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.
285
datasets available to search
ShareScore release 0.9.0
Dataset results
285 results for “Machine learning dataset”
pywaterinfo dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>This forcings dataset is the output of the pywaterinfo (https://fluves.github.io/pywaterinfo/) read in of forcing data (rain and potential evapotranspiration).</p> <p>Code related to this dataset can be found here: https://github.com/olivierbonte/master_thesis</p>
OpenEO dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>This dataset is the output of the <a href="https://openeo.org/">OpenEO</a> processing of satellite data (SAR backscatter and LAI). </p> <p>Code related to this dataset can be found <a href="https://github.com/olivierbonte/master_thesis">here</a></p>
Machine learning meets pKa - Datasets
<p>Datesets belonging to the publication:</p> <p>Baltruschat M and Czodrowski P. Machine learning meets pK<sub>a</sub> [version 2; peer review: 2 approved]. <em>F1000Research</em> 2020, <strong>9</strong>(Chem Inf Sci):113 (<a href="https://doi.org/10.12688/f1000research.22090.2">https://doi.org/10.12688/f1000research.22090.2</a>)</p> <p>Corresponding GitHub repository: <a href="https://github.com/czodrowskilab/Machine-learning-meets-pKa">https://github.com/czodrowskilab/Machine-learning-meets-pKa</a></p>
Dataset of Flow Velocity Prediction in Vegetated Alluvial Channels Comparing Empirical and State-of-the-art Hybrid Machine Learning Models
<p>We compiled 447 datasets from different sources and lab- and field-based measurements. These datasets included Einstein and Banks (1950), Fenzl (1962), Kouwen et al. (1969), Ree and Crow (1977), Murota (1984), Tsujimoto and Kitamura (1990), Tsujimoto (1991), Tsujimoto (1993), Shimizu (1994), Dunn et al. (1996), Ikeda and Kanazawa (1996), Meijer (1998), Jarvela (2002), Rowinski and Kubrak (2002), Stone and Shen (2002), Poggi et al. (2004), Carollo et al. (2005), and Murphy et al. (2007).</p>
Minimal dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>The minimal dataset needed of data which can not be retrieved from the internet by APIs in the preprocessing. Consists of shape, land use and rivers for the Zwalm catchment. </p> <p>Code related to this dataset can be found here: https://github.com/olivierbonte/master_thesis </p>
Datasets used in "Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management"
<p>Data sets used to train, validate, and test machine learning models for the prediction of 10-hour dead fuel moisture content. The data sets are for 375 m and 1 km resolutions. MADIS climatography data is also included. The datasets may be processed using the code supplied at https://github.com/NCAR/fmc_viirs. </p>
Dataset for "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique"
<p>This is the dataset used in a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>The content is</p> <ul> <li>Augmented images used in the U-Net training (aug_images.zip) <ul> <li>train/*.png: augmented original images (14,219 files)</li> <li>train_masks/*.png: augmented hand-masked images (14,219 files).</li> </ul> </li> </ul> <p>Note that original images include images obtained using <em>google-image-download</em>, a Python script published on GitHub (<a href="https://github.com/Joeclinton1/google-images-download/tree/patch-1">https://github.com/Joeclinton1/google-images-download/tree/patch-1</a>, Copyright © 2015-2019 Hardik Vasa). The whole images we obtained by <em>google-image-download</em> were labeled as noncommercial reuse with modification.</p> <p>For more details, please refer to a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>Correspondence: Rina Noguchi (r-noguchi@env.sc.niigata-u.ac.jp)</p>
AneuploidyML_Datasets: Machine-learning analysis of factors that shape cancer aneuploidy landscapes reveals an important role for negative selection
<p>This is raw data that were used to build the processed dataset for training and testing the machine learning</p>
Dataset and Model Files for the Paper "Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving"
<p>Dataset and model files for the paper "Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving".</p>
Dataset for manuscript "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning"
<p>Dataset for the github repository containing the code for the manuscript "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning".</p>
Training dataset for Nile delta shoreline change prediction until 2050 using machine learning
Open the record for dataset details and reuse information.
PPARγ-bla dataset curated and enriched using the Enalos tools and Enalos KNIME nodes for machine learning analysis (SCENARIOS project)
<p>A curated and enriched dataset for PPARγ-bla, intended for in silico model development, was obtained from PubChem BioAssay under the numeric identifier AID 743194 using Enalos tools and Enalos KNIME nodes. This dataset specifically utilizes compounds from the Tox21 10K chemical library that underwent screening against the PPARγ-bla HEK293H cell line. The cell line contains a beta-lactamase reporter gene, and all the information was extracted from PubChem Bioassay ID 743194 using Enalos tools and Enalos KNIME nodes. The original bioassay, consisting of 6587 compounds, assessed the antagonist activity of small molecules and classified them as 'active', 'inactive' or 'inconsistent' based on their AC50 (potency) score. The curated PPARγ dataset comprises 1230 compounds selected from the original bioassay and enriched with 777 molecular descriptors extracted from their 2D structure using EnalosMold2 KNIME nodes.</p>
Dataset: Machine Learning Based Prediction of Polaron-Vacancy Patterns on the TiO$_2$(110) Surface
Open the record for dataset details and reuse information.
Dataset for Application of machine learning to choke performance
Open the record for dataset details and reuse information.
Dataset [ref. paper "Predictive modeling of drivers' brake reaction time through machine learning methods"]
Open the record for dataset details and reuse information.
Figure 1 from: Wilf P, Wing SL, Meyer HW, Rose JA, Saha R, Serre T, Cúneo NR, Donovan MP, Erwin DM, Gandolfo MA, González-Akre E, Herrera F, Hu S, Iglesias A, Johnson KR, Karim TS, Zou X (2021) An image dataset of cleared, x-rayed, and fossil leaves vetted to plant family for human and machine learning. PhytoKeys 187: 93-128. https://doi.org/10.3897/phytokeys.187.72350
Figure 1 Selected image pairs of confamilial extant and fossil (see Appendix 1) leaves from the dataset ABatesia floribunda Spruce ex. Benth. (Fabaceae), NCLC-W 6417, showing typical layout of a cleared-leaf slide with original annotations (other examples are cropped in this figure); source voucher Froes 12074, DS 291771 (at CAS), Amazonas, Brazil BFabaceae sp. CJ1, SGC-ICP-10173; Cerrejón mine, middle-late Paleocene of Guajira Peninsula, Colombia CCrataegus viridis L. (Rosaceae), NCLC-W 11951b; H. Meyer s/n (collected 1974, no other voucher), cultivated, California, USA DCrataegus copeana (Rosaceae), UCMP 3610; Florissant, late Eocene of Colorado, USA; H. Meyer photograph number 0420 ETetracentron sinense Oliv. (Trochodendraceae), S. Wing negative 71-002; E.H. Wilson 659, US 599036, Szechuan, China FZiziphoides flabellum (Trochodendraceae), USNM 560134; Mexican Hat, early Paleocene of Montana, USA GQuercus prinus L. (Fagaceae), NCLC-W 6137; H. Foster 8223, US 1730249, Florida, USA HFagopsis longifolia (Fagaceae), FLFO 003432A; Florissant, late Eocene of Colorado, USA IEucalyptus astringens (Maiden) Maiden (Myrtaceae), NCLC-W 10489; J.H. Maiden (9 November 1909), Western Australia, UC 437518 JEucalyptus frenguelliana (Myrtaceae), MPEF-Pb 2344; Laguna del Hunco, early Eocene of Chubut, Argentina KCercidiphyllum obtritum (Cercidiphyllaceae), DMNH 25061; Republic, early Eocene of Washington, USA LCercidiphyllum japonicum Siebold & Zucc. ex J.J.Hoffm. & J.H.Schult.bis (Cercidiphyllaceae), Axelrod cleared leaf 166; UCMP (no other voucher) MPlatanus racemosa Nutt. (Platanaceae), NCLC-H 6631; Handel s/n (collected 1985, no other voucher), California, USA NErlingdorfia montana (compound-leaved Platanaceae), DMNH 7642; Hell Creek Formation, Late Cretaceous of North Dakota, USA. Scale bars: centimeters as labeled (A, B, L, M); 1 cm when not labeled (C–K, N).
OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis
<p>Logic synthesis is a challenging and widely-researched combinatorial optimization problem during integrated circuit (IC) design. It transforms a high-level description of hardware in a programming language like Verilog into an optimized digital circuit netlist, a network of interconnected Boolean logic gates, that implements the function. Spurred by the success of ML in solving combinatorial and graph problems in other domains, there is growing interest in the design of ML-guided logic synthesis tools. Yet, there are no standard datasets or prototypical learning tasks defined for this problem domain. Here, we describe OpenABC-D,a large-scale, labeled dataset produced by synthesizing open source designs with a leading open-source logic synthesis tool and illustrate its use in developing, evaluating and benchmarking ML-guided logic synthesis. OpenABC-D has intermediate and final outputs in the form of 870,000 And-Inverter-Graphs (AIGs) produced from 1500 synthesis runs plus labels such as the optimized node counts, and de-lay. We define a generic learning problem on this dataset and benchmark existing solutions for it. The codes related to dataset creation and benchmark models are available athttps://github.com/NYU-MLDA/OpenABC.git.</p>
Dataset for accelerating the calculation of electron-phonon coupling strength with machine learning
Open the record for dataset details and reuse information.
Datasets for "Leveraging Machine Learning and Natural Language Processing Techniques for Agriculture Experiment Station Project Classification"
Open the record for dataset details and reuse information.
Raw dataset for evaluating the effects of M sand and fly ash on concrete strength using experimental and machine learning approaches
Open the record for dataset details and reuse information.
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.