Find research datasets worth reusing

opencc-zeroAug 2022View details →

Summary data for plots in: Eco-evolutionary extinction and recolonization dynamics reduce genetic load and increase time to extinction in highly inbred populations

Understanding how genetic and ecological effects can interact to shape genetic loads within and across local populations is key to understanding ongoing persistence of systems that should otherwise be susceptible to extinction through mutational meltdown. Classic theory predicts short persistence times for metapopulations comprising small local populations with low connectivity, due to accumulation of deleterious mutations. Yet, some such systems have persisted over evolutionary time, implying the existence of mechanisms that allow metapopulations to avoid mutational meltdown. We first hypothesize a mechanism by which the combination of stochasticity in the numbers and types of mutations arising locally (genetic stochasticity), resulting in local extinction and recolonization through evolving dispersal, facilitates metapopulation persistence. We then test this mechanism using a spatially and genetically explicit individual-based model. We show that genetic stochasticity in highly structured metapopulations can result in local extinctions, which can favour increased dispersal, thus allowing recolonization of empty habitat patches. This causes fluctuations in metapopulation size and transient gene flow, which reduces genetic load and increases metapopulation persistence over evolutionary time. Our suggested mechanism and simulation results provide an explanation for the conundrum presented by the continued persistence of highly structured populations with inbreeding mating systems that occur in diverse taxa.

opencc-by-4.0Aug 2022View details →

Data from: The Effects of Multi-Level Identity Dynamics in New Venture Creation

Anonymized interview data from SNF project The Effects of Multi-Level Identity Dynamics in New Venture Creation (in the context of collective entrepreneurial ventures) that has been used for publications (as stated in Data Management Plan). Additional data available upon request.

openother-openAug 2022View details →

Symbiodiniaceae dynamics in Pocillopora spp. during the 2015 to 2016 El Nino

Cleaned code after reviews

opencc-bySep 2022View details →

Molecular Dynamics Simulation of Solar Wind Implantation in the Permanently Shadowed Regions on the Lunar Surface

Supporting data for "Molecular Dynamics Simulation of Solar Wind Implantation in the Permanently Shadowed Regions on the Lunar Surface"

opencc-by-4.0Aug 2022View details →

BioExcel Use Case 1: collection of output data from molecular dynamics simulation

The Use Case aims to address all the challenges related to antibody design through an integrative approach combining the core BioExcel software comprising of GROMACS, HADDOCK and PMX. The folder  contains the GROMACS output files (xtc and pdb file). Molecular Dynamics simulations have been performed with GROMACS version 2020 and CHARMM36 force field. The input files and scripts of the final protocol are publicly available on BioExcel GitHub https://github.com/bioexcel/BioExcel-UseCase1. The Use Case 1 protocol was presented at the BioExcel Summer School on Biomolecular Simulation in 2021 (see <a href="https://doi.org/10.5281/zenodo.7009238">https://doi.org/10.5281/zenodo.7009238</a> or <a href="https://youtu.be/_TDKfKX4kwM">https://youtu.be/_TDKfKX4kwM</a>)

opencc-zeroAug 2022View details →

Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner

The spiking activity of populations of cortical neurons is well described by a small number of population-wide covariance patterns, the "latent dynamics". These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable across behaviour: in each of the primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recording.

Zircon dates long-lived plume dynamics in oceanic islands

Here we present a new data set from ten islands of the hot-spot related Galapagos Archipelago. That includes zircon U-Pb geochronology and δ18O - εHf(t) isotope geochemistry and whole rock geochemistry. The methodology includes different analytical technics like SHRIMP II, LA-MC-ICPMS, ICP, and XRD. This repository contains the following files: Table S1. LA-MC-ICPMS Lu-Hf isotope data of Galápagos zircons measured at Hong Kong University. Table S2. LA-MC-ICPMS Lu-Hf isotope data of Galápagos zircons measured at Frankfurt University. Table S3. Major and trace element concentrations for Galápagos samples. Table S4. Oxygen isotope ratios for Galápagos zircons. Table S5. Thermo-mechanical properties of the rock phase. Table S6. List of the numerical experiments. Table S7: List of the experiments used to calibrate the permeable boundary condition. Table S8.  (A) Hf Isotope Ratios in Galapagos Lavas (After (Blichert-Toft & White, 2001). (B) Oxygen isotopic ratios of plagioclase separates from Galapagos lavas and xenoliths (Geist et al., 1998). (C) Oxygen isotope compositions of olivine grains from subaerial lavas (Peterson et al., 2019). (D) Hf Isotope Compositions (±2s) of Submarine Ridges in the Panama Basin and of CLIP (Caribbean Large Igneous Province) rocks (Geldmacher et al., 2003). Movie S1

Raw Data for 'Efficient Access of Phenyl-Spaced 5,5´-Bridged Dinuclear Ruthenium Metal Complexes and the Effect of Dynamic Ligand Exchange on Catalysis'

Herein, we provide the raw data for all figures being part of either the mansucript or the supporting information of the publication 'Efficient Access of Phenyl-Spaced 5,5’-Bridged Dinuclear Ruthenium Metal Complexes and the Effect of Dynamic Ligand Exchange on Catalysis'.

LaMEM source code and input files corresponding to Present‐day upper‐mantle architecture of the Alps: Insights from data‐driven dynamic modelling

This repository contains LaMEM source code and input files for the models presented in Kumar, A., Cacace, M., Scheck-Wenderoth, M., Götze, H.-J., & Kaus, B. J. P. (2022). Present-day upper-mantle architecture of the Alps: Insights from data-driven dynamic modeling. Geophysical Research Letters, 49, e2022GL099476. https://doi. org/10.1029/2022GL099476

opencc-by-4.0Oct 2022View details →

Dataset for the EPSL article: Structure and dynamics of the Tonga subduction zone: new insight from P-wave anisotropic tomography

The obtained 3-D P-wave anisotropic and isotropic velocity models in the Tonga subduction zone. Please find this article: Z. Yu*, D. Zhao and J. Li*. Structure and dynamics of the Tonga subduction zone: New insight from P-wave anisotropic tomography. Earth and Planetary Science Letters, https://doi.org/10.1016/j.epsl.2022.117844

opencc-zeroSep 2022View details →

Plant litter chemistry controls coarse-textured soil carbon dynamics

1. As soils store more carbon (C) than the Earth's atmosphere and terrestrial biomass together, the balance between soil C uptake in the form of soil organic matter and release as CO2 upon its decomposition is a critical determinant in the global C cycle regulating our planet's climate. Although plant litter is the predominant source of C fueling both soil C build-up and losses, the issue of how litter chemistry influences this balance remains unresolved. 2. As a contribution to solving that issue, we traced the fate of C during near-complete decomposition of 13C-labelled leaf and root litters from 12 plant species in a coarse-textured soil. We separated the soil organic carbon (SOC) into mineral-associated organic matter (MAOM) and particulate organic matter (POM) pools, and investigated how 14 litter chemical traits affected novel SOC formation and native SOC mineralization (i.e., the priming effect) in these soil fractions. 3. We observed an overall net increase in SOC due to the addition of litter, which was stronger for root than for leaf litters. The presumed stable MAOM-C pool underwent both substantial stabilization and mineralization, whereas the presumably less stable POM-C pool showed substantial stabilization and reduced mineralization. Overall, the initial increase in soil C mineralization was fully counterbalanced by a later decrease in native soil C mineralization. POM-C formation as well as MAOM-C formation and mineralization were positively related to the initial litter lignin concentration and negatively to that of the nitrogen leachates, whereas the opposite was observed for POM-C mineralization. 4. Synthesis. Our results highlight the importance of litter chemical traits for SOC formation, and stabilization, destabilization, and mineralization. In our coarse-textured soil, the amount of MAOM-C did not change despite large C fluxes through this pool. The litter chemical traits that drove these processes differed from those frequently reported for fine-textured soils far from mineral-associated C saturation. To account for these discrepancies, we propose an integrative perspective in which litter quality and soil texture interactively control soil C fluxes by modulating several SOC stabilization and destabilization mechanisms. Irrespective, our results open new critical perspectives for managing soil C pools globally.

Poroelastic response of water level in Tengchong well aquifer to dynamic stress

Data used in manuscript named "Poroelastic response of water level in Tengchong well aquifer to dynamic stress"

Data samples for Flow-matching -- efficient coarse-graining molecular dynamics without forces

CG samples generated during the training and validation processes in the flow-matching project. Accompanying the preprint "Flow-matching -- efficient coarse-graining molecular dynamics without forces": https://arxiv.org/abs/2203.11167. Detailed descriptions can be found in the preprint as well as the included README.

opencc-zeroSep 2022View details →

Connectivity among thermal habitats buffers the effects of warm climate on life-history traits and population dynamics

1. Contemporary climate change affects population dynamics, but its influence varies with landscape structure. It is still unclear whether landscape fragmentation buffers or enhances the effects of climate on population size and on the age and body size of individuals composing these populations. 2. This study aims to investigate the impacts of warm climates on lizard life-history traits and population dynamics in habitats varying in their connectivity. 3. We monitored common lizard (Zootoca vivipara) populations for three years in an experimental system in which both climatic conditions and connectivity among habitats were simultaneously manipulated. We considered two climatic treatments (i.e., present-day climate and warm climate (+1.4°C than present-day climate)) and two connectivity treatments (i.e., a connected treatment in which individuals could move from one climate to the other and an isolated treatment in which movement between climates was not possible). We monitored survival, reproduction, growth, dispersal, age and body size of each individual in the system as well as population density through time. 4. We found that the influence of warm climates on the life-history traits and population dynamics depended on the connectivity among thermal habitats. Populations in warm climates were i) composed of younger individuals only when isolated; ii) larger in population size only in connected habitats; and iii) composed of larger age-specific individuals independently of the landscape configuration. The connectivity among habitats altered population responses to climate warming likely through asymmetries in the flow and phenotype of dispersers between thermal habitats. 5. Our results demonstrate that landscape fragmentation can drastically change the dynamics and persistence of populations facing climate change.

opencc-by-4.0Jun 2022View details →

Supplementary data for "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"

Supplementary data for the article "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"

opencc-by-4.0Aug 2022View details →

Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device"

Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device". <pre>Code to analyze data produced by the Quantum-Si benchtop device and semiconductor chip is provided in a Python library qsi_algo under several submodules: - rs_caller.py: Algorithm for calling RS segments (also called ROI segments throughout code). - rs_caller_controller.py: Code framework for executing RS calling and property computation in a distributed manner - rs_properties: Code for computing properties of identified RS - rs_classifier: Algorithms for identifying peptide states (i.e. residue calls) associated with an RS - utils.py: shared helper code - pulse_reader: reader for binary pulse file - filters: ROI and pulse filtering utilities - plotting: functions for visualization of data relevant to the analyses presented Jupyter notebooks (.ipynb) files are named according to the manuscript figure they are associated with. Analysis code inside uses provided RS (recognition segment) data to demonstrate filtering and residue-calling techniques required to replicate analyses shown in manuscript figures. Please note: several methods rely on randomization for model initialization and/or data sampling which can cause small deviations from equivalent analyses in published figures. The raw data produced from the Quantum-Si benchtop device and semiconductor chip for the assays presented in the accompanying study is presented in a pulse-called binary file format. Pulses can be used as input for RS identification and peptide state identification. Pre-segmented (RS-identified) files are included for convenience. The data contained in the files include: {run_id}.bin: Binary format for storing pulse info. The reader provided in qsi_algo.pulse_reader produces the following columns: - aperture_index: unique aperture index on chip - start_f: index of first frame in pulse, counted from the beginning of the run - end_f: index of last frame in pulse, counted from the beginning of the run - dur_f: duration of pulse in frames - dur_s: duration of pulse in seconds - ipd_f: interpulse duration in frames (number of frames since end of preceding pulse) - ipd_s: interpulse duration in seconds (time in seconds elapsed since end of preceding pulse) - snr: signal-to-noise ratio (bin1_intensity / bin1_bg_std) - intensity: intensity of pulse (counts above baseline in bin1) - bin0_intensity: counts above baseline in bin0 - intensity_display: bin1_intensity + bin1_bg_mean - binratio: bin0_intensity / bin1_intensity - bg_mean: bin1 background mean in region of pulse - bg_std: bin1 background standard deviation in region pulse - bin0_bg_mean: bin0 background mean in region of pulse - bin0_bg_std: bin0 background standard deviation in region pulse {run_id}.csv.gz: Compressed comma-separated value file containing RS/ROI properties computed from raw pulses.bin file by included RS caller (example in rs_caller.py). - ap: unique aperture index on chip - ROI: ordinal ROI number in the aperture, 0-indexed - start_p: index (.loc) of first pulse in the ROI (inclusive) in pulse dataframe - end_p: index (.loc) of last pulse in the ROI (inclusive) in pulse dataframe - start_f: first frame of the first pulse in the ROI (inclusive) - end_f: Last frame of the last pulse in the ROI (exclusive) - start_s: Time (in seconds elapsed from beginning of run) of the start of the ROI - end_s: Time (in seconds elapsed from beginning of run) of the end of the ROI - dur_f: Duration in frames of the ROI - dur_s: Duration in seconds of the ROI - num_pulses: Number of pulses in the ROI (that also passed filtering during ROI-calling) - pw_mean: Mean pulse duration (in seconds) of pulses in the ROI - ipd_mean: Mean inter-pulse duration (in seconds) of pulses in the ROI - snr_mean: Mean signal-to-noise ratio of pulses in the ROI - intensity_mean: Mean intensity above baseline of pulses in the ROI - binratio_norm: Estimated pulse bin ratio of pulses in the ROI, according to the following equation: sum(bin0_intensity*dur_f) / np.sum(bin1_intensity*dur_f) - ROI_score: ROI quality score (0-1 from least to most likely to contain recognizer-peptide recognition pulsing) - binratio_skew: bin ratio correction factor accounting for binning signal timing differences across the chip. This factor has already been applied to the binratio_norm column</pre>

opencc-by-nc-nd-4.0Aug 2022View details →

Materials used in "Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality"

The materials in this record are the audio and video files, together with various configuration files, used by the "SEAT" software in the study described in Moore, Green, Brookes & Naylor (2022) "Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality" They are shared in this form so that the experiment may be reproduced.  For any other use please contact the authors to obtain the original database(s) from which these materials are derived. The materials were created to be compatible with v0.3 of SEAT, which is available from <a href="https://github.com/ImperialCollegeLondon/sap-elospheres-audiovisual-test/releases/tag/v0.3">GitHub</a>. Note that the materials must be placed at <code>C:\seat_experiments\cafe_AV.</code>

Autophagosomal/autolysosomal/lysosomal dynamics of FaDu and HGFb cells affected by autophagy modulators (dataset of dual labeling of autophagosomes and labeling lysosomes, confocal microscopy)

In this dataset, the impact of autophagy modulators on the auophagosomes and lysosomes in FaDu and HGF cells was investigated. Experimental details are described in the accompanying paper. This dataset contains total 702 CZI confocal image Z-stacks. Relevant paper: HANELOVA, Klara, RAUDENSKA, Martina, KRATOCHVILOVA, Monika, NAVRATIL, Jiri, VICAR, Tomas, BUGAJOVA, Maria, GUMULEC, Jaromir, MASARIK, Michal and BALVAN, Jan. Autophagy modulators influence the content of important signalling molecules in PS-positive extracellular vesicles. Cell Communication and Signaling. 24 May 2023. Vol. 21, no. 1, p. 120. DOI <a href="https://doi.org/10.1186/s12964-023-01126-z">10.1186/s12964-023-01126-z</a>. <div> <div> <div> </div> </div> </div> Model cell lines   The cell line FaDu (HTB-43TM), derived from a squamous cell carcinoma (SCC) of the hypopharynx, and the human gingival fibroblast cell line HGF (derived from the histologically normal gingival biopsy) were used in this study. The authenticated cell lines were purchased from the American Type Culture Collection (ATCC; Manassas, Virginia, USA) within the last five years.  Autophagy modulation For autophagy modulation, FaDu cells were treated for 24 h with 5nM bafilomycin A1 (Sigma-Aldrich, B1793), 50 µM of hydroxychloroquine sulphate (Sigma-Aldrich, H0915), 100 µM of Cpd18 (Calbiochem), 50 nM of autophinib (Sigma, SML2632), 10 µM of EACC (MedChemExpress), 200 nM of rapamycin (Sigma-Aldrich, R0395), 3 nM of Torin-1 (MedChemExpress), or 30 nM of NVP-BEZ235 (MedChemExpress). To induce starvation, cells were cultured in DMEM F12 without glutamine and without FBS (Biosera). Modulation of autophagy did not reduce the viability  of FaDu cells. Conditioned media preparation see details in the accompanying paper Fluorescence Microscopy The autophagosomal/autolysosomal/lysosomal dynamics of affected cells were observed using the combination of PremoTM Autophagy Tandem Sensor (P36239, Invitrogen) with the far-red emitting LysoTracker® Deep Red (L12492, Invitrogen) (Ex 647 nm/Em 668 nm). By combining acid-sensitive Emerald GFP (Ex 488 nm/Em 509 nm) with acid-insensitive TagRFP (Ex 555 nm/Em 584 nm) in the PremoTM kit, autophagosomes and autolysosomes labelling (yellow and red, respectively) is possible. Immediately after transduction with 12 µl PremoTM Autophagy Tandem Sensor/2ml cell suspension, cells were seeded at 5 × 105 into 35-mm glass-bottomed gelatin-coated dishes (Ibidi, μ-Dish 35 mm, high Glass Bottom) and cultured for 48 h to equilibrate expression levels. Subsequently, cells were exposed to the selected agents for 6, 12, 24 and 48 hours before imaging.  LysoTracker® Deep Red staining was performed 1 h before imaging.  To monitor the uptake of isolated PS-EVs by fibroblasts, we stained EVs with PKH67 (Sigma, PKH67GL) and then removed the remaining dye using Exosome Spin Columns (MW 3000) (Thermo Scientific, #4484449). The stained EVs were then suspended in 400 ul of cultivation medium and added to HGFB cells growing for 24 h in Ibidi µ-Slide I Luer (Ibidi, 80176). Image acquisition was performed 24 hours after EVs addition. 1 µl of 1 µg/ml of Hoechst 33342 (Enzo) (Ex 350 nm/ Em 461 nm) nuclear stain was added 1 hour before imaging. To determine the viability of the cell population prior to isolation of EVs, cells were left in a culture dish with 1 ml of culture medium to which propidium iodide (Sigma-Aldrich) and Hoechst 33342 were added 45 minutes before capturing.  To maximize the possibility of comparison between samples, all samples (from a single cell line) were captured on the same day in a single run using the same microscope settings. For each time and each treatment, 10–12 fields of view were captured from randomized sites of the culture dish. Epifluorescent microscopy images and confocal microscopy images were acquired using Laser scanning confocal microscope Zeiss LSM 880 with AiryscanFast module (Carl Zeiss Inc.) using a C-apochromat 40x/1.20 W and C-Apochromat 63 /1.20 W. LysoTracker® Deep Red was excited HeNe 633 nm solid-state laser and emitted light was detected at 638–759 nm. Emerald GFP was excited 488 nm ArgonRemote laser, and emitted light was detected at 493–576 nm. TagRFP was excited DPSS 561 nm laser, and emitted light was detected at 570–650 nm. Hoechst 33342 was excited with a 405 nm solid-state laser, and emitted light was detected at 410–508 nm. Fluorescence images were acquired by the transmitted light detector.  Images were analyzed using ImageJ software and custom MATLAB software developed in our laboratory. The analysis process consists of the segmentation of cells from the background and extraction of the intensity of fluorescence channels (TagRFP, GFP, LysoTracker) inside cells. For segmentation, a thresholding-based method was used, where a manually selected threshold was applied. Segmentation was applied to the image created as the sum of all fluorescence channels to achieve segmentation independent of the intensity of individual channels. To achieve better segmentation without noisy pixels, fluorescence images were preprocessed with median filter (7x7) and Gaussian filter (standard deviation 1); additionally, binary segmentation was post-processed with morphological closing and removal of small binary connected components (<5000px). For intensity extraction, the mean value of segmented cell pixels was used for each field of view. Besides individual fluorescence channels, the mean colocalization of TagRFP and GFP was calculated with a pixel-wise multiplication of TagRFP and GFP channels. File Naming CZI images are organised in folders according to cell line, treatment time, and treatment. Names include magnification, cell line measured, treatment, and time of treatment, <blockquote> 40x_FADU_BAF_12h_BF_4.czi </blockquote> The abbreviations used for treatments are as follows: BAF, bafilomycin A1; HCQ, hydroxychloroquine sulphate; Cpd18; APB, autophinib; EACC; RAPA, rapamycin; TOR1, Torin-1; BEZ, NVP-BEZ235; starv, starvation;

opencc-by-4.0Jun 2022View details →

Thermal conductivity of hydrous wadsleyite determined by non-equilibrium molecular dynamics based on machine learning

This repository contains data used in "Thermal conductivity of hydrous wadsleyite determined by non-equilibrium molecular dynamics based on machine learning" submitted by Dong Wang, Zhongqing Wu and Xin Deng. Figure S4 : "MLP test-Energy" in <a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/Data%20for%20Figures.xlsx?versionId=96ac296c-13f9-4871-976a-d7ad087e0b62">Data for Figures.xlsx</a>、<a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/MLP%20test-force.txt?versionId=acfeccd1-ecbf-42c8-a6a3-0d6daf72b078">MLP test-force.txt</a> Figure 1 : "MLP test-NEMD" in <a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/Data%20for%20Figures.xlsx?versionId=96ac296c-13f9-4871-976a-d7ad087e0b62">Data for Figures.xlsx</a> Figure 3 : "Modeing" in <a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/Data%20for%20Figures.xlsx?versionId=96ac296c-13f9-4871-976a-d7ad087e0b62">Data for Figures.xlsx</a>