Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
79
datasets available to search
ShareScore release 0.9.0
Dataset results
79 results for “Active layer”
Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)
<p>Files:</p> <ul> <li><strong>Active_Layer_Water_Table_03-17.xlsx</strong> - Data file, with main data in the "DATA" tab.</li> <li><strong>IsoGenieSite_AL_WTD_MapsVisualNotes_200310.pdf</strong> - Visual notes on the measurement locations.</li> </ul> <p>The following site labels (with chamber numbers in parentheses) correspond to the main autochamber sites:</p> <ul> <li>Dry (1,3,5) = Palsa Autochamber Site</li> <li>Mesic (2,4,6) = Sphagnum Autochamber Site</li> <li>Wet (7,8) = Eriophorum Autochamber Site</li> </ul> <p>Water table depth (W D) was measured in wells.</p> <p>Active layer depth (A L) was measured by inserting a metal rod into the surface. The original instruction page is included in page 3 of the pdf.</p> <p>All depths are in centimeters (cm) below peat surface (i.e. peat or <em>Sphagnum</em> spp. vegetation surface = 0), with negative values indicating depth below the surface and positive values (for water table) indicating height of standing water above the surface. Blank data in the Palsa or water table column means no water table observed.</p> <p>Staff gauge was added July 2006 at the edge of a small pond in the fen visible from the shack, with measurements reported in meters. All other measures are in cm.</p> <p> </p> <p>FUNDING:</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. The IsoGenie Project (which funded much of the work at these sites during the measurement period) was funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.</p>
Data for "Sintering activation energies of anisotropic layered and particle alumina/zirconia-based composites and their mechanical response"
<p>The dataset contains several folders that provide open data files for the manuscript "Sintering activation energies of anisotropic layered and particle alumina/zirconia-based composites and their mechanical response".</p>
The role of catchment characteristics, discharge, and active layer thaw on seasonal stream chemistry across ten permafrost catchments
<p>Data used for the paper: The role of catchment characteristics, discharge, and active layer thaw on seasonal stream chemistry across ten permafrost catchments. Contains water quality and discharge data. See paper for more details.</p>
Active layer depths: 150 mature black spruce sites in interior Alaska (2000-2003)
Maximum active layer depths at 150 extensive black spruce sites in interior Alaska collected in the summers of 2000, 2001, 2002 across the interior of Alaska along the Taylor highway, Alaska highway, Parks highway, Elliot highway, Steese highway, and Dalton highway.
Eight Mile Lake Research Watershed, Thaw Gradient: Active Layer thickness 2004-2013.
In this larger study, we are asking the question: Is old carbon that comprises the bulk of the soil organic matter pool released in response to thawing of permafrost? We are answering this question by using a combination of field and laboratory experiments to measure net carbon exchange, radiocarbon isotope ratios in soil organic matter, soil respiration, and dissolved organic carbon, in tundra ecosystems. The objective of these proposed measurements is to develop a mechanistic understanding of the SOM sources contributing to C losses following permafrost thawing. We are making these measurements at an established tundra field site near Healy, Alaska in the foothills of the Alaska Range. Field measurements center on a natural experiment where permafrost has been observed to warm and thaw over the past several decades. This area represents a gradient of sites, each with a different degree of change due to permafrost thawing. In this area, we eatablished three sites with different degrees of disturbance from the permafrost thaw: 1) a relatively undisturbed tussock tundra (Minimal Thaw site), 2) a site adjacent to to the borehole which is monitoring permafrost thaw since 1985 with intermediate degree of disturbance (Moderate thaw site) and 3) a site where permafrost thaw apeared to have started more than three decades ago (Extensive thaw site) As such, this area is unique for addressing questions at the time and spatial scales relevant for change in arctic ecosystems. This data set includes the active layer depth (ALD), which is the maximum seasonal thaw depth from each of the three sites mentioned before.
Layer-dependent activity in human prefrontal cortex during working memory
Open the record for dataset details and reuse information.
Data set for "Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task"
<p>Data set for: Vavladeli A, Daigle T, Zeng H, Crochet S, Petersen CCH (2020) Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task. FUNCTION 1: zqaa008. doi: 10.1093/function/zqaa008</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2020_Vavladeli_FUNCTION.pdf" is the Open Access pdf file of the manuscript published in FUNCTION.</p> <p>2. The file named "Vavladeli_data_code.zip" (~2 GB) is a zipped version of a folder named "Vavladeli_data_code" (~2 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and two '.mat' data files. In order to run the analysis of the data set, you need to execute the '.m' file with the corresponding figure name.</p>
Tuned Bis-Layered Supported Ionic Liquid Catalyst (SILCA) for Competitive Activity in the Heck Reaction of Iodobenzene and Butyl Acrylate
<p>This dataset contains the measurement data for figures (graphs) published in journal article:</p> <p>Tuned Bis-Layered Supported Ionic Liquid Catalyst (SILCA) for Competitive Activity in the Heck Reaction of Iodobenzene and Butyl Acrylate</p> <p>by Nemanja Vucetic, Pasi Virtanen, Ayat Nuri, Andrey Shchukarev, Jyri-Pekka Mikkola, and Tapio Salmi</p> <p>Published in: Catalysts 2020, 10(9), 963 </p> <p>https://doi.org/10.3390/catal10090963 </p>
Local wakefulness-like activity of layer 5 cortex under general anaesthesia [Dataset]
<p>These datasets contain the raw electrophysiology recordings analysed in the article "Local wakefulness-like activity of layer 5 cortex under general anaesthesia". Recordings are stored in MATLAB format (.mat). The sampling frequency of the recordings is 16667 Hz in all cases. Further information can be found in the main article.</p>
Comparison Results for Global Optimization Solvers Using a MIMO System With Two, Three and Four Active Layers as a Benchmark
<p>Benchmark data for GPU-based interval optimization of a simulated MIMO system with two, three and four active layers. We compare popular optimization tools from C-XSC and GNU Octave to purely brute-force based techniques for the GPU. Execution times were measured in seconds.</p>
A dominant-negative SOX18 mutant disrupts multiple regulatory layers essential to transcription factor activity
<p>Few genetically dominant mutations involved in human disease have been fully explained at the molecular level. In cases where the mutant gene encodes a transcription factor, the dominant-negative mode of action of the mutant protein is particularly poorly understood. Here, we studied the genome-wide mechanism underlying a dominant-negative form of the SOX18 transcription factor (SOX18<sup>RaOp</sup>) responsible for both the classical mouse mutant <u>Ra</u>gged <u>Op</u>ossum and the human genetic disorder Hypotrichosis-Lymphedema-Telangiectasia-Renal Syndrome. Combining three single-molecule imaging assays in living cells together with genomics and proteomics analysis, we found that SOX18<sup>RaOp</sup> disrupts the system through an accumulation of molecular interferences which impair several functional properties of the wild-type SOX18 protein, including its target gene selection process. The dominant-negative effect is further amplified by poisoning the interactome of its wild-type counterpart, which perturbs regulatory nodes such as SOX7 and MEF2C. Our findings explain in unprecedented detail the multi-layered process that underpins the molecular aetiology of dominant-negative transcription factor function.</p>
Activation patterns of afferent synapses on layer 5 tufted pyramidal cells in a biologically detailed simulation
<p>The data set is based on a biologically detailed simulation of a circuit of cortical neurons. The circuit was activated by thalamo-cortical inputs every 1 s for 500 ms. We report for a number of exemplary tufted pyramidal cells in layer 5 the pattern activation of their afferent synapses.</p> <p>Specifically, we report for all afferent excitatory synapses the pairwise path distances along the dendrite / soma (note that the soma was simplified to a point for the purpose of calculating path distances, but not during the simulation), and the times of activation of each of these synapses.</p> <p>The simulation is based on the model described in <a href="https://www.biorxiv.org/content/10.1101/2022.08.11.503144v1">this preprint</a>. The model can also be found <a href="https://zenodo.org/record/6906785">here on Zenodo</a>. For more details on the simulation, contact the author.</p> <p>For more details about the format of the data, refer to the included jupyter notebook.</p>
A dominant-negative SOX18 mutant disrupts multiple regulatory layers essential to transcription factor activity
Open the record for dataset details and reuse information.
Predicting <em>Agriotes</em> larval activity: Validation of the prognosis model SIMAGRIO-W to predict larval activity in top soil layers for <em>Agriotes</em> larvae in Eastern Austria
Open the record for dataset details and reuse information.
Data used in "Evaluation of topography and vegetation coverage impacts on watershed-scale active layer freeze-thaw processes with a simple algorithm in permafrost region on the Qinghai-Tibet Plateau"
<p>This is the data used in the manuscript "Evaluation of topography and vegetation coverage impacts on watershed-scale active layer freeze-thaw processes with a simple algorithm in permafrost region on the Qinghai-Tibet Plateau" (JGR earth surface 2020JF005564 ).</p>
Data from: Statistical forecasting of current and future circum-Arctic ground temperatures and active layer thickness
Mean annual ground temperature (MAGT) and active layer thickness (ALT) are key to understanding the evolution of the ground thermal state across the Arctic under climate change. Here a statistical modeling approach is presented to forecast current and future circum-Arctic MAGT and ALT in relation to climatic and local environmental factors, at spatial scales unreachable with contemporary transient modeling. After deploying an ensemble of multiple statistical techniques, distance-blocked cross-validation between observations and predictions suggested excellent and reasonable transferability of the MAGT and ALT models, respectively. The MAGT forecasts indicated currently suitable conditions for permafrost to prevail over an area of 15.1 ± 2.8 × 106 km2. This extent is likely to dramatically contract in the future, as the results showed consistent, but region-specific, changes in ground thermal regime due to climate change. The forecasts provide new opportunities to assess future Arctic changes in ground thermal state and biogeochemical feedbacks.
Dynamic Visualization of ResNet Layer Activations for Brain Health Classification
<p>This GIF file provides a dynamic visualization of the internal representations (activations) from the ResNet 18 model layers (2 to 69) during a brain health classification task. The sequence begins by showing the original input image, followed by successive activation maps visualized using the "jet" colormap. Each frame corresponds to the activations extracted from a specific layer in the ResNet, resized to match the input image dimensions for better interpretability. </p> <p>The dataset used for this visualization is from S. Bhuvaji, "Brain Tumor Classification MRI," published on Kaggle in 2023. This dataset contains MRI images of brain tumors and has been utilized to train and evaluate the ResNet model for the classification of brain health states. The input sample displayed in the GIF is one such MRI image from the dataset, highlighting the model's ability to extract and analyze features relevant to brain tumor diagnosis.</p> <p>The activations reveal how the ResNet processes the input image hierarchically. In the <strong>early layers (e.g., Layers 2-10)</strong>, the network preserves much of the spatial structure of the original image, focusing on edges and low-level features. Moving to the <strong>intermediate layers (e.g., Layers 11-40)</strong>, the network begins to emphasize localized patterns while filtering out irrelevant structures such as the skull, concentrating instead on regions associated with tumors or health-related features. Finally, the <strong>deep layers (e.g., Layers 41-69)</strong> extract highly abstract and classification-relevant patterns, concentrating on tumor-related features while discarding most of the background.</p> <p>The progression of the activations in the GIF demonstrates how the network transitions from general image features to highly specialized, diagnostic features that are critical for the classification task. This visualization helps provide an intuitive understanding of the hierarchical processing capabilities of convolutional neural networks (CNNs) in medical image analysis.</p> <h3>Key Features:</h3> <p>The visualization includes an input brain image and its corresponding activation maps, extracted from each ResNet layer. The activation maps are resized to match the original image for consistency, and the "jet" colormap is applied to enhance visual interpretation of activation intensities. Each frame in the GIF dynamically updates to show the activations of the next layer, offering an engaging representation of the network's internal behavior.</p> <h3>Use Cases:</h3> <p>This GIF is a valuable resource for education, research, and presentations. It can be used to illustrate how deep learning models process medical images, providing insights into the hierarchical feature extraction process. Researchers and educators can leverage this visualization to explain the concept of feature abstraction in CNNs. It is also ideal for inclusion in talks, posters, and papers to showcase the dynamic analysis of neural network activations.</p>
Active layer in the northern hemisphere during 2001-2017
<p>This dataset includes the original data of the active layer change in the Northern Hemisphere from 2000 to 2018, the Spatial distribution of average soil factor E in permafrost in northern Hemisphere, Theil–Sen median trend spatial distribution of ALT in permafrost in the Northern hemispshere. </p>
Brief Communication: Monitoring active layer dynamic using a lightweight nimble Ground-Penetrating Radar system. A laboratory analog test case
<p>GPR dataset from Leger et al., 2023 (https://tc.copernicus.org/preprints/tc-2022-214/)</p> <p> </p>
Data from: Statistical forecasting of current and future circum-Arctic ground temperatures and active layer thickness
Open the record for dataset details and reuse information.
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.