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1,891 results for “Field data”
Data: Effects of broadband sound exposure on the interaction between foraging crab and shrimp – a field study
<p>Data abstract:</p> <p>Data on foraging crabs and shrimps during trials with or without broadband sound exposures. Trials were conducted in situ using baited cameras.</p> <p> </p> <p>Paper abstract:</p> <p>Aquatic animals live in an acoustic world in which they often rely on sound detection and recognition for various aspects of life that may affect survival and reproduction. Human exploitation of marine resources leads to increasing amounts of anthropogenic sound underwater, which may affect marine life negatively. Marine mammals and fishes are known to use sounds and to be affected by anthropogenic noise, but relatively little is known about invertebrates such as decapod crustaceans. We conducted experimental trials in the natural conditions of a quiet cove. We attracted shore crabs (<em>Carcinus maenas</em>) and common shrimps (<em>Crangon crangon</em>) with an experimentally fixed food item and compared trials in which we started playback of a broadband artificial sound to trials without exposure. During trials with sound exposure, the cumulative count of crabs that aggregated at the food item was lower, while variation in cumulative shrimp count could be explained by a negative correlation with crabs. These results suggest that crabs may be negatively affected by artificially elevated noise levels, but that shrimps may indirectly benefit by competitive release. Eating activity for the animals present was not affected by the sound treatment in either species. Our results show that moderate changes in acoustic conditions due to human activities can affect foraging interactions at the base of the marine food chain.</p> <p> </p> <p>Reference:</p> <p>Hubert, J., Campbell, J., van der Beek, J.G., den Haan, M.F., Verhave, R., Verkade L.S., and Slabbekoorn H. (2018) Effects of broadband sound exposure on the interaction between foraging crab and shrimp - a field study. Environ. Pollut. 243, 1923–1929. DOI:10.1016/j.envpol.2018.09.076</p>
Subpixel offsets of Copernicus Sentinel 2 data, related to the displacement field of the Sulawesi Earthquake (2018, Mw 7.5)
<p><a href="https://en.wikipedia.org/wiki/Sulawesi">Sulawesi</a> lies within a complex fault system located between the <a href="https://en.wikipedia.org/wiki/Australian_Plate">Australian</a>, <a href="https://en.wikipedia.org/wiki/Pacific_Plate">Pacific</a>, <a href="https://en.wikipedia.org/wiki/Philippine_Sea_Plate">Philippine</a> and <a href="https://en.wikipedia.org/wiki/Sunda_Plate">Sunda Plates</a>. The main active structure onshore at the western part of Central Sulawesi is the left-lateral NNW-SSE trending <a href="https://en.wikipedia.org/wiki/Palu-Koro_fault">Palu-Koro</a> <a href="https://en.wikipedia.org/wiki/Strike-slip_Fault">strike-slip fault</a> that forms the boundary between the North Sula and Makassar blocks. On 28 September 2018, a large tsunamigenic <a href="https://en.wikipedia.org/wiki/Earthquake">earthquake</a> (Mw 7.5) struck the <a href="https://en.wikipedia.org/wiki/Minahasa_Peninsula">Minahasa Peninsula</a>, Indonesia. The earthquake caused massive damages near Palu city, including onshore gravitational instabilities and a tsunami.</p> <p>These data are the result of subpixel image correlation on Copernicus Sentinel-2 data (17 September 2018 and 2 October 2018) to derive the two-dimensional (East-West and North-South) horizontal co-seismic displacement field. In these data, the displacement field is expressed in meters. These results show a dominant senextral strike-slip motion on the onshore part of the Palu-Koro fault. Maximum displacement at the surface reached more than 8 meters at the location of Palu city. Processing is performed with COSI-CORR (Leprince et al., 2007). Data value higher than | 10 | meters should be considered as noise and disregarded. I used a correlation window size of 32 pixels with a sampling step of 16 pixels. A ramp has been removed from (separately) the North-South offsets and from the East-West offsets. The files are rasters of floating point values, served with a header file readable by ENVI software.</p> <p>Sign conventions:</p> <p>-North-South offsets: positive values to the North.</p> <p>- East-West offsets: positive values to the West.</p> <p> </p> <p><a href="http://www.esa.int/spaceinimages/Images/2018/10/Indonesia_earthquake_displacement_data">http://www.esa.int/spaceinimages/Images/2018/10/Indonesia_earthquake_displacement_data</a></p> <p><strong>Copyright:</strong> Contains modified Copernicus Sentinel data (2018), processed at the French Geological Survey (BRGM)</p> <p> </p> <p> </p>
Data sets for " The nature of mean-field generation in three classes of optimal dynamos"
<pre>The tar archive Optimal_Dynamos.tar contains and index.html file with links to the run directories for each figure and the two tables of the paper "The nature of mean-field generation in three classes of optimal dynamos" by Axel Brandenburg (Nordita) and Long Chen (Durham University) with the temporary URL http://norlx51.nordita.org/~brandenb/tmp/long_chen. Corrections and updates are available on the active URL to this tar archive: https://www.nordita.org/~brandenb/projects/Optimal_Dynamos/</pre>
Time Variable Ionospheric Electric Field Model (TiVIE) light data v 1.0
<p>This is the TiVIE light data to accompany TiVIE model v 1.0 produced by Maria-Theresia Walach, Lancaster University for the publication Walach, M.-T., and Grocott, A. (submitted 2024). </p>
Field data used in "A field-based estimation of the variability of particle entrainment in coarse-bed rivers"
<p>This file is a companion file to "A field-based estimation of the variability of particle entrainment in coarse-bed rivers ", co-authored by Daniel Vázquez-Tarrío, Estrella Carrero-Carralero, Raúl López, Fanny Ville, Damià Vericat and Ramon J. Batalla .</p> <p>This is a paper published in the <em>Journal of Geophysical Research: Earth Surface</em>, 129 (10) (https://doi.org/10.1029/2024JF007695).</p> <p>This Excel file contains all the raw data used in this research.</p>
Relativistic Electron Precipitation Events (driven by waves or field line scattering) from POES 2-second data
<p>This repository contains the list of relativistic electron precipitation from POES data since 2012.</p> <p>Please refer to the read_me file for further details.</p> <p> </p> <p><strong>You are free to use this for your research. However, before doing so, please contact Luisa Capannolo at luisacap@bu.edu.</strong></p> <p> </p> <p>This dataset is associated with the paper under review titled "Properties of Relativistic Electron Precipitation: A Comparative Analysis of Wave-Induced and Field Line Curvature Scattering Processes" by Capannolo, Staff, Li, Duderstadt, Sivadas, Petitt, Elliot, Qin, Shen, and Ma.</p>
Raw data to "Order-by-disorder in the antiferromagnetic $J_1$-$J_2$-$J_3$ transverse-field Ising model on the ruby lattice"
<p>This directory contains the data used to generate the results in the work "Order-by-disorder in the antiferromagnetic $J_1$-$J_2$-$J_3$ transverse-field Ising model on the ruby lattice" [1].</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README.txt file.</p> <p>[1]: A. Duft et al., Order-by-disorder in the antiferromagnetic $J_1$-$J_2$-$J_3$ transverse-field Ising model on the ruby lattice. arXiv:2312.12941</p>
Lithium-Ion Battery Field Data: 28 LFP battery systems with 8 cells in series, up to 5 years of operation
<div> <div> <div>This data set contains data from 28 portable 24V lithium iron phosphate (LFP) battery systems with approximately 160Ah nominal capacity. Each system's specific use case is unknown, but battery systems of this size are typically used as power sources for recreational vehicles, solar energy storage, and more.</div> <br> <div>All battery systems in this data set showed some form of unsatisfactory behavior and were returned to the manufacturer. Many reasons can cause a consumer to return a battery to the manufacturer for maintenance. The user's individual decisions may be motivated by personal judgment, BMS warnings, or customer support advice. This data set comprises a very small fraction of batteries sold of this version. Therefore, this data set is biased and not representative of the operational data of the entire population of this system version. An improved version replaced this battery system type. The battery system manufacturer provided the data set for this study and allowed its open-source release under the condition of anonymity.</div> <br> <div>Each battery system consists of 8 prismatic cells in series. Each system has one load current sensor, and each cell has one voltage sensor. The four temperature sensors are placed between adjacent cells, i.e., each temperature sensor is shared by two cells. Furthermore, the battery systems have active cell balancing. The available measurements vary from a single month to five years. Consequently, the number of data rows per system varies from several thousand to millions, depending on the duration of battery operation. The data set contains a total of 133 million rows of measurements.</div> </div> </div> <div> </div> <div> <div><strong>Associated Python Library</strong></div> <div>BattGP <a href="https://github.com/JoachimSchaeffer/BattGP" target="_blank" rel="noopener">https://github.com/JoachimSchaeffer/BattGP</a></div> <div>This library contains classes and functions to analyze the data set with Gaussian processes.</div> <div>Furthermore, data visualization functions are part of the library.</div> <div><br><strong>Associated Article<br></strong>Gaussian Process-based Online Health Monitoring and Fault Analysis of Lithium-Ion Battery Systems from Field Data</div> <div>Cell Report Physical Science <br> <div><a href="https://doi.org/10.1016/j.xcrp.2024.102258">https://doi.org/10.1016/j.xcrp.2024.102258</a></div> <div> </div> <div> </div> </div> </div>
Data for "Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil"
<p>This dataset contains soybean yield, soil property values, and apparent electrical conductivity (ECa) from two farms located in the South of Brazil. The data description and methods can be found in the paper:</p> <p><strong>Bottega, E. L., Safanelli, J. L., Zeraatpisheh, M., Amado, T. J. C., Queiroz, D. M. de, & Oliveira, Z. B. de. (2022). Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil. In Agronomy (Vol. 12, Issue 6, p. 1390). MDPI AG. https://doi.org/10.3390/agronomy12061390</strong></p> <p>Abstract:</p> <p>Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable management zones (MZ) delineated based on the soil apparent electrical conductivity (ECa), using fuzzy k-means, in two fields with contrasting soil textures in southern Brazil. For this, a grid scheme (50 × 50 m) was applied to measure ECa, conduct soil sampling for analysis, and determine soybean yield. The MZ were delineated based on the ECa spatial distribution, and statistical non-parametric tests (p < 0.05) were employed to compare the soil chemical and physical attributes among MZ. The management zones were able to distinguish the average values of Clay, Silt, pH, Ca<sup>2+</sup>, Mg<sup>2+</sup>, SB, Al<sup>3+</sup>, H<sup>+</sup> + Al<sup>3+</sup>, AS%, and BS%. In the field classified as sandy clay loam texture, management zones were able to differentiate the average values of soybean yield, Clay, Ca<sup>2+</sup>, Mg<sup>2+</sup>, SB, and CEC. Thus, this study supports the ECa as an efficient tool for delineating MZ of contrasting cropland soils in southern Brazil to understand the within-field soil variability and adjust the inputs accordingly.</p>
Hyperspectral Unmixing Dataset of UAV Gathered Blueberry Field Data
<p>Hyperspectral Unmixing dataset created from hyperspectral data gathered usign SPECIM push-broom hyperspectral camera mounted on a UAV flying over blueberry fields in Lithuania. Created dataset contains six data classes and linear mixtures from raw data. All data is given in Python Numpy array .npy files. </p> <p>To keep the annonimity of data owners only the non georectified data cubes are published.</p> <p>Dataset includes three hyperpectral data cubes of blueberry fields and Dark reference cube to show camera noise.</p> <p><strong>Data structure:</strong></p> <p>cube_1, cube_2, cube_2 and Dark - folder with hyperspectral data.</p> <p>calibration_data.npy - Data of calibration plates (with 40%, 10% and 5% reflectance values) from hyperspectral flight that were used to conver DN to reflectance.</p> <p>endmembers.npy - Spectra of siz endmembers (classes) used in the dataset.</p> <p><strong>cube_x folders include:</strong></p> <p>class_matrix.npy - Numpy matrix file of hyperspectral image classes (classification results)</p> <p>raw_data.npy - Hyperspectral cube created from raw camera data (with DN values)</p> <p>data_cube_3_0.npy and abundances_3_0.npy - Classified and mixed (using slidin window of 3x3 pixels with no overlap) hyperspectral data cube and class abundance matrix. </p> <p>endmember_errors.npy - matrix of variation for each of endmembers in the hyperspectral cube (used for evaluation mostly.)</p> <p><strong>Dark folder:</strong></p> <p>includes data folder with raw-dark_fl1_20230830_140006_radiance.dat and .hdr ENVI raster data files (library like <em>rasterio</em> for Python can used to read these files). This is the dark (0% reflectance) data cube and header file used in calibration.</p>
V 1.0 Dataset for "Emergence and Evolution of Big Data Research: A 30-year (1993-2022) Scientometric Analysis of The Knowledge Field"
<p>This dataset includes the bibliometric data used in the scientometric analysis of the field of big data research over a 30-year period (1993-2022). The data was collected from the Scopus database, and contains information on 70,163 articles and 315,235 author keywords. The dataset is structured by 17 interrelated data categories that trace the conceptual emergence and evolution of the big data field, focusing on keyword co-occurrences, disciplinary distributions, and the temporal growth of publications. This dataset supports the analyses presented in the related manuscript.</p>
A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations - velocity field data
<p>Velocity field to be processed by the methodology described in </p> <p>Biassoni, D, Russo, M, Viviani, P, Vitali, G, & Lengani, D. "A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations." <em>Proceedings of the ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition</em>. <em>Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions</em>. London, United Kingdom. June 24–28, 2024. V12CT32A039. ASME. <a href="https://doi.org/10.1115/GT2024-127953" target="_blank" rel="noopener">https://doi.org/10.1115/GT2024-127953</a></p> <p>and with the code provided at </p> <p><a href="https://gitlab.linksfoundation.com/across-public/aeronautics-workflows/asme-turboexpo-2024">https://gitlab.linksfoundation.com/across-public/aeronautics-workflows/asme-turboexpo-2024</a></p> <h3> </h3>
Linked collectors and determiners for: The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure.
Natural history specimen data linked to collectors and determiners held within, "The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3c438ae3-9ea0-4eda-b893-acf6d5756c90">https://bionomia.net/dataset/3c438ae3-9ea0-4eda-b893-acf6d5756c90</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3c438ae3-9ea0-4eda-b893-acf6d5756c90">https://gbif.org/dataset/3c438ae3-9ea0-4eda-b893-acf6d5756c90</a>. Formatted as a Frictionless Data package.
Towards the $\beta$ function of SU(2) with adjoint matter using Pauli–Villars fields—Data release
<p>This release contains all data generated in preparing the poster <a href="../records/13361520">Towards the function of SU(2) with adjoint matter using Pauli–Villars fields</a>, presented at <a href="https://conference.ippp.dur.ac.uk/event/1265/overview">Lattice 2024</a> in Liverpool, and its corresponding proceedings paper. It includes two classes of data, each provided both for the one- and two-flavour theories:</p> <ol> <li>Raw data, as generated from the HMC measurement code running on HPC, in their native formats.</li> <li>Metadata around the analysis of the ensembles, in YAML format.</li> </ol> <p>Due to their size, raw gauge configurations are not included in this release.</p> <p>Further documentation on the directory structure and file formats is provided in the file README.md</p>
Long-term biochar and soil organic carbon stability– Evidence from field experiments in Germany-ROW DATA
<p> Row data for researcher paper Long-term biochar and soil organic carbon stability– Evidence from field experiments in Germany</p>
Research project on field data collection for honey bee colony model evaluation - datasets
<p><strong>Description of the datasets</strong></p> <p>The file 00_MUSTB_field_data_model.docx contains the data model according to which the data collected in the context of the MUSTB field data collection were reported to EFSA. The current data model description includes some modifications with respect to the specifications published before the beginning of the project (EFSA, 2017, https://doi.org/10.2903/sp.efsa.2017.EN-1234). All the tables included in the data model are published here in csv format. The underlying schemas are also published in xsd format.</p> <p>Sites: General information about the sites where the data collection took place;</p> <p>Polygons: General information about the polygons where the botanical survey took place.</p> <p>Table I: Pesticide application, reporting data on experimental spraying events;</p> <p>Table II: Resource providing unit and landscape fitness, reporting data on abundance of flowering plants in polygons mostly within 1.5 km, but in some cases up to 3 km of the experimental colony;</p> <p>Table III: Master list of all hives included in the study;</p> <p>Table IV: Colony management, reporting the log of the beekeeper regarding input (if material was added to the hive: e.g. empty frames, chemicals for varroa treatment, sugar), output (if material was removed from the hive, e.g. honey combs, supers), queen loss, swarming, or clinical signs observed in the experimental hives;</p> <p>Table V: Hive inspection, reporting data on in-hive measurements in the experimental colonies. This table contained several types of data, including:</p> <ul> <li>Data on brood development and food provision (“cell utilization”) obtained from image analysis of combs;</li> <li>Data on forager activity obtained from automatic video recordings and image analysis by a bee counter;</li> <li>Data on hive weight obtained from automatic logging by a hive scale;</li> <li>Data on adult bee strength, obtained by weight assessment of combs with and without adult bees (“bees per comb data”);</li> </ul> <p>Table VI: SSD2, reporting data on results of laboratory analyses of pollen, pesticide residues and parasites/pathogens. These four types of laboratory analyses involved different methods, and were reported according to different standards. Therefore, a number of the fields in the technical specifications for the SSD2 table (EFSA, 2017) were not applicable for records reporting results of some analyses, in particular palynological, parasite and pathogen analyses. These fields were left empty;</p> <p>Table VII: Colony observation, reporting observations of honey bee waggle dances from observation hives. Orientation denotes the angle of the waggling phase relative to the vertical axis on the comb. Direction denotes the actual direction in the landscape, as calculated from the orientation of the waggle dance.</p> <p>In all the csv files, columns with the suffix "_desc" have been included, where relevant, to include the name corresponding to the EFSA controlled terminology used in the previous column (e.g. resUnit contains EFSA term codes while resUnit_desc contains the term names).</p> <p><strong>Data storage</strong></p> <p>All data collected during the project was stored in a relational database. The database was developed in .NET Entity Framework Core, ran on a PostgreSQL, and was hosted by Amazon Web Service during the whole duration of the project development. Data could be imported or entered manually in the database through a web form. Administrators could create new users and administrators, new sites, and new colonies, i.e., administrators were allowed to enter or change data of all tables. Users were allowed to enter data, and could view, retrieve, and modify their own data of all tables, except for Table III (description of experimental colonies). Administrators could view and retrieve all data. Data was retrieved in CSV and XML formats, and were structured to secure a smooth transmission of data to the Data Collection Framework of EFSA. Furthermore, data flow from the field data collection to the development of ApisRAM was secured by direct communication between the field and modelling teams.</p> <p> </p> <p><strong>Version 2</strong> contains the UTM coordinates in tables Sites, Polygons and Resource providing unit.</p>
Data from: Spectroscopic approach to correction and visualisation of bright-field light transmission microscopy biological data
<p>The most realistic information about the transparent sample such as a live cell can be obtained only using bright-field light microscopy. At high-intensity pulsing LED illumination, we captured a primary 12-bit-per-channel (bpc) response from an observed sample using a bright-field wide-field microscope equipped with a high-resolution (4872x3248) image sensor. In order to suppress data distortions originating from the light interactions with undesirable elements in the optical path, poor sensor reproduction (geometrical defects of the camera sensor and some peculiarities of sensor sensitivity), this uncompressed 12-bpc data underwent a kind of correction after simultaneous calibration of all the parts of the experimental arrangement. Moreover, the final intensities of the corrected images are proportional to the photon fluxes detected by a camera sensor. It can be visualized in 8-bpc intensity depth after the Least Information Loss compression [Lect. Notes Bioinform. 9656, 527 (2016)].</p>
Agronomic performance of cultivar mixtures of winter wheat varieties, obtained from mixture field trials at 5 locations in Switzerland from 2019 to 2020, together with yield data from the varieties in pure stand obtained from the national variety testing trial network
<p>This dataset contains agronomic parameters of 32 winter wheat variety mixtures tested during 2 growing seasons (2019-2020) at 5 locations in Switzerland, as well as yield data of these varieties in pure stands originating from the Swiss national variety testing network. The dataset has been used to investigate the links between asynchrony and yield stability, published in <a href="https://doi.org/10.1002/csc2.21151">https://doi.org/10.1002/csc2.21151</a>. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h2>Methods </h2> <p><em>Field trials </em></p> <p>The experiment took place in five sites across Switzerland, in 2019 and 2020. The sites were located in Nyon (1260), Delley (1567), Utzenstorf (3428), Zurich (8046), and Ellighausen (8566).</p> <p>Experimental communities consisted of 32 different two-variety mixtures grown in 7.1-m<sup>2</sup> plots (1.5 × 4.7 m). We replicated the mixture experiment three times per site with the exact same variety composition. We used a randomized block design, with plots being randomized at each site within each block. Density of sowing was 350 seeds/m<sup>2</sup>, and seeds were mixed beforehand at a 50:50 ratio in terms of mass. We used the 50:50 mass ratio as this is what is generally done in practice by farmers and seed suppliers. Plots were sown mechanically each autumn. The plots were mechanically fertilized according to the Principles of Agricultural Crop Fertilisation in Switzerland (Federal Office for Agriculture) with an average of 140 kg N/ha (ammonium nitrate), applied in three splits (40 at the tillering stage—60 at stem elongation stage—40 when the flag leaf is visible). The experimental trials were conducted following the extenso Swiss scheme, which means that there was no application of any fungicide, insecticide, or plant growth regulator. </p> <p>The performances of single varieties were obtained by going through the trials of the national variety testing program. We gathered the data for the years 2018/2019 and 2019/2020. The data regarding single varieties could be obtained for three out of the five sites used for the mixtures: 1260, 1567, and 8566. Because there were no national variety trials at the two other sites (8046, 3428), we could not get any data for single varieties in these sites. Thus, all further analyses including single variety data were only done for the three sites mentioned above. At each of these sites, the variety trials were located on the same plot as the mixture trials, even though a little further apart. Therefore, soil parameters and crop precedents were the same between the mixture and variety testing trials. Furthermore, we only selected the national variety testing trials that respected the <em>extenso</em> conditions, that is, no fungicide, pesticide, or growth regulator application, and that received the same amount of fertilization as the mixture trials. In 8566 and 1567, sowing and harvesting dates were identical between the two trials; in 1260, sowing and harvesting dates could vary but remained within a week of each other.</p> <p> </p> <p><em>Data collection </em></p> <p>For each plot, heading dates were monitored, and average height at BBCH 59–75 was measured.</p> <p>The prevalence of diseases was scored twice in the growing season. Specifically, the severity of brown rust, yellow rust, powdery mildew, and Septoria tritici blotch was assessed. This was performed by grading each individual plot from 1 to 9 for each disease, with 1 representing no disease and 9 a complete infection. The scoring scale follows a logistic progression based on the symptoms of the top three leaves. We used the data from the final scoring for statistical analysis, as the disease severity was usually more important then.</p> <p>At maturity, we harvested each plot with a combine harvester. The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured specific weight and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content was measured at the site level with a near-infrared instrument (ProxiMate; Büchi instruments).</p>
Active nematics director and flow field data
<p>We conduct our experiments on the microtubule-kinesin active nematic system pioneered in Sanchez et al. 2012. The long rod-like MTs are bundled together via depletion interactions and are driven out of equilibrium by the action of kinesin-streptavidin motor protein complexes, which are units that induce relative motion utilizing ATP<br> as the energy source. Depletion forces also aid in driving the MT bundles to form bundles to the oil-water<br> fluid interface, where they execute self-sustained bending and buckling instabilities. The system is extensile,<br> which means that active stresses cause the MT bundles to extend in length and contract in width.</p> <p> </p> <p>To investigate the dynamics of defects in 2D flat space, we prepare the active nematic in a flow-cell setup<br> where the entire pool of ingredients is confined in a 2D sealed cell roughly 10 cm2 in area and 100 μm in<br> thickness. The lower surface of the cell is subjected to hydrophobic treatment (using Aquapel) and the<br> upper surface to hydrophilic treatment (using polyacrylamide coating) to enhance wetting by the respective<br> fluid phases. A fluorinated oil (HFE-7500 with surfactant E2K0660) forms the oil-phase, and the active MT<br> suspension forms the water-phase. We obtained purified tubulin monomers and kinesin–streptavidin motor<br> protein complexes from the Dogic Group at Brandeis University. The polymerization of tubulin to<br> MTs is performed in our lab before mixing with other biomaterials as per the protocols described in previous<br> works. The final active mix has 20% MTs by volume aided with 144μM ATP. The entire flow<br> cell is sealed by epoxy resin and centrifuged at 1000 RPM to accelerate the depletion mechanism to the<br> interface.</p> <p><br> We use confocal fluorescence microscopy for visualization. The MTs are labeled with AlexaFlour 647 dye<br> and illuminated at 633 nm; the excitation and emission peaks are at 651 nm and 667 nm, respectively. After<br> sample preparation and centrifugation, we wait for 15-20 minutes to allow for uniform depletion, and then<br> image at a constant framerate till the activity ceases. Typically, the MTs stay active for 6+ hours. Imaging<br> is done using 10× and 20× objectives to focus on regions with area on the order of mm2, away from the<br> edges of the flow cell. The imaging process results in a time series of 8-bit grayscale images, which are stored<br> as the raw data.</p> <p> </p> <p>For more information, see "Physically-informed data-driven modeling of active nematics" by Golden et al.</p>
Data for paper "Constructing precisely quasi-isodynamic magnetic fields"
<p>This archive contains data and source code used for the paper "Constructing precisely quasi-isodynamic magnetic fields".</p>
ScienceDex guides
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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.