Find research datasets worth reusing

Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses

Raw data and code to reproduce figures in the manuscript "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses" # README ## Introduction This README provides essential information about the codebase for the manuscript titled "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses." The code in this repository is self-contained and is expected to run smoothly given the appropriate versions of the required libraries/packages. ## Directory structure and execution details ### R code - Main Figures 2A-2D, 3A-3C, and 4A-4E, as well as supplemental figures S2A-S2H, S3A-S3E, S4L, and S5A-S5I, were generated using R. Execute the `R_figs_master.r` script located in the `r_code` directory. - All figures will be saved within the `r_code/code_generated_figures` directory. - Note: Exact UMAP representations might vary across different hardware and operating systems, likely due to an issue with the UWOT package ([Reference Issue](https://github.com/satijalab/seurat/issues/5514)). If figures appear outside their designated plot ranges, set "FixAxes" to 'FALSE' in the `single_cell_variables.r` script. ### MATLAB code - Main figures 1B, 1D-1F, and 6A-6H, as well as supplemental figures S1A-S1J and S6A-S6I, were generated using MATLAB (version 9.11.0.1809720 (R2021b) Update 1). Execute the `get_the_figs_matlab.m` script located in the `matlab_code` directory. - All figures will be saved within the `matlab_code/code_generated_figures` directory. - Required: [fca_readfcs, version 2020.06.22](https://ch.mathworks.com/matlabcentral/fileexchange/9608-fca_readfcs). ### Python code - Figures 5B-5F panels were generated using Python (version 3.6.8). Run the `fig_5_analysis_code.py` script located in the `python_code` directory. - All figures will be saved within the `python_code/code_generated_figures` directory. - The preprocessed images located in `python_code/data_repository/Adamts2_processed`, `python_code/data_repository/Agmat_processed`, and `python_code/data_repository/Baz1a_processed` were generated using the ImageJ macro `python_code/cropped_to_processed_macro.ijm` from the raw images in `python_code/data_repository/Adamts2_cropped`, `python_code/data_repository/Agmat_cropped`, and `python_code/data_repository/Baz1a_cropped`. ## Supplementary code (for reference only as raw data is not included) ### Mapping code and genome construction code - Initial processing of Single-cell RNA-sequencing was performed with Cell Ranger, coordinated by the Python script:   `python_code/mapping_and_genome_construction/single_cell_mapping_pipeline.py`. Some components of this script are deprecated and were primarily used to pass .fastq files to Cell Ranger and organize the outputs. - A custom genome was constructed to account for the expression of CaMPARI2 in the single-cell RNA-sequencing dataset:   `python_code/mapping_and_genome_construction/campari2_genome_construction.py`. - Processing of Bulk RNA-sequencing, either single or paired-end, was executed through Python:   `python_code/mapping_and_genome_construction/bulk_single_end_mapping.py` and `python_code/mapping_and_genome_construction/bulk_paired_end_mapping.py`. - A custom genome was constructed to account for the expression of various artificial promoter viruses:   `python_code/mapping_and_genome_construction/bulk_seq_genome_construction.py`.

opencc-bySep 2023View details →

Advancements in QSAR modelling: Decision trees and rotation forest for prediction of Aspergillus anti-inflammatory metabolites

This study presents applications of advancements in QSAR modelling for predicting nitric oxide (NO) inhibitors and anti-inflammatory metabolites from the Aspergillus genus. Inflammation-related diseases remain a pressing concern, necessitating the identification of effective anti-inflammatory compounds. Using decision trees, the Ranker method, and CorrelationAtrributeEval as a base classifier for attribute selection together with Rotation Forest and Adaboost as enhancers, we explored their potential with different classifiers including Artificial Neural Networks and J48 Trees. The proposed QSAR models employed an ensemble approach with Rotation Forest and Adaboost.M1, applying an automated KNIME workflow. Seven molecular descriptors were selected and trained on a comprehensive dataset of diverse anti-inflammatory Aspergillus specialised metabolites. Results showed that the Rotation Forest-enhanced version outperformed other models, capturing complex structure-activity relationships and improving predictive performance. Chemical characteristics of electrotopological state, topological distances, and functional groups including secondary amides and alcohols contribute to important anti-inflammatory effects. The developed QSAR model showed good predictive performance for anti-inflammatory Aspergillus metabolites, focusing on their NO inhibitory activity. These results can contribute to the discovery of novel anti-inflammatory drugs based on computational techniques.

opencc-zeroAug 2023View details →

Data from: Evolvability predicts evolutionary divergence in extant and extinct species

Heritable variation is a prerequisite for evolutionary change. Yet, whether genetic potential for microevolution is relevant on macroevolutionary timescales is debated. Here we show that evolutionary divergence among populations, and to a lesser extent among species, increases with microevolutionary evolvability in both extant and extinct taxa. We evaluate and reject a number of hypotheses put forward to explain this relationship and propose that an effect of evolvability on population and species divergence can be explained by the influence of genetic constraints on population's ability to track rapid stationary environmental fluctuations.

Predicting the Relative Static Permittivity: a Group Contribution Method Based on Perturbation Theory

Permittivity-over-temperature diagram for the publication "Predicting the Relative Static Permittivity: a Group Contribution Method Based on Perturbation Theory" published under DOI 10.1021/acs.jced.3c00323 in the Journal of Chemical and Engineering Data.

A computer vision pipeline for predicting zoonotic reservoir distribution

Shared here is the complete project repository of code and data supporting the manuscript, "Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision." Note that all project subdirectories (with the exception of the "Data" subdirectory) were compressed into single .zip files and thus need to be decompressed upon download. The "Data" subdirectory, which contains numerous large data files, was stored as a split zip archive to facilitate upload to Zenodo and consists of the files "Data.z01", "Data.z02", "Data.z03", and "Data.zip". These files may need to be decompressed using dedicated archiver software (such as The Unarchiver [https://theunarchiver.com/] on macOS). The "Data" subdirectory should be ~17 GB upon decompression. Note that the entire project repository is ~40 GB when uncompressed.

Prediction of Spheroid Cell Death using Fluorescence Staining and Convolutional Neural Networks

This repository contains training, validation, and testing of fluorescence image data sets with their label for spheroid cell death classification.  These data are intended to be used in the paper "Prediction of Spheroid Cell Death using Fluorescence Staining and Convolutional Neural Networks" currently submitted

opencc-zeroAug 2023View details →

Data and code for: Veterinary Expert System for Outcome (VESOP) Prediction

Timely detection and understanding of causes for population decline are essential for effective wildlife management and conservation. Assessing trends in population size has been the standard approach but we propose that monitoring population health could prove more effective. We collated data from seven bottlenose dolphin (Tursiops truncatus) populations in the southeastern U.S. to develop the Veterinary Expert System for Outcome Prediction (VESOP), which estimates survival probability using a suite of health measures identified by experts as indices for inflammatory, metabolic, pulmonary, and neuroendocrine systems. VESOP was implemented using logistic regression within a Bayesian analysis framework, and parameters were fit using records from five of the sites that had robust stranding network and frequent photographic identification (photo-ID) surveys to document definitive survival outcomes. We also conducted capture-mark-recapture (CMR) analyses of photo-ID data to obtain separate estimates of population survival rates for comparison with VESOP survival estimates. VESOP analyses found multiple measures of health, particularly markers of inflammation, were predictive of 1- and 2-year individual survival. The highest mortality risk one year following health assessment related to low alkaline phosphatase, with an odds ratio of 10.2 (95% CI 3.41–26.8), while 2-year mortality was most influenced by elevated globulin (9.60; 95% CI 3.88–22.4); both are markers of inflammation. The VESOP model predicted population-level survival rates that correlated with estimated survival rates from CMR analyses for the same populations (1-year Pearson's r=0.99; p=1.52e-05, 2-year r=0.94; p=0.001). While our proposed approach will not detect acute mortality threats that are largely independent of animal health, such as harmful algal blooms, it is applicable for detecting chronic health conditions that increase mortality risk. Random sampling of the population is important and advancement in remote sampling methods could facilitate more random selection of subjects, obtainment of larger sample sizes, and extension of the approach to other wildlife species.

Metal halide thermoelectrics: prediction of high-performance CsCu2I3

Optimized crystal structures of CsCu2I3 (Cmcm and Amm2); force constant sets of Amm2 structure; and raw AMSET input/output files.

opencc-zeroAug 2023View details →

Data and code for: Spatial cell type enrichment predicts mouse brain connectivity

A fundamental neuroscience topic is the link between the brain's molecular, cellular and cytoarchitectonic properties and structural connectivity (SC). Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the Matrix Inversion and Subset Selection (MISS) algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell type densities, demonstrating surprisingly strong prediction accuracy of cell types in general and particular cells like oligodendrocytes. We found evidence of a strong distance-dependency in the cell-connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia are salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.

opencc-by-4.0Dec 2022View details →

Images of daphnids (control and exposed to NMs) over multiple generations, scored by experts as toxic or non-toxic and the resulting deepDaph predictions

Background This study showcases a pioneering application of deep learning methodologies in ecotoxicology, aimed at facilitating hazard assessment and safer design of engineered nanomaterials (ENMs). The research hinges on a high-quality dataset comprising microscopic images of Daphnia magna exposed to various ENMs, collected systematically under controlled conditions. The Dataset: A Cornerstone of Nanoinformatics Our dataset, which will be openly accessible on Zenodo, serves as a foundational resource for the ecotoxicology community. It contains high-resolution images tagged with intricate details like malformations, tail lengths, lipid concentrations, and lipid deposit shapes. Researchers can use this exhaustive dataset to train a variety of predictive models for diverse applications. Methodology We employ two different deep learning architectures to process the dataset. These architectures automatically detect malformations and assess the impact of ENMs on D. magna by classifying various biological structures based on lipid densities. Results and Validation The developed models demonstrate high statistical validation, confirming their prediction accuracy on external D. magna images. Our dataset and the associated models not only accelerate manual procedures but also pave the way for automated, high-throughput analyses in ecotoxicology. Future Prospects The dataset holds the potential to extend investigations into predicting the impacts on future generations from parental exposures, thus reducing the time and cost of multi-generational toxicity assays.

opencc-zeroSep 2023View details →

Functional traits of young seedlings predict trade-offs in seedling performance in three neotropical forests

<ol> <li>Understanding the mechanisms that promote the coexistence of hundreds of species over small areas in tropical forest remains a challenge. Many tropical tree species are presumed to be functionally equivalent shade-tolerant species that differ in performance trade-offs between survival in shade and the ability to quickly grow in sunlight.</li> <li>Variation in plant functional traits related to resource acquisition is thought to predict variation in performance among species, perhaps explaining community assembly across habitats with gradients in resource availability. Many studies have found low predictive power, however, when linking trait measurements to species demographic rates.</li> <li>Seedlings face different challenges recruiting on the forest floor and may exhibit different traits and/or performance trade-offs than older individuals face in the eventual adult niche. Seed mass is the typical proxy for seedling success, but species also differ in cotyledon strategy (reserve vs photosynthetic) or other seedling traits. These can cause species with the same average seed mass to have divergent performance in the same habitat.</li> <li>We combined long-term studies of seedling dynamics with functional trait data collected at a standard developmental stage in three diverse neotropical forests to ask whether variation in coordinated suites of traits predicts variation among species in demographic performance.</li> <li>Across hundreds of species in Ecuador, Panama, and Puerto Rico, we found seedlings displayed correlated suites of leaf, stem, and root traits, which strongly correlated with seed mass and cotyledon strategy. Variation among species in seedling functional traits, seed mass, and cotyledon strategy were strong predictors of trade-offs in seedling growth and survival.</li> <li>Our findings highlight the importance of cotyledon strategy in addition to seed mass as a key component of seed and seedling biology. These results also underscore the importance of matching the ontogenetic stage of the trait measurement to the stage of demographic dynamics.</li> <li> Synthesis: With strikingly consistent patterns across three tropical forests, we find strong evidence for the promise of functional traits to provide mechanistic links between seedling form and demographic performance. </li> </ol>

opencc-by-4.0Oct 2022View details →

Predicting RMSW R2data

R2 data and ancillary information as used in "Predicting turbulent vertical velocity in the ocean surface layer under mixed convective and wind-wave forcing"

Dataset of Intention to Whistleblow: Perception of Reporting Skill Mediates the Predicting Role of Class Consciousness and Perceived Probability of Revenge

Dataset of Intention to Whistleblow: Perception of Reporting Skill Mediates the Predicting Role of Class Consciousness and Perceived Probability of Revenge

Data from: Prospection of potential actions during visual working memory starts early, is flexible, and predicts behavior.

Raw data (EEG and behavior) reported in the manuscript "Prospection of potential actions in visual working memory starts early, is flexible,  and predicts behavior" by Rose Nasrawi, Sage E.P. Boettcher, and Freek van Ede

Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'

Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article:  Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405 The .zip file has the following folders: 1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents 2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction 3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction 4) soil_texture_quantile05: quantile 5% of the soil texture prediction 5) soil_texture_quantile95: quantile 95% of the soil texture prediction

Rating results obtained during a review of original articles on radiomics and machine learning for outcome prediction based on PET

This upload provides Open Data associated with the publication "Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)" by Rogasch JMM et al. (2023). The upload contains the item-by-item results of rating for all criteria and all 100 original articles. PubMed IDs are also included. Furthermore, a description of all variable names and how the rating categories were encoded in the data tables can be found in the PDF file "ML_prediction_Dictionary_2023_08_27.pdf".

Soil chemistry dataset from the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica"

Bases sum, H+Al (potential acidity), pH, phosphorous, remaining P (P-rem), sodium and total organic carbon distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The quantile and prediction interval data represent the spatial uncertainty of the predictions. As soon as the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica" is published, the paper will be cited here.  The .zip file contains the following folders: 1) soil_chemistry_antarctica: data containing the soil chemical attributes distribution 2) soil_chemistry_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil attributes prediction 4) soil_texture_quantile05: quantile 5% of the soil attributes prediction 5) soil_texture_quantile95: quantile 95% of the soil attributes prediction

Automated benchmarking of combined protein structure and ligand conformation prediction

The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions. This repository contains: <ol> <li> all_validation_clustering_data.tsv - X-ray validation data and MMSeqs cluster identifiers at different sequence identities for over a million small molecule and ion-binding pockets in the PDB.  </li> <li> hqr_dataset.tsv - PDB IDs and ligand information for the high quality representative (HQR) dataset described in the manuscript </li> <li> score_files.tar.gz - Full docking results for all detected pockets for the PDBBind time-split test-set, the HQR dataset, and the subsets of AF models created for both datasets. One file per tool benchmarked with the following columns: Tool, Complex, Pocket, Rank, lDDT-PLI, lDDT-LP, BiSyRMSD, Reference_Ligand, Tool-generated Score </li> <li> errors_all_sets.csv - Report of failures running the pipeline with the following columns: Process, Complex/Ligand/Receptor, Problem </li> </ol>

TableS1 of ML-based predictive gut microbiome analysis for health assessment

Complete list of species associated with the COVID and Control cohort (ANOVA F score > 1). Comparison with respect to the original set of species used by Gupta et al., and ANOVA-F values are reported.