Keyword refinement training
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This document provides guidance on refining keywords when researching a topic. It recommends reviewing the original research question and considering whether keywords are too specific or broad. Authors may use alternative terms, so the checklist suggests adding synonyms found from sources like Google, colleagues, or related articles. Database thesauruses contain controlled vocabularies that can help identify additional keywords to represent the topic.
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Keyword refinement training
This document provides guidance on refining keywords when searching for information on a research topic. It recommends reviewing the topic statement and keywords to ensure they represent all main ideas. Terms related to future predictions, like "future", may not apply if the article is older. The checklist suggests considering whether keywords are too specific or broad, and adding synonyms found from sources like colleagues, related articles, or a database's controlled vocabulary thesaurus. Broadening or narrowing the focus of some keywords is also advised.
Keyword refinement training
This document provides guidance on refining keywords when searching for information on a research topic. It recommends reviewing the topic statement and keywords to ensure they represent all main ideas. Terms related to future predictions, like "future", may not apply if the article is older. The checklist suggests considering whether keywords are too specific or broad, and adding synonyms found from sources like colleagues, related articles, or a database's controlled vocabulary thesaurus. Broadening or narrowing the focus of some keywords is also advised.
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Keyword refinement training
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Purpose of lit review
Literature reviews have specific purposes and end products depending on their type. There are two major types: those that introduce or frame original research, and those that only collect and summarize existing literature. Common products of summarizing reviews include comprehensive assessments of a knowledge domain, quantitative/qualitative assessments of current research through sampling or examining all literature, summaries of other reviews to synthesize information, and critical reviews of influential sources. The intent and end product of the review determines how comprehensive the literature search should be.
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This document discusses how to judge the relevance of information based on its utility. Relevance refers to how well an item meets an information need and depends on its relationship to the topic and practical application. Relevance is often a matter of degree rather than binary. To determine relevance, one must understand the purpose of the information search. Ultimately, for information to be relevant it must be useful - it must add new information not already known. Information from other domains can also be relevant if it helps solve a problem in one's own domain.
Relevance vs credibility
Credibility is an important factor in determining the relevance of information to an information need. To judge credibility, researchers should consider the author's credentials, the publication process such as peer review, and how the knowledge community has responded to the work. Several sources can provide information to assess credibility, such as details about peer review in a database record, information on journals from Ulrichsweb, and metrics on the prestige of the publishing journal. Sections within articles like the methods, results, and conclusions can also provide insight into credibility. Ultimately, individual researchers must make their own judgment about whether a work is credible enough to be relevant for their specific information need.
Relevance vs Subject
This document discusses judging the relevance of information based on its subject. It defines relevance as how well an item meets an information need. Relevance is determined on a degree, not a binary scale. Information can fall into three zones of relevance - the specific subject domain, broad subject domain, or outside the domain but still tangentially related. When assessing relevance, one should understand their information need and scan different parts of research articles like the abstract, keywords, background, and conclusions to determine the subjects discussed. Database records and paper sections like the abstract and keywords can help identify the topics covered.
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This document discusses judging the relevance of information based on information needs. It defines relevance as how well an item meets an information need, which can be a matter of degree rather than binary. When judging relevance, one must understand the purpose of the information search. There are many types of reviews with different purposes that affect what is considered relevant. Systematic reviews take an exhaustive look at literature, critical reviews focus on influential sources, and literature reviews for research papers select sources that frame the research. The relevance of articles depends on the objective of the specific review.
Similarity of sources
This document discusses two techniques for finding relevant articles: searching for similar articles based on references from an initial article, or searching based on subject words if no initial articles have been identified yet. Searching for similar articles utilizes the references and information from an article that has already been found to locate related articles, while searching based on subject words relies on descriptive terms when no initial articles are available as a starting point.
Database information domains
Library databases collect and index information on defined subject areas, with some overlap between databases. Each database represents a subset of journals focused on a specific information need. To determine the best database to search, categorize your topic into broad subject areas like general/cross-disciplinary, general science education, or engineering & technology as a starting point, then search multiple databases as needed to find the required information.
Leveraging articles
Each source references other related sources, creating a network that can be followed forward or backward in time to find additional relevant information. Useful information like keywords, subject terms, related documents, and authors from a known source can also be leveraged to discover other related sources. This document discusses how existing sources can be used to find new sources by following citation links between documents and utilizing metadata like titles and authors.
Type of database searches
Searching a database can be done in different ways:
1) Searching for works by a specific author or institution looks for all records containing that author or institution. This provides a narrow search focused on one entity.
2) Searching for sources about a known topic uses what is already known about the topic to find additional publications on that topic. The topic is defined with some level of detail.
3) Both author/institution searches and topic searches may find sources that were previously unknown or found, depending on the search terms and database searched.
Specific needs
This document defines two types of information needs based on specificity: searching for a known reference where the publication details are already known, and searching for sources that meet certain criteria where the topic is defined but specific publications are unknown. It provides examples of each, such as finding a specific published article with a known title and authors, or finding any articles on a general topic like coordinated control of UAVs.
Topic specificity
The document discusses topic specificity for research, noting that while a perfectly defined topic is not necessary to begin searching, a well-defined topic allows for more efficient searching. It recommends thinking of a topic as a focused area of interest within a broader domain and provides examples of poorly defined, broadly defined, and narrowly defined research topics, with autonomous vehicles, autonomous UAVs, and image recognition algorithms for vision systems in autonomous UAVs as examples ranging from broad to narrow.
Strategies concept block
Concept blocking is a search strategy that breaks topics down into major concepts represented by search terms. Each concept is described with synonyms to capture alternative wordings. The search strings combine the concepts with Boolean operators - using AND between concepts and OR between synonyms. This intuitive approach represents the intersection of knowledge domains that comprise topics. An example breaks down the topic of coordinated control of UAVs into search terms for unmanned aerial vehicles, control/automation, and coordination/cooperation.
Currency
This document discusses how to determine the currency, or recency, of information sources. It notes that what is considered current depends on how rapidly the field is changing, with articles about new materials potentially remaining current for 5 years while those on additive manufacturing may date quickly. It also explains that different source types, like newspapers, conference papers, journal articles, and books, cover events and research at different points along the information cycle, from immediate coverage to discussion years later.
Types of sources
Peer-reviewed journals can be identified by searching Ulrichsweb or checking the publication's website for information about their peer review process. Conference articles found in proceedings may contain more current information than journal articles but are typically not reviewed to the same extent. Other sources like books, theses, dissertations, reports and patents can be identified from their search record but are generally not considered peer-reviewed.
Strategies concept block subject terms
This document discusses concept blocking strategy, an intuitive method for searching topics that breaks them down into major concepts represented by search terms. It recommends using subject terms from databases and considering related terms from thesauri to broaden searches. Search strings should use AND between concepts to narrow the topic and OR between similar terms, while removing concepts broadens the topic. An example search string is provided to coordinate control of unmanned aerial vehicles using subject terms from Compendex.
Strategies successive fractions
This document describes the successive fractions strategy for searching, where a searcher starts with a broad search term related to their topic of interest. They then review the results to identify more specific ideas or topics to refine the search with, adding these additional terms with Boolean AND operators. An example is provided where a searcher starts with a search on "UAV" or "drone", then examines results to add a second search term like "control" or "coordination" to narrow the results. The strategy aims to iteratively refine the search through multiple steps until a manageable number of relevant results are obtained.
Database selection citation indexes
Web of Science and Scopus are citation indexes that provide access to articles in the broad sciences and assist researchers by tracking who cites whom in their articles. Citation indexes allow researchers to find a known article and then click "Cited by" to get a list of articles that have cited the original article.
Subject terms
Subject terms, also known as controlled vocabulary, are special words used by databases to describe what an article is about. Using subject terms allows for a precise search, as the selected terms do not need to be explicitly stated in an article's text. To perform a subject search, you must know the specific terms used by the database. Reviewing subject terms from previous relevant articles found in that database can help identify appropriate search terms, as controlled vocabularies vary between databases.
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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Keyword refinement training
- 1. Review your research question to make sure your keywords represent each main idea it contains Example: Future adoption of drone delivery z In general, time-related terms such as “future” are best added later in the search process. Often an article written several years ago about the future will not have the information you desire about today’s future. KEYWORD REFINEMENT CHECKLIST
- 2. Consider whether your terms are specific enough (or too specific) to adequately represent your topic z Some terms have subtle meanings that unintentionally change the direction of your search • Example of narrowing the focus of terms: • Example of broadening the focus of terms: Goods -> Cargo (goods that are shipped) Unmanned -> Autonomous (there are many levels of autonomy)
- 3. Consider adding other terms to your list of keywords. Authors may use industry- specific words to describe the main ideas in their research. Use of these special terms may help you more quickly find their work.
- 4. z To find synonyms, you can use Google, brainstorm with colleagues, or find examples of language used in related articles. • If your list of keywords only includes a few basic terms, try to think of synonyms for those terms that other authors may use. UAV World “public acceptance” Commercial Application?
- 5. z Indexers of article databases use a “controlled vocabulary” to ensure subjects are represented consistently. Use the thesaurus in the database to find the controlled vocabulary for your topic. • Many databases provide a thesaurus that is valuable in helping determine which words to use in your search.
- 6. v When you are finished viewing this material, please close this tab on your browser and return to the Decision-based Learning tab. v