Find research datasets worth reusing

The theory of planned behavior and the prediction of pre-service biology teachers' intention to teach evolution

We developed the project to identify and analyze variables that promote or hinder prospective biology teachers’ intentions to teach evolution. We adopted the model of the theory of planned behavior (TPB). We extended it to include additional variables described by teacher education research as key determinants of behavioral intention to teach evolution. We initially hypothesized that attitudes toward teaching evolution, subjective norms, perceived behavioral control, personal religious beliefs, perceived usefulness, and knowledge about evolution would determine a person’s behavioral intentions. To test the hypotheses, we developed an online questionnaire and conducted a quantitative cross-sectional survey in the field of teacher education. The data included information on N = 309 participants. Because we initially analyzed the data using a two-stage structural equation model (SEM), we uploaded two data files that were created in subprocesses of our original analyses (for more information, see the original publication). The dataset “data3” contains 77 variables and has missing values. Since we wanted to use complete data for the SEM, we trimmed the data set “data3” to include only the 67 variables necessary for the SEM, then applied an expectation-maximum (EM) algorithm with multiple imputations, and obtained the data set “data4”.

Data from: Differential gene expression during recall of behaviorally conditioned immune enhancement in rats: a pilot study

Background: Behaviorally conditioned immune functions are suggested to be regulated by bidirectional interactions between CNS and peripheral immune system via the hypothalamic-pituitary-adrenal (HPA) axis, sympathetic nervous system (SNS), and the parasympathetic nervous system (PNS). Since the current knowledge about biochemical pathways triggering conditioned immune enhancement is limited, the aim of this pilot study was gaining more insights into that. Methods: Rats were conditioned with camphor smell and poly I:C injection, mimicking a viral infection. Following stimulus re-exposure, animals were sacrificed at different time points, and neural tissues along the HPA axis was analyzed with a rat genome array together with plasma protein using Luminex analysis. Results: In the hypothalamus, we observed a strong upregulation of genes related to Wnt/β-catenin signaling (Otx2, Spp1, Fzd6, Zic1), monoaminergic transporter Slc18a2 and opioid-inhibitory G-protein Gpr88 as well as downregulation of dopaminergic receptors, vasoactive intestinal peptide Vip, and pro-melanin-concentrating hormone Pmch. In the pituitary, we recognized mostly upregulation of steroid synthesis in combination with GABAergic, cholinergic and opioid related neurotransmission, in adrenal glands, altered genes showed a pattern of activated metabolism plus upregulation of adrenoceptors Adrb3 and Adra1a. Data obtained from spleen showed a strong upregulation of immunomodulatory genes, chemo-/cytokines and glutamatergic/cholinergic neurotransmission related genes, as also confirmed by increased chemokine and ACTH levels in plasma. Conclusions: Our data indicate that in addition to the classic HPA axis, there could be additional pathways as e.g. the cholinergic anti-inflammatory pathway (CAIP), connecting brain and immune system, modulating and finetuning communication between brain and immune system.

Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset

Abstract: (from [1]) <blockquote> The addition of nano-sized filler particles enhances the mechanical performance of polymers. The resulting properties of the polymer nanocomposite depend on a complex interplay of influence factors such as material pairing, filler size, and content as well as filler-matrix adhesion. As a complement to experimental studies, numerical methods, such as molecular dynamics (MD), facilitate an isolated examination of the individual factors in order to understand their interaction better. However, particle-based simulations are, in general, computationally very expensive, rendering a thorough investigation of nanocomposites’ mechanical behavior both expensive and time-consuming. Therefore, this paper presents a fast coarse-grained MD model for a generic nanoparticle-reinforced thermoplastic. First, we examine the matrix and filler phase individually, which exhibit isotropic elasto-viscoplastic and anisotropic elastic behavior, respectively. Based on this, we demonstrate that the effect of filler size, filler content, and filler-matrix adhesion on the stiffness and strength of the nanocomposite corresponds very well with experimental findings in the literature. Consequently, the presented computationally efficient MD model enables the analysis of a generic polymer nanocomposite. In addition to the obtained insights into the mechanical behavior, the material characterization provides the basis for a future continuum mechanical description, which bridges the gap to the engineering scale.  </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All MD simulations were performed with LAMMPS [2], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [3] Post-processing Matlab R2019b Evaluation of polymer entanglements with Z1-Algorithm [4]   License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information. Content: supplementary material: supplementary_information.pdf data:     folder names vary depending on the context, explained in the following:   01_matrix <ul> <li> 01_equilibration sample equilibration to different temperatures nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> <li> batch_ID: 2-5  </li> </ul> </li> <li> 02_temperature_dependence uniaxial tension simulations to identify temperature dependence nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> </ul> </li> <li> 03_directional_dependence uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-5 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 04_rate_dependence uniaxial tension simulations to identify strain rate dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 05_cyclic_loading sinusoidal uniaxial deformation nomenclature: 05_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 06_relaxation relaxation subsequent to time-proportional deformation nomenclature: 07_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude>_relax <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 07_simple_shear time-proportional simple shear deformation with different strain rates nomenclature: SS_P2VPSi-rate_<strain_rate>-<batchID> <ul> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 08_large_deformation uniaxial deformation up to 100% strain nomenclature: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> max_strain: 1 </li> </ul> </li> </ul> 02_filler <ul> <li> 01_Silica_equilibration sample equilibration </li> <li> 02_time_proportional time-proportional uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> max_strain: 0.1 </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> </ul> </li> <li> 03_time_periodic time-periodic uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_amplitude: 0.025 </li> </ul> </li> </ul> 03_composite <ul> <li> 01_equilibration sample equilibration nomenclature: equil_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 02_uniaxial-tension uniaxial tension simulations nomenclature: UT_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 03_filler-maxtrix-adhesion equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension nomenclature: see above </li> <li> 04_IP_equilibration equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5 nomenclature: P2VPSi-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> filler_radius: 0 (neat), 2.5, 5.0, 7.5 </li> <li> filler_number: 0 (neat), 1 </li> <li> batchID: 1-20 </li> </ul> </li> </ul>       Each simulation directory contains: <ul> <li> lammps input file (*.in) of the specific simulation </li> <li> data file (*.data) containing the initial sample configuration </li> <li> input.prm: input parameters of the specific simulation (read by the input file) </li> <li> meta.info: meta data of the specific simulation run </li> <li> LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li> thermo_out.Dat: raw output  </li> <li> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter) </li> <li> thermo_out_STD.Dat: standard deviation of raw output </li> </ul> </li> </ul>   Output quantities (columns of *.Dat files): Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1]. <ul> <li> Step: time step  </li> <li> Time: time  </li> <li> TotEng: total energy  </li> <li> PotEng: potential energy </li> <li> KinEng: kinetic energy  </li> <li> E_pair: pair energy  </li> <li> E_bond: bond energy  </li> <li> E_angle: angle energy  </li> <li> E_dihed: dihedral energy  </li> <li> Temp: temperature </li> <li> Press: hydrostatic pressure </li> <li> Pxx: xx component of pressure tensor  </li> <li> Pyy: yy component of pressure tensor  </li> <li> Pzz: zz component of pressure tensor  </li> <li> Pxy: xy component of pressure tensor </li> <li> Pxz: xz component of pressure tensor </li> <li> Pyz: yz component of pressure tensor </li> <li> Volume: volume of simulation box  </li> <li> Lx: box length in x direction   </li> <li> Ly: box length in y direction   </li> <li> Lz: box length in z direction   </li> <li> Density: density   </li> <li> c_RG: radius of gyration scalar  </li> <li> c_RG[1]: squared radius of gyration tensor (xx component)   </li> <li> c_RG[2]: squared radius of gyration tensor (yy component)   </li> <li> c_RG[3]: squared radius of gyration tensor (zz component)   </li> <li> c_RG[4]: squared radius of gyration tensor (xy component)   </li> <li> c_RG[5]: squared radius of gyration tensor (xz component)   </li> <li> c_RG[6]: squared radius of gyration tensor (yz component)   </li> <li> c_bondave[1]: bond energy averaged over all atoms   </li> <li> c_bondave[2]: bond distance averaged over all atoms   </li> <li> c_bondave[3]: squared bond distance averaged over all atoms   </li> <li> c_angleave[1]: angle energy averaged over all atoms   </li> <li> c_angleave[2]: angle averaged over all atoms degree </li> <li> c_angleave[3]: cosine of angle  </li> <li> c_angleave[4]: squared cosine of angle  </li> <li> c_MSD[1]: mean squared displacement x-direction   </li> <li> c_MSD[2]: mean squared displacement y-direction   </li> <li> c_MSD[3]: mean squared displacement z-direction   </li> <li> c_MSD[4]: total mean squared displacement   </li> <li> c_COM[1]: x coordinate of center of mass   </li> <li> c_COM[2]: y coordinate of center of mass   </li> <li> c_COM[3]: z coordinate of center of mass   </li> <li> v_strain_xx: xx component of engineering strain tensor    </li> <li> v_strain_yy: yy component of engineering strain tensor     </li> <li> v_strain_zz: zz component of engineering strain tensor     </li> <li> v_vMisesequivstress: von Mises equivalent stress  </li> <li> v_Cauchy_xx: xx component of stress tensor   </li> <li> v_Cauchy_yy: yy component of stress tensor </li> <li> v_Cauchy_zz: zz component of stress tensor </li> <li> v_Cauchy_xy: xy component of stress tensor  </li> <li> v_Cauchy_xz: xz component of stress tensor  </li> <li> v_Cauchy_yz: yz component of stress tensor  </li> <li> v_strain_xy: xy component of engineering strain tensor    </li> <li> v_strain_xz: xz component of engineering strain tensor    </li> <li> v_strain_yz: yz component of engineering strain tensor    </li> </ul>   References: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. [2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19. [3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022. [4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, Zenodo, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a>

Homophily in Voting Behavior: Evidence from Preferential Voting

This is a dataset for the paper "Homophily in Voting Behavior: Evidence from Preferential Voting" (co-authored by Lucie Coufalová and Michal &Scaron;evčík) that allows for the replication of all regression tables and descriptive statistics. There are four files in the dataset: <ul> <li>"rdata.csv" is the main file with data used in regressions</li> <li>"homo_occupation.csv" is an auxiliary file with data used to calculate some descriptive statistics</li> <li>"municipalities_age.csv" is an auxiliary file with data used to calculate some descriptive statistics</li> <li>"replication_data_20220910.RData" is a file that contains all tables packed and compressed for use in R.</li> </ul>  Tables  rdata...main data table used in regressions  homo_occupation...auxiliary data table used in descriptive stats  municipality_ages...auxiliary data table used in descriptive stats Variables  Outcome:  pref...number of preferential votes  Variables of interest:  homo_municipality...indicator variable for a candidate running in the municipality of his/her residence  homo_(education/occupation/age/gender)...percentage of the population sharing the characteristic of the candidate  Other variables:  KOD_OBEC...municipality ID in CISOB classification  total_fe...ID of candidate-election pair  maxPORCISLO...number of candidates on the ballot  cluster_ID...ID for error term clustering  POC_HLASU...total number of votes cast for the party in the given municipality  small_municipality...indicator variable for a municipality with a population below the median (defined separately   for each year and constituency)  year...election year (election ID)  VOLKRAJ...constituency ID  PORCISLO...position of the candidate on the ballot  VEK...age  agecat...age category  MANDAT...indicator variable for elected candidates  tertiary_educ...indicator variable for candidates with tertiary education  gender...male/female  ger_share...indicator variable for municipality being dominated by ethnic Germans in 1930

Data and Source codes: Ancestral sex-role plasticity facilitates the evolution of same-sex sexual behavior

This repository provides access to the tracking data and analysis code used for the manuscript: Ancestral sex-role plasticity facilitates the evolution of same-sex sexual behavior by Nobuaki Mizumoto1, Thomas Bourguignon1, and Nathan W. Bailey2 1 Okinawa Institute of Science & Technology Graduate University, Onna-son, Okinawa, Japan 2 School of Biology, University of St Andrews, St Andrews, U.K. published in the Proceedings of the National Academy of Sciences of the United States of America.

Inhibitory Kcnip2 neurons of the spinal dorsal horn control behavioral sensitivity to environmental cold

Excel file containing datasets for all Figures published in the article "Inhibitory Kcnip2 neurons of the spinal dorsal horn control behavioral sensitivity to environmental cold" by Albisetti et al.

opencc-by-4.0Apr 2020View details →

Figs 19–24 in NEW RECORDS OF CELONITES KOZLOVI KOSTYLEV, 1935 AND C. SIBIRICUS GUSENLEITNER, 2007 (HYMENOPTERA: VESPIDAE: MASARINAE), WITH OBSERVATIONS ON THEIR BEHAVIOR AT FLOWERS

Figs 19–24. SEM micrographs of Celonites spp. structures: 19 – head of C. kozlovi Kostylev; 20 – its frons covered with short setae not suitable for pollen collecting; 21 – the

opencc-by-4.0Jul 2019View details →

Figure 1 in How Spiromesifen affects some biological parameters and switching behavior of predatory mite Amblyseius swirskii (Acari: Phytoseiidae) when feeding on different ratios of mixed preys

Figure 1. Linear relation between initial number of Bemisia tabaci (left)/ Tetranychus urticae (right) treated with recommended concentration of Spiromesifen and number of preys eaten by predatory mite Amblyseius swirskii.

opencc-by-4.0Jul 2019View details →

Figure 2 in How Spiromesifen affects some biological parameters and switching behavior of predatory mite Amblyseius swirskii (Acari: Phytoseiidae) when feeding on different ratios of mixed preys

Figure 2. Fitted regression equation between the proportion of consumed mite to total preys and preference index (β) of Amblyseius swirskii.

Figure 5 in Reciprocal intraguild predation between Neoseiulus barkeri and Amblyseius swirskii (Mesostigmata: Phytoseiidae): Does experience affect anti-intraguild predation behaviors?

Figure 5. Survival of ten IG predator larvae of either A. swirskii (above) or N. barkeri (below) in choice experiments with naïve or experienced IG prey females of N. barkeri and A. swirskii, respectively.

Figure 4 in Reciprocal intraguild predation between Neoseiulus barkeri and Amblyseius swirskii (Mesostigmata: Phytoseiidae): Does experience affect anti-intraguild predation behaviors?

Figure 4. Total oviposition and proportion of eggs laid by experienced or naïve IG prey females N. barkeri (a, b) and A. swirskii (c, d) in risky (black bars) and safe (white bars) patches.

Figure 3 in Reciprocal intraguild predation between Neoseiulus barkeri and Amblyseius swirskii (Mesostigmata: Phytoseiidae): Does experience affect anti-intraguild predation behaviors?

Figure 3. Effect of experience on patch choice behavior of N. barkeri and A. swirskii IG prey females within choice experiments. Shown are mean fractions (± SE) for residence frequency of either experienced or naïve females in risky patches with IG predator cues and juveniles (black bars) or in safe patches with only spider mites (white bars).

Figure 2 in Reciprocal intraguild predation between Neoseiulus barkeri and Amblyseius swirskii (Mesostigmata: Phytoseiidae): Does experience affect anti-intraguild predation behaviors?

Figure 2. Comparative survival to adulthood of 20 IG prey eggs of either N. barkeri (black bar, n = 25) or A. swirskii (grey bar, n = 25) on arenas with 5 IG predator females of A. swirskii or N. barkeri, respectively.

Figure 1 in Reciprocal intraguild predation between Neoseiulus barkeri and Amblyseius swirskii (Mesostigmata: Phytoseiidae): Does experience affect anti-intraguild predation behaviors?

Figure 1. Survival to adulthood (mean number ± SE) of 20 IG prey eggs of either N. barkeri (a) or A. swirskii (b) on arenas with only spider mites (control arenas, white bars) or with spider mites and 5 IG predator females (risky arenas, black and grey bars).

Figure 6 in Reciprocal intraguild predation between Neoseiulus barkeri and Amblyseius swirskii (Mesostigmata: Phytoseiidae): Does experience affect anti-intraguild predation behaviors?

Figure 6. Comparative survivorship of ten IG predator larvae of either A. swirskii or N. barkeri in choice experiments with experienced or naïve IG prey females of N. barkeri or A. swirskii, respectively.

opencc-by-4.0Oct 2022View details →

Data set for "Cortical sensory processing across motivational states during goal-directed behavior"

Data set for: Matteucci G, Guyoton M, Mayrhofer JM, Auffret M, Foustoukos G, Petersen CCH, El-Boustani S, Cortical sensory processing across motivational states during goal-directed behavior (2022). Neuron https://doi.org/10.1016/j.neuron.2022.09.032 There are 2 files in this upload: 1. The file named "Matteucci2022.pdf" is the Open Access pdf file of the manuscript published in Neuron. 2. The file named "Matteucci_data_code.zip" (~26.5 GB) is a zipped version of a folder "Matteucci_data_code" (~33 GB), which contains the data analysed in the study along with Matlab code used to generate all main figures of the paper. The analysis code is in a subfolder named "code". This subfolder in turn has three subfolders "analysis_scripts", “analysis_functions” (containing the original code for intermediate data processing) and “paper_figures_scripts” (containing the code for generating each figure panel from pre-processed data). The main script “reproduce_figures.m” will call the subscripts contained in the  “paper_figures_scripts” folder to reproduce the plots contained in all main figures of the paper (and take care of adding the relevant code and data folders and subfolders to Matlab file path). The raw and pre-processed data analysed in the study can be found in the folder named "data". A “README.txt” file provides further details on the content of each subfolder.

opencc-by-4.0Dec 2022View details →

Interseismic and Coseismic Slip Behaviors Along the Tuolaishan-Lenglongling Faults From InSAR, GPS and Optical Observations

This repository contains: (1) The coseismic horizontal displacements measured from Planet-Lab and Landsat-9 optical data in grd format. The matlab script (i.e., grdread2.m) can be used to read the data in grd format. (2) Interseismic fault-parallel and fault-norm veloctiy profiles projected by the east-west and north-south velocity maps, the vertical and InSAR-derived descening (Track 33) LOS velocity profiles perpendicular to the seismogenic fault of the 2022 Menyuan Mw 6.7 earthquake. The  data in profile files is formated as longitude, latitude, velocity and fault-perpendicular distance.