Find research datasets worth reusing

Data Set for Sandanbata et al. (2022: JGR-Solid Earth) entitled "Sub-decadal Volcanic Tsunamis Due to Submarine Trapdoor Faulting at Sumisu Caldera in the Izu-Bonin Arc"

Descriptions This dataset contains supplementary materials for the following research article: <ul> <li>Sandanbata, O., Watada, S., Satake, K., Kanamori, H., Rivera, L., & Zhan, Z. (2022). Sub-decadal volcanic tsunamis due to submarine trapdoor faulting at Sumisu caldera in the Izu–Bonin Arc. Journal of Geophysical Research: Solid Earth, 127, e2022JB024213. <a href="https://doi.org/10.1029/2022JB024213">https://doi.org/10.1029/2022JB024213</a></li> </ul> We construct earthquake source models for the 2015 earthquake at Sumisu caldera in the Izu-Bonin Arc. Four source models, presented in Figures 4a, S8a, S9a, and S14a, are contained in this dataset.

Figure data sets for the paper "Non-classical correlations over 1250 modes between telecom photons and 979-nm photons stored 171Yb3+:Y2SiO5"

Processed datasets corresponding to the Figures published in the article.

Data Set_Curantz et al

Data Set for Curantz et al, PLOS Biology, "Cell shape anisotropy contributes to self-organized feather pattern fidelity in birds"

openSep 2022View details →

Mutational Signatures identification in clinical-setting assays using neural embedding based representations

This dataset includes supporting information for the manuscript titled "Mutational Signatures identification in clinical-setting assays using neural embedding based representations".  Code and custom scripts are available in https://github.com/Adarya/MESiCA.

Experimental single-setting quantum state tomography

Source data underlying the graphical representations used in the figures.

Evaluation data-set for APICarv

Data-set to replicate results reported in the APICarv submission

Figure data sets for the paper "Coherent optical-microwave interface for manipulation of low-field electronic clock transitions in 171Yb3+:Y2SiO5"

Figure data sets.

openAug 2022View details →

Data set to Remote sensing-supported mapping of the activity of a subterranean landscape engineer across an afro-alpine ecosystem

This data set is part of the article Wraase et al. (2022): Remote sensing -supported mapping of the activity of a subterranean landscape engineer across an afro-alpine ecosystem. Remote sensing in Ecology and Conservation. (https://doi.org/10.1002/RSE2.303) The repository contains a Readme file ("readme.txt") and two additional folders labeled: “input data” and “script”. The first folder contains 13 data files further divided into three subfolders “cca_analysis”, “main_modelling_prc_texture_idx” and “vectors”. Data formats are .csv format for all tables, .rds files for model objects from R and .shp format for all vector data. The second folder contains all 31 R-scripts necessary to do the analysis, as described in the article. Additionally, the folder is further categorized into five subfolders equivalent to the main analysis operations: “cca_analysis”, “landsat_temp_modelling”, “main_modelling_prc”, “maxent” and “texture_idx”.

Data set

Data set from Conventional and sustainable agricultural management effect of contrasting crops on carbon and water fluxes in a Mexican semi-arid region

Data set: Native forest conversion alters soil macroinvertabrate diversity and soil quality in tropical mountain landscapes of northern Ecuador

Data set an related material of the article entitled: Native forest conversion alters soil macroinvertabrate diversity and soil quality in tropical mountain landscapes of northern Ecuador

Data set containing the energy landscapes for GPO and GPP tropocollagen models under pulling forces

Energy landscapes (databases of minima and transition states) for GPO and GPP repeat collagen models under constant pulling forces as explored with OPTIM and PATHSAMPLE with an AMBER force field. The systems are seven GPO or GPP per chain capped with ACE and NME.  The forces applied are 0 pN (F0), 10 pN (F1), 50 pN (F2), 100 pN (F3), 250 pN (F4), 500 pN (F5) and 750 pN (F6). The folders contains numerous analysis scripts and graphs. Most of these assume python with numpy and pandas, as well as cpptraj from AMBERTools.

Data set for energy landscapes for interrupted sequences

Databases of minima and transition states produced with PATHSAMPLE for interrupted tropocollagen segments. The two databases are for a GPO and a  GPP repeat model (7 repeats per chain, with ACE and NME caps). One strand contains a deletion of the form GPO-GP-GPO and GPP-GP-GPP.

opencc-by-4.0Oct 2019View details →

Santa Rosa Xtampak, Campeche: 2018 map set.

Maps from the 2018 survey in Santa Rosa Xtampak.

Boundary Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

Boundary data set used to evaluate the continuity predictions for different models in boundary zones. It is composed of labelled and unlabelled pixels for a boundary size of 100m and 200m. For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>. To compute the predictions in the boundary zones with different models (GP, RF, MLP, LTAE), the code is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.

Application of machine learning to improve appropriateness of treatment in an orthopaedic setting of personalized medicine

Raw Data

Scalable mixed model approaches for set-based association studies on large-scale categorical data analysis and its application to 450k exome sequencing data in UK Biobank

The ongoing release of large-scale sequencing data in the UK Biobank allows for identifying associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a novel method for rare-variant association tests, POLMM-GENE, in which a proportional odds logistic mixed model was used to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole exome-sequencing data for 5 ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.

opencc-zeroSep 2022View details →

dryad32/100

A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues

Although cellular senescence drives multiple age-related co-morbidities through the senescence-associated secretory phenotype (SASP), in vivo senescent cell identification remains challenging. Here, we generated a gene set (SenMayo) and validated its enrichment in bone biopsies from two aged human cohorts. We further demonstrated reductions in SenMayo in bone following genetic clearance of senescent cells in mice and in adipose tissue from humans following pharmacological senescent cell clearance. We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from human and murine bone marrow/bone scRNA-seq data. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Using this senescence panel, we were able to characterize senescent cells at the single-cell level and identify key intercellular signaling pathways. SenMayo also represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.

openmit-licenseSep 2022View details →

Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models

Training and test datasets and scripts for training models.

opencc-by-4.0Oct 2022View details →

Data set for "The effect of pulling and twisting forces on chameleon sequence peptides"

Data set for the publication "The effect of pulling and twisting forces on chameleon sequence peptides" The data contains all energy landscapes explored, structures extracted and analysed, and the necessary utility scripts, written by James Meadows. The README details which data is in each directory. The image directory contains the disconnectivity graphs associated with this data.