Find research datasets worth reusing

opencc-by-4.0Apr 2023View details →

BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy - Supplementary material

Figure S1: Distribution of the reviewed 141 BRCA1 missense variants; Figure S2: The Shapely values for the BRCA1 XGBoost models; Figure S3: The Shapely values of the BRCA1 XGBoost model used to predict the functional assays’ results for variants of uncertain significance; Table S1: The receiver operating characteristic (ROC) curve analysis for the different in silico predictions; Table S2: Cross validation of the BRCA1 model in 5 different random training and test samples; Table S3: Pathogenicity prediction and prioritization of the 31,058 unreviewed BRCA1 variants from the BRCA Exchange database.

CausalOrca: An ORCA-based Diagnostic Dataset for Causally-aware Multi-agent Trajectory Prediction

CausalOrca is a synthetic diagnostic dataset created through controlled simulations. It is designed to provide annotations of ground-truth causal effects and fine-grained agent categories for social interactions in multi-agent scenarios. The dataset is constructed using a modified RVO2 simulator and incorporates the ORCA optimization-based collision avoidance algorithm known for crowd simulation. With full control over scene configurations, the dataset enables the collection of motion behaviors in paired scenes before and after agent removal, generating a large set of counterfactual pairs with annotations of ground-truth causal effects. CausalOrca can serve as a valuable resource for studying and developing causally-aware neural representations of social interactions and trajectory prediction models. Please see the <a href="https://github.com/rebuttal-anonymous/causalorca">GitHub repository</a> for a more detailed description of the dataset, including dataset statistics and documentation on how to use, visualize, and generate the data.

The Prairie State: Using Ecological Niche Modeling to Predict Distributions of Early Land Plants

This data includes raw data of over 12,000 occurrences were downloaded from the Consortium of Bryophyte Herbaria (<a href="http://www.bryophyteportal.org/portal">www.bryophyteportal.org/portal</a>), that were listed to be in Illinois and included longitude and latitude data. This data set was screened and cleaned to investigate species distribution models as well as generate models of selected bryophytes investigating future changes in distribution across climate change scenarios.

Processed data and scripts supporting the manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma"

This submission contains the compiled count table, processed R objects and various scripts and output files accompanying our manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma" (Nature Communications, 2023, https://doi.org/10.1038/s41467-023-38886-8)

Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity

GENERAL INFORMATION This data is described in the following publication:  Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity, Somayeh Shahsavarani1,2,5, David N. Thibodeaux1,5, Weihao Xu1, Sharon H. Kim1, Fatema Lodgher1, Chinwendu Nwokeabia1, Morgan Cambareri1, Alexis J. Yagielski1, Hanzhi T. Zhao1, Daniel A. Handwerker2, Javier Gonzalez-Castillo2, Peter A. Bandettini2,3, Elizabeth M. C. Hillman1,4,6,* Cell Reports (2023): <a href="https://doi.org/10.1016/j.celrep.2023.112527">https://doi.org/10.1016/j.celrep.2023.112527</a> 1. Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA 2. Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA 3. Functional MRI Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA 4. Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA 5. These authors contributed equally 6. Lead contact *Correspondence: elizabeth.hillman@columbia.edu Preprocessing and analysis code that generated / can be used with this data is posted at:  GitHub: <a href="https://doi.org/10.5281/zenodo.7860561">https://doi.org/10.5281/zenodo.7860561</a> DATA OVERVIEW This dataset comprises simultaneous neuronal and hemodynamic data collected using wide-field optical mapping (WFOM) techniques. The data were obtained from head-fixed mice that were allowed to behave spontaneously without any external stimulation. For more detail, please refer to the Readme file.

Biometeorological Dataset for 'Novel algorithms for high resolution prediction of canopy evapotranspiration in grapevine'

A head trained Vitis vinifera L. cv. Zinfandel vine was grafted on St. George rootstock (V. rupestris) then planted in a 1.1 m3 plastic container filled with Yolo County, CA sourced sandy loam. To estimate evapotranspiration, we measured the wind speed, air temperature and relative humidity in vine canopies by mounting each vine with a suite of research grade sensors. We measured wind speed (units m ᐧ s-1) inside the vine canopy using a single needle anemometer (East 30 Sensors; Pullman, WA) that took instantaneous wind speed measurements every 10 seconds and recorded the average of the previous 12 instantaneous measurements for every 2-minute interval. We measured temperature (units oC) and relative humidity (units %) using HMP60L sensors (Campbell Scientific; Logan, UT) mounted both inside and outside of each vine canopy and recorded instantaneous measurements at each 2-minute interval. We filtered all biometeorological data using a 3-hour moving average to remove noise without causing any significant over or under-approximation of daily maxima and minima. We automated all data collection using two CR1000 data loggers (Campbell Scientific; Logan, UT), with 1 or 2 vines and associated sensors per logger, using custom CR1 programs.  A single 30W solar cell and 12V lead acid battery powered the entire vine-sensor system. This dataset represents all sensor data from a single vine, as measured in August 2020. Columns are named accordingly and include units. Please Note: The column named 'load_cell_kg' is not named accurately. The values given are in units of millivolts, and need to be translated from millivolts to kilograms. The 2020 calibration coefficient is 0.00330693663 millivolts per kilogram.

Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning

3d morphology evolution of diblock copolymer via 3D UNet. Manuscript: Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning

Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"

RHEED raw dataset and Gaussia fitting parameter dataset for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."

The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity

Raw data and code to generate the figures of the publication "The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity" <a href="https://doi.org/10.7554/eLife.85111.2">https://doi.org/10.7554/eLife.85111</a>.

In silico design, docking simulation, and ANN-QSAR model for predicting the anticoagulant activity of thiourea isosteviol compounds as FXa inhibitors

The present work combined molecular modeling and docking approach for searching and designing novel thiourea isosteviol-based compounds as potential FXa inhibitors. Elaborated regression model establishes the relationships between experimentally determined anticoagulant activity and molecular descriptors and enables the prediction of FXa inhibitory activity for novel compounds. The obtained results proved that the Artificial Neural Network algorithm facilitates the search for the most promising isosteviol derivatives incorporating thiourea fragments as FXa inhibitors. Moreover, docking simulation confirms the prominent binding of the newly in silico designed molecules with the active sites of the protein, which may be the lead molecules and can be further optimized for the efficient pharmacodynamic and pharmacokinetic profiles. The enclosed files are representations of molecular structures of thiourea isosteviol compounds with experimentally tested FXa inhibitory activity (i20-i39) geometrically optimized in hyperchem, newly in silico designed thiourea isosteviol compounds geometrically optimized in hyperchem (e1-e11), one file contains molecular descriptors for optimized structures calculated in Dragon and there is also a code for ANN QSAR model for predicting activity of novel thiourea isosteviol compounds.

opencc-zeroJun 2023View details →

dryad40/100

Data for: Shy-boldness cannot predict egg rejection in the Japanese tit

In response to brood parasitism, which significantly reduces the reproductive success of avian hosts, hosts have evolved the ability to recognize and reject parasitic eggs. Many studies have revealed the influencing factors of egg recognition and egg rejection, and recent study found that host personality, along the bold-shy continuum, can influence egg rejection behavior. In addition, the egg rejection behavior of hosts differed among populations, suggesting that individuals with different personalities may evolve various adaptive behaviors under different parasitic pressures, but there has been few relevant experiments to verify this. In the present study, two populations of the Japanese tit Parus minor distributed in China were used to examine whether there were differences in egg rejection behavior of individuals with different personalities. The results showed that egg rejection rates of bold individuals (who remained in the nest box when encountering human invaders) and shy individuals (who immediately flew away from the nest box when encountering human invaders) were similar in both populations of the Japanese tit, and there were also no significant differences in egg rejection rates between individuals of the same personality in the two populations. This study suggests that host personality, along the bold-shy continuum, does not account for egg rejection behavior in the Japanese tits. This may be due to either other factors that affect egg rejection behavior counteracting the effect of personality, or the presence of multiple personalities that act together in the Japanese tits counteracting the effect of a single personality trait.

dryad40/100

Data from: The impact of task context on predicting finger movements in a brain-machine interface

A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes in a task. With functional electrical stimulation (FES) for example, the patient's own hand will be used to produce a wide range of forces in otherwise similar movements. To investigate the impact of task changes on BMI performance, we trained two rhesus macaques to control a virtual hand with their physical hand while we added springs to each finger group (index or middle-ring-small) or altered their wrist posture. Using simultaneously recorded intracortical neural activity, finger positions, and electromyography, we found that predicting finger kinematics and finger-related muscle activations across contexts led to significant increases in prediction error, especially for muscle activations. However, with respect to online BMI control of the virtual hand, changing either training task context or the hand's physical context during online control had little effect on online performance. We explain this dichotomy by showing that the structure of neural population activity remained similar in new contexts, which could allow for fast adjustment online. Additionally, we found that neural activity shifted trajectories proportional to the required muscle activation in new contexts, possibly explaining biased kinematic predictions and suggesting a feature that could help predict different magnitude muscle activations while producing similar kinematics.

opencc-zeroJun 2023View details →

Supplementary data for wave current scour prediction

This CSV file has data obtained, which is obtained from various works of literature containing Normalized scour depth around monopile foundations due to waves and currents.

Predicting International and Internal Migration in Guatemala with Social, Physical and Climatic Variables (Data)

Accompanying data for the Depsky and Pons (2023) PLoS ONE publication - Predicting International and Internal Migration in Guatemala with Social, Physical and Climatic Variables. This repository contains tables of the raw 2018 Guatemalan national census values at the individual, household, and residence levels, as well as its migration-specific data table. Municipality-average climate values from ERA5-Land are provided as well, in addition to a shapefile of the municipal boundaries. All standardized outcome and predictor variables used for each of the five models are provided as separate tables as well.

PYFOREST-ML-Predictions

Results data from the Master's Capstone project PYFOREST. Predictions of deforestation under different policy scenarios are provided. See attached metadata for additional information, or refer to the <a href="https://github.com/cp-PYFOREST">GitHub project repository.</a>

Predicted drug-likeness and toxicity properties of Salvia fruticosa compounds

The datasets present computationally predicted drug-likeness and toxicity profiles of Salvia frutocosa compounds.

opencc-zeroJun 2023View details →

Dataset for the article: Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment

This open access repository houses the dataset utilized in the research article:Pankiewicz, M. (2023). Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1413-1418). Vienna, Austria: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/222666The repository includes these files:"submissions.csv": This data file captures the evaluation results of programming assignments. It is organized by the following columns: <ul><li>"user_id": The unique identifier for each student who submitted the assignment.</li><li>"task_id": The unique identifier for each task that received submissions.</li><li>"submission_seconds": The number of seconds since the first user accessed the initial task's description within the system.</li><li>"correct": The outcome of the evaluation (1 denotes correct; 0 denotes incorrect).</li><li>"subject": The specific subject matter that the task addresses.</li></ul> "subjects.csv": This data file comprises the roster of subjects for which tasks have been assigned within the system. It includes these columns: <ul><li>"subject_id": The unique identifier for each subject.</li><li>"subject": The actual name of the subject.</li></ul>

Predicting the Habitat Suitability of Asian Elephants in Madhesh Landscape of Southern Nepal

This is the dataset used during my thesis entitled " Predicting the Habitat Suitability of Asian Elephants (Elephas maximas) in Madhesh landscape of southern Nepal.

opencc-byJun 2023View details →

Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis

Dataset used in Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis [<a href="https://arxiv.org/abs/2211.11784">arXiv: 2211.11784</a>]. This dataset will be helpful to validate different continuum prediction model and study absorption systems. The descriptions of individual files can be found here: <ul> <li><a href="/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-dr16.tar.gz?versionId=427c1aca-6f12-4e84-b91e-f9a3d678329e">sdss-dr16.tar.gz</a> : continuum prediction for ~100,000 quasar spectra from SDSS DR16, see Section 3.1 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>;</li> <li><a href="https://zenodo.org/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-mock-with-dla-with-perturb.tar.gz?versionId=55bc466d-4cbf-43ba-9b83-209ffba4ec30">sdss-mock-with-dla-with-perturb.tar.gz </a> : ~150,000 mock quasar spectra to validate QFA performance with perturbation on  quasar continuum from PCA template, see Section 3.2 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>;</li> <li><a href="https://zenodo.org/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-mock-with-dla-without-perturb.tar.gz?versionId=efa5d05a-2a40-4a43-801e-c95b29a4efb2">sdss-mock-with-dla-without-perturb.tar.gz</a>: ~150,000 mock quasar spectra to validate QFA performance with quasar continuum directly from PCA template, see Section 3.2 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>.  </li> </ul>

Predicted models of S receptor kinase ectodomain (eSRK), S-locus protein 11 (SP11), and eSRK-SP11 complexes

Predicted models of S receptor kinase ectodomain (eSRK), S-locus protein 11 (SP11), and their complexes using ColabFold and curated multiple sequence alignments (MSAs).