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2,371 results for “platform”
Initial evaluation of the MOVING platform
<p>This resource contains information about the laboratory study carried out as an initial evaluation of the MOVING platform.</p> <p>It contains the information gathered from the questionnaires, and qualitative notes taken during the study for two different use cases. The design of the study and the results of the analysis can be found in the deliverable 1.3 "Initial evaluation, updated requirements and specifications"</p> <p><a href="http://moving-project.eu/deliverables/">http://moving-project.eu/deliverables/</a></p>
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
<p>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).</p>
Dataset: An Open-hardware Platform for MPSoC Thermal Modeling
<p>This repository contains the experimental data for the paper</p> <p>"An Open-hardware Platform for MPSoC Thermal Modeling"</p> <p>published at 2019 samos conference</p> <p>http://samos-conference.com</p> <p>The data is released under a CC-BY Creative Commons license.<br> If you use this dataset, cite the following paper:<br> Federico Terraneo, Alberto Leva, William Fornaciari, "An Open-hardware Platform for MPSoC Thermal Modeling", 2019 IEEE International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS)</p>
A proteome-wide quantitative platform for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs
<p><strong>EM Quantitation:</strong></p> <p>Raw data gathered from EM images taken to determine nanodisc population size distribution.</p> <p> </p> <p><strong>NNB TGN46 analysis:</strong></p> <p>Data analysis of the Native Nanobleach experiments of TGN46 in native nanodiscs to determine population distribution of oligomeric organizations.</p> <p> </p> <p><strong>Polymer conditions:</strong></p> <p>Physiochemical characteristic and extraction conditions for all polymers in the library both commercially available and in-house.</p> <p> </p> <p><strong>Protein groups polymer screen original file:</strong></p> <p>Original output of MaxQuant data processing of polymer screen data.</p> <p> </p> <p><strong>Organelle matching:</strong></p> <p>Code used for mathcing proteins identified in the proteomics output to organelle or residence for all organellar annotations.</p> <p> </p> <p><strong>Polymer code:</strong></p> <p>Code used to process and normalize the MaxQuant output and calulate extraction efficiency across all detected proteins.</p> <p> </p> <p><strong>MAP Library Details:</strong></p> <p>Graphic and table explaining chemical details of all polymer used in the screen, both commerically available and in-house synthesized.</p> <p> </p> <p><strong>NNB TGN46:</strong></p> <p>Raw scope files for the TIRF microscopy single molecule step photobleaching experiment with TGN46.</p> <p> </p> <p><strong>Organellar Breakdown Database:</strong></p> <p>Proteins detected in the polymer screen through proteomics experiments stratified into organelle of residence.</p> <p> </p> <p><strong>Human Proteome FASTA:</strong></p> <p>The FASTA file used for proteome searching in processing the proteomics data to build the screening database.</p> <p> </p> <p><strong>Hand Curated Organellar Proteomes:</strong></p> <p>Organellar proteomes used for organellar sorting and identification of proteins detected in the screen.</p> <p> </p> <p><strong>Polymer SEC Superdex75:</strong></p> <p>Size exculsion chromatography traces for chloroSMA series of polymers. Was used to characterize length and population polydispersity.</p> <p> </p> <p><strong>Negative Stain Raw:</strong></p> <p>RAW TEM scope images of purified synaptophysin-vamp2 containing nanodiscs. Populatoin size distribution was determined.</p> <p> </p> <p><strong>FSEC Polymer CS80:</strong></p> <p>Fluoresence size exclusion chromatogram for purified synaptophysin-vamp2 containing nanodiscs to ensure population homogeneity and purity.</p> <p><strong>NMR Raw data:</strong></p> <p>NMR raw files for characterizing the in-house synthesized Chloro-SMA series and AASTY series.</p> <p> </p>
RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).
<p>This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a></p> <p>The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki</p>
RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)
<p>This is an RDFied version of the dataset published in Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. <em>Nanomaterials</em> <strong>2020</strong>, <em>10</em>, 2017.</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.3390/nano10102017">https://doi.org/10.3390/nano10102017</a></p> <p>The Original publication authors: Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A.</p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Open Pit Extraction, Valea Sesei and Roșia Poieni (Romania)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Open Pit Extraction (mine located at Valea Sesei and Roșia Poieni (Romania)) (3D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Underground Extraction, Pyhäsalmi (Finland)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Underground Extraction (mine located at Pyhäsalmi (Finland)) (2D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Datasets of the work named Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform
<pre>- 1_Motion_Simulator/ - IMU_results/ - 20211028101756.csv - 20220114101543.csv - 20220117000000.csv - Rotary_Table/ - 15/ - rover_20220105.nav - rover_20220105.obs - solution_20220105_CAS.log - 360/ - rover_20211221.nav - rover_20211221.obs - solution_20211221_CAS.log - 360-15/ - rover_20220202_CAS.nav - rover_20220202_CAS.obs - solution_20220202_CAS.log - Static_Tests/ - solution_SSRA00CAS0 - solution_SSRA00WHU0 - 2_GNSS_Signal_Simulator/ - platformmov_C1.xtd - platformmov_C2.xtd - platformmov_C3.xtd - TestBetaNoneMov_C1 - TestBetaNoneMov_C1.nav - TestBetaNoneMov_C1.obs - TestBetaNoneMov_C1.ubx - TestBetaNoneMov_C2 - TestBetaNoneMov_C2.nav - TestBetaNoneMov_C2.obs - TestBetaNoneMov_C2.ubx - TestBetaNoneMov_C3 - TestBetaNoneMov_C3.nav - TestBetaNoneMov_C3.obs - TestBetaNoneMov_C3.ubx - 3_Test_Sea/ - 20220503000000.xlsx - solution_28.nav - solution_28.obs - solution_28.ubx Background: {Journal Article using this dataset} 'Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform' Paper DOI: <a href="https://doi.org/10.3390/s23020925">https://doi.org/10.3390/s23020925</a> Abstract: a low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved. </pre>
Image descriptions for 7471 of the DEArt images, obtained via the Zooniverse crowdsourcing platform
<p>This dataset contains crowdsourced image descriptions for 7471 images from the DEArt dataset. There are typically 4-5 descriptions per image, provided by volunteers of the Zooniverse crowdsourcing platform. The guidelines we used, together with some examples of possible captions, are also published in Zooniverse. We are in debt with all the Zooniverse volunteers and coordinators, and in particular with Samantha Blickhan, for making this possible. Some image descriptions were filtered out whenever they did not contain any textual data, nevertheless the dataset may still contain some descriptions that are not according to the guidelines we provided to the annotators. The dataset is a data export from Zooniverse in CSV format, in which the user information has been anonymized. The CSV also includes a field with the filename of the described image in the DEArt dataset.</p>
PHUSICOS project platform dataset
<p>The PHUSICOS dataset gathers :</p> <p>- a dataset of Nature Based Solutions (NBS) actions implemented in mountainous or rural areas to cope with hydrometeorological events</p> <p>- datasets of documents of interest on NBS (publications, medias, reports, ect.)</p>
ΝEW ABC_ WP2 _ Task 2-1 Mapping the NEW ABC platform requirements_20230609_v1
<p>This data set consists of an analysis of users of existing platforms of other European projects, and the level of engagement they create, to inform the creation and successful use of NEW ABC project platform. This analysis will assess the number and typology of users who access these platforms and what kind of information and material is available to them. The data set will contain numerical and textual tabular data converted into digital format (survey made by online interviews with platform users), quantitative and qualitative data.</p> <p>Content of the files:</p> <ul> <li>file <strong><em>ΝEWABC_WP2_T2-1_Mapping the NEW ABC platform requirements_20230609_v1.csv </em></strong>contains a description of the data collected for each Horizon project relevant to the NEW ABC.</li> </ul> <p> </p> <ul> <li>file <strong>README_</strong><strong><em> ΝEWABC_WP2_T2-1_Mapping the NEW ABC platform requirements_20230609_v1.rtf</em></strong> contains basic information on the dataset uploaded.</li> </ul>
Risk Commodity Dataset (RCDD) from Alibaba's e-commerce platform
<p>This is a risk commodity detection dataset (RCDD) that is based on a real risk detection scenario from Alibaba's e-commerce platform. All the <em>.csv </em>files are the initial data which consists of edges, node features, supervised information as well as IDs of candidate items. More details can see in <em>README.md</em>, we list the type of each column in each file as follows:</p> <p>1. RCDD_edges.csv (edge file): <br> source_node_id int, target_node_id int, source_node_type string, target_node_type string, edge_type string<br> 2. RCDD_nodes.csv (node file):<br> node_id int, node_type string, node_atts string (notice that node_atts are 256-dimensional feature vector strings with delimiter ":")<br> 3. RCDD_train_labels.csv (training labels):<br> item_id int, label int<br> 4. RCDD_test_ids.csv (testing ids):<br> item_id int<br> 5. RCDD_test_labels.csv (testing labels):<br> item_id int, label int </p> <p>Besides that,<em> graph.bin</em> is the format in DGL which is constructed by all <em>*.csv</em> files, and a general method to load this graph as follows:</p> <pre><code class="language-python">from dgl import load_graphs #should install dgl ds,_ = load_graphs("./graph.bin") g = ds[0] print(g)</code></pre> <p>And then you can easily get a large-scale heterogeneous graph with 157,814,864 edges and 13,806,619 nodes, our graph task is node classification: detect risk product.</p>
Simulation output for seven simulations with CLM-FATES in the Land Sites Platform
<p>Supporting data for a manuscript involving simulation of vegetation with the<a href="https://www.cesm.ucar.edu/models/clm"> Community Land Model </a>and<a href="https://github.com/NGEET/fates"> FATES</a>. Simulations were run using the <a href="https://noresmhub.github.io/noresm-land-sites-platform/">Land Sites Platform </a>on two virtual linux machines provided by <a href="https://nrec.no/">NREC</a>. The manuscript is part of my (EL) PhD thesis. <br> <br> See the associated GitHub repository for notebooks and workflow documentation, and the thesis chapter or manuscript for further information.</p> <p>In this dataset:</p> <ul> <li>Concatenated model history files for the entire simulation period (e.g. "alp4-1500-cosmo-IA.0-1500.nc")</li> <li>Readme files per simulation specifying the variables passed to the Land Sites Platform to start the simulation (e.g. "readme.md")</li> <li>Zipped case folders, containing the standard CLM case folder structure. Individual monthly history files are found under /archive/lnd/hist. E.g. "2985cbd23a2e3d7b1ac057abd862abb7_alp4-1500-cosmo-ia.zip"</li> </ul>
OneNet Cross-Platform Services
<p>The goal of the OneNet System is to facilitate data exchanges among existing platforms, services, applications, and devices by the power of interoperability techniques. To ensure that system requirements are technically -implementable and widely adopted, internationally standardized file formats, metadata, vocabularies and identifiers - are required.</p> <p>The OneNet “Cross-Platform Access” pattern is the fundamental characteristic of an interoperable ecosystem, leading to the definition of the exposed list OneNet Cross-Platform services (CPS). The pattern entails that an application accesses services or resources (information or functions) from multiple platforms through the same interface. For example, a “grid monitoring” application gathers information on different grid indicators provided by different platforms that conduct measurements or state estimations. The challenge of realizing this pattern lies in allowing applications or services within one platform to interact with other platforms (eventually from different providers) with relevant services or applications via the same interface and data formats. Thereby, reuse and composition of services as well as easy integration of data from different platforms are enabled. </p> <p>Based on the defined concept for CPS, an extensive analysis has been performed regarding data exchange patterns and roles involved for system use cases (SUCs) from other H2020 projects and the OneNet demo clusters. This has resulted into a first list of CPS, that has been thereafter taxonomized into 10 categories. The different entries have been defined providing a set of classes such as service description, indicative data producer/consumer etc. Each CPS can be assigned with multiple business objects describing the context of it. For a specific set of widely used by the Demo CPS, there have been formal semantic definitions provided in the "CrossPlatformServices-Semantic" excel worksheet.</p>
Codebook - Knowledge-Action Platforms (Data & Policy article) Bream McIntosh et al.,
<p>Codebook used for Round 2 coding against key criteria of a 'Platform' definition that responds to matrix coding of Sustainability Knowledge Action Platforms. To accompany the article in Data & Policy 'The role of sustainability knowledge-action platforms in advancing multi-stakeholder engagement on sustainability'.</p>
Turbulence and flux data from eddy flux platform on Toolik Lake, Alaska 2009-2015.
Yearly file describing the turbulence conditions on Toolik Lake including the CH4, CO2 and H2O fluxes over the lake adjacent to the Toolik Field Research Station (68 38'N, 149 36'W). This location is a floating platform where eddy flux measurements have been made, and should not be confused with either the Toolik Field Station Climate site, which is a land-based station, or the Toolik Lake Climate Station that is lake-based but at a different location (approximately 300 m from the eddy platform). These data were used in the Eugster et al. (2020) article that appeared in Environmental Science: Processes and Impacts. Note that the quality flagging system in this file is only for reference, it was not used in the named article. Instead, the 9-level system by Foken et al. was calculated (see Eugster et al. 2020), but no data were eliminated to avoid bias from wrong assumptions made in this approach if measurements are not performed over a terrestrial vegetated land surface.
Cross-platform mentions of the QAnon conspiracy theory
<p>This dataset contains mentions of the QAnon conspiracy theory across the Web between 28 October 2017 and 1 November 2018. The following list details the data per platform and its collection process:</p> <ul> <li><strong>4chan: </strong>Posts and comments on 4chan/pol/ mentioning "Q" or "QAnon". The data is collected through 4CAT, a data capturing and analysis tool that hosts all posts and comments made on 4chan/pol/ since 2014.</li> <li><strong>8chan:</strong> Posts and comments on the /qresearch/ board and other smaller boards mentioning "Q" or "QAnon". The data is derived qanon.news, a grassroots archive. Considering its amateur nature, the dataset is likely not 100% complete, but still includes over 200,000 posts.</li> <li><strong>Reddit:</strong> Comments made on politically-oriented subreddits mentioning "Q" or "QAnon". The data is gathered through the Pushshift API.</li> <li><strong>YouTube:</strong> Videos mentioning QAnon or "Q" in the title or video decription. The data is collected via the YouTube v3 API using the search endpoint. Multiple keywords were queried ("qanon", "qanon 4chan", etc) to collect a large sample. False positives were then filtered out manually.</li> <li><strong>Breitbart:</strong> Disqus comments on Breitbart.com mentioning QAnon or "Q". The data was gathered by crawling all of Breitbart.com in the timeframe and using the Disqus API.</li> <li><strong>Online news media</strong>: Articles from English online news sources mentioning QAnon. The data is derived from Nexis Uni and ContextualWeb Search by searching for "QAnon". Irrelevant sources and false positives were filtered manually.</li> </ul> <p>The datasets include timestamps, text bodies, and platform-specific information like subreddits and channel titles. To collect data from 4chan, 8chan, Reddit, and Breitbart, we used the same SQL query, sampled 200 comments, and edited the query to so it would have sufficient number of true positives (> 94%). The YouTube and online news media datasets are filtered manually.</p> <p>For Breitbart and Reddit, the data is anonymised by omitting author information. The online news media article text is omitted because of copyright concerns.</p> <p>See <a href="https://journals.uic.edu/ojs/index.php/fm/article/view/10643/9998">the article on First Monday</a> for the full collection process.</p>
The SV callsets of the HG002 human sample produced by cuteSV with multi long-read sequencing platforms.
<p>The SV callsets of the HG002 human sample produced by cuteSV with multi long-read sequencing platforms.</p>
Experimental Results of "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms"
<p>This repository contains the experimental results of the article "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms" by Duarte Campos, D., Lindsay, C., Roth, J., LeSavage, B., Seymour, A., Krajina, B., Ribeiro, R., Costa, P., Heilshorn, S., published in <em>Front. bioeng. biotechnol. </em><strong>8, 374</strong> (2020). https://doi.org/10.3389/fbioe.2020.00374</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.