Find research datasets worth reusing

opencc-by-4.0Jan 2022View details →

Supplementary information for 'Distinct gene expression dynamics in developing and regenerating crustacean limbs', by Sinigaglia et al.

Supplementary data and code for the manuscript 'Distinct gene expression dynamics in developing and regenerating crustacean limbs', by Sinigaglia et al.

opencc-zeroJun 2022View details →

Modelling seasonal dynamics of secondary growth in R

The monitoring of seasonal radial growth of woody plants addresses the ultimate question of when, how, and why trees grow. Assessing the growth dynamics is important to quantify the effect of environmental drivers and understand how woody species will deal with the ongoing climatic changes. One of the crucial steps in the analyses of seasonal radial growth is to model the dynamics of xylem and phloem formation based on increment measurements on samples taken at relatively short intervals during the growing season. The most common approach is the use of the Gompertz equation, while other approaches, such as general additive models (GAMs) and generalised linear models (GLMs), have also been tested in recent years. For the first time, we explored artificial neural networks with Bayesian regularisation algorithm (BRNNs) and show that this method is easy to use, resistant to overfitting, tends to yield s-shaped curves and is therefore suitable for deriving temporal dynamics of secondary tree growth. We propose two data processing algorithms that allow more flexible fits. The main result of our work is the XPSgrowth() function implemented in the radial Tree Growth (rTG) R package, that can be used to evaluate and compare three modelling approaches: BRNN, GAM and the Gompertz function. The newly developed function, tested on intra-seasonal xylem and phloem formation data, has potential applications in many ecological and environmental disciplines where growth is expressed as a function of time. Different approaches were evaluated in terms of prediction error, while fitted curves were visually compared to derive their main characteristics. Our results suggest that there is no single best fitting method, therefore we recommend testing different fitting methods and selection of the optimal one.

All-atom molecular dynamics simulations of phenylalanine-4-hydroxylase (PAH) tetramer to investigate the impact of two novel heterozygous mutations, p.Y198N and p.Y204F, observed in a classical phenylketonuria patient

Phenylalanine-4-hydroxylase (PAH) tetramer system (Robetta modelling to complete the structure with template PDB ID: 6hyc) with parametrised BH4 ligand (parameters are available in the dataset) and Fe(II) metal ions in a TIP3P water box ionised with 0.15 M KCl were presented as wild-type and carrying two novel mutations as Y198N on dimeric chains A and B, and Y204F on dimeric chains C and D. In addition, E353 and E422 are protonated as predicted by PROPKA. BH4 molecule parametrization was performed by using GAFF, Antechamber and “amb2chm_par.py” program of Amber2018. 5,000-step minimization and 1 ns equilibration were performed by fixing the protein to relax the system. Then, another 5,000-step minimization and 1 ns equilibration were performed without any constraints, except the SHAKE algorithm on water molecules, to relax the protein and system. The production simulations were performed along 100 ns trajectory at 310 K collected under NpT ensemble. All system preparation and simulation details for this dataset is available with the related background, results and conclusions in the following article: Tolga Aslan, Aslı Yenenler-Kutlu, Umut Gerlevik, Ayşe Çiğdem Aktuğlu Zeybek, Ertuğrul Kıykım, Osman Uğur Sezerman & Necla Birgul Iyison (2021) Identifying and elucidating the roles of Y198N and Y204F mutations in the PAH enzyme through molecular dynamic simulations, Journal of Biomolecular Structure and Dynamics, DOI: <a href="https://doi.org/10.1080/07391102.2021.1921619">10.1080/07391102.2021.1921619</a>

Ambient temperature CiPA dynamic hERG protocol dataset recorded on the SyncroPatch 384SE

Abstract  The Comprehensive in vitro Proarrhythmic Assay (CiPA) has promoted use of in silico models of drug effects on cardiac repolarization to improve proarrhythmic risk prediction. These models contain a pharmacodynamic component describing drug binding to hERG channels that required in vitro data for kinetics of block, in addition to potency, to constrain them. To date, development and validation has been undertaken using data from manual patch-clamp. The application of this approach at scale requires the development of a high-throughput, automated patch-clamp (APC) implementation. Here, we present a comprehensive analysis of the implementation of the Milnes, or CiPA dynamic protocol, on an APC platform, including quality control and data analysis. Kinetics and potency of block were assessed for bepridil, cisapride, terfenadine and verapamil with data retention/QC pass rate of 21.8% overall, or as high as 50.4% when only appropriate sweep lengths were considered for drugs with faster kinetics. The variability in IC50 and kinetics between manual and APC was comparable to that seen between sites/platforms in previous APC studies of potency. Whilst the experimental success is less than observed in screens of potency alone, it is still significantly greater than manual patch. With the modifications to protocol design, including sweep length, number of repetitions, and leak correction recommended in this study, this protocol can be applied on APC to acquire data comparable to manual patch clamp. Description High-throughput patch clamp electrophysiology dataset acquired on Nanion SyncroPatch 384PE platform describing the potency and kinetics of drug block of hERG channels stably expressed in CHO cells. The voltage protocols used were 10 or 40 s 0mV steps with a 15s, -80mV inter-pulse intervals, repeated 5 or 10 times in the absence (control) and presence of drug. Drug was added and cells clamped at -80 mV for 4 minutes prior to recording to ensure drug equilibration prior to recording the onset (kinetics) of drug block. This dataset accompanies the paper titled 'High throughput measurement of hERG drug block kinetics using the CiPA dynamic protocol' by Monique J. Windley, Jessica Farr, Clifford TeBay, Jamie I. Vandenberg and Adam P. Hill.  File information Raw and leak corrected files for individual plates run on the SyncroPatch 384PE Folder naming convention: Plate identifier (6 digit date_user initials_plate number)_type of data (leak uncorrected raw / leak corrected). <ul> </ul> Raw data <ul> <li>File naming convention: Plate identifier (6 digit date_user initials_plate number)_well number</li> <li>Each raw data folder also contains a file named QC_plate identifier no. which contains information used for cell/patch and hERG current quality controls including seal resistance, series resistance, capacitance, baseline (average current at -80mV), average (average leak uncorrected current during last 100 ms of 0mV step). QC details are reported for each sweep along with the drug and concentration, with external and 0 M reported for control sweeps.</li> <li>Full SyncroPatch plates were not used for all runs, only applicable wells were included where  partial plates were run (see plates 240719_MW1-3, 170719_MW1-3, 180719_MW1 and  291119_MW1)</li> </ul> Leak corrected data <ul> <li>File naming convention: well number_drug_concentration in M_leak_correction</li> <li>Refer to folder for plate identifier number.</li> <li>Each folder containing leak corrected data also contains a file labelled 'percent block' which contains percentage block measurements for each leak correction file in the same folder as identified by the well number.</li> <li>Only files passing the first pass QC were leak corrected (>125 MOhm seal, <80 MOhm series, 10-200 pA capacitance, >-300 pA baseline, >50 pA hERG current size and <20% change in peak control current)</li> </ul>

Design Principles for the Development of Gd(III) Polarizing Agents for Magic Angle Spinning Dynamic Nuclear Polarization

This is the raw dataset for publication  Design Principles for the Development of Gd(III) Polarizing Agents for Magic Angle Spinning Dynamic Nuclear Polarization. with the DOI of 10.1021/acs.jpcc.2c01721. It contains all NMR, EPR raw data and the MATLAB codes that are used in this paper. For details, please refer to the readme file.

opencc-by-4.0Mar 2022View details →

Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography

This repository contains the raw K-space data and all MATLAB (The MathWorks, Natick, MA) code used for reconstruction, simulations, data analysis, and figure generation used in the article titled "Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography". ## Referencing If you use any of the data or the code provided here, please cite: 1. Woods JG, Schauman SS, Chiew M, Chappell MA, Okell TW. Time-encoded pseudo-continuous arterial spin labeling: Increasing SNR in ASL dynamic angiography. Magnetic Resonance in Medicine. 2023; doi:10.1002/mrm.29491 2. This Zenodo repository: Joseph G. Woods, S. Sophie Schauman, Mark Chiew, Michael A. Chappell & Thomas W. Okell. (2022). Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6791097   If you use the reconstruction code provided here, please also cite: 1. Fessler JA, Sutton BP. Nonuniform fast fourier transforms using min-max interpolation. IEEE Transactions on Signal Processing. 2003;51(2):560-574. doi:10.1109/TSP.2002.807005 2. Fessler JA. Michigan Image Reconstruction Toolbox. https://web.eecs.umich.edu/~fessler/code/. Accessed February 26, 2018. 3. Schauman SS. Accelerated_TEASL. https://github.com/SophieSchau/Accelerated_TEASL. Accessed October 19, 2020. 4. Chiew M. MR Linear Encoding Operators. https://users.fmrib.ox.ac.uk/~mchiew/Tools.html. Accessed March 30, 2020. ## Notes for use: All data processing is performed in MATLAB (tested with 2021a and 2021b on macOS 10.14 (Mojave) and 12 (Monterey)) The "code/" and "data/" folders should be placed within the same folder. The necessary file paths within each script in "code/" will be set up automatically. **Note**: the file paths use "/", so would need to be manually changed for use on Windows, which uses "\" in file paths. - For performing full reconstruction of in vivo K-space data, **run reconalldata.m**.     - All code provided     - Uses:         1. Jeff Fessler's MIRT: http://web.eecs.umich.edu/~fessler/irt/fessler.tgz         2. Mark Chiew's: xfm_NUFFT.m, etc: https://users.fmrib.ox.ac.uk/~mchiew/Tools.html         3. Sophie Schauman's Accelerated_TEASL: https://github.com/SophieSchau/Accelerated_TEASL - For plotting in-vivo data, **run figures_invivo.m**.     - FSL's read_avw to read in NIFTIs (but can replace this with MATLAB's in-built niftiread instead).         - (Install FSL from here: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation)     - All other code provided - For performing simulation and plotting results, **run figures_simulation.m**.     - All code provided - Location of specific Figure generation code:     - Figure 1: no code - manually made.     - Figure 2: figures_invivo.m     - Figure 3: figures_simulation.m     - Figure 4: figures_invivo.m     - Figure 5: figures_invivo.m     - Figure 6: figures_invivo.m     - Figure 7: figures_invivo.m     - Figure 8: figures_invivo.m     - Figure 9: figures_simulation.m     - Figure 10: figures_invivo.m     - Supporting Information Figure S1: figures_simulation.m     - Supporting Information Figure S2: figures_invivo.m     - Supporting Information Figure S3: figures_invivo.m     - Supporting Information Figure S4: figures_invivo.m     - Supporting Information Figure S5: figures_invivo.m     - Supporting Information Figure S6: figures_invivo.m     - Supporting Information Figure S7: figures_invivo.m     - Supporting Information Figure S8: figures_invivo.m     - Supporting Information Figure S9: figures_invivo.m     - Supporting Information Figure S10: figures_simulation.m

Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities

This repository contains raw data and analysis routines of the publication “Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities” in Optics Express (<a href="https://doi.org/10.1364/OE.456139">doi.org/10.1364/OE.456139</a>). The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 5-10 minutes. For ideal scanning alignment each dataset includes diffusion, focus (beam waist) calibration, and flow measurements (using both M-scan and B-scan methods) for all used sample lengths. The names “M-scan” and “A-scan” are used interchangeably. The analysis process is as follows: firstly, the diffusion coefficient is determined for every sample size (time series length) to be analyzed using the script ‘Diffusion.py’. Secondly, the beam waist (focus) calibration is performed using the script ‘Beam Waist.py’. Since the beam waist should be constant for each dataset, choose the value obtained from the file with a largest time series length for minimizing the statistical uncertainty and fix it for a given dataset. Beam scanning for our setup is not exactly perpendicular to the optical axis. Therefore, for B-scan Doppler flow measurements the calibration parameter v_d, quantifying the axial scan bias, must be used. This calibration parameter varies with time series length and needs to be obtained for each sample size. This is done with the same script as the beam waist calibration. Thirdly, the Doppler angle is determined using M-scan measurement with the lowest discharge rate using the script ‘Angle.py’. Finally, the flow profiles are obtained both for M-scan and B-scan methods with predetermined calibration parameters using the script ‘Flow.py’. All file names are sufficiently descriptive, showing sample size, scan mode, measurement type and discharge rate. The number on the file name represents the time series length. For arbitrary scanning alignment, the dataset includes one diffusion and one focus (beam waist) measurements for calibration purposes. The diffusion measurement is used only for the beam waist calibration and not for flow measurements. It also contains several B-scan flow measurements (with different scan speeds) for every discharge rate. The analysis process is same as before but without the angle calibration step. Use the script ‘Omnidirectional.py’ for this step. The table below summarizes all datasets and Python scripts uploaded to this repository. <table align="center"> <tbody> <tr> <td> Name </td> <td> Applicability </td> <td> Description </td> </tr> <tr> <td> Dataset, 12-04-2021.zip </td> <td> Ideal scan alignment </td> <td> Dataset for Doppler angle of 0.39 deg and alignment angle of 0 deg. </td> </tr> <tr> <td> Dataset, 16-04-2021.zip </td> <td> Ideal scan alignment </td> <td> Dataset for Doppler angle of 0.94 deg and alignment angle of 0.94 deg. </td> </tr> <tr> <td> Dataset, 20-04-2021.zip </td> <td> Ideal scan alignment </td> <td> Dataset for Doppler angle of 1.58 deg and alignment angle of 2.26 deg. </td> </tr> <tr> <td> Dataset, 18-05-2021.zip </td> <td> Arbitrary alignment </td> <td> Dataset for alignment angle of 2.7 deg. </td> </tr> <tr> <td> Chirp.data </td> <td> Both methods </td> <td> File containing k-interpolation data </td> </tr> <tr> <td> ReadOCTFile.py </td> <td> Both methods </td> <td> Written by Jos de Wit, this module reads and imports spectra from raw OCT files. </td> </tr> <tr> <td> DataProcessing.py </td> <td> Both methods </td> <td> This module contains all analysis and processing routines. </td> </tr> <tr> <td> Diffusion.py </td> <td> Both methods </td> <td> This script determines diffusion coefficient from raw OCT spectra. </td> </tr> <tr> <td> Beam Waist.py </td> <td> Both methods </td> <td> This script determines focus beam waist from raw OCT spectra. </td> </tr> <tr> <td> Angle.py </td> <td> Ideal scan alignment </td> <td> This script determines Doppler angle from raw OCT spectra. </td> </tr> <tr> <td> Flow.py </td> <td> Ideal scan alignment </td> <td> This script determines M-scan and B-scan flow profiles from raw OCT spectra. </td> </tr> <tr> <td> Omnidirectional.py </td> <td> Arbitrary alignment </td> <td> This script determines flow profiles for arbitrary scan alignment. </td> </tr> </tbody> </table>

opencc-zeroJul 2022View details →

Data from: Diversity dynamics of microfossils from the Cretaceous to the Neogene show mixed responses to events

Microfossils have a ubiquitous and well-studied fossil record with temporally and spatially fluctuating diversity, but how this arises and how major events affect speciation and extinction is uncertain. We present the first application of PyRate to a micropaleontological global occurrence data set, reconstructing diversification rates within a Bayesian framework from the Mesozoic to the Recent in four microfossil groups: planktic foraminiferans, calcareous nannofossils, radiolarians and diatoms. Calcareous and siliceous groups demonstrate opposed, but inconsistent, responses in diversification. Siliceous groups increased origination from ~104 Ma, maintaining high rates into the Cenozoic. Calcareous microfossils diversification rates significantly decline across the Cretaceous–Paleogene boundary, while rates in siliceous microfossil groups remain stable until the Paleocene–Eocene transition. Diversification rates in the Cenozoic are largely stable in calcareous groups, whereas the Paleogene is a turbulent time for diatoms. Diversification fluctuations are driven by climate change and fluctuations in sea surface temperatures, promoting selectivity in both microfossil composition and foraminiferal size. Extinctions appear induced by changes in anoxia, acidification, and stratification, while speciation tends to be associated with upwelling, productivity, and ocean circulation. These results show promise for further quantitative analyses in micropaleontological diversity studies and effects of major transitions in the fossil record. Despite extensive occurrence data, regional diversification events were not recovered, neither were some global events. These unexpected results show the need to consider multiple spatiotemporal levels of diversity and diversification analyses, and implies occurrence data sets of different clades may be more appropriate to testing some hypotheses than others.

opencc-by-4.0Apr 2016View details →

Two Dynamic Attributed Networks: Enron & Jazz LastFM

Description. This repository contains two dynamic and attributed social networks extracted from the well-known Enron email dataset, and from the LastFM online music platform. We used both networks in the following papers: <ol> <li>G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “A Method for Characterizing Communities in Dynamic Attributed Complex Networks,” in IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), 2014, pp. 481–484. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01011913">hal-01011913</a>⟩ DOI: <a href="http://doi.org/10.1109/ASONAM.2014.6921629">10.1109/ASONAM.2014.6921629</a></li> <li>G. K. Orman, V. Labatut, M. Plantevit, and J.-F. Boulicaut, “Interpreting communities based on the evolution of a dynamic attributed network,” Social Network Analysis and Mining, vol. 5, p. 20, 2015. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01163778">hal-01163778</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-015-0262-4">10.1007/s13278-015-0262-4</a></li> </ol> Citation. If you use these data, please cite the paper [1]. <code>@InProceedings{Orman2014,</code> <code>  author    = {Orman, Günce Keziban and Labatut, Vincent and Plantevit, Marc and Boulicaut, Jean-François},</code> <code>  title     = {A Method for Characterizing Communities in Dynamic Attributed Complex Networks},</code> <code>  booktitle = {IEEE/ACM International Conference on Advances in Social Network Analysis and Mining},</code> <code>  year      = {2014},</code> <code>  pages     = {481-484},</code> <code>  address   = {Beijing, CN},</code> <code>  publisher = {IEEE Publishing},</code> <code>  doi       = {10.1109/ASONAM.2014.6921629},</code> <code>}</code> ---------------------------------------- Enron dataset. Enron is a well-known dataset in network science and text mining. It has been widely studied in academia. In network science, several different static networks appear in the literature. However, up to now, no dynamic network has been published, even though the email conversations have timestamps. We processed the original dataset to extract a dynamic network. There are 158 nodes representing Enron employees between 1997 and 2002. All the addresses in the From and To fields of each email are considered, resulting in a network of 28,802 nodes representing a distinct email addresses. A time span of one month is chosen for the time slices, generating 46 time slices. Two nodes are connected if the corresponding persons emailed each other during the given time slice. We did not make any distinction between sender and receiver, and thus produced an undirected dynamic network.  ---------------------------------------- LastFM dataset. LastFM is a music website that allows its members to register and listen to music online. It is also a social network platform, because its members can declare friendship relationships. In LastFM, members can join a predefined group related to their music tastes, and  participate in music-related events such as concerts. Using the LastFM API, One can retrieve the information of the artist and track a user has listened to, with the exact timestamp. Moreover, it is also possible to get some information regarding the music-related events the users joined, including the exact timestamps. We extracted a network by focusing on the members of the Jazz group, which is supposed to include users appreciating this type of music. We took advantage of the LastFM API to retrieve the members of this group and the existing friendship connection between them. In the end, our network contains 1,702 nodes representing the Jazz users. The friendship relationships between them is static, though, in the sense that the LastFM API does not give access to any temporal information regarding their beginning or end. So, we decided to take advantage of some additional information to get a dynamic structure. We put a link between two nodes if two conditions were simultaneously true: 1) both considered users listened to at least one common artist for a specific period of time, and 2) they are friends on the LastFM platform. For the mentioned period of time, we decided to use 3 months with 1 month overlap, after having analyzed the dynamics of the platform. In other words, we extracted a dynamic network in which each time slice represents three months of LastFM usage for our 1,702 users of interest. There are one month overlap between two consecutive time slices.

opencc-zeroJul 2022View details →

Data from: Bateman gradients reflect variation in sexual selection in a species with dynamic sex roles

Bateman gradients, the slope of the regression of reproductive success on mating success, are among the most commonly reported measures of sexual selection. They are particularly insightful in species with reversed sex roles, where females are expected to be under sexual selection. We measured Bateman gradients in replicate experimental populations of the spermatophore gift-giving bushcricket Kawanaphila nartee (Orthoptera: Tettigoniidae). In this species, the operational sex ratio (OSR) and thus the sex competing for mates varies depending on the availability of pollen food resources: under pollen-limited regimens females are more competitive, whereas under pollen-rich regimens males are more competitive. We maintained populations in enclosures with either limited or supplemented pollen, and calculated Bateman gradients for males and females under both conditions. Bateman gradients were significantly positive in males, and the slope was steeper in pollen-supplemented populations where the OSR was more male-biased. Bateman gradients for females were shallow and nonsignificant regardless of pollen availability. Our results show that the strength of sexual selection on males can depend on environmental context. The lack of significant gradients among females may reflect experimental limitations on our ability to estimate Bateman gradients in female K. nartee.

opencc-by-4.0Jul 2022View details →

Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning

This is the source code and data for the publication "Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning". Preprint - https://arxiv.org/abs/2112.09182

opencc-by-4.0May 2021View details →

Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings - The 4th China Physiological Signal Challenge 2021

This dataset is part of the available dataset for Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021, available at https://physionet.org/static/published-projects/cpsc2021/paroxysmal-atrial-fibrillation-events-detection-from-dynamic-ecg-recordings-the-4th-china-physiological-signal-challenge-2021-1.0.0.zip (last accessed today 2022-07-22). The dataset is licensed under Creative Commons Attribution 4.0 International Public License: Permissions: <ul> <li>Share — copy and redistribute the material in any medium or format</li> <li>Adapt — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> Conditions: <ul> <li> Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. </li> </ul> Limitations: <ul> <li>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.</li> </ul> For more information about the license, check: https://creativecommons.org/licenses/by/4.0/ The following modifications were made: <ul> <li>Only the training and test set folders are used</li> <li>Only the files *.hea, *.dat and *.atr are used, being the *.dat and *.atr converted to CSV and compressed in .bz2 format</li> <li>Added the LICENSE.txt file as required.</li> </ul>

opencc-by-4.0Jul 2022View details →

Molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants

This data set accompanies the publication by S. Conti, E. Lau, and V. Ovchinnikov entitled "On the rapid calculation of binding affinities for antigen and antibody design and affinity maturation simulations", to be published in the MDPI journal Antibodies. It contains molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants, as described in the paper. The format of the trajectory files is CHARMM-compatible dcd. The files can be visualized with the program Visual Molecular Dynamics (VMD) (see paper by Humphrey et al. 1996, J. Molec. Graphics).  The accompanying file "view" is a tcl-based script for VMD that can be executed in the Linux environment using: "vmd -e view", which will display the trajectory of the mutant specified by editing the first noncomment line of the script.

opencc-by-4.0Jul 2022View details →

Engineering Dust Coma Model (EDCM) for ESA's Comet Interceptor mission to a dynamically new comet

This data-set contains all results from the Engineering Dust Coma Model (EDCM) for ESA's Comet Interceptor (CI) mission to a dynamically new comet. A full description of the model behind the data can be found in the peer-reviewed paper Marschall, Zakharov et al. (2022), <a href="https://doi.org/10.1051/0004-6361/202243648">https://doi.org/10.1051/0004-6361/202243648</a>. Please cite this data-set and the paper when using the data. Contemporary numerical models of dusty-gas coma are used to obtain spatial distribution of dust for a given set of parameters. By varying parameters within a range of possible values we obtain an ensemble of possible dust distributions. Then, this ensemble is statistically evaluated in order to define the most probable cases and hence reduce the dispersion. This ensemble can be used to estimate not only the likely dust abundance along e.g. a fly-by trajectory of a spacecraft but also quantify the associated uncertainty. The dust environment assessment for the case when the target comet is not known beforehand (or when its parameters are known with large uncertainty) is critical for spacecraft safety and planning. The EDCM provides an assessment of dust environment for the CI mission.

opencc-by-4.0Mar 2020View details →

Fig. 5. A in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region

Fig. 5. A) Mean monthly whorl increment of Xerolenta obvia in two growth seasons in SW (solid line) and NE (dashed line) populations; B) mean (black lines), maxi- mum and minimum (grey lines) monthly temperature, and C) total monthly precipitation during the study period in SW (solid lines) and NE (dashed lines) sites. Data from nearest meteorological stations in Wrocław and Suwałki (IMGW-PIB data)

opencc-by-4.0Mar 2020View details →

Fig. 2 in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region

Fig. 2. Distribution of shell pattern types of Xero- lenta obvia snails in the two populations studied, SW (n = 781) and NE (n = 1387)

opencc-by-4.0Mar 2022View details →

Data for: "Dynamic species distribution modeling reveals the pivotal role of human-mediated long-distance dispersal in plant invasion"

All the data needed to reproduce the results and Figures of our article: Botella, C., Bonnet, P., Hui, C., Joly, A., & Richardson, D. M. (2022). Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion. Biology, 11(9), 1293. <a href="https://doi.org/10.3390/biology11091293">https://doi.org/10.3390/biology11091293</a> Please, find the R scripts and guidelines to reproduce our results on the article's Github repository : <a href="https://github.com/ChrisBotella/plectranthus_barbatus/tree/main">https://github.com/ChrisBotella/plectranthus_barbatus/tree/main</a>

opencc-zeroAug 2022View details →

Spatiotemporal influences of climate and humans on muskox range dynamics over multiple millennia

Processes leading to range contractions and population declines of Arctic megafauna during the late Pleistocene and early-Holocene are uncertain, with intense debate on the roles of human hunting, climatic change, and their synergy. Obstacles to a resolution, have included an over reliance on correlative rather than process-explicit approaches for inferring drivers of distributional and demographic change. Using process-explicit macroecological models that integrate modern and fossil occurrence records, spatiotemporal reconstructions of past climatic change, speciesspecific population ecology and the growth and spread of anatomically modern humans, we disentangle the ecological mechanisms and threats that were integral in the decline and extinction of the muskox (Ovibos moschatus) in Eurasia, and in its expansion in North America. We show that accurately reconstructing inferences of past demographic changes for muskox over the last 21,000 years requires high dispersal abilities, large maximum densities, and a small Allee effect. Climatic change was the primary driver of muskox distribution shifts and demographic changes across its previously extensive (circumpolar) range, with populations responding negatively to rapid warming events. Regional analyses reveal that the range collapse and extinction of the muskox in Europe (~ 13 thousand years ago) was caused by humans operating in synergy with climatic warming. In Canada and Greenland, climatic change and human activities combined to drive recent population sizes. The impact of past climatic change on the range and extinction dynamics of muskox during the Pleistocene-Holocene transition signals a vulnerability of this species to future increased warming. By disentangling the ecological processes that shaped the distribution of the muskox through space and time, process-explicit models have important applications for the future conservation and management of this iconic species in a warming Arctic.

opencc-by-4.0Aug 2022View details →

Dataset for "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling"

Dataset for figures of the upcoming article "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling" submitted to "AIP Advances".