Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11,506
datasets available to search
ShareScore release 0.9.0
Dataset results
11,506 results for “Dynamics”
Dataset for: "Dynamical properties of solid and hydrated collagen: Insight from nuclear magnetic resonance relaxometry"
<p>The dataset contains a full set of 1H magnetization curves (1H magnetization versus time) for solid and hydrated collagen and collagen-based artificial tissues.</p> <p>DOI of article: <a href="https://doi.org/10.1063/5.0191409" target="_blank" rel="noopener">https://doi.org/10.1063/5.0191409</a></p> <p>This research was funded by the National Science Centre, Poland, Grant No. 2021/43/B/NZ5/01602.</p>
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
PIBE project- Experimental characterization of stall noise in static and dynamic regimes using a NACA 63(3)418 airfoil
<p>Dynamic stall noise is one of the potential sources of amplitude modulations associated with wind turbine noise. This phenomenon is related to the periodic separation and reattachment of the boundary layer on the wind turbine blade suction side during its rotation. Within the framework of the PIBE project (Predicting the Impact of Wind Turbine Noise - <a href="https://www.anr-pibe.com/en">https://www.anr-pibe.com/en</a>), experiments were conducted in the anechoic wind tunnel of the École Centrale de Lyon in order to characterize stall noise on a pitching airfoil in both static and dynamic conditions.</p> <p>In version 1.0.0 of the database, <span>data from the second campaign using an instrumented NACA63(3)418 airfoil in static and dynamic conditions are provided. The static data can be found in the file static_data_NACA63418.h5 that contains:</span></p> <ol> <li>static wall pressure data : lift and pressure coefficients;</li> <li>dynamic wall pressure data : Power Spectral Density (PSD) of fluctuating wall pressure;</li> <li>far-field acoustic data : Power Spectral Density (PSD) of acoustic pressure.</li> </ol> <p><span>The structure of the file is described in Tree_structure_static_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_static_Matlab.zip can be used.</span></p> <p><span>The dynamic data can be found in the file dynamic_data_NACA63418.h5 that contains:</span></p> <ol> <li><span>static wall pressure data : phase-averaged lift coefficients;</span></li> <li><span>dynamic wall pressure data : phase-averaged spectrograms of fluctuating wall pressure;</span></li> <li><span>far-field acoustic data : phase-averaged spectrograms of acoustic pressure.</span></li> </ol> <p><span>The structure of the file is described in Tree_structure_dynamic_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_dynamic_Matlab.zip can be used. Only the results for a mean angle of attack of 15° and an amplitude of 15° are provided in this file.</span></p>
Past and future effects of climate on the metapopulation dynamics of a NorthEast Atlantic seabird across two centuries
<p>Datasets required to run code for contribution:</p> <p>Past and future effects of climate on the metapopulation dynamics of a NorthEast Atlantic seabird across two centuries</p> <p>Jana WE Jeglinski, Holly I Niven, Sarah Wanless, Robert T. Barrett, Mike P. Harris, Jochen Dierschke and Jason Matthiopoulos</p> <p>Extension of a Bayesian metapopulation model fit to colony census data for all Northeast Atlantic colonies of the Northern gannet (<em>Morus bassanus</em>) described in Jeglinski et al. (2023) to investigate mechanistic relationships with climate and forecast metapopulation dynamics under two climate scenarios. </p>
Dataset For Molecular Dynamics Simulations of Thin Film Rupture
<p>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. </p> <p>Codes to run these files, and process the data are described in github: https://github.com/MuhammadRRahman/Thin-Film-Rupture-NEMD.git.</p>
Kinematic and Electromyographic Recordings during Dynamic, Repetitive, Low-Force Movements
<p>Experimental recordings of kinematic (XSens Awinda, Full-Body) and electromyographic (Delsys Trigno, 8 Right Arm Muscles) data during 4 repetive upper limb exercises with a 1.5Kg dumbell: A) elbow counter-gravity flexion and gravity-assisted extension, with torso tilted forwards; B) shoulder counter-gravity abduction and gravity-assisted adduction; C) shoulder counter-gravity flexion and gravity-assisted extensio; D) composite sequence of elbow/shoulder flexion/extension motions, executed until self-reported fatigue.</p> <p>A total of 17 healthy volunteers (11 Male; 6 Female; 23.82 ± 2.79 years old, 69.06 ± 14.75 Kg) were recruited. Each participant willingly agreed to participate in the study and gave their signed, informed consent, following the standard set by the declaration of Helsinki and the Oviedo Conventions.</p> <p>Additionally, self-reported fatigue after each exercise, according to Borg's Perceived Exertion Scale (Borg, 1998), is provided for all subjects.</p> <p>For a detailed description of the experimental protocol and instrumentation, refer to associated research paper (submission under review).</p>
Topic Labels of "Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique"
<p>These are the labels generated with the method proposed in the article <em>"Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique".</em> These labels are for the 100 and 200 DTM topic models, trained both with the whole corpus and with only the COVID-19 period data </p> <p> </p> <p>For the generation of these labels you can go to the original published work or to the linked Zenodo resource.</p>
UC2017 Static and Dynamic Hand Gestures
<p>We introduce the UC2017 static and dynamic gesture dataset. Most researchers use vision-based systems such as the Microsoft Kinect to acquire and classify hand gesture data. Despite that, we believe that we can achieve more reliable results and allow the use of more complex gestures with wearable systems. There are not many datasets with wearable systems due to the plethora of data gloves in the market and their relative high cost. For these reasons, we opted by creating a new dataset to present and evaluate our gesture recognition framework. The objectives of the dataset are: (1) provide a superset of hand gestures for HRI, (2) have user variability, (3) to be representative of the actual gestures performed in a real-world interaction.</p> <p>We divide the dataset in two types of gestures: SG and DG. SG are described by a single timestep of data, therefore representing a single hand pose and orientation. DGs are variable-length timeseries of poses and orientations with particular meanings. Some of the gestures of the dataset are correlated with a certain meaning in the context of HRI, while others are arbitrary, to enrich the dataset and add complexity to the classification problem.</p> <p>The library is composed of 24 SG classes and 10 DG. The dataset includes SG data from eight subjects with a total of 100 repetitions for each of the 24 classes (2400 samples in total). The DG samples were obtained from six subjects and has cumulatively 131 repetitions of each class (1310 samples in total). All of the subjects are right-handed and performed the gestures with their left hand.</p> <p>We used a data glove (CyberGlove II) and a magnetic tracker (Polhemus Liberty) to capture the hand shape, position and orientation over time. The glove provides digital signals that are proportional to the bending angle of each one of the 22 sensors which are elastically attached to a subset of the hand's joints. In this way we have an approximation of the hand's shape. The tracker's sensor is rigidly attached to the glove on the wrist and measures its position and orientation in respect to a ground-fixed frame. The orientation is the rotation between the fixed frame and the frame of the sensor, given a quaternion (WXYZ). We fuse the sensor data together online since the sensors have slightly different acquisition rates -- 100Hz for the glove and 120Hz for the tracker. The tracker data are under-sampled by gathering only the closest tracker frame in time.</p> <p>The files are in the h5df format. The dimensions are (sample, time, variables).</p>
DPMFA_EU_ENM_2000-2020: Dynamic Probabilistic Material Flows of Engineered Nanomaterials from 2000 to 2020 - Raw results
<p>This dataset is related to the following publication:</p> <p>Title: Dynamic probabilistic material flow analysis of engineered nanomaterials in European waste treatment systems</p> <p>Authors: Sana Rajkovic, Nikolaus A. Bornhöf<span>t</span>, Renata van der Weijden, Bernd Nowack, Véronique Adam</p> <p>Submitted to the journal Waste Management in September 2019.</p> <p>The files contain key values of probability distributions associated with the emissions of selected engineered nanomaterials to the environment.</p>
Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip: Experimental and simulation data sets
<p>This data set collates the experimental and simulation data for the research paper "Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip".</p>
Structure and dynamics of growing networks of Reddit threads
<p>Data used in the paper "<a href="https://doi.org/10.1007/s41109-024-00654-y" target="_blank" rel="noopener">Structure and dynamics of growing networks of Reddit threads</a>".</p> <p>This dataset is made of 6366 threads collected from the r/AmITheAsshole community on Reddit. The dataset contains a total of 6,372,251 comments. The collected threads constitute the “top” submissions — those having the highest score, measured as the difference between upvotes and downvotes of a post. We downloaded them using PRAW, running 10 different queries across various temporal scopes, and then cleaning the obtained dataset by removing duplicated threads. Please refer to the paper, specifically to <a href="https://appliednetsci.springeropen.com/articles/10.1007/s41109-024-00654-y/tables/3" target="_blank" rel="noopener">Table 3</a>, for more details about the dataset.</p> <p><strong>If you use this data, please cite the following source:</strong> Goglia, D., Vega, D. Structure and dynamics of growing networks of Reddit threads. Appl Netw Sci 9, 48 (2024). https://doi.org/10.1007/s41109-024-00654-y</p>
Growth of hexagonal boron nitride from molten nickel solutions: a reactive molecular dynamics study
<p>Authors: Amin Ahmadisharaf and Jeffrey Comer</p> <p><br>Publication: Amin Ahmadisharaf, Bin Liu, James H. Edgar, and Jeffrey Comer (2025) Growth of Hexagonal Boron Nitride from Molten Nickel Solutions: A Reactive Molecular Dynamics Study. ACS Applied Materials & Interfaces. <a href="https://doi.org/10.1021/acsami.4c16991">doi.org/10.1021/acsami.4c16991</a></p> <p>Funding: Department of Energy Office of Science, grant DE-SC0021264, <a>https://pamspublic.science.energy.gov/WebPAMSExternal/Interface/Common/ViewPublicAbstract.aspx?rv=5e6ffff5-0daa-47e8-a3d0-20f594b7bfb8&rtc=24&PRoleId=10</a></p> <p>**************************************</p> <p>This data set for the manuscript entitled "Growth of Hexagonal Boron Nitride from Molten Nickel Solutions: A Reactive Molecular Dynamics Study" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software LAMMPS, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text. They include force field parameter files (in ReaxFF format), LAMMPS configuration files (*.in), ReaxFF control files (*.control), LAMMPS log files (*.log), and LAMMPS output including restart files (in binary LAMMPS format) and trajectories in dcd format (downsampled to 12.5 or 25 ps per frame) and also PDB and PSF files are useful for visualization with VMD. Analysis is performed by python and shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.</p> <p>The species analysis is performed by the VMD Tcl script "Figure3/analysis/count_hBN_species_NNB_BN.tcl" using the parameters given in "Figure3/analysis/doCount.sh".</p> <p>The directory contents are as follows.</p> <p>--------------------------------------------------------------<br>Figure-1: Parallel tempering simulations of the boron-nickel system and calculation of the boron concentration along the z-dimension of the nickel slab.</p> <p>The analysis of the boron concentration profile is performed by the VMD Tcl scripts calcRatioZRef.tcl and calcConcZRef.tcl using the parameters given in "Figure1/analysis/step3_conc_profile.sh". Also, the reorganization of the parallel tempering trajectories into frames at a single temperature is performed by the VMD Tcl script extractReplicaFrames.tcl based on "Figure1/analysis/step1_sort_frames.sh".</p> <p><br>--------------------------------------------------------------<br>Figure-2: Simulation of hBN sheet growth at 1750 K and calculation of largest cluster.</p> <p><br>--------------------------------------------------------------<br>Figure-3: Simulations of different boron-to-nickel ratios at varying nitrogen pressures at 1750 K and the calculation of the largest hBN cluster formed under different scenarios. The suffixes "liu", "long_liu", and "low_liu" correspond to pressures of 100.0, 50.0, and 25.0 atm respectively.</p> <p>The analysis of hBN clusters is performed by the VMD Tcl script "Figure3/analysis/" using the parameters given in "Figure3/analysis/doCount.sh". The related simulations files and outputs for panel A in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-4: Recognition and counting the different boron-nitrogen motifs in the simulation was performed in Figure 2.</p> <p>The related simulations files and outputs for panel B and C in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-5: Simulation of the temperature effect on hBN growth, and recognition and counting of the different boron-nitrogen motifs at 1750, 1800, 1900, 2000, 2200, and 2700 K.. </p> <p><br>--------------------------------------------------------------<br>Figure-6: Recognition of existing motifs for nitrogen atoms in the growth path of hBN and calculation of the probabilities of transitions between different motifs across all nitrogen atoms.</p> <p>The related simulations files and outputs for all panels in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-7: Comparing the ReaxFF and ab initio simulations of small B-N motifs(B--N--B and B--N) in a nickel slab and calculation of bond lengths and angle values.</p> <p><br>--------------------------------------------------------------<br>Figure-8: Diffusion simulations of four different systems at 1800 K: nickel with a single B atom, nickel with a single N atom, nickel with a free B-N-B molecule, and nickel with a small hBN sheet and Mean Squared Displacement (MSD) values were calculated and compared to assess the surface mobility of the different particles.</p> <p> </p>
Impact Areas and Dynamical Features associated with Mediterranean Cyclones (1980-2019)
<p>The dataset includes NetCDF files of Impact Areas and Dynamical Features associated with Mediterranean Cyclones (henceforth MedCyclones).</p> <p>MedCyclone tracks correspond to confidence-level 5 tracks from Flaounas et al. (2023), <a href="https://doi.org/10.5194/wcd-4-639-2023">https://doi.org/10.5194/wcd-4-639-2023</a>.</p> <p>Temporal frequency: 6h (00, 06, 12, 18 UTC)<br>Years: 1980 – 2019<br>Spatial resolution: 0.5 deg<br>Grid extension: 0-70N, 40W-65E</p> <p> </p> <h2>Dynamical Features</h2> <p>Files "dynfeats_bool_rmax2000_YYYY.nc" include the following list of variables, describing connected boolean objects:</p> <ul> <li><strong>r_500</strong>, <strong>r_1000</strong>: central areas of fixed 500 or 1000 km radius around MedCyclone centres;</li> <li><strong>WCB</strong>: warm conveyor belts related to MedCyclones (i.e., overlapping with r_500 in at least one grid point). Each <strong>WCB</strong> is eventually separated into inflow (<strong>WCBin</strong>, up to 800 hPa) and ascent (<strong>WCBout</strong>, between 800 and 400 hPa) regions. Ref. at <a href="https://doi.org/10.1175/JCLI-D-12-00720.1">https://doi.org/10.1175/JCLI-D-12-00720.1</a>, <a href="https://doi.org/10.5194/wcd-5-537-2024">https://doi.org/10.5194/wcd-5-537-2024</a>;</li> <li><strong>fronts</strong>: cold fronts related to MedCyclones (i.e., overlapping with r_500 in at least one grid point). Ref. at <a href="https://doi.org/10.5194/gmd-17-6137-2024">https://doi.org/10.5194/gmd-17-6137-2024</a>;</li> <li><strong>DI</strong>: dry instrusions related to MedCyclones (i.e., overlapping with r_1000 in at least one grid point). Ref. at <a href="https://doi.org/10.1175/JCLI-D-16-0782.1">https://doi.org/10.1175/JCLI-D-16-0782.1</a>;</li> <li><strong>r_1000_Nodynfeat</strong>: the central 1000 km area excluding regions of MedCyclone WCB, fronts and DI objects.</li> </ul> <p>The criteria for the identification of WCB, fronts and DI objects are described in Section 2.3 of Portal et al. (2024), <a href="https://doi.org/10.5194/wcd-5-1043-2024">https://doi.org/10.5194/wcd-5-1043-2024</a>.</p> <p>Additionally, we note that :<br>i. a weaker overlap constraint was used to associate DI objects to MedCyclones (r_1000 compared to r_500 for WCB and fronts objects) because of the relatively large distance of the DI airstream from the cyclone centre;<br>ii. in this dataset, all connected objects related to MedCyclones are cropped within a 2000 km area circle from the cyclone centre for two reasons. Firstly, the dynamical-feature related surface impacts usually weaken with the distance from the cyclone centre. Secondly, to cut connected objects composed by multiple overlapping features of the same kind - this often happens for fronts in summer because of their high detection density. Far from the cyclone centre, these objects are usually unrelated with the MedCyclone circulation.</p> <p> </p> <h2>Impact Areas</h2> <p>Files "IAs_bool_rmax2000_YYYY.nc" include boolean impact areas, combining a central area (r_1000 or r_500) and cyclone-related WCB, CF and DI objects. The three types of impact area are described in the following :</p> <ol> <li><strong>IA01</strong> is composed by a 1000 km radius circle around the cyclone centre (r_1000) extended by cyclone-related WCB, fronts and DI;</li> <li><strong>IA02</strong> is composed by a 500 km radius circle around the cyclone centre (r_500) extended by cyclone-related WCB, fronts and DI</li> <li><strong>IA03</strong> is composed by a 500 km radius circle around the cyclone centre (r_500) extended by cyclone-related WCB and fronts (DI is neglected).</li> </ol> <p>As discussed in Section 3.1 and Appendix A of Portal et al. (2024) (<a href="https://doi.org/10.5194/wcd-5-1043-2024">https://doi.org/10.5194/wcd-5-1043-2024</a>), IA01, composed by a central area of 1000 km, is adequate for intercepting long-range wind impacts associated with MedCyclones. IA02 and IA03, on the contrary, are better devised for detecting impacts expected at shorter distances from the cyclone centre, such as rainfall, thunderstorm and storm surges. In particular, IA03 neglects the DI region, which is normally of little interest for cyclone-related moist processes, involved in producing precipitation. Noetheless, DI remains relevant for the identification of strong cyclone-related winds.</p> <p> </p> <h3>Case Studies</h3> <p>A pdf file providing the visualisation and description of impact areas and dynamical features of all MedCyclones occurring in 1980 is available at the link <a href="https://boris.unibe.ch/192315/">https://boris.unibe.ch/192315/</a>. Note that in the examples the dynamical features are not cropped at 2000 km from the cyclone centres, as for the present dataset. </p> <p>Note that many of the "Annotations and Limitations" listed below derive from the attentive analysis of these study cases.</p> <h3>Annotations and limitations</h3> <ul> <li>In the case of more than one MedCyclone centre per timestep, the dasaset does not distinguish the impact areas / dynamical features associated with each centre.</li> <li>Because of the automated criteria for associating WCB, fronts and DI objects to MedCyclones, at times objects close to the centre but unrelated to the MedCyclone's circulation, are considered to be cyclone-related and included in the impact area.</li> <li>Elaborating on the point above, at times fronts responsible for Mediterranean cyclogenesis (and not produced by the cyclonic circulation itself) are included in the MedCyclone impact area.</li> <li>When computing statistics over a long time interval (e.g., climatology), the effects of erroneous associations of dynamical features to MedCyclone impact areas are attenuated by the aggregation of large quantity of data. </li> <li>Over a long time interval (e.g., climatology) the choice of a 1000 km fixed-radius impact area provides similar statistics to IA01, although in the first case it is not possible to isolate the role played by the different features composing the MedCyclones.</li> </ul>
Data: Dynamics of star clusters with tangentially anisotropic velocity distribution (Pavlik+ 2024)
<p>This dataset represents the results of our <em>N</em>-body simulations of star clusters (SCs). The initial conditions of the models are fully described in the referenced journal article. In short, the SCs start from isotropic, radially anisotropic or tangentially anisotropic initial velocity distributions, and each model is evolved in an external Galactic tidal field, for two different choices of the filling factor.</p>
POLCAND_ELEC (Allocation of Seats on Electoral Lists: A Dynamic Analysis of Candidate Positioning in Parliamentary Elections from 1991 to 2023)
<p>The aim of the study was to supplement and expand the data contained in the EAST PaC database (Joshua Kjerulf Dubrow: East European Parliamentarian and Candidate Data (EAST PaC), 1985 - 2015 [dane]. Institute of Philosophy and Sociology, Polish Academy of Sciences [producent], Warsaw, 2016. PADS21320. Polish Social Data Archive [dystrybutor], Repozytorium Danych Społecznych [wydawca], 2021. <a href="https://doi.org/10.18150/LSBNLO" target="_blank" rel="noopener">https://doi.org/10.18150/LSBNLO</a>, V1).</p> <p>Currently, an open-access database called EAST PaC (with a data structure similar to panel data, which allows for identifying candidates each time they participate in subsequent elections) contains information on all candidates who have ever run in elections to the Sejm in Poland, from the last elections in the People's Republic of Poland in 1985 to the elections in 2015.</p> <p>This study allowed for the expansion of previous analyses regarding the ways of forming political representation and the associated quality of political elites. This is essential for conducting a dynamic analysis of candidate positioning on electoral lists, which enables tracing the electoral activity of all candidates in elections from 1991 to 2023. It allows for illustrating the occurrence of events over time by tracking candidates' electoral activities. Many previous empirical studies on the course and consequences of parliamentary elections have significant gaps, as they are limited to analyzing the situation of parliamentarians (often focusing on only selected categories). They lack the history of candidacies in elections, even though voters' final decisions are based on the assessment of previous results of candidates and the political entities they represent.These analyses are significant for explaining the dynamics of the political system and assessing the quality of democracy. They also enable empirical verification of hypotheses concerning the periodization of the institutionalization of the electoral system in Poland from 1991 to 2023.</p>
17O-EPR determination of the structure and dynamics of copper single-metal sites in zeolites
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>spc</strong>, <strong>par</strong>, <strong>m</strong>, <strong>f34</strong>,<strong> xyz</strong>, <strong>out</strong>, <strong>in</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA</strong>,<strong> spc </strong>and<strong> par.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>Periodic DFT computations with(out) filename extensions <strong>out</strong> and <strong>f34</strong> in ASCII format.</li> <li>Molecular cluster DFT computations with filename extensions <strong>in</strong> and <strong>out</strong> in ASCII format.</li> <li>Geometry information of cluster models is stored in <strong>xyz</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>Q-band and X-band Pulsed-EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with SHQ cavity and ER035 M NMR gaussmeter produced by Bruker.</li> <li>Periodic DFT computations were generated using distributed parallel version of CRYSTAL17 code.</li> <li>Molecular cluster DFT computations were generated using the ORCA (v4.2.1) code.</li> <li><strong>If t</strong> <ul> <li>Files in <strong>PARACAT_WP3_20210625_01_CW</strong> folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and spc/par formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_02_HYSCORE</strong> folder includes HYSCORE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_03_ESE</strong> folder includes ESE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_04_ENDOR</strong> folder includes ENDOR spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_05_MATLAB</strong> folder includes computer simulations/analyses of the EPR measurements; data are in m formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_06_DFT </strong>folder includes periodic and cluster DFT computation inputs, outputs and geometries in ASCII format.</li> </ul> </li> </ul> <ul> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>ESE</strong> – Electron Spin Echo detected EPR, <strong>HYSCORE</strong> – HYperfine Sublevel CORrelation spectroscopy, <strong>ENDOR</strong> – Electron Nuclear DOuble Resonance spectroscopy, <strong>DFT </strong>– Density Functional Theory, <strong>CHA </strong>– Chabazite, zeolite topology.</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> <li>abbreviations: <strong>6MR, 8MR </strong>are the Cu docking sites; <strong>2Al-3NN</strong>, <strong>2Al-2NN</strong>, <strong>1Al</strong> are the different aluminium distributions analysed; <strong>1w</strong>, <strong>2w</strong>, <strong>3w, 4w</strong> indicates the number of water ligands considered in the models; <strong>eq</strong> and <strong>ax</strong> indicates equatorial and axial ligands. Periodic DFT computations with filename extension <strong>.f34</strong> include structural/symmetry information of optimized structure. Molecular cluster DFT computations with filename extension <strong>.in</strong>/<strong>.out</strong>/<strong>.xyz</strong> are inputs, outputs, and structure of cluster models.</li> </ul> </li> </ul>
Structure and dynamics of water confined in cylindrical nanopores with varying hydrophobicity
<p>Supporting data for Phil. Trans. R. Soc. A 379: 20200403 (2021)</p> <p><a href="https://doi.org/10.1098/rsta.2020.0403">http://doi.org/10.1098/rsta.2020.0403</a></p>
Dynamic reconfiguration of macaque brain networks during natural vision
<p>Raw data acquired under awake imaging conditions in the macaque monkey during free-viewing on natural scenes. The movie presented is also shared which is based on 30 sec 0N and OFF periods. Time-series echo planar imaging (EPI) data for each subject (AL, DP, FL and VL). Data is named based on the subject.session.run. The format structure is on NIFTI. Anatomical files are also named according to the same nomenclature. EPI mask are also available for each in-session subject. </p>
Data for the manuscript "Enhanced microscopic dynamics in mucus gels under a mechanical load in the linear viscoelastic regime" (PNAS).
<p>Data files for the figures published in</p> <p>D. Larobina, A. Pommella, A.-M. Philippe, M. Y. Nagazi, and L. Cipelletti, <em>Enhanced Microscopic Dynamics in Mucus Gels under a Mechanical Load in the Linear Viscoelastic Regime</em>, Proc Natl Acad Sci USA <strong>118</strong>, e2103995118 (2021).</p> <p>DOI: 10.1073/pnas.2103995118</p> <p>Each data set is available as a plain text file (description in the file __README__DataDescription.txt), and as an Excel file.<br> The Excel files typically contain the data sets of several panels of a given figure, as separated sheets. See the description<br> provided in the "GeneralInfo" sheet of each Excel file.</p>
Surface water and flooding dynamics based on seasonally continuous Landsat data (1986-2011) in a dryland river basin (monthly, seasonally, and yearly animations)
<p>The animations provided here are part of the following publication:<br> Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466</p> <p>Please refer to the above mentioned publication for a description of the data and interpretation of the patterns.</p> <p>The animations are based on statistically validated surface water and flooding extent dynamics data derived from seasonally continous Landsat TM/ETM+ and random forest models from 1986 to 2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157 and available here: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.