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41 results for “cellular dataset”
DUCC - Dataset for UAS Cellular Communications
<p><strong>Motivation</strong><br>The Dataset for Unmanned Aircraft System (UAS) Cellular Communications, short DUCC, was created with the aim of advancing communications for Beyond Visual Line of Sight (BVLOS) operations. With this objective in mind, datasets were generated to analyse the behaviour of cellular communications for UAS operations.</p> <p><strong>Measurement</strong><br>A measurement setup was implemented to execute the measurements. Two Sierra Wireless EM9191 modems possessing both LTE and 5G capabilities were utilized in order to establish a connection to the cellular network and measure the physical parameters of the air-link. Every modem was equipped with four Taoglas antennas, two of type TG 35.8113 and two of type TG 45.8113. To capture the measurements a Raspberry Pi 4B is used. All hardware components were integrated into a box and attached to a DJI Matrice 300 RTK. A connection to the drone controller has been established to obtain location, speed and attitude. To measure end-to-end network parameters, dummy data was exchanged bidirectionally between the Raspberry Pi and a server. Both the server as well as the Raspberry Pi are synchronized with the GPS time in order to measure the one-way packet delay. For this purpose, we utilised Iperf3 and customised it to suit our requirements. To ensure precise positioning of the drone a Real Time Kinematik (RTK) station was placed on the ground during the measurements.</p> <p>The measurements were performed at three distinct rural locations. Waypoint flights were undertaken with the points arranged in a cuboid formation maximizing the coverage of the air volume. Thereby, the campaigns were conducted with varying drone speeds. Moreover, for location A, different flight routes with rotated grids were implemented to reduce bias. Finally, a validation dataset is provided for location A, where the waypoints were calculated according to Quality of Service (QoS) based path-planning.</p> <p><strong>Dataset Structure and Usage</strong><br>The dataset's structure consists of:<br>-- Dataset<br> |-- LocationX<br> |-- RouteX (in case different routes at LocationX were created)<br> |-- LocXRouteX.kml (file containing the waypoints in the kml format)<br> |-- SpeedXMeterPerSecond (folder containing the datasets recorded with a specific drone speed)<br> |-- YYYY-MM-DD hh_mm_ss.s.pkl.gz (Dataset file)<br> |-- RouteY<br> |-- ...<br> |-- ...</p> <p>The dataset files can be loaded using the pandas module in python3. The file "load.py" provides a sample script for loading a dataset as well as the corresponding .kml file which contains the predefined waypoints. In the file "Parameter_Description.csv" each parameter measured is further explained.</p> <p><strong>License</strong><br>All datasets are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us at automotive@oth-aw.de and we will immediately remove the respective data from our server.</p> <p><strong>Achnowledgement</strong><br>The authors gratefully acknowledge the following European Union H2020 -- ECSEL Joint Undertaking project for financial support including funding by the German Federal Ministry for Education and Research (BMBF): ADACORSA (Grant Agreement No. 876019, funding code 16MEE0039).</p>
Dataset of "Comparison of Localization Methods for Internet of Things in 5G Cellular Networks: A Wide-scale Assessment"
<p>As the 3rd generation partnership project (3GPP) organization pushes out new releases,<br>positioning in heterogeneous mobile networks enables the achievement of the accuracy required<br>in the majority of industrial applications without dependence on global navigation<br>satellite systems (GNSS). This study presents the results gathered during an extensive measurement<br>campaign related to the practical applicability of localization in next-generation<br>heterogeneous networks. We present an accuracy comparison of basic timing advance (TA)<br>localization with the k-nearest neighbor (KNN), decision tree-based random forest (RF),<br>extreme gradient boosting (XGBoost), and long short-term memory (LSTM) recurrent neural<br>network. Our results demonstrate that TA cannot be considered an optimal solution<br>from the perspective of localization accuracy because the error roughly corresponds to the<br>average separation distance from the base station (BS) to the end device (ED). In addition,<br>we found that the LSTM approach is not optimal for the outdoor localization of moving<br>ED because of the combination of multiple factors, with sparse deployment being the most<br>important. The median value of the location error of the LSTM was more than 200m higher<br>than that of the TA for the self-validation dataset. However, a simple KNN regression shows<br>solid results for 5G New Radio (NR) operating in the non-standalone (NSA) mode. KNN<br>provided the most accurate results of all methods, with median error values of approximately<br>12 (k=3) and 82 (k=5) m for the self-validated and cross-validated datasets, respectively.</p>
Cryo-4D-STEM datasets on cells and cellular organelles for demonstrating a dose-Efficient cryo-EM technique: tilt-Corrected Scanning Transmission Electron Microscopy
<p>This upload contains three 4D-STEM datasets in .raw format for demonstrating a dose-efficient cryo-EM technique for thick samples: tilt-corrected Scanning Transmission Electron Microscopy (tcBF-STEM). The dataset dimension is 128130256*256. Data were acquired on vitrified intact E.coli cells and isolated human cell organelles. This upload also contains the EFTEM images in .mrc acqired in the same ROI as the 4D-STEM dataset. </p> <p>It also contains analysis of the manuscript's Fig 3 and Ext. data fig 8. </p>
SBF-SEM datasets related to the manuscript "Trans-cellular tunnels induced by the fungal pathogen Candida albicans facilitate invasion through successive epithelial cells without host damage" by Lachat et al, 2022.
<p>11 serial block face- scanning electron microscopy (SBF-SEM) datasets described in the manuscript "Trans-cellular tunnels induced by the fungal pathogen Candida albicans facilitate invasion through successive epithelial cells without host damage" by Lachat et al, 2022.</p> <p>Resolution: 10 nm x,y, 100 nm Z.</p> <p>Datasets description and quantification can be found in the Supplementary information.</p>
Dataset: Millicom International Cellular S.A. (TIGO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Intra-Cellular Therapies, Inc. (ITCI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset of Cellular Network Measurements
<p>This dataset contains 326,157 measurements in cellular networks, collected from August 2021 until Juni 2023 in Germany. The measurement focus on a high density in rural areas and were performed by car, bicycle and by foot. The measurements cover primary the Telefonica/o2 network.</p>
Dataset part one to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"
<p>This study was supported in parts by research funding from the Swiss National Science Foundation (grant number 310030_189144), the Thurgauische Stiftung für Wissenschaft und Forschung, and the State Secretariat for Education, Research and Innovation to DFL.</p>
Towards true volumetric refractive index investigation in tomographic phase microscopy at cellular level - dataset
<p>This dataset contains 6 files with reconstrucions of 3 different types of cells (SH-SY5Y neuroblastoma cells, A549 adenocarcinoma lung cells and HL-60 white blood cells), retrieved from optical diffraction tomography system measurements carried out at Warsaw University of Technology. The measurements were reconstructued with 2 different algorithms: direct inversion algorithm (DI) and Gerchberg-Papoulis algorithm with finite object support regularization (GPSC) [1]. </p> <p>All files are *.mat files.</p> <p>In the files there are 5 variables:</p> <p>REC - reconstruction matrix with information about 3D refractive index values in the cells<br> dx - sample size <br> RI2D - values of mean refractive index calculated from 2D lateral cross-sections<br> RI3D - volumetric mean refractive index<br> mask - binary mask of the cell</p> <p><br> [1] W. Krauze, “Optical diffraction tomography with finite object support for the minimization of missing cone artifacts,”277<br> Biomed. optics express 11, 1919–1926 (2020)<br> </p>
DATA7: A dataset that uses synthetic trajectories of vehicles and real cellular tower locations to simulate the workload of Edge nodes in the city of Pisa
<p><strong>Description</strong></p> <p>The dataset contains observations of vehicles in the range of edge nodes (cellular towers). The trajectories of vehicles are synthetically generated with <a href="https://www.eclipse.org/sumo/">SUMO</a>. The cellular tower positions have been taken from <a href="https://opencellid.org/">OpenCelliD</a>. The dataset is in the comma-separated values (CSV) format, and is around 220MB decompressed.</p> <p><br> The CSV contains the following fields:<br> * edge_id: unique identifier of the edge devices<br> * edge_lat: latitude coordinate of the edge device<br> * edge_lon: longitude coordinate of the edge device<br> * time: simulation step of the observation<br> * vehicle_id: unique identifier of the vehicle<br> * vehicle_lat: latitude coordinate of the vehicle<br> * vehicle_lon: longitude coordinate of the vehicle<br> * distance: geodesic distance in meters from the vehicle and the edge device</p>
Data for: The ErythronDB Dataset of the cellular and molecular underpinnings of mammalian erythropoiesis
<p>The Erythron Database (ErythronDB) was a resource dedicated to facilitating better understanding of the cellular and molecular underpinnings of mammalian erythropoiesis. For more information about this project, please visit <a href="https://cstoeckert.github.io/past-projects/ErythronDB.html" rel="nofollow">https://cstoeckert.github.io/past-projects/ErythronDB.html</a>.</p>
Data for: The ErythronDB Dataset of the cellular and molecular underpinnings of mammalian erythropoiesis
Open the record for dataset details and reuse information.
List of publicly available cellular reprogramming HTS datasets
<p>These datasets were obtained by manual curation via the following procedure: First, I queried GEO using keywords in the list below. </p> <ol> <li>Yamanaka</li> <li>Yamanaka AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>Reprogramming AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>OSKM AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>Sox2 AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>Sox2 reprogramming AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>somatic reprogramming AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>somatic cell reprogramming AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>Reprogramming pluripotency AND ("Genome binding/occupancy profiling by high throughput sequen"[Filter]) </li> <li>Reprogramming pluripotency </li> <li>Reprogramming pluripotency AND ("Methylation profiling by high throughput sequencing"[Filter]) </li> <li>OSKM AND ("Methylation profiling by high throughput sequencing"[Filter]) </li> <li>Sox2 Reprogramming AND ("Methylation profiling by high throughput sequencing"[Filter]) </li> </ol> <p>Results were selected for manual input if they contained either transcriptomic (e.g., RNA-Sequencing) or epigenomics (e.g., RRBS) data and had at least one intermediate cellular reprogramming timepoint. </p> <p> </p>
GeoMx DSP dataset of "Integrated multi-omics reveals cellular and molecular interactions governing the invasive niche of basal cell carcinoma"
<p>This data set is linked to the paper "Integrated multi-omics reveals cellular and molecular interactions governing the invasive niche of basal cell carcinoma". It contains datasets and images from GeoMx DSP analysis. </p>
Dataset: Phytoplankton Cell-States: multiparameter lifetime flow monitoring reveals cellular heterogeneity
<h1>Dataset for Multiparameter-based photosynthetic state transitions of single phytoplankton cells data.</h1> <h2>Creating environment</h2> <p>yaml and spec file are provided of the conda environment used. Most packages are typical, with the key exception of fretbursts</p> <p>A custom/development version of fretbursts was used, which contains support for more flexible photon selection (Ph_sel) objects, necessary for the larger parameter space. This version is in the polarization branch of harripd fork of fretbursts. The simple route to install is </p> <p><code>pip install git+https://https://github.com/harripd/FRETBursts@polarization</code></p> <p>however, if you want to ensure the exact same version is installed use:</p> <p><code>pip install git+https://github.com/harripd/FRETBursts@12a5168</code></p> <h2>Data organization</h2> <p>All zip files are meant to be unziped into the same destination folder (ie if a file is in a folder in the zip file, it should be in a subfolder inside the shared highest level folder, but if two files inside different zip folders are in the root of their respective zip files, they should be places in the same folder when all files are unzipped.</p> <p>After unzipping everything your folder for this article should contain the following items (normal text indicates file, <strong>bold text indicates folder</strong>).</p> <ol> <li>Flow_PhytoCellStates_figures.ipynb</li> <li><strong>mpAlgae</strong></li> <li><strong>figures</strong></li> <li>20230905_cyanoWH8102_diurnal</li> <li>20230707_cyanolightjump</li> <li>20230609_ppurp</li> <li>20230724_redalgaelightjump</li> <li>20231211_pmicans_6umol_diurnal</li> <li><strong>20231217_pmicans_6umol_lightjump</strong></li> <li><strong>20231019_WH8102_imaging</strong></li> <li><strong>bulk_cyano_lightjump</strong></li> <li>synWH8102_di_REPO_20230905.hdf5</li> <li>synWH8102_di_REPO_20230905.csv</li> <li>synWH8102_lj_REPO_20230707.hdf5</li> <li>synWH8102_lj_REPO_20230707.csv</li> <li>ppurp_di_REPO_20230609.hdf5</li> <li>ppurp_di_REPO_20230609.csv</li> <li>ppurp_lj_REPO_20230724.hdf5</li> <li>ppurp_lj_REPO_20230724.csv</li> <li>pmicans_di_REPO_20231211.hdf5</li> <li>pmicans_di_REPO_20231211.csv</li> <li>ppurp_lj_REPO_20230724.hdf5</li> <li>ppurp_lj_REPO_20230724.csv</li> </ol> <p>Flow_API_all.ipynb (item 1) is the jupyter notebook that analyses the data and produces the figures. It relies on having mpAlgae (item 2) in the same folder, as it contains the underlying code that adapts FRETBurts to work with the cell based data, adding functionality to combine multiple data files into one experiment, dynamic gating of data, storing calculated values to disk, which also reduces the memory requirements of the system, as well as a number of advanced plotting functions. Flow_API_all.ipynb also expects a figures folder (item 3) to exist so that it can save the produced figures. It also expects items 4-9 to exist, as those folder names are recorded as where the different raw data sets are stored. Even numbered items 12-22 (all items 12-23 that are hdf5 files) are produced by Flow_API_all.ipynb, and are where processed values of burst search are stored. the mpAlgae api makes it so that if these files are present, burst search is skipped, and instead loaded from these result files. Thus if you desire to examine the data, you only need the top level hdf5 files. These files also save results of histogram fittings, so that results can be loaded instead of recalculatd. </p> <p>The csv files are export of calculated parameters to csv format. Note that these are ungated, and thus proper gating must be implemented if plots are to be recapitulated from these files, this is done so that alternative gating strategies can also be explored.</p> <p> </p>
Dataset related to article "Tumor Treating Fields enhance chemotherapy efficacy by increasing cellular drug uptake and retention in mesothelioma cells"
<p>This record contains data related to article “Tumor Treating Fields enhance chemotherapy efficacy by increasing cellular drug uptake and retention in mesothelioma cells”</p> <p><span>Tumor Treating Fields (TTFields) applied with standard chemotherapy have been approved for the first-line treatment of unresectable pleural mesothelioma (PM), an aggressive malignancy with limited effective therapy options.</span></p> <p><span>In this study, we demonstrated that the simultaneous exposure to TTFields and doxorubicin or vinorelbine enhanced treatment efficacy in patient-derived PM cells by increasing intracellular drug concentrations. This was achieved by modulating several genes that encode transport proteins, such as the downregulation of P-glycoprotein.</span></p> <p><span>Using specific, sensitive and quantitative analytical techniques, we observed a more than 70% increase in intracellular concentrations of doxorubicin and vinorelbine in samples treated with TTFields, and a greater than 50% increase in drug uptake in cells exposed to TTFields and pemetrexed. This result indicates that the increased drug concentration observed in TTFields treated cells is significant not only for drugs that are P-gp substrates but also suggests that TTFields could potentially affect other efflux pumps. However, the co-exposure to the drug and TTFields was critical to increasing intracellular drug levels, highlighting the necessity of concurrent use with drugs to enhance the antiproliferative effects of treatment.</span></p> <p><span>The <em>in vitro</em> findings were further corroborated by <em>in vivo</em> pharmacokinetic experiments in mice subcutaneously injected with epithelioid PM tumors. Indeed, a 30% increase in intratumor concentrations was observed when vinorelbine was administered with TTFields.</span></p> <p><span>Our findings suggest that TTFields could be a well-tolerated approach for enhancing intratumoral drug levels and potentially achieving a more significant therapeutic impact on PM treatment.</span></p>
Dataset part two to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"
<p>Additional dataset to dataset part one (doi: 10.5281/zenodo.4719596) to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"</p>
Cellular dataset for: The anterior Hox gene ceh-13 and elt-1/GATA activate the posterior Hox genes nob-1 and php-3 to specify posterior lineages in the C. elegans embryo
Open the record for dataset details and reuse information.
Dataset related to article "Computer-aided assessment of the extra-cellular matrix during pancreatic carcinogenesis: a pilot study."
<p>BACKGROUND:</p> <p>A hallmark of pancreatic ductal adenocarcinoma is the desmoplastic reaction, but its impact on the tumor behavior remains controversial. Our aim was to introduce a computer -aided method to precisely quantify the amount of pancreatic collagenic extra-cellular matrix, its spatial distribution pattern, and the degradation process.</p> <p>METHODS:</p> <p>A series of normal, inflammatory and neoplastic pancreatic ductal adenocarcinoma formalin-fixed and paraffin-embedded Sirius red stained sections were automatically digitized and analyzed using a computer-aided method.</p> <p>RESULTS:</p> <p>We found a progressive increase of pancreatic collagenic extra-cellular matrix from normal to the inflammatory and pancreatic ductal adenocarcinoma. The two-dimensional fractal dimension showed a significant difference in the collagenic extra-cellular matrix spatial complexity between normal versus inflammatory and pancreatic ductal adenocarcinoma. A significant difference when comparing the number of cycles necessary to degrade the pancreatic collagenic extra-cellular matrix in normal versus inflammatory and pancreatic ductal adenocarcinoma was also found. The difference between inflammatory and pancreatic ductal adenocarcinoma was also significant. Furthermore, the mean velocity of collagenic extra-cellular matrix degradation was found to be faster in inflammatory and pancreatic ductal adenocarcinoma than in normal.</p> <p>CONCLUSION:</p> <p>These findings demonstrate that inflammatory and pancreatic ductal adenocarcinomas are characterized by an increased amount of pancreatic collagenic extra-cellular matrix and by changes in their spatial complexity and degradation. Our study defines new features about the pancreatic collagenic extra-cellular matrix, and represents a basis for further investigations into the clinical behavior of pancreatic ductal adenocarcinoma and the development of therapeutic strategies.</p>
Datasets generated for the manuscript: The basement membrane regulates the cellular localization and the cytoplasmic interactome of Yes-Associated Protein (YAP) in mammary epithelial cells
<p><strong>File proteinGroups-CoIP-Yap1:</strong> Dataset of co-Immunoprecipitation followed of Yes-associated protein (YAP) followed by proteomics to identify YAP interactants.</p> <p><strong>File Gene_set_file_YAP: </strong>Gene sets used for gene set enrichment analysis.</p> <p><strong>Files enrichr_x: </strong>output of the EnrichR tool</p>
ScienceDex guides
Understand access before you commit
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.