Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,452
datasets available to search
ShareScore release 0.9.0
Dataset results
6,452 results for “Application”
Figure 2 from: Hudz N, Yezerska O, Shanaida M, Horčinová Sedláčková V, Wieczorek PP (2019) Application of the Folin-Ciocalteu method to the evaluation of Salvia sclarea extracts. Pharmacia 66(4): 209-215. https://doi.org/10.3897/pharmacia.66.e38976
Figure 2 Dependence of absorbance on wavelength for E-4 of Salvia sclarea (dilution 1:10) with the FCR (after 60 min of reaction time).
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
<p>This dataset consists of 1,181,993 Tweet Ids, obtained as a result from the paper - A large-scale Twitter dataset for drug safety applications mined from publicly existing resources.</p> <p>The Tweet Ids can be hydrated using <strong>twarc</strong>, a command line tool and Python library for archiving Twitter JSON data. Please follow these instructions to install twarc - <a href="https://github.com/DocNow/twarc">https://github.com/DocNow/twarc</a></p> <p>It will take less than 4 hours to hydrate the tweet ids using twarc. </p> <p>If you intend to use the dataset, please cite the paper. </p>
RYDL: the sample data of the RY product for deep learning applications
<p>RYDL is the sample data of the RY product provided by the German Weather Service (DWD). This data has been utilized for training the RainNet model -- a deep convolutional neural network for radar-based precipitation nowcasting.</p> <p>You can find the latest RY product data on DWD open data repository: https://opendata.dwd.de/weather/radar/.</p> <p>The RainNet model is available on GitHub: https://github.com/hydrogo/rainnet.</p>
CroCoord v1.0 – an Android application for nearly real-time 3D PPP multi-frequency multi-GNSS static surveying using smartphones
<p>Two hour of GNSS data collected on 31st December 2019 on one point with Android dual-frequency smartphone Xiaomi Mi Mix-3 and permanent station in the surrounding of the measured point.</p>
3D Visualization in Geo-applications
<p>The use of 3D visualization technologies has increased rapidly in many applied fields, including geovisualization, and has been researched from many different perspectives. However, the findings for the benefits of 3D visualization, especially in stereoscopic 3D forms, remain inconclusive and disputed. Stereoscopic “real” 3D visualization was proposed as encouraging the visual perception of shapes and volume of displayed content yet criticised as problematic and limited in a number of ways, particularly in visual discomfort and increased response time in tasks. In order to assess the potential of real 3D visualization for geo-applications, 91 participants were engaged in this study to work with digital terrain models in different 3D settings. The researchers examined the effectivity of stereoscopic real 3D visualization compared to monoscopic 3D (or pseudo 3D) visualization under static and interactive conditions and applied three tasks with experimental stimuli representing different geo-related phenomena, i.e. objects in the terrain, flat areas marked in the terrain and terrain elevation profiles. The authors explored the significant effects of real 3D visualization and interactivity factors in terms of response time and correctness (effectiveness and efficiency). Further, mouse clicks were captured while completing tasks. </p>
Data supplementing the article "Aquatic biofilms as passive environmental DNA samplers: application to benthic macroinvertebrate communities in rivers" - raw MiSeq data inventories
<p>These data supplement the article “Aquatic biofilms as passive environmental DNA samplers: application to benthic macroinvertebrate communities in rivers” Sinziana F. Rivera, Valentin Vasselon, Nathalie Mary, Olivier Monnier, Fréderic Rimet & Agnès Bouchez submitted to “Molecular Ecology Resources” journal.</p> <p>The directory is composed of: “38_samples_fastq:files”: contains raw demultiplexed fastq files (R1. fastq and R2. fastq) for each of the 38 samples used in this study to produce OTUs and taxonomic inventories.</p> <p>“Samples id.xlsx”: contains the samples ID of the fastq files</p> <p>“Inventories.xlsx”: contains single and multi-habitat morphological inventories as well as molecular inventories resulting from the study</p>
Figure 5 from: Ruan Y, Konstantinov AS, Shi G, Tao Y, Li Y, Johnson AJ, Luo X, Zhang X, Zhang M, Wu J, Li W, Ge S, Yang X (2020) The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg. ZooKeys 915: 87-105. https://doi.org/10.3897/zookeys.915.38348
Figure 5 The dynamics of the catapult mechanism of the flea beetle hind leg powered by the elastic plate (Asiophrida xanthospilota, micro-CT 3D reconstructions from four different phases of the jumping leg). (F1 indicates the force generated by tibial extensor muscles responsible for extension of the tibia; F2 indicates the force responsible for flexion of the tibia which is indirectly generated by tibial flexor muscles). A–D 3D reconstructions of four hind legs through each of the four phases of the jump, indicating variable d1 (moment arm of F1) and d2 (moment arm of F2). d1 is at its minimal length at the start of the hind leg extension and at its maximal length during the middle; by contrast, d2 is at its maximal length at the start of the hind leg extension and at its minimal at the end A phase I B phase II C phase III D phase IV E the positive feedback mechanism in the take-off process. F2 comprises F21, which is the constrained force generated by the elastic plate, and F22, which is generated by the tibial flexor muscle F the triangular plate – elastic plate complex (lateral view) G the triangular plate – elastic plate complex (dorsal view) H the elastic plate (dorsal view, under light microscope, dark-field microscopy) I the triangular plate (dorsal view, under light microscope, dark-field microscopy). Abbreviations: epl: elastic plate; tli-2: secondary tibial ligament; tpl: triangular plate; ten: tendon of tibial flexor.
Supplementary material 1 from: Ruan Y, Konstantinov AS, Shi G, Tao Y, Li Y, Johnson AJ, Luo X, Zhang X, Zhang M, Wu J, Li W, Ge S, Yang X (2020) The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg. ZooKeys 915: 87-105. https://doi.org/10.3897/zookeys.915.38348
: Data type: multimedia
Figure 4 from: Ruan Y, Konstantinov AS, Shi G, Tao Y, Li Y, Johnson AJ, Luo X, Zhang X, Zhang M, Wu J, Li W, Ge S, Yang X (2020) The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg. ZooKeys 915: 87-105. https://doi.org/10.3897/zookeys.915.38348
Figure 4 Take-off strategy of flea beetles (Asiophrida xanthospilota is shown in the photos at the top of the figure). Acceleration data were calculated based on three typical jumps recorded by a high-speed camera. The three different species were chosen to represent flea beetles with different sizes A Phase I (Crunching): tibial flexor muscles contract, causing flexion of the tibia B Phase II (Co-contraction): tibial extensor muscles and tibial flexor muscles contract simultaneously, catching the triangular plate and hindering the extension of the tibia C Phase III (Triggering and Acceleration), the triangular plate is dislodged, causing the explosive release of energy D Phase IV (Relaxation), the flea beetle is catapulted into the air and the muscles begin to relax.
Figure 1 from: Ruan Y, Konstantinov AS, Shi G, Tao Y, Li Y, Johnson AJ, Luo X, Zhang X, Zhang M, Wu J, Li W, Ge S, Yang X (2020) The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg. ZooKeys 915: 87-105. https://doi.org/10.3897/zookeys.915.38348
Figure 1 Jumping apparatus in the flea beetle hind leg. A a model of a generic flea beetle (lateral view), indicating the enlarged hind leg B hind leg of Trachytetra obscura under a light microscope (dark-field microscopy) C–H X-ray computer tomography-based 3D reconstructions of the hind leg of the flea beetle Asiophrida xanthospilotaC lateral view of the hind leg and internal structures D dorsal view of the hind leg E lateral view of the hind leg (view from an opposite direction of inset C) F lateral view of the metafemoral spring G ventral view of the metafemoral spring H femorotibial joint. Abbreviations: epl: elastic plate; piv: tibial pivot of femorotibial joint; tli-1: primary tibial ligament; tli-2: secondary tibial ligament; tpl: Lever's triangular plate.
Supplementary material 4 from: Ruan Y, Konstantinov AS, Shi G, Tao Y, Li Y, Johnson AJ, Luo X, Zhang X, Zhang M, Wu J, Li W, Ge S, Yang X (2020) The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg. ZooKeys 915: 87-105. https://doi.org/10.3897/zookeys.915.38348
: Data type: multimedia
Figure 3 from: Ruan Y, Konstantinov AS, Shi G, Tao Y, Li Y, Johnson AJ, Luo X, Zhang X, Zhang M, Wu J, Li W, Ge S, Yang X (2020) The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg. ZooKeys 915: 87-105. https://doi.org/10.3897/zookeys.915.38348
Figure 3 Variation in the metafemoral spring of different flea beetle species (3D reconstructions). A hind femur of Altica cirsicolaB hind femur of Podontia luteaC hind femur of Psylliodes sp. D hind femur of Nonarthra sp. E hind femur of Clavicornaltica sp. F hind femur of Hespera lomasa. Scale bar: 0.2 mm.
Figure 2 from: Ruan Y, Konstantinov AS, Shi G, Tao Y, Li Y, Johnson AJ, Luo X, Zhang X, Zhang M, Wu J, Li W, Ge S, Yang X (2020) The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg. ZooKeys 915: 87-105. https://doi.org/10.3897/zookeys.915.38348
Figure 2 Internal structures of the hind leg of Altica cirsicola. A the flea beetle Altica sp. on foliage B–F X-ray computer tomography 3D reconstructions of the hind leg of A. cirsicolaB lateral view of the left hind leg C lateral view of the metafemoral spring D distal view of the metafemoral spring E distal view of the metafemoral spring and tibial extensors F features of the femorotibial joint. Abbreviations: cox: coxa; exa: extended arm of the metafemoral spring; epl: elastic plate; fem: femur; msp: metafemoral spring; ner: nerve; piv: tibial pivot of the femorotibial joint; rfl: recurve flange of the metafemoral spring; tar: tarsi; tex-1: primary tibial extensor; tex-2: secondary tibial extensor; tfl: tibial flexor; tib: tibia; tli-1: primary tibial ligament; tli-2: secondary tibial ligament; tpl: Lever's triangular plate; tra: trachea; tro: trochanter.
Supplementary material 2 from: Ruan Y, Konstantinov AS, Shi G, Tao Y, Li Y, Johnson AJ, Luo X, Zhang X, Zhang M, Wu J, Li W, Ge S, Yang X (2020) The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg. ZooKeys 915: 87-105. https://doi.org/10.3897/zookeys.915.38348
: Data type: multimedia
Supplementary material 3 from: Ruan Y, Konstantinov AS, Shi G, Tao Y, Li Y, Johnson AJ, Luo X, Zhang X, Zhang M, Wu J, Li W, Ge S, Yang X (2020) The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg. ZooKeys 915: 87-105. https://doi.org/10.3897/zookeys.915.38348
: Data type: multimedia
Figure 6 from: Ruan Y, Konstantinov AS, Shi G, Tao Y, Li Y, Johnson AJ, Luo X, Zhang X, Zhang M, Wu J, Li W, Ge S, Yang X (2020) The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg. ZooKeys 915: 87-105. https://doi.org/10.3897/zookeys.915.38348
Figure 6 Bionic design of a jumping limb inspired by the flea beetle leg. A preparation position for jumping B building up elastic strain energy C triggering the jump D acceleration. The required energy for a jump is provided by two 'motors' operating simultaneously and generating the forces F1 and F2, respectively. At the beginning (inset B), F1 > F2, the 'tibia' rotates clockwise, but the 'trigger' blocks the process and leads to the stretching of 'volute spring'. Thus, the work generated by the 'motors' is stored in the 'volute spring'. At a certain stage (inset C), when the 'trigger' can no longer constrain the huge tension built up inside the femur, the 'latch element' dislodges suddenly from the 'trigger' and the huge amount of energy stored in the 'volute spring' is released. This leads to the explosive clockwise movement of the tibia. The leg thereby propels the robot into the air in an explosive manner. The leg can be switched to regular walking mode as required by operating only one 'motor' (one to flex the leg and the another to extend it) at a time.
Venusian Habitable Climate Scenarios: Modeling Venus through time and applications to slowly rotating Venus-Like Exoplanets
<p>All files in these zip files are related to the paper titled "Venusian Habitable Climate Scenarios: Modeling Venus through time and applications to slowly rotating Venus-Like Exoplanets" by M.J. Way & Anthony D. Del Genio in The Journal of Geophysical Research: Planets</p> <p>https://www.essoar.org/doi/10.1002/essoar.10501118.2</p> <p>See Table 1 for a list of the 45 Experiments. The files used to generate the data presented in Table 2 are included herein.</p> <p>RUNDECKS.zip: ROCKE-3D model configuration files (179KB)</p> <p>INPUT_FILES.zip: Boundary conditions files required for a given rundeck (194MB)</p> <p>ACC.zip: Raw NetCDF model output files (733MB)</p> <p>AIJ.zip: ACC Processed aij NetCDF files (118MB)</p>
Link to dataset related to article "Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry"
<p>The interrogation of single cells is revolutionizing biology, especially our understanding of the immune system. Flow cytometry is still one of the most versatile and high-throughput approaches for single-cell analysis, and its capability has been recently extended to detect up to 28 colors, thus approaching the utility of cytometry by time of flight (CyTOF). However, flow cytometry suffers from autofluorescence and spreading error (SE) generated by errors in the measurement of photons mainly at red and far-red wavelengths, which limit barcoding and the detection of dim markers. Consequently, development of 28-color fluorescent antibody panels for flow cytometry is laborious and time consuming. Here, we describe the steps that are required to successfully achieve 28-color measurement capability. To do this, we provide a reference map of the fluorescence spreading errors in the 28-color space to simplify panel design and predict the success of fluorescent antibody combinations. Finally, we provide detailed instructions for the computational analysis of such complex data by existing, popular algorithms (PhenoGraph and FlowSOM). We exemplify our approach by designing a high-dimensional panel to characterize the immune system, but we anticipate that our approach can be used to design any high-dimensional flow cytometry panel of choice. The full protocol takes a few days to complete, depending on the time spent on panel design and data analysis.</p> <p> </p> <p> </p> <p>link related to dataset: https://flowrepository.org/id/FR-FCM-ZYV3</p>
[Dataset] Quality of Experience Measurements of Multipath TCP Applications on iOS Mobile Devices
<p>This dataset is related to the publication “Quality of Experience Measurements of Multipath TCP Applications on iOS Mobile Devices” (Katharina Keller, Patrick Felka, Jan Fornoff, Oliver Hinz, Amr Rizk) MMSYS 2020.</p>
Data for "An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore"
<p>Data for the article “An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore”</p> <p>By LT Beumer, J Pohle, NMS Schmidt, M Chimienti, JP Desforges, LH Hansen, R Langrock, SH Pedersen, M Stelvig, FM van Beest</p> <p> </p> <p>The data set includes three files: A readme file describing the data files and two data files accompanying the above publication.</p> <p>Combined, the two data files represent the dataset collected by GPS collars fitted on 19 female muskoxen in northeast Greenland (28 muskox-years with 153-1062 observation days/animal) and associated extracted covariates, divided into a summer and winter season dataset as modelled in the article. Data here are given as included in the models (for a description of cleaning procedures, see article). All continuous, non-cyclical covariates were standardised to have zero mean and unit standard deviation to improve numerical stability of parameter estimation. This is indicated by “_scaled” in the column name.</p> <p>For further queries please contact nms@bios.au.dk</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.