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19,799 results for “STEM”
Efficient embryoid-based method to improve generation of optic vesicles from human induced pluripotent stem cells data
<p>Animal models have provided many insights into ocular development and disease, but they remain suboptimal for understanding human oculogenesis. Eye development requires spatiotemporal gene expression patterns and disease phenotypes can differ significantly between humans and animal models, with patient-associated mutations causing embryonic lethality reported in some animal models. The emergence of human induced pluripotent stem cell (hiPSC) technology has provided a new resource for dissecting the complex nature of early eye morphogenesis through the generation of three-dimensional (3D) cellular models. By using patient-specific hiPSCs to generate <em>in vitro </em>optic vesicle-like models, we can enhance the understanding of early developmental eye disorders and provide a pre-clinical platform for disease modelling and therapeutics testing. A major challenge of <em>in vitro </em>optic vesicle generation is the low efficiency of differentiation in 3D cultures. To address this, we adapted a previously published protocol of retinal organoid differentiation to improve embryoid body formation using a microwell plate. Established morphology, upregulated transcript levels of known early eye-field transcription factors and protein expression of standard retinal progenitor markers confirmed the optic vesicle/presumptive optic cup identity of <em>in vitro </em>models between day 20 and 50 of culture. This adapted protocol is relevant to researchers seeking a physiologically relevant model of early human ocular development and disease with a view to replacing animal models.</p>
Data from: Radial stem growth of the clonal shrub Alnus alnobetula at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring Pinus cembra
<p><strong>Data are documented in the following article:</strong></p> <p>Oberhuber W., G Wieser, F. Bernich, A. Gruber (2022) Radial stem growth of the clonal shrub <em>Alnus alnobetula</em> at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring <em>Pinus cembra</em>. Forests 2022, 13, 440. doi: 10.3390/f13030440.</p> <p> </p> <p><strong>Summary:</strong></p> <p>Global change is affecting species areal distribution in many regions. A better understanding of how land-use change and climate warming affects shrub growth is essential for improved predictions of forest dynamics at the alpine treeline. Evaluation of radial stem growth of the clonal shrub <em>Alnus alnobetula</em> (= <em>Alnus viridis</em>) and the co-occurring tree species Swiss stone pine (<em>Pinus cembra</em>) within an alpine treeline ecotone revealed that mean ring width of nitrogen fixing <em>A. alnobetula</em> was about four times lower compared to <em>P. cembra</em>. Our findings are based on ring width data from <em>A. alnobetula</em> and <em>P. cembra</em> stems sampled at the alpine treeline ecotone on Mt. Patscherkofel (47°12’N, 11°27’E, Central European Alps, Austria, elevation range 2050 to 2190 m asl). Ring width time series include 86 radii from 51 stems of <em>A. alnobetula</em> (stems had mean age of 18±7 yrs) and 24 radii from 16 stems of <em>P. cembra </em>(18±4 yrs). We explain our findings by different carbon allocation strategies, i.e., preference of “vertical” stem growth in late successional <em>P. cembra</em> vs. favoring “horizontal” spread in the pioneer shrub<em> A. alnobetula.</em> By favouring clonal propagation over individual stem growth <em>A. alnobetula</em> is able to quickly spread at the alpine treeline ecotone.</p>
Human-mouse syntenic Long Range Interactions in Neural Stem Cells
<p>This repository contains the list of human DNA regions corresponding to long-range interactions in RNA polII-mediated long-range interactions in mouse. They are named human-mouse syntenic Long Range Interactions (hmsLRI) and were obtained via synteny from mouse neonatal forebrain stem cells. They were also annotated for their overlap with DNA sequence variants (SNPs; CNVs) associated with human neurodevelopmental disease (NDD). The lists of genes identified via inferred that are potentially involved in NDD, eye-development, and traits (schizophrenia, bipolar disorder, intelligence).</p> <p>This is a resource for exploring the potential of the non-coding regions of DNA, the largest part of the genome (~98%), in NDD. Indeed, despite the numerous NDD-causative genes identified, only 42% of patients with severe developmental disorders carry pathogenic <em>de novo</em> mutations within coding sequences. More than a half of patient could be diagnosed and treated with further research and technologies, one option is studying the non-coding genome and how alterations affect genes. </p> <p>Tracks for visualization onto UCSC Genome Browser and WashU, are also provided.<br> See README for more details. </p> <p>The peer-reviewed publication for this dataset has now been published in International Journal of Molecular Science, available OA at: <a href="https://www.mdpi.com/1422-0067/23/14/7964">https://www.mdpi.com/1422-0067/23/14/7964</a>. Please cite this when using the dataset.</p>
Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Training Data)
<p><strong>Overview </strong></p> <p>This repository contains 742,688 samples of simulated Convergent Beam Electron Diffraction patterns (CBEDs); the training data for the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The folder contains multiple hdf5 datasets. Each dataset has a corresponding Excel-sheet containing detailed information and simulation parameters for every datapoint, as well as a summary-report containing the parameter distributions, hdf5-infos and random number generator settings. This makes every dataset reproducible, using the simulation codes provided in <a href="https://github.com/ThFriedrich/ap_data_generation">https://github.com/ThFriedrich/ap_data_generation</a>.</p> <p><strong>Technical details</strong></p> <p>Every Datapoint consists of a 3x3 set of adjacent Convergent Beam Electron Diffraction pattern (CBEDs), the coherent exit wave phase and amplitude in real and reciprocal space, and the probe functions phase and amplitude in real space. All patterns are 64x64 pixel in 16 bit unsigned integer data format.</p> <p>Every hdf5 file has the following structure:</p> <table> <tbody> <tr> <td>Attributes</td> <td>'Seed': 6108236<br> 'State': 251786606 ...<br> 'Type': 'twister'<br> 'arch': 'glnxa64'<br> 'gpu': 'NVIDIA GeForce RTX 3080'<br> 'matlab_ver': '2021a'</td> </tr> <tr> <td>Dataset 'features'</td> <td> <p>Size: 64x64x9x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'labels_k'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'labels_r'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'probe_r'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'meta'</td> <td> <p>Size: 19x5000<br> Datatype: H5T_IEEE_F32LE (single)</p> </td> </tr> </tbody> </table> <p>The data was written to hdf5 in matlab. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly. The integer arrays were scaled to use the full range of the uint16 datatype. The scaling values are stored under "meta". To restore the original values in floating point numbers, convert the arrays like this:</p> <p>Matlab:</p> <pre><code>hdf_file = ['db_h5_b_5_Training.h5']; n = 128; % load `n` k-space exit waves x = single(h5read(hdf_file, '/labels_k', [1,1,1,1], [64,64,2,n])); % `meta` contains parameters and scaling factors for a given datapoint in following order: [E_0(keV), cond_lens_outer_aper_ang(mrad), collection angle(rA), step_size(A), scale_cbed_1 ... scale_cbed_9, scale_phase_k, scale_amp_k, scale_phase_r, scale_amp_r, scale_probe_phase_r, scale_probe_amp_r] s = h5read(hdf_file, '/meta', [14,1], [2,n]); amplitude = zeros(64,64,n); phase = zeros(64,64,n); for ix = 1:n phase(:,:,n) = (x(:,:,1,n)*s(1,ix) / 65536) - pi; amplitude(:,:,n) = (x(:,:,2,n)*s(2,ix)) / 65536; end % The 9 CBEDs correspond to a 3x3 kernel of patterns. The order in [x,y] is: %[[3, 6, 9]; % [2, 5, 8]; % [1, 4, 7]] </code></pre>
Nanoscale mapping of point defect concentrations with 4D-STEM
<p>The following 4D-STEM data sets were collected on the ThemIS and TitanX scanning transmission electron microscopes located at the National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. </p> <p>1• Au_thermal_beforeHT_17C.dm4, Au_thermal_HT_800C.dm4, Au_thermal_HT_1000C.dm4 and Au_thermal_afterHT_17C.dm4 are datasets from 4D-STEM measurements conducted <em>in situ</em> on an FEI ThemIS image corrected microscope at 300 kV during a thermal cycling experiment. These digital micrograph (.dm4) files were collected at 17 C before heat treatment, 800 C during heat treatment, 1000 C during heat treatment, and 17 C after heat treatment, respectively. Nano-diffraction data was collected using a Gatan K2-IS (2k x 2k) detector at 400 frames per second. Each dataset contains a set of electron diffraction patterns taken at each scan position with a ~ 1 nm probe step size. Approximately 80 x 80 scan positions were recorded from each region with a dwell time of 0.0025 seconds per frame. A custom 40µm patterned “bullseye” circular probe forming aperture was used to enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 3.20 milli-radians, spot size of 8, and diffraction pixel size of 0.16 Å<sup>-1</sup> was used in micro-probe lens configuration. The data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Data processing were performed using strain mapping scripts provided in the open source py4DSTEM software package. Au_thermal_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</p> <p>2• Al_irradiated.dm4 is a dataset from 4D-STEM measurement conducted <em>in situ </em>on an FEI TitanX microscope equipped to do high-angle STEM tomography and operating at 300 kV. Nano-diffraction data was acquired using a Gatan Orius 830 (2k x 2k) detector capable of collecting 30 frames per second. Each dataset contains a stack of convergent beam electron diffraction (CBED) patterns taken at each scan position with maximum resolution equivalent to 1.6 nm probe size. Approximately 50 x 50 frame scan regions were recorded with a dwell time of 0.01 seconds per frame. A custom 70 µm patterned “bullseye” circular C2 aperture was used to greatly enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 2.7 milli-radians, spot size 10, and camera length 195 mm was used in micro-probe lens configuration. With a measured screen current of 300 pA in this configuration, the total sum of electrons incident in a region of the sample, commonly known as the fluence (total dose), was determined at 67,100 electronsÅ<sup>-2</sup> per 4D-STEM scan. The 4D-STEM data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Al_irradiated_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</p>
The 0.1° stem area index dataset over Tibetan Plateau from 1981 to 2018
<p><strong>Description:</strong></p> <p>Based on the method of Zeng et al. (2002), we built the monthly stem area index (Ls) data product over the Tibetan Plateau (TP) at 0.1°×0.1° spatial resolution from 1981-09 to 2018-12 by using leaf area index data (LAI) from GLASS (Liang et al., 2021) and grass fractional cover data from Lawrence and Chase (2007). Here, We revised the method, considering that grass is completely green (no WGS) from May to August, the Ls,min is set to 0; in September when the grass begins to wither (Xiao et al., 2023), its Ls value is obtained by subtracting the Lgv in September from that in August; from October (when grass turns completely withered) to the April of next year, the Ls is calculated without adding of withered leaves, considering the small magnitude of LAI (<0.2) in non-growing season; the monthly remaining rate of withered leaves and stems (α) is obtained from the observed total area of leaf and stem data of Xiao et al. (2023), which actually represents the neutralization of monthly removal of dead leaves and the withering part, especially in October. The calculation of Ls starts from September, 1981. Based on the above, the Ls is calculated on the sub-grid scale, then it multiplies by the fractional vegetation cover of grass to obtain the stem area index on the grid scale.</p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 75º~105ºE, 25º~40ºN;</p> <p> Temporal Coverage: Sep. 1981-Dec. 2018;</p> <p> Spatial Resolution: 0.1º;</p> <p> Temporal Resolution: Monthly;</p> <p> Data Format: NetCDF.</p> <p><strong>Citation</strong><strong>:</strong></p> <p>Qi, Q., Yang, K., Li, H., Ai, L., Wang, C., Wu, T. (2024). Negative impacts of the withered grass stems on winter snow cover over the Tibetan Plateau. Agric. For. Meteorol., 352, 110053. https://doi.org/10.1016/j.agrformet.2024.110053</p> <p>If you have any questions, please contact <strong>Dr. Kai Yang (yangkai@lzu.edu.cn)</strong></p>
Three-dimensional Reconstructions and Quantitative Indicators for colloidal particles in Dry and Liquid Conditions in Scanning Transmission Electron Microscope (STEM)
<p>This dataset accompanies the research presented in the paper:</p> <div>Esteban, D.A., Wang, D., Kadu, A., Olluyn, N., Iglesias, A.S., Perez, A.G., Casablanca, J.G., Nicolopoulos, S., Liz-Marzán, L.M. and Bals, S., 2023. Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies. <em>arXiv preprint arXiv:2311.05309</em>. [<a href="https://arxiv.org/pdf/2311.05309" target="_blank" rel="noopener">link</a>]</div> <p>It provides a comprehensive collection of three-dimensional reconstructions and quantitative descriptors for small colloidal particles. These gold nanoparticles are arranged in tetrahedral and other intricate geometries under both dry and liquid conditions. The dataset contains 3D reconstructions and quantitative indicators such as centroids, volumes, surface areas, solidity measures, and principal axis lengths for assemblies with 4, 5, and 6 particles. </p> <p>The dataset includes: <code>N4_dry_dart.rec</code> and <code>N4_liquid_dart.rec</code> for the 3D reconstructions of an assembly with 4 particles in dry and liquid conditions respectively; <code>N4_quant_descriptors_dry.mat</code> and <code>N4_quant_descriptors_liquid.mat</code> providing quantitative descriptors for these conditions. Similar files are provided for assemblies with 5 and 6 particles, such as <code>N5_dry_dart.rec</code>, <code>N5_liquid_dart.rec</code>, <code>N5_quant_descriptors_dry.mat</code>, <code>N5_quant_descriptors_liquid.mat</code>, and the corresponding files for N6. </p> <p>This dataset can be used to study the structural dynamics of nanoparticle assemblies and studies in colloidal chemistry, materials science, and nanotechnology. The <code>.rec</code> files can be visualized using volume rendering software (e.g. Amira or Avizo), while the <code>.mat</code> files contain structured data for analysis in MATLAB. The supporting code and scripts for this dataset are available on the GitHub repository: <a href="https://github.com/ajinkyakadu/LiquidET_NatComm2024" target="_new" rel="noreferrer">https://github.com/ajinkyakadu/LiquidET_NatComm2024</a>. </p>
Leaf water and stem cellulose oxygen isotope ratios simulated with global dynamic vegetation model LPX-Bern
<p>Description of leaf water and stem cellulose oxygen isotope ratios simulated with LPX-Bern</p> <p>Citation of describing paper:</p> <p>Keel SG, Joos F, Spahni R, Saurer M, Weigt RB, Klesse S. 2016. Simulating oxygen isotope ratios in tree ring cellulose using a dynamic global vegetation model, Biogeosciences, 13, 3869–3886, 2016 doi:10.5194/bg-13-3869-2016</p> <p>download: www.biogeosciences.net/13/3869/2016/</p> <p>General Information: Format: NetCDF, gridded</p> <p>Model: Dynamic global vegetation model LPX-Bern Version 1.0 (Land surface Processes and eXchanges, Bern) (Spahni et al., 2013; Stocker et al., 2013)</p> <p>Resolution: 3.75° x 2.5° lat/lon global Time: Monthly from Jan 1960 to Dec 2012</p> <p>Variables:</p> <p>cellu18: monthly stem cellulose δ18O (per mil) lw18: monthly leaf water δ18O (per mil) -2 NPP: monthly net primary production (g C m ) FPC: monthly fractional plant cover</p> <p>Dimensions: i=longitude, j=latitude, l=time, k=plant functional type Codes for plant functional types (k):</p> <ol> <li> <p>1 tropical broad-leaved evergreen</p> </li> <li> <p>2 tropical broad-leaved deciduous (raingreen)</p> </li> <li> <p>3 temperate needle-leaved evergreen</p> </li> <li> <p>4 temperate broad-leaved evergreen</p> </li> <li> <p>5 temperate broad-leaved deciduous (summergreen)</p> </li> <li> <p>6 boreal needle-leaved evergreen</p> </li> <li> <p>7 boreal needle-leaved deciduous (summergreen)</p> </li> <li> <p>8 boreal broad-leaved deciduous (summergreen)</p> </li> <li> <p>9 temperate herbaceous</p> </li> <li> <p>10 tropical herbaceous</p> </li> </ol>
Scraped tweets about women in STEM from April 2022 to May 2023
<p>The data comprises one csv file named tweets. It has 168,677 tweets scraped with the help of snscrape spanning April 2022 to May 2023. Each entry contains metadata regarding the tweet and it's author. The tweets are curated to be representative of discourse regarding women in STEM. The search queries used while scraping are "womeninSTEM", "womeninTech", etc.</p> <p> </p>
Generation of beta-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids
<p>This repository contains single-cell RNA-sequencing data files (raw FASTQ files generated from Illumina HiSeq sequencing) related to the article entitled "Generation of beta-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids" by Lisa Morisseau et al. (2023) published in the Molecular Omics journal (DOI: 10.1039/d3mo00050h).</p> <p> </p>
Snag-fall patterns following stand-replacing fire vary with stem characteristics and topography in subalpine forests of Greater Yellowstone
We assessed the stem- and landscape-level drivers of snag persistence and snag-fall mode within the area burned as stand-replacing fire in the 1988 Yellowstone Fires in Yellowstone National Park, Wyoming, USA. Snags were sampled 14-15 years postfire (n = 131) and again in a separate set of plots 34 years postfire (n = 55). Stem characteristics such as species identity (e.g., lodgepole pine, whitebark pine, Engelmann spruce, subalpine fir, and Douglas-fir), diameter at breast height, whether the tree was alive or dead at the time of fire, and the mode of snag-fall (snapping or uprooting) were measured and used to explain patterns of snag persistence and modes of snag-fall. In addition, plot-level environmental variables (e.g., slope, aspect, elevation, stand density) were measured and related to the proportion of stems still standing as snags at 14-15 and 34 years postfire. Data collection is complete and is part of a forthcoming manuscript in revision at Forest Ecology and Management.
Stem maps in 4 1-hectare plots in the FASET and DIRT experiments at the University of Michigan Biological Station, Pellston, MI (2006-07)
Our primary objective is to provide an improved understanding of the biological and climatic controls over carbon (C) and energy cycles during and after a successional shift from a mature aspen to a young mixed confer/deciduous forest ecosystem that will be widely distributed across the upper Great Lakes region in coming decades. In Spring 2008, we implemented the Forest Accelerated Succession ExperimenT (FASET) by stem girdling all aspen and birch (gt;6,700 trees, ~35% canopy LAI) within a 39 ha area. A suite of ongoing ecological and meteorological measurements conducted in treatment and control stands before (2007) and after (2008 onwards) the succession treatment are used to quantify effects of climate, species composition, and canopy structure on the forest C cycle. We have established paired treatment and control plots, and surveyed a 25 m grid system within our treatment plots, begun operation of a eddy-covariance tower within the 33 ha treatment plot, conducted intercomparisons of carbon exchange and N allocation between treatment and control plots, remote sensed forest canopy structure, and begun or continued collaborative projects with investigators utilizing this project as a platform for further studies. Our overarching hypothesis is that forest NEP across much of the upper Great Lakes region will increase following transition from aspen dominated ecosystems to those of later-successional species with biologically and structurally more complex canopies. Specific hypotheses: a) Tree mortality will prompt a short-term reduction in NEP. A rapid recovery and stabilization of NEP above that of the control forest will be linked to the magnitude of N leaching losses and the pattern of redistribution of available N. Stands with more species and structurally diverse canopies and greater allocation of N to photosynthetic tissues will have higher NEP. b) Successional change will increase spatial variation in microclimate and nutrient distribution, both of which con
Forest metrics derived from the 2008 Lidar point clouds, includes canopy closure, percentile height, and stem mapping for the Andrews Experimental Forest.
There are three types of forest metrics within this database. They all are derived from the raw Lidar point clouds using the FUSION software. The three types are canopy closure, height metric, and stem mapping. The canopy closure and height metric grids cover a variety of canopy heights and grid cell sizes. 1. Canopy closure: This metric measures the canopy closure of a given horizontal cell above a given vertical threshold (height break). Canopy closure can inform many landscape models and provide insight on how much light will reach the forest floor. 2. Height Metric: This metric measures the height at which a given percent of the first return points are below. This analysis is done in a given grid cell size. Height metrics give various statistics of the elevation above ground for a given set of Lidar points. In forested landscapes, first return height metrics describe the forest canopy. 3.This stem map locates the approximate center of all trees in the HJ Andrews Research Forest greater than 10 meters. In addition to the stem location, a canopy radius is also provided. FUSION and TreeVaWA software programs were used to develop this data. Watershed Sciences, Inc. (WS) collected Light Detection and Ranging (LiDAR) data from HJ Andrews and the Willamette National Forest (NF) on August 10th and 11th 2008. Total area for this AOI is 17,705 acres. The total area of delivered LiDAR including 100 m buffer is 19,493 acres.
Above ground plant and below ground stem biomass in the Arctic LTER dry heath tundra experimental plots, 2006, Toolik Lake, Alaska
Above ground plant and below ground stem biomass, percent nitrogen, and percent carbon were measured in the Arctic LTER dry heath tundra experimental plots. Treatments included control, and nitrogen and phosphorus amended plots for 10 years, and exclosure plots with and without added nitrogen and phosphorus.
Above ground plant and below ground stem biomass of samples from the moderately burned site at Anaktuvuk River, Alaska
Above ground plant and below ground stem biomass were measured in 2011 from three sites at and around the Anaktuvuk River Burn: severely burned, moderately burned and unburned. These samples were analyzed for carbon and nitrogen concentrations.
Aboveground plant and belowground stem biomass were measured in moist acidic and moist non-acidic tussock tundra experimental plots, Toolik Field Station, Alaska, Arctic LTER 2000.
Aboveground plant and belowground stem biomass were measured in moist acidic and moist non-acidic tussock tundra experimental plots. Treatments at the acidic site include control and nitrogen (N) plus phosphorus (P) amendments; treatments at the non-acidic site include N, P, N+P, greenhouse warming, and greenhouse+N+P. Note: Version 8 corrected an error where Carex vaginata was listed twice under treatment of "Nitrogen Phosphorus". The tissues with 8 quadrats were "Greenhouse" treatment.
Stem diameter of trees and shrubs in thinleaf alder sites along the Tanana River floodplains and severity of stem canker infection of alder in 2006.
This dataset includes the stem diameter of each woody plant >2m tall, by species (or genus), found in each of the thinleaf alder sites we established in the Tanana River floodplains in 2006, as well as an assessment of the severity of canker infection in each thinleaf alder stem. Species (or genus), diameter at breast height (dbh), and severity of canker infection are the variables included here. This dataset can be used in conjunction with a dataset of alder size/age relationships (DN_alder_stand_structure2.xls) to estimate the age structure of alder stands.
Age, size, and disease severity of thinleaf alder stems along the Tanana River floodplains, collected in 2006 and 2007.
Thinleaf alder stands on the Tanana River floodplains, sampled in 2006 and 2007: A subsample of thinleaf alder stems were aged using tree-ring analysis, and the age structures of alder populations were estimated from their size structures (DN_alder_stand_structure1), based on the size/age relationships derived from this dataset. Relationships between disease incidence/severity and size/age were also explored. Included in this dataset are: the stem diameter at breast height (dbh), age of stem at ground level, age at breast height, and disease severity.
Alaskan Peatland Experiment: Community structure and productivity data for 2007-2010 IX - Stem Density
This dataset contains stem density counts for each species within sub-collar plots (5 per collar) with the gas flux collars at each plot and site of the Alaskan Peatland Experiment. Stem counts were started at the fen in 2008, while counts started at the bog in 2009. Within the fen site a water table manipulation has been ongiong since 2005, with control, lowered and raised water table treatment plots. Samples at the bog were collected in a plot established within the lowland black spruce permafrost plateau (permafrost), and two plots established within collapse scars embedded within the plateau. One collapse scar formed ~ 45 years ago (old collapse) and the other formed ~ 25 years ago based upon aerial photography provided by the BCEF LTER. The data provided in this data set can be sorted by site and plot.
Post-fire succession in Delta Junction burns: Measurements of aspen and spruce stem density and biomass in 1987, 1990, 1994 and 1999 burns
This dataset contains measurements of stem density and biomass of aspen and black spruce taken during summer 2008 in 4 burns located near Delta Junction (1987, 1994, 1999) and Tok (1990). These data can be found in Shenoy et al. 2011.
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.