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114 results for “Multimodal data”
Supporting data for "Entanglement between a Telecom Photon and an On-Demand Multimode Solid-State Quantum Memory"
<p>This repository contains the data supporting the article "Entanglement between a Telecom Photon and an On-Demand Multimode Solid-State Quantum Memory" by Jelena V. Rakonjac, Dario Lago-Rivera, Alessandro Seri, Margherita Mazzera, Samuele Grandi and Hugues de Riedmatten, Phys Rev Lett 2021.</p> <p>The data files used for the figures in the main text are included here, as well as a version of the final article submission.</p>
Raw experimental data for `Tailoring the Rotational Memory Effect in Multimode Fibers`
<p>Raw data for the article [**Tailoring the Rotational Memory Effect in Multimode Fibers**](https://arxiv.org/abs/2310.19337)</p><p>Measurement of transmission matrices and rotational memory effect for 4 segments of 50 micron core graded index multimode fibers with a numerical aperture of 0.2.</p>
Section 5.3 "Task Area 3: Multimodal data linking and integration" Figure 10
<p>Figure 10. Data flow to obtain a multimodal data structure (mmDS) with an overarching graph database (MUGDAT).</p> <p>from NFDI Grant Application, "<strong>National Research Data Infrastructure for Microscopy and Bioimage Analysis</strong>" (NFDI4BIOIMAGE)</p>
Data: multimodal cell tracking from systemic administration to tumour growth by combining gold nanorods and reporter genes
<p>This data set includes multispectral optoacoustic tomography images supporting an article on cell tracking (preprint: bioRxiv 199836; https://doi.org/10.1101/199836). The corresponding bioluminescence results are included too, as well as the spectra used for the multispectral processing. </p>
Data for "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots"
<p>This dataset contains all the data and CAD models needed to replicate the study presented in "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots".</p>
OpenMapCD: A Multimodal Benchmark Dataset for Change Detection Between Optical Remote Sensing and Map Data
<p><strong>Overview: </strong></p> <ol> <li>OpenMapCD, the <strong>first large-scale multimodal dataset</strong> for change detection on optical remote sensing imagery and map (OpenStreetMap) data, <strong>supporing basic binary change detection and further semantic change detection</strong></li> <li>OpenMapCD is highly geographically diverse, with <strong>1288</strong> benchmark samples with 1024x1024 pixels from <strong>40 </strong>regions across six continents and out-of-distribution data in two areas in Japan</li> <li>Advancing land-cover mapping, binary change detection and semantic change detection tasks, and GIS system updating<br><br></li> </ol> <p><strong>Research Paper: <br></strong></p> <ul> <li>Arxiv paper: <a href="https://arxiv.org/abs/2310.02674v3">https://arxiv.org/html/2310.02674v3</a></li> <li>TGRS paper: <a href="https://ieeexplore.ieee.org/document/10551264">https://ieeexplore.ieee.org/document/10551264</a></li> </ul> <p><strong><br>Project Page:</strong><br>The benchmark code is available at: <a href="https://github.com/ChenHongruixuan/ObjFormer">https://github.com/ChenHongruixuan/ObjFormer</a><br><br><strong>Reference:</strong></p> <pre><code>@ARTICLE{Chen2024ObjFormer, author={Chen, Hongruixuan and Lan, Cuiling and Song, Jian and Broni-Bediako, Clifford and Xia, Junshi and Yokoya, Naoto}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer}, year={2024}, volume={62}, number={}, pages={1-22}, doi={10.1109/TGRS.2024.3410389} }</code></pre>
Data Accompanying "Dynamics of Water Absorption in Callovo-Oxfordian Claystone Revealed With Multimodal X-Ray and Neutron Tomography"
<p>These are the datasets analysed in the publication "Dynamics of Water Absorption in<br> Callovo-Oxfordian Claystone Revealed With Multimodal X-Ray and Neutron Tomography" by Stavropoulou <em>et al.</em> in 2020 in Frontiers in Earth Science, DOI: <a href="https://doi.org/10.3389/feart.2020.00006">https://doi.org/10.3389/feart.2020.00006</a></p> <ol> <li>File 1 contains the 3D reconstructed x-ray and neutron tomography volumes analysed in the paper. State 002 is taken as a reference and the greylevels of all images in the times series for both x-ray and neutrons are rescaled using two characteristic image features (top-cap and air in the case of x-rays) linearly to align with 002. Neutron volumes are rescaled to the same pixel size as 2-bin x-ray volumes, and a mean registration is applied to align neutrons with x-rays.<br> Furthermore, a bilateral filter is applied to the neutron tomographies (domain sigma = 1, range sigma=3000).<br> Full scale images are available at <a href="https://doi.ill.fr/10.5291/ILL-DATA.UGA-42">https://doi.ill.fr/10.5291/ILL-DATA.UGA-42</a><br> </li> <li>File 2 contains the joint histograms for registered pairs of images, as visible in Figure 4 and Figure 5.<br> </li> <li>File 3 contains the results of the digital volume correlation performed with the <a href="https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/intro.html">spam</a> tookit.<br> The overall "registration" is used to create Figure 6<br> The global correlations available in "gdic" are used to create Figures 7, 8 and 9.</li> </ol>
Raw data accompanying the manuscript "Multiscale and multimodal optical imaging of the human liver"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Multiscale and multimodal optical imaging of the human liver". The file CARS_SRS.zip contains folders with all raw CARS and SRS data (TIFF format). The file CLSM.zip contains confocal laser scanning microscopy data using the manufacturers data format (Zeiss). The file LSFM.zip contains light sheet fluorescence microscopy data files using the manufacturers data format (LaVision Biotec). The file OPT.zip contains raw optical projection tomography data at different excitation wavelengths (TIFF format). The file SRSIM.zip contains reconstructed structured illumination microscopy data files (TIFF format).</p>
Data from: Multimodal in situ datalogging quantifies inter-individual variation in thermal experience and persistent origin effects on gaping behavior among intertidal mussels (Mytilus californianus)
In complex habitats, environmental variation over small spatial scales can equal or exceed larger-scale gradients. This small-scale variation may allow motile organisms to mitigate stressful conditions by choosing benign microhabitats, whereas sessile organisms may rely on other behaviors to cope with environmental stresses in these variable environments. We developed a monitoring system to track body temperature, valve gaping behavior, and posture of individual mussels (Mytilus californianus) in field conditions in the rocky intertidal zone. Neighboring mussels' body temperatures varied by up to 14°C during low tides. Valve gaping during low tide and postural adjustments, which could theoretically lower body temperature, were not commonly observed. Rather, gaping behavior followed a tidal rhythm at a warm, high intertidal site; this rhythm shifted to a circadian period at a low intertidal site and for mussels continuously submerged in a tidepool. However, individuals within a site varied considerably in time spent gaping when submerged. This behavioral variation could be attributed in part to persistent effects of mussels' developmental environment. Mussels originating from a wave-protected, warm site gaped more widely, and they remained open for longer periods during high tide than mussels from a wave-exposed, cool site. Variation in behavior was modulated further by recent wave heights and body temperatures during the preceding low tide. These large ranges in body temperatures and durations of valve closure events - which coincide with anaerobic metabolism - support the conclusion that individuals experience "homogeneous" aggregations such as mussel beds in dramatically different fashion, ultimately contributing to physiological variation among neighbors.
Context-dependent multimodal behaviour in a coral reef fish: Stage 1 & 2 total duration and count data in behaviour trials
<p>Animals are expected to respond flexibly to changing circumstances, with multimodal signalling providing potential plasticity in social interactions. Whilst numerous studies have documented context-dependent behavioural trade-offs in terrestrial species, far less work has considered such decision-making in fish, especially in natural conditions. Coral reef ecosystems host 25% of all known marine species, making them hotbeds of competition and predation. We conducted experiments with wild Ambon damselfish (<em>Pomacentrus amboinensis)</em> to investigate context-dependent responses to a conspecific intruder; specifically, how nest defence is influenced by an elevated predation risk. We found that nest-defending male Ambon damselfish responded aggressively to a conspecific intruder, spending less time sheltering and more time interacting, as well as signalling both visually and acoustically. In the presence of a model predator compared to a model herbivore, males spent less time interacting with the intruder, with a tendency towards reduced investment in visual displays compensated for by an increase in acoustic signalling instead. We therefore provide ecologically valid evidence that the context experienced by an individual can affect its behavioural responses and multimodal displays towards conspecific threats.</p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
Raw data for the main figures of the paper: "Demixing fluorescence time traces transmitted by multimode fibers"
<p><strong>RawMovies.zip </strong></p> <p>This zip file includes the raw movies for each main figure of the paper. </p> <p>For figures 02, 03, 04, 04 and 06, we included 2 tiff files (2 stacks of images): <br>- The first one gives the measured footprints of the sources (ground truth). <br>- The second one is the raw movie (temporal sequence of images) acquired on the microscope for the specific experiment. </p> <p>For figure 07, the tiff file corresponds to a movie acquired while moving a single fluorescent bead away from the optical axis of the microscope (as in figure 7c). </p> <p><strong>RawRata.zip</strong></p> <p>This zip file contains raw data for figures 03, 04, 05 and 06. We have included two files for each figure:<br>- the _gt file is a matrix of the GT time traces (dimensions: number of sources x number of time bins)<br>- the other file is a 3D matrix corresponding to all the images acquired during the experiment. The first time frames are the measured footprints of each of the sources (ground truth). They were acquired by illuminating each source sequentially. The remaining frames correspond to the raw movie acquired during the experiment while illuminating the sources with the GT time traces. Dimensions of this 3D matrix are: (number of pixels in the x dimension) x (number of pixels in the y dimension) x (number of sources + number of time bins)</p> <p>This raw data is the input data to the python analysis function located in :<br>https://github.com/comediaLKB/DemixedFiberPhotometry. </p>
Data underlying the paper titled "Integrating multimodal Raman and photoluminescence microscopy with enhanced insights through multivariate analysis"
<p>The folder includes Raman and Photoluminescence surface maps of microsamples from Cultural Heritage materials. The maps were obtained using a multimodal optical microscope that integrates Raman and Photoluminescence optical techniques to perform a raster scanning of microsample surface. </p> <p>Data refer to the publication: https://doi.org/10.1088/2515-7647/ad5773</p> <p> </p>
BSL-Hansard: A parallel, multimodal corpus of English and interpreted British Sign Language data from parliamentary proceedings
<p>BSL-Hansard is a novel open source and multimodal resource composed by combining Sign Language video data in BSL and English text from the official transcription of British parliamentary sessions. This paper describes the method followed to compile BSL-Hansard including time alignment of text using the MAUS (Schiel, 2015) segmentation system, gives some statistics about this dataset, and suggests experiments. These primarily include end-to-end Sign Language-to-text translation, but is also relevant for broader machine translation, and speech and language processing tasks.</p> <p>This dataset will be useful for translation between BSL and English, or for studies in BSL or English down to the phonetic level.</p>
Data from: Multimodal in situ datalogging quantifies inter-individual variation in thermal experience and persistent origin effects on gaping behavior among intertidal mussels (Mytilus californianus)
Open the record for dataset details and reuse information.
Context-dependent multimodal behaviour in a coral reef fish: Stage 1 & 2 total duration and count data in behaviour trials
Open the record for dataset details and reuse information.
Data from: Multimodal mimicry of hosts in a radiation of parasitic finches
<p>Brood parasites use the parental care of others to raise their young and sometimes employ mimicry to dupe their hosts. The brood-parasitic finches of the genus <i>Vidua</i> are a textbook example of the role of imprinting in sympatric speciation. Sympatric speciation is thought to occur in <i>Vidua</i> because their mating traits and host preferences are strongly influenced by their early host environment. However, this alone may not be sufficient to isolate parasite lineages, and divergent ecological adaptations may also be required to prevent hybridisation collapsing incipient species. Using pattern recognition software and classification models, we provide quantitative evidence that <i>Vidua</i> exhibit specialist mimicry of their grassfinch hosts, matching the patterns, colours and sounds of their respective host's nestlings. We also provide qualitative evidence of mimicry in postural components of <i>Vidua</i> begging. Quantitative comparisons reveal small discrepancies between parasite and host phenotypes, with parasites sometimes exaggerating their host's traits. Our results support the hypothesis that behavioural imprinting on hosts has not only enabled the origin of new <i>Vidua</i> species, but also set the stage for the evolution of host-specific, ecological adaptations.</p>
Dataset and Data analysis "Multimodal vibrational studies of drug uptake in vitro: Is the whole greater than the sum of their parts?"
<p>Data Analysis for the publication 10.1002/jbio.202000264.</p> <p>It is divided in three different folders describing three different part of the data analysis:</p> <p><strong>A. DATA TREATMENT RAMAN (Folder 1)</strong></p> <p><em>1. Import data using the Import_Raman script.<br> 2. Plot Spectra and integrate DOX band<br> Figure 1A<br> Figure 1B<br> 3. PCA<br> Figure 1D<br> Figure 1C<br> SM 1<br> 4. PLS<br> Figure 1F<br> Figure 1E</em></p> <p><strong>B. ANALYSIS OF IR DATA AND MULTIMODAL IR-RAMAN OF DOX UPTAKE (Folder 2)</strong></p> <p><em>1 Load Data IR<br> 2 Exploratory Analysis IR<br> Figure 2A<br> 3 PCA <br> SM 2<br> 4. Partial Least Squares vs time<br> Figure 2C<br> Figure 2B<br> 5. Partial Least Squares vs Raman Signal<br> Figure 2E<br> Figure 2D<br> 6. Make Averages and clean up Data for DATA Fusion<br> IR<br> Raman<br> 7. 2DCORR<br> Figure 3B<br> 8. MCR_ALS WITH DATA FUSION<br> Fitting of the concentration of Raman using the method in [9].<br> MCR-ALS<br> Figures 4 A, B and C</em></p> <p> </p> <p><strong>C. SIMULATION (Folder 3)</strong></p> <p><em>1. Load Raman DATA<br> 2. Simulate Raman DAta<br> 3. Load and simulate IR Data<br> 4. 2D corr<br> Figure 3A</em></p> <p> </p> <p>. Each folder contains a .mlx with the data analysis performed. Figures numbering corresponds to the one found in the article.</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024)
<p>The data contains simulation results from 2000-2024, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671245</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671253</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999)
<p>The data contains simulation results from 1975-1999, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671245</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254</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.