Data for "Machine Learning-based Prediction of Passive Gears from Vessel Tracking Data in Small-Scale Multi-gear Fisheries" by Pamela Lattanzi (16454871)

Supplementary file 1_Modelling the spatial distribution of total nitrogen and phosphorus stock in dryland terrestrial ecosystems of China using machine learning algorithms.docx by Bin Liu (5899)

Data Sheet 1_Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model.pdf by Xiaosong Zhu (11721357)

“…</p>Methods<p>This study utilizes six machine learning algorithms—Gradient Boosting Machine (GB), Random Forest (RF), AdaBoost Regressor (AB), Neural Network (NN), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN)—to predict ground substrate thickness. …”

China’s annual forest age dataset at a 30-m spatial resolution from 1986 to 2022 by Rong Shang (17306089)

WISpR spatial deconvolution workflow overview. by Nuray Sogunmez Erdogan (21533872)

An Explainable Percolation-based Clustering Framework for China’s Transport Carbon Emissions Analysis by Yifu Ou (22619717)

“…Here, we integrate the percolation theory in physics with a spatial-temporal clustering algorithm to objectively delineate clusters of transport-related carbon emissions (TCE) for 323 Chinese cities in 2019. …”

Table 1_Erannis jacobsoni disturbance detection based on unmanned aerial vehicle red edge spectral features.docx by Liga Bai (22156009)

“…Therefore, we used unmanned aerial vehicle (UAV) imagery from representative areas affected by E. jacobsoni, calculated conventional and red edge spectral indices, extracted features sensitive to pest infestation levels, detected disturbances using machine-learning algorithms, and analyzed the pest’s spatial distribution. …”

Image 2_Erannis jacobsoni disturbance detection based on unmanned aerial vehicle red edge spectral features.jpg by Liga Bai (22156009)

“…Therefore, we used unmanned aerial vehicle (UAV) imagery from representative areas affected by E. jacobsoni, calculated conventional and red edge spectral indices, extracted features sensitive to pest infestation levels, detected disturbances using machine-learning algorithms, and analyzed the pest’s spatial distribution. …”

Image 1_Erannis jacobsoni disturbance detection based on unmanned aerial vehicle red edge spectral features.jpg by Liga Bai (22156009)

“…Therefore, we used unmanned aerial vehicle (UAV) imagery from representative areas affected by E. jacobsoni, calculated conventional and red edge spectral indices, extracted features sensitive to pest infestation levels, detected disturbances using machine-learning algorithms, and analyzed the pest’s spatial distribution. …”

Spatial characterization of cancer-associated cell types in human breast cancer malignancies. by Nuray Sogunmez Erdogan (21533872)

The application of machine learning in the integration and optimization of natural protected areas by Qiuyan Liang (21585203)

“…This study constructed a spatial integration and optimization path for NPAs based on the random forest classification model and the regional growing algorithm, and conducted an empirical test using the northern Guangdong region with basic data as an example. …”

Predicted ecological suitability for OROV transmission across Brazil based on ecological niche modelling. by Camila Lorenz (3481175)

A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms by Xingan Chen (11334186)

Data Sheet 1_Hybrid machine learning algorithms accurately predict marine ecological communities.pdf by Luciana Erika Yaginuma (10477013)

“…In the supervised stage, these associations were modeled as a function of the environmental features by five supervised algorithms (Support Vector Machine, Random Forest, k-Nearest Neighbors, Naive Bayes, and Stochastic Gradient Boosting), using 80% of the samples for training, leaving the remaining for testing. …”

Data and codes for the paper "Machine-Learning-Enabled Spatial Pattern Mining: Evaluating the Impact of Imperfect Inputs". by Zhili Li (18836803)

“…Traditional formulations of SPM tasks are mainly based on true observations, which tend to have limited spatial coverage, availability, and timeliness. …”

Susceptibility occurrence maps of OROV in two scenarios (2020/2021 and 2024) and expansion areas based on ecological niche modelling. by Camila Lorenz (3481175)

Database of SatScan cluster analyses on confirmed ORO cases from 2024, based on data from the Brazilian Ministry of Health. by Camila Lorenz (3481175)

Spatial analysis clusters with high and low incidences of ORO according to relative risk (RR) during 2024 period. by Camila Lorenz (3481175)

Landslide susceptibility maps of the study area were developed using the RF algorithm for (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. by Raja Das (1841107)

Smart and Low-Cost Flexible Strain Sensor Based on Graphene-MWCNT Porous Elastic Sponge for Home Control and Object Grasping Recognition Using Machine Learning by Xiao-Hai Chen (22245876)