Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

Datasets underlying the paper 'Electrotaxis of self-propelling artificial swimmers in microchannels'

Datasets underlying the paper 'Electrotaxis of self-propelling artificial swimmers in microchannels', arXiv:2401.14376 Source data for all figures in the manuscript. Figures were produced by Python/matplotlib, a corresponding Jupyter notebook is included in the root folder. Compressed videomicrographs of the experiments, and abridged numerical data sets; all raw data are available from the authors on reasonable request.

opencc-by-4.0Oct 2024View details →

Crossroads of Commerce: How the Taiwan Strait Propels the Global Economy - Supplemental Material

Supplemental Material to the CSIS Report: <a href="https://features.csis.org/chinapower/china-taiwan-strait-trade">Crossroads of Commerce: How the Taiwan Strait Propels the Global Economy</a> <ol> <li>tws_2022_trade_value_estimates.csv contains estimated trade value in USD for 2022. Denominators for % estimates based on <a href="https://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=37">2022 CEPII BACI bilateral trade flows</a> <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>ISO</td> <td>ISO3 Country Code</td> </tr> <tr> <td>Economy </td> <td>Country/Region Name</td> </tr> <tr> <td>TWS Imports_bln</td> <td>US$ value (billions) of imports that transit the Taiwan Strait</td> </tr> <tr> <td>TWS Imports_%</td> <td>% of total imports that transit the Taiwan Strait</td> </tr> <tr> <td>TWS Exports_bln</td> <td>US$ value (billions) of exports that transit the Taiwan Strait</td> </tr> <tr> <td>TWS Exports_%</td> <td>% of total exports that transit the Taiwan Strait</td> </tr> <tr> <td>Total TWS Trade_bln</td> <td>US$ value (billions) of trade (imports+exports) that transits the Taiwan Strait</td> </tr> <tr> <td>Total TWS Trade_%</td> <td>% of total trade (imports+exports) that transits the Taiwan Strait</td> </tr> </tbody> </table> </li> <li> ChinaPower_CrossroadsCommerce_TaiwanStrait_methodology.pdf provides and overview of methodology and data sources. </li> <li> ChinaPower_CrossroadsCommerce_TaiwanStrait_factsheet.pdf provides key highlights derived from the data.</li> </ol> For questions about the data, please contact David Peng (dpeng@csis.org)

opencc-by-4.0Jul 2021View details →

SI for Cherns et al "Correlative tomography of exceptionally preserved Jurassic ammonite implies hyponome-propelled swimming"

================================================================================ About ================================================================================ This repository is associated with: "Correlative tomography of exceptionally preserved Jurassic ammonite implies hyponome-propelled swimming" Authored by: Lesley Cherns (1), Alan R. T. Spencer (2,3), Imran A. Rahman (3,4), Russell J. Garwood (3,5), Chris Reedman (1,6), Genoveva Burca (7,8), Martin J. Turner (9), Neville T. J. Hollingworth (10), and Jason Hilton (11) 1 School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK 2 Department of Earth Science and Engineering, Imperial College London, London, UK 3 Earth Sciences Department, Natural History Museum, London, UK 4 Oxford University Museum of Natural History, University of Oxford, Oxford, UK 5 Department of Earth and Environmental Sciences, University of Manchester, Manchester, UK 6 Jurassic Coast Trust HQ, Bridport, Dorset, UK 7 Science and Technology Facilities Council, Rutherford Appleton Laboratory, ISIS Facility, Harwell, UK 8 Faculty of Science and Engineering, University of Manchester, Manchester, UK 9 Department of Computer Science, University of Manchester, Manchester, UK 10 Science and Technology Facilities Council, Swindon, UK 11 School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK ================================================================================ Contents ================================================================================ This archive contains the following file(s): - ct_dataset.zip - dragonfly_sigaloceras_enodatum.zip - neutron_dataset.zip - neutron_dataset_cropped_cleaned.zip - photogrammetry_block_model.ply - photogrammetry_dataset.zip - stl_model_meshes.zip - video.mkv - video_blender.zip + metadata.txt (this file) ================================================================================ Note on .zip archives: ================================================================================ All .zip archive where created using 7-zip (https://www.7-zip.org/), using the default .zip settings. ================================================================================ File Descriptions and Metadata: ================================================================================ -------------------------------------------------------------------------------- ct_dataset.zip -------------------------------------------------------------------------------- X-ray CT data. A Nikon Metrology XTek XT H 225 at the University of Manchester was used. The scan used a 0.5mm Copper filter, 145 kV, uA 85, capturing 6000  projections on a 3192 x 2296 detector. The projection data was reconstructed  using Nikon Metrology NV’s XT 5.1.4.3 software. Dataset is consists of a .vol and .vgi (metadata) file. The .vol contains the raw data for the individual slices. Each image has the following properties: Images format: 32-bit float (raw) Image x/y: 3189 x 3191 px  Number of images: 1150 Caculated voxel size: 19.6419 µm Uncompressed total size: 45 GB -------------------------------------------------------------------------------- dragonfly_sigaloceras_enodatum.zip -------------------------------------------------------------------------------- Contains: sigaloceras_enodatum_final.ORSSession | 8.8 GB (uncompressed) The sigaloceras_enodatum_final.ORSSession file is a save DragonFly session. The session conatains the full setup (aligned Nutron and CT datasets, ROIs,  segments, and meshes) used to produce and interpret the ammonite.  DragonFly version: 2021.1.0.977 See: https://www.theobjects.com/dragonfly/index.html DragonFly was used under the terms of its Non-Commercial Use License. Non-commercial licenses are granted free-of-charge to qualified researchers,  academics, and non-commercial developers for non-profit research or development  purposes. Non-commercial licenses are valid for a one-year period and can be renewed  annually at no charge. See: https://www.theobjects.com/dragonfly/get-non-commercial-licensing-program.html -------------------------------------------------------------------------------- neutron_dataset.zip -------------------------------------------------------------------------------- Neutron tomography was performed at the IMAT beamline of ISIS Neutron and Muon  Source, UK. Imaging was undertaken using an optical camera box with a  field-of-view of 120 x 120 mm2. A total of 1801 projections were acquired  over 360° rotation, with an exposure time of 30 s per projection, giving a total  duration of ~15 hours. Projections were reconstructed as two-dimensional  slices using Octopus Imaging Software. These slices have an uncompressed total  size of 15.4 GB. Each slice in the stack has the following properties: Images format: 16-bit .tiff Image x/y: 2048 x 2048 px  Number of images: 2048 Caculated voxel size: 58 µm Image size (uncompressed): 8192 MB each. -------------------------------------------------------------------------------- neutron_dataset_cropped_cleaned.zip -------------------------------------------------------------------------------- Neutron tomographic slices that have been cropped and cleaned (ie. had background  noise removed) based on images in neutron_dataset.zip. Images in .tiff format. 1715 images in stack. -------------------------------------------------------------------------------- photogrammetry_block_model.ply -------------------------------------------------------------------------------- Model in .ply format (with coloured vertex) showing the limestone block and  counter-part of the ammonite. 257136 vertices and 513513 faces. -------------------------------------------------------------------------------- photogrammetry_dataset.zip -------------------------------------------------------------------------------- Photographs, with backgrounds removed, used to generate the 3D block model via photogrammetry. The .zip archive contains 142 photographs. -------------------------------------------------------------------------------- stl_model_meshes.zip -------------------------------------------------------------------------------- .zip archive containing the 49 .stl meshes used in the creation of the 3D reconstructions. These meshes have been smoothed within DragonFly prior to  exportation. -------------------------------------------------------------------------------- video.mkv -------------------------------------------------------------------------------- Video showing annimation starting with the limestone block and ammonite in place, ammonite is then shown on its own, before the combinded neutron and x-ray tomography results are presented. Length: 00:43 min Size: 23.9MB Format: .mkv -------------------------------------------------------------------------------- video_blender.zip -------------------------------------------------------------------------------- This .zip contains the Blender file used in the creation of the video.

dryad40/100

Geometric latches enable tuning of ultrafast, spring-propelled movements

The smallest, fastest, repeated-use movements are propelled by power-dense elastic mechanisms, yet the key to their energetic control may be found in the latch-like mechanisms that mediate transformation from elastic potential energy to kinetic energy. Here we test how geometric latches enable consistent or variable outputs in ultrafast, spring-propelled systems. We constructed a reduced-order mathematical model of a spring-propelled system that uses a torque reversal (over-center) geometric latch. We parameterized the model to match the scales and mechanisms of ultrafast systems, specifically snapping shrimp. We simulated geometric and energetic configurations that enabled or reduced variation of their strike durations and dactyl rotations given variation of stored elastic energy and latch mediation. We then collected an experimental dataset of the energy storage mechanism and ultrafast snaps of live snapping shrimp (Alpheus heterochaelis) and compared our simulations to their configuration. We discovered that snapping shrimp store elastic energy through deformation of the propodus exoskeleton. Regardless of the amount of variation in spring loading duration, strike durations were far less variable than spring loading durations. When we simulated this species' morphological configuration in our mathematical model, we found that the low variability of strike duration is consistent with their torque reversal geometry. Even so, our simulations indicate that torque reversal systems can achieve either variable or invariant outputs through small adjustments to geometry. Our combined experiments and mathematical simulations reveal the capacity of geometric latches to enable, reduce, or enhance variation of ultrafast movements in biological and synthetic systems.

opencc-zeroJan 2023View details →

opencc-by-4.0Feb 2023View details →

dataset related to article: " Cerebrospinal fluid neuropathological biomarkers in beta-propeller protein-associated neurodegeneration, with complicated parkinsonian phenotype"

analysis sanger electropherograms in the patient's in .abi format and segregation in the family (mother; father and sister

opencc-by-4.0Feb 2023View details →

Data associated with "Decoding the hydrodynamic properties of microscale helical propellers from Brownian fluctuations"

Deskewed data, thresholded data, and data analysis associated with https://arxiv.org/abs/2208.13854

opencc-by-4.0Mar 2023View details →

Cold/warm gas thruster propellant ranking and performance data

Datasets list all propellants, and their corresponding propulsion system, mission design, and performance values, considered in the open-access research paper: <a href="https://www.sciencedirect.com/science/article/pii/S0094576523003806">J. Martinez Martinez and T. Lafleur, "On the selection of propellants for cold/warm gas propulsion systems", Acta Astronautica 212, 54 (2023).</a> In each of the four files, propellants are ranked according to a specific performance metric: <ol> <li>Propellant_Ranking_FP.xlsx = Thrust-to-power ratio</li> <li>Propellant_Ranking_Isp.xlsx = Specific impulse (impulse per propellant mass)</li> <li>Propellant_Ranking_Ivol.xlsx = Volumetric impulse (impulse per propellant volume)</li> <li>Propellant_Ranking_Ispstar.xlsx = Effective specific impulse (impulse per propulsion system wet mass; defined in the above paper)</li> </ol> Each column in the datasets is labelled and corresponds to a specific variable used in the performance model in the paper above. The last column "Comments (E = extended properties)", highlights whether additional extrapolation of any model input physical/thermodynamic data was required. That is to say, for each propellant considered, physical data (such as the viscosity) is needed and was obtained from existing chemical databases. Such databases typically perform a regression analysis and propose empirical equations for calculating physical/thermodynamic data at a given temperature. The regression coefficients are often only valid within a certain temperature range. The label "E" in the last column indicates that data for that propellant was required at a temperature outside of this validity range. In most cases, the temperature is only just outside the validity range for some properties, and the empirical equation is still used within the model, but the label "E" has been used to signal to the reader that an extension of some empirical equations was needed.

opencc-by-4.0Sep 2023View details →

ONERA Numerical Database Configuration A1 (static propeller)

This database contains the numerical results obtained by ONERA on the configuration A1, using ZDES mode 2 and ZDES mode 3 methods. The ZDES mode 3 approach offers an explicit resolution of the turbulent scales present in the outer region of the flat plate boundary layer and their interaction with the propeller.

dryad40/100

Geometric latches enable tuning of ultrafast, spring-propelled movements

Open the record for dataset details and reuse information.

publicJan 2023View details →

dryad36/100

Data from: K-13 Propeller gene polymorphisms isolated between 2014 and 2017 from Cameroonian Plasmodium falciparum malaria patients

The emergence of artemisinin-resistant parasites since the late 2000s at the border of Cambodia and Thailand poses serious threats to malaria control globally, particularly in Africa which bears the highest malaria transmission burden. This study aimed to obtain reliable data on the current state of the kelch13 molecular marker for artemisinin resistance in Plasmodium falciparum in Cameroon. DNA was extracted from the dried blood spots collected from epidemiologically distinct endemic areas in the Center, Littoral and North regions of Cameroon. Nested PCR products from the Kelch13-propeller gene were sequenced and analyzed on an ABI 3730XL automatic sequencer. Of 219 dried blood spots, 175 were sequenced successfully. We identified six K13 mutations in 2.9% (5/175) of samples, including 2 non-synonymous, the V589I allele had been reported in Africa already and one new allele E612K had not been reported yet. These two non-synonymous mutations were uniquely found in parasites from the Littoral region. One sample showed two synonymous mutations within the kelch13 gene. We also observed two infected samples with mixed K13 mutant and K13 wild-type infection. Taken together, our data suggested the circulation of the non-synonymous K13 mutations in Cameroon. Albeit no mutations known to be associated with parasite clearance delays in the study population, there is need for continuous surveillance for earlier detection of resistance as long as ACTs are used and scaled up in the community.

opencc-zeroSep 2020View details →

opencc-by-4.0Dec 2023View details →

Quantum Material-Based Self-Propelled Microrobots for the Optical "On-the-Fly" Monitoring of DNA

Quantum dot-based materials have been found to be excellent platforms for biosensing and bioimaging applications. Herein, self-propelled microrobots made of graphene quantum dots (GQD–MRs) have been synthesized and explored as unconventional dynamic biocarriers toward the optical “on-the-fly” monitoring of DNA. As a first demonstration of applicability, GQD–MRs have been first biofunctionalized with a DNA biomarker (i.e., fluorescein amidite-labeled, FAM-L) via hydrophobic π-stacking interactions and subsequently exposed toward different concentrations of a DNA target. The biomarker–target hybridization process leads to a biomarker release from the GQD–MR surface, resulting in a linear alteration in the fluorescence intensity of the dynamic biocarrier at the nM range (1–100 nM, R2 = 0.99), also demonstrating excellent selectivity and sensitivity, with a detection limit as low as 0.05 nM. Consequently, the developed dynamic biocarriers, which combine the appealing features of GQDs (e.g., water solubility, fluorescent activity, and supramolecular π-stacking interactions) with the autonomous mobility of MRs, present themselves as potential autonomous micromachines to be exploited as highly efficient and sensitive “on-the-fly” biosensing systems. This method is general and can be simply customized by tailoring the biomarker anchored to the GQD–MR’s surface.

opencc-by-4.0May 2022View details →

PROCRAFT_Form A_Propeller_Fecamps

Information on the propeller of a WWII airplane (not identified) found near Fécamps, France

opencc-by-nc-2.5Jul 2019View details →

Flights of a Multirotor UAS with Structural Faults: Failures on Composite Propeller(s)

Data acquired from several flights of a custom-fabricated Hexacopter UAS with composite structure (carbon fiber arms and central hub) and composite (carbon fiber) propellers is presented here. The Hexacopter was assembled from a commercially available kit (Tarot 690) and was flown in manual and autonomous mode; take-offs and landings were under manual control and the bulk of the flight tests were conducted with the Hexacopter in a “Position Hold” mode. All flights were flown within the UAS flight cage at Parks College of Engineering, Aviation and Technology at Saint Louis University, for approximately 5 minutes each. Several failure conditions (different types of manually induced) on the composite (carbon fiber) propellers were tested, including failures on up to two propellers. The data set described in this article contains flight data from the onboard flight controller (Pixhawk) as well as three 3-axis accelerometers mounted on the arms of the Hexacopter UAS. The data is included as supplemental material.

dryad36/100

Data from: Smart self-propelled particles: A framework to investigate the cognitive bases of movement

We present a framework specifically developed to develop theories of spatial decision-making and to fully understand the rational of decisions embedded in an environment (and therefore the underlying evolutionary processes). This is achieved by the means of cognitive agents, moving thanks to artificial neural networks controlling movements and whose parameters are optimised with a genetic algorithms. Specifically, we investigate a simple task in which single agents need to learn to explore their square arena without leaving its boundaries. We show that agents evolve by developing increasingly optimal strategies to solve a spatially-embedded learning task while not having an initial arbitrary model of movements. The process allows the agents to learn how to move (i.e. by avoiding the arena walls) in order to make increasingly optimal decisions (improving their exploration of the arena). Our dataset is made of 4 sets of simulations: parameters of reference, a survival objective function, introducing a turning penalty and with a slower speed (see details of parameters below). Each set of simulations is made of 60 trials with different initial conditions, each of which has been simulated with 20,000 agents for 150 generations. For each generation, we record: <ul> <li>the score of the 20,000 agents across four runs with different and random initial conditions</li> <li>the score of the 20,000 agents from the same controlled initial condition (files with _com suffix)</li> <li>metadata with parameters used in this simulation</li> <li>parameters (weights and biases) of the artificial neural network of the best agent (i.e. with highest score) of each generation</li> <li>parameters (weights and biases) of the artificial neural network of the best agent (i.e. with highest score in the controlled initial condition run) of each generation (files with _com suffix)</li> </ul>

opencc-zeroJul 2023View details →

opencc-by-4.0Oct 2022View details →

Inertial self-propelled particles in anisotropic environments

Supplementary data for the following manuscript: Alexander R. Sprenger, Christian Scholz, Anton Ldov, Raphael Wittkowski, and Hartmut Löwen, "Inertial self-propelled particles in anisotropic environments".

controlledIPD-YESFeb 2026View details →

A Study to Assess the Total Systemic Exposure Bioequivalence of of Budesonide, Glycopyrronium, and Formoterol Delivered by BGF MDI With Next-Generation Propellant Compared With BGF MDI With HFA Propel

ClinicalTrials.gov study NCT05569421. IPD Sharing: YES. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

Self-Propelled Versus Standard Percutaneous Endoscopic Gastrojejunostomy(PEG-J); RCT

ClinicalTrials.gov study NCT01892267. IPD Sharing: Not stated. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

Safety and Efficacy of the Propel Mini and Propel Nova Steroid-Eluting Sinus Implant in Frontal Sinus

ClinicalTrials.gov study NCT02266810. IPD Sharing: NO. Countries: 1. Publications: 2.