against the standard 1-m2 quadrat method that has been in use since 1998. Transects were laid out across the 5 mammal trapping webs as well as across burned and unburned plots of the Mixed Shrub site (MS). Repeated measures of the same bushes are performed seasonally. Whole shrubs of various size classes are collected, sorted, and weighed to develop regressions for biomass. Purpose: Data was collected initially to determine density and dimensions of creosote bush in the Five points area on core rodent webs and on burned and unburned plots following the 2003 prescribed burn. It was decided to expand the project by continuing measurements through time to quantify the change in shrub size and with simultaneous harvest of shrubs to measure NPP.
EEG: Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms
Open the record for dataset details and reuse information.
Catalogues of Semantic Artefacts - Maturity Dimensions and Features
<p>This dataset contains, in three different formats (XSLX, CSV, and PDF), a description of twelve maturity dimensions identified from the literature that can be used to measure the maturity of the semantic artefacts catalogues (SAC). For each dimension, a number from 2 to 6 features has been added, for a total of 43 features overall.</p>
Tree mortality in Forest and Biodiversity 2: a tree diversity experiment to understand the consequences of multiple dimensions of diversity and composition for long-term ecosystem function and resilience
The Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity planted in 100 m2 and 400 m2 plots at 1 m spacing, appropriate for testing long-term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m2 plots at 0.5 m spacing. Both are adjacent to the BioDIV prairie-grassland diversity experiment, enabling comparative investigations of diversity and ecosystem function relationships between experimental grasslands and forests at different planting densities and plot sizes. This data package examines mortality in the first six years of the experiment.
FAB2_sapling_volume_2021-2022 in Forest and Biodiversity 2: a tree diversity experiment to understand the consequences of multiple dimensions of diversity and composition for long-term ecosystem function and resilience
The Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity planted in 100 m2 and 400 m2 plots at 1 m spacing, appropriate for testing long-term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m2 plots at 0.5 m spacing. Both are adjacent to the BioDIV prairie-grassland diversity experiment, enabling comparative investigations of diversity and ecosystem function relationships between experimental grasslands and forests at different planting densities and plot sizes. This data package examines mortality in the first six years of the experiment.
fab2_allometry_2016-2022 in Forest and Biodiversity 2: a tree diversity experiment to understand the consequences of multiple dimensions of diversity and composition for long-term ecosystem function and resilience
The Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity planted in 100 m2 and 400 m2 plots at 1 m spacing, appropriate for testing long-term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m2 plots at 0.5 m spacing. Both are adjacent to the BioDIV prairie-grassland diversity experiment, enabling comparative investigations of diversity and ecosystem function relationships between experimental grasslands and forests at different planting densities and plot sizes. This data package examines mortality in the first six years of the experiment.
Institutional Dimensions of Restoring Everglades Water Quality -Interview Notes (FCE), September 2014-July 2015
These data were compiled through semi-structured and open-ended interviews as part of the Institutional Dimensions of Restoring Everglades Water Quality research project. The notes represent responses from farmers, agricultural extension agents, state and federal government officials, private water consultants, and nonprofit officials involved in the implementation of the Everglades Forever Act regulations. These mandate that farms in the Everglades Agricultural Area implement best management practices to reduce phosphorus enrichment. Since the regulations started in 1994, water quality has steadily improved. The research sought to explain why the regulations have been effective.
The source of energy, the fifth dimension and definition of time
<p>**TITLE: ENERGY SOURCE, THE FIFTH DIMENSION, AND THE DEFINITION OF TIME.**</p><p> </p><p>**INTRODUCTION:** It is known that each object in our universe is defined by these coordinates x, y, z, and that time, as concluded by Albert Einstein, is referred to as the fourth dimension. I am confident that the very existence of each object in our universe, in addition to these four dimensions (x, y, z, t), the energy source is the fifth dimension.</p><p> </p><p>This can be defined as the distance from any object to its energy source, which divides based on the time needed for this energy to reach the object. On our Earth, it is the sun, and in our galaxy, it is the black hole at the center. Thus, as a first deduction, black holes ⚫️ are sources of energy.</p><p> </p><p>These four forces - strong force, weak force, electromagnetic force, gravitational force - have grounded humanity on Earth 🌎, and to leave, it requires a lot of energy and many risks. So, it takes a new approach to physics, starting with an essential element, which is time.</p><p> </p><p>**METHODOLOGY:** The cosmos and universes are eternal; nothing is lost, and nothing is created; everything transforms. Thus, the term time is the beginning of an event or phenomenon, and time does not flow the same way from one star to another, from one galaxy to another, or from one individual to another.</p><p> </p><p>For example, you and I are the same age but show different signs of aging. So, time is sequential and manifests through temporal energy power.</p><p> </p><p>**DEFINITION OF THE ENERGY SOURCE:** The energy source is the speed of any energy to reach the surface of an object while embracing the gravitational lines of that object. We can determine it by a simple physical concept, which is \(S_e = C/G^2\); the energy source is the FIFTH DIMENSION.</p><p> </p><p>**DEFINITION OF TIME:** We can start talking about the moment when temporal energy is just at the point of contact between light and gravitational curves. Light slips between gravitational curves to reach us on Earth 🌎, knowing that light and gravity travel at the same speed in the universe. Thus, the beginning of the term TIME ⌛️ can be expressed in this way:</p><p> </p><p>\[T = cĥ \times \frac{C}{G²}\]</p><p> </p><p>\[E_t = M \times \frac{C}{\pi G²}\]</p><p> </p><p>This new physics conception could one day allow us to leave our Earth 🌎 easily with less energy and fewer risks, making interstellar travel more accessible.</p><p> </p><p>**DISCUSSION:** Rainbows and auroras are visible events to the naked eye of this interaction between light and gravitational curves. In a black hole, gravity is so intense that light does not travel inside, and time stops ⌛️ completely.</p><p> </p><p>This definition of time \(T=cĥ \times \frac{C}{G²}\) summarizes everything. So, time + space + light form the temporal sphere and manifest as temporal energy, which can be called the 5th dimension.</p><p> </p><p>TIME + SPACE = SPACE-TIME = 4TH DIMENSION. TIME + SPACE + LIGHT = TIME SPHERE = 5TH DIMENSION.</p><p> </p><p>In the fifth dimension, light excites gravity (gravitons), and the latter contracts to bend space-time and objects, stars, galaxies, stars... etc. This gives us the ability to bring distances between point A and point B closer without affecting or modifying anything, and this will be the next mechanism for future spacecraft.</p><p> </p><p>**CONCLUSION:** The model I have just published will complete physics in its fifth dimension, allowing humanity to create very sophisticated devices that can shorten routes and interstellar travel by using a space characteristic never used before, which is the CURVATURE OF SPACE.</p><p> </p><p>**PREDICTION AND DEDUCTION:** Each galaxy has its own black hole at the center, and each black hole is a source of energy. Each black hole has two faces, one that gives life to stars, planets, stars, nebulae, etc.</p><p> </p><p>1. A dying world gives birth to another world; nothing is lost, nothing is created, everything transforms.</p><p>2. Black holes dictate the rotation speed of each star and its inclination.</p><p>3. Celestial bodies, stars, planets regularly come from the side of the black hole to take charge of destiny, time, and memory to start their journeys in the universe again.</p><p>4. Just at the exit of the black hole, physically speaking, it's the moment t=0, the beginning of all life.</p><p>5. In the universe, time runs in two directions, one-way and a return to the energy source.</p><p>6. A place in the universe devoid of black holes will see accumulations of galaxies and planets and large voids.</p><p>7. Black holes uniformly distribute matter and energy in all corners of the universe.</p><p>8. On Earth, mass generates energy, but in the universe, energy generates mass for a potential balance. This means that masses already have their own accelerations or are simply fueled by energy present everywhere in the universe. Of course, in contact with an energy source like our sun, all planets around our sun are powered by the energy released by our sun in contact with the dark energy of our universe.</p><p>9. End of the use of fossil fuels and the end of air travel by plane, making way for a practical and non-polluting technique to move in the universe by bending space and bringing distant points closer without altering the texture of space.</p><p>10. End of wars and fights for earthly wealth; each country will have its galaxies to supply essential materials.</p>
Supplemental Material to "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments"
<p>Supplemental material to manuscript "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments" published in IMMJ "Integrating Materials and Manufacturing Innovation" 2022</p>
Data for manuscript "Creating boundaries along a synthetic frequency dimension" in Nature Communications
<p>Data for manuscript "Creating boundaries along a synthetic frequency dimension"</p> <p>https://www.nature.com/articles/s41467-022-31140-7</p> <p>https://arxiv.org/abs/2203.11296</p>
Raw data to "Scaling at quantum phase transitions above the upper critical dimension"
<p>This directory contains the data used to generate the numerical results in the work "Scaling at quantum phase transitions above the upper critical dimension[1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README file.</p> <p>[1]: A. Langheld et al., Scaling at quantum phase transitions above the upper critical dimension, <a href="https://scipost.org/10.21468/SciPostPhys.13.4.088">SciPost Phys. <strong>13</strong> 088</a>, 2022.</p>
Data for paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions"
<p>Data for paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions" including:</p> <p>-Raw SEM imaging and EBSD data</p> <p>-Processed SEM images</p> <p>-3D slices</p> <p>-Supplementary summary figure</p> <p>-Supplementary video</p>
Exploring the economic, social, and environmental dimensions of community-supported agriculture in Italy (dataset)
<p>Dataset inherent to the following article:</p> <p>Medici, M., Canavari, C., Castellini, A., 2021. <em>Exploring the economic, social, and environmental dimensions of community-supported agriculture in Italy</em>, Journal of Cleaner Production, 316, 128233, DOI: <a href="https://doi.org/10.1016/j.jclepro.2021.128233">10.1016/j.jclepro.2021.128233</a></p>
Customer experience dimension in service provider commenters (bahasa)
<p>A dataset containing customer commenters that obtain from user-generated content on Twitter and Instagram @byu_id. The dataset is used Bahasa, and it includes 32.684 raws. The following is an explanation of the variables in each column:</p> <p>- <strong>comment</strong>: comments using Indonesian obtained from January 1 to June 30, 2021. This comment has been through a preprocessing process.</p> <p>- <strong>sentiment</strong>: consists of neutral, positive, and negative sentiments of customers.</p> <p>- <strong>dimension</strong>: there are six dimensions using customer experience proposed by Malviya and Varma (2012).</p>
Task and Dimension Switching Data Set
<p>This data set contains the data of 8 experiments concerned with the question how multi-component task sets are organised. Four different views on task set organisation were tested in these experiments. Experiments 1-4 were used in Vandierendonck, A., Christiaens, E., & Liefooghe, B. (2008). On the representation of task information in task switching: Evidence from task and dimension switching. <em>Memory & Cognition, 36</em>(7), 1248-1261. doi: 10.3758/mc.36.7.1248. The entire data set was used for a test of the utility of integrated measures of speed and accuracy. This is currently a submitted paper: Vandierendonck, A. Further tests of the utility of integrated speed-accuracy measures in task switching. Journal of Cognition.</p>
Extracting interpretable rules with Bayesian Networks. A case study of intrinsic human hazardous properties of silver nanoforms for the Safety Dimension of Safe and Sustainable by design paradigm.
<p>Three different datasets: toxicological attributes in i) lung and ii) intestinal cell line along with system dependent features and iii) system independent pchem properties) were merged. Each row represents one set of experimental testing conditions and related system dependent nanodescriptors based on the exposure dose and NFs pre-treatment (for intestinal assessments). The system independent inputs are NF specific and independent of experimental conditions. Data is captured via FAIR principles where the reader can find the origin (institution) of each data, the responsible data creators (experimentalists), the raw measurements, the protocols followed and the instrumentations used for each experiment. .</p>
Availability of information on citizen science activities, checked against the Activities & Dimensions Grid of Citizen Science on the basis of some projects
<p>The research resulting in this report aimed at answering the following questions:</p> <ul> <li> <p>Which information on citizen science activities is online available that matches the Activity & Dimension Grid of Citizen Science or goes beyond it? </p> </li> <li> <p>Is there any contradictory information?</p> </li> <li> <p>What can be the reason for the availability or non-availability of information about citizen science activities?</p> </li> <li> <p>How does/could this impact on the CS Track’s recommendations?</p> </li> </ul> <p>The corresponding dataset consists of the results of a keyword-based search in the WP2 project database. The information retrieval resulted in 3318 projects on which information is available in German or English.</p> <p>More information on this research can be found in D2.2 section 3.2.</p>
Data and scripts to reproduce the results shown in "Stability of attractor local dimension estimates in non-Axiom A dynamical systems"
<p>Here we make available all the codes and datasets to reproduce the results of the paper "Stability of attractor local dimension estimates in non-Axiom A dynamical systems" by Flavio Pons, Gabriele Messori and Davide Faranda.</p> <p>The pre-print of the article is available at https://hal.science/hal-04051659/document.</p> <p>Any question/comment can be sent to flavio.pons@gmail.com.</p> <p>License for the codes and simulation/analysis results (*.Rda files): the code is shared under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, see https://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p>License and terms of use for the ERA5 data (z500_daily_euro.nc): the ERA5 500 hPa geopotential was downloaded from https://climexp.knmi.nl/start.cgi</p>
Connecting the multiple dimensions of global soil fungal diversity
<p>How the multiple facets of soil fungal diversity vary worldwide remains virtually unknown, hindering the management of this essential species-rich group. By sequencing high-resolution DNA markers in over 4000 topsoil samples from natural and human-altered ecosystems across all continents, we illustrate the distributions and drivers of different levels of taxonomic and phylogenetic diversity of fungi and their ecological groups. We show the impact of precipitation and temperature interactions on fungal local species richness (alpha diversity) across different climates. Our findings reveal how temperature drives fungal compositional turnover (beta diversity) and phylogenetic diversity, linking them with regional species richness (gamma diversity). Our work integrates fungi into the principles of global biodiversity distribution and presents detailed maps for biodiversity conservation and modeling of global ecological processes.</p> <p><strong>### Data overview</strong></p> <p>These datasets contain comprehensive estimates of alpha, beta, and gamma diversity. The data are provided in two formats: TIFF (Tagged Image File Format) and GeoPackage formats, which are commonly used to store geospatially-referenced data.</p> <p><strong>Alpha Diversity</strong>:</p> <ul> <li>`<em>Alpha_S_</em>*` files: These files contain estimates of alpha diversity (local species diversity) for each grid cell of a raster file.</li> <li>`<em>Alpha_AOA_</em>*` files: These files outline the 'Area of Applicability' for the alpha diversity estimates.</li> <li>`<em>Alpha_Uncertainty_</em>*` files: These files contain data related to the uncertainty of the alpha diversity predictions. Uncertainty here represents the range or degree of error associated with the diversity estimates.</li> <li> `<em>Alpha_Hotspots_and_ProtectedAreas</em>` contains information on fungal diversity hotspots and their area under protection (based on IUCN classification). 'Hotspots' are areas with exceptionally high alpha diversity.</li> </ul> <p><strong>Beta Diversity</strong>:</p> <ul> <li>`<em>Beta_</em>*` files: These files include results of beta diversity analyses: maps of global compositional dissimilarity among soil fungal communities and maps of compositional turnover rate.</li> </ul> <p><strong>Other files</strong>:</p> <ul> <li>`<em>EcM_and_AM_GlobalDistribution</em>`: the global distribution of areas with high richness of ectomycorrhizal and arbuscular mycorrhizal fungi.</li> <li>`<em>Ecoregions_Alpha,Beta,Gamma_Diversities</em>`: estimates of alpha, beta, and gamma diversity at the level of ecoregion cf. Tedersoo et al., 2022 (DOI:10.1111/gcb.16398).</li> </ul> <p> </p> <p><strong>### Data description</strong></p> <p>Alpha diversity, which is a measure of local species richness (number of Operational Taxonomic Unit (OTU) representing distinct taxa, roughly corresponding to species level). Alpha diversity is represented by the residuals from a model adjusting for sequencing depth, with zero equating to the average OTU richness in the training data set.</p> <p><br> `<strong>Alpha_S_AllFungi_Consensus.tif</strong>`: This file provides consensus estimates for total fungal alpha diversity.<br> Within the file, there are two types of consensus estimates:</p> <ul> <li> <em>AvgW</em> - weighted consensus estimates for alpha diversity. The weighting takes into account both the area of applicability and the goodness-of-fit for the model used to generate the estimates.</li> <li> <em>Avg</em> - non-weighted consensus estimates for alpha diversity. Unlike <em>AvgW</em>, these estimates give equal weight to all models regardless of their goodness-of-fit or area of applicability.</li> </ul> <p><br> `<strong>Alpha_AOA_*</strong>`: Files containing Area of Applicability information:</p> <ul> <li> A raster value of '1' represents areas that are outside the Area of Applicability</li> <li> A raster value of '2' denotes areas that are inside the Area of Applicability</li> </ul> <p><br> In the files containing prediction uncertainties (`<strong>Alpha_Uncertainty_*</strong>`), two types of data are presented to quantify the amount of uncertainty in model predictions, each represented by a different band:</p> <ul> <li>The SD band represents the standard deviation of predictions based on different folds of cross-validation. A larger standard deviation indicates greater variability in the predictions.</li> <li>The IQR band represents the interquartile range (the difference between the upper and lower quartiles) of predictions. The wider the IQR, the greater variability in the predictions.</li> </ul> <p><br> `<strong>Alpha_Hotspots_and_ProtectedAreas.tif</strong>`: This file provides information on regions of exceptionally high species richness, referred to as 'hotspots', along with information about protected areas. Hotspots are identified as the top 2.5% quantiles of the richest grid cells on the map in terms of OTU richness.</p> <ul> <li><em>IUCN_1_4</em> - terrestrial protected areas that fall into categories I-IV, as classified by the International Union for Conservation of Nature (IUCN). These categories typically represent areas with high levels of protection, often prohibiting extractive and destructive activities to preserve biodiversity.</li> <li><em>IUCN_all</em> - all terrestrial protected areas as recorded in the World Database on Protected Areas (WDPA) database v.1.6. It includes a wider range of protected areas beyond the categories I-IV.</li> <li><em>All_Avg</em> - Hotspots of total fungal alpha diversity, based on the consensus map</li> <li><em>GSM_All</em> - Hotspots of total fungal alpha diversity, based on the GSMc dataset</li> <li><em>GSM_EcM</em> - Hotspots of ectomycorrhizal alpha diversity</li> <li><em>GSM_AM</em> - Hotspots of arbuscular mycorrhizal alpha diversity</li> <li><em>GSM_AgarNM</em> - Hotspots of non-EcM Agaricomycetes alpha diversity</li> <li><em>GSM_Mold</em> - Hotspots of mold alpha diversity</li> <li><em>GSM_Pathog</em> - Hotspots of opportunistic human parasitic fungal alpha diversity</li> <li><em>GSM_OHP</em> - Hotspots of putative pathogenic fungal alpha diversity</li> <li><em>GSM_Unicel</em> - Hotspots of unicellular, non-yeast fungal alpha diversity</li> <li><em>GSM_Yeast</em> - Hotspots of yeast alpha diversity</li> <li><em>GSMc_PD</em> - Hotspots of phylogenetic alpha diversity</li> <li><em>GSM_PDst</em> - Hotspots of phylogenetic dispersion</li> </ul> <p><br> `<strong>EcM_and_AM_GlobalDistribution.tif</strong>`: To illustrate the worldwide distribution of ectomycorrhizal (EcM) and arbuscular mycorrhizal (AM) fungi, we have categorized their richness into three distinct groups with low (1), medium (2), and high (3) alpha diversity. These categories have been encoded in the raster file using a bitcode system. Specifically, a value of '9' indicates that both EcM and AM fungal communities have low alpha diversity, while a value of '27' signifies that both groups of fungi are OTU-rich To assist with interpretation, a color legend has been provided in a separate QML style file (`<strong>EcM_and_AM_GlobalDistribution.qml</strong>`). This should be automatically recognized by geographic information system software, such as QGIS, to aid in visual analysis.</p> <p><br> `<strong>Beta_Taxonomic_AllFungi.tif</strong>` and `<strong>Beta_Phylogenetic_AllFungi.tif</strong>`: These files quantify the degree of difference in OTU composition of fungal communities. The measurements are based on the Generalized Dissimilarity Modelling (GDM) framework, as described by Mokany et al., 2022 (DOI:10.1111/geb.13459). Each file provides a different perspective on beta diversity: taxonomic (which is the change in species composition between different locations), and phylogenetic (the change in phylogenetic lineage composition between different locations). Each of these raster files contains three bands, with each band representing a scaled axis from a Principal Component Analysis (PCA) of the GDM-transformed environmental predictors.</p> <p><br> `<strong>Beta_LocalTurnover.tif</strong>`: This file contains estimates of local turnover in fungal communities composition estimated as the median expected compositional dissimilarity (taxonomic or phylogenetic) between each location and its closest neighbors within a 150 km radius. In addition, interquartile range (IQR) of dissimilarities is also provided.</p> <p> </p> <p>`<strong>Ecoregions_Alpha,Beta,Gamma_Diversities.gpkg</strong>`: Median alpha, beta, and gamma diversity estimates within ecoregions.</p> <ul> <li><em>Ecoregion</em> - Ecoregion name (cf. Tedersoo et al., 2022, DOI:10.1111/gcb.16398)</li> <li><em>area</em> - Ecoregion area, m<sup>2</sup></li> <li><em>Alpha_S_AllFungi_Consensus</em> - Richness of all fungi (S'<sub>tot</sub>), consensus map</li> <li><em>Alpha_S_AllFungi_GSMc</em> - Richness of all fungi (S'<sub>GSMc</sub>), based on GSMc dataset</li> <li><em>Alpha_S_EcM_GSMc</em> - Richness of ectomycorrhizal fungi (S'<sub>ecm</sub>)</li> <li><em>Alpha_S_AM_GSMc</em> - Richness of arbuscular mycorrhizal fungi (S'<sub>am</sub>)</li> <li><em>Alpha_S_NMA_GSMc</em> - Richness of non-EcM Agaricomycetes (S'<sub>nma</sub>)</li> <li><em>Alpha_S_Mold_GSMc</em> - Richness of molds (S'<sub>mold</sub>)</li> <li><em>Alpha_S_OHP_GSMc</em> - Richness of opportunistic human parasitic fungi (S'<sub>ohp</sub>)</li> <li><em>Alpha_S_Path_GSMc</em> - Richness of putative pathogenic fungi (S'<sub>path</sub>)</li> <li><em>Alpha_S_Ucel_GSMc</em> - Richness of unicellular, non-yeast fungi (S'<sub>ucel</sub>)</li> <li><em>Alpha_S_Yeast_GSMc</em> - Richness of yeasts (S'<sub>yeast</sub>)</li> <li><em>Alpha_SESPD_GSMc</em> - Phylogenetic dispersion of fungal communities (SES<sub>PD</sub>)</li> <li><em>Beta_Taxonomic_Median</em> - Median taxonomic dissimilarity of fungal communities (Simpson's index)</li> <li><em>Beta_Taxonomic_IQR</em> - Interquartile range of taxonomic dissimilarities of fungal communities</li> <li><em>Beta_Phylogenetic_Median</em> - Median phylogenetic dissimilarity of fungal communities</li> <li><em>Beta_Phylogenetic_IQR</em> - Interquartile range of phylogenetic dissimilarities of fungal communities</li> <li><em>Gamma_AllFungi</em> - Gamma diversity (regional species richness) for all fungi (G<sub>tot</sub>)</li> <li><em>Gamma_EcM</em> - Gamma diversity of ectomycorrhizal fungi (G<sub>ecm</sub>)</li> <li><em>Gamma_AM</em> - Gamma diversity of arbuscular mycorrhizal fungi (G<sub>am</sub>)</li> <li><em>Gamma_NMA</em> - Gamma diversity of non-EcM Agaricomycetes (G<sub>nma</sub>)</li> <li><em>Gamma_Mold</em> - Gamma diversity of molds (G<sub>mold</sub>)</li> <li><em>Gamma_Path</em> - Gamma diversity of opportunistic human parasitic fungi (G<sub>ohp</sub>)</li> <li><em>Gamma_OHP</em> - Gamma diversity of putative pathogenic fungi (G<sub>path</sub>)</li> <li><em>Gamma_Ucel</em> - Gamma diversity of unicellular, non-yeast fungi (G<sub>ucel</sub>)</li> <li><em>Gamma_Yeast</em> - Gamma diversity of yeasts (G<sub>yeast</sub>)</li> </ul> <p> </p> <p><strong>### Source code</strong></p> <p>The code used for data analysis and visualization of the main results of the study are available at GitHub:</p> <p><a href="https://github.com/Mycology-Microbiology-Center/Global_fungal_diversity">https://github.com/Mycology-Microbiology-Center/Global_fungal_diversity</a></p> <p> </p>
Dimensions, cover, volumes, mass and nutrient stores of Coarse Woody Debris (bark and wood from logs, snags, and stumps) from forests plots in the western United States and Mexico, 1977 to 2005
These data provide an inventory of the mass and nutrients stored within various forest types by coarse woody debris (CWD). CWD inventoried includes standing dead trees (greater than 10 cm diameter at breast height) and dead and downed wood (greater than 10 cm large end and >1 long). The majority of measurements are from permanent sample plots associated with the Andrews LTER permanent sample plots network. This includes clusters of plots at and near the H. J. Andrews Experimental Forest (OR), Cascade Head Experimental Forest (OR), Mount Rainier National Park (WA), Olympic National Park (WA), and Fraser Experimental Forest (CO). There are also data from plots in the Yucatan in Mexico; however, the live tree data for these plots is not available. The species of CWD inventoried are primarily those found in the Pacific Northwest; the dominants being Douglas-fir, western hemlock, mountain hemlock, western redcedar, Pacific silver fir, noblel fir, lodgepole pine, ponderosa pine, sitka spruce, and Englemann spruce. The majority of measurements were made in the 1975 to 1995 period; however plots are periodically remeasured and the intent to eventually remeasure all the plots except those in Mexico. In each plot measurements of log and snag dimensions (length and diameters), as well as decay class and species were recorded in the stands (td01201 file). These dimension data are combined with data on density and nutrient content for each species and decay class (td01202 file) and plot area and slope (td01203 file) to calculate CWD volume, cover, biomass and nutrient storage (td01204 file). When data on density and nutrient concentrations is not known, a list of substitutions is used (td01206). The adjust tree diameters if measurements are taken at the base of the dead tree, taper regressions are used (td01205).
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.