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568 results for “condensate”
Co-condensation of proteins with single- and double-stranded DNA. Source Data
<p>Source data for: Co-condensation of proteins with single- and double-stranded DNA, PNAS, 2022</p>
Data related to "Bilayer WSe2 as a natural platform for interlayer exciton condensates in the strong coupling limit"
<p>Data related to "Bilayer WSe2 as a natural platform for interlayer exciton condensates in the strong coupling limit"</p>
Hijacking of transcriptional condensates by endogenous retroviruses
<p>This dataset provides companion data to the paper "Hijacking of transcriptional condensates by endogenous retroviruses", Nature Genetics, 2022 (NG-A58214). It includes raw data, images and computational code to generate the figures in the study.</p>
The material properties of a bacterial-derived biomolecular condensate tune biological function in natural and synthetic systems. Source data
<p>Supplementary information for manuscript titled "The material properties of a bacterial-derived biomolecular condensate tune biological function in natural and synthetic systems"</p>
FIGURE 1. Condensed Bayesian tree inferred from cyt b in Capoeta anamisensis, a new species from the Minab and Hasan Langhi River drainages in Iran (Teleostei: Cyprinidae)
FIGURE 1. Condensed Bayesian tree inferred from cyt b. Numbers right of the slash, indicate the posterior probabilities of the Bayesian analysis, using MrBayes, while numbers left of the slash are the bootstrap support for 10,000 replicates in the Maximum Likelihood tree, using RaxML. Asterisks (*) indicate less than 50% Maximum Likelihood support for the node.
Bioconcentration and lethal effects of gas-condensate and crude oil on nearshore copepod assemblages
<p> To assess the toxicity of petroleum pollutants on copepod assemblages, we conducted microcosm experiments simulating slick scenarios at five different concentrations (0, 50, 250, 500, 100 ppm) of gas-condensate and crude oil. We collected natural zooplankton from a coastal station (32°51'07.1 N 34°59'26.2 E) on July 18, 2021 (“gas-condensate experiment”) and July 27, 2021 (“crude oil experiment”) by two horizontal Bongo net tows (200 and 65 µm mesh size; 300 µm mesh size), and surface seawater (sieved through a 50 µm mesh). The seawater was enriched with the collected zooplankton (65 µm - 2 mm size fraction) and incubated in 10 L polycarbonate container microcosms for 48 hours. After incubation, a subsample of 5 L was taken from each experimental microcosm using a hand pump. Each sample was sieved through a 50 µm mesh, rinsed with 0.2 μm filtered seawater, and concentrated in 200 ml in a glass beaker. The samples were examined under the stereomicroscope (SZX16, Olympus, Japan) and the number of dead (not swimming and/or not moving) and live individuals (swimming and/or moving after being gently touched with tweezers) of calanoid and cyclopoid copepods were counted for estimating survival. e data included the counts of the dead and live individuals (copepods, including calanoid and cyclopoid copepods). Survival (%) was estimated for each treatment as the live/dead ratio at the end of each incubation (48h).</p>
Data for "Predicting Rubisco:Linker Condensation from Titration in the Dilute Phase"
<p>Data for the Figures of "Predicting Rubisco:Linker Condensation from Titration in the Dilute Phase" are stored on this repository.</p> <p>Microscopy images are stored on BioStudies: <a href="https://doi.org/10.6019/S-BIAD1097" target="_blank" rel="noopener">https://doi.org/10.6019/S-BIAD1097</a>. </p> <p>Code used to analyse the microscopy images: <a href="https://github.com/ejh516/pystachio-smt">https://github.com/ejh516/pystachio-smt</a></p> <p>Code used for single-molecule calculations: <a href="https://github.com/CharleySchaefer/RubiscoMonomers">https://github.com/CharleySchaefer/RubiscoMonomers</a></p>
Data for critical fluctuations in a confined driven-dissipative quantum condensate
<p>All the raw data sets collected for this project are included in this submission. 'Readme.text' files are included with the data sets explaining what the data sets are and how to read them. </p> <p>We divided the real space data into 8 diifferent files for uploading purposes. We had trouble uploading it as a single file due to the large file size so we divided the data into 8 different files. </p> <p>Files real_space_data_1,real_space_data_2, real_space_data_3, real_space_data_4, real_space_data_5, real_space_data_6,real_space_data_7 and real_space_data_8 are a SINGLE data set. </p>
Oscillating superflow in multicomponent Bose-Einstein condensates
<p>Two movies 1) "<a href="../api/records/12714022/draft/files/Oscillatorysuperflow_supp1.mov/content" target="_blank" rel="noopener noreferrer">Oscillatorysuperflow_supp1.mov</a>" shows superflow with an oscillating direction of flow around an elliptical potential while 2) "<a href="../api/records/12714022/draft/files/Superflow_supp2.mov/content" target="_blank" rel="noopener noreferrer">Superflow_supp2.mov</a>" shows superflow of a two-component condensate around the same elliptical potential. Simulation parameters are included in the movies and the accompanying manuscript. </p>
Co-condensation of silica and lignin: STEM tomography and EDS analysis
<p>Data files associated with publication: "Silica Biomineralization with Lignin Involves Si−O−C Bonds That<br>Stabilize Radicals", https://doi.org/10.1021/acs.biomac.4c00061 </p> <p>A) Tomographic reconstruction of the silica-lignin particles by HAADF-STEM, a single .mrc volume file.</p> <p>B) Energy Dispersive X-ray Spectroscopy analysis of in vitro condensation of silica and lignin. Bruker format .emd files with screen previews included. </p>
EXTRA DATA 2/2 Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs (repeat)
<p>Extra data that did not fit the file size limit, for the main repository</p> <p>10.5281/zenodo.13788846</p> <pre><a href="https://doi.org/10.5281/zenodo.13788846">https://doi.org/10.5281/zenodo.13788846</a></pre>
EXTRA DATA 1/2 Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs (repeat)
<p>Extra data that did not fit the file size limit, for the main repository</p> <p>10.5281/zenodo.13788846</p> <pre><a href="https://doi.org/10.5281/zenodo.13788846">https://doi.org/10.5281/zenodo.13788846</a></pre>
EXTRA DATA 1/1 Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs
<p>Extra data that did not fit the file size limit, for the main repository</p> <p>10.5281/zenodo.13788484</p> <pre><a href="https://doi.org/10.5281/zenodo.13788484" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13788484</a></pre>
EXTRA DATA 2/2 Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs
<p>Extra data that did not fit the file size limit, for the main repository</p> <p>10.5281/zenodo.13788484</p> <pre><a href="https://doi.org/10.5281/zenodo.13788484" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13788484</a></pre>
Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs
<p>The dataset contains microscopy image data of DNA-nanomotifs (color channel 2) that can form liquid-like condensates, especially in association with target DNA strands that can act as condensation surfaces (color channel 1). The data were recorded on a VisiTech iSIM microscopy with a 40X water immersion objective (Apo LWD 40x WI λS DIC N2, NA 1.15). Images were recorded using dual ORCA-Flash4.0 V3 cameras with simultaneous two-channel acquisition to avoid displacement of objects due to consecutive acquisition of color channels. The file names contain the flow channel number, the presence of condensation surfaces (SX4), and the amount of added nanomotif solution in microliters (NX in µl).</p> <p>The image files are in the .nd2 Nikon proprietary format, but can be read with the OME bioformats libraries.</p> <p>Scripts for the analysis of these data are available via a GitHub repository: <a href="https://github.com/lhilbert/SurfacesAndCondensates" target="_blank" rel="noopener">https://github.com/lhilbert/SurfacesAndCondensates</a></p> <p>This repository contains data recorded on 13 March, 2023</p> <p>The file size of data were too large, so additional repositories were opened. The complete date need to be also downloaded from these additional repositories:</p> <p>EXTRA DATA 1/2: 10.5281/zenodo.13788608 </p> <pre><a href="https://doi.org/10.5281/zenodo.13788608" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13788608</a></pre> <p>EXTRA DATA 2/2: 10.5281/zenodo.13788711</p> <pre><a href="https://doi.org/10.5281/zenodo.13788711">https://doi.org/10.5281/zenodo.13788711</a></pre> <p>There is another data set that contains a repeat of the same conditions, recorded on 10 March, 2023</p>
Simulation script and data for "Second sound in the crossover from the Bose-Einstein condensate to the Bardeen-Cooper-Schrieffer superfluid"
<p>This upload includes numerical scripts and data used for the results in the main text and the supplementary material of the paper "Second sound in the crossover from the Bose-Einstein condensate to the Bardeen-Cooper-Schrieffer superfluid", arXiv:2003.06847.</p>
CRM Variance Transport validation in E3SM-MMF - analysis code and condensed data
<p>This tarball contains a collection of code and data used to performa a detailed analysis of the CRM variance transport feature added to E3SM-MMF. The data subset is condensed from the model output and observational datasets, so the ability to reproduce results directly is limited, but this archive mainly serves to document the methods used for our analysis. </p>
Code and data from: Influence of heat transfer and wetting angle on condensable fluid flow through nanoporous anodic alumina membranes
<p>Data and matlab code to compute all figures contained in a manuscript submitted to the linked journal.</p>
Condensed GCHP-CAM simulation output
<p>Condensed output dataset from GCHP-CAM. This data was generated for the paper "Rapid estimation of climate-air quality interactions in integrated assessment using a response surface model", published in ACS Environmental Au in 2023. An analysis code which applies this dataset has the DOI 10.5281/zenodo.7618827. The full zip file contains five directories, one for each ensemble member (w10, w13...). For each ensemble member, there are 10 subdirectories, reflecting two different climate scenarios (pol3.7 and ref, for 3.7 W/m2 and 10 W/m2 of warming by 2100 respectively) and five different emissions perturbations (either no perturbation or a 10% reduction in NH3, NOx, SOX, or VOCs respectively). Each subdirectory contains a single file called "AQ_annual_2080.nc4". This file is a c24 output dataset with entries for 2080 to 2099.</p>
Adaptive preservation of orphan ribosomal proteins in chaperone-stirred condensates
<p>Python and Fiji code used for the study "Adaptive preservation of orphan ribosomal proteins in chaperone-stirred condensates"</p>
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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.