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47 results for “cross-platform”
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>
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>
WeBCMD: A Cross-Platform Interface for the BCMD Modelling Framework
<p>Datasets for the WeBCMD example use cases.</p> <p>Those named *-output-*.csv are intended for comparing with model output whilst synthetic-input-data.csv is intended to drive the model for the BrainSignals/Systemic Data use case.</p>
Preprocessed data for find motives behind cross-platform forks from SWHGD dataset
<p>The fork-based development mechanism provides the flexibilityand the unified processes for software teams to collaborate easilyin a distributed setting without too much coordination overhead.Currently, multiple social coding platforms support fork-based de-velopment, such as GitHub, GitLab, and Bitbucket. Although thesedifferent platforms virtually share the same features, they havedifferent emphasis. As GitHub is the most popular platform and thecorresponding data is publicly available, most of the current stud-ies are focusing on GitHub hosted projects. However, we observedanecdote evidences that people are confused about choosing amongthese platforms, and some projects are migrating from one platformto another, and the reasons behind these activities remain unknown.With the advances of Software Heritage Graph Dataset (SWHGD),we have the opportunity to investigate the forking activities acrossplatforms. In this paper, we conduct an exploratory study on 10popular open-source projects to identify cross-platform forks andinvestigate the motivation behind. Preliminary result shows thatcross-platform forks do exist, for the 10 subject systems in thisstudy, we found 81,357 forks in total among which 179 forks areon GitLab. Based on our qualitative analysis we found that most ofthe cross-platform forks that we identified are mirrors of anotherplatform, but we still find cases that were created due to preferenceof using certain functionalities (e.g. Continuous Integration(CI))supported by different platforms. This study lays the foundation offuture research directions, such as understanding the differencesbetween platforms and supporting cross-platform collaboration.</p>
Cross-Platform Autonomous Control of Minimal Kitaev Chains
<p>This folder contains the raw data and code used to generate the plots for the paper <em>Cross-Platform Autonomous Control of Minimal Kitaev Chains</em>.</p> <p>To run the Jupyter notebook, install Anaconda and execute:</p> <blockquote> <p><em>conda env create -f </em>environment.yml</p> </blockquote> <p>followed by:</p> <blockquote> <p><em>conda activate KitaevML</em></p> </blockquote> <p>Finally,</p> <blockquote> <p><em>jupyter lab</em></p> </blockquote> <p>to launch the notebook.</p> <p>Raw data are stored in netCDF (.nc) format, or as full QCoDeS databases (.db). Logs of autonomous tuning runs are saved as log files (.txt). The experimental datasets are exported by the data acquisition package QCoDeS and can be read as an xarray Dataset. The ipython notebook <em>Data labelling notebook.ipynb </em>contains the code for labelling the 2DEG and nanowire datasets. The ipython notebook <em>Zenodo Paper Figures.ipynb</em> contains all the code for generating the paper figures. </p> <p>The machine learning model and automated tuning algorithm can be publicly accessed here: https://gitlab.com/QMAI/papers/crossplatformkitaev.<br>This repo contains a pip-installable module ``PMMSGD` that can be used to predict (Delta-t)/(Delta+t) using:</p> <pre><code>pred = PMMSGD.get_Delta_minus_t(dbx, 'CNN_weighted_ratio_theory', ['G_LL*G_RR'])</code></pre> <p>which uses the neural network that was exclusively trained on theory data</p> <p>For the automated tuning algorithm we used this predictor:</p> <pre><code>pred = PMMSGD.get_Delta_minus_t(dbx, 'CNN_weighted_ratio', ['G_LL*G_RR'])</code></pre> <p><br>which uses the neural network that was additionally trained on data from the 2DEG device.</p>
Data from: Cross-platform compatibility of de novo-aligned SNPs in a non-model butterfly genus
High-throughput sequencing methods for genotyping genome-wide markers are being rapidly adopted for phylogenetics of non-model organisms in conservation and biodiversity studies. However, the reproducibility of SNP genotyping and degree of marker overlap or compatibility between datasets from different methodologies have not been tested in non-model systems. Using double-digest restriction site associated DNA sequencing, we sequenced a common set of 22 specimens from the butterfly genus Speyeria on two different Illumina platforms, using two variations of library preparation. We then used a de novo approach to bioinformatic locus assembly and SNP discovery for subsequent phylogenetic analyses. We found a high rate of locus recovery despite differences in library preparation and sequencing platforms, as well as overall high levels of data compatibility after data processing and filtering. These results provide the first application of NGS methods for phylogenetic reconstruction in Speyeria, and support the use and long-term viability of SNP genotyping applications in non-model systems.
Data from: Cross-platform compatibility of de novo-aligned SNPs in a non-model butterfly genus
Open the record for dataset details and reuse information.
Cross-platform study
GEO Series GSE4854. Mus musculus. 91 samples. Type: Expression profiling by array.
Cross-platform toxicogenomics for the prediction of nongenotoxic hepatocarcinogenesis in rat (miRNA)
GEO Series GSE53083. Rattus norvegicus. 63 samples. Type: Non-coding RNA profiling by array.
crossNN is an explainable framework for cross-platform DNA methylation-based classification of tumors
GEO Series GSE289137. Homo sapiens. 687 samples. Type: Methylation profiling by genome tiling array.
Cross-Platform Validation of Neurotransmitter Release Impairments in Schizophrenia Patient-Derived NRXN1-Mutant Neurons
GEO Series GSE168762. Homo sapiens; Mus musculus. 30 samples. Type: Expression profiling by high throughput sequencing.
Cross-platform reproducibility of oligonucleotide microarray expression profiles
GEO Series GSE1722. Homo sapiens. 24 samples. Type: Expression profiling by array.
Cross-platform toxicogenomics for the prediction of nongenotoxic hepatocarcinogenesis in rat (protein)
GEO Series GSE53084. Mus musculus; Rattus norvegicus. 63 samples. Type: Protein profiling by protein array.
Microarray gene expression analysis: Batch effect removal improves the cross-platform consistency
GEO Series GSE54275. Homo sapiens. 486 samples. Type: Expression profiling by array.
Mergen experiments for cross-platform study including site 2 data
GEO Series GSE4837. Mus musculus. 20 samples. Type: Expression profiling by array.
Cross-platform expression microarray performance in a mouse model of mitochondrial disease therapy
GEO Series GSE18677. Mus musculus. 18 samples. Type: Expression profiling by array.
crossNN is an explainable framework for cross-platform DNA methylation-based classification of tumors [sequencing-based methylome profiling]
GEO Series GSE289246. Homo sapiens. 139 samples. Type: Methylation profiling by high throughput sequencing; Other.
Cross-platform toxicogenomics for the prediction of nongenotoxic hepatocarcinogenesis in rat
GEO Series GSE53085. Mus musculus; Rattus norvegicus. 189 samples. Type: Expression profiling by array; Non-coding RNA profiling by array; Protein profiling by protein array.
Cross-Platform Assessment of Genomic Imbalance Confirms the Clinical Relevance of Genomic Complexity and Reveals Loci with Potential Pathogenic Roles in Diffuse Large B-Cell Lymphoma
GEO Series GSE74025. Homo sapiens. 107 samples. Type: Genome variation profiling by array.
GE Healthcare (Amersham) experiments for cross-platform study including site 2 data
GEO Series GSE4835. Mus musculus. 20 samples. Type: Expression profiling by array.
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.