Results 231 to 240 of about 38,763 (257)

REDMANE: A Lightweight Ecosystem for Research Data Management Across Organisations and Diverse Data Types #RSAA25

Managing research data in multi-organisational, multi-omics environments is increasingly complex yet most existing systems are built for institutions, not researchers. REDMANE (REsearch Data Management & ANalysis Environment) is a lightweight, researcher-focused ecosystem that addresses this critical gap.
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An overview of real‐world data sources for oncology and considerations for research

Ca-A Cancer Journal for Clinicians, 2022
Lynne Penberthy, Donna R Rivera, Jennifer L Lund, Msph   +2 more
exaly  

Comprehensive assessment of Research Data Management: Practices and data quality indicators in a social sciences organisation

This dataset includes information on quality control and data management of researchers and data curators from a social science organization. Four data curators and 24 researchers provided responses for the study. Data collection techniques, data processing strategies, data storage and preservation, metadata standards, data sharing procedures, and the ...
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Addressing distress management challenges: Recommendations from the consensus panel of the American Psychosocial Oncology Society and the Association of Oncology Social Work

Ca-A Cancer Journal for Clinicians, 2021
Teresa L Deshields, Sharla Wells-Di Gregorio, Ryan D Nipp   +2 more
exaly  

Quantitative Text Analysis:

2017
Text data is ubiquitous. This type of data is not limited to organizational reports anymore and can come from a wide variety of sources (e.g. Internet Data). Even though beneficial, the traditional quantitative text analysis techniques simply fail to deal with the size and the dimension of the data.
Bayat, Ali, Kawalek, Peter
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An interdisciplinary consensus on the management of brain metastases in patients with renal cell carcinoma

Ca-A Cancer Journal for Clinicians, 2022
Elshad Hasanov, Brian I Rini, Cosmin-Victor Ene   +2 more
exaly  

Innovations in research and clinical care using patient‐generated health data

Ca-A Cancer Journal for Clinicians, 2020
H S L Jim, Aasha I Hoogland, Naomi C Brownstein   +2 more
exaly  

AI in action: The role of machine learning in systematic reviews, data organisation and management for medical researchers

This editorial explores the transformative potential of AI, specifically machine learning, in the realm of medical research, particularly focusing on systematic reviews, data organisation, and management. Traditional methods of conducting systematic reviews are often time-consuming and labor-intensive, typically taking between 12 to 24 months. AI tools
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Evolving standards of care and new challenges in the management of HER2‐positive breast cancer

Ca-A Cancer Journal for Clinicians, 2020
Grace M Choong, Grace D Cullen, Ciara C O'sullivan   +2 more
exaly  

Researching with records in management and organisation studies: archives, data corpus, and reflexivity

2023
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