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138
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Dataset results
138 results for “dynamic mapping”
Data from: Mapping serotonergic dynamics using drug-modulated molecular connectivity
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Molecular dynamics simulations in: High-resolution structures with bound Mn2+ and Cd2+ map the metal import pathway in an Nramp transporter
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Mapping structures and dynamics with frequency-correlated diffusion exchange
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Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data
While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect detectability, ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Here, we focused on wolves (Canis lupus) that have been recolonizing France since the early 90's. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1994 to 2016, while accounting for species imperfect detection and time- and space-varying sampling effort using dynamic site-occupancy models. Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by more than 50% on average. Colonization appeared to be negatively influenced by the proportion of a site with an altitude higher than 2500m and positively influenced by the number of observed occupied sites at short and longdistances , forest cover, farmland cover and mean altitude. The expansion rate, defined as the number of occupied sites in a given year divided by the number of occupied sites in the previous year, decreased over the first years of the study, then remained stable from 2000 to 2016. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decisionmakers to target sites where large carnivores are likely to occur and mitigate conflicts.
Figure S4.20. Dynamic weekly suitability map for sei whales around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p> Dynamic weekly suitability map for sei whales around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Figure S4.16. Dynamic weekly suitability map for blue whales around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p>Dynamic weekly suitability map for blue whales around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Figure S4.2. Dynamic weekly suitability map for sperm whales around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p>Dynamic weekly suitability map for sperm whales around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Figure S4.12. Dynamic weekly suitability map for short-beaked common dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p>Dynamic weekly suitability map for short-beaked common dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Figure S4.14. Dynamic weekly suitability map for bottlenose dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p>Dynamic weekly suitability map for bottlenose dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Figure S4.6. Dynamic weekly suitability map for Risso's dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p>Dynamic weekly suitability map for Risso's dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Figure S4.4. Dynamic weekly suitability map for short-finned pilot whales around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p>Dynamic weekly suitability map for short-finned pilot whales around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Figure S4.8. Dynamic weekly suitability map for stripped dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p>Dynamic weekly suitability map for stripped dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Figure S4.10. Dynamic weekly suitability map for Atlantic spotted dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p>Dynamic weekly suitability map for Atlantic spotted dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Figure S4.18. Dynamic weekly suitability map for fin whales around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p>Dynamic weekly suitability map for fin whales around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Learning Markovian Dynamics with Spectral Maps
<p>Used in the following publications:</p> <ul> <li> <p>J. Rydzewski, T. Gökdemir<br><em>Learning Markovian Dynamics with Spectral Maps</em><br>J. Chem. Phys. 160, 091102 (2024)<br><a href="https://doi.org/10.1063/5.0189241">doi:10.1063/5.0189241</a> / <a href="https://arxiv.org/abs/2311.16411">arXiv:2311.16411</a> / <a href="https://zenodo.org/records/10678142">Zenodo:10678142</a> / <a href="https://www.plumed-nest.org/eggs/24/005/">plumID:24.005</a></p> </li> <li> <p>J. Rydzewski<br><em>Spectral Map: Embedding Slow Kinetics in Collective Variables</em><br>J. Phys. Chem. Lett. 14, 5216 (2023)<br><a href="https://doi.org/10.1021/acs.jpclett.3c01101">DOI:10.1021/acs.jpclett.3c01101</a> / <a href="https://arxiv.org/abs/2404.01809">arXiv:2404.01809</a></p> </li> </ul>
High resolution dynamic mapping of the C. elegans intestinal brush border
<p>The intestinal brush border is made of an array of microvilli that increases the membrane surface area for nutrient processing, absorption, and host defense. Studies on mammalian cultured epithelial cells uncovered some of the molecular players and physical constrains required to establish this apical specialized membrane. However, the building and maintenance of a brush border <em>in vivo</em> has not been investigated in detail yet. Here, we combined super-resolution imaging, transmission electron microscopy and genome editing in the developing nematode <em>C. elegans</em> to build a high-resolution and dynamic localization map of known and new brush border markers. Notably, we show that microvilli components are dynamically enriched at the apical membrane during microvilli outgrowth and maturation but become highly stable once microvilli are built<em>.</em> This new toolbox will be instrumental to understand the molecular processes of microvilli growth and maintenance <em>in vivo</em> as well as the effect of genetic perturbations, notably in the context of disorders affecting brush border integrity.</p>
Mapping global lake dynamics reveals the emerging roles of small lakes: code and data
<p>This repository contains the relevant code and data for the paper <strong>Mapping global lake dynamics reveals the emerging roles of small lakes (</strong><a href="http://dx.doi.org/10.1038/s41467-022-33239-3">https://www.nature.com/articles/s41467-022-33239-3</a><strong>)</strong>.</p> <p>Specifically, the <strong>U-Net.zip</strong> file includes the associated codes for segmenting global lakes by using the U-Net model, and the labels used in this process. The <strong>GLAKES.zip</strong> file includes the GLAKES lake polygon product (.gdb & .shp) and the corresponding water probability-weighted area of GLAKES lakes during the three periods (1984-1999, 2000-2009, 2010-2019). Please refer to the README file in both the <strong>U-Net.zip</strong> and the <strong>GLAKES.zip </strong>file for more detailed information.</p>
Dynamics, synchronization and analog circuit implementation of a discrete neuron-like map with pulsating spiral dynamics
<p>These are experimental time series for an electronic model of neural dynamics. They are provided to support the replication of the results reported in the associated publication, as well as any further public-domain academic research in the field of neural dynamics, nonlinear electronic circuits, and related aspects, in compliance with the specified license terms and all applicable legal clauses.</p> <p>The following reference must be cited when using these data:</p> <div> <div> <div>Zhu W, Sun K, Wang H, Fu L, Minati L, Dynamics, synchronization and analog circuit implementation of a discrete neuron-like map with pulsating spiral dynamics, Chaos, Solitons and Fractals 186 (2024) 115281, DOI 10.1016/j.chaos.2024.115281</div> </div> </div> <p>This document is the results of the research project funded by the National Natural Science Foundation of China (Nos. 62071496, 62061008), and the Innovation Project of Graduate of Central South University (Nos. 2024ZZTS0241). L.M. gratefully acknowledges the support of the "Hundred Talents" program of the University of Electronic Science and Technology of China, of the "Outstanding Young Talents Program (Overseas)" program of the National Natural Science Foundation of China, and of the talent programs of the Sichuan province and Chengdu municipality.</p>
Mapping circuit dynamics during function and dysfunction
<p>Contains reduced data for analysis. Most scripts that make figures will operate on these data.</p>
Deep and fast label-free Dynamic Organellar Mapping - Imaging data
<p>Imaging data from the article "Deep and fast label-free Dynamic Organellar Mapping", published in Nature Communications by Schessner et al., from Fig. 6, 7 and Supp. Fig. 6c.</p> <p>Widefield images were captured on a Leica DMi8 inverted microscope equipped with an iTK LMT200 motorised stage, a 63x/1.47 oil objective (HC PL APO 63x/1.47 OIL) and a Leica DFC9000 GTC Camera, and controlled with LAS X (Leica Application Software X).</p> <p>Fig6_GLG1_TGOLN2_GALNT2_SuppFig6c_LC3B: Widefield imaging of wild-type HeLa cells cultured for 1h in either: 1) full growth medium (Control); 2) EBSS to starve the cells (Starve); 3) full growth medium plus 100 nM BafA (Control + BafA); or 4) EBSS plus 100 nM BafA (Starve + BafA). Cells were labelled with anti-GALNT2 (Alexa Fluor 488), in combination with either anti-GLG1 (Alexa Fluor 568) or anti-TGOLN2 (Alexa Fluor 680), as shown in Fig. 6, or were labelled with anti-LC3B (Alexa Fluor 488), as shown in Supp. Fig. 6c. In all images, cells were stained with DAPI to label nuclei.</p> <p>Fig7_GALNT2_GLG1_TM9SF2_TGOLN2_GOLIM4_SDF4: Widefield imaging of HeLa cells left untreated in full growth medium (0h) or cultured in the presence of 100 nM BafA for 0.5, 1, 2, 4, 6 or 8 hours, before fixation. Cells were labelled with anti-GALNT2 (Alexa Fluor 647) in combination with either anti-GLG1, anti-TM9SF2, anti-TGOLN2, anti-GOLIM4 or anti-SDF4 (Alexa Fluor 555). In all images, cells were stained with DAPI and phalloidin-488 to label nuclei and cytoplasm, respectively.</p>
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.