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61 results for “Digital solution”
Harvesting the Value of Data: A Data Architectural Smart Solutions Approach for Enabling Digital Water - Dataset
<p>This database includes the test data used to produce the results for the following article:</p> <p>Harvesting the Value of Data: A Data Architectural Smart Solutions Approach for Enabling Digital Water by S. Seshan, D. Vries, M. Zandvoort, A. W. C. van der Helm, J. Poinapen, Smart Water - WaterAge Magazine, February 16-23</p>
Dataset: First Trust Nasdaq Lux Digital Health Solutions ETF (EKG) 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.
Needs and preferences of different groups of informal caregivers towards designing digital solutions
<p>The project aimed to understand whether young adults who take care of a loved-one (young adult caregivers; YACs) differ in their perceived life balance and psychosocial functioning as compared to young adults without care responsibilities (non-YACs). In addition, this project aimed to understand how YACs evaluated a tool to support informal careg</p> <p>ivers. This tool (“Caregiver Balance”; <a href="https://balans.mantelzorg.nl">https://balans.mantelzorg.nl</a>) is specifically designed to support informal caregivers taking care of a loved-one in the palliative phase and could potentially be adapted to meet the needs of YACs. </p> <p>In this project, we collected data of 74 YACs and 246 non-YACs. Both groups completed questionnaires, and the YACs engaged in a usability test. The questionnaire data was used to compare the perceived life balance and psychological functioning between YACs and non-YACs, aged 18-25 years, and studying in the Netherlands (study 1). Furthermore, we examined the relationship between positive aspects of caregiving and relational factors, in particular, relationship quality and collaborative coping among YACs (study 2). Finally, we conducted a usability study where we interviewed YACs to understand the needs and preferences towards a supportive web-based solution (study 3).</p> <p><strong><em>Table: Study details and associated files</em></strong></p> <table> <tbody> <tr> <td>Number</td> <td>Study Name</td> <td>Study Aim</td> <td>Study Type</td> <td>Type of data</td> <td>Associated Files</td> </tr> <tr> <td>1</td> <td>Perceived life balance among young adult students: a comparison between caregivers and non-caregivers</td> <td>Compare the perceived life balance and psychological functions among student young adult caregivers aged 18-25 years (YACs) with young adult without care responsibilities</td> <td>Survey study</td> <td>Quantitative</td> <td> <ol> <li>ENTWINE_YACs_nonYACsSurvey_RawData</li> <li>ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData</li> <li>ENTWINE_ PerceivedLifeBalanceSurvey _Syntax</li> <li>ENTWINE_YACs_nonYACsSurvey_codebook</li> </ol> </td> </tr> <tr> <td>2</td> <td>Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers</td> <td>Examine the relationship of positive aspects of caregiving with relational factors, in particular, relationship quality and collaborative coping among a particular group of ICGs, young adult caregivers (YACs), aged 18-25 years.</td> <td>Survey study</td> <td>Quantitative</td> <td> <ol> <li>ENTWINE_YACs_nonYACsSurvey_RawData</li> <li>ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData</li> <li>ENTWINE_PositiveAspectsCaregiving_Survey_Syntax</li> <li>ENTWINE_YACs_nonYACsSurvey_codebook</li> </ol> </td> </tr> <tr> <td>3</td> <td>Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool</td> <td>Explore (i) challenges and support needs of YACs in caregiving, (ii) their needs towards the content of the ‘MantelzorgBalans’ tool, and (iii) issues they encountered in using the tool and their preferences for adaptation of the tool.</td> <td>Usability study</td> <td> <p>Qualitative and Quantitative</p> </td> <td> <ol> <li>ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData [to be determined whether data can be shared]</li> <li>ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData</li> </ol> </td> </tr> </tbody> </table> <p><strong>Description of the files to be uploaded</strong></p> <p><strong><em>Study 1: Perceived life balance among young adult students: a comparison between caregivers and non-caregivers</em></strong></p> <ol> <li>ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw, pseudonomyzed survey data. The following cleaned dataset ‘ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData’ was generated from this raw data.</li> <li>ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData: SPSS file with the cleaned dataset having the following metadata - <ol> <li><strong>Population:</strong> young adult caregivers and young adult non-caregivers aged 18-25 years studying in the Netherlands;</li> <li><strong>Number of participants:</strong> 320 participants in total (74 young adult caregivers and 246 young adult non-caregivers)</li> <li><strong>Time point of measurement:</strong> Data was collected from December 2020 till March 2022</li> <li><strong>Type of data:</strong> quantitative</li> <li><strong>Measurements included, topics covered:</strong> perceived life balance (based on the Occupational balance questionnaire [1]), burnout (Burnout Assessment Tool [2]), negative and positive affect (Positive and Negative Affect Schedule [3]), and life satisfaction (Satisfaction with Life Scale [4])</li> <li><strong>Short procedure conducted to receive data:</strong> online survey on Qualtrics platform</li> </ol> </li> <li>SPSS syntax file ‘ENTWINE_ PerceivedLifeBalanceSurvey _Syntax’ was used to clean and analyse ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset</li> <li>ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset</li> </ol> <p><strong><em>Study 2: Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers</em></strong></p> <ol> <li>ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw survey data. The following cleaned dataset ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ was generated from this raw data.</li> <li>ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData: SPSS file with the cleaned dataset having the following metadata - <ol> <li><strong>Population:</strong> young adult caregivers aged 18-25 years studying in the Netherlands;</li> <li><strong>Number of participants:</strong> 74 young adult caregivers</li> <li><strong>Time point of measurement:</strong> Data was collected from December 2020 till March 2022</li> <li><strong>Type of data:</strong> quantitative</li> <li><strong>Measurements included, topics covered:</strong> positive aspects of caregiving (positive aspects of caregiving scale [5]), relationship quality (Relationship Assessment Scale [6]), collaborative coping (Perception of Collaboration Questionnaire [7] )</li> <li><strong>Short procedure conducted to receive data:</strong> online survey on Qualtrics platform.</li> </ol> </li> </ol> <ol> <li>SPSS syntax file ‘ENTWINE_PositiveAspectsCaregiving_Survey_Syntax’ was used to clean and analyse ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ dataset.</li> <li>ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData dataset.</li> </ol> <p><strong><em>Study 3: Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool </em></strong></p> <ol> <li>ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData: Pseudonymized word file including 13 transcripts having the qualitative data from interview and usability testing with the following metadata – <ol> <li><strong>Population:</strong> young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total</li> <li><strong>Time point of measurement:</strong> data was collected from October 2021 till February 2022</li> <li><strong>Type of data:</strong> qualitative and quantitative</li> <li><strong>Measurements included, topics covered: </strong>Caregiving challenges, support needs and barriers, usability needs, preferences and issues towards eHealth tool</li> <li><strong>Short procedure conducted to receive data</strong>: Online interviews</li> </ol> </li> <li>ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData: Excel sheet having the quantitative questionnaire raw data with the following metadata <ol> <li><strong>Population:</strong> young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total</li> <li><strong>Time point of measurement:</strong> data was collected from October 2021 till February 2022</li> <li><strong>Type of data:</strong> qualitative and quantitative</li> <li><strong>Measurements included, topics covered: </strong>User experience (user experience questionnaire [8]), satisfaction of using the web-based tool (After scenario questionnaire [9]), Intention of use and persuasive potential of the eHealth tool (persuasive potential questionnaire [10])</li> <li><strong>Short procedure conducted to receive data</strong>: Online questionnaire</li> </ol> </li> </ol> <p><strong>Data collection details</strong></p> <ol> <li>All data was collected, processed, and archived in accordance with the General Data Protection Regulation (GDPR) and the FAIR (Findable, Accessible, Interoperable, Reusable) principles under the supervision of the Principal Investigator.</li> <li>The principal researcher and a team of experts (supervisors) in the field of health psychology and eHealth (University of Twente, The Netherlands) reviewed the scientific quality of the research. The studies were piloted and tested before starting the collection of the data. For the survey study, the researchers monitored the data collection weekly to ensure it was running smoothly.</li> <li>The ethical review board, Centrale Ethische Toetsingscommissie of the University Medical Center Groningen, The Netherlands (CTc), granted approval for this research (Registration number: 202000623).</li> <li>Participants digitally signed informed consent for participating in the study.</li> </ol> <p><strong>Terms of use</strong></p> <ol> <li>Interested persons can send a data request by contacting the principal investigator (Prof. dr. Mariët Hagedoorn, University Medical Center Groningen, the Netherlands <a href="mailto:mariet.hageboorn@umcg.nl">mariet.hageboorn@umcg.nl</a>).</li> <li>Interested persons must provide the research plan (including the research question, methodology, and analysis plan) when requesting for the data.</li> <li>The principal investigator reviews the research plan on its quality and fit with the data and informs the interested person(s).</li> <li>(Pseudo)anonymous data of those participants who agreed on the reuse of their data is available on request for 15 years from the time of completion of the PhD project.</li> <li>Data will be available in Excel or SPSS format alongside the variable codebook after the completion of this PhD project and publication of the study results.</li> </ol> <p><strong>References</strong></p> <p>1. Wagman P, Håkansson C. Introducing the Occupational Balance Questionnaire (OBQ). Scand J Occup Ther 2014;21(3):227–231. PMID:24649971</p> <p>2. Schaufeli WB, Desart S, De Witte H. Burnout assessment tool (Bat)—development, validity, and reliability. Int J Environ Res Public Health 2020;17(24):1–21. PMID:33352940</p> <p>3. Watson D, Clark LA, Tellegen A. Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales. J Pers Soc Psychol 1988;54(6):1063–1070. PMID:3397865</p> <p>4. Pavot W, Diener E, Colvin CR, Sandvik E. Further Validation of the Satisfaction With Life Scale; Evidence for the Cross-Method Convergence of Well-Being Measures. J Pers Assess 1991;57(1):149–161. PMID:1920028</p> <p>5. Tarlow BJ, Wisniewski SR, Belle SH, Rubert M, Ory MG, Gallagher-Thompson D. Positive aspects of caregiving: Contributions of the REACH project to the development of new measures for Alzheimer’s caregiving. Res Aging 2004;26(4):429–453. [doi: 10.1177/0164027504264493]</p> <p>6. Hendrick SS, Dicke A, Hendrick C. The relationship assessment scale. J Soc Pers Relat 1998;15(1):137–142. [doi: 10.1177/0265407598151009]</p> <p>7. Berg CA, Schindler I, Maharajh S. Adolescents’ and Mothers’ Perceptions of the Cognitive and Relational Functions of Collaboration and Adjustment in Dealing With Type 1 Diabetes. J Fam Psychol 2008;22(6):865–874. PMID:19102607</p> <p>8. Laugwitz B, Held T, Schrepp M. Construction and evaluation of a user experience questionnaire. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2008;5298 LNCS:63–76. [doi: 10.1007/978-3-540-89350-9_6]</p> <p>9. Lewis JR. Psychometric evaluation of an after-scenario questionnaire for computer usability studies. ACM SIGCHI Bull 1991;23(1):78–81. [doi: 10.1145/122672.122692]</p> <p>10. Meschtscherjakov A, Gärtner M, Mirnig A, Rödel C, Tscheligi M. The Persuasive Potential Questionnaire (PPQ): Challenges, drawbacks, and lessons learned. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2016;9638:162–175. [doi: 10.1007/978-3-319-31510-2_14]</p> <p> </p>
Digital Contact Tracing: Overview of technological solutions for the fight against pandemics
<p>In late 2019, Covid-19 emerged and was soon declared a pandemic causing until now a massive health disruption and a huge impact on the global economy. Several governments around the world are still forced to take containment measures to curb the spread of the virus including partial or full lockdowns. At the same time, they rely heavily on human resources to perform manual Contact Tracing (CT) for alerting known contacts of the confirmed cases and breaking the infection chains early enough. However, CT does not scale well when the cases increase exponentially, due to the limited capacity of national public health authorities, and cannot identify possible <em>hidden</em> infections due to random encounters with strangers in crowded spaces such as restaurants, bars, theaters, public transportation, etc. To this end, Digital Contact Tracing (DCT) is becoming increasingly popular to enhance and empower CT, enabling automatic and faster identification and notification of exposed users. This presentation will first overview the different generations of DCT solutions from the privacy-invasive use of subscriber location data provided by cellular operators, to location monitoring mobile apps on GPS-equipped smartphones, to privacy-preserving mobile apps based on <em>proximity</em> sensing through Bluetooth. It will discuss the findings of recent studies with regards to the effectiveness of DCT and debate whether it has been – or has the potential to become – a game changer. Finally, it will outline the latest developments and trends in this active research field that are of interest to the IPIN community including <em>presence</em> tracing that aims to notify anonymously those users that have been in the same place (especially indoors) with an infected user, without necessarily satisfying the proximity constraint.</p>
Participant responses - Enhancing Workplace Well-being through Digital Solutions
Open the record for dataset details and reuse information.
Digital solutions and early warning system for decision support and risk management in water reuse for irrigation
<p>Video presentation for IWA World Water Congress & Exhibition, 11-15 September 2022, Copenhagen, Denmark.</p>
Me & You-Tech: A Socio-Ecological Solution to Teen Dating Violence for the Digital Age
ClinicalTrials.gov study NCT05225727. IPD Sharing: NO. Countries: 1. Publications: 0.
Clinical Impact and Utility of Digital Health Solutions in Participants Receiving Systemic Treatment in Clinical Practice
ClinicalTrials.gov study NCT05694013. IPD Sharing: YES. Countries: 5. Publications: 1.
Connecting digital citizen science data quality issue to solution mechanism table
<p>A table explaining how to solve data quality issues in digital citizen science. A total of 35 issues and 64 mechanisms to solve them are proposed</p>
PMcardio ECG Image Database (PM-ECG-ID): A Diverse ECG Database for Evaluating Digitization Solutions
<p>The dataset presents the collection of a diverse electrocardiogram (ECG) database for testing and evaluating ECG digitization solutions. The Powerful Medical ECG image database was curated using 100 ECG waveforms selected from the PTB-XL Digital Waveform Database and various images generated from the base waveforms with varying lead visibility and real-world paper deformations, including the use of different mobile phones, bends, crumbles, scans, and photos of computer screens with ECGs. The ECG waveforms were augmented using various techniques, including changes in contrast, brightness, perspective transformation, rotation, image blur, JPEG compression, and resolution change. This extensive approach yielded 6,000 unique entries, which provides a wide range of data variance and extreme cases to evaluate the limitations of ECG digitization solutions and improve their performance, and serves as a benchmark to evaluate ECG digitization solutions.<br><br>PM-ECG-ID database contains electrocardiogram (ECG) images and their corresponding ECG information. The data records are organized in a hierarchical folder structure, which includes metadata, waveform data, and visual data folders. The contents of each folder are described below:<br><br></p> <ul> <li><strong>metadata.csv:</strong> <br>This file serves as a key-to-key bridge between the image data and the corresponding ECG information. It contains the following columns: <ul> <li><strong>Image name: </strong>image name with extension,</li> <li><strong>ECG ID:</strong> this ID corresponds to the specific ECG identifier from the original PTB-XL dataset. Under this ID you can find a cutout array in the <em>leads.npz </em>and <em>rhythms.npz,</em></li> <li><strong>Image relative path: </strong>relative path to the image in question,</li> <li><strong>Image page: </strong>page number of the particular image (starting from 0),</li> <li><strong>ECG number of pages: </strong>number of pages in the whole ECG,</li> <li><strong>ECG number of columns per page: </strong>number of columns per page in the ECG,</li> <li><strong>ECG number of rows per page: </strong>number of rows in the ECG,</li> <li><strong>ECG number of rhythm leads: </strong>number of rhythms in the ECG,</li> <li><strong>ECG format: </strong>short version of the ECG format.</li> </ul> </li> <li><strong>data </strong>folder: <ul> <li><strong>leads.npz: </strong>NPZ file containing all underlying cutout lead signals; each signal is there under its ECG ID.</li> <li><strong>rhythms.npz:</strong> NPZ file containing all underlying rhythm signals; each signal is there under its ECG ID. If no rhythm lead is in the ECG, you will find an empty array in the NPZ.</li> </ul> </li> <li><strong>visual_data</strong> folder: <br>This folder contains subfolders for various image data, including augmented photos and visualization and different types of photos of ECG printouts. The subfolders are organized based on the specific augmentation or type of photograph. These folders contain images with various augmentation settings, such as different levels of blur, brightness, contrast, padding, perspective transformation, resolution scaling, and rotation. The database is organized in a way that allows for easy navigation and understanding of the different augmentations applied to the image data. Each of these subfolders contains images relevant to the specific augmentation or type of photograph. The <em>metadata.csv</em> file provides a direct link to each image and its associated ECG information.</li> </ul>
supplement videos of Solution for Mass Production of High-Throughput Digital Microfluidic Chip Based on Thin Film Transistor Technology
<p>supplement videos of Solution for Mass Production of High-Throughput Digital Microfluidic Chip Based on Thin Film Transistor Technology</p>
Digital Solution for Salutogenic Brain Health (DiSaB): A Pilot Protocol for Clinical Implementation
ClinicalTrials.gov study NCT06582316. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Development and Measurement of the Effects of a Digital Education Solution for Patients Undergoing Total Hip or Knee Arthroplasty
ClinicalTrials.gov study NCT07345299. IPD Sharing: NO. Countries: 1. Publications: 5.
EARLY-COGN^3 - Smart Digital Solutions for EARLY Treatment of COGnitive Disability: a Neuropsychological, Neurophysiological and Neurobiological Perspective in Chronic Neurological Diseases - PNRR-MCN
ClinicalTrials.gov study NCT06657274. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Assessment of the Effectiveness, Socio-economic Impact and Implementation of a Digital Solution for Severe Patients
ClinicalTrials.gov study NCT05575336. IPD Sharing: NO. Countries: 5. Publications: 0.
Impact of a Digital Solution (CardiCare™) on Cardiorespiratory Fitness Improvement in Patients Discharged From a Phase 2 Cardiac Rehabilitation Following an Acute Coronary Syndrome
ClinicalTrials.gov study NCT04294940. IPD Sharing: Not stated. Countries: 4. Publications: 1.
Co-design of a Digital Health Solution to Monitor Persisting Symptoms Related to COVID-19 Using Voice
ClinicalTrials.gov study NCT05546918. IPD Sharing: NO. Countries: 1. Publications: 4.
A Dialectical Behavioral Therapy Digital Health Solution for Outpatients Seeking Support for Substance Use
ClinicalTrials.gov study NCT05094440. IPD Sharing: NO. Countries: 1. Publications: 1.
Long-term Maintenance Benefits of a Pulmonary Rehabilitation Program Using a Mobile Digital Solution: a Prospective, Randomized, Controlled, Multicenter Study in a Population of COPD Patients
ClinicalTrials.gov study NCT04550741. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Digital Solution for Individuals With Non Alcoholic Fatty Liver Disease
ClinicalTrials.gov study NCT05426382. IPD Sharing: NO. Countries: 1. Publications: 1.
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.