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147 results for “dynamic processes”
Dynamic 1D search and processive nucleosome translocations by RSC and ISW2 chromatin remodelers
<p>Eukaryotic gene expression is linked to chromatin structure and nucleosome positioning by ATP-dependent chromatin remodelers that establish and maintain nucleosome-depleted regions (NDRs) near transcription start-sites. Conserved yeast RSC and ISW2 remodelers exert antagonistic effects on nucleosomes flanking NDRs, but the temporal dynamics of remodeler search, engagement and directional nucleosome mobilization for promoter accessibility are unknown. Using optical tweezers and 2-color single-particle imaging, we investigated the Brownian diffusion of RSC and ISW2 on free DNA and sparse nucleosome arrays. RSC and ISW2 rapidly scan DNA by one-dimensional hopping and sliding respectively, with dynamic collisions between remodelers followed by recoil or apparent co-diffusion. Static nucleosomes block remodeler diffusion resulting in remodeler recoil or sequestration. Remarkably, both RSC and ISW2 use ATP hydrolysis to translocate mono-nucleosomes processively at ~30 bp/sec for surprising distances on extended linear DNA. Processivity and opposing push-pull directionalities of nucleosome translocation shown by RSC and ISW2 shape the distinctive landscape of promoter chromatin.</p>
1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence.<br>The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka.</p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation,<br>n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.</p>
Data and code for Decoding dynamic landslide hazard processes for a massive refugee camp (KTP) in Bangladesh
<p>The codes have been implemented using R 4.4.0. Landslide priority zonation using Monte Carlo simulation is implemented in Google Colab.</p> <p>A Dynamic Landslide Hazard Assessment has been conducted using a Generalized Additive Model (GAM). The results of the GAM are also compared with standard machine learning algorithms (MLs): NNET, RF, LDA, xgBoost, and SVM.</p> <p>The code is jointly developed by Dewan Haque and Ritu Roy, with collaboration from many others. The GAM code is an update from the study published by Zhice, F. (2023), <a href="https://doi.org/10.5281/zenodo.10395153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10395153</a>, adapted to apply it across settings. The ML code has been developed from scratch.</p> <p>The required data from intensive fieldwork and satellite image analysis is uploaded here to reproduce the results. Additionally, R Markdown files are provided.</p> <p>The ReadMe file here, as well as on GitHub, will be useful for further instructions.</p> <p>GitHub Link: https://github.com/Dewan-cpu/Decoding-Landslide-Hazard-Assessment</p>
r-process abundances in neutron star merger dynamical ejecta given different fission yields
<p>This data release contains nucleosynthesis predictions following Vassh et al. (2020) for the r-process abundances of binary neutron star merger ejecta based on the simulation trajectories of Radice et al. (2018), which were mapped to parametrized trajectories based on their neutron-richness (Ye), entropy (s), and expansion timescale (tau). The neutron star merger scenarios considered here cover a wide range of neutron star masses. Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating. Results are reported for two fission yield sets, the Finite Range Liquid Drop Model (FRLDM, Mumpower et al. 2020) and 50/50 symmetric splits. All calculations assumed FRDM12 (Möller et al. 2016) for the nuclear mass model and apply the SFHO equation of state when obtaining the initial composition from nuclear statistical equilibrium (NSE).</p> <p>When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh 2022). Refer to Vassh et al. (2020) for further details on the nuclear physics inputs, to Mumpower et al. (2018) for further details on FRLDM, and to Radice et al. (2018) for further details on the merger ejecta trajectories.</p>
Dataset for acceleration measurements at the research bridge openLAB in Bautzen, Germany - Change of dynamic behavior in the concrete hardening process
<p>This data set was collected during the construction phase of the openLAB in Bautzen, Germany during the period from 22.01.2024 - 30.04.2024. It includes acceleration measurements and temperature measurements that record the dynamic behavior of the bridge over a period of 49 days (from 22.01.2024 to 11.03.2024; the remaining data cannot be uploaded due to the Zenodo upload restriction, but can be released on request). The detailed documentation of the data set can be found in the file "Bartels, Dunkel, Marx_2024_Documentation.pdf". The documentation describes the structure, the applied monitoring system and the collected data in detail.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 6. Mutation process
<p>The individuals obtained at the end of crossing over might not provide the desired level of variability. In that case, the produced individuals are mutated independently from another individual in such a way that their own gene sequence will change. The mutation process is performed in the event that the mutation possibility that is specified in the beginning comes true. The results obtained from mutation can enhance the outcome or make it worse. It is of utmost importance to specify the most suitable mutation possibility. This possibility should be high enough to prevent the method from becoming stuck at a local point, but at the same time, low enough to allow the best results produced by crossing over and multiplexing. In this study, the mutation possibility was selected as 10%, and the locations of two randomly selected bus stops were changed during the mutation process. As in the crossing over, also during this process, the limitations regarding producing a new individual (route) were adapted. Figure 6 shows an example to mutation process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 5. Crossover process
<p>In the selection mechanism, the individuals passed down from the previous generation occasionally cannot produce a better individual. In that case, the compatibility of the individuals might worsen, while producing the exact opposite is what is expected. To avoid this, the elitism operator is used and it is ensured that the best individual of the previous generation is passed down to the next generation, even though the current population is diminishing on an overall basis as a result of the production operators (Goldberg, 1989). In the current study, the consecutive selection method and elitism selection were preferred. For this aim, after calculating the compatibility function, the population was ranked according to the population function values (total route length). In case the crossover possibility is realized, the number of individuals to select will be determined according to the parameter related to the crossover size. To ensure a high level of variability in the generation, it is suggested that this possibility is taken as 50% and 95% (Goldberg, 1989). The crossover process allows the production of a new individual using the genes taken from two individuals, based on the selected crossover method. In this study, a single point crossover method was selected. During the crossing over, limitations that were previously mentioned regarding the formation of a new initial population were taken into consideration. The identified initial or final point was fixed and kept out of the context of crossing over. An example of crossover can be seen in Figure 5.</p>
Processed Hi-C contact matrices for "Single-cell DNA replication profiling identifies spatiotemporal developmental dynamics of chromosome organization"
<p>Processed Hi-C interaction matrices (iterative correction) saved in .hic format (40kb bins).</p> <p>.hic files were generated by juicer pipeline using processed Hi-C interaction matrices.</p> <p>Only <em>cis </em>interactions were available.</p> <p>To extract the data, please see </p> <p>https://github.com/aidenlab/juicer/wiki/Data-Extraction</p>
Data and Processing from "Carbon-centric dynamics of Earth's marine phytoplankton"
<div><strong>Brief Summary:</strong></div> <div>This documentation is for associated data and code for: </div> <div>A. Stoer, K. Fennel, Carbon-centric dynamics of Earth's marine phytoplankton. Proceedings of the National Academy of Sciences (2024).</div> <div> </div> <div>To cite this software and data, please use:</div> <div> <div>A. Stoer, K. Fennel, Data and processing from "Carbon-centric dynamics of Earth's marine phytoplankton". Zenodo. <a href="https://doi.org/10.5281/zenodo.10949682" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10949682</a>. Deposited 1 October 2024.</div> </div> <div> </div> <div><strong>List of folders and subfolders and what they contain:</strong></div> <div> <ol> <li>raw data: Contains raw data used in the analysis. This folder does not contain the satellite imagery, which will need to be downloaded from the NASA Ocean Color website (https://oceancolor.gsfc.nasa.gov/). <ol> <li>bgc-argo float data (subfolder): Includes Argo data from its original source or put into a similar Argo format</li> <li>global region data (subfolder): Includes data used to subset the Argo profiles into each 10deg lat region and basin.</li> <li>graff et al 2015 data (subfolder): Include the data digitized from Graff et al.'s Fig. 2.</li> </ol> </li> <li>processed data: data processing by this study (Stoer and Fennel, 2024) <ol> <li>processed bgc-argo data (subfolder): A binned processed file is present for each Argo float used in the analysis. Note these files include those describe in Table S1 (these are later processed in "3_stock_bloom_calc.py")</li> <li>processed satellite data (subfolder): includes a 10-deg latitude averaged for each satellite image processed (called "chl_sat_df_merged.csv"). This is later used to calculate a satellite chlorophyll-a climatology in "3_stock_bloom_calc.py".</li> <li>processed chla-irrad data (subfolder): includes the quality-controlled light diffuse attenuation data coupled with the chlorophyll-a fluorescence data to calculate slope factor corrections (the file is called "processed chla-irrad data.csv").</li> <li>processed topography data (subfolder): includes smoothed topography data (file named "ETOPO_2022_v1_60s_N90W180_surface_mod.tiff").</li> </ol> </li> <li>software: <ol> <li>0_ftp_argo_data_download.py: This program downloads the Argo data from the Global Data Assembly Center's FTP. Running this program will provide new Argo float profiles. However, there will be new floats and profiles present if downloaded. This will not match the historical record of Argo floats used in this analysis but could be useful for replicating this analysis when more data becomes available. The historical record of BGC-Argo floats are present in "/raw data/bgc-argo float data/" path. If you wish to downloaded other float data, see Gordon et al. (2020), Hamilton and Leidos (2017) and the data from the misclab website (https://misclab.umeoce.maine.edu/floats/).</li> <li>1_argo_data_processing.py: This program quality-controls and bins the biogeochemical data into a consistent format. This includes corrections and checks, like the spike/noise test or the non-photochemical quenching correction.</li> <li>2_sat_data_processing.py: this program processes the satellite data downloaded from the NASA Ocean Color website.</li> <li>3_stock_bloom_calc.py: this is the main program used to described the results of the study. The program takes the processed Argo data and groups it into regions and calculates slope factors, phytoplankton carbon & chlorophyll-a, global stocks, and bloom metrics.</li> <li>4_stock_calc_longhurst_province.py: This program repeats the global stocks calculations performed in "3_stock_bloom_calc.py" but bases the grouping on Longhurst Biogeochemical Provinces.</li> </ol> </li> </ol> </div> <div><strong>How to Replicate this Analysis:</strong></div> <div>Each program should be run in the order listed above. Path names where the data files have been downloaded will need to be updated in the code.</div> <div> </div> <div>To use the exact same Sprof files as used in the paper, skip running "0_ftp_argo_data_download.py" and start with "1_argo_data_processing.py" instead. Use the float data from the folder "bgc-argo float data". The program "0_ftp_argo_data_download.py" downloads the latest data from Argo database, so it is useful for updating the analysis. The program "1_argo_data_processing.py" may also be skipped to save time and the processed BGC-Argo float data may be used instead (see folder named "processed bgc-argo data"). </div> <div> </div> <div>Similarly, the program "2_sat_data_processing.py" may also be skipped, which otherwise can take multiple hours to process. The raw data is available from the NASA Ocean Color website (<a href="https://oceancolor.gsfc.nasa.gov/">https://oceancolor.gsfc.nasa.gov/</a>). The processed data from "2_sat_data_processing.py" is available so this step may be skipped to save time as well.</div> <div> </div> <div>The program "3_stock_bloom_calc.py" will require running "ocean_toolbox.py" (see below) in another tab. The portion of the program that involves QC for the irradiance profiles has been commented out to save processing time, and the pre-processed data used in the study has been linked instead (see folder "processed light data"). Similarly, pre-processed topography data is present in this repository. The original Earth Topography data can be accessed at <a href="https://www.ncei.noaa.gov/products/etopo-global-relief-model">https://www.ncei.noaa.gov/products/etopo-global-relief-model.</a></div> <p> </p> <p>A version of "3_stock_bloom_calc.py" using Longhurst provinces is available for exploring alternative groupings and their effects on stock calculations. See the program named "4_stock_calc_longhurst_province.py". You will need to download the Longhurst biogeochemical provinces from <a href="https://www.marineregions.org/">https://www.marineregions.org/</a>.</p> <p>To explore the effects of different slope factors, averaging methods, bbp spectral slopes, etc, the user will likely want to make changes to "3_stock_bloom_calc.py". Please do not hesitate to contact the correponding author (Adam Stoer) for guidance or questions.</p> <p><strong>ocean_toolbox.py:</strong></p> <p>import statsmodels.formula.api as smf<br>import os<br>import matplotlib.pyplot as plt<br>import numpy as np<br>from uncertainties import unumpy as unp<br>from scipy import stats</p> <p>def file_grab(root,find,start): #grabs files by file extensions and location<br> filelst = []<br> for subdir, dirs, files in os.walk(root):<br> for file in files:<br> filepath = subdir + os.sep + file<br> if filepath.endswith(find):<br> if filepath.startswith(start):<br> filelst.append(filepath)<br> return filelst</p> <p>def sep_bbp(data, name_z, name_chla, name_bbp):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_z: name of the depth variable in data<br> name_chla: name of the chlorophyll-a variable in data<br> name_bbp: name of the particle backscattering variable in data <br> <br> returns: the data variable with particle backscattering partitioned into <br> phytoplankton (bbpphy) and non-algal particle components (bbpnap).<br> '''<br> #name_chla = 'chla'<br> #name_z = 'depth'<br> #name_bbp = 'bbp470'<br> dcm = data[data.loc[:,name_chla]==data.loc[:,name_chla].max()][name_z].values[0] # Find depth of deep chla maximum<br> part_prof = data[(data.loc[:,name_bbp]<np.median(data.loc[:,name_bbp]))] # find median bbp of profile<br> <br> mod = smf.quantreg('bbp470 ~ ' + str(name_z), <br> part_prof).fit(q=0.01) # Find model to 1 percentile<br> y_pred = mod.predict(part_prof.loc[:,name_z]) # Create predicted bbp_nap<br> <br> part_prof.loc[:,'bbp_back'] = y_pred.values # Predicted bbp NAP from linear trend<br> z_lim = part_prof.loc[(part_prof.loc[:,'bbp_back'].div(part_prof.loc[:,name_bbp])>=1), name_z].min() <br> <br> # Find depth where bbp NAP and bbp intersect<br> data.loc[data[name_z]>=z_lim, 'bbp_back'] = data.loc[data[name_z]>=z_lim, name_bbp].tolist()<br> data.loc[data[name_z]<z_lim,'bbp_back'] = data.loc[data[name_z]==z_lim, name_bbp].values[0] #data.loc[data[name_z]<z_lim, name_z].mul(lr.slope).add(lr.intercept)<br> <br> <br> data.loc[:,'bbpphy'] = data.loc[:, name_bbp].sub(data.loc[:,'bbp_back']) # Subtract bbp NAP from bbp for bbp from phytoplankton<br> data.loc[(data['bbpphy']<0)|(data['depth']>z_lim),'bbpphy'] = 0 # Subtract bbp NAP from bbp for bbp from phytoplankton</p> <p> return data['bbpphy'], z_lim</p> <p>def bbp_to_cphy(bbp_data, sf):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_bbp: name of the particulate backscattering variable in data<br> name_bbp_err: name of particulate backscattering error variable in data<br> <br> returns: the data variable with particle backscattering [/m] converted into<br> phytoplankton carbon [mg/m^3].<br> '''<br> <br> cphy_data = bbp_data.mul(sf) </p> <p> return cphy_data</p>
Processed data for "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER"
<p>This dataset contains the processed data for the figures and analyses performed in the publication "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER" from Lucas O., Ward S., Zaidi R., Bunkum A., ..., Zaccaria S. Nature genetics, in press, 2024.</p> <p>The processed data are separated into three respective folders:</p> <ul> <li>GT contains all the data related to the analysis of the generated ground truth datasets;</li> <li>NSCLC contains all the data related to the analysis of the NSCLC dataset;</li> <li>TNBC_HGSC contains all the data related to the analysis of the TNBC and HGSC datasets. </li> </ul>
Dynamic Binaural Processing (sBTRF) data
<p>This dataset is associated with a publication exploring processing of dynamic binaural cues. </p>
Geochronology and Geochemistry of Late Cenozoic Magmatism on Naozhou Island, Leiqiong Area: Implications for Deep Dynamic Processes
<p>The files of "Metadata 1" and "Metadata 2" are new measured data. The file of "References dataset" is published data from previous studies.</p>
Fig. 8. A-D in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 8. A-D − Nomarski (leftmost lane), FITC-immunofluorescence images labeled with anti-α-tubulin monoclonal antibody and their magnified images (middle two lanes), and red fluorescence images (rightmost lane) stained with Acti-stain 555 phalloidin (detection for F-actin) of encysting cells of C. cucullus Nag-1. Each set of photomicrographs arranged in a horizontal row shows an identical cell except for Fig. 8C (FITC image, inset). A − Vegetative cell. B-D − Encysting cells of C. cucullus Nag-1 at 1.5 h (B), 3 h (C) and 3 days (D) after encystment induction. E − Nomarski image (left), red fluorescence images (middle) stained with Acti-stain 555 phalloidin, and a Nomarski image superimposed with a red fluorescence image obtained by Acti-stain 555 phalloidin staining (right) in encysting cells of C. cucullus Nag-1 at 3 h after encystment induction. F − Silver impregnation of a 3-day-aged cyst showing the basal structure of cilia. This photograph was reproduced from our previous work (Watoh et al. 2005, Fig. 9b). ant: anterior end, le: lepidosome, mu: mucus layer, ec/en: ectocyst layer lined with endocyst layer, m: plasma membrane. B − arrowheads: swollen tip of cilia. C − arrowhead: oral apparatus.
Fig. 6 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 6. Ca2+/overpopulation-stimulated in vivo phosphorylation of p43 (actin, identified by MS) during resting cyst formation of C. cucullus Nag-1, detected by biotinylated Phos-tag/ECL assays (A), and blots stained with CBB after the biotinylated Phos-tag/ECL detection (B). Figures above the photographs indicate time lapse after onset of encystment induction.
Fig. 5 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 5. Photomicrographs (Nomarski images) (A) and transmission electron micrographs (B) of C. cucullus Nag-1 after onset of encystment induction, showing resorption of cilia. (A) Vegetative cell at 0 h (A-1) and 2.5 h (A-2) after onset of encystment induction. (B) Encysting 3-h-aged cell (B-1) and 4-h-aged cell (B-2). ci: cilia, m: plasma membrane, ec: ectocyst layer, le: lepidosome. (B-2) a different electron micrograph of the same ultrathin section used in a previous paper (Funatani et al. 2010; Fig. 3).
Fig. 3 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 3. Immunoblotting assay using anti α-tubulin antibody showing total α-tubulin content during resting cyst formation of C. cucullus Nag-1. Figures above the photographs indicate time lapse after on- set of encystment induction.
Fig. 2 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 2. Changes of the amount of β-tubulin (p56) and its fragments (p37 and p19) contained in water-soluble fraction during resting cyst formation of C. cucullus Nag-1. Figures above the photographs indicate time lapse after onset of encystment induction.
Fig. 1. 2-D in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 1. 2-D PAGE showing an alteration of the water-soluble protein composition at 0 h–4 weeks after the onset of encystment induction of C. cucullus Nag-1. Arrowheads indicate the proteins (p56, p37, p19) whose amount uniquely and markedly changed during resting cyst formation. These proteins were identified as β-tubulin and its fragments by MS analysis (see Table 1).
Fig. 7 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 7. Effects of 10 µM taxol (A) and 10 µM cytochalasin B (B) on Ca2+/overpopulation-mediated globulation of C. cucullus Nag-1 (A-1, B-1) and ciliary resorption (A-2, B-2). A-1, B-1 − The rate of encysting (rounded) cells was expressed as a percentage of the total number of tested cells (100 randomly selected cells). Open squares (negative control). The cells were suspended in 1 mM Tris-HCl (pH 7.2) solu- tion without inhibitors at low cell density (<2,000 cells/ml). Under this condition, encystment was hardly induced. Closed circles (positive control). The cells were suspended in an encystment-inducing medium [1 mM Tris-HCl (pH 7.2) and 0.1 mM CaCl2] without inhibitors at high cell density (> 30,000 cells/ml) (Ca2+/overpopulation stimulation). In this condition, the encystment was markedly induced. Open circles (experiment). The cells were suspended in an encystment-inducing medium containing taxol (Ta) or cytochalasin B (CB) at high cell density (> 30,000 cells/ml). Points and attached bars correspond to the means of 5 measurements (100 cells per measurement) obtained from different batches and standard errors, respectively. A-2, B-2 − Length of cilia at 2 h after onset of encystment induction in the presence or absence of taxol (Ta) or cytochalasin B (CB). In the negative control [Induced without 'Ta' (0 h) or Induced without 'CB' (0 h)], the cultured cells were collected, then suspended in encystment-inducing medium, and quickly fixed with 3.7% paraformaldehyde. In the positive control [Induced without 'Ta' (2 h) or Induced without 'CB' (2 h)], the cells were suspended for 2 h in an encystment-inducing medium without inhibitors at high cell density (> 30,000 cells/ml), and then fixed with 3.7% paraformaldehyde. In the experimental groups [Induced with 'Ta' (2 h) or Induced with 'CB' (2 h)], the cells were suspended for 2 h in an encystment-inducing medium containing inhibitors at high cell density (> 30,000 cells/ml), and then fixed with 3.7% paraformaldehyde. Columns and attached bars correspond to the means in 26 cells and standard errors, respectively.
Fertility loss and recovery dynamics after repeated heat stress across life stages in male Drosophila melanogaster: Patterns and processes
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.