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.
5,371
datasets available to search
ShareScore release 0.9.0
Dataset results
5,371 results for “Flow”
CTC identification and characterization in prostate cancer using K/V protocol and imaging flow cytometry - part 10
<p>This project aimed to identify and characterize circulating tumor cells (CTCs) in peripheral blood of 33 prostate cancer patients. Fraction of peripheral blood mononuclear cells putatively containing CTCs was isolated using density gradient centrifugation. Pan-keratin/vimentin/CD45/CD31/DAPI immunofluorescent staining was performer and DAPI+ or DAPI+CD45/CD31- objects were collected using Amnis® ImageStream®XMk II, Cytek® Biosciences, Inc (lasers: 405 nm, 488 nm, 642 nm, magnification 40x, slow speed flow).</p>
CTC identification and characterization in prostate cancer using K/V protocol and imaging flow cytometry - part 9
<p>This project aimed to identify and characterize circulating tumor cells (CTCs) in peripheral blood of 33 prostate cancer patients. Fraction of peripheral blood mononuclear cells putatively containing CTCs was isolated using density gradient centrifugation. Pan-keratin/vimentin/CD45/CD31/DAPI immunofluorescent staining was performer and DAPI+ or DAPI+CD45/CD31- objects were collected using Amnis® ImageStream®XMk II, Cytek® Biosciences, Inc (lasers: 405 nm, 488 nm, 642 nm, magnification 40x, slow speed flow).</p>
CTC identification and characterization in prostate cancer using K/V protocol and imaging flow cytometry - part 6
<p>This project aimed to identify and characterize circulating tumor cells (CTCs) in peripheral blood of 33 prostate cancer patients. Fraction of peripheral blood mononuclear cells putatively containing CTCs was isolated using density gradient centrifugation. Pan-keratin/vimentin/CD45/CD31/DAPI immunofluorescent staining was performer and DAPI+ or DAPI+CD45/CD31- objects were collected using Amnis® ImageStream®XMk II, Cytek® Biosciences, Inc (lasers: 405 nm, 488 nm, 642 nm, magnification 40x, slow speed flow).</p>
CTC identification and characterization in prostate cancer using K/V protocol and imaging flow cytometry - part 4
<p>This project aimed to identify and characterize circulating tumor cells (CTCs) in peripheral blood of 33 prostate cancer patients. Fraction of peripheral blood mononuclear cells putatively containing CTCs was isolated using density gradient centrifugation. Pan-keratin/vimentin/CD45/CD31/DAPI immunofluorescent staining was performer and DAPI+ or DAPI+CD45/CD31- objects were collected using Amnis® ImageStream®XMk II, Cytek® Biosciences, Inc (lasers: 405 nm, 488 nm, 642 nm, magnification 40x, slow speed flow).</p>
CTC identification and characterization in prostate cancer using K/V protocol and imaging flow cytometry - part 7
<p>This project aimed to identify and characterize circulating tumor cells (CTCs) in peripheral blood of 33 prostate cancer patients. Fraction of peripheral blood mononuclear cells putatively containing CTCs was isolated using density gradient centrifugation. Pan-keratin/vimentin/CD45/CD31/DAPI immunofluorescent staining was performer and DAPI+ or DAPI+CD45/CD31- objects were collected using Amnis® ImageStream®XMk II, Cytek® Biosciences, Inc (lasers: 405 nm, 488 nm, 642 nm, magnification 40x, slow speed flow).</p>
AI-based imaging flow cytometry approach to study erythrophagocytosis
<p><span>Erythrophagocytosis is a process consisting of recognition, engulfment and digestion by phagocytes of antibody-coated or damaged erythrocytes. Understanding the dynamics that are behind erythrophagocytosis is fundamental to comprehend this cellular process under specific circumstances. Several techniques have been used to study phagocytosis. Among these, an interesting approach is the use of Imaging Flow Cytometry (IFC) to distinguish internalization and binding of cells or particles. However, this method requires laborious analysis. Here, we introduce a novel approach to analyze the phagocytosis process by applaying Amnis Artificial Intelligence (AAI) to IFC. Our study demonstrates that this approach is<span> </span>highly suitable to study erythrophagocytosis, categorizing internalized, bound and non-bound erythrocytes. Validation experiments showed that our pipeline performs with high accuracy and reproducibility.</span></p>
Numerical Modeling for Choked Flow in Old Faithful Geyser
<div>These ANSYS archive files were made to investigate the choked flow in Old Faithful Geyser. Here, Peng and Robinson Equation of State and Tait Equation of State are used as an equation of state for gas phase of H2O and CO2, and liquid phase of H2O, respectively. Also, Lee model was used to describe the phase transition between liquid and gas phase of H2O. The archive files contain the geometry of Old Faithful Geyser obtained from Hutchinson et al., 1997, mesh and numerical settings properties for calculating. Note that the archive files were created with ANSYS 2020 R1 version. </div> <div> <div> <h2>Files</h2> </div> </div>
A unified flow resistance formular for open-channels with natural and engineered obstacles
<p>This repository contains data set used in the manuscript "A unified flow resistance formular for open-channels with natural and engineered obstacles" submitted to Water Resources Research by Xingyu Chen, Tao Wang, Jiamei Wang, Hongbo Ma, Marwan A. Hassan and Xudong Fu</p>
Dataset for the paper 'Debris-flow return times and pre-event terrains: a novel framework using copula functions and quantile-based approaches'
<p>here attached you can find the dataset we used for our paper. While in the first two columns, you have X, and Y coordinates of the domain's pixels, the other five columns are DEMs' elevations (2006, 2010, 2011, 2015, and 2021). See in the paper's 'Methodology' Section how to employ these data. For any questions, please reach us at massimiliano.schiavo@unipd.it. Thanks!</p>
Supporting data and code for Leach et al. 'Zero-flow dynamics for headwater streams in a humid forested landscape'
<p>Supporting data and code for Leach et al. 'Zero-flow dynamics for headwater streams in a humid forested landscape'. The R script contains details on the data files.</p>
Dataset: Advanced Similarity Metrics for IP Flow Data Analytics
<p>Analysis of encrypted traffic in computer networks is intricate due to reduced visibility in transmitted content. Machine-learning techniques applied to data representing characteristics of traffic flows provide powerful tools for network monitoring or intrusion detection. Since real-world datasets are scarce, we present a novel traffic classification dataset with TLS traffic. The dataset contains three days (19-08-2022 -- 21-08-2022) of anonymized communication on CESNET3 ISP network, which is used by approximately half a million users daily.</p> <p><strong>Ethic statement </strong>The privacy of the CESNET network users is a fundamental concern in our work, leading us to conduct our research with careful consideration. The indisputable advantages of real traffic generated by hundreds of thousands of people come with understandable privacy concerns. Thus, we used only automatic data processing with immediate data anonymization. With this, we declare that we did not analyze or manually process non-anonymized data or perform any procedures that could allow us to track users or reveal their identities. </p> <p><strong>Data description</strong> The dataset consists of network flows describing encrypted TLS communications. Flows are extended with packet sequences, histograms, and fields extracted from the TLS ClientHello message, which is transmitted in the first packet of the TLS connection handshake. The most important extracted handshake field is the SNI domain, which is used for ground-truth labeling. </p> <p><strong>Packet Sequences</strong> Sequences of packet sizes, directions, and inter-packet times are standard data input for traffic analysis. For packet sizes, we consider the payload size after transport headers (TCP headers for the TLS case). We omit packets with no TCP payload, for example ACKs, because zero-payload packets are related to the transport layer internals rather than services’ behavior. Packet directions are encoded as ±1, where +1 means a packet sent from client to server, and -1 is a packet from server to client. Packet timing depends on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate a response. Packet sequences have a maximum length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time-stamps, and TCP flags. </p> <p><strong>Flow statistics</strong> Each data record also includes standard flow statistics, representing aggregated information about the entire bidirectional connection. The fields are the number of transmitted bytes and packets in both directions, the duration of the flow, and packet histograms. The packet histograms include binned counts (not limited to the first 30 packets) of packet sizes and inter-packet times in both directions. There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes (More information in the <a href="https://github.com/CESNET/ipfixprobe/tree/master#phists">PHISTS plugin documentation).</a> </p> <p><strong>Dataset structure</strong> The dataset is organized per individual days and hours. The flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. The following list describes flow data fields in CSV files:</p> <ul> <li><strong>TCP_FLAGS: </strong>Logical OR of all TCP flags transmitted from client to server</li> <li><strong>TCP_FLAGS_REV: </strong>Logical OR of all TCP flags transmitted from server to client</li> <li><strong>TLS_SNI:</strong> Server Name Indication domain</li> <li><strong>TIME_FIRST:</strong> Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff</li> <li><strong>TIME_LAST:</strong> Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff</li> <li><strong>DURATION:</strong> Duration of the flow in seconds</li> <li><strong>BYTES:</strong> Number of transmitted bytes from client to server</li> <li><strong>BYTES_REV:</strong> Number of transmitted bytes from server to client</li> <li><strong>PACKETS:</strong> Number of packets transmitted from client to server</li> <li><strong>PACKETS_REV:</strong> Number of packets transmitted from server to client</li> <li><strong>PPI_PKT_DIRECTIONS:</strong> Direction of PPI sequence </li> <li><strong>PPI_PKT_FLAGS:</strong> TCP flags of PPI sequence</li> <li><strong>PPI_PKT_TIMES:</strong> Timestamps of individual packets in PPI sequence</li> <li><strong>PPI_PKT_LENGTHS:</strong> Lengths of individual packets in PPI sequence</li> <li><strong>S_PHISTS_SIZES:</strong> Histogram of packet sizes from client to server</li> <li><strong>D_PHISTS_SIZES: </strong>Histogram of packet sizes from server to client</li> <li><strong>S_PHISTS_IPT: </strong>Histogram of inter-packet times from client to server</li> <li><strong>D_PHISTS_IPT:</strong> Histogram of inter-packet times from server to client</li> </ul> <p> </p> <p>The dataset also contains a service map in the form of a CSV file. The service map can be used to extract high-level labels from SNI domain names. </p> <p> </p> <p>The directory tree of the dataset is:</p> <pre><code>. ├── 20220819 │ ├── flows.202208190000.csv │ ├── flows.202208190100.csv | ├── ... │ └── flows.202208192300.csv ├── 20220820 │ ├── flows.202208200000.csv │ ├── flows.202208200100.csv | ├── ... │ └── flows.202208202300.csv └── 20220821 ├── flows.202208210000.csv ├── flows.202208210100.csv ├── ... └── flows.202208212300.csv<br></code></pre>
NOVAFOODIES Market Place software user flow
Open the record for dataset details and reuse information.
Out of Control: Reducing Probabilistic Models by Control-Flow Reduction - Experiments
<p>Scripts and benchmark files for reproducing the experiments regarding the reduction of probabilistic models reported in our submission.</p>
Solving the multi-commodity flow problem using an evolutionary routing algorithm in a computer network environment
<p>The continued increase in Internet traffic requires that routing algorithms make the best use of all available network resources. Most of the current deployed networks are not doing so due to their use of single path routing algorithms. In this work we propose the use of a multipath capable routing algorithm using Evolutionary Algorithms (EAs) that takes into account all the traffic going over the network and the link capacities by leveraging the information available at the Software Defined Networks (SDN) controller. The use of such information ensures that no link is used beyond its capacity, eliminating network congestion. We use EAs as true multi-objective solvers to provide a set of valid routing solutions from a single run of the algorithm. Modifications to the Multipath TCP (MPTCP) protocol are proposed to overcome the multipath problems associated with TCP.</p>
Tmem119 IAB flow cytometry dataset
<p>These files contain raw data from Tmem119 CreERT2/+ or Tmem119 CreERT2/+ IAB fl/fl mice transduced with AAV2-SYN. Brains were collected and processed 4 weeks post-transduction and run on an Attune Nxt flow cytometer. A workspace file is included to demonstrate how gating and quantification were performed.</p>
Turbulent pipe flow up to Re_tau = 1000
<p>Data from the paper:</p> <p><a href="https://scholar.google.com/citations?view_op=view_citation&hl=no&user=a-0MJGMAAAAJ&citation_for_view=a-0MJGMAAAAJ:qjMakFHDy7sC">Direct numerical simulation of turbulent pipe flow at moderately high Reynolds numbers</a></p> <p>GK El Khoury, P Schlatter, A Noorani, PF Fischer, G Brethouwer, ...</p> <p>Flow, turbulence and combustion 91 (3), 475-495</p>
Lateral flow immunoassay-based laboratory algorithm for rapid diagnosis of diphtheria
<p><strong>Results of the ‘DIFTERIA’ Lateral Flow Immunoassay (LFIA) validation.</strong></p>
A verified open-access AI-based chemical microparticle image database for in-situ particle visualization and quantification in multi-phase flow
<p>This report provided a new method and idea for the detection, segmentation, classification, and quantitative analysis of four dispersed phase particles - "DPPs" (agglomeration, bubble, crystal, and droplet) in chemical multi-phase flow processes.</p>
Images of Construction and Demolition Waste Consting of Brick and Sand-lime Brick Transported on a Conveyor Belt - Recording with a Mass Flow of 15 g per s and a Mixing Ratio of 25 vs. 75 %
<p>This data set comprises images of bulk material of brick and sand-lime brick on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden,Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer</em>, <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>,Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/insideschuettgut">project website</a>.</li> </ul> <p>This dataset is part of a batch of recordings of construction and demolition waste consisting of brick and sand-lime brick on optical sorters. Please use the search function with the keywords "Images of Construction and Demolition Waste" (in quotes) to find them. The data set was recorded with a Bonito CL-400C. Please see the debayer script on GitHub <a href="https://github.com/geomai/debayer_bonito">https://github.com/geomai/debayer_bonito</a>. The frame rate is approximately 192 Hz.</p> <p><br> To this date, publications that used this data include</p> <ul> <li><em>Marcel Reith-Braun, Albert Bauer, Maximilian Staab, Florian Pfaff, Georg Maier, Robin Gruna, Thomas Längle, Jürgen Beyerer, Harald Kruggel-Emden and Uwe D. Hanebeck</em>, <strong>GridSort: Image-based Optical Bulk Material Sorting Using Convolutional LSTMs</strong>, 22nd IFAC World Congress, Yokohama, Japan, July 2023.</li> </ul> <p><strong>Acknowledgment</strong><br> The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Climate Action on the basis of a resolution of the German Bundestag.</p>
Dataset and Software for High-resolution mantle flow models reveal importance of plate boundary geometry and slab pull forces on generating tectonic plate motions
<p>This repository contains the plugin and dataset used to setup models in the manuscript: "High-resolution mantle flow models reveal importance of plate boundary geometry and slab pull forces on generating tectonic plate motions". It also includes the corresponding versions of the geodynamics software ASPECT and Worldbuilder which were used to develop the models in the paper.</p> <p>The material model plugin used in our models is in the "plugins" folder. The "models" folder contains the reference input parameter file described in the paper. All other model configurations shown in the paper can be obtained by modifying this parameter file. The input files used to set up the models are in the respective folder. Additionally, the Jupyter notebook used to compute residuals of our models is provided in the "scripts" folder.</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.