Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities

Neural Networks: A Comprehensive Foundation

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Analysis of Incomplete Multivariate Data

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Classical and Modern Regression With Applications.

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The Dip Test of Unimodality

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A product perspective on total data quality management

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Identifying Multiple Outliers in Multivariate Data

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Post-randomisation exclusions: the intention to treat principle and excluding patients from analysis

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Attrition in longitudinal studies

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CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

Lucas, A., Palmiero, N. E., Mcguigan, J., Passero, K., Zhou, J., Orie, D., Ritchie, M. D., & Hall, M. A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01240 · Cited by 20

External Validation of Model-Based Dosing Guidelines for Vancomycin, Gentamicin, and Tobramycin in Critically Ill Neonates and Children: A Pragmatic Two-Center Study

Hartman, S. J. F., Orriëns, L. B., Zwaag, S. M., Poel, T., de Hoop‐Sommen, M. A., & de Wildt, S. N. (2020) · Pediatric Drugs · doi:10.1007/s40272-020-00400-8 · Cited by 19

Maritime education factors and presenteeism: a comparative quantitative study

Ferritto, V. R. (2016) · WMU Journal of Maritime Affairs · doi:10.1007/s13437-015-0098-9 · Cited by 20

Food Safety in Informal Markets: How Knowledge and Attitudes Influence Vendor Practices in Namibia

Sheehama, W. L. N., & Singh, T. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22040631 · Cited by 18

Clinical relevance assessment of animal preclinical research (RAA) tool: development and explanation

Gurusamy, K. S., Moher, D., Loizidou, M., Ahmed, I., Avey, M. T., Barron, C. C., Davidson, B. R., Dwek, M. V., Gluud, C. N., Jell, G., et al. (2021) · PeerJ · doi:10.7717/peerj.10673 · Cited by 18

Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks

Wanjawa, B., Wanzare, L., Indede, F., McOnyango, O., Ombui, E., & Muchemi, L. (2023) · LDV-Forum/Journal for language technology and computational linguistics · doi:10.21248/jlcl.36.2023.243 · Cited by 28

How to deal with Uncertainty in Machine Learning for Medical Imaging?

Gillmann, C., Saur, D., & Scheuermann, G. (2021) · IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) · doi:10.1109/trex53765.2021.00014 · Cited by 18

Physical Activity Among Foreign-Born Older Adults in Canada: A Mixed-Method Study Conducted in Five Languages

Tong, C., Sims‐Gould, J., & McKay, H. A. (2017) · Journal of Aging and Physical Activity · doi:10.1123/japa.2017-0105 · Cited by 19

Why so Emotional? An Analysis of Emotional Bot-generated Content on Twitter

Kušen, E., & Strembeck, M. (2018) · International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS) · doi:10.5220/0006699500130022 · Cited by 18

Prediction of groundwater quality assessment by integrating boosted learning with DE optimizer

Subudhi, S., Pati, A. K., Bose, S., Sahoo, S., Pattanaik, A., Acharya, B. M., & Thakur, R. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-30122-1 · Cited by 17

Improving farm decisions: The application of data engineering techniques to manage data streams from contemporary dairy operations

Wangen, S. R., Zhang, F., Fadul-Pacheco, L., da Silva, T. É., & Cabrera, V. E. (2021) · Livestock Science · doi:10.1016/j.livsci.2021.104602 · Cited by 17

Contextual learning for energy forecasting in buildings

Jozi, A., Pinto, T. D. O., & Vale, Z. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107707 · Cited by 17

Preparing CT imaging datasets for deep learning in lung nodule analysis: Insights from four well-known datasets

Wang, J., Sourlos, N., Zheng, S., van der Velden, N., Pelgrim, G. J., Vliegenthart, R., & van Ooijen, P. M. A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17104 · Cited by 19

Investigation of relationships between body weight and age among domestic cats stratified by breed and sex

Campigotto, A. J., Poljak, Z., STONE, E. A., Stacey, D., & Bernardo, T. M. (2019) · Journal of the American Veterinary Medical Association · doi:10.2460/javma.255.2.205 · Cited by 17

Outlier detection and missing data filling methods for coastal water temperature data

Cho, H.-Y., Oh, J. H., Kim, K. O., & Shim, J. S. (2013) · Journal of Coastal Research · doi:10.2112/si65-321.1 · Cited by 17

A data-driven standardised generalisable methodology to validate a large energy performance Certification dataset: A case of the application in Ireland

Raushan, K., Uidhir, T. M., Llorens, M., Norton, B., & Ahern, C. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114774 · Cited by 16

Too Much Information

Talbert, S., & Sole, M. L. (2013) · Clinical Nurse Specialist · doi:10.1097/nur.0b013e3182819111 · Cited by 16

Use of Urban Residential Community Parks for Stress Management During the COVID-19 Lockdown Period in China

Kang, N., Bell, S., Thompson, C. W., Zheng, M., Xu, Z., & Sun, Z. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.816417 · Cited by 20

Hypothesis test for paired samples in the presence of missing data

Martínez‐Camblor, P., Corral, N., & de la Hera, J. M. (2012) · Journal of Applied Statistics · doi:10.1080/02664763.2012.734795 · Cited by 16

A review of routinely collected data studies in urology: Methodological considerations, reporting quality, and future directions

Welk, B. K., & Kwong, J. (2017) · Canadian Urological Association Journal · doi:10.5489/cuaj.4101 · Cited by 16

A 40-year study of child maltreatment over the early life course predicting psychiatric morbidity, using linked birth cohort and administrative health data: protocol for the Childhood Adversity and Lifetime Morbidity (CALM) study

Kisely, S. R., Leske, S., Arnautovska, U., Siskind, D. J., Warren, N., Northwood, K. S., Suetani, S., & Najman, J. M. (2023) · BJPsych Open · doi:10.1192/bjo.2023.29 · Cited by 15

A review on the significance of body temperature interpretation for early infectious disease diagnosis

Zaman, N. I. D., Hau, Y. W., Leong, M. C., & Al‐Ashwal, R. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10528-x · Cited by 16

Relationship between smartphone use and sedentary behavior: a school-based study with adolescents

Lourenço, C. L. M., de Souza, T. F., & Mendes, E. L. (2019) · Revista Brasileira de Atividade Física & Saúde · doi:10.12820/rbafs.24e0078 · Cited by 15

Network intrusion detection system for Internet of Things based on enhanced flower pollination algorithm and ensemble classifier

Gangula, R., Mohan, M., V, & Ranjeeth Kumar, M. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7103 · Cited by 15

A Domain-Expert Centered Process Model for Knowledge Discovery in Medical Research: Putting the Expert-in-the-Loop

Girardi, D., Kueng, J., & Holzinger, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23344-4_38 · Cited by 15

Exploring the Associations of Walking Behavior with Neighborhood Environments by Different Life Stages: A Cross-Sectional Study in a Smaller Chinese City

Sun, Z., Lai, K. Y., Bell, S., Scott, I., & Zhang, X. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17010237 · Cited by 15

Size fractionation, chemotaxonomic groups and bio-optical properties of phytoplankton along a transect from the Mediterranean Sea to the SW Atlantic Ocean

Nunes, S. O., Pérez, G. L., Latasa, M., Zamanillo, M., Delgado, M., Ortega‐Retuerta, E., Marrasé, C., Simó, R., & Estrada, M. (2019) · Scientia Marina · doi:10.3989/scimar.04866.10a · Cited by 14

Automation of cleaning and ensembles for outliers detection in questionnaire data

Uher, V., Dráždilová, P., Platoš, J., & Baďura, P. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117809 · Cited by 14

Evaluation of reporting quality for observational studies using routinely collected health data in pharmacovigilance

Nie, X., Zhang, Y., Wu, Z., Jia, L., Wang, X., Langan, S., Benchimol, E. I., & Peng, X. (2018) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2018.1484106 · Cited by 14

The Contribution of Physical Education to Physical Activity Within a Comprehensive School Health Promotion Program

McLoughlin, G. M., & Graber, K. C. (2020) · Research Quarterly for Exercise and Sport · doi:10.1080/02701367.2020.1765952 · Cited by 16

Older age and online health information search behaviors: The mediating influence of executive functions

Kordovski, V. M., Tierney, S. M., Rahman, S., Medina, L. D., Babicz, M. A., Yoshida, H., Holcomb, E. M., Cushman, C., & Woods, S. P. (2021) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/13803395.2021.1990866 · Cited by 13

The role of credibility and negative feelings in comparative perceptual bias related to environmental hazards

Sarabia-Sánchez, F. J., & Rodríguez-Sánchez, C. (2016) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2016.04.011 · Cited by 13

Predictive Importance of Social Contacts on U.S. Veteran Suicide Safety Plans

Chalker, S. A., Parrish, E. M., Ceren, C. S. M., Depp, C. A., Goodman, M. S., & Doran, N. (2022) · Psychiatric Services · doi:10.1176/appi.ps.202100699 · Cited by 14

Insiders, Outsiders, and the Editing of Inconsistent Survey Data

Sana, M., & Weinreb, A. A. (2008) · Sociological Methods & Research · doi:10.1177/0049124107313857 · Cited by 13

Psychometric testing of the revised “Families’ Importance in Nursing Care—Nurses’ Attitudes instrument”

Mackie, B. R., Marshall, A. P., Mitchell, M. L., & Ireland, M. J. (2017) · Journal of Advanced Nursing · doi:10.1111/jan.13442 · Cited by 13

Moderating Effect of Employee Participation on Factors that Determine Effective Performance Appraisal (EPA): Data Screening and Preliminary Analysis

Babagana, S. A., Mat, N., & Ibrahim, H. B. (2019) · International Journal of Academic Research in Business and Social Sciences · doi:10.6007/ijarbss/v9-i4/5826 · Cited by 12

The Embodied Teaching of Spatial Terms: Gestures Mapped to Morphemes Improve Learning

Janzen Ulbricht, N. (2020) · Frontiers in Education · doi:10.3389/feduc.2020.00109 · Cited by 13

Efficacy, Use, and Usability of the VIP-HANA App for Symptom Self-management in PLWH with HANA Conditions

Schnall, R., Porras, T., Cho, H., Jia, H., Siegel, K., Mohr, D. C., Henry, S. B., & Olender, S. (2021) · AIDS and Behavior · doi:10.1007/s10461-020-03096-6 · Cited by 13

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

Portela, A. S. D., Saxena, V., Rosenn, E. H., Wang, S., Masieri, S., Palmieri, J., & Pasinetti, G. M. (2024) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.202300605 · Cited by 12

Hitchhikers’ guide to analysing bird ringing data

Harnos, A., Fehérvári, P., & Csörgő, T. (2015) · Ornis Hungarica · doi:10.1515/orhu-2015-0018 · Cited by 12

Interpretation and display of research results

Kulkarni, D. K. (2016) · Indian Journal of Anaesthesia · doi:10.4103/0019-5049.190622 · Cited by 12

Performance Analysis of Multilayer Perceptron Neural Network Models in Week-Ahead Rainfall Forecasting

Velasco, L. C., Ruth, P. N. J., Shahin, M., Bryan, F., & Junneil, C. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100374 · Cited by 12

Data Cleaning

Calabrese, B. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20458-5 · Cited by 12

Are accuracy discernment and sharing of COVID-19 misinformation associated with older age and lower neurocognitive functioning?

Matchanova, A., Woods, S. P., Neighbors, C., Medina, L. D., Podell, K., Beltran‐Najera, I., Alex, C., Babicz, M. A., & Thompson, J. L. (2023) · Current Psychology · doi:10.1007/s12144-023-04464-w · Cited by 13

Risk Prediction Model for Chronic Kidney Disease in Thailand Using Artificial Intelligence and SHAP

Tsai, M., Lojanapiwat, B., Chang, C., Noppakun, K., Khumrin, P., Li, S.-H., Lee, C.-Y., Lee, H.-C., & Khwanngern, K. (2023) · Diagnostics · doi:10.3390/diagnostics13233548 · Cited by 11

A Novel Clinical Risk Score to Predict Vasoplegia After Adult Cardiac Surgery

Miles, T. J., Guinn, M. T., Suero, O. R., Rosengart, T. K., Moon, M. R., Coselli, J. S., Ghanta, R. K., & Chatterjee, S. (2025) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2025.02.022 · Cited by 11

Effects of a Disability Awareness Clinic on Boy Scouts’ Attitudes toward Disability

Anderson, J. J., Wilbers, L. E., Loepp, E. D., Malek, S., Scherer, K. J., & Bero, C. M. (2019) · Journal of Social Service Research · doi:10.1080/01488376.2019.1705959 · Cited by 11

Time Distances to Residential Food Amenities and Daily Walking Duration: A Cross-Sectional Study in Two Low Tier Chinese Cities

Sun, Z., Scott, I., Bell, S., Zhang, X., & Wang, L. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18020839 · Cited by 11

Backpropagation Neural Network for Processing of Missing Data in Breast Cancer Detection

Zhang, L., Cui, H., Liu, B., Zhang, C., & Horn, B. K. P. (2021) · IRBM · doi:10.1016/j.irbm.2021.06.010 · Cited by 11

Organizing and Analyzing Data from the SHARE Study with an Application to Age and Sex Differences in Depressive Symptoms

Lusa, L., & Huebner, M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18189684 · Cited by 11

The "Measuring Outcomes of Clinical Connectivity" (MOCC) Trial: Investigating Data Entry Errors in the Electronic Primary Care Research Network (ePCRN)

Fontaine, P., Mendenhall, T. J., PETERSON, K. A., & Speedie, S. M. (2007) · The Journal of the American Board of Family Medicine · doi:10.3122/jabfm.2007.02.060069 · Cited by 11

Investigating the management of alcohol‐related presentations in an Australian teaching hospital

Qian, S., Irani, M. Z., Brighton, R. M., Yeo, W. W., Reid, D., Sinclair, B., Bresnahan, S., Lynch, P., Feng, X., & Yu, P. (2019) · Drug and Alcohol Review · doi:10.1111/dar.12906 · Cited by 10

Data Cleansing: An Omission from Data Analytics Coursework

Snyder, J. (2019) · Information Systems Education Journal · Cited by 10

Making a distinction between data cleaning and central monitoring in clinical trials

Love, S., Yorke-Edwards, V., Díaz-Montaña, C., Murray, M. L., Masters, L., Gabriel, M., Joffe, N., & Sydes, M. R. (2021) · Clinical Trials · doi:10.1177/1740774520976617 · Cited by 10

Targeting Non-obvious Errors in Death Certificates

Johansson, L. A. (2008) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 10

Sensor data cleaning for applications in dairy herd management and breeding

Schodl, K., Stygar, A. H., Steininger, F., & Egger-Danner, C. (2024) · Frontiers in Animal Science · doi:10.3389/fanim.2024.1444948 · Cited by 9

Building a Research Data Repository for Chronic Condition Self-Management Using Harmonized Data

Moore, S. M., Musil, C. M., Alder, M. L., Pignatiello, G. A., Higgins, P. A., Webel, A. R., & Wright, K. (2020) · Nursing Research · doi:10.1097/nnr.0000000000000435 · Cited by 9

Factors influencing Australian nursing and midwifery students COVID-19 vaccination intentions

Ford, A. J., Heidke, P., Kurup, C., Wirihana, L., Kroll, J., & Calleja, P. (2023) · Vaccine X · doi:10.1016/j.jvacx.2023.100352 · Cited by 9

Prediction of cyanotic and acyanotic congenital heart disease using machine learning models

Shahid, S., Khurram, H., Lim, A., Shabbir, M. F., & Billah, B. (2024) · World Journal of Clinical Pediatrics · doi:10.5409/wjcp.v13.i4.98472 · Cited by 10

Open research practices: unintended consequences and suggestions for averting them. (Commentary on the Peer Reviewers' Openness Initiative)

Bishop, D. (2016) · Royal Society Open Science · doi:10.1098/rsos.160109 · Cited by 12

The myths of ‘big data’ in health care

Jacofsky, D. (2017) · The Bone & Joint Journal · doi:10.1302/0301-620x.99b12.bjj-2017-0939 · Cited by 9

Intelligent Monitoring and Controlling of Public Policies Using Social Media and Cloud Computing

Singh, P., Dwivedi, Y. K., Kahlon, K. S., & Sawhney, R. S. (2018) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-04315-5_11 · Cited by 9

Outlier detection for questionnaire data in biobanks

Sakurai, R., Ueki, M., Makino, S., Hozawa, A., Kuriyama, S., Takai‐Igarashi, T., Kinoshita, K., Yamamoto, M. P., & Tamiya, G. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz012 · Cited by 9

Comprehensive Trends and Patterns of Antihypertensive Prescriptions Using a Nationwide Claims Database in Korea

Jung, M., Choo, E., & Lee, S. (2020) · Clinical Epidemiology · doi:10.2147/clep.s265966 · Cited by 9

ImageDC: Image Data Cleaning Framework Based on Deep Learning

Zhang, Y., Jin, Z., Liu, F., Zhu, W., Mu, W., & Wang, W. (2020) · International Conference on Artificial Intelligence and Information Systems (ICAIIS) · doi:10.1109/icaiis49377.2020.9194803 · Cited by 10

The National ReferAll Database: An Open Dataset of Exercise Referral Schemes Across the UK

Steele, J., Wade, M., Copeland, R. J., Stokes, S., Stokes, R., & Mann, S. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18094831 · Cited by 9

Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis

Grady, S. K., Dojcsak, L., Harville, E. W., Wallace, M. E., Vilda, D., Donneyong, M. M., Hood, D. B., Valdez, R. B., Ramesh, A., Im, W., et al. (2023) · Environmental Health Perspectives · doi:10.1289/ehp12901 · Cited by 8

Effect of interventions to reduce malaria incidence among military personnel on active duty: study protocol for a cluster randomised controlled trial of the impact of etofenprox-treated uniforms, permethrin-treated uniforms and DEET insect repellent

Msellemu, D. F., Ross, A. J., Temu, L., Moshi, I. R., Hofer, L. M., Mwanziva, C. E., Kohi, Y. M., & Moore, S. (2021) · Trials · doi:10.1186/s13063-021-05801-9 · Cited by 9

Optimal Feature Selection with Weight Optimised Deep Neural Network for Incremental Learning-Based Intrusion Detection in Fog Environment

Abdussami, A. A., & Farooqui, M. F. (2022) · Journal of Information & Knowledge Management · doi:10.1142/s0219649222500423 · Cited by 8

Person-, Job-, and Environment-Related Factors Associated with Long-Term Job Retention of People with Physical Disabilities

Kudla, A., Dinelli, E. J., Capraro, P., Crown, D., Sheth, M., Trierweiler, R., Munsell, E. G. S., Wong, J., & Heinemann, A. W. (2024) · Journal of Occupational Rehabilitation · doi:10.1007/s10926-024-10245-4 · Cited by 8

Can echocardiographically estimated pulmonary arterial elastance be a non-invasive predictor of pulmonary vascular resistance?

Sinha, N., Devabhaktuni, S., Kadambi, A., McClung, J. A., Aronow, W. S., & Lehrman, S. G. (2014) · Archives of Medical Science · doi:10.5114/aoms.2014.44860 · Cited by 8

Ideological shift and employees’ relationships: evidence from Ukraine

Bogodistov, Y., & Lizneva, A. (2016) · Baltic Journal of Management · doi:10.1108/bjm-11-2015-0220 · Cited by 9

A Model for Types and Levels of Automation in Visual Analytics: A Survey, a Taxonomy, and Examples

Domova, V., & Vrotsou, K. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3163765 · Cited by 12

Exercise treatments for chronic low back pain: a network meta-analysis

Hayden, J. A., Ogilvie, R., Kashif, S., Singh, S., Boulos, L., Stewart, S. A., Wieland, L. S., de Jesus‐Moraleida, F. R., Saragiotto, B. T., Yamato, T. P., et al. (2023) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd015608 · Cited by 9

Coordination and management of multisite complementary and alternative medicine (CAM) therapies: Experience from a multisite reflexology intervention trial

Rahbar, M. H., Wyatt, G. K., Sikorskii, A. E., Victorson, D. E., & Hessabi, M. (2011) · Contemporary Clinical Trials · doi:10.1016/j.cct.2011.05.015 · Cited by 8

The Development and Initial Validation of the COPLAG Scale: Measuring the Concerns of Parents of Lesbians and Gays

Conley, C. L. (2011) · Sexual and Gender Diversity in Social Services · doi:10.1080/10538720.2010.532083 · Cited by 8

Validade das medidas do tempo sentado do questionário IPAQ-versão curta em universitários brasileiros

Franco, D. C., Farias, G. S., Pelegrini, A., Júnior, J. S. V., & de Sousa, T. F. (2021) · Revista Brasileira de Atividade Física & Saúde · doi:10.12820/rbafs.26e0223 · Cited by 7

Algorithmic approaches to detecting interviewer fabrication in surveys

Borriello, G., Karlin, A. R., & Birnbaum, B. (2012) · ResearchWorks at the University of Washington (University of Washington) · Cited by 8

Effectiveness of a virtual reality-based sensory stimulation intervention in preventing delirium in intensive care units: a randomised-controlled trial protocol

Liang, S., Liu, Y., Wen, T., Luo, D., He, M., & Tian, J. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-083966 · Cited by 7

Unveiling the Relationship Between Cultural Values and Public (Dis)Value: The Mediation Role of Corruption Perception and Citizen Trust

Esposito, P., Doronzo, E., Tufo, M., & Riso, V. (2025) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.3192 · Cited by 9

Time Series Analysis of Dengue, Zika, and Chikungunya in Ecuador: Emergence Patterns, Epidemiological Interactions, and Climate-Driven Dynamics (1988–2024)

Sánchez, J. D., Ramírez, C. Á., Carrillo, E. C., Salazar, J. A., & Cevallos, C. B. (2025) · Viruses · doi:10.3390/v17091201 · Cited by 7

Identification of risk factors of 30-day readmission and 180-day in-hospital mortality, and its corresponding relative importance in patients with Ischemic heart disease: a machine learning approach

Okere, A. N., Sanogo, V., Alqhtani, H., & Diaby, V. (2020) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2021.1842200 · Cited by 7

A structural equation model of CFIR inner and outer setting constructs, organization characteristics, and national DPP enrollment

Madrigal, L., Haardörfer, R., Kegler, M. C., Piper, S., Blais, L. M., Weber, M. B., & Escoffery, C. (2023) · Implementation Science Communications · doi:10.1186/s43058-023-00522-3 · Cited by 6

Pledging after nudging improves uptake of plant-based diets: a field experiment in a German university cafeteria

Thamer, P., Banerjee, S., & John, P. C. (2024) · Environmental Research Communications · doi:10.1088/2515-7620/ad2625 · Cited by 7

The Role of Artificial Intelligence in Biofertilizer Development

Thakur, B., & Kaur, S. (2024) · Springer eBooks · doi:10.1007/978-981-97-2910-4_9 · Cited by 9

A prospective longitudinal cohort study on risk factors for COVID-19 vaccination failure (RisCoin): methods, procedures and characterization of the cohort

Koletzko, S., Thi, T. G. L., Zhelyazkova, A., Osterman, A., Wichert, S. P., Breiteneicher, S., Koletzko, L., Schwerd, T., Völk, S., Jebrini, T., et al. (2023) · Clinical and Experimental Medicine · doi:10.1007/s10238-023-01170-6 · Cited by 7

Usability of a Consumer Health Informatics Tool Following Completion of a Clinical Trial: Focus Group Study

Cho, H., Porras, T., Sanabria, G., & Schnall, R. (2020) · Journal of Medical Internet Research · doi:10.2196/17708 · Cited by 8

Methodology for Ensuring Accuracy and Validity of Infusion Pump Alarm Data

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RCS-Based Flight Target Recognition using Deep Networks with Convolutional and Bidirectional GRU Layer

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