Find research datasets worth reusing

opencc-by-4.0Feb 2022View details →

Raw data supporting "mScarlet fluorescence lifetime reports lysosomal pH quantitatively"

Original dataset and processing code supporting the preprint (scientific publication) "mScarlet fluorescence lifetime reports lysosomal pH quantitatively." Publication Abstract: The lysosome maintains a highly acidic pH, which is critical for successful lysosomal catabolism. Lysosomal pH (pHlys) is difficult to measure because of the simultaneous need for a sensor with large dynamic range, genetic targetability, low pKa, and a quantitative readout. Here, we demonstrate that the fluorescence lifetime of the mScarlet-LAMP1 fusion protein quantitatively reports lysosomal pH, exhibiting a large dynamic range and a pKa well-tuned for the lysosome. Because fluorescence lifetime is an intrinsic property, pH measurements can be achieved in a single fluorescence channel. mScarlet-LAMP1 lifetime allows for individual lysosome-resolved recordings, a critical advance in describing and understanding pHlys heterogeneity. Using this biosensor, we quantify heterogeneity of pHlys in cultured cells at rest and over time during drug treatment. We anticipate that mScarlet-LAMP1 will enable new insights into the diversity of lysosomal physiology and ionic milieu.

opencc-by-4.0Feb 2022View details →

Data set for the journal article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO2-to-CO conversion"

In the article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO2-to-CO conversion" we demonstrated that Fe impurities in a hybrid CoPc@MWCNT catalyst lead to its performance deterioration during long-term CO2 electrolysis. Here we present the dataset the work was based on. The data are divided into four groups: (i) Current transients and gas chromatography data for short-term electrolysis at different potentials (in an excel file we give the numbers of chromatograms for each potential; current transients are given as an origin file with datasets and plots inside) (ii) Current transients and gas chromatography data for long-term electrolysis at different potentials and with different catalysts (in respective excel files we give the numbers of chromatograms; figure numbers are given in the folder names) (iii) Electron microscopy images and EDX datasets (the images and datasets are collected in the folders with respective figure numbers used in the paper) (iv) Calibration curves for ICP-MS

opencc-by-4.0Jun 2020View details →

Learning to embed lifetime social behavior from interaction dynamics - Data

Interaction matrices and metadata used in "Learning to embed lifetime social behavior from interaction dynamics" The following files are included: <ul> <li>interactions_bn16_sparse.npz and interactions_bn19_sparse.npz: These are the interaction affinity matrices for the BN16 and BN19 datasets as described in the publication. The data is stored as compressed sparse tensors with time on the first, and the individuals on the second and third dimensions. The data was stored using the <a href="http://sparse.pydata.org">pydata/sparse</a> library 0.9.1</li> <li> alive_bn16.csv and alive_bn19.csv: These files contain the dates of emergence (also corresponding to the dates they were introduced into the colonies) and heuristically determined number days alive for all individuals in the interaction matrices. Death dates were determined using a bayesian changepoint model and the number of daily detections of each individual </li> <li> rhythmicity_bn16.csv and rhythmicity_bn19.csv: These files contain the circadian rhythmicity values used in the evaluation of the method. The circadian rhythmicity is the \(R^2\) value of a sine with a 24 hour period fitted to the individuals' movement velocities over a three day window </li> <li> indices_bn16.csv and indices_bn19.csv: These files contain the mapping between the original marker IDs used during the recording of the data (which has gaps, because not all markers were used) and the sequential indices used in the interaction matrices. These files can therefore be used to look up the original ID of an individual based on it's index in the interaction matrix and vice versa </li> <li> time_spent_on_substrates.csv: This data was used for the mapping from factors to the proportion of time spent on various cell substrates (Figure 5). The positions of the individuals were accumulated by minute, and the column "location_descriptor_count" contains the total number of minutes on the respective day that the individual was detected </li> </ul> See <a href="https://doi.org/10.1101/2020.05.06.076943">10.1101/2020.05.06.076943</a> for more details about the bayesian changepoint model, circadian rhythmicity calculation, and location mapping.

opencc-by-4.0May 2024View details →

Insights into metabolic changes during epidermal differentiation as revealed by multiphoton microscopy with fluorescence lifetime imaging

Rapid developments in the field of organotypic cultures has generated a growing need for effective quality control measures during tissue development. In this study, we correlate metabolic changes with epidermal differentiation and demonstrate that multiphoton microscopy with fluorescence lifetime imaging (MPM-FLIM) can be applied as a non-invasive approach to monitor epidermal differentiation of keratinocytes with respect to proliferative and differentiated states.  Keratinocytes grown at 1.5 mM Ca2+ exhibited increased expression of differentiation markers KRT1 and KRT10 compared to 60 μM Ca2+, and a metabolic shift from glycolysis to mitochondrial respiration. Fitting the fluorescence decay with a biexponential model revealed a decreased relative fraction of intracellular NADH and FAD after high calcium treatment, consistent with increased oxidative phosphorylation. Using these two parameters, the epidermal differentiation process could be monitored over a 96 h period. Implementing discriminating analysis based on k-means clustering generated clusters that correlated well with culturing time, suggesting that this methodology can be employed as part of an automated pipeline for monitoring keratinocyte differentiation.

opencc-by-4.0Aug 2021View details →

Data and code for: Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats, Suricata suricatta

Data and code to go with our publication "Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats, Suricata suricatta", Nature Communications (2021). FILE DESCRIPTIONS ****** DATA ****** meerkat_16S_data.tar.gz    # 16S V4 amplicon sequences  sequenced on an Illumina MiSeq platform using primer pair 515F and 806R, including all faecal samples, controls, and sand samples. Sequence identifiers and basic metadata are in sequence_identifiers.csv. sequence_identifiers.csv    # Simple metadata and identifiers for all sequences/samples (what type of sample/sequencing run, etc), required for QIIME2 processing of the raw fasta.gz files contained in meerkat_16S_data.tar.gz. It contains a column for whether the sample was included in the final analysis. Does not include sample biological metadata as generating this data requires access to Kalahari Meerkat Project database. Biological metadata for samples included in the final analysis are instead provided in processed_data_phyloseq.RDS and can be accessed via phyloseq::sample_data(processed_data_phyloseq). processed_data_phyloseq.RDS # Phyloseq object containing the processed data used in the presented analysis. Contains data for 1109 samples, and includes the ASV table, the taxonomic classification, the phylogenetic tree, and the sample metadata used in the analysis. technical_replicate_data_phyloseq.RDS # Phyloseq object containing data from the 16 technical replicates. pilot_study_data_phyloseq.RDS # Phyloseq object containing data from the pilot study on captive meerkats. ****** CODE ****** CODE1_QIIME_script.R   # QIIME2 script to generate ASV table, taxonomy, and phylo tree from meerkat_16S_data.tar.gz. Requires a reference taxonomy (SILVA) and a reference phylogeny (SEPP) for taxonomic and phylogenetic placements. CODE2_processing_QIIME_output.Rmd  # R markdown script that processes the QIIME2 output generated by CODE1_QIIME_script.R. Does not generate meerkat metadata as this requires access to the Kalahari Meerkat Project database. This metadata is provided in processed_data_phyloseq.RDS. CODE3_data_analysis_script.Rmd # R markdown script that generates data and figures presented in paper, using data from processed_data_phyloseq.RDS, technical_replicate_data_phyloseq.RDS, and pilot_study_data_phyloseq.RDS. ****** R MARKDOWN REPORTS ****** The following reports are html files that show the code output for the two RMD files above. RMARKDOWN_data_processing.html # R markdown report for CODE2_processing_QIIME_output.Rmd RMARKDOWN_data_analysis.html # R markdown report for CODE3_data_analysis_script.Rmd ***************************** For general queries, unexpected errors and/or inconsistencies, please contact riselya@gmail.com.

opencc-by-4.0Feb 2023View details →

Foresail-2 Orbital Lifetime Analysis

The dataset has been created with the following inputs for DRAMAs CROC. <table> <tbody> <tr> <td>Inputs for DRAMA</td> <td> </td> <td>Outputs for DRAMA</td> </tr> <tr> <td>Orbital elements</td> <td> </td> <td>CROC</td> </tr> <tr> <td>Inclination</td> <td>15</td> <td>°</td> <td> </td> <td>F3. Randomly tumbling satellite</td> </tr> <tr> <td>RAAN</td> <td>0</td> <td>°</td> <td> </td> <td>Average cross section for 6U</td> <td>0.170</td> <td>m^2</td> </tr> <tr> <td>Argument of perigee</td> <td>0</td> <td>°</td> <td> </td> <td>Minimum Cross Section</td> <td>0.028</td> <td>m^2</td> </tr> <tr> <td>Drag coefficient</td> <td>2.2</td> <td>N/A</td> <td> </td> <td>Maximum Cross Section</td> <td>0.268</td> <td>m^2</td> </tr> <tr> <td>SRP coefficient</td> <td>1.3</td> <td>N/A</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Orbit Predigtion Update</td> <td>08.02.2023</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>6U CubeSat with 2 folding panel + boom</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Dry mass</td> <td>14</td> <td>kg</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Main body dimensions</td> <td>0.11x0.34x0.25</td> <td>m^3</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Solar Panel X+</td> <td>0.007x0.34x0.25</td> <td>m^3</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Solar Panel X-</td> <td>0.007x0.34x0.25</td> <td>m^3</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Extended boom dimensions</td> <td>0.07x0.68x0.01</td> <td>m^3</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Magnetometer dimensions</td> <td>0.05x0.05x0.05</td> <td>m^3</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>   OSCAR simulations have been conducted with orbits with apogee varying from 15000 km to 65000 km in 5000 km steps. The perigee has been varied for every apogee height from 250 km to 400 km in 50 km steps. Every simulation has been done for 4 different launch dates: <table> <tbody> <tr> <td>21.03.2025 12:00:00</td> <td>Spring</td> </tr> <tr> <td>21.06.2025 12:00:00</td> <td>Summer</td> </tr> <tr> <td>21.09.2025 12:00:00</td> <td>Autumn</td> </tr> <tr> <td>21.12.2025 12:00:00</td> <td>Winter</td> </tr> </tbody> </table> If not specified otherwise all settings in DRAMA have been kept to the default values.

opencc-by-4.0Mar 2020View details →

Precise Lifetime Measurement of the Cesium 5²D₅⸝₂ State

This repository contains data and software related to an experiment in which we determine the lifetime of the cesium 52D5/2 state using atoms in a vapor cell. More information is available in the following paper: <ul> <li>arXiv:1912.10089</li> </ul> We provide the data and Python scripts for data evaluation in six folders. We zipped these folders with Windows 10 Enterprise, Version 1903. In the following, we describe how to use data and scripts to get the lifetime results published in our paper.   Raw Time-Tags Here, we provide the raw measurement data. We perform several experiment cycles. An excitation laser is switched on at the beginning of each cycle. In the middle of the cycle, it is switched off. We use two single-photon counting modules (SPCM): one detects fluorescence photons emitted by the atoms, the other reference light from the excitation laser beam. We record the arrival times of those photons with respect to the beginning of the cycle. These time delays can be used to create a histogram and to determine the lifetime of the cesium 52D5/2 state. For each measurement, we provide two data files which are encoded in ‘UTF-8’: <ul> <li>‘figx_xxx_reference_time_tags.dat’</li> <li>‘figx_xxx_fluorescence_time_tags.dat’</li> </ul> where ‘figx_xxx’ is a unique tag indicating the figure and point to which this data corresponds in our paper. The ‘figx_xxx_fluorescence_raw_data.dat’ and ‘figx_xxx_reference_raw_data.dat’ files contain the raw time delays in picoseconds of the fluorescence and the reference photons, respectively. We provide raw time delays in the following folders: <ul> <li>‘fig3_time_tags’: The data used in figure 3.</li> <li>‘fig4_time_tags’: The data used in figure 4. This folder has six subfolders, named ‘point_x’, where x indicates to which point of figure 4 the data belongs. The data of the subfolders ‘point_x_y’ was used for points x and y of figure 4 (the time-tags of the fluorescence photons were split into two sub-datasets with equal size).</li> <li>‘fig5_time_tags’: The data underlying figure 5. This folder has subfolders from ‘23C’ to ‘116C’ where the name indicates the temperature in units of °C of the vapor cell during the measurement. Note that the various measurements have different cycle lengths because reabsorption makes the decay of the fluorescence signal longer. For the lifetime value at a temperature of 23 °C, we used  the lifetime which we found in figure 4. For some measurements, the time-tags of the reference SPCM are missing because only one SPCM was available for these measurements.</li> </ul>   Histograms Since the files of the raw measurement data are large, we also provide histograms of the time tags. For all datasets discussed above, we generated a histogram with a bin length of 5 ns. We save these histograms with the same file name as the files with the raw time tags but with the ending ‘_histo’ instead of ‘_time_tags’, e.g., ‘fig3_fluorescence_histo.dat’ and ‘fig3_reference_histo.dat’. We always provide two file formats: <ul> <li>a data file (.dat), containing rows with the start time of a bin in microseconds, and the number of SPCM counts due to the fluorescence signal until the start of the next bin, separated by a comma. These files are encoded in ‘UTF-8’.</li> <li>a NumPy compressed array format file (.npz), which includes two arrays: The first array is called ‘time’ and contains the starting times of the bins. The second array is called ‘counts’ and includes the corresponding measured number of fluorescence photons per bin. It is possible to load the arrays into a Python script with numpy.load (tested with NumPy version 1.18.1).</li> </ul>   Additional Information on the Measurements We provide a JavaScript Object Notation file (.json) for each measurement. These files provide the following information about every measurement: temperature of the cell, number of detected photons, photons per cycle, and the total measurement duration. They are named ‘figx_xxx_info.json’, where ‘figx_xxx’ is the same indicator as discussed in section ‘Raw Time-Tags’.   Scripts This folder contains two sample scripts to illustrate how our data can be processed with Python. The first Python script (generate_histograms.py) generates a histogram of the photon arrival times. The second Python script performs a fit in order to determine the lifetime of the cesium 52D5/2 state. We wrote these scripts with Python 3.6.5. To avoid errors, one should download all zipped folders and extract them to the same folder. <ul> <li>The script ‘generate_histograms.py’ processes the fluorescence photon detection events stored in the folder ‘fig3_time_tags’. The file ‘fig3 _fluorescence_time_tags.dat’ is read into the script, and a histogram is generated. To run the script, the following Python libraries are required: NumPy (version 1.18.1), os (version 0.1.4), and json (version 2.0.9).</li> <li>The script ‘fit_data.py’ loads the file ‘fig3_fluorescence_histogram.npz’ from the folder ‘histograms\ fig3_time_tags’ in NumPy arrays. We perform a least-square fit on the histogram of the fluorescence decay. From the fit, we get the lifetime of the cesium 52D5/2 state. Optionally, it is possible to print a fit report and to plot the fit with its residuals. The following Python libraries are required to run the script: NumPy (version 1.18.1), os (version 0.1.4), json (version 2.0.9), pyplot from matplotlib (version 3.1.1), and Parameters, ExponentialModel, and ConstantModel from LmFit (version 1.0.0).</li> </ul>   Figures In the folder ‘figures’, we provide the values of the points which we used to generate figure 4 and figure 5. For both figures, we made a JavaScript Object Notation file (.json) where the data of every point is stored in a dictionary. This data contains the fit result of the lifetime and the temperature of the measurement. Additionally, it contains the corresponding errors and the units of every value.

opencc-by-4.0Jul 2020View details →

Hippocampal hub neurons maintain distinct connectivity throughout their lifetime

The temporal embryonic origins of cortical GABA neurons are critical for their specialization. In the neonatal hippocampus, GABA cells born the earliest (ebGABAs) operate as ‘hubs’ by orchestrating population synchrony. However, their adult fate remains largely unknown. To fill this gap, we have examined CA1 ebGABAs using a combination of electrophysiology, neurochemical analysis, optogenetic connectivity mapping as well as ex vivo and in vivo calcium imaging. We show that CA1 ebGABAs not only operate as hubs during development, but also maintain distinct morpho-physiological and connectivity profiles, including a bias for long-range targets and local excitatory inputs. In vivo, ebGABAs are activated during locomotion, correlate with CA1 cell assemblies and display high functional connectivity. Hence, ebGABAs are specified from birth to ensure unique functions throughout their lifetime. In the adult brain, this may take the form of a long-range hub role through the coordination of cell assemblies across distant regions.

Figure 4 in Influence of CO -induced seawater acidification on the development and lifetime reproduction of Tigriopus japonicus Mori, 1938

Figure 4. Effect of carbon dioxide (CO2)-driven seawater acidification on total number of nauplii (N = 3) produced by Tigriopus japonicus females over the duration of the experiment (median indicated with a bar; quartiles, minimum and maximum shown).

Figure 3 in Influence of CO -induced seawater acidification on the development and lifetime reproduction of Tigriopus japonicus Mori, 1938

Figure 3. Proportion of egg sacs that successfully produced nauplii (N = 3) at four pH levels (median indicated with a bar; quartiles, minimum and maximum also shown).

Figure 6 in Influence of CO -induced seawater acidification on the development and lifetime reproduction of Tigriopus japonicus Mori, 1938

Figure 6. Variation in number of nauplii [mean ± standard deviation (SD), N = 3] produced by Tigriopus japonicus females over successive broods at four pH levels.

Figure 2 in Influence of CO -induced seawater acidification on the development and lifetime reproduction of Tigriopus japonicus Mori, 1938

Figure 2. Effect of carbon dioxide (CO2)-induced seawater acidification on the number of broods (N = 3) produced by females of Tigriopus japonicus over the duration of the experiment (median indicated with a bar; quartiles, minimum and maximum also shown).

opencc-by-4.0Jan 2021View details →

Grid-Interactive Efficient Building Technology Cost, Performance, and Lifetime Characteristics

This record includes the raw data associated with the Lawrence Berkeley National Laboratory (LBNL) report "Grid-Interactive Efficient Building Technology Cost, Performance, and Lifetime Characteristics," which is published <a href="https://escholarship.org/uc/item/44t4c2v6">here</a>. Data were collected by Guidehouse under the objective of developing current and projected performance, cost, and lifetime characteristics for residential and commercial building technologies and equipment with the potential to provide grid services. The list of technologies was developed based on data gathered from the U.S. Department of Energy (DOE) Grid Interactive Efficient Buildings (<a href="https://www.energy.gov/eere/buildings/grid-interactive-efficient-buildings">GEB</a>) Technical Report Series, ENERGY STAR Connected Certified Products, and input from researchers at the U.S. national laboratories. For each technology, characteristics are provided for a typical case and a connected or grid-interactive case. Where data are available, current DOE appliance standard levels are given. Definitions vary by technology and are provided with each data table. Current data is provided for 2020 and projections are available for 2030, 2040, and 2050.  Note: enabling technologies for grid-interactive efficient buildings such as smart meters and distributed energy management software were out of scope for this report. Non-building technologies such as EV chargers and PV inverters were also out of scope for this report.

dryad40/100

Lifetimes and timescales of tropospheric ozone: Ozone emission experiments

The lifetime of tropospheric O3 is difficult to quantify because we model O3 as a secondary pollutant, without direct emissions. For other reactive greenhouse gases like CH4 and N2O, we readily model lifetimes and timescales that include chemical feedbacks based on direct emissions. Here, we devise a set of artificial experiments with a chemistry-transport model where O3 is directly emitted into the atmosphere at a quantified rate. We create three primary emission patterns for O3, mimicking secondary production by surface industrial pollution, that by aviation, and primary injection through stratosphere-troposphere exchange (STE). The perturbation lifetimes for these O3 sources includes chemical feedbacks and varies from 6 to 27 days depending on source location and season. Previous studies derived lifetimes around 24 days estimated from the mean odd-oxygen loss frequency. The timescales for decay of excess O3 varies from 10–20 days in NH summer to 30–40 days in NH winter. For each season, we identify a single O3 chemical mode applying to all experiments. Understanding how O3 sources accumulate (the lifetime) and disperse (decay timescale) provides some insight into how changes in pollution emissions, climate, and stratospheric O3 depletion over this century will alter tropospheric O3. This work incidentally found two distinct mistakes in how we diagnose tropospheric O3, but not how we model it. First, the chemical pattern of an O3 perturbation or decay mode does not resemble our traditional view of the odd-oxygen family of species that includes NO2. Instead, a positive O3 perturbation is accompanied by a decrease in NO2. Second, heretofore we diagnosed the importance of STE flux to tropospheric O3 with a synthetic 'tagged' tracer O3S, which had full stratospheric chemistry and linear tropospheric loss based on odd-oxygen loss rates. These O3S studies predicted that about 40 % of tropospheric O3 was of stratospheric origin, but our lifetime and decay experiments show clearly that STE fluxes add about 8 % to tropospheric O3, providing further evidence that tagged tracers do not work when the tracer is a major species with chemical feedbacks on its loss rates, as shown for CH4.

opencc-zeroJan 2024View details →

opencc-by-4.0Nov 2024View details →

Example binding lifetime analysis: Kymographs and tracks

Kymograph recorded on the LUMICKS C-Trap. LacI is labeled in green. The binding events on the kymograph were tracked using Pylake. The corresponding tracks are included in this dataset as tracks1.csv and tracks.csv.

opencc-by-4.0Oct 2024View details →

Data set for the study "Assessing the lifetime of anthropogenic CO2 and its sensitivity to different carbon cycle processes"

This repository contains the data necessary to reproduce the results of the paper:  "Assessing the lifetime of anthropogenic CO2 and its sensitivity to different carbon cycle processes"  <a href="https://doi.org/10.5194/bg-22-2767-2025" target="_blank" rel="noopener">https://doi.org/10.5194/bg-22-2767-2025</a> <h3>Data organization:</h3> The Zenodo upload is organized as the following inside of <code>results.zip</code>: <ul> <li>Data analysis and figure generation are given by "*.pynb" and "*.m" files </li> <li>Data files as NetCDF output are organized with the following structure inside of <code>data</code>: <ul> <li>Experiment: <code>REF</code>, <code>noLAND</code>, <code>noWEATH</code>, <code>ECS2</code>, <code>ECS4</code>, <code>intCH4</code>, <code>PATH1</code>, <code>PATH2</code>, and <code>PULSE</code> <ul> <li>Emissions scenario: <code>0_gtc</code>, <code>500_gtc</code>, <code>1000_gtc</code>, <code>2000_gtc</code>, <code>3000_gtc</code>, <code>4000_gtc</code>, and <code>5000_gtc</code> <ul> <li>Component: atmosphere (<code>atm</code>), land (<code>lnd</code>), ocean (<code>ocn</code>), biogeochemistry (<code>bgc</code>), and the carbon cycle (<code>co2</code>) <ul> <li>Note: for <code>intCH4</code>, there is another file concerning methane (<code>ch4</code>)</li> <li>Note: surface ocean pH and surface ocean DIC were not part of the standard output in the original CLIMBER-X model. Instead, these variables were calculated during post-processing using 2D spatial data. Since the 2D data was only output every 1 kyr, the first millennium of data was missing. To address this, we re-ran the experiments with surface ocean pH and DIC included in the output for the first 1 kyr. This is why there are additional individual files for pH, DIC, and the Revelle factor (see "fig5_7_8_9.ipynb" for further details). </li> </ul> </li> <li>File type: for each component, files are divided into timeseries (<code>*_ts.nc</code>) or 2D data with a 1 kyr output frequency (<code>*.nc</code>) <ul> <li>Note: due to size constraints of the Zenodo repository, only some 2D spatial data presented in the publication (for the <code>REF</code> experiment) is available. However, this is not an exhaustive dataset. For inquiries regarding additional data, please contact the corresponding author to explore potential availability.</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0May 2022View details →

SupportingDataset Identification and Quantification of Within-Burst Dynamics in Singly-Labeled Single-Molecule Fluorescence Lifetime Experiments

The Jupyter notebooks and resulting files used to demonstrate divisor-based mpH2MM. The analysis is demonstrated with both simulations and analyses of alpha-synuclein. <ol> <li> Notebooks.zip:* Zip file containing the Jupyter notebooks for producing, analyzing and visualizing the simulated photon trajectories. Note: this folder contains all code needed to reproduce simulations. All other files related to the simulations are produced by one of the notebooks in this trajectory. However, as simulations can take a long time, the various results files are included in this repository so that notebooks can be run from intermediate steps. <ol> <li> 1-PIFE-pybromo-sims.ipynb : The code for producing simulated diffusion trajectories and photon-HDF5 files of two-state systems undergoing transition dynamics (the results of this notebook are stored in the sub-folder PyBroMo_photonHDF5) </li> <li> 2-PIFE-mpH2MM-sim-[lifetime components].ipynb : Notebooks performing divisor-based mpH2MM on simulated datasets for a given combination of lifetime states. (these notebooks store files that are contained in the sub-folder H2MMresults) </li> <li> 3-PIFE-mpH2MM-compiled-plots.ipynb : Jupyter notebook for producing figures comparing all results globally </li> <li> 532nm_IRF_19-10-2021.csb: the file containing the experimental IRF used in the simulations </li> </ol> </li> <li> PyBroMo_photonHDF5.zip:* Zip file containing the simulated results of 1-PIFE-pybromo-sims notebook as photon-HDF5 files (1 file per transition rate/lifetime combination) </li> <li> PIFE-sim-dynamicmix_[lifetime components]_result.hdf5: special HDF5 files containing the results of each notebook in Notebooks, which are used by 3-PIFE-mpH2MM-compiled-plots </li> <li> PIFE-mpH2MM-alpha-syn-vFinal.ipynb: divisor-based mpH2MM analysis of alpha-synuclein smPIFE data </li> <li> H2MM-Lifetime_example.ipynb: A demonstration of divisor-based mpH2MM using nsALEX-smFRET data. This method could potentially demonstrate states differentiated in lifetimes independently of potential changes in E & S. </li> <li> Template_ltH2MM.ipynb: An easy-to-follow implementation of divisor-based mpH2MM demonstrated on a single alpha-synuclein experimental data acquisition file. This can be used for learning how to implement and analyze single dye fluorescence lifetime data with mpH2MM </li> </ol>   * For running these notebooks, generally, all files in PyBroMo_photonHDF5.zip should be placed into a single directory (i.e., the files in Notebooks.zip should be placed into the same directory as the files in PyBroMo_photonHDF5.zip) as the notebooks are set to read in files from their current directory.