Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Data and structures for "How accurately can we predict binding poses with AlphaFold models?

Contains structures generated by AlphaFold, models from GPCRdb, and structures of proteins from PDB.  Additionally computed rmsds for pockets, backbone, and poses, and scripts to create figures.

opencc-by-4.0Nov 2023View details →

Demo datasets for the protocol to identify shared transcriptional risks between diseases and compounds predicted to result in mutual benefit

We present a computational protocol (https://github.com/ghbore/protocol-cancer-cvd-similarity), implemented as a Snakemake workflow, that was used in previous works (Gao et al., 2022; Baylis et al., 2023). This protocol allows researchers to identify shared transcriptional processes that drive disease and to screen existing compounds for mutual benefit. The protocol also includes a description of the pharmacovigilance study design used to validate the effect of novel compounds using electronic health records, where applicable. This repository bundles the datasets used in previous works as an example to run through the Snakemake workflow. These datasets include the TCGA cancer dataset, the STARNET and BiKE CVD datasets, and other dependent resources.

opencc-by-4.0Oct 2023View details →

Intracranial and behavioral data from "Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex"

Preprocessed intracranial EEG and behavioral data from Hoy, Quiroga-Martinez, et al. manuscript titled "Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex" published in Nature Communications (2023). Source data files for figures are included as well.

Coupling deep learning and physically-based hydrological models for monthly streamflow predictions

Revision in journal Water Resources Research, Manuscript number: 2023WR035618R Abstract: This study proposes a new hybrid model for monthly streamflow predictions by coupling a physically-based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. It is then driven by bias corrected general circulation model (GCM) predictions to generate soil moistures for the forecasting months. Finally, model-simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future monthly streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) as the DL model, is applied to predict 1-, 3-, and 6-month ahead reservoir inflows for the Danjiangkou Reservoir in China. The results show that the hybrid model consistently performs better than VIC and CNN-GRU models with great improvement in Kling‐Gupta efficiency (KGE) values for lead times up to 6 months. Additional tests indicate that hybrid models based on CNN-GRU outperform those based on LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model greatly reduces the computation burden of rolling prediction. It also saves decision-makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. Overall, the new hybrid model demonstrates great potential for monthly streamflow prediction where training data are limited. Keywords: monthly streamflow prediction; deep learning; physically-based distributed hydrological model; VIC model; soil moisture; hybrid model

DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.

test and training data.

opencc-by-4.0Jan 2024View details →

Data supporting the article "Replication of extinction retrieval prediction by prefrontal reactivations and non-replication of L-DOPA effects on retrieval and reactivations in males"

These datasets contain sAA, sCORT, US expectancy, questionnaire and rating raw data generated in this replication study. Furthermore, the processed MRI data (vmPFC reactivations) and derived SCR data are available. The custom-made analysis script for scoring SCR can be run on Matlab 2015b.

DSMBind: SE(3) denoising score matching for unsupervised binding energy prediction and nanobody design

This record provides the training and evaluation dataset for DSMBind Paper link: https://www.biorxiv.org/content/10.1101/2023.12.10.570461v1.abstract Github repo: https://github.com/wengong-jin/DSMBind

Objective sleep quality predicts subjective sleep ratings: a multiday observational study

This dataset serves as supplementary material for the referenced article.

opencc-zeroFeb 2024View details →

dryad32/100

Geographic drivers more important than landscape composition in predicting bee beta diversity and community structure

The importance of microhabitat traits such as floral availability is well known; however, forest bee spatial dynamics have been variably studied across local to broad geographic scales. Past literature suggests that landscape factors from proximate to distal are important in determining forest bee community metrics, including richness, abundance, and taxonomic composition. Leveraging the interest and assistance of citizen science volunteers, we employed standard bee bowl trap transects across Maryland, Delaware, northern Virginia, and the District of Columbia and identified correlations between bee community composition, local and regional landcover, and broader geospatial patterns. We also identified the partial contributions of both specific species and sampling sites to total beta diversity. Various landcover metrics were significantly related to bee community structure, with bee abundance positively and negatively correlated with forest and wetland cover, respectively. In general, land cover metrics within a 1,000-m buffer exhibited stronger correlations with bee communities; however, broader geographic variation, using Cartesian coordinates north and east as indices, was most significantly correlated with the bee community. Specifically, bee communities were less rich to the east and south of the study area. We also identified similar correlations with the bee community as categorized both by trophic and nesting behaviors, with both geographic northing and easting proving to be most strongly correlated with the forest bee community. Results of beta diversity and cluster analyses showed that the most species-depauperate sites exhibited the highest contributions to beta diversity and that species-poor sites consisted of a reduced subset of the greater community. Our results show that successful bee conservation must consider, beyond local-scale resource availability, broad geospatial considerations and forest habitat connectivity across political and administrative boundaries.

opencc-by-4.0Feb 2024View details →

RavenBuild: Context, Relevance, and Dependency Aware Build Outcome Prediction

Replication and raw data of RavenBuild

opencc-by-4.0May 2024View details →

Causes and multiyear predictability of the rapid acceleration of U.S. Southeast Sea level rise after 2010

Authors: Liping Zhang1*,2, Thomas L. Delworth1, Xiaosong Yang1, Fanrogn Zeng1, Qinxue Gu1,3 and Shouwei Li1,3                         1NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA            2University Corporation for Atmospheric Research, Boulder, Colorado, USA             3The Program in Atmospheric and oceanic Sciences, Princeton University, Princeton, NJ, USA     Replication of paper entitled "Causes and multiyear predictability of the rapid acceleration of U.S. Southeast Sea level rise after 2010". This file contains datasets used to replicate figures in a paper published in npj Climate and Atmospheric Science. These datasets are in .mat format, which can be easily read by Matlab software using load command.

Extended Data Fig. 2-27 Geographical information of bioinformatic predicted samples based on the analysis of 16S rRNA gene in four PE degrading bacteria

Open the record for dataset details and reuse information.

Antibody-Antigen Models for McCoy 2024 Paper: "A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases"

Up to the top 20 models generated for each method tested in the 2024 Paper: "A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases"

Machine Learning and Radiomics analysis by Computed Tomography in colorectal liver metastases patients for RAS mutational status prediction

We uploaded the Radiomics features raw data of the manuscript "Machine Learning and Radiomics analysis by Computed Tomography in colorectal liver metastases patients for RAS mutational status prediction"

AssemblyNetV2: A Dataset for Predicting Exploded Views Through Point Clouds

AssemblyNetV2 contains two datasets: the original AssemblyNet dataset, resampled with face normals, and a novel dataset for predicting whether parts can be disassembled, also containing both points and normals. The original AssemblyNet dataset contains 5420 samples while the new dataset contains 16337 samples of 512-point and 1024-point point clouds of mixed industrial assemblies and LEGO models. The new dataset for part removal contains annotations for binary classification of whether a part can be removed from an assembly. The intended use case of the two datasets is to predict exploded views of assemblies by iteratively removing parts in the predicted directions.

Skillful Seasonal Prediction of Wind Energy Resources in the contiguous United States

This dataset contains data files used to replicate figures in a paper published in communications earth & environment. These datasets are in .mat format, which can be readable by Matlab software.

Benchmark dataset for protein function prediction that integrates various protein information

This research was funded by the National Science Centre in Poland (grant number 2021/41/N/ST6/01919)

opennotspecifiedJun 2022View details →

Fig. 4 in Characterization of undescribed melanoma inhibitors from Euphorbia mauritanica L. cultivated in Egypt targeting BRAF and MEK 1 kinases via in-silico study and ADME prediction

Fig. 4. Experimental and TDDFT-simulated electronic circular dichroism spctra of (A) euphomauritanol A (1), (B) euphomauritanol B (2), and euphomauritanophane A (3).

opennotspecifiedJun 2022View details →

Fig. 6 in Characterization of undescribed melanoma inhibitors from Euphorbia mauritanica L. cultivated in Egypt targeting BRAF and MEK 1 kinases via in-silico study and ADME prediction

Fig. 6. Bioavailability radar chart from Swiss ADME online web tool for compounds (A) 1, (B) 2 and (C) 3. The pink area represents the range of the optimal property values for oral bioavailability and the red line is compounds (A) 1, (B) 2 and (C) 3 predicted properties. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)