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453 results for “Dynamic dataset”
Dataset of Dynamic Strength training and Dynamic Endurance training
<p>Here is the dataset for Numeric pain rating scale and Neck disability index for Dynamic strength training and dynamic endurance training groups in patients with mechanical neck pain before and after treatment. The objective was to compare effectiveness of two different exercise regimen in individuals with mechanical neck pain.</p>
A 24-hour dynamic population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland
<p><strong>Related article:</strong> Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. <a href="https://www.nature.com/articles/s41597-021-01113-4"><em>Scientific Data</em> 9, 39</a>.<br> </p> <p><strong>In this dataset:</strong></p> <p>We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning. </p> <p><strong>Please cite this dataset as:</strong><br> <br> Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4<br> </p> <p><strong>Organization of data</strong></p> <p>The dataset is packaged into a single Zipfile <em>Helsinki_dynpop_matrix.zip</em> which contains following files:</p> <ol> <li> <em>HMA_Dynamic_population_24H_workdays.csv</em> represents the dynamic population for average workday in the study area.</li> <li> <em>HMA_Dynamic_population_24H_sat.csv</em> represents the dynamic population for average saturday in the study area.</li> <li> <em>HMA_Dynamic_population_24H_sun.csv</em> represents the dynamic population for average sunday in the study area.</li> <li><em>target_zones_grid250m_EPSG3067.geojson</em> represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.</li> </ol> <p><strong>Column names</strong></p> <ol> <li><em>YKR_ID </em>: a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.</li> <li><em>H0, H1 ... H23 </em>: Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. <br> The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)</li> </ol> <p>In order to visualize the data on a map, the result tables can be joined with the <em>target_zones_grid250m_EPSG3067.geojson</em> data. The data can be joined by using the field <em>YKR_ID</em> as a common key between the datasets.</p> <p><strong>License</strong><br> Creative Commons Attribution 4.0 International.</p> <p><strong>Related datasets</strong></p> <ul> <li>Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612</li> <li>Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564</li> </ul> <p><br> </p>
Dataset from paper "Dynamic Concentration Factor: A Novel Parameter For The Rigorous Evaluation Of Solar Compound Parabolic Collectors"
<ul> <li>Dynamic Concentration Factor as a function of the beam angle (α) for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> <li>Diffuse Dynamic Concentration factor for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> <li>Experimental validation of the Dynamic Concentration Factor using actinometry results</li> <li>Independence test of Dynamic Concentration results of the number of rays cast in the software tool.</li> <li>Full-precision data of the Dynamic Concentration Factor as a function of the beam angle (α) for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> </ul>
Dataset related to Balazs et al. The dynamics of forearc – back-arc basin subsidence: numerical models and observations from Mediterranean subduction zones
<p>Additional model data to publication by Balazs et al. The dynamics of forearc – back-arc basin subsidence: numerical models and observations from Mediterranean subduction zones</p>
Dataset (II) related to publication: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist
<p>MD simulation data related to the publication Rashidian et al.: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist. <a href="https://doi.org/10.1016/j.csbj.2022.06.020">https://doi.org/10.1016/j.csbj.2022.06.020</a></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files)</p> <p>dataset I: systems SRL+Co, C-100 and BAY-1797</p> <p>dataset I: each file contains all branched replicas and the main replica.</p> <p>dataset1: C_100_Replica1 contains four branched replicas and the main replica. The two of four branched replicas which stem from the middle of the main replica named: b_c_D1_r1_2285 (corresponding name in the SI data is R1_a) and b_c_D1_2285_r1_2 (corresponding name in the SI data is R1_b).</p> <p>dataset1: C_100_Replica2–5 , each file contains one main replica and the two branches.</p> <p>dataset I: system SRL+Co ;each file contains one main replica</p> <p>dataset I: system BAY-1797; each file contains one main replica</p> <p>dataset II:system SRL ; each file contains one main replica.</p>
Dataset (I) related to publication: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist
<p>MD simulation data related to the publication Rashidian et al.: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist. <a href="https://doi.org/10.1016/j.csbj.2022.06.020">https://doi.org/10.1016/j.csbj.2022.06.020</a></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files)</p> <p>dataset I: systems SRL+Co, C-100 and BAY-1797</p> <p>dataset I: each file contains all branched replicas and the main replica.</p> <p>dataset1: C_100_Replica1 contains four branched replicas and the main replica. The two of four branched replicas which stem from the middle of the main replica named: b_c_D1_r1_2285 (corresponding name in the SI data is R1_a) and b_c_D1_2285_r1_2 (corresponding name in the SI data is R1_b).</p> <p>dataset1: C_100_Replica2–5 , each file contains one main replica and the two branches.</p> <p>dataset I: system SRL+Co ;each file contains one main replica</p> <p>dataset I: system BAY-1797; each file contains one main replica</p> <p>dataset II:system SRL ; each file contains one main replica.</p>
Dynamic deformation calculation of articular cartilage and cells using resonance-driven laser scanning microscopy - Deformable Registration Validation Dataset
<p>This dataset includes the supporting input files, scripts, and output files for validation tests of lsmgridtrack v0.3 applied to resonance scanned images. </p>
Dataset (III) related to publication: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist
<p>MD simulation data related to the publication Rashidian et al.: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist. <a href="https://doi.org/10.1016/j.csbj.2022.06.020">https://doi.org/10.1016/j.csbj.2022.06.020</a></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files)</p> <p>dataset I: systems SRL+Co, C-100 and BAY-1797</p> <p>dataset I: each file contains all branched replicas and the main replica.</p> <p>dataset1: C_100_Replica1 contains four branched replicas and the main replica. The two of four branched replicas which stem from the middle of the main replica named: b_c_D1_r1_2285 (corresponding name in the SI data is R1_a) and b_c_D1_2285_r1_2 (corresponding name in the SI data is R1_b).</p> <p>dataset1: C_100_Replica2–5 , each file contains one main replica and the two branches.</p> <p>dataset I: system SRL+Co ;each file contains one main replica</p> <p>dataset I: system BAY-1797; each file contains one main replica</p> <p>dataset II:system SRL ; each file contains one main replica.</p> <p>dataset III:system C-100+Co (compound 100 in presence of SRC-1 coactivator) ; each file contains one main replica.</p>
Molecular dynamics dataset of Synaptotagmin-1
<p>This dataset contains all-atom molecular dynamics trajectories of synaptotagmin-1 C2A (calcium free). Each trajectory is 2 µs long and the total cumulated simulation time is 184 µs. Details, including the molecular dynamics setup, are given in Hempel, T.; Plattner, N.; Noé, F. Coupling of Conformational Switches in Calcium Sensor Unraveled with Local Markov Models and Transfer Entropy. <em>J. Chem. Theory Comput.</em> <strong>2020</strong>, <em>16</em> (4), 2584–2593. <a href="https://doi.org/10.1021/acs.jctc.0c00043">https://doi.org/10.1021/acs.jctc.0c00043</a>.</p>
DATASET_Effect Of Electronic Doping And Traps On Carrier Dynamics In Tin Halide Perovskites_BOLLA_v1
<p>This document contains the data supporting the peer-reviewed publication : Effect of electronic doping and traps on carrier dynamics in tin halide perovskites by Treglia et al. (<em>Materials Horizons</em>, 2022, <strong>9</strong>, 1763 - 1773) .</p> <p> </p>
Dataset - Experimental analysis of the dynamic inflow effect due to coherent gusts
<p>Here the processed experimental data of the accepted paper given below is documented and made available:</p> <p>Berger, F., Neuhaus, L., Onnen, D., Hölling, M., Schepers, G., and Kühn, M.: Experimental analysis of the dynamic inflow effect due to coherent gusts, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-2, accepted, 2022.</p>
Dataset for Conformational dynamics of loop L3 in OmpF: Implications towards antibiotic translocation and voltage gating
<p>Dataset supporting the work in the publication titled, "Conformational dynamics of loop L3 in OmpF: Implications towards antibiotic translocation and voltage gating".</p>
Dataset for the EPSL article: Structure and dynamics of the Tonga subduction zone: new insight from P-wave anisotropic tomography
<p>The obtained 3-D P-wave anisotropic and isotropic velocity models in the Tonga subduction zone.</p> <p>Please find this article: Z. Yu*, D. Zhao and J. Li*. Structure and dynamics of the Tonga subduction zone: New insight from P-wave anisotropic tomography. Earth and Planetary Science Letters, https://doi.org/10.1016/j.epsl.2022.117844<br> </p>
Autophagosomal/autolysosomal/lysosomal dynamics of FaDu and HGFb cells affected by autophagy modulators (dataset of dual labeling of autophagosomes and labeling lysosomes, confocal microscopy)
<p>In this dataset, the impact of autophagy modulators on the auophagosomes and lysosomes in FaDu and HGF cells was investigated. Experimental details are described in the accompanying paper. This dataset contains total 702 CZI confocal image Z-stacks.</p> <p><strong>Relevant paper</strong>: HANELOVA, Klara, RAUDENSKA, Martina, KRATOCHVILOVA, Monika, NAVRATIL, Jiri, VICAR, Tomas, BUGAJOVA, Maria, GUMULEC, Jaromir, MASARIK, Michal and BALVAN, Jan. Autophagy modulators influence the content of important signalling molecules in PS-positive extracellular vesicles. <em>Cell Communication and Signaling</em>. 24 May 2023. Vol. 21, no. 1, p. 120. DOI <a href="https://doi.org/10.1186/s12964-023-01126-z">10.1186/s12964-023-01126-z</a>.</p> <div> <div> <div> </div> </div> </div> <p><strong>Model cell lines</strong></p> <p> </p> <p>The cell line FaDu (HTB-43TM), derived from a squamous cell carcinoma (SCC) of the hypopharynx, and the human gingival fibroblast cell line HGF (derived from the histologically normal gingival biopsy) were used in this study. The authenticated cell lines were purchased from the American Type Culture Collection (ATCC; Manassas, Virginia, USA) within the last five years. </p> <p><strong>Autophagy modulation</strong></p> <p>For autophagy modulation, FaDu cells were treated for 24 h with 5nM bafilomycin A1 (Sigma-Aldrich, B1793), 50 µM of hydroxychloroquine sulphate (Sigma-Aldrich, H0915), 100 µM of Cpd18 (Calbiochem), 50 nM of autophinib (Sigma, SML2632), 10 µM of EACC (MedChemExpress), 200 nM of rapamycin (Sigma-Aldrich, R0395), 3 nM of Torin-1 (MedChemExpress), or 30 nM of NVP-BEZ235 (MedChemExpress). To induce starvation, cells were cultured in DMEM F12 without glutamine and without FBS (Biosera). Modulation of autophagy did not reduce the viability of FaDu cells.</p> <p><strong>Conditioned media preparation</strong></p> <p>see details in the accompanying paper</p> <p><strong>Fluorescence Microscopy</strong><br>The autophagosomal/autolysosomal/lysosomal dynamics of affected cells were observed using the combination of PremoTM Autophagy Tandem Sensor (P36239, Invitrogen) with the far-red emitting LysoTracker® Deep Red (L12492, Invitrogen) (Ex 647 nm/Em 668 nm). By combining acid-sensitive Emerald GFP (Ex 488 nm/Em 509 nm) with acid-insensitive TagRFP (Ex 555 nm/Em 584 nm) in the PremoTM kit, autophagosomes and autolysosomes labelling (yellow and red, respectively) is possible.<br>Immediately after transduction with 12 µl PremoTM Autophagy Tandem Sensor/2ml cell suspension, cells were seeded at 5 × 10<sup>5</sup> into 35-mm glass-bottomed gelatin-coated dishes (Ibidi, μ-Dish 35 mm, high Glass Bottom) and cultured for 48 h to equilibrate expression levels. Subsequently, cells were exposed to the selected agents for 6, 12, 24 and 48 hours before imaging. LysoTracker® Deep Red staining was performed 1 h before imaging. <br>To monitor the uptake of isolated PS-EVs by fibroblasts, we stained EVs with PKH67 (Sigma, PKH67GL) and then removed the remaining dye using Exosome Spin Columns (MW 3000) (Thermo Scientific, #4484449). The stained EVs were then suspended in 400 ul of cultivation medium and added to HGFB cells growing for 24 h in Ibidi µ-Slide I Luer (Ibidi, 80176). Image acquisition was performed 24 hours after EVs addition. 1 µl of 1 µg/ml of Hoechst 33342 (Enzo) (Ex 350 nm/ Em 461 nm) nuclear stain was added 1 hour before imaging.<br>To determine the viability of the cell population prior to isolation of EVs, cells were left in a culture dish with 1 ml of culture medium to which propidium iodide (Sigma-Aldrich) and Hoechst 33342 were added 45 minutes before capturing. <br>To maximize the possibility of comparison between samples, all samples (from a single cell line) were captured on the same day in a single run using the same microscope settings. For each time and each treatment, 10–12 fields of view were captured from randomized sites of the culture dish. Epifluorescent microscopy images and confocal microscopy images were acquired using Laser scanning confocal microscope Zeiss LSM 880 with AiryscanFast module (Carl Zeiss Inc.) using a C-apochromat 40x/1.20 W and C-Apochromat 63 /1.20 W. LysoTracker® Deep Red was excited HeNe 633 nm solid-state laser and emitted light was detected at 638–759 nm. Emerald GFP was excited 488 nm ArgonRemote laser, and emitted light was detected at 493–576 nm. TagRFP was excited DPSS 561 nm laser, and emitted light was detected at 570–650 nm. Hoechst 33342 was excited with a 405 nm solid-state laser, and emitted light was detected at 410–508 nm. Fluorescence images were acquired by the transmitted light detector. <br>Images were analyzed using ImageJ software and custom MATLAB software developed in our laboratory. The analysis process consists of the segmentation of cells from the background and extraction of the intensity of fluorescence channels (TagRFP, GFP, LysoTracker) inside cells. For segmentation, a thresholding-based method was used, where a manually selected threshold was applied. Segmentation was applied to the image created as the sum of all fluorescence channels to achieve segmentation independent of the intensity of individual channels. To achieve better segmentation without noisy pixels, fluorescence images were preprocessed with median filter (7x7) and Gaussian filter (standard deviation 1); additionally, binary segmentation was post-processed with morphological closing and removal of small binary connected components (<5000px). For intensity extraction, the mean value of segmented cell pixels was used for each field of view. Besides individual fluorescence channels, the mean colocalization of TagRFP and GFP was calculated with a pixel-wise multiplication of TagRFP and GFP channels.</p> <p><strong>File Naming</strong></p> <p>CZI images are organised in folders according to cell line, treatment time, and treatment. Names include magnification, cell line measured, treatment, and time of treatment,</p> <blockquote> <p>40x_FADU_BAF_12h_BF_4.czi</p> </blockquote> <p>The abbreviations used for treatments are as follows: BAF, bafilomycin A1; HCQ, hydroxychloroquine sulphate; Cpd18; APB, autophinib; EACC; RAPA, rapamycin; TOR1, Torin-1; BEZ, NVP-BEZ235; starv, starvation; </p>
Datasets for The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations
<p>The data in this repository contains the information needed to reproduce the core results of the paper, "The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations" submitted to the Journal of Advances in Modeling Earth Systems (JAMES) on 10/3/2022.</p> <p> </p> <p>The "intsmoke*.txt" files are text files with the globally integrated smoke mass above 150 hPa for all simulations in the paper. A header is provided in each file.</p> <p>The "spectra*.mat" files are matlab files that contain fields used to plot the kinetic energy spectra for all simulations. The variable "kes" is the kinetic energy as a function of wavelength, "wvl" are the associated wavelengths of "kes". The variables "hcut" and "lcut" represent the indices of the data "kes" and "wvl" that are used to make the kinetic energy spectra plots in the paper.</p> <p>The "budget*.nc" files contain the budget terms for the relative, vertical vorticity evolution equation at the appropriate date/time. The fields in the netcdf file are self-describing. These budget terms are for the "optimal simulation" described in the paper.</p> <p>The "vort*.nc" file contains the vorticity fields just before the plume forcing starts based on the date/time on the file.</p>
Dataset for "Towards Better Evaluation for Dynamic Link Prediction"
<p>These are the datasets used in <em>Towards Better Evaluation for Dynamic Link Prediction</em></p> <p>For preparing the datasets, we closely follow the baseline methods' data preparation strategy.<br> The original networks are saved as <network>.csv.</p> <p>The networks are formatted as follows:<br> * Each edge is denoted in one line.<br> * Each line has the following format: source_node, destination_node, timestamp, edge_label, comma-separated arrays of edge features.<br> * Please note that if there is no edge label available, the edge_label column will be filled with 0s only for loading purpose; these labels are not used in the link prediction task.<br> * The first line denotes the network format.<br> * Edge features should include at least one feature. If there is no edge feature available, a 0 value is used for all the edges.</p> <p>The network edge-lists are pre-processed for different methods to use them (Specifically, for preprocessing the data, we use the scripts available in "preprocess_data.py" file of the corresponding baseline).<br> Ater preprocessing the network edge-list, there are three files that are used by the models:<br> * <ml_network>.csv: this file contains the timestamp edge-list.<br> * <ml_network>.npy: this file contains the edge features in the dense `npy` format that has the features in binary format.<br> * <ml_network_node>.npy: this file contains the node features in the dense `npy` format that contains the node features in binary format.<br> Please note that when the edge features or node features are absent, we use a vector of zeros is used as the node/edge features in line with the baseline methods.</p>
Dataset and scripts from: Predicting organismal response to marine heatwaves using dynamic thermal tolerance landscape models
<p>Marine heatwaves (MHWs) can cause thermal stress in marine organisms, experienced as extreme 'pulses' against the gradual trend of anthropogenic warming. When thermal stress exceeds organismal capacity to maintain homeostasis, organism survival becomes time-limited and can result in mass mortality events. Current methods of detecting and categorizing MHWs rely on statistical analysis of historic climatology, and do not consider biological effects as a basis of MHW severity. The reemergence of ectotherm thermal tolerance landscape models provides a physiological framework for assessing the lethal effects of MHWs by accounting for both the magnitude and duration of extreme heat events. Here, we used a simulation approach to understand the effects of a suite of MHW profiles on organism survival probability across 1) three thermal tolerance adaptive strategies, 2) interannual temperature variation, and 3) seasonal timing of MHWs. We identified survival isoclines across MHW magnitude and duration where acute (short duration-high magnitude) and chronic (long duration-low magnitude) events had equivalent lethal effects on marine organisms. While most research attention has focused on chronic MHW events, we show similar lethal effects can be experienced by more common but neglected acute marine heat spikes. Critically, a statistical definition of MHWs does not accurately categorize biological mortality. By letting organism responses define the extremeness of a MHW event, we can build a mechanistic understanding of MHW effects from a physiological basis. Organism responses can then be transferred across scales of ecological organization and better predict marine ecosystem shifts to MHWs. </p>
Dataset associated with "Constraints on the subsecond modulation of striatal dynamics by physiological dopamine signaling"
<p>This repository contains behavioral data, dLight photometry measurements, smoothed spiking data from recorded striatal neurons, and miniscope recordings. Text files with further explanation are provided to assist with replication of the main analyses. Matlab code to perform key analyses can be found in the accompanying GitHub repository (https://github.com/sotmasman/Dopamine-constraints)</p>
Dataset for Engineering multifunctional dynamic hydrogel for biomedical and tissue regenerative applications
<p><span>Hydrogels have emerged in various biomedical applications, including tissue engineering and medical devices, due to their ability to imitate the natural extracellular matrix (ECM) of tissues. However, conventional static hydrogels lack the ability to dynamically respond to changes in their surroundings to withstand the robust changes of the biophysical microenvironment and to trigger on-demand functionality such as drug release and mechanical change. In contrast, multifunctional dynamic hydrogels can adapt and respond to external stimuli and have drawn great attention in recent studies. It is realized that the integration of nanomaterials into dynamic hydrogels provides numerous functionalities for a great variety of biomedical applications that cannot be achieved by conventional hydrogels. This review article provides a comprehensive overview of recent advances in designing and fabricating dynamic hydrogels for biomedical applications. We describe different types of dynamic hydrogels based on breakable and reversible covalent bonds as well as noncovalent interactions. These mechanisms are described in detail as a useful reference for designing crosslinking strategies that strongly influence the mechanical properties of the hydrogels. We also discuss the use of dynamic hydrogels and their potential benefits. This review further explores different biomedical applications of dynamic nanocomposite hydrogels, including their use in drug delivery, tissue engineering, bioadhesives, wound healing, cancer treatment, and mechanistic study, as well as multiple-scale biomedical applications. Finally, we discuss the challenges and future perspectives of dynamic hydrogels in the field of biomedical engineering, including the integration of diverse technologies.</span></p>
Dataset from Mannerfelt et al., (2024): Dynamic LIA advances hastened the demise of small valley glaciers in central Svalbard
<h1>Data from Bolterdalen and Foxdalen, Svalbard</h1> <p>This repository contains geospatial data from 1914 to 2019 of the valleys Bolterdalen and Foxdalen on Svalbard, associated with (and explained further in) Mannerfelt et al., (2024); <a href="https://cdnsciencepub.com/doi/10.1139/as-2024-0024">https://cdnsciencepub.com/doi/10.1139/as-2024-0024</a>.</p> <p><br>Its contents are:</p> <ul> <li>Shapefiles of geomorphological features</li> <li>Glacier outlines</li> <li>DEMs of the glaciers and their forefields</li> <li>Orthomosaics of the glaciers and their forefields</li> <li>Interpreted GPR measurements; interpolated and as point data.</li> </ul> <p>The 2009/2011 DEM has no associated orthomosaic in the repository. This is available as a WMTS service from the Norwegian Polar Institute:</p> <p>https://geodata.npolar.no/arcgis/rest/services/Basisdata/NP_Ortofoto_Svalbard_WMTS_25833/MapServer/WMTS/1.0.0/WMTSCapabilities.xml</p> <p>For more info (or there is an issue with the link), visit: <a href="https://geodata.npolar.no/">https://geodata.npolar.no/</a></p> <p> </p> <p><strong>NOTE</strong>: One file is slightly misnamed; "<em>Rieperbreen_forefield_ortho_2019.tif</em>" was collected in the autumn of 2017.</p> <p><br>To cite the dataset, please cite the associated paper:</p> <p>Mannerfelt, E. S., Hodson, A. J., Håkansson, L., and Lovell, H. (2024). Dynamic LIA advances hastened the demise of small valley glaciers in central Svalbard. Arctic Science.</p>
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.