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.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “Shap”
Local explanation SHAP approach applied to MIROC5,RCP8.5-forced multi-model ensemble study of GrIS future sea-level contributions
<p>The repository contains materials for analysing the results of the Local explanation named SHAP-CTREE (Redelmeier et al., 2020) approach applied to the MIROC5,RCP8.5-forced multi-model ensemble study of GrIS future sea-level contributions from Goelzer et al. (2020).</p> <p>The available files are:<br> - run_SupplMat.R: the main R script to perform the diagnostics and the different analyses (levels 1 - 3)<br> - utilsPLOT.R: functions for plotting<br> - Diagnostics.zip: the zip file with the png figures, named 'GrIS_CaseXXX_yYYY.png', that depict the diagnostic for case XXX for prediction time YYY<br> - SupplementaryMaterials.zip<br> - RData files for each prediction time YYY "Shapley_yYYY" with:<br> S: matrix N=55 cases x d+1: SHAP values for the d inputs (+ average sea level value at time YYY)<br> YHAT: ML-based predictions of the sea level for the 55 cases<br> YTRUE: true values for the 55 cases<br> mae: mean absolute error<br> - RData file containing the design of experiments "DOE_GrIS_MIROC5-RCP85.RData"<br> doe: matrix with values of the d=9 inputs</p> <p>These constitute the supplementary materials of Rohmer et al. (2022, The Cryosphere). All technical details are provided in this reference.</p>
ACCESS-AM2 Southern Ocean cloud and radiation data and code for SHAP analysis
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) and SHAP analysis code and data used for the study described in Fiddes et al. (2024) '<em>A machine learning approach for evaluating Southern Ocean cloud-radiative biases over the Southern Ocean in a global atmosphere model</em>' accepted in Geoscientific Model Development</p> <p>Included files: </p> <p>- code.zip, inc: </p> <p> - pre-process_modis.ipynb: process the modis data, described in Fiddes et al. 2022 (https://doi.org/10.5194/acp-22-14603-2022)<br> - pre-process.ipynb: organises model and modis data for analysis. Produces the files: COSP_vars_MODIS_2015-2019.nc, COSP_vars_cg207_2015-2019.nc and COSP_vars_bx400_2015-2019.nc<br> - run_XGBoost+SHAP_control.ipynb: run the XGBoost model and SHAP analysis for the control run (bx400). Produces the files: SHAP_values_SWCRE_2015-2019_bx4002.nc, XGBoost_predicted_SWCRE_2015-2019_bx4002.nc, SHAP_interactions_bx400.nc<br> - run_XGBoost+SHAP_ice.ipynb: run the XGBoost model and SHAP analysis for the ice experiment run (cg207). Produces the files: SHAP_values_SWCRE_2015-2019_cg2072.nc, XGBoost_predicted_SWCRE_2015-2019_cg2072.nc<br> - analysis+plots_ML.ipynb: plots and stats presented in paper </p> <p>- COSP_vars_MODIS_2015-2019.nc</p> <p>- COSP_vars_cg207_2015-2019.nc</p> <p>- COSP_vars_bx400_2015-2019.nc</p> <p>- SHAP_values_SWCRE_2015-2019_bx4002.nc</p> <p>- SHAP_values_SWCRE_2015-2019_cg2072.nc</p> <p>- XGBoost_predicted_SWCRE_2015-2019_cg2072.nc</p> <p>- XGBoost_predicted_SWCRE_2015-2019_bx4002.nc</p> <p>- SHAP_interaction_bx400.nc</p> <p>The cloud types used in this work can be found at https://doi.org/10.5281/zenodo.6004061 </p>
Non-exhaustive record of locations of Shap granite (Sg) boulders and Shap microgranite (Sa) clasts in the Lake District region, UK.
<p><strong><em>Non-exhaustive record (~10,000) of locations of Shap granite (Sg) boulders and Shap microgranite (Sa) clasts. Search area was limited from: in the NE, the eastern margin of Gaythorn Plain; in the NW, Shap Pink Quarry; in the SE, Farleton Fell and; in the SW, the locale of Kendal (approximate coordinates of the study area: NE: 54.499850° -2.519770°; NW: 54.468401° -2.685596°; SE: 54.226849° -2.519770°; SW: 54.227170° -2.770000°)</em></strong></p> <p>The northern limit to the study area was selected to exclude the northern plume, which disrupted the earlier eastern plume over Crosby Ravensworth Fell. The western limit was selected to enclose the known western limit of the Mint plume. The eastern limit was selected to enclose the known eastern limit of the Lune plume. The southern limit was selected to enclose the broadening of the Mint plume in the vicinity of Kendal. </p> <p>A comprehensive field survey was made of lower Borrowdale and northwards. The western Sg plume down the Mint valley south to Kendal, and the area of Lune Gorge, south of Dillicar, were surveyed cursorily, mainly checking published locations and adding additional locations encountered during this exercise. Prior reports for the Mint valley show a well-defined narrow plume, so to reduce the workload, a more complete record of locations has yet to be compiled. Similarly, from prior reports, boulder locations are few in the region around Kendal and east to the southern portion of the Lune valley and the overall distribution, before clearances, was recorded adequately in the 19<sup>th</sup> C.</p>
Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP
<p>Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a "black box" that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model's predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model's predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97\% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment.</p>
Code and Data For Silva et al. Lightning SHAP
<p>This is the code and software for the paper: "Using an Interpretable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash OccurrenceUsing an Interpretable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence"</p>
Pre-calculated SHAP Values for Drug Synergy Model
<p>Pre-calculated SHAP Values used to evaluate feature importance in the drug synergy model.</p>
Whole transcriptomes analyses of gastric cancer HGC-27 cell line treated with SHAP or control peptide
GEO Series GSE135086. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Applying the LightGBM model and SHAP interpretation to understand the urban land price index and its influencing factors in China
Open the record for dataset details and reuse information.
Finite Element and z Shap Mini Plate With Lingual Extension
ClinicalTrials.gov study NCT06899568. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
The integrated stress response finetunes 18S nonfunctional rRNA decay (SHAP-MaP)
GEO Series GSE255783. Homo sapiens. 12 samples. Type: Other.
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.