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5,371 results for “Flows”
Abundance, biovolume, and biomass of Synechococcus, eukaryote pico- and nano- phytoplankton, and heterotrophic bacteria from flow cytometry for water column bottle samples on NES-LTER Transect cruises, ongoing since 2018
These data represent the abundance, biovolume, and biomass of prokaryotic phytoplankton, eukaryotic pico- and nano- phytoplankton, and heterotrophic bacteria from discrete flow cytometry samples collected during the Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018. Samples were collected and preserved from the water column at multiple depths using Niskin bottles on a CTD rosette system along the NES-LTER transect, and analyzed post cruise. Cells were identified and enumerated from the flow cytometry data files based on their scattering, SYBR (525 nm), phycoerythrin (575 nm) and chlorophyll (680 nm) fluorescence signals. Gating was completed manually in the Attune NXT software interface.
stationary_granular_flow_seismicity_and_optics
<p>Raw data acquired during the study of seismic sources emitted by a laboratory landslide: a stationary granular flow in an inclined flume. The data consists in images acquired by a fast camera and accelerometers. The scripts to treat the data are also shared.</p>
Liquid Flow and Control Without Solid Walls
<p>This repository contains additional data related to the publication: 10.26434/chemrxiv.7207001</p> <p>Contained in python_magneto_fluidics.zip are all the files needed to calculate magnetic fields of any assembly of cuboid permanent magnets such as used in this paper, along with the equilibrium diameters for each antitube-ferrofluid combination.</p> <p>data figures.zip contains all the experimental data plotted in the figures, consisting of data in figures:</p> <p>Main Text: 2, 3, 4<br> Extended Data: E2, E3, E4, E6, E8</p>
Flow Magnetic Tweezers example video
<p>The example video contains a section of a field-of-view from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils.</p>
One-minute average horizontal wind velocity data (not corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains the one-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. This data set has not been corrected for air-flow distortion, which was caused by the ship's super structure. The flow-distortion corrected data should be used for studies interested in the actual true wind speed near the ship's location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Measuring individual and group flow in collaborative improvisational dance.
<p>Flow is a state of being fully absorbed and experiencing feelings of energised focus, deep involvement, and success in the process of doing things. Flow plays a vital role in innovation and creativity, as all such processes require high intrinsic motivation to break through to a new level of complexity of thoughts and ideas, while the social environment rarely provides sufficient extrinsic rewards to motivate people to extensive creative work. Meanwhile, the vast majority of creative activities have a primarily social character: e.g. theatre making, music, and dancing. Thus, group flow became central in group creativity research.</p> <p>Group flow shares many aspects with individual flow, but inevitably has differences, due to its collaborative nature. In this study, we compare individual and group flow in dance improvisation, to explore the cognitive processes and strategies underlying group improvisation and their relation to flow experience; in particular, those that might support the aspects of group flow that are dependent upon understanding the other group members’ states and intentions.</p> <p>To assess flow experience, we used a video-stimulated recall method, <em>Flow </em>(Łucznik, Loesche, 2017), which allowed participants to mark on the video-recording of the activity those moments when they remembered experiencing flow. We identified group flow as the moments when then the majority of a group declared themselves as being in flow.</p> <p>This dataset consists of the data and analysis used in the 'Measuring individual and group flow in collaborative improvisational dance.' article (in press).</p>
Supplementary data to accompany Information flow, cell types and stereotypy in a full olfactory connectome
<p>Supplemental file 1</p> <p>Layers assigned by the probabilistic graph traversal model. bodyId refers to neurons’ unique ID in ne- uPrint. layer mean contains the mean layer after 10,000 iterations of the main model (Figure 2). layer - olf mean and layer th mean contain the mean layers from running the traversal model with ORNs and THN/HRNs, respectively (Figure S2).</p> <p>S1 hemibrain neuron layers.csv</p> <p>Supplemental file 2</p> <p>Sensory meta-information related to each glomerulus. Columns: glomerulus (canonical name for one of the 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli), laterality (whether the glomerulus receives bilateral or only unilateral innervation from ALRNs), expected cit (a citation that describes the expected number of RNs in this glomerulus), expected RN female 1h (number of expected RNs in one hemi- sphere), expected RN female SD (standard deviation in the expected number of RNs), missing (qualitative assessment of glomeruli truncation), RN frag (if the RNs in that glomerulus are fragmented), receptor (the OR or IR expressed by cognate ALRNs (Bates et al., 2020; Task et al., 2020)), odour scenes (the general ‘odour scene(s)’ which this glomerulus may help signal (Mansourian and Stensmyr, 2015; Bates et al., 2020)), key ligand(the ligand that excites the cognate ALLRN or receptor the most, based on pooled data from multiple studies (Mu ̈nch and Galizia, 2016)), valence (the presumed valence of this odour chan- nel (Badel et al., 2016)). Exists as hemibrain glomeruli summary in our R package hemibrainr.</p> <p>S2 hemibrain olfactory information.csv</p> <p>Supplemental file 3</p> <p>File listing all identified antennal lobe receptor neurons (ALRNs) in the hemibrain, including information shown in neuPrint. See above for column explanations. Exists as rn.info in our R package hemibrainr.</p> <p>S3 hemibrain ALRN meta.csv</p> <p>Supplemental file 4</p> <p>All the hemibrain neurons we have classed as antennal lobe local neurons (ALLNs). See above for column explanations. Exists as alln.info in our R package hemibrainr.</p> <p>S4 hemibrain ALLN meta.csv</p> <p>Supplemental file 5</p> <p>All the hemibrain neurons we have classed as antennal lobe projection neurons (ALPNs). See above for column explanations. In addition, across dataset cluster refers to the clustering with left and right FAFB PNs; is canonical indicates whether that ALPN is one of the well studied “canonical” uPNs. Exists as pn.info in our R package hemibrainr.</p> <p>40</p> <p>S5 hemibrain ALPN meta.csv</p> <p>Supplemental file 6</p> <p>All the hemibrain neurons we have classed as third-order olfactory neurons (TOONs) including lateral horn neurons (LHNs), as well as wedge projection neurons (WEDPNs), lateral horn centrifugal neurons (LHCENT) and other projection neuron classes (Figure 1). See above for column explanations. Exists as ton.info in our R package hemibrainr.</p> <p>S6 hemibrain TOON meta.csv</p> <p>Supplemental file 7</p> <p>All the hemibrain neurons we have classed as neurons that descend to the ventral nervous system (DNs). See above for column explanations. Exists as dn.info in our R package hemibrainr.</p> <p>S8 hemibrain DN meta.csv</p> <p>Supplemental file 8</p> <p>The root point in hemibrain voxel space, for each hemibrain neuron. This is either the location of the soma, or the tip of a severed cell body fibre tract, where possible. Exists as hemibrain somas in our R package hemibrainr.</p> <p>S8 hemibrain root points.csv</p> <p>Supplemental file 9</p> <p>The start points for different neuron compartments. Nodes downstream of this position in the 3D structure of the neuron indicated with bodyid, belong to the compartment type designated by Label. A product of running flow centrality on hemibrain neurons, exists as hemibrain splitpoints in our R package hemi- brainr.</p> <p>S9 hemibrain compartment startpoints.csv</p> <p>Supplemental file 10</p> <p>3D triangle mesh for the hemibrain surface as a .obj file. This mesh was generated by first merging individual ROI meshes from neuPrint and then filling the gaps in between in a semi-manual process. It also exists as hemibrain.surf in our R package hemibrainr.</p> <p>S10 hemibrain raw.obj</p> <p>Supplemental file 11</p> <p>3D meshes of 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli for the hemibrain volume, generated from ALRN presynapses.</p> <p>41</p> <p>Note that hemibrain coordinate system has the anterior-posterior axis aligned with the Y axis (rather than the Z axis, which is more commonly observed).</p> <p>S11 hemibrain AL glomeruli meshes RN-based.zip</p> <p>Supplemental file 12</p> <p>3D meshes of 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli for the hemibrain volume, generated from ALPN presynapses.</p> <p>Note that hemibrain coordinate system has the anterior-posterior axis aligned with the Y axis (rather than the Z axis, which is more commonly observed).</p> <p>These meshes are also available as hemibrain al.surf in our R package hemibrainr. S12 hemibrain AL glomeruli meshes PN-based.zip</p>
LMU Fast Decompression Experiment Data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This data is camera images and nozzle pressure gauge voltage traces from rapid decompression shots at the LMU shock tube facility.</p> <p>This data is discussed in the "Materials and Methods" section of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The prefixes of the filenames correspond to the shot dates and times listed in table S1 of the paper. </p> <p>The "_camera.zip" files contains tiff images of the camera frames. The ".ixc" file in each zip lists camera settings in plain text.</p> <p>The ".dat" file contains the voltage measurement of the nozzle pressure gauge. Row 1 is the header, row 2 is the time in seconds, and row 3 is the voltage of the pressure gauge in Volts. The peak pressure in the header can be used to relate the voltage to pressure.</p>
Compressible Hydrodynamics Simulation Data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This data is a 2D cross-section from a 3D compressible hydrodynamics simulation (Hyburn / AMRex code) of a rapid decompression / shock tube experiment at Special Technologies Laboratory. The simulated shot is a pure argon gas decompression from 1000Psi to atmosphere. </p> <p>This data is used in figures 3 and 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The data is saved as python numpy zipped archives numbered by the timestep in the simulation. Files starting with 'tube' contain data from inside the shock tube. Files starting with 'near_vent' contain data from the expansion chamber above the nozzle. All units are in SI.</p> <p>Each .npz file is an array file generated with python numpy.savez(). It can be opened with:</p> <p><em>import numpy as np</em></p> <p><em>data = np.load('<name>.npz')</em></p> <p>The data is an python dictionary. The dictionary keys can be displayed with:</p> <p><em>print(data.files)</em></p> <p>The numpy arrays can be accessed by keyname:</p> <p><em>print(data['keyname'])</em></p> <p>The key names correspond to physical quantities (density, temperature, etc.). All particle quantities are 0 as the simulation did not include particles.</p>
Videos of fluid flow in contact interfaces
<p>These videos demonstrate the capabilities of the computational framework presented in [1] to solve complex coupled problem of viscous thin fluid flow in contact interfaces while handling the possibility of the fluid to be trapped in pockets surrounded by contact zones.</p> <p>[1] Andrei G. Shvarts, Julien Vignollet, Vladislav A. Yastrebov "Computational framework for monolithic coupling for thin fluid flow in contact interfaces" https://arxiv.org/abs/1912.11292v3</p>
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
Oxidation of Ammonia/Methanol Mixtures in a plug-flow reactor with TOF-MS at 373-973K
<p>TOF-mass spectrometric measurements with a plug-flow reactor for ammonia/methanol gas mixtures (neat, 10% and 20% methanol in ammonia for equivalence ratios 1 and 2)</p> <ul> <li>temperature range: 373-973 K</li> <li>pressure: 3 bar</li> <li>dilution: 98 %</li> </ul> <p>Dataset described, analyzed and discussed in: A. Welp, C. Rudolph, B.R. Giri, K.P. Shrestha, R. Verma, F. Mauss, and B. Atakan, Oxidation of Ammonia Methanol Blends: An Experimental and Kinetic Modeling Study, 2025, accepted for publication in Combustion and Flame.</p>
Network Digital Twin-Generated Dataset for Machine Learning-based Detection of Benign and Malicious Heavy Hitter Flows
<h3>Overview</h3> <p>This record provides a dataset created as part of the study presented in the following publication and is made <strong>publicly available for research purposes</strong>. The associated article provides a comprehensive description of the dataset, its structure, and the methodology used in its creation. If you use this dataset, please <strong>cite the following article </strong>published in the journal <strong>IEEE Communications Magazine</strong>:</p> <blockquote> <p><strong>A. Karamchandani, J. Nunez, L. de-la-Cal, Y. Moreno, A. Mozo, and A. Pastor, “On the Applicability of Network Digital Twins in Generating Synthetic Data for Heavy Hitter Discrimination,” IEEE Communications Magazine, pp. 2–8, 2025, DOI: 10.1109/MCOM.003.2400648.</strong></p> </blockquote> <p>More specifically, the record contains several synthetic datasets generated to differentiate between benign and malicious heavy hitter flows within a realistic virtualized network environment. Heavy Hitter flows, which include high-volume data transfers, can significantly impact network performance, leading to congestion and degraded quality of service. Distinguishing legitimate heavy hitter activity from malicious Distributed Denial-of-Service traffic is critical for network management and security, yet existing datasets lack the granularity needed for training machine learning models to effectively make this distinction.</p> <p>To address this, a Network Digital Twin (NDT) approach was utilized to emulate realistic network conditions and traffic patterns, enabling automated generation of labeled data for both benign and malicious HH flows alongside regular traffic.</p> <h3>Feature Set:</h3> <p>The feature set includes the following flow statistics commonly used in the literature on network traffic classification:</p> <ul> <li>The protocol used for the connection, identifying whether it is TCP, UDP, ICMP, or OSPF.</li> <li>The time (relative to the connection start) of the most recent packet sent from source to destination at the time of each snapshot.</li> <li>The time (relative to the connection start) of the most recent packet sent from destination to source at the time of each snapshot.</li> <li>The cumulative count of data packets sent from source to destination at the time of each snapshot.</li> <li>The cumulative count of data packets sent from destination to source at the time of each snapshot.</li> <li>The cumulative bytes sent from source to destination at the time of each snapshot.</li> <li>The cumulative bytes sent from destination to source at the time of each snapshot.</li> <li>The time difference between the first packet sent from source to destination and the first packet sent from destination to source.</li> </ul> <h3>Dataset Variations:</h3> <p>To accommodate diverse research needs and scenarios, the dataset is provided in the following variations:</p> <ol> <li> <p><strong><code>All at Once</code></strong>:</p> <ol> <li>Contains a synthetic dataset where all traffic types, including benign, normal, and malicious DDoS heavy hitter (HH) flows, are combined into a single dataset.</li> <li>This version represents a holistic view of the traffic environment, simulating real-world scenarios where all traffic occurs simultaneously.</li> </ol> </li> <li> <p><strong><code>Balanced Traffic Generation</code></strong>:</p> <ol> <li>Represents a balanced traffic dataset with an equal proportion of benign, normal, and malicious DDoS traffic.</li> <li>Designed for scenarios where a balanced dataset is needed for fair training and evaluation of machine learning models.</li> </ol> </li> <li> <p><strong><code>DDoS at Intervals</code></strong>:</p> <ol> <li>Contains traffic data where malicious DDoS HH traffic occurs at specific time intervals, mimicking real-world attack patterns.</li> <li>Useful for studying the impact and detection of intermittent malicious activities.</li> </ol> </li> <li> <p><strong><code>Only Benign HH Traffic</code></strong>:</p> <ol> <li>Includes only benign HH traffic flows.</li> <li>Suitable for training and evaluating models to identify and differentiate benign heavy hitter traffic patterns.</li> </ol> </li> <li> <p><strong><code>Only DDoS Traffic</code></strong>:</p> <ol> <li>Contains only malicious DDoS HH traffic.</li> <li>Helps in isolating and analyzing attack characteristics for targeted threat detection.</li> </ol> </li> <li> <p><strong><code>Only Normal Traffic</code></strong>:</p> <ol> <li>Comprises only regular, non-HH traffic flows.</li> <li>Useful for understanding baseline network behavior in the absence of heavy hitters.</li> </ol> </li> <li> <p><strong><code>Unbalanced Traffic Generation</code></strong>:</p> <ol> <li>Features an unbalanced dataset with varying proportions of benign, normal, and malicious traffic.</li> <li>Simulates real-world scenarios where certain types of traffic dominate, providing insights into model performance in unbalanced conditions.</li> </ol> </li> </ol> <p>For each variation, the output of the different packet aggregators is provided separated in its respective folder.</p> <p>Each variation was generated using the NDT approach to demonstrate its flexibility and ensure the reproducibility of our study's experiments, while also contributing to future research on network traffic patterns and the detection and classification of heavy hitter traffic flows. The dataset is designed to support research in network security, machine learning model development, and applications of digital twin technology.</p>
Mean current velocity sections along 11°S, 5°S, 35°W, and 23°W from shipboard measurements used in "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements"
<p>This data set contains current velocity measurements used in the study "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements“ by <em>Tuchen et al. (2022)</em> published at <em>Journal of Geophysical Research: Oceans</em>.</p> <p>For the meridional mean sections along 35°W and 23°W, and for the quasi-zonal sections along 11°S and 5°S, one ".mat" file is provided for each of the sections. Please note that the section along 11°S consists of a zonal part (east of 34.2°W) and a cross-shore part closer to the coast. The meridional velocities along the cross-shore part of the 11°S-section are rotated clockwise by 36° in order to derive along-shore velocities.</p> <ul> <li>11°S: meridional velocity / alongshore velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>5°S: meridional velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>35°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> <li>23°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> </ul> <p>Mean velocity data in the upper 10 m are replaced by the gridded mean surface current velocities at 1/4° horizontal resolution derived from satellite-tracked surface drifting buoys (<em>Laurindo et al. 2017</em>) that were horizontally interpolated to the resolution of the individual ship sections.</p>
Data to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"
<p>This data set corresponds to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"</p>
Data for "Globally widespread and increasing violations of environmental flow envelopes"
<p>Data and code for</p> <p><strong>Globally widespread and increasing violations of environmental flow envelopes</strong></p> <p>Vili Virkki*#, Elina Alanärä#, Miina Porkka, Lauri Ahopelto, Tom Gleeson, Chinchu Mohan, Lan Wang-Erlandsson, Martina Flörke, Dieter Gerten, Simon N. Gosling, Naota Hanasaki, Hannes Müller Schmied, Niko Wanders, and Matti Kummu*</p> <p># equal contribution to the article<br> * Correspondence to: Vili Virkki (vili.virkki@aalto.fi), Matti Kummu (matti.kummu@aalto.fi)</p> <p><br> link to published version: https://hess.copernicus.org/articles/26/3315/2022/</p> <p><strong>Please cite the published version of the article when using these data.</strong></p> <p><strong>See readme.txt in data for a detailed description of attached files.</strong></p>
Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this halo-based statistical dataset, i) all halos in N-body system are identified with all particles divided into halo and out-of-halo particles; ii) halos are grouped into halo groups including all halos of the same mass (m<sub>h</sub>); iii) the redshift (z) and mass scale (m<sub>h</sub>) dependence of all halo properties (momentum, energy, size, shape, velocity, acceleration, etc.) are presented . </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this correlation-based statistical dataset, i) all particle pairs with any given separation r in a N-body system are identified; ii) statistical measures are calculated over all particle pairs with the same separation r (pairwise average); iii) the redshift (z) and scale (r) dependence of all statistical measures (correlation/moment/structure/dispersion/spectrum functions for density, velocity and potential etc.) are presented. </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks
<p>The publication titled "Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks" is supported by the STRIDE K3 project. The dataset used in the publication is uploaded here.</p>
Flow manipulation in a Hele-Shaw cell with an electrically-controlled viscous obstruction
<p>The dataset named “Dataset: Flow manipulation in a Hele-Shaw cell with an electrically-controlled viscous obstruction” consists of Raw time-averaged images, which are generated by sequence of 100 frames extracted from experimental videos captured at various voltages (5V, 10V, 15V, 20V, and 50V), and saved as .tif files. These images were analysed to produce the data used in figure 2 and 3 of the article. The dataset also includes two Excel files named as “Figure 2_Experimental data.xlsx” and “Figure 3_Experimental data.xlsx”. These excel files contain the data used to create the experimental plots shown in Figure 2C, and Figure 3 of the research article respectively.</p> <p>In the “Figure 2C_Experimental Data.xlsx” excel file, each sheet corresponds to a different voltage value shown in the figure, and contains three columns: A, B, and C. which represents the X-location, Y-location, and orientation angle (in degrees) of the experimental plot (red rods in the figure) respectively. This plot is overlaid on the model data (black rods in the figure) and displayed in Figure 2C given in the article.</p> <p><span>The “Figure 3_Experimental data.xlsx” file contains three sheets for each voltage (5V, 10V, 15V, 20V, and 50V) and each of these three sheets provide data at three different X-locations (X=579, X= 1079, and X= 1779) as a function of Y-location as shown in the Figure 3 of the article. Each sheet has five columns: A, B, C, D, and E. These columns represent the X-location, Y-location, Orientation angle (in degrees), Coherency, and Error in the orientation angle (in degrees), respectively. These data points are used to create the experimental scatter plot shown in Figure 3 of the article.</span></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.