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67 results for “High accuracy”

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zenodo48/100

Plant image identification application demonstrates high accuracy in Northern Europe dataset

Images and data for the study "Plant image identification application demonstrates high accuracy in Northern Europe" Details: Jaak Pärtel, Meelis Pärtel, Jana Wäldchen, Plant image identification application demonstrates high accuracy in Northern Europe, AoB PLANTS, Volume 13, Issue 4, August 2021, plab050, <a href="https://doi.org/10.1093/aobpla/plab050">https://doi.org/10.1093/aobpla/plab050</a> The data table displays Flora Incognita's identification results together with species and observations characteristics. All (3199) used images are included. The study was conducted in two parts: database and field study. Database study images have been taken from eBiodiversity database (https://elurikkus.ee/en) under Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). Please cite the original source for the images as well when using the dataset. Field study images were taken by Jaak Pärtel in 2020 in field conditions from different habitats across Estonia.

opencc-by-4.0Jul 2021View details →

zenodo44/100

High Accuracy Barrier Heights, Enthalpies, and Rate Coefficients for Chemical Reactions

This Zenodo repository contains the data presented in Spiekermann, K. A.; Pattanaik, L.; Green, W. H.* <a href="https://www.nature.com/articles/s41597-022-01529-6">High Accuracy Barrier Heights, Enthalpies, and Rate Coefficients for Chemical Reactions</a>, Sci. Data 9, 417, (2022). We recommend people refer to this dataset as RDB7 i.e. a diverse reaction database whose transition states contain up to 7 heavy atoms. Atom-mapped SMILES, barrier heights, reaction enthalpies, and Reaction Mechanism Generator (RMG) reaction family for each reaction are listed in the comma-separated values files b97d3.csv, wb97xd3.csv, ccsdtf12_dz.csv, and ccsdtf12_tz.csv. ccsdtf12_dz_individual_heats_of_formation.csv containing the individual heats of formation for each stable species (i.e., reactant and product). The values in all of these files are in kcal/mol. Q-Chem output files from the reoptimized products are provided for 16,302 reactions at B97-D3/def2-mSVP and for 11,926 reactions at ωB97X-D3/def2-TZVP level of theory. For convenience, these also include the original log files for the reactant, transition state, and non-reoptimized products from Grambow et al. (10.5281/zenodo.3715478) since they were used to calculate barrier heights, enthalpies, and rate constants in this work. The numbering of reaction indices matches that from the originally published dataset to facilitate easy comparison. MOLPRO output files from the single point calculations are provided for 11,926 reactions at the CCSD(T)-F12/cc-pVDZ-F12 level of theory as well as for the 15 validation reactions run at CCSD(T)-F12/cc-pVTZ-F12. The raw log files for all calculations are stored in b97d3.tar.gz, wb97xd3.tar.gz, ccsdtf12_dz.tar.gz, and ccsdtf12_tz.tar.gz. Each archive contains a separate folder for each reaction, which contains log files for the reactant, transition state, and product/s. The Q-Chem log files contain the output from a geometry optimization and harmonic vibrational analysis while the MOLPRO log files contain output from an energy calculation. Transition state theory rate constants, fitted Arrhenius parameters, and average percentage error between the calculated and fitted rate constants can be found for the rigid reactions in ccsdtf12_dz_rigid.csv. The list of 50 temperatures (K) used during Arrhenius fitting is provided in arkane_temperatures.csv, and the raw Arkane outputs are provided in ccsdtf12_dz_rigid.tar.gz. The improvement from fitting bond additivity corrections at B97-D3/def2-mSVP, ωB97X-D3/def2-TZVP, CCSD(T)-F12/cc-pVDZ-F12//ωB97X-D3/def2-TZVP, and CCSD(T)-F12/cc-pVTZ-F12//ωB97X-D3/def2-TZVP is shown in b97d3_def2msvp_BAC.csv, wb97xd3_def2tzvp_BAC.csv, ccsdtf12_ccpvdzf12__wb97xd3_def2tzvp_BAC.csv, and ccsdtf12_ccpvtzf12__wb97xd3_def2tzvp_BAC.csv respectively. The files contain the experimental and calculated enthalpies for the reference species from the RMG-database used for fitting. The correction values are publicly stored on the RMG-database GitHub on the AEC_BAC branch, though they are also provided in fitted_corrections.pkl for convenience. Further validation of the BACs at the double zeta level was done by comparing to experimental values from the Pedley set since over half of these molecules were not in the RMG-database training set used for fitting. The comparison is shown in ccsdtf12_dz_vs_Pedley_experimental.csv.

opencc-by-4.0Apr 2022View details →

zenodo44/100

Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers

Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography. Publication  <a href="https://www.nature.com/articles/s41597-020-0479-6">https://www.nature.com/articles/s41597-020-0479-6</a>

opencc-by-4.0Nov 2018View details →

zenodo44/100

Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers

opencc-by-4.0Nov 2018View details →

zenodo40/100

Data for "High-accuracy determination of Paul-trap stability parameters for electric-quadrupole-shift prediction", J. Appl. Phys. 132, 124401 (2022)

Data required to reproduce the key results in: "High-accuracy determination of Paul-trap stability parameters for electric-quadrupole-shift prediction", J. Appl. Phys. 132, 124401 (2022). <a href="https://doi.org/10.1063/5.0106633">https://doi.org/10.1063/5.0106633</a> <ul> <li>The file "sec_freq.dat" contains measured secular frequencies, the rf frequency, the applied bias voltages, and the MJD of the measurement: <ul> <li>Figures 4-5 use rows 31-33 of this data.</li> <li>Figure 6 uses all data corresponding to -1.1e-3 < ax < -0.6e-3.</li> <li>Figure 7(a) uses all the data.</li> </ul> </li> <li>The file "data2021-12-21_MJD.txt" contains the secular frequency data used to derive Eq. (16) and plot Figure 8.</li> <li>The file "RF_monitor_rectifier_1d.txt" contains the rectified monitor voltage used in Figure 8.</li> <li>The file "Temperature_108_1d.txt" contains the helical-resonator temperature used in Figure 8.</li> <li>The file "Fig9_EQS.dat" contains the measured electric quadrupole shift (EQS) used for Figure 9.</li> <li>The file "interleavedEQS.dat" contains the data from the interleaved EQS measurement used to determine a lower value of 1070 for the cancellation factor.</li> </ul>

opencc-by-4.0Oct 2022View details →

zenodo40/100

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 7 . Test Accuracy with prograess generation

In the training phase, the neural network weights errors are minimized and network design problem which the objective function to an acceptable level. In test step we have better results because weights of neural network are adjusted by genetic algorithm and back propagation method. Of course achievement to accuracy with 83.5% is reason using of good feature with minimum error.

opencc-by-4.0Oct 2013View details →

zenodo40/100

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 6. Training Accuracy with prograess generation

There are many features will reduce the efficiency of the algorithm and its complexity. Among the methods for selecting the appropriate features, the algorithm is a GA. One of the important parameters for testing methods is accuracy rate on progress generation. In fact accuracy is reverse error in algorithm results. As reader can compare the results of our paper with another works. Figure 4 show that accuracy present for Training step. We achieve to best answers of 800 generation to after generation.

opencc-by-4.0Oct 2013View details →

zenodo40/100

Two-step fusion method for generating 1 km seamless multi-layer soil moisture with high accuracy in the Qinghai-Tibet plateau

Current remote sensing techniques fail to observe and generate large scale multi-layer soil moisture (SM) due to the inherent features of the satellite sensors. The lack of comprehensive understanding of multi-layer SM hinders the sustainable development of agriculture, hydrology, and food security. In order to overcome the depth barrier of traditional SM assimilation and downscaling methods, we developed a Two-step Multi-layer SM Downscaling (TMSMD) framework by fusing multi-source remotely sensed, reanalysis, and in-situ data through both machine learning and state-of-the-art deep learning models to generate multi-layer SM. The produced multi-layer SM was characterized by high resolution (1 km), high spatio-temporal continuity (cloud-free and daily), and high accuracy (i.e., 3H data). Firstly, the coarse resolution SMAP SM was downscaled to 1 km spatial resolution using LightGBM to weaken the effects of scale mismatch issue and provide high-resolution input for the subsequent calibration. Results indicated that the downscaled SMAP SM remained high consistency with the original SMAP SM product. With the high-resolution inputs, we calibrated the downscaled SMAP SM using multi-layer in-situ SM through state-of-the-art attention-based LSTM. Results demonstrated that the average PCC, RMSE, ubRMSE, and MAE were improved by 22.3%, 50.7%, 26.2%, and 56.7% compared to SMAP L4 SM while 38.5%, 52.1%, 29.5%, and 58.7% compared to downscaled SMAP SM. Further spatio-temporal and comparative analysis confirmed that the multi-layer SM produced by the TMSMD framework had excellent performance in capturing the spatial and temporal dynamics. In conclude, the proposed TMSMD framework successfully generated 3H multi-layer SM data and is promising for accurate assessment and monitoring in agriculture, water resources, and environmental domains.   The remaining data will be uploaded soon.

opencc-by-4.0Jan 2023View details →

zenodo40/100

BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy - Supplementary material

Figure S1: Distribution of the reviewed 141 BRCA1 missense variants; Figure S2: The Shapely values for the BRCA1 XGBoost models; Figure S3: The Shapely values of the BRCA1 XGBoost model used to predict the functional assays’ results for variants of uncertain significance; Table S1: The receiver operating characteristic (ROC) curve analysis for the different in silico predictions; Table S2: Cross validation of the BRCA1 model in 5 different random training and test samples; Table S3: Pathogenicity prediction and prioritization of the 31,058 unreviewed BRCA1 variants from the BRCA Exchange database.

opencc-by-4.0Apr 2023View details →

zenodo40/100

The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 1 (2001-2010)

Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.

opencc-by-4.0Jun 2023View details →

zenodo40/100

BQE WIM Data Year 6 Project (Evaluation of Integrated Overweight Enforcement System using High Accuracy WIM System and Non-Proprietary ALPR System)

NEW BQE (Brooklyn-Queens Expressway) WIM Data for QB (Queens Bound) for Direct Overweight Enforcement

opencc-by-4.0Aug 2023View details →

zenodo36/100

Data for "The effect of pattern overlap on the accuracy of high resolution electron backscatter diffraction measurements"

Data for "The effect of pattern overlap on the accuracy of high resolution electron backscatter diffraction measurements" Vivian Tong1, Jun Jiang1, Angus J Wilkinson2, and T Ben Britton1 1.    Department of Materials, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK 2.    Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK For more information please contact: b.britton@imperial.ac.uk (Ben Britton) -- The zip contains three subfolders: Fig4 Interaction volume measurement Fig14 Error approaching gb Fig16 GrainBoundaryProbability -- Further details: Fig4 Interaction volume measurement - Measurement and simulation data of EBSD inteaction volume Includes calculated model & EBSD patterns for measurement EBSD patterns are from Zircaloy-4 and scanned on a Bruker eFlashHR camera in high resolution mode (1600 x 1200) attached to a Zeiss Auriga-40 SEM. The sample was tilted to 70 degrees and the SEM image shows the tilt corrected scanned region. Fig14 Error approaching gb - 15 patterns are included that were used to create many simulated grain boundary pairs. These were captured from the same sample as used in Fig4. The spreadsheet details results shown in Fig 4. Fig 16 GrainBoundary Pobability - This describes results from the simple Voronoi tessalation model (virtual grain structure) and sampling with a fixed step size, similar to a real EBSD scan. Probabilities were calcualted for different interaction volume sizes and critical distances.

opencc-by-sa-4.0Apr 2015View details →

zenodo36/100

Dataset: High-accuracy simulations of highly spinning binary neutron star systems

Dataset for Gravitational Waveforms for the work of Dudi et al.,  "High-accuracy simulations of highly spinning binary neutron star systems"; arXiv: 2108.10429    The naming of the files:  EOS_SpinSetup_NumberOfPoints_Mode.dat EOS: SLy SpinSetup: dd - down down; ud - up, down; uu037 -- up, up with dimensionless spin of 0.37; uu057 -- up, up with dimensionless spin of  0.57 NumberOfPoints: Points inside the finest level: 96, 144, 192, 240 Mode: Only 2,2-mode available

opencc-by-4.0Feb 2022View details →

dryad36/100

Data from: Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy

Mice are the most commonly used model animals for itch research and for the development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID (Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine. The best-trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and the chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that could replace manual quantification for mouse scratching behavior in different itch models and for drug screening. This dataset includes all videos for the study to establish a novel deep learning-based system for automatic mouse scratching behavior quantification.

opencc-zeroMay 2024View details →

zenodo36/100

Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening

<div> <h2>Overview</h2> <div>Data and Results used in the publication entitled "Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening"</div> </div> <div> <h3>Data</h3> <div>This folder contains the raw data used during this work.</div> <div>`EvoEF.csv` contains information on the library used (sequences, number of mutations, etc.) and the fitness (energy) used as continuous mean values. `mut.csv` contains the information about the combinatorial scaling (N vs N_norm), the number of mutations (m) and the probability of each variant using different distributions (uniform and binomial) at different $p_{WT}$.</div> <div>For further details on how the fitness values were calculated and how the combinatorial scale works, please refer to our prevoius [Paper](https://arxiv.org/abs/2405.05167).</div> <div> </div> <h3>Results</h3> <div> <div>This folder contains the results (outputs) of all scripts used. Such results are included in the form of `.npy` and `.npz` files. To load such files with numpy you should include the option `allow_pickle=True`.</div> </div> </div>

opencc-by-4.0Sep 2024View details →

zenodo36/100

The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 2 (2011-2020)

opencc-by-4.0Jun 2023View details →

dryad36/100

Data from: Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy

Open the record for dataset details and reuse information.

publicMay 2024View details →

dryad36/100

High-speed photoacoustic and ultrasonic computed tomography of the breast tumor for early diagnosis with enhanced accuracy

Open the record for dataset details and reuse information.

publicSep 2025View details →

zenodo32/100

GAP model parameter files for "Combining phonon accuracy with high transferability in Gaussian approximation potential models"

GAP model parameter files to accompany the publication "Combining phonon accuracy with high transferability in Gaussian approximation potential models" by Janine George, Geoffroy Hautier, Albert P. Bartók, Gábor Csányi, and Volker L. Deringer All models are defined by the main parameter file "gp_iter6C.xml" and the associated file "gp_iter6C.xml.sparseX.GAP_00001", where "GAP_0000" is a placeholder for the unique identifier of the potential (also given in the XML header), and the trailing "1" indicates that only one set of descriptor (here, SOAP) parameters is given. The directories in this dataset follow the figures in the publication for which the respective potentials have been first used. Fig_2/only_random/M_1000 Fig_2/only_random/M_3000 Fig_2/only_random/M_5000 Fig_2/only_random/M_7000 Fig_2/only_random/M_9000 Fig_2/only_individual/M_1000 Fig_2/only_individual/M_3000 Fig_2/only_individual/M_5000 Fig_2/only_individual/M_7000 Fig_2/only_individual/M_9000 Fig_2/combined/M_1000 Fig_2/combined/M_3000 Fig_2/combined/M_5000 Fig_2/combined/M_7000 Fig_2/combined/M_9000 Fig_4/only_individual/f_0.1000 Fig_4/only_individual/f_0.0100 Fig_4/only_individual/f_0.0010 Fig_4/only_individual/f_0.0001 Fig_4/combined/f_0.1000 Fig_4/combined/f_0.0100 Fig_4/combined/f_0.0010 Fig_4/combined/f_0.0001 Fig_5/GAP-18_plus_SCs_f0.01   Fig_5/GAP-18_plus_SCs_f0.001

opencc-by-4.0Jun 2020View details →

dryad32/100

Data from: Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars

Taphonomists have long struggled with identifying carnivore agency in bone accumulation and modification. Now that several taphonomic techniques allow identifying carnivore modification of bones, a next step involves determining carnivore type. This is of utmost importance to determine which carnivores were preying on and competing with hominins and what types of interaction existed among them during prehistory. Computer vision techniques using deep architectures of convolutional neural networks (CNN) have enabled significantly higher resolution in the identification of bone surface modifications (BSM) than previous methods. Here, we apply these techniques to test the hypothesis that different carnivores create specific BSM that can enable their identification. To make differentiation more challenging, we selected two types of carnivores (lions and jaguars) that belong to the same mammal family and have similar dental morphology. We hypothesize that if two similar carnivores can be identified by the BSM they imprint on bones, then two more distinctive carnivores (e.g. hyenids and felids) should be more easily distinguished. The CNN method used here shows that tooth scores from both types of felids can be successfully classified with an accuracy greater than 82%. The first hypothesis was successfully tested. The next step will be to differentiate diverse carnivore types involving a wider range of carnivore-made BSM. The present study demonstrates that resolution increases when combining two different disciplines (taphonomy and artificial intelligence computing) in order to test new hypotheses that could not be addressed with traditional taphonomic methods.

opencc-zeroJul 2020View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record