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.
1,943
datasets available to search
ShareScore release 0.9.0
Dataset results
1,943 results for “machine learning”
Machine Learning Guided Optimization of Dynamic Peptide Organocatalysts
<p>Associated data for publication "<strong>Machine Learning Guided Optimization of Dynamic Peptide Organocatalysts". </strong>Full description of the dataset is in the SI of the associated manuscript. </p>
Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
<p>Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center.</p> <p>Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. </p> <p><span>The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.</span></p> <p> </p>
Research data supporting 'Machine-Learned Interatomic Potentials for Transition Metal Dichalcogenide Mo1−xWxS2−2ySe2y Alloys'
<p>Research data supporting 'Machine-Learned Interatomic Potentials for Transition Metal Dichalcogenide Mo1−xWxS2−2ySe2y Alloys'</p>
Supplemental data for "Benchmarking machine learning interatomic potentials via phonon anharmonicity"
<p>Supplementary data including all training data, machine learning models and irreducible derivatives used in the study.</p>
Dataset and code of groudwater nitrate for machine learning
<p>Data of groudwater nitrate and related data in North China Plain (NCP). The data including nitrate concentration of groudwater collected from more than 4,000 sites (wells) in NCP from 2005 to 2021. The groundwater samples were collected in 2005–2021, and the collection was conducted in May (before rainy season) and October (after rainy season) in each year for every site.During sampling, basic information about well location, groundwater depth, farmland planting pattern and soil types were collected. Sampling wells were divided into three types according to depth, shallow (0–30 m), medium (30–100 m) and deep (> 100 m). The planting pattern mainly involved intensive croplands, grain crops, vegetable crops and orchards. Soil types of each sampling site were obtained from the China soil database (<a href="http://vdb3.soil.csdb.cn/">http://vdb3.soil.csdb.cn/</a>).The socio-economic and agricultural information of the study areas (take the districts of municipalities and prefecture-level cities of provinces as basic units) were acquired via the <a>China Statistical Yearbook</a> (<a href="http://www.stats.gov.cn/sj/ndsj/">http://www.stats.gov.cn/sj/ndsj/</a>). The data includes agricultural planting area, grain crop area, vegetable planting area, orchard planting area, total facility agricultural area; fertilizer amount, nitrogen fertilizer amount, unit area nitrogen fertilizer amount; total output value of agricultural, forestry, animal and fishery husbandry, agricultural output value, forestry output value, animal husbandry output value, fishery output value; Gross Domestic Product (GDP), per capita GDP; total population, and rural population.</p>
Dataset: Uncovering Obscured Phonon Dynamics from Powder Inelastic Neutron Scattering using Machine Learning
<p>Dataset of simulated and experimental spectra for the manuscript: Uncovering Obscured Phonon Dynamics from Powder Inelastic Neutron Scattering using Machine Learning. </p> <p>The data.zip contains all the simulated spectra and labels.</p> <p>The dataset.zip contains the divided subsets for training, validation and testing purposes, as well as the experimental dataset.</p>
Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning
<p>In this repository we share processed data that were generated using the bioinformatics framework we developed in publication "Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning".</p> <p>These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification. </p> <p>The datasets are as follows:</p> <ol> <li>cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches</li> <li>ctrl_annotated.RDa : the annotated cohort of seven healthy donors</li> <li>data_umap.RDa : UMAP representation of the data used to generate the figures in our paper</li> <li>DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper</li> <li>median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper</li> <li>patient_annotated.RDa : the annotated cohort of leukemia patients (n=43)</li> </ol> <p>We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH</p> <p> </p>
Graph Machine Learning Dataset AIFB (AIFB-GML)
<p>AIFB-GML is a comprehensive, heterogeneous graph machine learning dataset derived from the <a href="https://figshare.com/articles/dataset/AIFB_DataSet/745364" target="_blank" rel="noopener">AIFB RDF knowledge graph</a>. This dataset integrates a variety of node types, including individuals (:person), research groups (:group), and scholarly publications (:publication).</p> <p>Developed using the innovative <a href="https://github.com/davidlamprecht/AutoRDF2GML" target="_new">AutoRDF2GML framework</a>, AIFB-GML is specifically formatted to facilitate graph-based tasks such as node classification and link prediction.</p>
Machine learning algorithm reveals surface deoxygenation in the Agulhas Current due to warming
<p>This file contains ML – random forest constructed oxygen in the Agulhas Current.</p>
Use of Machine Learning in virtual learning environments: a bibliometric review
Open the record for dataset details and reuse information.
Supplementary Material for "Architecture-Based Mitigation of Confidentiality Violations Utilizing Machine Learning"
<p>Supplementary material for the paper "Architecture-Based Mitigation of Confidentiality Violations Utilizing Machine Learning". For more information, please see the README.md</p>
Open Data Set for the article Analysing the impact of renewables on Iberian wholesale electricity market prices using machine learning techniques. Green Finance, 2024, 6 (2), 363-382
<p>The datasets available for open access from the article ‘<em>Ballester, C. and Furió, D. (2024). Analysing the impact of renewables on Iberian wholesale electricity market prices using machine learning techniques. Green Finance, 6 (2), 363-382</em>’ are provided here. This study has been supported by funding from the Spanish Ministry of Science, Innovation, and Universities (Project PGC2018-093645-B-100).</p> <p>The data encompasses the price series of the components of the final wholesale prices, other than the day-ahead market price, at an hourly frequency, from January 2017 to December 2021. In particular, the daily average of the hourly price series of the intraday market, which captures the net effect of the six sessions of the intraday market on the final price, the daily average of the hourly net effect on the final price of the procedure to solve technical constraints, the daily average of the hourly costs resulting from ancillary services and deviation management, the daily average of the hourly costs related to capacity payments and the daily average of the hourly costs associated with the interruptibility service. In addition, we compute the daily average of the hourly series of bids (price and amount) individually submitted by market participants to buy or sell energy, distinguishing between matched and non-matched bids, both in the day-ahead market and in the first session of the intraday market. Other energy-related price series included in the analysis are: the Dutch TTF futures price, the API2 index for the coal price, the EUA futures price, the percentage of hours with 100% use from the France-Spain interconnection, the spread from the France-Spain interconnection, the percentage of water reserves in the reservoirs of the Iberian Peninsula. All shareable data are made available in accordance with open data principles to promote transparency and reproducibility in research. However, there is a specific dataset that we are not authorized to share publicly. Specifically, this includes the data corresponding to the Dutch TTF futures price and the API2 index. Consequently, this dataset is published under restricted access.</p>
Repository for "Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics"
<p>These files contain data and generation codes for figures, a numerical code for the analysis of structure factors, and a numerical code for the simulation, in an article "Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics".</p>
Field and Lab_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Field and Lab observations_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Orthomosaic_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Orthomosaic of the aerial images_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Data for Machine Learning Guided Design of Nerve-on-A-Chip Platforms with Promoted Neurite Outgrowth
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.