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Dataset results
4 results for “no-code”
Design of Blockchain-based Applications using Model-Driven Engineering and Low-Code / No-Code Platforms - SLR Dataset
<p>Dataset of the publications collected at various stages of the Structured Literature Review titled "<a href="https://link.springer.com/article/10.1007/s10270-023-01109-1">Design of Blockchain-based Applications using Model-Driven Engineering and Low-Code / No-Code Platforms</a>" published in Software and Systems Modeling:</p> <p>The creation of blockchain-based software applications requires today considerable technical knowledge, particularly in software design and programming. This is regarded as a major barrier in adopting this technology in business and making it accessible to a wider audience. As a solution, low-code and no-code approaches have been proposed that require only little or no programming knowledge for creating full-fledged software applications. In this paper we extend a review of academic approaches from the discipline of model-driven engineering as well as industrial low-code and no-code development platforms for blockchains. This includes a content-based, computational analysis of relevant academic papers and the derivation of major topics. In addition, the topics were manually evaluated and refined. Based on these analyses we discuss the spectrum of approaches in this field and derive opportunities for further research.</p>
Data and code used for 'Thigh-Worn Accelerometry: A Comparative Study of Two No-Code Classification Methods for Identifying Physical Activity Types'
<p>This repository contains all data necessary to reproduce the results for the manuscript titled 'Thigh-Worn Accelerometry: A Comparative Study of Two No-Code Classification Methods for Identifying Physical Activity Types'.</p> <p> </p> <h2><strong>File structure</strong></h2> <p><strong>- analysis</strong></p> <p>The analysis subfolder contains all R scripts used for the study:</p> <p>1. Sample size estimation<br>2. Synchronisation of the timestamps<br>3. Processing of the raw data<br>4. Calculating the interrater agreement<br>5. Calculating all performance metrics and producing the plots</p> <p>In addition, the two subfolders contain the plots and result tables produced when running the scripts.</p> <p> </p> <p><strong>- data</strong></p> <p>The data folder contains all raw data as well as the processed data. A subfolder for each subject contains the video annotations (.eaf), the raw acceleration data (.csv) and the SENS motion classification data (.csv).</p> <p>The ActiPASS subfolder contains the raw acceleration files in binary file format as well as the ActiPASS output.</p> <p>The shiny subfolder contains the R code used for running the shiny app during data collection as well as the logged data and timestamps.</p> <p> </p> <p><span><strong><span>- documents</span></strong></span></p> <p><span>This folder contains any additional documents used in the study.</span></p> <p> </p> <h2><strong><span>Requirements</span></strong></h2> <p>The data processing and analysis was performed in R (Version 4.3.2). To run the full analysis in R, the following packages need to be installed:</p> <ul> <li>tidyverse</li> <li>xml2</li> <li>lubridate</li> <li>here</li> <li>dygraphs</li> <li>hms</li> <li>irr</li> <li>yardstick</li> <li>cowplot</li> <li>gt</li> </ul>
Supplementary Material for "Turning Low-Code Development Platforms into True No-Code with LLMs"
<p>This repository contains additional material supporting the paper titled "Turning Low-Code Development Platforms into True No-Code with LLMs"</p>
No-Code Augmented Reality Platforms
<p>Survey and description of a set of no-code AR authoring tools currently available online (until 27/10/2022), using the collection method of the systematic literature review through web search, described by Stansfield et al. (2016)*. The collection entailed three phases: (1) planning the search, (2) executing the search and (3) screening records for relevance and managing the results.</p> <p><sub>* Stansfield, C., Dickson, K., & Bangpan, M. (2016). Exploring issues in the conduct of website searching and other online sources for systematic reviews: how can we be systematic? <em>Systematic Reviews</em>, <em>5</em>(1), 191. https://doi.org/10.1186/s13643-016-0371-9 </sub></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.