Find research datasets worth reusing

Data of Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media

Fully data of the paper "Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media" in the Astrophysical Journal. The Astrophysical Journal, 911:119 (18pp), 2021 April 20.

opencc-by-3.0-usJul 2023View details →

CePNEM model analysis data and ANTSUN and microscopy neural network weights

Citation and publication To cite this work or access the publication, please use the citation information listed here: <a href="https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation">https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation</a>   Initially published as preprint in: Brain-wide representations of behavior spanning multiple timescales and states in C. elegans Adam A. Atanas*, Jungsoo Kim*, Ziyu Wang, Eric Bueno, McCoy Becker, Di Kang, Jungyeon Park, Cassi Estrem, Talya S. Kramer, Saba Baskoylu, Vikash K. Mansingkha, Steven W. Flavell bioRxiv 2022.11.11.516186; doi: <a href="https://doi.org/10.1101/2022.11.11.516186">https://doi.org/10.1101/2022.11.11.516186</a> * Equal Contribution   Contents 1. deepnet-weights.tar.bz2 contains the trained weights of the neural networks used in this project. 3dunet_540nm_voxels: 3D U-Net for segmenting neurons head_detector_unet: finding worm head landmark used in ANTSUN registration head_detector_unet_0622: an alternative version of the above, optimal for NeuroPAL datasets microscope_tracker: detecting keypoints for online tracking on the microscope behavior_nir: segmentation of the recorded NIR behavior images for behavior quantification 2. data files ANTSUN processed datasets and CePNEM processed model fits and analysis data. Check the project packages and notebooks in the project github repository (<a href="https://github.com/flavell-lab/AtanasKim-Cell2023/">https://github.com/flavell-lab/AtanasKim-Cell2023/</a>) on using these datasets.

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Data from: Personality determines population-level effects of microplastics consumption in a modelled population of stream-dwelling rainbow trout (Oncorhynchus mykiss)

Microplastics in freshwater habitats are consumed by fish, including stream-dwelling salmonids, which can alter food consumption or negatively affect swimming and foraging behaviour. As population-level effects are largely unknown, a simulated population of stream-dwelling rainbow trout (Oncorhynchus mykiss) was created using the agent-based model 'inSTREAM 7' to model population-level effects (biomass) of behavioural changes caused by microplastics consumption. Individual fish were assigned all possible combinations of two personality traits (dominance, boldness/shyness), and consumed microplastics while foraging, while their abundance, body size, and microplastics consumption were tracked for three different life stages (fry, juvenile, adult) for a 10-year simulation period. Three additive scenarios were explored: a low-impact scenario with decreased food consumption, a medium-impact scenario with added lower swimming speed, and a high-impact scenario with added reductions in prey capture efficiency. Each was tested with microplastics concentrations of 0%, 1% (i.e., current levels), and 3% (i.e., future levels) of drift food. Overall, microplastics consumption did not strongly affect trout population abundance. Dominant adult trout consumed disproportionally more microplastics than all other fish, especially with higher microplastics concentrations. Different personality types were affected differently in the three scenarios: dominant and bold adults were smaller when food consumption was reduced, shy and subordinate adults were smaller when swimming speed was lowered, and all dominant adults, regardless of boldness, were smaller when foraging efficiency was impeded, with dominant and bold fry also less abundant in this scenario. However, effects on fish body size were only found with microplastic concentrations of 3%, indicating these outcomes can be prevented, as current levels of microplastics pollution are unlikely to affect salmonid body size. Nevertheless, microplastics ingestion represents an additional stressor that may potentially interact with a myriad of anthropogenic impacts that already affect wild salmonid populations.

opencc-zeroJul 2023View details →

Data and code for: A quantitative model for spatio-temporal dynamics of root gravitropism

This repository contains the experimental data presented in "A quantitative model for spatio-temporal dynamics of root gravitropism" and Python scripts for the presented root model.

Data, scripts, and figures of the article: Evaluation of oregano essential oil in broilers challenged with a mixed Eimeria spp. and high dietary protein model of subclinical coccidiosis

Data, scripts, and figures of the article "Evaluation of oregano essential oil in broilers challenged with a mixed Eimeria spp. and high dietary protein model of subclinical coccidiosis" to be published in the journal Animal - Open Space.

openmit-licenseJul 2023View details →

ThoughtSource: A central hub for large language model reasoning data (code snapshot)

ThoughtSource is a meta-dataset and software library for chain-of-thought reasoning in large language models (LLMs). This repository contains a snapshot of the associated GitHub repository.

OpenAlex Author Name Disambiguation V3 Data - Disambiguation Model

5 Separate files used in the OpenAlex (https://openalex.org) V3 Author Name Disambiguation Model Creation: <ol> <li>ORCID_hard_negative_pairs: Pairs of ORCIDs where either the full name, family name, or given name are a match and would therefore be more difficult to disambiguate.</li> <li>Disambiguator_all_possible_training_data: Dataset created which contains all possible features for modeling and all possible samples of data. Eventually, this was split into train/val/test and also processed more to create a better balance of positive to negative samples for our purposes.</li> <li>Disambiguator_final_train_data: Final data which the disambiguator was trained on.</li> <li>Disambiguator_final_val_data: Data which was used to test the model during training to optimize the features/hyperparameters chosen.</li> <li>Disambiguator_final_test_data: Final dataset which gave model performance indication after all hyperparameters were tuned and features were chosen.</li> </ol> More details can be found at https://github.com/ourresearch/openalex-name-disambiguation

Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks

This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, currently under review.  The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis. The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.

Results of a Survey on Obstacle Resolving Model Data

This publication includes the results of the online survey "Obstacle resolving model data survey"  which was available via https://www.uhh.de/orm-survey until June 2022. The data was collected between October 2019 and June 2022. The survey was created with the survey tool limesurvey (https://www.limesurvey.org/). The aim of the survey was to collect information on obstacle-resolving atmospheric models and their data, targeting the modeler community. As a part of the project AtMoDat (www.atmodat.de (https://www.atmodat.de/)) we invited the obstacle-resolving modeling community to contribute with their experience with obstacle-resolving models (ORM) and its model data, what models were used and how the user handles the data. We want to develop a data standard for obstacle-resolving model data, which shall be similar to existing standards. Therefore we need some information about the use and processing of model data. This survey asks about the usage of different obstacle-resolving models and/or outputs.  The survey is divided into two parts, the first part contains questions about model properties and the second part contains questions about how the participants handle model data.

opencc-by-4.0Aug 2022View details →

Data presented in "Fitting cumulus cloud size distributions from idealized cloud resolving model simulations"

Updated repository containing the data produced and analzed in the manuscript entitled "Fitting cumulus cloud size distributions from idealized cloud resolving model simulations" by J. Savre and G. Craig, submitted to the Journal of Advances in Modeling Earth Systems. New uploads: time series file (T_S), vertical profiles (profiles_tot.nc) and horizontal slices at 2km (slice_z_2000_bis.nc). Uploaded files concern the LBA case only.

Numerical model and natural river data for the timescale analysis of meandering channel migration

This is the archive of the numerical model and river centerline data used for analyzing the timescale related to meandering channel migration, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Timescale of the morphodynamic feedback between planform geometry and lateral migration of meandering rivers. Running this model needs a MATLAB® software environment. The model can be launched by the wrapper scripts saved under the folder "software code/example wrappers". The wrapper script called "wrapper01a_channelOnly_runModel.m" is used to generate all model simulations in this study.

opencc-zeroAug 2023View details →

dryad40/100

Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning

This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep – 2023), Automated cataloging of oyster toadfish (Opsanus tau) calls using template matching and machine learning. The software accompanying this paper is known as ToadFishFinder, and the classifier model presented in the paper is v4. It consists of more than 10000 labeled toadfish and 10000 labeled other signals. Labeled spectrogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 1350 ms long. The center 850 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'boatwhistle' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the ToadFishFinder model or develop alternative classifiers.

Data and model from "An upper-crust lid over the Long Valley magma chamber" by Biondi et al., 2023

Dataset and velocity model from the fiber-tomography study entitled "An upper-crust lid over the Long Valley magma chamber". The npz files within the PickedRepo.zip contain the following keys: <ul> <li>ChannelID: ID of the channel (similar to station ID)</li> <li>ChannelPos: Lat/Lon/Elevation of each channel</li> <li>P_TT: Pick P-wave traveltime for each channel</li> <li>P_W: P-wave traveltime probability/confidence for each channel</li> <li>S_TT: Pick S-wave traveltime for each channel</li> <li>S_W: S-wave traveltime probability/confidence for each channel</li> </ul> To read and plot the velocity models in the file <a href="https://zenodo.org/api/files/9a704e85-28de-495e-b09f-2578440aa304/FinalVpVsLongValleyDAS2023.npz">FinalVpVsLongValleyDAS2023.npz</a>, one can modify the basic script named ReadPlotVel.py

Input and output data from simulations of 2D valves and 3D inflow-outflow model using particle methods

Input and output data of open-source softwares for computational fluid dynamics simulation involving fluid-structure interaction.   Data from two studies <ol> <li>Verifications of the weakly-compressible smoothed particle hydrodynamics (WCSPH) method, open-source code <a href="https://www.sphinxsys.org">SPHinXsys</a>, when applied to the flow of idealized 2D valve models.</li> <li>Validations of inflow-outflow model in moving particle semi-implicit (MPS) method, open-source code <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow">PolyMPS</a>.</li> </ol>   Folders and Files valve-2D.zip is the folder with data from the idealized models of vertical and curved 2D valves: <ul> <li>Vertical valves with parameters provided in <a href="https://doi.org/10.1016/j.jcp.2010.08.005">Gil et al., 2010</a></li> <li>Curved valves with parameters provided in <a href="http://doi.org/10.1007/s00466-013-0890-3">Wick, 2014</a></li> <li>source files (.cpp): input data (physical and numerical parameters) for SPHinXsys</li> <li>text files: SPHinXsys (.dat) and Reference (.tsv) results</li> <li>python files (.py): Generates the graphics</li> </ul>   inflow-outflow-3D.zip is the folder with data from the inflow-outflow model in MPS: <ul> <li>Fluid physical properties of water <ul> <li>\(\rho=1000kg/m^3 , \,\, \nu=10^{-6}m/s^{-2}\)</li> </ul> </li> <li>Pipes of length \(L=0.15m\): <ul> <li>circular section of diameter \(D=0.1m\).</li> <li>square section of sides \(S=0.1m\).</li> </ul> </li> <li>Constante pressure variation (\(\Delta P = 30 \,\, or \,\, 50 \,\, Pa\)) between inflow and outflow: <ul> <li>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L}, \\ \Delta P = P_{outflow} - P_{inflow}\)</li> </ul> </li> </ul> <ul> <li>Sinusoidal pressure variation (\(\Delta P =700Pa \,\, , \,\, T = 2.0s\)) between inflow and outflow <ul> <li>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L} \sin \omega t \, \\ \omega = \frac{2\pi}{T} \\ Delta P = P_{outflow} - P_{inflow}\)</li> </ul> </li> <li>input data (.json, .grid, .stl): physical properties, numerical parameters and geometries for PolyMPS can be found at <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow/input">https://github.com/rubensamarojr/polymps/tree/inOutflow/input</a></li> <li>text files (.txt): PolyMPS and OpenFOAM results</li> <li>python files (.py): Generates the graphics</li> </ul>   References <a href="https://doi.org/10.1016/j.jcp.2010.08.005">A. J. Gil. The Immersed Structural Potential Method for haemodynamic applications. J. Comput. Phys., 229 (2010), pp. 8613-8641</a> <a href="https://doi.org/10.1007/s00466-013-0890-3">T. Wick. Flapping and contact FSI computations with the fluid–solid interface-tracking/interface-capturing technique and mesh adaptivity. Comput Mech 53, 29–43 (2014)</a> <a href="https://doi.org/10.1016/j.cma.2014.10.040">D. Kamensky, et al. An immersogeometric variational framework for fluid–structure interaction: Application to bioprosthetic heart valves Comput. Methods Appl. Mech. Engrg., 284 (2015), pp. 1005-1053</a> <a href="https://doi.org/10.1016/j.cma.2015.12.023">C. Kadapa et al. A fictitious domain/distributed Lagrange multiplier based fluid–structure interaction scheme with hierarchical B-Spline grids. Comput. Methods Appl. Mech. Engrg., 301 (2016), pp. 1-27</a> <a href="https://doi.org/10.1016/j.jcp.2015.10.015">Jie Liu. A second-order changing-connectivity ALE scheme and its application to FSI with large convection of fluids and near contact of structures. J. Comput. Phys., 304 (2016), pp. 308-423</a>

Lake Ontario model data - 2013 & 2018

This is the dataset that was generated by the model during the calibration and validation process for the Lake Ontario Hydrodynamic model developed in DHI's Mike 3 framework. Contains:  <ul> <li>water temperature  data</li> <li>Current data</li> <li>Water level data</li> <li>Wave data</li> <li>metadata that identifies the position of the instruments and model observation points</li> </ul>

Surface Charge Boundary Condition Often Misused in CO2 Reduction Models (raw data for graphs)

This contains the raw data, in text file format, for the figures in the journal article "Surface Charge Boundary Condition Often Misused in CO2 Reduction Models" published in Journal of Physical Chemistry C, DOI 10.1021/acs.jpcc.3c05364. The files are in directories corresponding to their figure number, and the files are named with the capacitance.

opencc-by-nc-4.0Feb 2022View details →

Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

(DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning

Data points for "Modelling sorption of hydrocarbons in polyethylene with the SAFT-γ Mie approach combined with a statistical-mechanical model to describe semi-crystalline polymers"

A variety of thermodynamic calculations (VLE, sorption isotherms, etc.) performed with a combination of the SAFT-γ equation of state and a novel model to account for the constraints affecting the amorphous domains in semi-crystalline polyethylene (PE). Please refer to the original article (published in Macromolecules) for the bibliography and more details.

Data set: Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022

Version 2: Updates from version 1: Monthly, daily, and hourly dist-PDD and uni-PDD outputs have been added. https://doi.org/10.5194/tc-17-3667-2023   Version 1: This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere. This dataset contains annual PDD model output.