Find research datasets worth reusing

DAS Data for the figure in the paper entitled "A Hybrid Earthquake Detection Method for Distributed Acoustic Sensing Array Data and Its Application to the 2022 Menyuan Earthquake Sequence"

The DAS data can be loaded using numpy. The sampling rate is 100 Hz, and each row is a time series for that channel.

IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data Fusion and Sensing Technology_characterization of sensing system for grippers_v0

The dataset contain the data acquired from the multi-sensorized fingers developed in T5.1 and integrated into grippers used in IntelliMan UC3 and UC4. The data contain tactile data, proximity data and endoscopic camera data for the evaluation of sensor performance with respect to IntelliMan use cases requirements.

Rocktopo Sensing Mountains Summer School 2024: UAV images, pointcloud and TLS pointclouds

This dataset contains high-resolution 3D scans of an outdoor rock climbing wall, captured using both Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS). The dataset includes images from the UAV, the reconstructed point cloud derived from photogrammetry, and the point cloud from TLS. These scans were conducted on the following dates: The used TLS was a Trimble X7 and the UAV a DJI Spark. <ul> <li> TLS (Terrestrial Laser Scanning): Provided in <code>TLS_240924</code> or <code>TLS_240925</code>, this folder contains the point cloud data captured via TLS, offering highly accurate and detailed 3D geometry of the climbing wall surface. In LAS format. </li> <li> UAV Images: Stored in <code>UAV_240924</code> or <code>UAV_240925</code>, these subdirectories contain a set of aerial images taken from the UAV, which were later used to reconstruct the 3D point cloud of the rock climbing wall. In JPEG format. </li> <li> Photogrammetric Point Cloud (LAS): The resulting 3D point cloud from UAV photogrammetry is stored in <code>LAS_240924</code> or <code>LAS_240925</code>. These files represent the spatial data and geometry derived from processing the UAV images using photogrammetry techniques. In LAS format. </li> <li>Aligned Point Cloud (ICP): The ICP aligned clouds that were used for analysis are stored in <code>ICP</code>. These files represent the spatial data and geometry derived from aligning the TLS and UAV clouds. In LAS format.</li> <li>Potree Point Cloud (folder): Octree converted pointclouds used in the <a href="https://github.com/jurriandoorbos/potree-rocktopo" target="_blank" rel="noopener">Potree rocktopo</a> visualization.</li> <li>Route Points: Polylines of every route in the lower location, in .poly (x y z) format.</li> </ul> The combination of both photogrammetric and laser scanning data provides a comprehensive and high-fidelity 3D representation of the climbing wall, useful for geospatial analysis, surface reconstruction, and outdoor modeling applications. The pointclouds are not georeferenced. Please note: the TLS and UAV were taken simultaneously: the same location is therefore: UAV/LAS_240924 fits with TLS_250924 and vice-versa.

Data from systematic review of uses of remote sensing in disease ecology

These data accompany the paper "The potential of remote sensing for improved infectious disease ecology research and practice" by Teitelbaum, C., Ferraz, A., De La Cruz, S.E.W., Gilmour, M.E., and Brosnan, I.G.. Each .csv file contains data from primary articles that used remote sensing to study disease ecology. Studies are identified by a unique study ID in each table; relationships between tables are usually many-to-many, except for the biobliographic details, which contains only one entry per article. The metadata.csv file describes columns in all sheets.

Data-driven Soil Moisture Sensing with mmWave Radar

This is a mmWave radar soil moisture dataset, which includes two different environments, specifically two conference rooms with different layouts. In Environment 1, soil with 20 different moisture levels was collected, ranging from 6.20% to 43.82%, with an interval of approximately 2%.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 6.20%, 8.30%, 10.80%, 12.60%, 14.44%, 16.14%, 18.96%, 19.70%, 23.34%, 25.24%, 26.24%, 28.90%, 31.10%, 32.46%, 33.80%, 36.44%, 38.20%, 40.56%, 42.68%, and 43.82%, respectively.In the bin file names, the second number after "data" represents the height of the radar development board above the soil surface, measured in centimeters, specifically 16 cm, 20 cm, and 24 cm. The data was collected using the IWR1843 radar board, with 1 transmitting antenna and 4 receiving antennas. Each bin file contains 128 radar frames, with each frame consisting of 32 chirps, and each chirp containing 384 sampling points. In Environment 2, soil with 20 different moisture levels was collected, ranging from10.0% to 48.0%, with an interval of 2%.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 10.0%, 12.0%, 14.0%, 16.0%, 18.0%, 20.0%, 22.0%, 24.0%, 26.0%, 28.0%, 30.0%, 32.0%, 34.0%, 36.0%, 38.0%, 40.0%, 42.0%, 44.0%, 46.0%, and 48.0%, respectively.In the bin file name, the second number after 'data' indicates the environment: 0 represents a static environment, and 1 represents a dynamic environment, where there is human movement.In the bin file name, 'train' and 'test' after 'data' represent the training and testing data, which were collected in two separate sessions.The data was collected using the IWR1843 radar board, which has 3 transmitting antennas and 4 receiving antennas. Each bin file contains 64 radar frames, with each frame consisting of 96 chirps, and each chirp containing 384 sampling points.

Data-driven Soil Moisture Sensing with mmWave Radar In Environment 2

This is a millimeter-wave radar soil moisture dataset, which includes soil samples with 20 different moisture levels, ranging from 10.0% to 48.0% with approximately 2% intervals.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 10.0%, 12.0%, 14.0%, 16.0%, 18.0%, 20.0%, 22.0%, 24.0%, 26.0%, 28.0%, 30.0%, 32.0%, 34.0%, 36.0%, 38.0%, 40.0%, 42.0%, 44.0%, 46.0%, and 48.0%, respectively.In the bin file name, the second number after 'data' indicates the environment: 0 represents a static environment, and 1 represents a dynamic environment, where there is human movement.In the bin file name, 'train' and 'test' after 'data' represent the training and testing data, which were collected in two separate sessions.The data was collected using the IWR1843 radar board, which has 3 transmitting antennas and 4 receiving antennas. Each bin file contains 64 radar frames, with each frame consisting of 96 chirps, and each chirp containing 384 sampling points.

opencc-by-4.0Apr 2024View details →

A data set on "High-Yield Production of SiV-Doped Nanodiamonds for Spectroscopy and Sensing Applications"

The data set to paper:  High-Yield Production of SiV-Doped Nanodiamonds for Spectroscopy and Sensing Applications Alexander Kromka1,*, Marián Varga1,2, Kateřina Aubrechtová Dragounová1,3, Oleg Babčenko1, René. Pfeifer1, Assegid M. Flatae4, Florian Sledz4, Farzana Akther4, Mario Agio4,5, &Scaron;těpán Potocký1, &Scaron;těpán Stehlík1 1 Institute of Physics, Czech Academy of Sciences, Prague 6, Czech Republic 2 Institute of Electrical Engineering, Slovak Academy of Sciences, Bratislava, Slovakia 3 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague 1, Czech Republic 4 Laboratory of Nano-Optics and Cμ, University of Siegen, Walter-Flex-Str. 3, 57072 Siegen, Germany  5 National Institute of Optics (INO-CNR), Largo Enrico Fermi 6, 50125 Florence, Italy *corresponding authors: kromka@fzu.cz Data manager: Kristýna Dostálová: dostalovak@fzu.cz Date of data collection: 1. 2. 2023 - 31. 7. 2024 All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective figure to which the data belong is provided in high resolution.  The data are in the following formats:  Scheme 1: pdf Figure 1: pdf Figure 2: pdf Figure 3: pdf, csv Figure 4: pdf, csv Figure 5: pdf, csv Figure 6: pdf, csv Figure 7: pdf, csv Figure S1: pdf, csv Figure S2: pdf, csv The comma separated values file (csv) always contain the description of the columns in the first row. In case of composed image the name of the file corresponds to the corresponding figure. Data acquistion and processing is provided in the Experimental part in the publication: DOI: 10.1021/acsanm.4c04676

SynRS3D : A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery

<h1>SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery</h1> <h3>Neural Information Processing Systems (Spotlight), 2024</h3> For more details, please refer to our <a href="https://arxiv.org/pdf/2406.18151">paper</a> and visit our <a href="https://github.com/JTRNEO/SynRS3D">GitHub repository</a>. <h2>Overview</h2> TL;DR: SynRS3D is a comprehensive synthetic remote sensing dataset designed to improve global 3D semantic understanding from monocular high-resolution imagery. It includes data for three key tasks: <ul> <li>Height estimation</li> <li>Land cover mapping</li> <li>Building change detection</li> </ul> <h2>Dataset Structure</h2> The dataset consists of 17 folders and includes a total of 69,667 images at a resolution of 512x512. After downloading and extracting the files, ensure the directory structure follows this format: ${DATASET_ROOT}  # Example: /home/username/project/SynRS3D/data/grid_g05_mid_v1 ├── opt           # RGB images (.tif), also used as post-event images for building change detection ├── pre_opt       # RGB images (.tif), used as pre-event images for building change detection ├── gt_nDSM       # Normalized Digital Surface Model (nDSM) images (.tif) ├── gt_ss_mask    # Land cover mapping labels (.tif) ├── gt_cd_mask    # Building change detection masks (.tif, 0 = no change, 255 = change area) └── train.txt     # List of training data filenames The land cover mapping labels (`gt_ss_mask`) are mapped to the following categories: <ul> <li>Bareland: 1</li> <li>Rangeland: 2</li> <li>Developed Space: 3</li> <li>Road: 4</li> <li>Trees: 5</li> <li>Water: 6</li> <li>Agriculture land:  7</li> <li>Buildings: 8</li> </ul> <h2>Image Breakdown by Folder</h2> The dataset is organized into grid-like and irregular terrain. It includes a range of ground sampling distances (GSDs) and variations in building heights. The folder naming convention indicates these characteristics:   - `grid` = grid-like terrain   - `terrain` = irregular terrain   - `g005`, `g05`, `g1` = GSD ranges (0.05m–0.3m, 0.3m–0.6m, and 0.6m–1m, respectively)   - `low`, `mid`, `high` = building height variations The dataset includes the following image counts: - 1,430 images – `terrain_g05_mid_v1` - 10,000 images – `grid_g05_mid_v2` - 2,354 images – `terrain_g05_low_v1` - 3,707 images – `terrain_g05_high_v1` - 880 images – `terrain_g005_mid_v1` - 2,127 images – `terrain_g005_low_v1` - 11,325 images – `grid_g005_mid_v2` - 1,212 images – `terrain_g005_high_v1` - 348 images – `terrain_g1_mid_v1` - 4,285 images – `terrain_g1_low_v1` - 904 images – `terrain_g1_high_v1` - 3,000 images – `grid_g005_mid_v1` - 2,997 images – `grid_g005_low_v1` - 4,000 images – `grid_g005_high_v1` - 7,000 images – `grid_g05_mid_v1` - 7,098 images – `grid_g05_low_v1` - 7,000 images – `grid_g05_high_v1` <h2>Citation</h2> If you find SynRS3D useful in your research, please consider citing: <div> <div>@article{song2024synrs3d,</div> <div>title={SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery},</div> <div>author={Song, Jian and Chen, Hongruixuan and Xuan, Weihao and Xia, Junshi and Yokoya, Naoto},</div> <div>journal={arXiv preprint arXiv:2406.18151},</div> <div>year={2024}</div> <div>}</div> </div> <h2>Contact</h2> For any questions or feedback, feel free to reach out via email:  song@ms.k.u-tokyo.ac.jp. Enjoy using SynRS3D!

Data collection of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"

This dataset contains the definition and name of the data used in the study. It also contains rows of data for all flood parameters applied to the creation of flood vulnerability maps, namely rainfall data, landsat-8 files, DEM, DSMW and drainage survey data.

Data testing of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"

This dataset explains validation testing in a study of the Samarinda Seberang flood vulnerability map. There are two test methods, namely the Kappa accuracy test and the 3D simulation visualization test. The Kappa accuracy test tab displays a table of Kappa calculation results, and the second tab contains a 3D simulation scenario image.

opencc-by-nd-4.0Jul 2023View details →

DWUG DE Sense: A data set of historical word sense annotations in German

This data collection contains a subset of <a href="https://zenodo.org/record/5543723">DWUG DE</a> word usage data annotated with classical word sense definitions (DWUG DE Sense, see <code>data/*/judgments_senses.csv</code>). From these annotations aggregated and cleaned sense labels were derived (<code>labels/*/labels_senses.csv</code>). From these labels we derived additional binary semantic proximity labels between use pairs ('0' for different sense, '1' for same sense, <code>labels/*/labels_proximity.csv</code>) and change labels reflecting sense changes between the two time periods from which word usages were sampled (<code>stats/*/stats_groupings.csv</code>). The sense labels were derived from the sense annotation by removing instances where not at least 2/3 annotators agree on the label (<code>maj_2</code>/<code>maj_3</code>). Note that the binary proximity labels were derived from the sense annotation, and not directly judged by humans (in contrast to other <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUG data sets</a>). Note that consequently also the change scores EARLIER, LATER and COMPARE were not calculated directly from human judgments, but from the inferred binary proximity labels. Please find the code aggregating and cleaning the data, deriving proximity labels and deriving change labels in the <a href="https://github.com/Garrafao/WUGs">WUG repository</a>. Please find more information on the provided data in the paper referenced below. Version: 1.0.1, 01.11.2024. Correct or remove some normalization and lemmatization errors in the uses. Updated references. <h3>Reference</h3> Dominik Schlechtweg, Frank D. Zamora-Reina, Felipe Bravo-Marquez, Nikolay Arefyev. 2024. <a href="https://doi.org/10.1007/s10579-024-09771-7">Sense Through Time: Diachronic Word Sense Annotations for Word Sense Induction and Lexical Semantic Change Detection</a>. Language Resources and Evaluation. Dominik Schlechtweg. 2023. <a href="http://dx.doi.org/10.18419/opus-12833">Human and Computational Measurement of Lexical Semantic Change</a>. PhD thesis. University of Stuttgart.

dryad36/100

Data from: Split between two worlds: automated sensing reveals links between above- and belowground social networks in a free-living mammal

Many animals socialize in two or more major ecological contexts. In nature, these contexts often involve one situation in which space is more constrained (e.g. shared refuges, sleeping cliffs, nests, dens or burrows) and another situation in which animal movements are relatively free (e.g. in open spaces lacking architectural constraints). Although it is widely recognized that an individual's characteristics may shape its social life, the extent to which architecture constrains social decisions within and between habitats remains poorly understood. Here we developed a novel, automated-monitoring system to study the effects of personality, life-history stage and sex on the social network structure of a facultatively social mammal, the California ground squirrel (Otospermophilus beecheyi) in two distinct contexts: aboveground where space is relatively open and belowground where it is relatively constrained by burrow architecture. Aboveground networks reflected affiliative social interactions whereas belowground networks reflected burrow associations. Network structure in one context (belowground), along with preferential juvenile–adult associations, predicted structure in a second context (aboveground). Network positions of individuals were generally consistent across years (within contexts) and between ecological contexts (within years), suggesting that individual personalities and behavioural syndromes, respectively, contribute to the social network structure of these free-living mammals. Direct ties (strength) tended to be stronger in belowground networks whereas more indirect paths (betweenness centrality) flowed through individuals in aboveground networks. Belowground, females fostered significantly more indirect paths than did males. Our findings have important potential implications for disease and information transmission, offering new insights into the multiple factors contributing to social structures across ecological contexts.

opencc-zeroDec 2017View details →

opencc-by-4.0Jun 2021View details →

Analysis code and quantification for publication "The stress-sensing domain of activated IRE1α forms helical filaments in narrow ER membrane tubes"

Analysis code and input/output files for all quantifications performed for publication entitled "The stress-sensing domain of activated IRE1α forms helical filaments in narrow ER membrane tubes."  All questions on the analyses or code can be directed to han@walterlab.ucsf.edu

opencc-by-4.0Jun 2021View details →

Multi-type Aircraft of Remote Sensing Images: MTARSI 2

Multi-Type Aircraft of Remote Sensing Images (MTARSI 2) dataset of aircraft on runways. The dataset has had some reclassification into 42 classifications, and extra data augmentation in those classifications.  It is an example of an unbalanced dataset, with challenges of different light and viewing angles.  Originated from https://zenodo.org/record/3464319#.YNwk3-hKiUk. (MTARSI)

opencc-by-4.0Aug 2021View details →

Tunable Resistive Pulse Sensing data of "The impact of storage on extracellular vesicles: a systematic study" experiments

Raw data of Tunable Resistive Pulse Sensing (TRPS) of "The impact of storage on extracellular vesicles: a systematic study" experiments

opencc-by-4.0Aug 2021View details →

Data for "Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing"

This dataset includes the experiment and simulation data of a new structural health monitoring system using distributed fiber optic sensing (DFOS) and Isogeometric Analysis (IGA). The experiment setup was a 5mm thick PVC pipe with a fiber optic cable wrapped around the outer surface of the pipe. The PVC pipe was subjected to an applied deformation and the distributed strains along the optical fiber was measured with a Neubrescope (NBX7031) instrument using Rayleigh backscattering technology. The simulation was performed using the in-house code JWRIAN-IGA developed in Joining and Welding Research Institute, Osaka University. The simulated data includes deformation, stress and strain distributions of the pipe, and projected one-dimensional fiber strains. The visualization files are post-processed with ParaView software.

opencc-by-4.0Sep 2021View details →

Data Archive for: Hurricane Laura (2020): A Comparison of Drop Size Distribution Moments Using Ground and Radar Remote Sensing Retrieval Methods

This archive corresponds to the data described in Brauer et al. (2021) to be published in Journal of Geophysical Research: Atmospheres. Please see the included readme.txt file for details about each data file.

openother-openSep 2021View details →

A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach

This file is the .csv database compiling surge-type glaciers automatically identified in Guillet et al (2022). File format is compliant with the Randolph Glacier Inventory (RGI) V6.0. If you have questions about the dataset - please refer to the following reference or contact Dr. Guillet. <table> <tbody> <tr> <td>Guillet, G., King, O., Lv, M., Ghuffar, S., Benn, D., Quincey, D., & Bolch, T. (2022). A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach. The Cryosphere, 16(2), 603-623.</td> </tr> <tr> <td> </td> </tr> </tbody> </table>

dryad36/100

Making sense of virus size and the tradeoffs shaping viral fitness

Viruses span an impressive size range, with genome length varying a thousandfold and virion volume nearly a millionfold. For cellular organisms the scaling of traits with size is a pervasive influence on ecological processes, but whether size plays a central role in viral ecology is unknown. Here we focus on viruses of aquatic unicellular organisms, which exhibit the greatest known range of virus size. We outline hypotheses within a quantitative framework, and analyze data where available, to consider how size affects the primary components of viral fitness. We argue that larger viruses have fewer offspring per infection and slower contact rates with host cells, but a larger genome tends to increase infection efficiency, broaden host range, and potentially increase attachment success and decrease decay rate. These countervailing selective pressures may explain why a breadth of sizes exist and even coexist when infecting the same host populations. Oligotrophic ecosystems may be enriched in "giant" viruses, because environments with resource-limited phagotrophs at low concentrations may select for broader host range, better control of host metabolism, lower decay rate, and a physical size that mimics bacterial prey. Finally, we describe where further research is needed to understand the ecology and evolution of viral size diversity.

opencc-zeroOct 2021View details →