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Data Sharing (See Research Ethics, Data Sharing; Virus Sharing)

Henk ten Have, Maria do Céu Patrão Neves · 2021

Cited by 0

Sharing data, sharing methods, sharing science.

Sergio Pantano · MethodsX · 2021

Cited by 4Open access

Data sharing

Richard B. Slatcher, Giulia Zoppolat, Jacqueline Rodriguez et al. · OSF Preprints (OSF Preprints) · 2020

Cited by 0

Sharing Data

Takayama, Tomohiro, Unagami, Kohei, Omoto, Kazuya et al. · Figshare · 2023

Data from an questionnaire-based survey about knowledge, attitude and practice (KAP) survey regarding COVID-19 and vaccine intentions in kidney transplant recipients at the Yochomachi Clinic, Tokyo, Japan

Cited by 0Open access

Sharing Data

Tim Menzies, Ekrem Kocagüneli, Leandro L. Minku et al. · Elsevier eBooks · 2015

Cited by 14

Data Sharing

Zhang Jianhua · OSF Preprints (OSF Preprints) · 2025

Data shared for the submission to Learning & Instruction.

Cited by 0

Data sharing

Daradkeh, Yousef Ibrahim · CERN Document Server (European Organization for Nuclear Research) · 2018

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Data sharing

Rhonda Nicole Balzarini, Jacqueline Rodriguez-Stanley, Richard B. Slatcher et al. · OSF Preprints (OSF Preprints) · 2022

Cited by 0Open access

Sharing data

G. Hughes · Emergency Medicine Journal · 2009

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Data sharing

Data Sharing · Zenodo (CERN European Organization for Nuclear Research) · 2023

Data sharing

Cited by 0Open access

Data Sharing

Mathias Krebs · OSF Preprints (OSF Preprints) · 2023

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Data Sharing

Recker, Astrid · DSpace repository (University of Tartu) · 2015

Cited by 0Open access

Data Sharing

David L. Rodrigues, Richard de Visser, Diniz Lopes et al. · OSF Preprints (OSF Preprints) · 2021

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sharing data

Chenlu Ma · OSF Preprints (OSF Preprints)

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Data Sharing

nursini, nursini · Figshare · 2021

The data provided in this repository is the dataset of the article “Disaggregated Approach of Local Government Expenditures and Poverty Reduction: Empirical Evidence from Panel Data Analysis”. The raw data are gathered from The Ministry of Finance in Indonesia and the Central Bureau of Statistic Indonesia. In addition, this article uses a panel data set of 24 districts/cities in South Sulawesi for the period of 2015 to 2020.

Cited by 0Open access

Data Sharing

Hanzlíková, Dagmar, Janíček, Milan · Zenodo (CERN European Organization for Nuclear Research) · 2022

Presentation from a lecture on research data sharing.

Cited by 0Open access

Data Sharing

Dan L. Longo, Jeffrey M. Drazen · New England Journal of Medicine · 2016

The aerial view of the concept of data sharing is beautiful. What could be better than having high-quality information carefully reexamined for the possibility that new nuggets of useful data are lying there, previously unseen? The potential for leveraging existing results for even more benefit pays appropriate increased tribute to the patients who put themselves at risk to generate the data. The moral imperative to honor their collective sacrifice is the trump card that takes this trick. However, many of us who have actually conducted clinical research, managed clinical studies and data collection and analysis, and curated data sets have . . .

Cited by 428Open access

Data Sharing

Ryan Dwyer · OSF Preprints (OSF Preprints)

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Sharing data

Kwai Han Yoo · Figshare · 2020

This is data set for PONE-D-20-00626R2, DOI: 10.1371/journal.pone.0235503

Cited by 0Open access

Data sharing

Jin, Zihan · Figshare · 2025

Inspired by the natural mechanism of taste perception, artificial bionic electronic tongues have successfully enabled the detection and classification of various tastes. The liquid-solid contact electrification (LSCE) effect has emerged as a highly effective approach for developing self-inducted electronic tongues. However, droplet-based sensing structures often face challenges related to internal and environmental interferences, compromising their stability and repeatability. In this work, we developed a monolithically integrated self-inducted microfluidic bionic electronic tongue (SMET), combining the LSCE effect with deep learning algorithms to achieve highly reliable and intelligent sample identification and concentration detection. The incorporation of a multiplexed microchannel structure significantly reduced the required liquid sample volume while simultaneously increasing the electrical output amplitude (up to 10 V at multitone wave excitation), thereby enhancing sensitivity. Instead of micropumps, miniaturized exciters were employed as SMET drivers to generate multiple excitation waveforms, producing various signal types to improve specific algorithmic accuracy. The SMET achieved over 93% classification accuracy for five taste element samples (glacial acetic acid, anhydrous dextrose, quinine, edible chili essence, sodium chloride) and five concentrations of sodium chloride solutions using a single waveform signal, reaching 100% accuracy with the fusion of multiple waveform signals. Furthermore, the SMET was used to detect more than ten different taste samples, each exhibiting distinct signal variations. Thus, due to its ultra-high sensitivity to the electrical properties of liquids, SMET enables accurate and rapid analysis of liquid samples with high reliability, positioning it as a promising tool in the field of rapid liquid detection.

Cited by 0Open access