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.
96
datasets available to search
ShareScore release 0.9.0
Dataset results
96 results for “Machine learning algorithms”
The Camouflage Machine: Optimising protective colouration using deep learning with genetic algorithms
Evolutionary biologists frequently wish to measure the fitness of alternative phenotypes using behavioural experiments. However, many phenotypes are complex. For example colouration: camouflage aims to make detection harder, while conspicuous signals (e.g. for warning or mate attraction) require the opposite. Identifying the hardest and easiest to find patterns is essential for understanding the evolutionary forces that shape protective colouration, but the parameter space of potential patterns (coloured visual textures) is vast, limiting previous empirical studies to a narrow range of phenotypes. Here we demonstrate how deep learning combined with genetic algorithms can be used to augment behavioural experiments, identifying both the best camouflage and the most conspicuous signal(s) from an arbitrarily vast array of patterns. To show the generality of our approach, we do so for both trichromatic (e.g. human) and dichromat (e.g. typical mammalian) visual systems, in two different habitats. The patterns identified were validated using human participants; those identified as the best for camouflage were significantly harder to find than a tried-and-tested military design, while those identified as most conspicuous were significantly easier than other patterns. More generally, our method, dubbed the 'Camouflage Machine', will be a useful tool for identifying the optimal phenotype in high dimensional state-spaces.
INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells
Open the record for dataset details and reuse information.
Subseasonal to Seasonal (S2S) Prediction Algorithms using Hybrid Machine Learning Techniques
<p>< S2S dataset.zip ></p><p>1.ECMWF observations/hindcast realizations</p><ul><li>hindcast-like-observations_2000-2019_biweekly_deterministic.zarr</li><li>forecast-like-observations_2020_biweekly_deterministic.zarr</li><li>ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr</li><li>ecmwf_forecast-input_2020_biweekly_deterministic.zarr</li><li>hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc</li></ul><p>2. External variables</p><ul><li>"nino" folder -> nino12.long.anom.data, nino34.long.anom.data : El Niño data</li><li>"Oscillation" folder<ul><li>-> ersst.v5.pdo.dat.text : PDO (Pacific Decadal Oscillation)</li><li>-> norm.nao.monthly.b5001.current.ascii.table.txt : NAO (North Atlantic Oscillation)</li><li>-> qbo.dat : QBO (Quasi Biennial Oscillation)</li></ul></li><li>"great_lake" folder -> N_seaice_extent_daily_v3.0 : Great lakes ice cover</li><li>observed-solar-cycle-indices.json : Sunspot cycles (two variables: original value and smoothed value)</li></ul><p>3. Region.txt : Region and its bound</p><p>4. Biweekly historical statistics data</p><ul><li>biw_stat_w34 folder -> data (mean, standard deviation, median, skewness, kurtosis) for Week 3-4</li><li>biw_stat_w56 folder -> data (mean, standard deviation, median, skewness, kurtosis) for Week 5-6</li></ul><p> </p><p>< ML_code.zip ></p><ul><li>ML codes for training, testing, and calculating RPSS based on Python3</li><li>Check run_val.sh and run_2020.sh </li></ul><p> </p>
Modeling surface pCO2 variability in two contrasting basins of North Indian Ocean using advanced machine learning algorithms
<p>The dataset contains surface ocean <em>p</em>CO2, uncertainty and air-sea CO2 flux for the North Indian Ocean region. The data is available from 1993 to 2020 on a monthly time scale. Each of these data has a spatial resolution of 1/12º. Air-sea CO2 flux is calculated using a bulk parameterization, which is a function of wind speed. A positive CO2 flux value signifies CO2 outgassing, while a negative value indicates atmospheric CO2 uptake. </p> <p><strong>**It is recommended to use the latest version V3. Previous versions are depricated.</strong></p>
Supplementary data for: Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C
<p>Supplementary example data for the work presented in "<em>Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C</em>". </p> <p><strong>Abstract: </strong></p> <p>Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use specially designed graphene microelectrode arrays (G-MEAs), which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, we have developed an easy-to-implement machine learning algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML) spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signalling capacity.</p> <p> </p>
Randomized Cooperative Overtake Maneuvers for Use in Machine Learning Algorithms
<p>Randomized cooperative overtake maneuvers involving one host vehicle and up to three remote vehicles. Maneuver containers are defined as specified in Häfner et al., (2020) "CVIP: A Protocol for Complex Interactions Among Connected Vehicles."</p> <p>If for a maneuver container "rel-target-lane" is given, then the maneuver type is a lane change left/right.</p> <p>If, instead, "rel-target-speed" is given, then the maneuver type is "change speed".</p> <p>Within the raw data, the "validate_stats" files contain validation data as specified in the accompanying conference paper.</p>
Landslide mapping using satellite imagery and machine learning algorithms
<p>Cyclone Idai made landfall on 15th March near Beira, Mozambique, and caused heavy rainfall across Mozambique, Malawi, Madagascar, and eastern Zimbabwe. Chimanimani District of Zimbabwe received 200 to 400 mm rainfall between 15th and 19th March, which caused widespread flooding and triggered thousands of landslides. This study aims to map the landslides in Chimanimani District and differentiate concurrent flooding from the landslides using high resolution PlanetScope imagery and DEM. Three machine learning algorithms namely, Random Forest, Artificial Neural Network, and Support Vector Machine have been deployed for the supervised landslide classification. </p> <p> </p>
Generating a Labeled Dataset to Train Machine Learning Algorithms for Lithological Classification of Drill Cuttings
<p>This dataset contains 16,700 fully labeled SEM images of rock chips isolated from 14 thin sections of drill cutting samples. These samples come from a low-permeability reservoir in western Canada.</p>
Data Extraction table for the study Machine-based Stereotypes: How Machine Learning Algorithms Evaluate Ethnicity from Face Data
<p>This table contains the data extraction results for the study Machine-based Stereotypes: How Machine Learning Algorithms Evaluate Ethnicity from Face Data. It contains 24 columns and 74 rows.</p>
A Building Imagery Database for the Calibration of Machine Learning Algorithms
<p>An open database of 5276 building images from a parish in Lisbon (Alvalade), whose buildings have been classified according to a uniform taxonomy. This open database can be used for the testing and calibration of machine learning algorithms, as well as for the direct assessment of earthquake risk in Alvalade.</p> <p><strong>Full Changelog</strong>: <a href="https://github.com/vsilva028/ML/commits/v1.0.0">https://github.com/vsilva028/ML/commits/v1.0.0</a></p>
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at global scale (v1.0)
<p>This study investigates the estimation of daily SSM using eight optimised ML algorithms and ten ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using the international soil moisture network (ISMN) data collected from 1722 stations distributed across the World. </p>
Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate Gestational Age
ClinicalTrials.gov study NCT05433519. IPD Sharing: YES. Countries: 2. Publications: 1.
The Camouflage Machine: Optimising protective colouration using deep learning with genetic algorithms
Open the record for dataset details and reuse information.
Modelling the potential distribution of African Wormwood (Artemisia afra) using machine learning algorithm-based approach (MaxEnt) in Sekhukhune District, South Africa
Open the record for dataset details and reuse information.
Data from: QTG-Finder2: a generalized machine-learning algorithm for prioritizing QTL causal genes in plants
Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.
Echo-CGC: A Communication-Efficient Byzantine-tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network (video)
Full video presentation of the paper: Echo-CGC: A Communication-Efficient Byzantine-tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network.<br><br>Appears in Session 2 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
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>
K-Nearest-Neighbor algorithm to predict the survival time and classification of various stages of Oral Cancer: A machine learning approach
<p>This project predicts the survival time of a cancer patient in terms of the number of days and also classifies the dataset into various stages of cancer</p> <p>This is executed on the SPYDER platform using Python 3.7 on Anaconda Navigator.</p> <p>The dataset includes the oral cancer patient's record of 4 countries</p> <p> </p>
Training data for ship track detection machine learning algorithms
<p>The training data and labels used to train the linked machine learning algorithm</p>
Implementation of a Diabetes Status Prediction Application Using a Machine Learning Algorithm Approach
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.