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155 results for “Localisation”
Imaging data-set of mitochondrial Zink Finger localisation in mammalian cells
<p>This repository contains all raw and saved images for experiments with mitochondrial ZincFingers or mtZincFinger-Fluorophores conducted by Timo Rey during 2021 and 2022 at the MRC Mitochondrial Biology Unit without curation (unfiltered).<br> A more detailed description with a selection of representative images and proposed conclusions will follow eventually</p> <p>File-titles should be explicable of cell line and over-expressed plasmid constructs and/or antibodies used. Please refer to the accompanying excel sheet as well as all available plasmid maps for further detail and do not hesitate to enquire by contacting Timo Rey via e-mail, if further clarifications are needed. All images were acquired on an LSM880 Confocal Microscope, except for those on June 2, 2022, which were acquired on an Andro DragonFly Spinning Disk.</p> <p>Have a look and draw your own conclusions!</p>
Fig. 3. Localisation des observations connues d in Isomira costessii (Bertolini, 1868) (Coleoptera, Tenebrionidae): une nouvelle espèce pour la Suisse
Fig. 3. Localisation des observations connues d'Isomira costessii (ronds orange et rouges), avec en rouge les observations suisses, et d'I. moroi (carrés verts).
Fig. 2. Localisation des 370 in Coléoptères capturés en Suisse par pièges attractifs aériens: bilan après trois années et discussion de la méthode
Fig. 2. Localisation des 370 pièges placés entre 2010 et 2012 en Suisse. Figurent en rouge ceux qui correspondent à des localités travaillées dans le cadre de la Liste Rouge des Coléoptères du bois. Les six régions biogéographiques (Gonseth et al. 2001) sont délimitées en noir.
Primary data for: "Remotely sensed localised primary production anomalies predict the burden and community structure of infection in long-term rodent datasets"
<p>Datasets</p>
Tracking small animals in complex landscapes: a comparison of localisation workflows for automated radio telemetry systems
Open the record for dataset details and reuse information.
Data from: Glossiness disrupts predator localisation of moving prey
Open the record for dataset details and reuse information.
FIG. 1. — Localisation d in Exploitation animale à l'Ancien Empire en Égypte: les apports d'Ayn Asil (oasis de Dakhla)
FIG. 1. — Localisation d'Ayn Asil, plan du site et des secteurs fouillés. Échelle: 1/2000.
Trypanosoma brucei bloodstream form tagging: Targeted subcellular protein localisation.
<p>Trypanosoma brucei bloodstream form tagging protein localisation data. Widefield epifluorescence microscope images of protein subcellular localisation in the bloodstream form life cycle stage of the unicellular eukaryotic pathogen Trypanosoma brucei by endogenous tagging with mNeonGreen (mNG). This master deposition includes a summary of the localisations, primer sequences and DOI indexing, provided in a directory structure analogous to the TrypTag genome-wide procyclic form project: <a href="https://doi.org/10.5281/zenodo.6862298">https://doi.org/10.5281/zenodo.6862298</a> It does not include any microscopy data, which are spread over multiple Zenodo DOIs. Instead, this deposition and the raw and processed data directories include an index to each DOI.</p> <p><strong>localisations.tsv</strong><br> Tab-delimited table, which can be opened in Excel, of localisation annotations for each gene tagged. Also includes primer sequences used, the 96 well plate in which tagging was carried out organised with one row per gene ID, with sets of columns for N and C terminal tagging.</p> <p><strong>geneselection.tsv</strong><br> Tab-delimited table of criteria used for gene selection for tagging. This includes presence/absence of a <em>Leishmania major </em>or <em>Trypanosoma cruzi </em>ortholog, localisation and signal intensity by procyclic form tagging (TrypTag) and upregulation at mRNA level.</p> <p><strong>id_doi_index.tsv</strong><br> Tab-delimited table listing all Trypanosoma brucei Lister 427 gene IDs, if tagging was attempted at the N or C terminus and, if so, the Zenodo DOI at which to find the microscopy data. To download data for a particular gene ID, find its entry in this table, go to the corresponding Zenodo DOI and download <plateid_date>.zip for the raw microscopy data or <plateid_date>_processed.zip for the processed microscopy data. In the latter, images are named by gene ID and tagged terminus.</p> <p><strong>plate_doi_index.tsv</strong><br> Tab-delimited table listing all 96 plates which were part of the targeted bloodstream form tagging project and the Zenodo DOI at which the data can be found. Downloading the data from all of these Zenodo DOIs gives the full microscopy dataset.</p> <p><strong>trypTag_BSF_master.zip</strong><br> Zip file containing the master directory structure for the targeted bloodstream form tagging project database. This contains all internal code which was used to build the bloodstream form tagging database from the raw microscopy data.</p> <p><strong>readme.docx</strong><br> Documentation on data access and rebuilding the database using trypTag_BSF_master.zip</p>
TrypTag: Genome-wide subcellular protein localisation in Trypanosoma brucei.
<p>TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <em>Trypanosoma brucei</em> by endogenous tagging with mNeonGreen (mNG). This master deposition includes a summary of the localisations, scripts, code and primer sequences used to build the TrypTag database, provided in the master directory structure. It does not include any microscopy data, which are spread over multiple Zenodo DOIs. Instead, this deposition and the raw and processed data directories include an index linking gene IDs to each Zenodo DOI. Data can also be browsed at <a href="http://tryptag.org/">TrypTag.org</a>.</p> <p>If you use this data resource please cite Billington <em>et al.</em> 2023 <em>Nature Microbiology </em>(<a href="https://doi.org/10.1038/s41564-022-01295-6">doi:10.1038/s41564-022-01295-6</a>). We recommend including this citation in the results or methods if TrypTag was used as part of a discovery process. If directly using TrypTag images, please also indicate in the figure legend or similar which images are from TrypTag. If carrying out a large-scale data analysis, please also cite this Zenodo deposition.</p> <p>Data can be mined via the cellular localization imaging or cellular component GO term searches at the genome database <a href="https://tritrypdb.org/">TriTrypDB.org</a> (part of <a href="https://veupathdb.org/">VEuPathDB</a>). If you do, please also <a href="https://tritrypdb.org/tritrypdb/app/static-content/about.html">cite</a> the genome database.</p> <p>You may also find the following papers informative: Dean <em>et al.</em> 2016 <em>Trends in Parasitology</em> (<a href="https://doi.org/10.1016/j.pt.2016.10.009">doi:10.1016/j.pt.2016.10.009</a>), which describes the original project aims and workflow. Halliday <em>et al.</em> 2019 <em>Molecular and Biochemical Parasitology</em> (<a href="https://doi.org/10.1016/j.molbiopara.2018.12.003">doi:10.1016/j.molbiopara.2018.12.003</a>), which describes the localisation ontology with example images and comparison to <em>Leishmania</em>.</p>
Localised Thermal Emission from Topological Interfaces
<p>Dataset and simulation files for the manuscript "Localised Thermal Emission from Topological Interfaces".</p>
Dataset: The effect of a keyhole defect on strain localisation in an additive manufactured titanium alloy
<p><strong>This is the dataset used in the following publication: </strong></p> <div> <div> <div> <p>S. Cao, R. Thomas, A.D. Smith, P. Zhang, L. Meng, H. Liu, J. Guo, J. Donoghue, D. Lunt, The effect of a keyhole defect on strain localisation in an additive manufactured titanium alloy, Journal of Materials Research and Technology, https://doi.org/10.1016/j.jmrt.2024.11.237</p> </div> </div> </div> <p><strong>Contained in this dataset are:</strong></p> <p>A Jupyter notebook which uses the open-source DefDAP Python package (https://github.com/MechMicroMan/DefDAP) to open enclosed HRDIC and EBSD data for two regions in an SLM Ti64 sample, one around a keyhole defect and one ~1mm away in the bulk.</p> <p>Please use the 'master' version of DefDAP: <a href="https://github.com/MechMicroMan/DefDAP/tree/51074e158b0131c69358ddf7eee319e41cf582ca">https://github.com/MechMicroMan/DefDAP/</a></p> <p><strong>Publication abstract:</strong></p> <p>The influence of a keyhole defect on local deformation behaviour in additive manufactured Ti-6Al-4V was investigated by comparing it to a representative bulk region without a defect. High resolution digital image correlation (HRDIC) was used to measure the differences in strain localisation at the microstructural length-scale. A nanoscale speckle pattern was used to allow small changes in strain to be detected and resolved within a single individual lamella and at pre-existing crack locations around the defect. Strain localisation was observed around the defect and formed well below the macroscopic yield stress. In contrast, minimal deformation was found in the bulk at this stress level. Following further deformation into the plastic regime, the strain localisation around the keyhole became more heterogenous with a distinct strain field. A large amount of strain localisation and <c+a> slip was observed either side of the defect normal to the loading direction compared to relatively little in the regions close to the defect in line with the loading direction. This HRDIC observation was consistent with finite element analysis of the expected strain fields around the defect both below and above the yield point. Furthermore, micro-cracks were observed in αp/αp and αp/βt interfaces in both regions with the more pronounced strain fields around the defect leading to an increased number of long micro-cracks than in the bulk. The formation mechanisms of micro-cracks have been discussed, emphasising the role of localised strain caused by the defect.</p> <p> </p>
Supplementary code and data for: Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE
<p>This repository contains code and data to reproduce the figures in the manuscript: Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE.</p> <p>Please refer to the readme in the repository. </p>
ANNEXE 1. — Localisation des 153 in Piscicola pojmanskae Bielecki, 1994 (Hirudinida, Piscicolidae), une nouvelle espèce de sangsue pour la faune de France
ANNEXE 1. — Localisation des 153 stations de prélèvement (fond de carte: Open Street Map).
WatchBLoc: A smartwatch IMU and ambient BLE dataset for room-level localisation
<p><strong>WatchBLoc dataset</strong></p> <p>This dataset consists of BLE RSSIs (emitted from identical BLE beacons) and IMU recordings (3-axial acceleration, 3-axial gyroscope, 3-axial magnetometer) recorded by a Sony Smartwatch 3. All participants wore the smartwatch in their right hand, which was the dominant hand in all participants.</p> <p>The data were recorded across two environments: a real-home and a demo-home. The demo-home consists of 6 rooms: big office, small office, kitchen, bathroom, meeting room, lab room. The real-home consists of 6 rooms: kitchen, living room, bedroom, bathroom, office, and loo. However, the living room and kitchen lie in the same open-plan space, and the loo lies within the office (i.e., it is an ensuite space). These can thus be accounted as:</p> <ul> <li>the aforementioned 6 rooms</li> <li>5 rooms, namely: open-plan kitchen/living room, bedroom, bathroom, office, loo</li> <li>5 rooms, namely: kitchen, living room, bedroom, bathroom, ensuite</li> <li>4 rooms, namely: open-plan kitchen/living room, bedroom, bathroom, ensuite.</li> </ul> <p>BLE beacons were installed in each room of the above environments. A total of three BLE configurations were considered for each room; one beacon was placed in the centre of each room (denoted as "centre" in the dataset), one by the entrance of each room (denoted "doors") and one at a location in each room chosen such that the pairwise distances between the beacons from all rooms are maximised (denoted as "far").</p> <p>The smartwatch was recording IMU at 100 Hz and BLE RSSIs at 0.2 Hz and the ground truth location which the participants had to report, by tapping the appropriate room label on the watch's screen every time they were entering a new room.</p> <p>Each participant performed the experiment for approximately 1 hour continuously; with the sensor recording application active and the user instructed on how to record the ground truth location, the participants moved around the environment, performing activities that are commonly encountered in each room in their own style. Not everyone performed the exact same activities, and the ground truth activity labels were not recorded.</p> <p>A total of 11 participants, noted as user1 to user11 performed the experiment across the two environments, yielding a total of 20 recordings, noted as rec1 to rec20 in the dataset. user1 performed the experiment in both environments; three times in the demo-home (rec1,5,8) and once in the real-home (rec13,15,17,19). user11 performed the experiment only in the real-home (rec14,16,18,20) while the rest of the users performed the experiment only in the demo home, yielding one recording each.</p> <p>Note that recording rec13,15,17,19 were recorded simultaneously, but account for the four different assumptions on what constitutes a room in the "real home", as described before. The same holds for recordings rec14,16,18,20.</p> <p>The data are organised per recording and user id. Note that some users have more than one corresponding recording.</p>
Data underpinning "Local integrals of motion and the stability of many-body localisation in disorder-free systems"
<p>Many-body localisation in disordered systems in one spatial dimension is typically understood in terms of the existence of an extensive number of (quasi)-local integrals of motion (LIOMs) which are thought to decay exponentially with distance and interact only weakly with one another. By contrast, little is known about the form of the integrals of motion in disorder-free systems which exhibit localisation. Here, we explicitly compute the LIOMs for disorder-free localised systems, focusing on the case of a linearly increasing potential. We show that while in the absence of interactions, the LIOMs decay faster than exponentially, the addition of interactions leads to the formation of a spatially extended plateau. We study how varying the linear slope affects the localisation properties of the LIOMs, finding that there is a significant finite-size dependence, and present evidence that adding a weak harmonic potential does not result in typical many-body localisation phenomenology. By contrast, the addition of disorder has a qualitatively different effect, dramatically modifying the properties of the LIOMs. Based on this, we speculate that disorder-free localisation is unlikely to be stable at long times and for large systems.</p>
Dataset for strain-localisation in helium implanted tungsten: CPFE, Laue diffraction, AFM
<p>The folder includes dataset for helium-implanted tungsten and pure tungsten. The data includes measurement of the following for both materials as observed experimentally, and predicted using crystal-plasticity simulations:</p> <p>1. Surface morphology of nano-indents,</p> <p>2. Lattice-distortions around and under nano-indents</p> <p>3. Computed geometrically necessary dislocations field around and under nano-indents in both materials</p> <p>4. Load-displacement curves</p> <p>Guidelines for using dataset:</p> <p>1. Extracting the folder will generate five individual folders</p> <p>2. In the AFM plots folder, use the matlab code and dataset in the the folder to generate the surface morphology of nano-indents.</p> <p>3. In the "CPFE implanted sample data" folder --> use "CPFE implanted matlab code" --> load "variables2" --> run the code (raw data is also provided in the folder)</p> <p>4. In the "CPFE unimplanted sample data" folder --> use "CPFE unimp matlab code" --> load "variables3" --> run the code (raw data is also provided in the folder)</p> <p>5. In the "Laue unimplanted data" folder --> use matlab code with relevant raw data provided in folder</p> <p>6. In the "Laue implanted data" folder --> use matlab code with relevant raw data provided in folder</p> <p>7. Excel sheet provides nano-indentation and CPFE predictions of load-displacement curves</p> <p>8. The folder "HR-EBSD code and data" includes related raw data and codes.</p>
Data of Listening Experiments for Azimuthal Localisation in (Local) Sound Field Synthesis
<p>Data of two listening experiments conducted at University of Rostock, Germany. The study investigated the four (Local) Sound Field Synthesis techniques</p> <ul> <li>Wave Field Synthesis</li> <li>Near-Field-Compensated Higher-Order Ambisonics</li> <li>Local Wave Field Synthesis using Spatial Bandwidth Limitation</li> <li>Local Wave Field Synthesis using Virtual Secondary Sources</li> </ul> <p>The corresponding binaural room scanning (BRS) files for the binaural simulation can be found in the directory `brs`. The employed noise stimulus is contained in `stimuli`. The localisation results are stored in `results`. The `analysis` directory includes scripts for parsing the data.</p>
Dataset for supervised learning with a deep neural network to assess azimuthal localisation in sound field synthesis
<p>Dataset for supervised learning with a deep neural network to assess azimuthal localisation in sound field synthesis.<br> Released as part of the Master Thesis 'An Auditory Model for Azimuthal Localisation in Sound Field Synthesis'.</p> <p>This database is calculated from the data of listening experiments.</p>
Arabidopsis TRM5 encodes a nuclear-localised bifunctional tRNA guanine and inosine-N1-methyltransferase that is important for growth
<p>Modified nucleosides in tRNAs are critical for protein translation. N<sup>1</sup>-methylguanosine-37 and N<sup>1</sup>-methylinosine-37 in tRNAs, both located at the 3’-adjacent to the anticodon, are formed by Trm5. Here we describe <em>Arabidopsis thaliana AtTRM5</em> (At3g56120) as a Trm5 ortholog. <em>Attrm5</em> mutant plants have overall slower growth as observed by slower leaf initiation rate, delayed flowering and reduced primary root length. In <em>Attrm5</em> mutants, mRNAs of flowering time genes are less abundant and correlated with delayed flowering. We show that <em>AtTRM5</em> complements the yeast <em>trm5</em> mutant, and <em>in vitro</em> methylates tRNA guanosine-37 to produce N<sup>1</sup>-methylguanosine (m<sup>1</sup>G). We also show <em>in vitro</em> that AtTRM5 methylates tRNA inosine-37 to produce N<sup>1</sup>-methylinosine (m<sup>1</sup>I) and in <em>Attrm5</em> mutant plants, we show a reduction of both N<sup>1</sup>-methylguanosine and N<sup>1</sup>-methylinosine. We also show that AtTRM5 is localized to the nucleus in plant cells. Proteomics data showed that photosynthetic protein abundance is affected in <em>Attrm5</em> mutant plants. Finally, we show tRNA-Ala aminoacylation is not affected in <em>Attrm5</em> mutants. However the abundance of tRNA-Ala and tRNA-Asp 5’ half cleavage products are deduced. Our findings highlight the bifunctionality of AtTRM5 and the importance of the post-transcriptional tRNA modifications m<sup>1</sup>G and m<sup>1</sup>I at tRNA position 37 in general plant growth and development.</p>
supplementary data for Bemelmans et al., 2023, High-resolution InSAR reveals localised pre-eruptive deformation inside the crater of Agung volcano, Indonesia.
<p>This repository contains the supplementary materials for the paper "High-resolution InSAR reveals localised pre-eruptive deformation inside the crater of Agung volcano, Indonesia." to be published in JGR: Solid Earth.</p> <p>The dataset contains files assiciated with the StaMPS time series processing. Each dataset has its own folder containing:</p> <ol> <li>*_data.csv : data file containing latitude, longitude, incidence angle, heading and LOS displacement for each acquistion (date is listed in the column name in the format yyyymmdd).</li> <li>parms.mat : parameter file used for StaMPS processing of that dataset</li> </ol> <p>The dataset also contains input and results from the GBIS modelling (/GBIS_results/). the *.inp files are the input files for each model inversion where the letter ('M','T','P','Y', or 'D') refer to the Mogi (point), McTigue (sphere), penny-shaped crack, Yang (ellipsoid), and dyke (also sill) model used for that run. folders with the same name as the *.inp file contain the inversion results (invert_*.mat), a summary table (summary_*.txt) and several figures showing the distribution and convergence of the model inversion.</p> <p>The input for the GBIS inversions is stored in /GBIS_results/INSAR_input/</p> <p>the file <a href="https://zenodo.org/api/files/f30be116-1e2f-4d52-8fcf-a54e11ab691f/matlab_functions.zip?versionId=490de786-f245-45b5-92bb-d2ae0d50be56">matlab_functions.zip </a>contains matlab functions used for data processing, visualisation, storage and conversion.</p> <p>the file <a href="https://zenodo.org/api/files/f30be116-1e2f-4d52-8fcf-a54e11ab691f/GBISv1_1_MJWB.zip?versionId=cec83c81-a0b7-4e4a-8aa8-ac81e32b8772">GBISv1_1_MJWB.zip </a>contains GBIS code adapted by the author to perform statistical analysis of the model inversion, perform region-of-interest based subsampling and store modeled results as shapefiles for further processing.</p> <p> </p>
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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.