Current cancer research on cancer patient supportive care and pain management.

NTIS: PB81-929403.

Current cancer research [on] etiology, epidemiology, and biology of melanomas and other skin cancers.

Prepared for the ICRDB Program by the Current Cancer Research Project Analysis Center.

Current cancer research on breast cancer : etiology, endocrinology and related biology.

Includes index.

Current cancer research on oral and head and neck cancer.

Prepared for the ICRDB Program by the Current Cancer Research Project Analysis Center.

Data analysis /

"In any comprehensive research project, there are essentially five steps. First, one starts with a literature review with regard to a particular research question. Second, one seeks to develop a theory. Third, the research question is finalized, frequently in the form of a hypothesis to be tested. Fourth, data are collected. Fifth, the subject matter of this paper, the data are analyzed in order to come to a resolution of the research question. There are two general approaches to analyzing research data. If the data were gathered concerning a 'research question,' a description of the data may be sufficient. However, if the data were gathered to accept or reject a formal hypothesis, statistical analysis is usually in order. This paper briefly surveys the principal data analysis methodologies that are available."

Current cancer research on breast cancer : diagnosis, prognosis, and treatment.

Prepared for the ICRDB Program by the Current Cancer Research Project Analysis Center.

Current cancer research on endocrine neoplasms : epidemiology, etiology and related biology.

Prepared for the ICRDB Program by the Current Cancer Research Project Analysis Center.

Current cancer research on etiology, epidemilogy and related biology of gastrointestinal, oral, esophageal and pancreatic cancers.

Includes index.

Current cancer research on hepatic neoplasms.

Prepared for the ICRDB Program by the Current Cancer Research Project Analyis Center.

Journey making group workshops as a research tool

Problem-structuring group workshops can be used in organizations as a consulting tool and as a research tool. One example of the latter is using a problem-structuring method (PSM) to help a group tackle an organizational issue; meanwhile, researchers collect the participants' initial views, discussion of divergent views, the negotiated agreement, and the reasoning for outcomes emerging. Technology can help by supporting participants in freely sharing their opinions and by logging data for post-workshop analyses. For example, computers let participants share views anonymously and without being influenced by others (as well as logging those views), and video-cameras can record discussions and intra-group dynamics. This paper evaluates whether technology-supported Journey Making workshops can be effective research tools that can capture quality research data when compared against theoretical performance benchmarks and other qualitative research tools. © 2006 Operational Research Society Ltd. All rights reserved.

Key Aspects and Approaches of Open Access, Open Data and Open Science

Overview of the key aspects and approaches to open access, open data and open science, emphasizing on sharing scientific knowledge for sustainable progress and development.

Data Resource Profile : Aberdeen Maternity and Neonatal Databank (AMND)

Conflict of interest: None of the authors have any conflict of interest.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.

Data Files

In order to become better prepared to support Research Data Management (RDM) practices in sciences and engineering, Queen’s University Library, together with the University Research Services, conducted a research study of all ranks of faculty members, as well as postdoctoral fellows and graduate students at the Faculty of Engineering & Applied Science, Departments of Chemistry, Computer Science, Geological Sciences and Geological Engineering, Mathematics and Statistics, Physics, Engineering Physics & Astronomy, School of Environmental Studies, and Geography & Planning in the Faculty of Arts and Science.