Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

Two multi-temporal datasets to track debris flow after the 2008 Wenchuan earthquake

We provide two datasets for tracking the debris flow induced by the 2008 Wenchuan Mw 7.9 earthquake on a section of the Longmen mountains on the eastern side of the Tibetan plateau (Sichuan, China). The database was obtained through a literature review and field survey reports in the epicenter area, combined with high-resolution remote sensing imagery and extensive data collection and processing. The first dataset covers an area of 892 km2, including debris flows from 2008 to 2020. 186 debris flows affecting 79 watersheds were identified. 89 rainfall stations were collected to determine the rainfall events for the post-earthquake debris flow outbreak. The second database is a list of mitigation measures for post-earthquake debris flows, including catchment name, check dam number, coordinates, construction time, and successful debris flow mitigation date. This two datasets can aid different applications, including the early warning monitoring and engineering prevention of post-earthquake debris flow, as well as provide valuable data support for research in related disciplines.

opencc-by-4.0Jul 2022View details →

Plots of 1962 and 1983 lava flows and scoriae in Miyakejima

Thermomagnetic curves (jst), hysteresis loops (hys), and Arai diagrams with Zijderveld diagrams of Thellier results (thl) by site.

opencc-by-4.0May 2022View details →

Spectral flow cytometric tracking of mitochondria transfer in adipose tissue by age and diet

Unmixed fcs files for the stromal vascular fraction of epididymal (eWAT), inguinal (iWAT), and brown adipose tissue (BAT) in mtD2F/+AdipoqCre+/- and different ages or diets.  For a breakdown of samples and code, please see: <a href="https://github.com/ncborcherding/mtD2">https://github.com/ncborcherding/mtD2</a>. The full github repository at time of manuscript acceptance is also in the Zenodo repository under mtD2_GitHubRepo.zip. Antibody Panel:  <table align="center"> <tbody> <tr> <td> Laser </td> <td> Channel </td> <td> Antigen </td> <td> Fluorophore </td> <td> Final Dilution Factor </td> <td> Vendor </td> <td> Clone </td> <td> Catalog Number </td> </tr> <tr> <td> Violet </td> <td> V1 </td> <td> SiglecF </td> <td> BV 421 </td> <td> 1:400 </td> <td> BD Biosciences </td> <td> E50-2440 </td> <td> 562681 </td> </tr> <tr> <td> </td> <td> V2 </td> <td> CD44 </td> <td> Super Bright 436 </td> <td> 1:300 </td> <td> eBioscience </td> <td> IM7 </td> <td> 62-044-180 </td> </tr> <tr> <td> </td> <td> V3 </td> <td> CD11b </td> <td> Pacific Blue </td> <td> 1:300 </td> <td> BioLegend </td> <td> M1/70 </td> <td> 101224 </td> </tr> <tr> <td> </td> <td> V5 </td> <td> IL-33R⍺-biotin </td> <td> n/a </td> <td> 1:300 </td> <td> BioLegend </td> <td> DIH9 </td> <td> 145308 </td> </tr> <tr> <td> </td> <td> V5 </td> <td> Streptavidin (2°) </td> <td> BV 480 </td> <td> 1:300 </td> <td> BD Horizon </td> <td> n/a </td> <td> 564876 </td> </tr> <tr> <td> </td> <td> V7 </td> <td> MHC-II (I-A/I-E) </td> <td> BV 510 </td> <td> 1:300 </td> <td> BioLegend </td> <td> M5/114.15.2 </td> <td> 107636 </td> </tr> <tr> <td> </td> <td> V8 </td> <td> Ly6C </td> <td> BV 570 </td> <td> 1:400 </td> <td> BioLegend </td> <td> HK1.4 </td> <td> 128030 </td> </tr> <tr> <td> </td> <td> V10 </td> <td> KLRG1 </td> <td> BV 605 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 2F1/KLRG1 </td> <td> 138419 </td> </tr> <tr> <td> </td> <td> V11 </td> <td> F4/80 </td> <td> BV 650 </td> <td> 1:300 </td> <td> BioLegend </td> <td> BM8 </td> <td> 123149 </td> </tr> <tr> <td> </td> <td> V13 </td> <td> Ly6G </td> <td> BV 711 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 1A8 </td> <td> 127643 </td> </tr> <tr> <td> </td> <td> V14 </td> <td> CD3 </td> <td> BV 750 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 17A2 </td> <td> 100249 </td> </tr> <tr> <td> </td> <td> V15 </td> <td> CD206 </td> <td> BV 785 </td> <td> 1:200 </td> <td> BioLegend </td> <td> C068C2 </td> <td> 141729 </td> </tr> <tr> <td> Blue </td> <td> B1 </td> <td> mtDendra2 </td> <td> n/a </td> <td> n/a </td> <td> n/a </td> <td> n/a </td> <td> n/a </td> </tr> <tr> <td> </td> <td> B2 </td> <td> TCRb </td> <td> AF 488 </td> <td> 1:300 </td> <td> BioLegend </td> <td> H57-597 </td> <td> 109215 </td> </tr> <tr> <td> </td> <td> B3 </td> <td> CD8 </td> <td> Spark Blue 550 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 53-6.7 </td> <td> 100780 </td> </tr> <tr> <td> </td> <td> B8 </td> <td> CD45 </td> <td> PerCP </td> <td> 1:200 </td> <td> BioLegend </td> <td> 30-F11 </td> <td> 103130 </td> </tr> <tr> <td> </td> <td> B9 </td> <td> CD71 </td> <td> PerCP-Cy5.5 </td> <td> 1:200 </td> <td> BioLegend </td> <td> RI7217 </td> <td> 113816 </td> </tr> <tr> <td> </td> <td> B10 </td> <td> NKp46 </td> <td> PerCP-eFluor710 </td> <td> 1:200 </td> <td> eBioscience </td> <td> 29A1.4 </td> <td> 46-3351-82 </td> </tr> <tr> <td> Green </td> <td> YG1 </td> <td> TCRgd </td> <td> PE </td> <td> 1:300 </td> <td> BioLegend </td> <td> GL3 </td> <td> 118108 </td> </tr> <tr> <td> </td> <td> YG3 </td> <td> CD64 </td> <td> PE/Dazzle 594 </td> <td> 1:300 </td> <td> BioLegend </td> <td> X54-5/7.1 </td> <td> 139320 </td> </tr> <tr> <td> </td> <td> YG4 </td> <td> CD4 </td> <td> PE/Fire 640 </td> <td> 1:300 </td> <td> BioLegend </td> <td> GK1.5 </td> <td> 100482 </td> </tr> <tr> <td> </td> <td> YG5 </td> <td> CD62L </td> <td> PE-Cyanine5 </td> <td> 1:300 </td> <td> BioLegend </td> <td> MEL-14 </td> <td> 104410 </td> </tr> <tr> <td> </td> <td> YG7 </td> <td> CD11c </td> <td> PE-Cyanine5.5 </td> <td> 1:300 </td> <td> eBioscience </td> <td> N418 </td> <td> 35-0114-82 </td> </tr> <tr> <td> </td> <td> YG9 </td> <td> CD25 </td> <td> PE/Cyanine7 </td> <td> 1:200 </td> <td> BioLegend </td> <td> PC61 </td> <td> 102016 </td> </tr> <tr> <td> Red </td> <td> R1 </td> <td> CD9 </td> <td> APC </td> <td> 1:300 </td> <td> BioLegend </td> <td> MZ3 </td> <td> 124812 </td> </tr> <tr> <td> </td> <td> R2 </td> <td> CD127 </td> <td> AF 647 </td> <td> 1:200 </td> <td> BioLegend </td> <td> A7R34 </td> <td> 135020 </td> </tr> <tr> <td> </td> <td> R3 </td> <td> CD19 </td> <td> Spark NIR 550 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 6D5 </td> <td> 115568 </td> </tr> <tr> <td> </td> <td> R4 </td> <td> CD90.2 </td> <td> AF 700 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 30-H12 </td> <td> 105320 </td> </tr> <tr> <td> </td> <td> R5 </td> <td> CD5 </td> <td> BUV 737 </td> <td> 1:300 </td> <td> BD Horizon </td> <td> 53-7-3 </td> <td> 612809 </td> </tr> <tr> <td> </td> <td> R6 </td> <td> Viability dye </td> <td> Zombie NIR </td> <td> 1:1,000 </td> <td> BioLegend </td> <td> n/a </td> <td> 423106 </td> </tr> <tr> <td> </td> <td> R7 </td> <td> NK1.1 </td> <td> APC/Fire 750 </td> <td> 1:300 </td> <td> BioLegend </td> <td> PK136 </td> <td> 108752 </td> </tr> <tr> <td> </td> <td> R8 </td> <td> B220 </td> <td> APC/Fire 810 </td> <td> 1:300 </td> <td> BioLegend </td> <td> RA3-6B2 </td> <td> 103278 </td> </tr> </tbody> </table>

opencc-by-4.0May 2022View details →

Darcy Flow Datasets for Paper "Variable-Input DeepONets for Operator Learning"

This repository contains all datasets required to reproduce the Darcy Flow tests presented in the paper "Variable-Input DeepONets for Operator Learning".

opencc-by-4.0Apr 2022View details →

Dataset to "Local thermodynamic description of isothermal single-phase flow in simple porous media"

This is the dataset presented in the article "Local thermodynamic description of isothermal single-phase flow in porous media" [1]. This dataset contains three files: "equilibrium.out", "equilibrium_bulk.out", and "flow.out". The first file contains data for equilibrium simulations of the porous medium, while the second file contains data for equilibrium simulations of bulk fluid, and the third file contains data for non-equilibrium simulations of the porous medium. [1] https://doi.org/10.1007/s11242-022-01844-x In the file "equilibrium.out" the columns from left to right are <ol> <li>Temperature</li> <li>Standard deviation of the temperature</li> <li>Lattice constant</li> <li>Fluid chemical potential</li> <li>Potential energy</li> <li>Standard deviation of the potential energy</li> <li>Kinetic energy</li> <li>Standard deviation of the kinetic energy</li> <li>Fluid mass density</li> <li>Standard deviation of the fluid mass density</li> </ol> In the file "equilibrium_bulk.out" the columns from left to right are <ol> <li>Temperature</li> <li>Standard deviation of the temperature</li> <li>Fluid chemical potential</li> <li>Potential energy</li> <li>Standard deviation of the potential energy</li> <li>Kinetic energy</li> <li>Standard deviation of the kinetic energy</li> <li>Fluid mass density</li> <li>Standard deviation of the fluid mass density</li> <li>Trace of the mechanical pressure tensor divided by 3</li> <li>Standard deviation of the trace of the mechanical pressure tensor divided by 3</li> </ol> In the file "flow.out" the columns from left to right are <ol> <li>Temperature</li> <li>Standard deviation of the temperature</li> <li>Lattice constant</li> <li>Fluid chemical potential</li> <li>Passing probability of reflecting particle method (RPM)</li> <li>Mass flux</li> <li>Standard deviation of the mass flux</li> <li>Integral pressure gradient</li> <li>Standard deviation of the integral pressure</li> </ol>

opencc-by-4.0Aug 2022View details →

PyFLOWGO Data for Arsia Mons Lava Flows

<ul> <li>PyFLOWGO results for five lava flows SW of Arsia Mons, Mars</li> <li>Flow measurements for five lava flows SW of Arsia Mons, Mars</li> </ul>

opengpl-2.0Aug 2022View details →

Data and code for "A decade of monitoring micropollutants in urban wet-weather flows: what did we learn?"

Data and code for publication Lena Mutzner, Viviane Furrer, Hélène Castebrunet, Ulrich Dittmer, Stephan Fuchs, Wolfgang Gernjak, Marie-Christine Gromaire, Andreas Matzinger, Peter Steen Mikkelsen, William R. Selbig, Luca Vezzaro, A decade of monitoring micropollutants in urban wet-weather flows: what did we learn? Water Research, 2022. https://doi.org/10.1016/j.watres.2022.118968 Data Micropollutants concentrations in µg/l (incl. heavy metals) for 77 wet-weather discharge sites (36 combined sewer overflows, 41 stormwater outlets). The provided data set is a collection of raw data sets based on publications referenced below. The data description is provided in the folder Data\AA_DataDescription.txt Abstract Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the “true value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.

Datasets of surface ice flow speed for Pine Island Glacier, Ferrigno Ice Stream and Leonardo Glacier

Here we make available the datasets used in my MRes dissertation as part of the School of Earth and Environment at the University of Leeds. The research paper presents a novel method to automatically detect speed anomalies in ice velocity datasets.

Observing flow of He II with unsupervised machine learning

Data repository for observing flow in He II with unsupervised machine learning.

opencc-zeroAug 2022View details →

Gait behavioral and neuromuscular characterization in response to increasing working memory load while walking under optic flow perturbations in young adults

The precise role of cognitive control on optic flow processing during locomotion has rarely been investigated. Therefore, this study aimed to determine whether coping with unreliable visual inputs during walking requires attentional resources. Twenty-four healthy young adults walked on an instrumented treadmill in a virtual environment under two optic flow conditions: normal (NOF) and perturbed (POF, continuous mediolateral pseudo-random oscillations). Each condition was performed under single-task and dual-task conditions of increasing difficulty (1-, 2-, 3-back). In all conditions, subjective mental workload was estimated (raw NASA-TLX). For kinematic variables, mean, standard deviation, statistical persistence and step-to-step error correction were computed from gait time series in mediolateral and anteroposterior directions. For EMG variables of soleus and gluteus medius, the full width at half maximum and the variance ratio were calculated. Performance on N-back tasks was assessed using mean reaction time and d-prime. Cognitive performance was not affected by simultaneous walking in any optic flow condition. Gait variability was altered under POF compared to NOF, so that young adults sought to counteract those perturbations by adopting an effortful gait control strategy, independently of concurrent working memory (WM) load. Increasing WM load led changes first at the neuromuscular level, then at the behavioral level, with a prioritization of gait control in the mediolateral direction. Interestingly, dual-tasking lowered the effects of POF but in the anteroposterior direction only. These findings and their theoretical implications provide valuable insight into the complex interaction effects of cognitive and visual constraints on gait control during treadmill walking.

Supplementary Videos for ``Influence of Fine Particle Content in Debris Flows on Alluvial Fan Morphology"

This dataset includes 8 videos for the 6 debris flow fan experiments: Video V1. Video record (side view) of the experiment process of a continuous (15 minute) flow with 6\% clay mixture. Video V2. Video record (side view) of the experiment process of three successive (5 minute) flow with 6\% clay mixture. Video V3. Video record (side view) of the experiment process of a continuous (15 minute) flow with 4\% clay mixture. Video V4. Video record (side view) of the experiment process of three successive (5 minute) flow with 4\% clay mixture. Video V5. Video record (side view) of the experiment process of a continuous (15 minute) flow with 8\% clay mixture. Video V6. Video record (side view) of the experiment process of three successive (5 minute) flow with 8\% clay mixture. Video V7. Video record (front view) of the experiment process of three successive (5 minute) flow with 4\% clay mixture. Video V8. Video record (front view) of the experiment process of three successive (5 minute) flow with 8\% clay mixture.

Data samples for Flow-matching -- efficient coarse-graining molecular dynamics without forces

CG samples generated during the training and validation processes in the flow-matching project. Accompanying the preprint "Flow-matching -- efficient coarse-graining molecular dynamics without forces": https://arxiv.org/abs/2203.11167. Detailed descriptions can be found in the preprint as well as the included README.

opencc-zeroOct 2022View details →

Viscoelastic properties of suspended cells measured with shear flow deformation cytometry

Numerous cell functions are accompanied by phenotypic changes in viscoelastic properties, and measuring them can help elucidate higher-level cellular functions in health and disease. We present a high-throughput, simple and low-cost microfluidic method for quantitatively measuring the elastic (storage) and viscous (loss) modulus of individual cells. Cells are suspended in a high-viscosity fluid and are pumped with high pressure through a 5.8 cm long and 200 μm wide microfluidic channel. The fluid shear stress induces large, near ellipsoidal cell deformations. In addition, the flow profile in the channel causes the cells to rotate in a tank-treading manner. From the cell deformation and tank treading frequency, we extract the frequency-dependent viscoelastic cell properties based on a theoretical framework developed by R. Roscoe that describes the deformation of a viscoelastic sphere in a viscous fluid under steady laminar flow. We confirm the accuracy of the method using atomic force microscopy-calibrated polyacrylamide beads and cells. Our measurements demonstrate that suspended cells exhibit power-law, soft glassy rheological behavior that is cell cycle-dependent and mediated by the physical interplay between the actin filament and intermediate filament networks.

opencc-by-4.0Oct 2022View details →

Quasi-Armored Mud Clasts as Indicators of Transport Processes of Subaqueous Sediment Gravity-Flow

Fig. S1  Quasi-armored mud clasts in different case studying. (A) Quasi-armored mud clasts in the H1 division of Hybrid event bed, Eocene Liushagang Formation; (B) Quasi-armored mud clasts in massive sandstone, Early Cretaceous Lingshandao Formation; (C) Quasi-armored mud clasts in massive sandstone, Early Cretaceous, North Falkland Basin (Dodd et al., 2018); (D) Quasi-armored mud clasts in the upper part of massive sandstone, Middle Eocene, Ainsa System (Pickering et al., 2015); (E) and (F) Quasi-armored mud clasts in massive sandstone, Upper Jurassic, North Sea rift system (Jackson et al., 2011); (G) Quasi-armored mud clasts in the H3 division, Late Jurassic, in the northern North Sea (Haughton et al., 2003) ; (H) and (I) Quasi-armored mud clasts in massive sandstone, Late Jurassic, in the northern North Sea (Williams, 2015). Fig. S2 (A) Location of Beibuwan Basin in China. (B) Location of Weixi’nan depression in the Beibuwan Basin. (C) Sedimentary facies distribution of the upper member of the Liushagang Formation in the Weixi’nan depression. (D) Sedimentary sequence and stratigraphic framework of the Weixinan Depression, Beibuwan Basin (Huang et al., 2013). Fig. S3 The lateral distribution of gravity-flow deposits in the Upper of the Liushagang Formation in the research area from proximal to distal. For the location of the section, see Fig. S2 C. Fig. S4 Facies analysis of Well WZ11-7-1 (in relatively distal), dominated by hybrid event beds with common quasi-armored mud clasts in different units. Fig. S5 Facies analysis of Well WZ11-7-4 (in relatively proximal), dominated by high-density turbidities beds with occasional quasi-armored mud clasts. For the legend of facies analysis, see Fig. S4.

opencc-by-4.0Oct 2022View details →

Data and movies for "Flow of the normal component of He II about bluff objects as recorded by 〖He〗_2^* excimers"

The directory is subdivided for the cylinder and flat plate bluff objects.  Each subdirectory is further subdivided into data and movie subdirectories. "Data" consists of raw data collected from the camera.  In the data directory is a listing of the filename with the power applied to the heater.  Filenames of background data are also listed.   "Movie" consists of frames integrated over 0.5 second intervals (28 frames).  The movies include only excimer peaks for which the intensity of the peak is >3 sigma (~45). Where sigma is the standard deviation of the per frame background. A basic Jupiter Notebook is included in the top-level directory.  This notebook (and the python code used by the notebook) will allow a user to upload a data file and a background file, subtract the background and place the net result into a NumPy array with dimensions of 256 x 256 x 1500, which represent the 2 directions in position and time in seconds.

opencc-by-4.0Oct 2022View details →

Dataset for High-resolution mantle flow models reveal importance of plate boundary geometry and slab pull forces on generating tectonic plate motions

This repository contains the plugin and dataset used to setup models in the manuscript: "High-resolution mantle flow models reveal importance of plate boundary geometry and slab pull forces on generating tectonic plate motions". The material model plugin used in our models is in the "plugins" folder. The "models" folder contains the reference input parameter file described in the paper. All other model configurations shown in the paper can be obtained by modifying this parameter file.  The input files used to set up the models are in the respective folder. Additionally, the Jupyter notebook used to compute residuals of our models is provided in the "scripts" folder.

opencc-byMay 2019View details →

Johnson Brothers Flow Blue Ceramic Dish

This dish fragment was found at the Gunson site at MSU in 2015. It's a flow blue earthenware dish, meaning the blue transfer print bled or flowed out of its bounds during the firing process. This style was invented in England in the 1820s but did not become popular in the US until the end of the 19th century. The maker's mark reads "MONTANA" (the specific style), shows a crown, and followed by "JOHNSON BROS | ENGLAND". Johnson Brothers was a ceramic company from Staffordshire that began in the 1890s. Although they had many styles, the became best known for their flow blue ceramics. These were cheaper and tougher while still presenting as elegant. The Montana style on this dish was started in the early 20th century and was likely used by the Gunson household during dinner parties. Gunson was a very popular professor at the institution and routinely dined with students, faculty, and alumni, making him a staple of the Expansion Phase of campus history. Model made by Jack A. Biggs with Agisoft Metashape. Source: Objaverse 1.0 / Sketchfab

opencc-by-4.0Sep 2017View details →

Dataset: Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering

This dataset contains all relevant data for the manuscript (in submission) "Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering". Code written to analyse this dataset (which may be adapted for other flow cytometry datasets) is found at https://zenodo.org/record/999747 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Naming convention for raw flow cytometry data files (located in /Script 3. Generating raw data subset/input/): [Allparameters] _ [Year] - [Month] - [Date] [Hour] [u] [Minute] _ [Depth] e.g: Allparameters_2014-07-31 08u08_1.0m The date, time and depth indicate the location and time at which the measurement was taken.

opencc-zeroApr 2024View details →

Revisiting the paradigm of reaction optimization in flow with a priori computational reaction intelligence

The use of micro/meso-fluidic reactors has resulted in both new scenarios for chemistry and new requirements for chemists. Through flow chemistry, large-scale reactions can be performed in drastically reduced reactor sizes and reaction times. This obvious advantage comes with the concomitant challenge of re-designing long-established batch processes to fit these new conditions. The reliance on experimental trial-and-error to perform this translation frequently makes flow chemistry unaffordable, thwarting initial aspirations to revolutionize chemistry. By combining computational chemistry and machine learning, we have developed a model that provides predictive power tailored specifically to flow reactions. We show its applications to translate batch to flow, to provide mechanistic insight, to contribute reagent descriptors, and to synthesize a library of novel compounds in excellent yields after executing a single set of conditions.