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5,875 results for “lively”
Dataset for "Changes in Global Terrestrial Live Biomass over the 21st Century"
<p>Live woody vegetation is the largest reservoir of biomass carbon with its restoration considered one of the most effective natural climate solutions. However, carbon fluxes associated with terrestrial ecosystems still remain the largest source of uncertainty of the global carbon balance. Here, we develop spatially explicit estimates of global carbon stock changes of live woody biomass from 2000 to 2019 using measurements from ground, air, and space. We show live biomass has removed 4.9-5.5 PgC yr<sup>-1 </sup>from the atmosphere in this century, offsetting 4.6±0.1 PgC yr<sup>-1</sup> of gross emissions from land-use and environmental disturbances and adding substantially (0.23-0.88 PgC yr<sup>-1</sup>) to the global carbon stocks. Gross emissions and removals in the tropics were four times larger than temperate and boreal ecosystems combined. Although live biomass is responsible for more than 80% of gross terrestrial fluxes, soil, dead organic matter, and lateral transport may play important roles in terrestrial carbon sink.</p>
Global Airborne Observatory: Hawaiian Islands Live Coral Cover in 2019
<p>An airborne mapping approach combining laser-guided imaging spectroscopy and deep learning models was used to quantify the geographic distribution of live corals to 16 m water depth throughout the eight main Hawaiian Islands. Full metadata and methods are provided in:</p> <p>Asner, G.P., N.R. Vaughn, J. Heckler, D.E. Knapp, C. Balzotti, E. Shafron, R.E. Martin, B.J. Neilson, J.M. Gove. 2020. Large-scale mapping of live corals to guide reef conservation. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.2017628117.</p> <p>Asner, G.P., N.R. Vaughn, J. Heckler. 2020. Global Airborne Observatory: Hawaiian Islands Live Coral Cover in 2019 (Version 3.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4292660</p> <p>Use of our data requires that you cite both of these sources together. In addition, we ask that these data be used to make the world a better place.</p> <p>The data are in standard GeoTIFF file format organized by island.</p> <p>These data files will be updated as further improvements are made.</p>
Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data
<p>The dataset contains raw imaging data from the work:</p> <p>"Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis"</p> <p>The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de).</p> <p><strong>Authors</strong></p> <p>Lotta Hof<sup>1</sup>*, Till Moreth<sup>1</sup>*, Michael Koch<sup>1</sup>, Tim Liebisch<sup>2</sup>, Marina Kurtz<sup>3</sup>, Julia Tarnick<sup>4</sup>, Susanna M. Lissek<sup>5</sup>, Monique M.A. Verstegen<sup>6</sup>, Luc J.W. van der Laan<sup>6</sup>, Meritxell Huch<sup>7</sup>, Franziska Matthäus<sup>2</sup>, Ernst H.K. Stelzer<sup>1</sup>, Francesco Pampaloni<sup>1§</sup></p> <p><sup>1</sup>Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>2</sup>Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>3</sup>Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>4</sup>Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom</p> <p><sup>5</sup>Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany</p> <p><sup>6</sup>Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands</p> <p><sup>7</sup>The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany</p> <p>*contributed equally</p> <p><sup>§</sup>corresponding author: fpampalo@bio.uni-frankfurt.de</p> <p><strong>Abstract</strong></p> <p><em>Background</em></p> <p>Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days.</p> <p><em>Results</em></p> <p>We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures.</p> <p><em>Conclusion</em></p> <p>Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.</p>
LEAD Madrid Living Lab Open Data
<p>December 2021 to May 2023 monthly list of all services used to conduct daily route optimization, calculate energy consumption, and calculate environmental KPIs in the project.</p>
BeBOD estimates of incidence, prevalence, and years lived with disability for 57 cancer types, 2004-2021
<p><strong>Belgian National Burden of Disease Study</strong></p><p><strong>Estimates of the morbidity burden of disease for 57 cancer sites</strong></p><p><i>Incidence</i></p><p>Data on new cancer cases in Belgium are collected by the <a href="https://kankerregister.org/Annual%20Tables">Belgian Cancer Registry</a> (BCR). For the current study, we selected 80 ICD-10 (C00.0-96.9 and chronic myeloid neoplasms) codes resulting in 57 cancer sites. Data were extracted by year (from 2004 to 2021), age group (5-years), sex and region (N=3). We excluded "Respiratory system and intrathoracic organs, NOS (not otherwise specified)" from further analyses because of too few cases.</p><p><i>Prevalence</i></p><p>Prevalence estimates were estimated using the above-described incidence estimates and the survival estimates also provided by BCR, derived from linkage with the Belgian Crossroads Bank for Social Security. We used a 10-year prevalence perspective meaning that from the year 2013 onwards, we were able to define the prevalence in a given year as the sum of person-months spent in the different health states. Specifically, we used a microsimulation approach to simulate future health states for each year-, age-, sex-, region- and cancer-specific cohort of incident cases.</p><p>See for more details: <a href="https://doi.org/10.1186/s12885-021-09109-4">https://doi.org/10.1186/s12885-021-09109-4</a></p><p><i>Years Lived with Disability</i></p><p>Years Lived with Disability (YLDs) were calculated using both an incidence and prevalence perspective as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p>
BeBOD estimates of mortality, years of life lost, prevalence, years lived with disability, and disability-adjusted life years for 38 causes, 2013-2020
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>Prevalence</em></p> <p>Our estimates are based on the GBD cause list for morbidity by <a href="https://www.healthdata.org/">IHME</a>. We first select for each of the 38 causes, the most suitable local data source as described in the <a href="https://www.sciensano.be/en/biblio/belgian-national-burden-disease-study-guidelines-calculation-dalys-belgium-2">protocol</a>. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.</p> <p><em>Years Lived with Disability</em></p> <p>In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p> <p><em>Disability-Adjusted Life Years</em></p> <p>Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.</p>
Growth of Merocyanine Dye J-Aggregate Nanosheets by Living Supramolecular Polymerization
<p>Data to report <a href="https://doi.org/10.1002/ange.202314667">https://doi.org/10.1002/ange.202314667</a>:</p><p>J-aggregates are highly desired dye aggregates but so far there has been no general concept how to accomplish the required slip-stacked packing arrangement for dipolar merocyanine (MC) dyes whose aggregation commonly affords one-dimensional aggregates composed of antiparallel, co-facially stacked MCs with H-type coupling. Herein we describe a strategy for MC J-aggregates based on our results for an amphiphilic MC dye bearing alkyl and oligo(ethylene glycol) side chains. In an aqueous solvent mixture, we observe the formation of two supramolecular polymorphs for this MC dye, a metastable off-pathway nanoparticle showing H-type coupling and a thermodynamically favored nanosheet showing J-type coupling. Detailed studies concerning the self-assembly mechanism by UV-Vis spectroscopy and the packing structure by atomic force microscopy and wide-angle X-ray scattering show how the packing arrangement of such amphiphilic MC dyes can afford slip-stacked two-dimensional nanosheets whose macrodipole is compensated by the formation of a bilayer structure. As an additional feature we demonstrate how the size of the nanosheets can be controlled by seeded living supramolecular polymerization.</p>
Data, plotting scripts, and figures for "A physics-based ignition model with detailed chemical kinetics for live fuel burning studies"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "A physics-based ignition model with detailed chemical<br>kinetics for live fuel burning studies" by Diba Behnoudfar and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs
<p>Supplementary data and videos for the manuscript 'Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs', by Çevrim,<sup> </sup>Laplace-Builhé,<sup> </sup>Sugawara, Rusciano, Labert, Brocard, Almazán and Averof.</p> <p>The supplementary data include:</p> <p><strong>Supplementary Data 1 (.csv file); Live imaging of regenerating <em>Parhyale</em> legs: image acquisition settings</strong></p> <p>Table with information on the 22 time lapse recordings presented in Figure 3, including image acquisition settings, temperature and duration of the recordings.</p> <p><strong>Supplementary Data 2 (.zip file); Live imaging of regenerated <em>Parhyale</em> legs: maximum projections</strong></p> <p>Compressed folder including maximum projections for each of the 22 time lapse recordings presented in Figure 3. These files were generated by projecting all or a subset of the z slices acquired at each time point. A 20 micron scale bar was added on the first time point. These files serve as a quick way to examine the 22 time lapse recordings.</p> <p><strong>Supplementary Data 3 (22 .tif files); Live imaging of regenerated <em>Parhyale</em> legs: complete datasets</strong></p> <p>Complete image 3D+T hyperstacks for each of the 22 time lapse recordings presented in Figure 3. These files have been generated by concatenating the original image stacks and correcting any image shifts, as described in the Methods section of the paper.</p> <p><strong>Supplementary Data 4 (.zip file); Analysis of trade-offs of imaging resolution and image quality</strong></p> <p>The data used for the analysis of trade-offs in imaging and the results shown in Table 1 are included in this compressed folder. Folders for the original recording (labelled 00), for each of the subsampled datasets (labelled 01 to 05), and for the denoised and deconvoluted datasets each include the corresponding image data and ground truth cell tracking files (.tif, .h5, .xml and .mastodon files) and three sets of cell track predictions (.mastodon files). There are also separate folders containing the Elephant detection and flow model parameters for each set of predictions.</p> <p dir="ltr"><strong>Supplementary Data 4 (.zip file); Analysis of trade-offs of imaging resolution and image quality</strong></p> <p dir="ltr">The data used for the analysis of trade-offs in imaging and the results shown in Table 1 are included in two folders. The folder named Image_and_tracking_data includes the image data (.tif, .h5, .xml), ground truth cell tracking files (.mastodon files) and three sets of cell track predictions (.mastodon files) for the original recording (labelled 00), for each of the subsampled datasets (labelled 01 to 05), and for the denoised and deconvoluted datasets. It also includes separate folders containing the Elephant detection and flow model parameters for each set of predictions. The folder named CTC_tracking_results includes the ground-truth data along with three sets of predictions for detection and tracking for each dataset, following the Cell Tracking Challenge format. For each dataset we include label image files (.tif) for every time point along with tracking results in .txt format, and each results directory (01_RES_*) also contains the evaluation results from the Cell Tracking Challenge Evaluation Software. For a detailed explanation of the folder structure, please refer to the Cell Tracking Challenge documentation.</p> <p><strong>Supplementary Data 5 (.zip file); Tracking the progenitors of spineless-expressing cells in the distal carpus</strong></p> <p>The data used to generate Figure 7 are included in this compressed folder, including the live imaging and cell tracking files (.h5, .xml and .mastodon files) and the image stack of the spineless and futsch HCR and DAPI stainings (.tif file). Channel 2 shows spineless expression (mostly nascent transcripts in nuclei), as well as background signal in epidermal nuclei (possibly due to photoconversion of DAPI, see Karg & Golic 2018, Chromosoma 127: 235-245) and strong autofluorescence in granular cells (also visible in channel 1, depicting futsch HCR).</p> <p><strong>Supplementary Data 6 (.txt file); Sequences of <em>Parhyale</em> genes targeted by the HCR probes</strong></p> <p>The sequences are provided in FASTA format.</p> <p dir="ltr"><strong>Supplementary Data 7 (.zip file); Apoptosis in legs that have not been subjected to live imaging</strong></p> <p dir="ltr">The data used to generate Figure 2 supplement 2 are contained in this compressed folder, including 9 image stacks of T4 and T5 legs fixed and stained with DAPI 3 days post amputation (with apoptotic nuclei marked) and a .txt file containing the apoptotic cell counts.</p> <p dir="ltr"><strong>Supplementary Data 8 (.zip file); Analysis of tracking performance in relation to imaging depth</strong></p> <p dir="ltr">The data used to generate Figure 5 are contained in this compressed folder, including separate folders for the data extracted from the analysis of datasets #1 to #5. Each folder includes data from three replicates (batches 001 to 003), with .csv files listing the z location of nucleus centroids (in µm) for the nuclei that were incorrectly detected by Elephant – either as false positives (FP) or as false negatives (FN) – and the ground truth data (GT). The folder also includes an .xlsx file gathering all the relevant data and the measurements of precision and recall.</p> <p dir="ltr"><strong>Supplementary Data 9 (.zip file); Detecting the temporal pattern of cell divisions in regenerating legs</strong></p> <p dir="ltr">The data used to generate Figure 4 are contained in this compressed folder, including the five image datasets (.tif, .h5, .xml), the detected cell divisions (.mastodon files), and an .xlxs file containing all the cell divisions counts and graphs.</p> <p><strong>Video 1. Time lapse recording of regeneration in a Parhyale T5 leg (dataset li48-t5)</strong></p> <p>Live imaging of nuclei labelled with H2B-mREFruby (maximum projection of z slices 3-10). Proximal parts of the leg are to the left and the amputation site is at the right of the frame. For annotations of different features please refer to Figure 2. Shortly after leg amputation (0 hpa) hemocytes adhere to the wound. By 16 hpa the wound has melanized. Up to ~32 hpa epithelial cells can be seen migrating and accumulating at the wound, below the melanized scab (Figure 2A,B). Around 31 hpa, the leg tissues become detached from the scab (Figure 2C). At 43 hpa, the carpus-propodus boundary first becomes visible, and thereafter many cells can be observed dividing at the distal part of the leg stump (Figure 2D). At 56 hpa, the propodus-dactylus boundary first becomes visible (Figure 2E). At later stages, tissues in more proximal parts of the leg retract, making space for the regenerating leg to grow (Figure 2F,G). After ~90 hpa cell proliferation there is less cell proliferation and cell movements, and the nuclear positions within the tissue become fixed. Scale bars, 20 µm.</p> <p><strong>Video 2. Time lapse recording of regeneration in a Parhyale T5 leg (dataset li36-t5)</strong></p> <p>Live imaging of nuclei labelled with H2B-mREFruby (maximum projection of z slices 3-15). Proximal parts of the leg are to the left and the amputation site is at the right of the frame. The sequence of events is similar to that described in Video 1, but the progression is slower: epithelial migration towards the wound is observed up to 40 hpa, tissues detach from the scab at 65 hpa, and the carpus-propodus and propodus-dactylus boundaries first become visible at 78 and 91 hpa. The tissues making up the carpus and propodus can be seen pulsating from 105 to 145 hpa. Scale bars, 20 µm.</p>
MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 (Greenland Sample Products)
<p>We provide 21 sample products of MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 in three test regions of Greenland Ice Sheet. The full archive of version 2 ITS_LIVE products (including image pair maps, data cubes and mosaics) from Sentinel-1 as well as other optical sensors (Landsat-4/5/6/7/8 and Sentinel-2) can be found at the ITS_LIVE project website: <a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov</a>.</p> <p><strong>Sensor</strong>: Sentinel-1A/B</p> <p><strong>Processor</strong>: <a href="https://github.com/isce-framework/isce2">ISCE</a>v2.4.1 (topsApp -> <a href="https://github.com/leiyangleon/Geogrid">Geogrid</a>v1.4.0 -> <a href="https://github.com/nasa-jpl/autoRIFT">autoRIFT</a>v1.4.0)</p> <p><strong>Project</strong>: NASA MEaSUREs project <a href="https://its-live.jpl.nasa.gov">ITS_LIVE</a></p> <p><strong>Region 1</strong> (69.13N, 50.88W; Jakobshavn Isbræ Glacier): 7 ascending image pairs</p> <p><strong>Region 2</strong> (77.61N, 42.79W; central north of interior Greenland): 3 ascending image pairs</p> <p><strong>Region 3</strong> (72.48N, 35.87W; central south of interior Greenland): 10 descending image pairs and 1 ascending image pair</p> <p>This serves as a supplementary dataset for the companion journal article submitted to Earth System Science Data (to appear).</p> <p> </p> <p><strong>Acknowledgement</strong>: This effort was funded by the NASA MEaSUREs program in contribution to the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (<a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov/</a>) and through Alex Gardner’s participation in the NASA NISAR Science Team.</p>
Additional Data - Phototoxicity induced in living HeLa cells by focused femtosecond laser pulses
<p>Nonlinear optical microscopy is a powerful label-free imaging technology, providing biochem-ical and structural information in living cells and tissues. A possible drawback is photodamageinduced by high-power ultrashort laser pulses. Here we present an experimental study on thou-sands of HeLa cells, to characterize the damage induced by focused femtosecond near-infraredlaser pulses as a function of laser power, scanning speed and exposure time, in both wide-field andpoint-scanning illumination configurations. Our data-driven approach offers an interpretation ofthe underlying damage mechanisms and provides a predictive model that estimates its probabilityand extension and a safety limit for the working conditions in nonlinear optical microscopy. Inparticular, we demonstrate that cells can withstand high temperatures for a short amount of time,while they die if exposed for longer times to mild temperatures. It is thus better to illuminatethe samples with high irradiances: thanks to the nonlinear imaging mechanism, much strongersignals will be generated, enabling fast imaging and thus avoiding sample photodamage.</p>
TULIPS: a Tool for Understanding the Lives, Interiors, and Physics of Stars
<p>This is a basic reproduction package for the paper "TULIPS: a Tool for Understanding the Lives, Interiors, and Physics of Stars".</p> <p>This package contains inlists for MESA, analysis scripts, animations created with TULIPS, and processed output from MESA.</p>
Dynamics of CTCF and cohesin mediated chromatin looping revealed by live-cell imaging
<p><strong>Overview</strong></p> <p>This repository contains all the raw and processed trajectory data associated with “paper title”. In this ReadMe file we provide the following information:</p> <ul> <li>The cell lines and conditions used in this study</li> <li>A summary of how the data was collected</li> <li>The structure of the chromosome locus tracking data</li> </ul> <p><strong>Cell lines and conditions</strong></p> <p>In total, the dataset covers 12 experimental conditions representing the following cell lines and treatment conditions:</p> <ul> <li>C36</li> <li>C65</li> <li>C27</li> <li>CTCF-AID (untreated)</li> <li>CTCF-AID (2 hours AID)</li> <li>CTCF-AID (4 hours AID)</li> <li>RAD21-AID (untreated)</li> <li>RAD21-AID (2 hours AID)</li> <li>RAD21-AID (4 hours AID)</li> <li>WAPL-AID (untreated)</li> <li>WAPL-AID (4 hours AID)</li> <li>WAPL-AID (6 hours AID)</li> </ul> <p> </p> <p><strong>Data and data processing</strong></p> <p>Trajectories were obtained from 3D timeseries of mouse embryonic stem cell colonies in the conditions listed above using a LSM900 Airyscan 2 Zeiss microscope. For each movie we recorded 365 frames of 49.69 µm x 49.69 µm (584 x 584 pixels, pixel size: 0.085 µm by 0.085 µm), separated by an interval of 20 seconds for a total of just over 2 hours. 3D images were composed of 30 z-stacks separated by 0.25 µm, for a total height of 7.25 µm. Imaging was performed in two colors allowing the tracking of two arrays of fluorophores on Chromosome 18 near the <em>Fbn2</em> gene. In all conditions, the fluorophore arrays were separated by 515 kb (except the C27 clone where separation was 10 kb).</p> <p>The 3D image time series were processed using ConnectTheDots: <a href="https://github.com/ahansenlab/connect_the_dots">https://github.com/ahansenlab/connect_the_dots</a> to obtain paired trajectories of chromosome loci over time. The trajectories have been corrected for chromatic shifts and aberrations.</p> <p>Data are provided in an “unfiltered” format (meaning that individual dot localizations were not quality control filtered) , or a filtered format (the same data set, but having undergone quality control). The filtered (quality controlled) trajectory data was used for all the quantitative analyses in the article “”.</p> <p>File names are formatted follows.</p> <ul> <li>Quality controlled data have the structure: {Clone_and_condition_name}.tagged_set.tsv</li> <li>Unfiltered data have the structure: {Clone_and_condition_name}.unfiltered.tagged_set.tsv</li> </ul> <p>For example, for RAD21-AID tagged clone, for imaging performed after two hours of protein degradation, the quality-controlled file name is: RAD21_2_hr.tagged_set.tsv. Please note that for all no-treatment conditions, we used “0 hours” as the tag. Thus, the RAD21 (untreated) becomes RAD21_0_hr.tagged_set.tsv.</p> <p> </p> <p><strong>Structure of Data</strong></p> <p>The trajectory data are provided as tab-separated text files consisting of 10 columns. The column headers are:</p> <ul> <li>id: a unique dot pair index</li> <li>t: the frame in which the dots were localized</li> <li>x: x-coordinate of the dot in the EGFP channel (units in µm)</li> <li>y: y-coordinate of the dot in the EGFP channel (units in µm)</li> <li>z: z-coordinate of the dot in the EGFP channel (units in µm)</li> <li>x2: x-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>y2: y-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>z2: z-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>dist: 3D distance between the dots across channels (units in µm)</li> <li>movie_index: an identifier used to link the dot pair back to the raw image timeseries.</li> </ul>
Mixed methods systematic review and metasummary about barriers and facilitators for the implementation of cotrimoxazole and isoniazid - preventive therapies for people living with HIV.
<p>This is the minimal data set underlying the findings of our systematic review and metasummary:</p> <p>We uploaded the following data extracted from the studies included in our review:</p> <p>- Systematic Review protocol, also published in PROSPERO (CRD42019137778).</p> <p>- detailed description of studies included in our review.</p> <p>- barriers identified in the review (metasummary).</p> <p>- facilitators identified in the review.</p>
Ash (Fraxinus excelsior L.) in vitro survival data for the UKs Living Ash Project
<p><em>In-vitro</em> propagation and survival data sets (including nursery survival) of the ash plants generated i.e. <em>Fraxinus excelsior</em> L., plus the PCR primers used and conditions applied.</p> <p>Surveyed from a range of ash seed material taken from across the UK, and held at the UK ash collection hosted by the Earth Trust in Oxfordshire, UK.</p> <p>A more detailed analysis of this data is currently expected to be be published in the <em>Annals of Forest Science</em>, and which has already provisionally accepted this work for publication, subject to the underlying data being made available i.e. here</p> <p>The data deposited here represents the underlying data that will be presented in graphical form in the forthcoming paper by Fenning et al., plus the associated metadata and statistical analyses, along with the original .jpg of the photos used.</p>
Effect of live cribwall on slope stability - modelling outputs
<p>These datasets contain outputs from a novel live cribwall model. The model assess the effect of a live cribwall on slope stability over time. The dataset contains Factor of Safety records under different plant cover and climate change scenarios. The model is still unpublished. For more detail, please get in touch aol3@gcu.ac.uk </p>
Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes
<p>Raw multichannel and/or Z-stack source data from time series images in TIF format to accompany publication of:</p> <p><strong>Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes</strong></p> <p>Jina Ko<sup>1</sup>, Martin Wilkovitsch<sup>2</sup>, Juhyun Oh<sup>1</sup>, Rainer Kohler<sup>1</sup>, Evangelia Bolli<sup>1,3</sup>, Mikael J. Pittet<sup>1,3,4,5</sup>, Claudio Vinegoni<sup>1</sup>, David B. Sykes<sup>6,7</sup>, Hannes Mikula<sup>2</sup>, Ralph Weissleder<sup>1,8</sup>*, Jonathan C. T. Carlson<sup>1,7</sup>*</p> <p><sup>1 </sup>Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA 02114 </p> <p><sup>2</sup> Institute of Applied Synthetic Chemistry, TU Wien, 1060 Vienna, Austria </p> <p><sup>3</sup> Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland</p> <p><sup>4</sup> Ludwig Institute for Cancer Research, Lausanne Branch, Switzerland</p> <p><sup>5</sup> AGORA Cancer Center, Lausanne, Switzerland</p> <p><sup>6</sup> Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA</p> <p><sup>7 </sup>Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA</p> <p><sup>8 </sup>Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115</p>
Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load
<p>For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained. </p>
Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span
<p>For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained. </p>
Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022)
<p>Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022), which has been accepted for publication in Atmospheric Chemistry and Physics.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.