Find research datasets worth reusing

opencc-by-4.0Jul 2020View details →

Dataset: Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean

This dataset is linked to this manuscript entitled "Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean" published in Elementa: Science of the Anthropocene (<a href="http://doi.org/10.1525/elementa.430">http://doi.org/10.1525/elementa.430</a>). Please find the abstract below: The decline of sea-ice thickness, area, and volume due to the transition from multi-year to first-year sea ice improves the under-ice light environment for pelagic Arctic ecosystems. One unexpected and direct consequence of this transition, the proliferation of under-ice phytoplankton blooms (UIBs), challenges the paradigm that waters beneath the ice pack harbor little planktonic life. Little is known about the diversity and spatial distribution of UIBs in the Arctic Ocean, or the environmental drivers behind their timing, magnitude, and species composition. Here, we compiled a unique and comprehensive dataset from seven major research projects in the Arctic Ocean (11 expeditions, covering the spring sea-ice-covered period to summer ice-free conditions) to identify the environmental drivers responsible for initiating and shaping the magnitude and assemblage structure of UIBs. The temporal dynamics behind UIB formation related to the ways that snow and sea-ice conditions impact the under-ice light field. In particular, the onset of snowmelt significantly increased under-ice light availability (> 0.1–0.2 mol photons m–2 d–1), marking the concomitant termination of the sea-ice algal bloom and initiation of UIBs. At the pan-Arctic scale, bloom magnitude (expressed as maximum chlorophyll a concentration) was predicted best by winter water Si(OH)4 and PO43– concentrations, as well as Si(OH)4:NO3– and PO43–:NO3– drawdown ratios, but not NO3– concentration. Two main phytoplankton assemblages dominated UIBs (diatoms or Phaeocystis), driven primarily by the winter nitrate:silicate (NO3–:Si(OH)4) ratio and the under-ice light climate. Phaeocystis co-dominated in low Si(OH)4 (i.e., NO3:Si(OH)4 molar ratios > 1) waters, while diatoms contributed the bulk of UIB biomass when Si(OH)4 was high (i.e., NO3:Si(OH)4 molar ratios < 1). The implications of such differences in UIB composition could have important ramifications for Arctic biogeochemical cycles, and ultimately impact carbon flow to higher trophic levels and the deep ocean.

opencc-by-4.0Oct 2024View details →

Dataset of "Molecular dynamics of evaporative cooling of water clusters"

The cooling of water clusters through evaporation into a vacuum is studied using classical molecular dynamics with the SPC water model, and the results are compared with semimacroscopic theory. A model based on the Hertz–Knudsen equation underestimates the cooling rates. A modified approach, which accounts for the Kelvin equation, provides better results. While the rotational temperature of the clusters is in equilibrium with their internal temperature, the translational temperature of the clusters “as individual particles” remains unchanged.

opencc-by-4.0Oct 2021View details →

Human Kino-Dynamic Measurements Dataset for Factory-like Activities

This dataset was created as a part of the study presented in IEEE Transactions on Human-Machine Systems with the title "An Online Multi-Index Approach to Human Ergonomics Assessment in the Workplace" by Marta Lorenzini, Wansoo Kim and Arash Ajoudani. This paper introduces an online approach to monitor kinematic and dynamic quantities on the workers, providing on the spot an estimate of the physical load required in their daily jobs. A set of ergonomic indexes is defined to account for multiple potential contributors to work-related musculoskeletal disorders (WMSDs), which remain one of the major occupational safety and health problems in the European Union nowadays. Thus, the continuous tracking of workers’ exposure to the factors that may contribute to their development is paramount. To evaluate the proposed framework, a throughout experimental analysis was conducted. Twelve healthy adult subjects were recruited in the experimental study to perform, in the laboratory settings, occupational activities that are commonly carried out by workers in the current industrial scenario. Three tasks were selected to encompass the most significant risk factors in the workplace: mechanical overloading of the body joints, variable and high-intensity interaction forces, and repetitive and monotonous movements. Accordingly, lifting/lowering of a heavy object, drilling, and painting with a lightweight tool were considered, respectively, in this study. While the subjects were carrying out such activities, the data regarding the whole-body motion and the forces exchanged with the environment (both ground reaction force (GRF) and interaction forces at the end-effector) were collected. In addition, ten surface electromyography (sEMG) sensors were placed on the body of each subject to measure muscle activity as a reference to the effective physical effort required for the tasks. The whole experimental procedure was carried out in accordance with the Declaration of Helsinki and the protocol was approved by the ethics committee azienda sanitaria locale (ASL) Genovese N.3 (Protocol IIT_HRII_ERGOLEAN 156/2020).

opencc-by-4.0May 2024View details →

Dataset of "Molecular Dynamics Simulations Unveil the Aggregation Patterns and Salting out of Polyarginines at Zwitterionic POPC Bilayers in Solutions of Various Ionic Strengths"

Molecular dynamics simulations are performed for a series of model cell-penetrating peptides (in particular nona-arginines) in aqueous solutions, in contact with model phosphocholine (POPC) membranes in conditions of different ionic strengths. The unusual aggregation properties of peptides at model lipid bilayers are analyzed and different sizes and lifetimes of aggregates are presented. This dataset contains molecular dynamics simulation data with trajectories, input files, and topology files for all studied systems. They contain low peptide concentration in water, low NaCl concentration, high NaCl concentration, low CaCl2 concentration, and high CaCl2 concentration. In addition to low peptide concentration, high peptide concentration in water, low NaCl concentration, high NaCl concentration, low CaCl2 concentration, and high CaCl2 concentration are also studied.

opencc-by-4.0Jan 2022View details →

Super-resolving ocean dynamics from space with computer vision algorithms: training datasets

We provide here the datasets used for the development of the dilated Adaptive Residual Network for the super-resolution of ocean Absolute Dynamic Topography described in Buongiorno Nardelli et al. (2022). The model is designed to combine satellite altimetry and thermal observations and provides super-resolved dynamic topography. The training/test datasets have been built starting from the data originally prepared for an Observing System Simulation Experiment carried out in the framework of the European Space Agency CIRCOL project [Ciani et al., 2021]. They consist of one year of synthetic daily Absolute Dynamic Topography (ADT), surface geostrophic currents and sea surface temperature data  obtained from Copernicus Marine Service Mediterranean Forecasting System (MFS) (Product ID: MEDSEA-ANALYSIS- FORECAST-PHY-006-013) [Clementi et al. 2021]. Synthetic Altimeter-derived ADT maps were obtained by first sampling the model output along the actual tracks of a synthetic constellation composed of 4 Radar Altimeters: Jason-3, Sentinel-3A, SARAL/Altika, and Cryosat-2 missions  (this step is achieved by running the SWOT simulator software [Gaultier et al., 2016]) and successively applying the DUACS (Data Unification and Altimeter Combination System) mapping method. The original input images cover the entire Mediterranean domain at 1/24° spatial resolution, leading to an individual image size of 380x1000 pixels. Here, we have randomly chosen 40 dates (~11% of the total) to be kept aside as fully independent test data, and successively re-sampled the original images extracting much smaller tiles (76x100), which are used as input to the network training. The tiles are extracted by going through a double loop on latitude and longitude, imposing a spatial overlap of 50%. Full details on data pre-processing (e.g.normalization strategies) are given in the paper: <ul> <li>Buongiorno Nardelli, B.; Cavaliere, D.; Charles, E.; Ciani, D. Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms. Remote Sens., 2022, 14, 1159. https://doi.org/10.3390/rs14051159</li> </ul>

opencc-by-4.0Mar 2024View details →

Dataset for: "Dynamical properties of solid and hydrated collagen: Insight from nuclear magnetic resonance relaxometry"

The dataset contains a full set of 1H magnetization curves (1H magnetization versus time) for solid and hydrated collagen and collagen-based artificial tissues. DOI of article: <a href="https://doi.org/10.1063/5.0191409" target="_blank" rel="noopener">https://doi.org/10.1063/5.0191409</a> This research was funded by the National Science Centre, Poland, Grant No. 2021/43/B/NZ5/01602.

opencc-by-4.0Jul 2024View details →

Dataset For Molecular Dynamics Simulations of Thin Film Rupture

Data files for production runs for the key results reported in "Life and Death of a Thin Liquid Film", (2024) by Muhammad Rizwanur Rahman, Li Shen, James P. Ewen, D. M. Heyes, Daniele Dini, and E. R. Smith. The directory named "spontaneous-rupture-equilibrated-state-for-production-runs" contains data files of different initial film thicknesses, and the directory named "synthetic-rupture-equilibrated-state-for-production-runs" contains data files for films with similar initial thickness, but with different patterns of synthetic damages caused to the film. These files should be used as the restart file for production phase under NVE ensemble.  Codes to run these files, and process the data are described in github: https://github.com/MuhammadRRahman/Thin-Film-Rupture-NEMD.git.

opencc-by-4.0Feb 2023View details →

Dataset for: Water column dynamics control nitrite-dependent anaerobic methane oxidation by Candidatus 'Methylomirabilis' in stratified lake basins

Dataset containing treated 16S rRNA amplicon sequence data, accompanying the manuscript "Water column dynamics control nitrite-dependent anaerobic methane oxidation by Candidatus ‘Methylomirabilis’ in stratified lake basins"  Files:  - Mapping file - ASV table - Refseq file -Tree file - Relative abundances of dominant methanotrophs in the water column of Lake Lugano North Basin (used to create Fig. 6c) - Multi annual dataset of water chemistry data of the Lake Lugano North Basin

opencc-by-4.0Apr 2023View details →

Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

Advection datasets from the paper:     Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679) The datasets are:   - AdvBox   - AdvInBox   - AdvTaylor   - AdvCircle   - AdvCircleAng   - AdvSquare   - AdvEllipseH   - AdvEllipseV   - AdvSpline   - AdvSquareAndCircle   - Adv3Circles Check the "README.txt" file for information on how the simulations are organised. The features of each dataset and how they were generated are explained in the journal publication.   To cite these datasets, use the following reference: Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022). <pre><code>@article{lino2022multi, author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris}, title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}}, journal = {Physics of Fluids}, volume = {34}, year = {2022}, url = {https://doi.org/10.1063/5.0097679}, }</code></pre>

opencc-by-4.0Jun 2023View details →

Dataset for "Light Scalar Meson and Decay Constant in SU(3) Gauge Theory with Eight Dynamical Flavors"

Decoding File Names: Consider the file name f8l24t48b48m00889_S0.csv.  We will break down the meaning of the various pieces of the filename <ul> <li>"f8" means 8 Dirac flavors.</li> <li>"l24t48" means 243×48 lattice.</li> <li>"b48" means beta=4.8, related to the inverse bare gauge coupling.</li> <li>"m00889" means fermion mass m=0.00889.</li> <li>"S" means flavor-singlet scalar meson. Other options are "P" for flavor non-singlet pseudoscalar meson and "C" for flavor non-singlet scalar meson.</li> <li>"0" an integer from 0 to 4 proportional to the squared length of the spatial momentum vector of the correlation function.</li> </ul> Columns of the CSV files: Each line of the CSV file should contain 41 entries, separated by commas. Refer to the Eq. (8) which defines model A in the accompanying paper to understand the physical interpretation of these parameters. <ol> <li>Model number: 1 is model A, 2 is model B, 3 is model C.</li> <li>nmax: the number of non-oscillating states in the fit.</li> <li>jmax: the number of oscillating states in the fit.</li> <li>tmin: the minimum t value used in the fit.</li> <li>tmax: the maximum t value used in the fit.</li> <li>𝜒2 of the fit.</li> <li>\(\log\ p\left(\left.M\right|D\right)\): log of model probability used in Bayesian model averaging.</li> <li>fit value for c0 (model A) or \(\overline{c}_0\) (model B).</li> <li>fit error for c0 (model A) or \(\overline{c}_0\) (model B).</li> <li>fit value for c1.</li> <li>fit error for c1.</li> <li>fit value for c2.</li> <li>fit error for c2.</li> <li>fit value for c3.</li> <li>fit error for c3.</li> <li>fit value for c4.</li> <li>fit error for c4.</li> <li>fit value for \(c_1^\prime\).</li> <li>fit error for \(c_1^\prime\)</li> <li>fit value for \(c_2^\prime\).</li> <li>fit error for \(c_2^\prime\).</li> <li>fit value for \(c_3^\prime\).</li> <li>fit error for \(c_3^\prime\).</li> <li>fit value for \(c_4^\prime\).</li> <li>fit error for \(c_4^\prime\).</li> <li>fit value for \(\log(E_2 - E_1)\).</li> <li>fit error for \(\log(E_2-E_1)\).</li> <li>fit value for \(\log(E_3-E_2)\).</li> <li>fit error for \(\log(E_3-E_2)\).</li> <li>fit value for \(\log(E_4-E_3)\).</li> <li>fit error for \(\log(E_4-E_3)\).</li> <li>fit value for \(\log(E_2^\prime - E_1^\prime)\).</li> <li>fit error for \(\log(E_2^\prime - E_1^\prime)\).</li> <li>fit value for \(\log(E_3^\prime - E_2^\prime)\).</li> <li>fit error for \(\log(E_3^\prime - E_2^\prime)\).</li> <li>fit value for \(\log(E_4^\prime - E_3^\prime)\).</li> <li>fit error for \(\log(E_4^\prime - E_3^\prime)\).</li> <li>fit value for \(\log(E_1)\).</li> <li>fit error for \(\log(E_1)\).</li> <li>fit value for \(\log(E_1^\prime)\).</li> <li>fit error for \(\log(E_1^\prime)\).</li> </ol>

opencc-by-4.0Apr 2020View details →

Dynamic Percussion Dataset

This dataset was recorded as a part of my Bachelor's thesis work for Tampere University. It is a collection of samples from different drums and percussion, recorded in an anechoic room. More details on the structure, labels and recording process can be found in [1]. In the thesis, this dataset was visualized in the 2-dimensional space with a method (described in [1]) that utilizes OpenL3 deep audio embeddings [2] and t-SNE dimensionality reduction algorithm [3]. Since the embeddings can take a significant time to compute without a sufficient CUDA environment installed, they are provided with the dataset (in a later version). Audio format: Data type: wav Sampling rate: 44,1 kHz Bit-depth: 24 Channels: 1 Added in version 1.1.0: OpenL3 pre-calculated embeddings have been added. Several different models were used to calculate the embeddings. Each file corresponds to a single pickle file containing the embeddings. The pickle files contain a metadata dictionary which has information about the original sample and the OpenL3 model used to calculate that particular embedding series. NOTE: The reference [1] will be added when the thesis has been published.

opencc-by-4.0Aug 2020View details →

The datasets used in the manuscript named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"

The hindcast and real-time prediction output of FGOALS-f2 V1.0 used in the study named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"

opencc-by-4.0Sep 2016View details →

1st_dataset-Minimal radius of magnetic skyrmions: statics and dynamics

In a broad range of applied magnetic fields and material parameters isolated magnetic skyrmions condense into skyrmion lattices. While the geometry of isolated skyrmions and their lattice counterparts strongly depend on field and Dzyaloshinski–Moriya interaction, this issue has not been adequately addressed in previous studies. Meanwhile, this information is extremely important for applications, because the skyrmion size and the interskyrmion distance have to be tuned for skyrmion based memory and logic devices. In this investigation we elucidate the size and density-dependent phase diagram showing traditional phases in field versus material parameters space by means of Monte-Carlo simulations on a discrete lattice. The obtained diagram permits us to establish that, in contrast to the continuum limit, skyrmions on a discrete lattice cannot be smaller than some critical size and have a very specific shape. These minimal skyrmions correspond to the micromagnetic configuration at the energy barrier between the ferromagnetic and the skyrmionic states. Furthermore, we use atomistic Landau–Lifshitz–Gilbert simulations to study dynamics of the skyrmion annihilation. It is shown that this procees consists of two stages: the continuous skyrmion contraction and its discontinuous annihilation. The detailed analysis of this dynamical process is given.

opencc-by-4.0Feb 2017View details →

Dataset for: "Incremental Semiparametric Inverse Dynamics Learning"

Dataset used in the experimental section of the paper: <blockquote> R. Camoriano, S. Traversaro, L. Rosasco, G. Metta and F. Nori, "Incremental semiparametric inverse dynamics learning," 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, 2016, pp. 544-550. doi: 10.1109/ICRA.2016.7487177 Abstract: This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. The result is an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7487177&isnumber=7487087 </blockquote> Description The file "iCubDyn_2.0.mat" contains data collected from the right arm of the iCub humanoid robot, considering as input the positions, velocities and accelerations of the 3 shoulder joints and of the elbow joint, and as outputs the 3 force and 3 torque components measured by the six-axis F/T sensor in-built in the upper arm. The dataset is collected at 10Hz at as the end-effector tracks circumferences with 10cm radius on the transverse (XY) and sagittal (XZ) planes (For more information on the iCub reference frames, see [4]) at approximately 0.6 m/s. The total number of points for each dataset is 10000, corresponding to approximately 17 minutes of continuous operation. Trajectories are generated by means of the Cartesian Controller presented in [5]. Input (X) columns 1-4: Joint (3 shoulder joints + 1 elbow joint) positions columns 5-8: Joint (3 shoulder joints + 1 elbow joint) velocities columns 9-12: Joint (3 shoulder joints + 1 elbow joint) accelerations Output (Y) Columns 1-3: Measured forces (N) along the X, Y, Z axes by the force-torque (F/T) sensor placed in the upper arm Columns 4-6: Measured torques (N*m) along the X, Y, Z axes by the force-torque (F/T) sensor placed in the upper arm Preprocessing - Velocities and accelerations are computed by an Adaptive Window Polynomial Fitting Estimator, implemented through a least-squares based algorithm on a adpative window (see [2], [3]). Velocity estimation max window size: 16. Acceleration estimation max window size: 25. - Positions, velocities and accelerations are recorded at 9Hz and oversampled to 20 Hz via cubic spline interpolation. - Forces and torques are directly recorded at 20Hz. This dataset was used in [1] for experimental purposes. See section IV therein for further details. For more information, please contact: Raffaello Camoriano - raffaello.camoriano@iit.it Silvio Traversaro - silvio.traversaro@iit.it References [1] Camoriano, Raffaello; Traversaro, Silvio; Rosasco, Lorenzo; Metta, Giorgio; Nori, Francesco, "Incremental Semiparametric Inverse Dynamics Learning", eprint arXiv:1601.04549, 01/2016 [2] F. Janabi-Sharifi ; Dept. of Mech. Eng., Ryerson Polytech. Univ., Toronto, Ont., Canada ; V. Hayward ; C. -S. J. Chen, "Discrete-time adaptive windowing for velocity estimation", IEEE Transactions on Control Systems Technology, 1003 - 1009, Vol. 8, Issue 6, Nov 2000 [3] https://github.com/robotology/icub-main/blob/master/src/libraries/ctrlLib/include/iCub/ctrl/adaptWinPolyEstimator.h [4] http://wiki.icub.org/wiki/ICubForwardKinematics [5] U. Pattacini; F. Nori; L. Natale; G. Metta; and G. Sandini; “An experimental evaluation of a novel minimum-jerk cartesian controller for humanoid robots,” in Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, Oct 2010, pp. 1668–1674.

opencc-by-4.0Jul 2024View details →

The 30 m annual land cover datasets and its dynamics in China from 1985 to 2024

Using 335,709 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual land cover product of China (CLCD) from 1985 to 2019. We collected the training samples by combining stable samples extracted from China's Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Several temporal metrics were constructed via all available Landsat data and fed to the random forest classifier to obtain classification results. A post-processing method incorporating spatial-temporal filtering and logical reasoning was further proposed to improve the spatial-temporal consistency of CLCD.  "*_albert.tif" are projected files via a proj4 string "+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs". CLCD in 2024 is now available. 1. Given that the USGS no longer maintains the Landsat Collection 1 data, we are now using the <a href="https://www.usgs.gov/landsat-missions/landsat-collection-2">Collection 2</a> SR data to update the CLCD. 2. All files in this version have been exported as Cloud Optimized GeoTIFF for more efficient processing on the cloud. Please check <a href="https://www.cogeo.org/">here</a> for more details. 3. Internal overviews and color tables are built into each file to speed up software loading and rendering.

opencc-by-4.0Dec 2023View details →

Dataset from paper "Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest"

<h3>Dataset from the paper “Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest”</h3>  This repository contains: <ul> <li>Dataset Description: “raster_labels.xlsx” (an Excel spreadsheet detailing raster pixel values and their respective fragmentation classes).</li> <li>Fragmentation Raster Files: “forest_fragmentation_mspa_2020.tif” and “forest_fragmentation_mspa_2020.tif” (GeoTIFF files of landscape forest fragmentation for 1985 and 2020).</li> </ul>  If you need anything else, please contact the corresponding author, Igor Broggio (<a href="mailto:isbbroggio@gmail.com">isbbroggio@gmail.com</a>).   If you use these data, please cite the paper: [Citation]

opencc-by-4.0Mar 2024View details →

Figures datasets for "Wave momentum shaping for moving objects in heterogeneous and dynamic media"

Source data for Figures used in the manuscript "Wave momentum shaping for moving objects in heterogeneous and dynamic media".

opencc-by-4.0Mar 2024View details →

Strong coupling electron-photon dynamics: a real-time investigation of energy redistribution in molecular polaritons - Dataset

Dataset complement to "Strong coupling electron-photon dynamics: a real-time investigation of energy redistribution in molecular polaritons" - includes output and video files obtained using the <a href="https://etprogram.org/">eT program</a>, an open-source electronic (and molecular-polaritonic) structure program. See the paper at <a href="https://doi.org/10.1103/PhysRevResearch.6.033283">https://doi.org/10.1103/PhysRevResearch.6.033283</a>