Find research datasets worth reusing

Farm-Flow | AG-IoT Security: Intrusion Detection in Smart Agriculture Dataset

<div> <div> <p><strong>Introduction:</strong></p> <p>The "Farm-Flow" dataset was created to emulate real-world Agricultural Internet of Things (AG-IoT) systems, encompassing network attacks and data collection. Following comprehensive cleaning and processing, the "Farm-Flow" dataset comprises 532 MB of data with 1,310,000 instances, structured around "flows," which represent consecutive series of packets transmitted from a single source to a specific destination. The dataset demonstrates an intrusion detection accuracy of 92.67% and is intended to enhance the security of AG-IoT systems, safeguarding information such as crop health, weather patterns, and soil conditions</p> <p><strong>Captures:</strong></p> <p>The captures comprises three months of network traffic: August, September, and October of 2022. Each month is divided into folders, which categorize the network traffic. These folders contain numerous .pcap files, which have been divided into 5-second intervals. This segmentation is necessary because, as previously mentioned, flows aggregate packets, resulting in only one row of flow data for ongoing connections. To address this, a script was developed to segment the .pcap files into 5-second increments. This approach allows for the generation of multiple rows of flow connections, thereby providing more quantity of data for model training.</p> <p><strong>Dataset:</strong></p> <p>The dataset comprises 532 MB of data, encompassing 1,310,000 instances. These instances have been classified into eight distinct attack types and one category for normal traffic. The identified attacks include Arp Spoofing, BotNet DDoS, HTTP Flood, ICMP Flood, MQTT Flood, Port Scanning, TCP Flood, and UDP Flood. Among the data set, there are 27,458 instances of normal traffic and 1,282,429 instances of aggregated attack traffic.</p> <p><strong>Zip Folder:</strong></p> <p>The zip folder is structured into two main directories: Captures and Dataset. The Captures directory is organized by the month of capture and further categorized by network traffic type. The Datasets directory includes the Farm-Flow Dataset, alongside four additional datasets that have undergone pre-processing: the training and testing datasets for binary classification, and the training and testing datasets for multiclass classification. Additionally, there are further datasets categorized by month and type of network traffic.</p> <p><strong> Article Information:</strong></p> <p>The work involved in developing the Farm-Flow dataset is described in the following paper. Please cite the paper and the dataset when using the Farm-Flow dataset.</p> <blockquote> <p>Rafael Ferreira, Ivo Bispo, Carlos Rabadão, Leonel Santos, and Rogério Luís de C. Costa (2025). <em>Farm-flow dataset: Intrusion detection in smart agriculture based on network flows</em>, Computers and Electrical Engineering, Volume 121, 109892, DOI: <a href="https://doi.org/10.1016/j.compeleceng.2024.109892." target="_blank" rel="noopener"> 10.1016/j.compeleceng.2024.109892</a></p> </blockquote> </div> </div>

Immersive Analytics: The Influence of Flow, Sense of Agency, and Presence on Performance and Satisfaction

<p>This are the datasets and R scripts for the experiments described in the paper "Immersive Analytics: The Influence of Flow, Sense of Agency, and Presence on Performance and Satisfaction".</p>

opencc-by-4.0Dec 2021View details →

Fig. 1 Screening flow, testing 1600 FDA compounds against S in Assessment of FDA-approved drugs against StrongyloideS rAtti in vitro and in vivo to identify potentially active drugs against strongyloidiasis

Fig. 1 Screening flow, testing 1600 FDA compounds against S. ratti

MFMET webinar - 02. Flow in microfluidics

<p>This video presents flow in microfluidics. It shows measurements of flow and pressure, and a characterisation method for determining hydrodynamic resistance.</p> <p><br>Further reading can be found:<br><a href="https://mfmet.eu/publications">https://mfmet.eu/publications</a><br>A2.2.2: Development of test protocols for microfluidic devices. (2023). <a href="https://doi.org/10.5281/zenodo.7845431">Link</a><br>Whitepaper flow resistivity testing (1.0). (2023) Zenodo. <a href="https://doi.org/10.5281/zenodo.7919134">https://doi.org/10.5281/zenodo.7919134</a><br>A1.3.2 - Existing operational conditions and existing operation protocols for flow control. (2023). <a href="https://doi.org/10.5281/zenodo.10116586">Link</a></p> <p>The project (20NRM02 MFMET) have received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>

[SUMMER - Flow Duration Curves & Hydropower Potential - RM4]

<p><span>Dataset containing the results of the flow duration curves (FDC) and the hydroelectric potential along the entire Orco Valley hydrographic network</span></p>

Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models [source code]

<p>J. Heiland & Y. Kim, 'Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models', GAMM 2024</p>

Additional data for: Directing Min protein patterns with advective bulk flow

<p>Data and analysis associated with 2024 addition to:<br>"Direction Min protein patterns with advective bulk flow" (NatComms, 2023)<br>Companion repository to <a href="../records/7339803">10.5281/zenodo.7339803</a></p> <p>Please consider the readme for more information.</p>

Stylidium armeria experimental gene flow data

<p>Gene flow can have rapid effects on adaptation and is an important evolutionary tool available when undertaking biological conservation and restoration. This tool is underused partly because of the perceived risk of outbreeding depression and loss of mean fitness when different populations are crossed. In this article we briefly review some theory and empirical findings on how genetic variation is distributed across species ranges, describe known patterns of gene flow in nature with respect to environmental gradients, and highlight the effects of gene flow on adaptation in small or stressed populations in challenging environments (e.g., at species range limits). We then present a case study involving crosses at varying spatial scales among mountain populations of a trigger plant (<em>Stylidium armeria</em>:<em> </em>Stylidiaceae) in the Australian Alps to highlight how some issues around gene flow effects can be evaluated. We found evidence of outbreeding depression in seed production at greater geographic distances. Nevertheless, we found no evidence of maladaptive gene flow effects in likelihood of germination, plant performance (size), and performance variance, suggesting that gene flow at all spatial scales produces many offspring with high adaptive potential. This case study demonstrates a path to evaluating how increasing sources of gene flow in managed wild and restored populations could identify some offspring with high fitness that could bolster the ability of populations to adapt to future environmental changes. We suggest further ways in which managers and researchers can act to understand and consider adaptive gene flow in natural and conservation contexts under rapidly changing conditions.</p>

opencc-by-4.0Feb 2024View details →

Dataset: Phytoplankton Cell-States: multiparameter lifetime flow monitoring reveals cellular heterogeneity

<h1>Dataset for Multiparameter-based photosynthetic state transitions of single phytoplankton cells data.</h1> <h2>Creating environment</h2> <p>yaml and spec file are provided of the conda environment used. Most packages are typical, with the key exception of fretbursts</p> <p>A custom/development version of fretbursts was used, which contains support for more flexible photon selection (Ph_sel) objects, necessary for the larger parameter space. This version is in the polarization branch of harripd fork of fretbursts. The simple route to install is </p> <p><code>pip install git+https://https://github.com/harripd/FRETBursts@polarization</code></p> <p>however, if you want to ensure the exact same version is installed use:</p> <p><code>pip install git+https://github.com/harripd/FRETBursts@12a5168</code></p> <h2>Data organization</h2> <p>All zip files are meant to be unziped into the same destination folder (ie if a file is in a folder in the zip file, it should be in a subfolder inside the shared highest level folder, but if two files inside different zip folders are in the root of their respective zip files, they should be places in the same folder when all files are unzipped.</p> <p>After unzipping everything your folder for this article should contain the following items (normal text indicates file, <strong>bold text indicates folder</strong>).</p> <ol> <li>Flow_PhytoCellStates_figures.ipynb</li> <li><strong>mpAlgae</strong></li> <li><strong>figures</strong></li> <li>20230905_cyanoWH8102_diurnal</li> <li>20230707_cyanolightjump</li> <li>20230609_ppurp</li> <li>20230724_redalgaelightjump</li> <li>20231211_pmicans_6umol_diurnal</li> <li><strong>20231217_pmicans_6umol_lightjump</strong></li> <li><strong>20231019_WH8102_imaging</strong></li> <li><strong>bulk_cyano_lightjump</strong></li> <li>synWH8102_di_REPO_20230905.hdf5</li> <li>synWH8102_di_REPO_20230905.csv</li> <li>synWH8102_lj_REPO_20230707.hdf5</li> <li>synWH8102_lj_REPO_20230707.csv</li> <li>ppurp_di_REPO_20230609.hdf5</li> <li>ppurp_di_REPO_20230609.csv</li> <li>ppurp_lj_REPO_20230724.hdf5</li> <li>ppurp_lj_REPO_20230724.csv</li> <li>pmicans_di_REPO_20231211.hdf5</li> <li>pmicans_di_REPO_20231211.csv</li> <li>ppurp_lj_REPO_20230724.hdf5</li> <li>ppurp_lj_REPO_20230724.csv</li> </ol> <p>Flow_API_all.ipynb (item 1) is the jupyter notebook that analyses the data and produces the figures. It relies on having mpAlgae (item 2) in the same folder, as it contains the underlying code that adapts FRETBurts to work with the cell based data, adding functionality to combine multiple data files into one experiment, dynamic gating of data, storing calculated values to disk, which also reduces the memory requirements of the system, as well as a number of advanced plotting functions. Flow_API_all.ipynb also expects a figures folder (item 3) to exist so that it can save the produced figures. It also expects items 4-9 to exist, as those folder names are recorded as where the different raw data sets are stored. Even numbered items 12-22 (all items 12-23 that are hdf5 files) are produced by Flow_API_all.ipynb, and are where processed values of burst search are stored. the mpAlgae api makes it so that if these files are present, burst search is skipped, and instead loaded from these result files. Thus if you desire to examine the data, you only need the top level hdf5 files. These files also save results of histogram fittings, so that results can be loaded instead of recalculatd. </p> <p>The csv files are export of calculated parameters to csv format. Note that these are ungated, and thus proper gating must be implemented if plots are to be recapitulated from these files, this is done so that alternative gating strategies can also be explored.</p> <p> </p>

Data from: Genetic divergence and one-way gene flow influence contemporary evolution and ecology of a partially migratory fish

<p>Recent work has revealed the importance of contemporary evolution for shaping ecological outcomes. In particular, rapid evolutionary divergence between populations has been shown to impact the ecology of populations, communities, and ecosystems. While studies have focused largely on the role of adaptive divergence in generating ecologically-important variation among populations, much less is known about the role of gene flow in shaping ecological outcomes. After divergence, populations may continue to interact through gene flow, which may influence evolutionary and ecological processes. Here we investigate the role of gene flow in shaping the contemporary evolution and ecology of recently diverged populations of anadromous steelhead / resident rainbow trout (<em>Oncorhynchus mykiss</em>). Results show that resident rainbow trout introduced above waterfalls have diverged evolutionarily from downstream anadromous steelhead, which were the source of introductions. However, the movement of fish from above to below the waterfalls has facilitated gene flow, which has reshaped genetic and phenotypic variation in the anadromous source population. In particular, gene flow has led to an increased frequency of residency, which in turn has altered population density, size-structure, and sex ratio. This result establishes gene flow as a contemporary evolutionary process that can have important ecological outcomes. From a management perspective, anadromous steelhead are generally regarded as a higher conservation priority than resident rainbow trout, even when found within the same watershed. Our results show that anadromous and resident <em>O. mykiss</em> populations may be connected via gene flow, with important ecological consequences. Such eco-evolutionary processes should be considered when managing recently diverged populations connected by gene flow.</p>

Data from: Salinity decline promotes growth and harmful blooms of a toxic alga by diverting carbon flow

<p>Global climate change intensifies the water cycle and makes freshest waters become fresher and vice‑versa. But how this change impacts phytoplankton in coastal, particularly harmful algal blooms (HABs), remains poorly understood. Here, we monitored a coastal bay for a decade and found a significant correlation between salinity decline and the increase of <em>Karenia mikimotoi</em> blooms. To examine the physiological linkage between salinity decreases and <em>K. mikimotoi</em> blooms, we compare chemical, physiological and multi-omic profiles of this species in laboratory cultures under high (33) and low (25) salinities. Under low salinity, photosynthetic efficiency and capacity as well as growth rate and cellular protein content were significantly higher than that under high salinity. More strikingly, the omics data show that low salinity activated the glyoxylate shunt to bypass the decarboxylation reaction in the tricarboxylic acid cycle, hence redirecting carbon from CO<sub>2</sub> release to biosynthesis. Furthermore, the enhanced glyoxylate cycle could promote hydrogen peroxide metabolism, consistent with the detected decrease in reactive oxygen species (ROS). These findings suggest that salinity declines can reprogram metabolism to enhance cell proliferation, thus promoting bloom formation in HAB species like <em>K. mikimotoi</em>, which has important ecological implications for future climate-driven salinity declines in the coastal ocean with respect to HAB outbreaks.</p>

Data products associated with "Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows"

<p>These are the data products associated with "Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows" by J. F. Crenshaw, et. al. This includes the input catalog and the outputs of the workflow described here https://github.com/jfcrenshaw/pzflow-paper, as well as a gzip of the github repo.</p>

Data from: Neuroprotection provided by hypothermia initiated with high transnasal flow with ambient air in a model of pediatric cardiac arrest

<p>Clinical trials of hypothermia after pediatric cardiac arrest have not seen robust improvement in functional outcome, possibly because of the long delay in achieving target temperature. Previous work in infant piglets showed that high nasal airflow, which induces evaporative cooling in the nasal mucosa, reduced regional brain temperature uniformly in half the time needed to reduce body temperature. The mouth is kept open to allow the high nasal airflow to easily exit. Here, we evaluated whether initiation of hypothermia with high transnasal airflow (32 L/min) provides neuroprotection without adverse effects in the setting of asphyxic cardiac arrest. Anesthetized, mechanically ventilated piglets (approximately 2-weeks-old) underwent sham-operated procedures (Group 1) or asphyxic cardiac arrest (Groups 2-6). The asphyxic insult consisted of reducing the inspired oxygen from 30% to 9.5-10% for 45 minutes (hypoxia period), then briefly increasing the inspired oxygen to 21% for 5 min (to improve the later success of cardiac resuscitation), and then completely stopping ventilation for 7 minutes. Cardiopulmonary resuscitation (CPR) commenced by re-establishing ventilation, performing chest compression, and injecting epinephrine as needed. The five cardiac arrest groups were further divided into those with normothermic recovery (38.5°C; Group 2), with mild hypothermia (34°C) initiated by surface cooling at 10 minutes (Group 3) or 120 minutes (Group 5) after resuscitation, or with mild hypothermia (34°C) initiated by transnasal cooling initiated at 10 minutes (Group 4) or 120 minutes (Group 6) after resuscitation. In the two transnasal cooling groups, the high nasal airflow continued for 2 hours and was then stopped; thereafter, surface cooling was used to maintain hypothermia. In all four groups with induced hypothermia, rectal temperature was sustained at the targeted temperature of 34°C with surface cooling until 20 hours after resuscitation, followed by 6 hours of gradual rewarming and cessation of fentanyl/70% nitrous oxide anesthesia.<strong> </strong>At four days of recovery, the piglets were euthanized and their brains were analyzed for the density of morphologically intact neurons in putamen, sensorimotor cortex, ventrolateral thalamus, and prefrontal cortex. The data sheet shows the density of viable neurons in these 4 brain regions for the 45 piglets that completed the study. The data sheet also shows the serial measurements of rectal temperature, mean arterial blood pressure, heart rate, and arterial blood measurements of the partial pressure of oxygen (PO2) and carbon dioxide (PCO2), oxyhemoglobin saturation, and pH obtained at baseline, during the period of hypoxia, at 4 minutes of ventilation with 21% O2, during the period of asphyxia, and during the first 24 hours of recovery. The piglets are assigned the same unique identifier number, labelled 1-45, for each set of measurements. Transnasal cooling initiated at 10 minutes after resuscitation was able to significantly rescue neurons in the highly vulnerable putamen without adverse effects.</p>

Data from: Amazonian rivers are leaky barriers to gene flow in forest understory birds

<p>Ever since Alfred Russel Wallace's nineteenth-century observation that related terrestrial species are often separated on opposing riverbanks, major Amazonian rivers have been recognized as key drivers of speciation. However, rivers are dynamic entities whose widths and courses may vary through time. It thus remains unknown how effective rivers are at reducing gene flow and promoting speciation over long timescales. We fit demographic models to genomic sequence to reconstruct the history of gene flow in three pairs of avian taxa fully separated by different Amazonian rivers, and whose geographic ranges do not make contact in headwater regions. Models with gene flow were best fit, but still supported an initial period without any gene flow which ranged from 187,000 to over 959,000 years, suggesting that rivers are capable of initiating speciation through long stretches of allopatric divergence. Allopatry was followed by either bursts or prolonged episodes of gene flow that retarded genomic differentiation but did not homogenize populations. Our results support Amazonian rivers as key barriers that promoted speciation and the buildup of species richness, but they also suggest that river barriers are often leaky, with genomic divergence accumulating slowly due to episodes of substantial gene flow.</p>

Fluid flow drives phenotypic heterogeneity in bacterial growth and adhesion on surfaces

<p>Raw data for the study reported by Hubert et al., Nature Communications 2024 (accepted).</p> <p>Each of the 4 folders within the archive, denoted 'Ulow', 'Low', 'Med', and 'High' contains 3 sets of raw experimental data, each corresponding to an experiment performed in the 'Ulow', 'Low', 'Med', and 'High' shear stress regime, in accordance with the folders' names.</p> <p>Each of these data sets is bundled into a single .avi video, consisting of raw images recorded at a 1/60 Hz acquisition frequency (i.e., 1 image per minute).</p> <p>The folder 'Supplementary_Software'contains Matlab and Python scripts used to treat the raw data, with a raw data sample. The file 'HOWTO_use_the_scripts.txt' explains how to use the scripts.</p> <p>Except for this HOWTO file, the files inside the various subfolders of folder 'Supplementary_Software' are not listed in the file tree below. Only the subfolders are listed.</p> <p>./<br>├── README_data_sets_and_treatment_scripts.txt (this file)<br>├── Ulow/<br> |     ├── Ulow_set1.avi<br> |     ├── Ulow_set2.avi<br> |     └── Ulow_set3.avi<br>├── Low/<br> |     ├── Low_set1.avi<br> |     ├── Low_set2.avi<br> |     └── Low_set3.avi<br>├── Med/<br> |     ├── Med_set1.avi<br> |     ├── Med_set2.avi<br> |     └── Med_set3.avi<br>└── High/<br> |     ├── High_set1.avi<br> |     ├── High_set2.avi<br> |     └── High_set3.avi<br>└── Supplementary_Software/<br>    ├── HOWTO_use_the_scripts.txt<br>    ├── step1_matlab/ <br>     |     ├── Pos0_100_every_1000/ <br>     |     ├── matlab_image_processing/ <br>     |     ├── images_treated_20201120_220854/ <br>     |     └── Outputs/<br>    └── step2_python/<br>            └── Outputs/</p> <p> </p> <p> </p>

opencc-by-4.0Jun 2024View details →

Volumetric flow datasets from the SWMM model for Bellinge

<p>This dataset is the volumetric flow (cubic metres / second) dataset for the simulations as run in the paper "Sensor Placement in Wastewater Networks, an Information-theoretic Approach to Enhance Network Monitorin" for Case Study 2, simulated from the SWMM hydrodynamic model.</p> <p>There are two data sets contained, the first is the merger of simulation one and simulation two. The second is the merger of simulation three and four respectively. The dates of these simulations are shown below (MM/DD/YYYY).</p> <p>Simulation 1: 01/01/2018 to 07/01/2018.<br>Simulation 2: 07/01/2018 to 01/01/2019.<br>Simulation 3: 01/01/2019 to 07/02/2019.<br>Simulation 4: 07/02/2019 to 01/03/2020.</p> <p>The simulations were run under the default conditions uploaded in the SWMM model. The SWMM model was taken from Nedergaard Pedersen, A., Wied Pedersen, J., Vigueras-Rodriguez, A., Brink-Kjær, A., Borup, M., and Steen Mikkelsen, P.: The Bellinge data set: open data and models for community-wide urban drainage systems research, Earth Syst. Sci. Data, 13, 4779–4798, https://doi.org/10.5194/essd-13-4779-2021, 2021.</p> <p> </p>

Orthophoto mosaic of the July 1974 lava flow of Kīlauea

<p><span>This 20 cm-resolution orthophoto mosaic was produced from digital images taken during a helicopter overflight in January 2018 during a mission by the Hawaiian Volcano Observatory and reconstructed using <em>Pix4D Mapper v.3.2.14. </em>Reference CRS is EPSG:32605.</span></p>

opencc-by-4.0Sep 2021View details →

Data from: Forest carbon sink in the U.S. (1870–2012) driven by substitution of forest ecosystem service flows

Open the record for dataset details and reuse information.

VORTEX 01: Analytic solution of the flow, aeroacoustic source and acoustic field from a co-rotating vortex pair

<div>This aeroacoustic reference example (often referred in literature as "Co-rotating vortex pair") provides the analytic solution of the flow, aeroacoustic source and acoustic field. Details on the example and derivation can be found in the article "Analysis of the co-rotating vortex pair as a test case for computational aeroacoustics" on which the analytic functions are based on can be found here: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jsv.2024.118496" target="_blank" rel="noopener">10.1016/j.jsv.2024.118496</a>. The following Matlab functions can be used for computing the aero-acoustic radiation of a co-rotating vortex pair:</div> <div> </div> <div><strong>Main script:</strong></div> <div>compute_pfar - Computes the radiated acoustic field. Use the "method" variable to change the aeroacoustic model</div> <div> </div> <div><strong>Computing the source terms:</strong></div> <div>vpair_pic - Incompressible pressure field p_ic and Dp_ic / Dt at a given time instance</div> <div>vpair_u -   Velocity field u, and its first and second spatial derivatives at a given time instance</div> <div> </div> <div>plot_source_terms - Create a plot of the source terms of both Lighthill and PCWE</div> <div>plot_quadrupole_field - Plots the analytical quadrupole field solution</div> <div> </div> <div><strong>Utility functions used by the main script:</strong></div> <div>gaussquad - Gauss-Legendre quadrature in 1D (original version from Mathworks FTP)</div> <div>subdiv_graded - Graded subdivision of one or more sections in 1D</div> <div>vpair_quadrature - Quadrature base points and weights for numerical convolution</div> <div> </div> <div><strong>Resource:</strong></div> <div>colormap.png - Blue -> Gray -> Red color scale</div> <div> </div> <div>All functions and scripts, except gaussquad.m were created by the authors:</div> <div>P. Rucz, M. Á. Ulveczki, J. Heinz, S. Schoder</div>