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1,961 results for “Sensing”
Detecting social interactions via mobile sensing - Dataset
<p>The goal of Markus' master thesis was to establish whether real-life social interactions could be detected using smartphone internal sensors.</p> <p>The present dataset is a labelled set of sensor data. It contains sensing data of six persons, who each used the sensing application for three days in a row. During these days, the test persons in addition created labels for their real-life sensing data.</p> <p>Participants in the first group in addition worked in the same office, and used the sensing at the same time. Their social interactions therefore overlap.</p>
Dataset: Six years ground-based remote sensing of microphysical properties of stratiform liquid clouds at Mace Head, Ireland
<p>A total of 118 stratiform water clouds observed by ground-based remote sensing instruments at the Mace Head Atmospheric Research Station at the West coast of Ireland from 2009 to 2015 were analyzed in terms of microphysical and optical characteristics as well as the impact of aerosols on these properties. The microphysical and optical cloud properties in the files were obtained using the algorithm SYRSOC (SYnergistic Remote Sensing Of Clouds).</p>
Literature search: Remote sensing in conservation and ecology
<p>This file provides the raw data of a literature search that was conducted to demonstate the growing relevance and rapid devleopment of remote sensing in relation to conservation and ecology within academia. The search was performed using Scopus, a database of peer-reviewed literature, using the string "remote sensing" AND ["conservation" OR "ecology"].</p>
Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset 2
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (<em>weighted_it_reconstructions.hdf5</em>).</li> <li>The Python script which was used to create the database (<em>weighted_it_reconstructions.py</em>).</li> <li>MD5 and SHA256 checksums of the database and Python script files (<em>weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The database is split into ten parts:</p> <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-04</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-05</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-06</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-07</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-08</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-09</li> </ol> <p>These tem parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:</p> <p><em>$ cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz</em></p> <p>after which the archive may be extracted, e.g., using:</p> <p><em>$ tar xfJ weighted_it_reconstructions.hdf5.tar.xz</em></p> <p><strong>WARNING: The extracted HDF5 database has a size of 114 GiB.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (<em>weighted_it_reconstructions.hdf5</em>).</li> <li>The Python script which was used to create the database (<em>weighted_it_reconstructions.py</em>).</li> <li>MD5 and SHA256 checksums of the database and Python script files (<em>weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The database is split into four parts:</p> <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> </ol> <p>These four parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:</p> <p><em>cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz</em></p> <p>after which the archive may be extracted, e.g., using:</p> <p><em>tar xfJ weighted_it_reconstructions.hdf5.tar.xz</em></p> <p><strong>WARNING: The extracted HDF5 database has a size of 70 GiB.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
<p>This dataset contains the models for interpretable Word Sense Disambiguation (WSD) that were employed in Panchenko et al. (2017; the paper can be accessed at https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/publications/EACL_Interpretability___FINAL__1_.pdf).</p> <p>The files were computed on a 2015 dump from the English Wikipedia. Their contents:</p> <ul> <li>Induced Sense Inventories: <strong>wp_stanford_sense_inventories.tar.gz</strong><br> This file contains 3 inventories (coarse, medium fine)</li> <li>Language Model (3-gram): <strong>wiki_text.3.arpa.gz</strong><br> This file contains all n-grams up to n=3 and can be loaded into an index</li> <li>Weighted Dependency Features: <strong>wp_stanford_lemma_LMI_s0.0_w2_f2_wf2_wpfmax1000_wpfmin2_p1000.gz</strong><br> This file contains weighted word--context-feature combinations and includes their count and an LMI significance score</li> <li>Distributional Thesaurus (DT) of Dependency Features: <strong>wp_stanford_lemma_BIM_LMI_s0.0_w2_f2_wf2_wpfmax1000_wpfmin2_p1000_simsortlimit200_feature expansion.gz</strong><br> This file contains a DT of context features. The context feature similarities can be used for context expansion</li> </ul> <p>For further information, consult the paper and the companion page: http://jobimtext.org/wsd/</p> <p>Panchenko A., Ruppert E., Faralli S., Ponzetto S. P., and Biemann C. (2017): Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL'2017). Valencia, Spain. Association for Computational Linguistics.</p> <p> </p> <p> </p>
A dataset of atmospheric ozone above the Mexico City basin retrieved from FTIR remote sensing observations made at two different ground altitudes
<p>This dataset of atmospheric ozone (O<sub>3</sub>) has been generated from solar absorption spectra measured in central Mexico using ground-based Fourier-Transform Infrared (FTIR) spectrometers. The FTIR experiments have been operated by the “Spectroscopy and Remote Sensing” Research Group of the Centro de Ciencias de la Atmósfera of the Universidad Nacional Autónoma de México (http://www.atmosfera.unam.mx/espectroscopia/index.html).</p> <p>The dataset covers measurements made between November 2012 and February 2014 applying two different FTIR spectrometers. The first instrument offers very high resolution spectra and contributes to NDACC (Network for the Detection of Atmospheric Composition Change). It is located at the mountain observatory of Altzomoni (ALTZ) about 1700m above the Mexico City basin. The second instrument has a medium spectral resolution and is located inside of Mexico City at the Universidad Nacional Autónoma de México (UNAM) at a horizontal distance of about 60km to the mountain observatory.</p> <p>The here provided dataset consists of two NETCDF data-files for each station and a MATLAB script for reading the NETCDF files. The files “ALTZ_IFS125_O3.nc” and “UNAM_IFS125_O3.nc” contain the retrieved O<sub>3</sub> state vectors, the O<sub>3</sub> averaging kernels and the O<sub>3</sub> a priori profiles, together with auxiliary data: observation time, observation geometry, instrumental settings, atmospheric temperature and humidity profiles. The data as well as the method for combining the two different observations are presented in Plaza-Medina et al. (2017), which should be consulted for more details.</p> <p>The files “ALTZ_IFS125_O3_Jac+Gain.nc” and “UNAM_IFS125_O3_Jac+Gain.nc” contain the Jacobians (for O<sub>3</sub> as well as for error sources) and the Gain matrix, together with the auxiliary data. The MATLAB script “readNETCDF_and_combine2FTIR.m” reads the NETCDF files and performs the operations needed for the generation of a combined product, thereby exploiting the synergetic effects of two observations made in coincidence but at different ground altitudes.</p> <p>A related dataset with Altzomoni O<sub>3</sub> profiles obtained by applying slightly different retrieval settings is available at the NDACC database (ftp://ftp.cpc.ncep.noaa.gov/ndacc/station/altzomoni/hdf/ftir/). Further datasets of atmospheric parameters as measured by different techniques are available at the webpage of the Red Universitario de Observaciones Atmosfericas (www.ruoa.unam.mx).</p>
A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
<p>Vertical <span>profile </span>observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profil<span>e</span> data. This study <span>introduces </span>a high-<span>time-</span>resolution (approximately 15 minutes) dataset of vertical <span>profile </span>observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal <span>variations and </span>diurnal <span>pattern</span> of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing <span>would </span>facilitate the scientific community <span>in </span>explor<span>ing</span> of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate <span>and </span>feedback mechanisms.</p>
Fire-D: Analysis and ML-Ready NASA-Centric Remote Sensing of Wildfire and Smoke
<p>Earth science remote sensing imagery is rich in structural and spectral information, making such data an ideal platform for benchmarking for a broad range of machine learning (ML) tasks, from pattern retrieval to physics-informed classification to anomaly detection to transfer learning. Nevertheless, the utility of Earth science remote sensing data remains largely unexplored by the broader ML community. Our goal is to bridge this gap and bring a rich variety of multisource multi-resolution Earth image data to a wider range of ML researchers who are non-experts in remote sensing, thereby increasing the utility and societal impact of such data products. In particular, motivated by the emerging wildfire crisis, we present radiometrically and geometrically calibrated radiance data from airborne and orbital instruments from the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the Korean Meteorological Administration (KMA).</p> <p>Given the scarce occurrence of wildfires and complex spatio-temporal dependencies in radiance data, these datasets are especially well suited for benchmarking unsupervised and self-supervised learning tasks both on images and non-Euclidean objects. Our experiments on these datasets indicate that contrastive learning and transfer learning algorithms can capture the structures of views and scenes, map pixel space of multi-sensor imagery to a high-level embedding space for further downstream tasks, and facilitate more cohesive integration of the state-of-the-art ML approaches into wildfire risk analytics.</p> <p>All NASA-based observations are freely usable under the <a href="https://science.data.nasa.gov/license/">Creative Commons Zero License</a>.There are also no restrictions on the use of <a href="https://registry.opendata.aws/noaa-goes/">GOES Data</a>. <a href="https://registry.opendata.aws/noaa-gk2a-pds/">GK2A data</a> are also open data without any restrictions on its use.<br><br>For the Planet data, we cannot not share the Radiances, but all masks within this dataset are freely usable with no restrictions.</p> <p> </p> <p>Use:</p> <p>On the data input, input geometrically and radiometrically calibrated radiance data has been pulled from various NASA, NOAA, Planet, and KMA archives. For instruments that have multiple different spatial resolutions within their spectral bands (GOES and GK2A), all bands have been resampled to the lowest collective spatial resolution.</p> <p>Geometric and radiometric calibration has been done by the science data processing pipelines of the various missions, and would not need to be done by anyone else looking to curate the same data. Further information for each instrument can be found in each of the publicly available Level-1 algorithm theoretical basis documents (ATBDs)</p> <p>All input and label data have been put in GeoTiff format. Each band is in a separate raster band and each scene is in a separate GeoTiff file. Label files and input files are in separate tar files, labeled respectively, and the file names match for input and labels, with the exception of an additional .fire and .smoke in the respective label filenames and subfolders.<br><br>The <a href="https://www.earthdata.nasa.gov/about/esdis/esco/standards-practices/geotiff">GeoTiff</a> data format natively contains geolocation metadata internally, and can be interfaced with via C/C++/Python <a href="https://gdal.org/en/stable">GDAL</a> packages, or other python packages that wrap GDAL, like <a href="https://rasterio.readthedocs.io/en/stable/">rasterio</a> and <a href="https://corteva.github.io/rioxarray/stable/">rioxarray</a> . The documentation for <a href="https://nicks-personal-organization-2.gitbook.io/sit-fuse">SIT-FUSE</a> , the package with which the labels were generated, also has examples on how to read and interface with various data formats, including GeoTiffs. Lastly, this data can be interfaced with using Geographic Information Systems (GIS), like the free and open-source <a href="https://qgis.org/">QGIS</a>.</p> <p>An example of programmatic data access and usage can be found in the dataset's associated <a href="https://github.com/Fire-D-Dataset/FIRE-D">GitHub repository</a>. </p> <p>A working example using data from this repository for ML tasks is available <a href="https://drive.google.com/drive/folders/16aJO6LhrxJ3gsWoTU9BNN3hsb8W0refG?usp=sharing">here</a>.</p> <p>Timing information can be found in the file names, which all use the standard formats from the various instruments' L1B datasets.</p> <p>V2 includes additional GOES-18 radiance data and associated smoke and fire labels for the recent LA fires (Palisades and Eaton fires in January of 2025).</p> <p>V3 provides a reorganization of all data, and an inclusion of improved and additional data from airborne and satellite platforms in 2019, associated with this study: https://arxiv.org/pdf/2501.15343 . </p> <p>V4 provides additional AVIRIS-C Radiances and fixes the spatial range of the GOES-17 radiances to match that of the associated labels. The AVIRIS-C radiances are split across 5 tar files, ordered temporally - all associated labels are in a single tar file.</p> <p><br>Current fire coverage includes:</p> <ul> <li>2019: Williams Flats, Sheridan, Horsefly, and Mosquito (US)</li> <li>2022: Uljin Forest Fire (S. Korea; largest fire on record in S. Korea)</li> <li>2025: Palisades and Eaton Fires (US)</li> </ul> <p>Additional data for the 2025 Palisades and Eaton fires from the TEMPO instrument is currently being validated and will be released in a V4 shortly.</p> <p>Croissant file for dataset metadata specification is also included</p> <p>Validation:</p> <p>These labels have been extensively validated and further information can be referenced in associated publications:<br><a href="https://doi.org/10.3390/rs13122364">https://doi.org/10.3390/rs13122364</a><br><a href="https://doi.org/10.3390/rs17071267">https://doi.org/10.3390/rs17071267</a></p> <p> </p> <p> </p>
Dataset for paper "Interpreting the shifts in forest structure, plant community composition, diversity, and functional identity by using remote sensing-derived wildfire severity"
<p>Interpreting the shifts in forest structure, plant community composition, diversity, and functional identity by using remote sensing-derived wildfire severity . New collected data</p>
Multi-modal pose estimation in XR applications leveraging integrated sensing and communication: Dataset
<p>This dataset refers to paper Multi-modal pose estimation in XR applications leveraging integrated sensing and communication in workshop of ACM Mobicom. CSI of 3 people performing a set of 8 poses. This dataset contain discrete classes and corresponding CSI data. Kinect poses can be found here (https://github.com/nisarnabeel/multi-modal-pose-estimation-CSI-mmWave).</p> <p> </p> <p>Abstract: Mobile extended reality (XR) applications are anticipated to generate substantial traffic for 6G. Such applications not only require high data rate and low-latency transmissions, but also accurate and real-time pose estimation to enable interactive and immersive experiences. While sub-6 GHz signals have been exploited for pose estimation, they cannot cope up with multi-gigabit data rates required by XR applications. Instead, mobile communications at mmWave frequencies can potentially support data rates up to several giga-bits per second (Gbps) and, therefore, can be used to deliver XR content wirelessly to the Head-Mounted Display (HMD). Moreover, mmWave frequencies can offer improved sensing due to the large available bandwidth. Therefore, mmWave communications can play a crucial role in enabling device-free interactivity by offering both high-speed communication and accurate sensing capabilities. However, mmWave propagation characteristics are different from sub-6 GHz. Path loss plays a significant role, and can lead to degraded sensing performance. Therefore, our proposal supplements wireless sensing at mmWave frequencies with wireless electromyography (EMG) armbands. By capturing patterns of muscle activities, we can counteract the limitations of mmWave-based pose estimation, thereby enriching the granularity and precision of pose estimation. This paper proposes a conceptual architecture to achieve multi-modal pose estimation for XR applications. Early results highlight the shortcomings of mmWave-based sensing, and we identify future steps and opportunities on integration of both approaches.</p>
data for publication "Benefits of biobased fertilizers as substitutes for synthetic nitrogen fertilizers: Field assessment combining minirhizotron and UAV-based spectrum sensing technologies"
<p>Dataset for the scientific publication "Benefits of biobased fertilizers as substitutes for synthetic nitrogen fertilizers: Field assessment combining minirhizotron and UAV-based spectrum sensing technologies" in the Journal Frontiers of Environmental Science. </p><p><a href="https://doi.org/10.3389/fenvs.2022.988932">https://doi.org/10.3389/fenvs.2022.988932</a></p>
Data used in '3D Printable Self-Sensing Magnetorheological Elastomer'
<p>This dataset contains the data used in the publication '3D Printable Self-Sensing Magnetorheological Elastomer'</p>
Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.
<p>For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised Geographic Object-Based Image Analysis (GeOBIA) paired with the Fuzzy C-Means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985-2019 time-period, each spanning five years. Our study site is the George River watershed (GRW), a 42,000 km<sup>2</sup> watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015-2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985-2014 to investigate past changes in hydrological regime. The seven-classification time series showed a homogenization of subwatershed types associated to increases in vegetation productivity and in water content<br> in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evapotranspiration and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km<sup>3</sup>y<sup>−1</sup>) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote sensing based approaches, particularly in areas of discontinuous and sporadic permafrost.</p>
Capacitive crosstalk in gate-based dispersive sensing of spin qubits
<p>supporting data from paper 'Capacitive crosstalk in gate-based dispersive sensing of spin qubits' by E. G. Kelly, A. Orekhov, N. Hendrickx, M. Mergenthaler, F. Schupp, S. Paredes, R. S. Eggli, A. V. Kuhlmann, P. Harvey-Collard, A. Fuhrer and G. Salis</p>
Data set: Al-Biruni Earth Radius Optimization with Deep Transfer Learning based Scene Image Classification on Remote Sensing Imagery
Open the record for dataset details and reuse information.
Large Apple Sculture Sense Scanner
6LB Foam that I hand shaped, hardcoated, painted for home. Nothing more classic than a red delicious apple. Sits on top of a great book, added fruits. Once I learn Photoscan I'll snap some photos and see what happens. Source: Objaverse 1.0 / Sketchfab
Morphology and ultrastructure of external sense organs of Drosophila larvae
<p>Sensory perception is the ability through which an organism is able to process sensory stimuli from the environment. This stimulus is transmitted from the peripheral sensory organs to the central nervous system, where it is interpreted. Drosophila melanogaster larvae possess peripheral sense organs on their head, thoracic, and abdominal segments. These are specialized to receive diverse environmental information, such as olfactory, gustatory, temperature, or mechanosensory signals. In this work, we complete the description of the morphology of external larval sensilla and provide a comprehensive map of the ultrastructure of the different types of sensilla that comprise them. This was achieved by 3D electron microscopic analysis of partial and whole body volumes, which contain high-resolution and complete three-dimensional data of the anatomy of the sensilla and adjacent ganglia. Our analysis revealed three main types of sensilla on thoracic and abdominal segments: the papilla sensillum, the hair sensillum, and the knob sensillum. They occur solitary or organized in compound sensilla such as the thoracic keilin's organ or the terminal sensory cones. We present a spatial map defining these sensilla by their position on thoracic and abdominal segments. Further, we identify and name the sensilla at the larval head and the last fused abdominal segments. We show that mechanosensation dominates in the larval peripheral nervous system, as most sensilla have corresponding structural properties. The result of this work, the construction of a complete structural and neuronal map of the external larval sensilla, provides the basis for following molecular and functional studies to understand which sensory strategies the Drosophila larva employs to orient itself in its natural environment.</p>
Forest disturbance detection by using remote sensing and artificial intelligence in Africa
<p>The dataset arises from the "Forest Disturbance Detection Using Remote Sensing and Artificial Intelligence in Africa" (EO4Forest) project, a collaboration funded by the European Space Agency and conducted by Wrocław University of Environmental and Life Sciences (Poland) and Lagos State University (Nigeria). Designed for forest monitoring in the Ogun and Lagos States, the dataset includes detailed land cover classification maps for the years 2015, 2019, 2022, and 2023, all at a 20-meter spatial resolution to ensure accurate representation of land cover. A legend file accompanies the maps, clarifying six defined land cover classes: water, urbanized areas, soil, cropland, grasslands, and forest.</p> <p>The dataset includes over 112,000 training and 11,900 validation samples, which are essential for the accurate development and evaluation of the Random Forest classification models used. Special emphasis was placed on the forest class, ensuring a diverse representation of forest types, including tropical humid forests, mangroves, and dry woodlands.</p> <p>In addition to land cover maps, the dataset also contains forest gain and loss maps, with a particular focus on recent updates for selected subareas in 2022 and 2023, available in the NTR_ForestUpdate folder.</p> <p>Based on optical satellite imagery from Sentinel-2 and Landsat-8, the dataset leverages spectral indices and the Random Forest algorithm to classify land cover types. It provides valuable insights for environmental research related to deforestation, reforestation, and afforestation. Forest change maps are included to highlight areas of forest loss and gain, capturing the dynamic shifts in Nigeria's forest cover and offering detailed geographic context.</p> <p>The methodology, including data acquisition, preprocessing, feature extraction (spectral index calculation), classification, and accuracy assessment, is fully documented in Python scripts available in the associated GitHub repository. This ensures transparency and reproducibility, offering users both the processed outputs and the tools necessary for custom analyses and advanced forest monitoring.</p>
IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_DataFusion and Sensing Technology_characterization of sensing system for grippers_v0
<p>The dataset contains data related to the simulations and experiments presented in the publication:<br>G. Laudante, O. Pennacchio, and S. Pirozzi, “Multiphysics simulation for the optimization of an optoelectronic-based tactile sensor,” in Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, 2023, pp. 101–110. (DOI: 10.5220/0012166900003543)</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.