Citation Analysis

Systematic Analysis of Citation Patterns and Research Impact

citation metrics
bibliometric analysis
citation tracking

1955 (citation indexes); 1975 (Impact Factor); 2005 (H-index)

Eugene Garfield (Citation Indexes, 1955); Jorge Hirsch (H-index, 2005)

Citation analysis is the systematic study of how scholarly works are cited by subsequent research, used as a proxy for research impact and influence. Founded formally by Eugene Garfield in 1955 (introducing citation indexes), the field encompasses metrics ranging from simple citation counts to sophisticated indices like the H-index (Hirsch, 2005) and field-normalized indicators. Citation analysis is used to evaluate researcher productivity, track influence of ideas, assess journal quality, and detect research trends. While citation counts are not perfect measures of quality (high citation does not equal high quality; time lag in citation accumulation), they provide valuable quantitative data for research evaluation alongside peer review and expert assessment.

Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.

Garfield, E. (1955). Citation indexes for science: A new dimension of bibliographic information. Science, 122(3159), 108–111.

Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634.

altmetrics doi-system orcid-researcher-id citation-management-tools


Citation Management Tools

Software for Organizing, Formatting, and Sharing Bibliographic References

reference manager
citation software
bibliographic management

2001 (modern era, EndNoteWeb); 2006 (Mendeley); 2006 (Zotero)

Academic researchers and librarians; developed since 1980s

Citation management tools are software applications that store, organize, and format bibliographic references. They allow researchers to import citations from databases and websites, annotate and tag articles, organize references by project, and automatically generate formatted in-text citations and bibliographies in multiple styles (APA, Vancouver, Chicago, Harvard). Popular tools include Zotero (free, open-source), Mendeley (Elsevier-owned, freemium), EndNote (commercial, Clarivate), and others. These tools are essential for managing the hundreds to thousands of references accumulate during a research career and for ensuring consistent, accurate citation formatting in academic writing.

Booth, A. (2012). Citation management tools. In R. Bosch & K. Winn (Eds.), Reference management and citation software. Library Technology Reports, 48(5), 12–18.

Bramer, W. M., Rethlefsen, M. L., Murad, M. H., & Landhuis, E. (2016). When updating systematic reviews, how often should new searches be applied to increase the chance of finding relevant studies? Systematic Reviews, 5(1), 94.

Zotero project team (2024). Zotero: Free reference management software. https://www.zotero.org.

pico-framework boolean-search-operators citation-analysis doi-system


Citizen Satisfaction Survey

CSS
Public Satisfaction Index
Citizen Service Satisfaction

The Citizen Satisfaction Survey (CSS) measures public satisfaction with government services, infrastructure, and institutions across multiple dimensions (access, responsiveness, quality, fairness, transparency). Rooted in expectancy-disconfirmation theory (James, 2009) and the American Customer Satisfaction Index (Fornell et al., 1996), the CSS operationalizes citizen satisfaction as a key accountability metric and driver of institutional legitimacy. Essential for government agencies, public utilities, and civic institutions seeking to monitor service performance, identify improvement priorities, and demonstrate responsiveness to public needs.

Nasco, S. A., Cleveland, M., & Laroche, M. (2010). Evaluating the public sector customer satisfaction construct in the context of public transit service. Journal of Public Sector Management, 23(2), 97-113.

Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American Customer Satisfaction Index: Nature, purpose, and findings. Journal of Marketing, 60(4), 7-18.

James, O. (2009). Evaluating the expectations disconfirmation and expectations anchoring theories of citizen satisfaction with local government. Journal of Public Administration Research and Theory, 19(1), 141-157.

Andersen, L. B., Jørgensen, T. B., Kjørup, A. M., & Sørensen, L. H. (2012). Public employees' motivations. International Journal of Public Administration, 35(1), 3-14.

public-service-motivation-scale e-government-adoption-scale tourist-satisfaction-scale hotel-service-quality-scale perceived-value-scale-tourism


Civic Engagement Scale

Civic Engagement and Participation Index

Cliff Zukin, Scott Keeter, Russell Dalton

The Civic Engagement Scale measures the extent and type of an individual's participation in civic, political, and community life. Rather than a single construct, it typically encompasses multiple dimensions: electoral participation (voting), political activity (contacting officials, campaign involvement), civic service (volunteering, organizational membership), and social participation (community meetings, neighborhood involvement). Developed by scholars including Zukin, Keeter, and Dalton, it captures how citizens actualize their democratic role.

Self-report questionnaire / Behavioral frequency

Zukin, C., Keeter, S., Andolina, M., Jenkins, K., & Delli Carpini, M. X. (2006). A new engagement? Political participation, civic life, and the changing American citizen. Oxford University Press.

Brady, H. E., Verba, S., & Schlozman, K. L. (1995). Beyond SES: A resource model of political participation. American Political Science Review, 89(2), 271-294.

Dalton, R. J. (2008). Citizenship norms and the expansion of political participation. Political Studies, 56(1), 76-98.

political-efficacy-scale social-capital-index generalized-trust-scale community-belonging-scale social-cohesion-scale


CK Metrics

Chidamber and Kemerer Object-Oriented Metrics

OO metrics
CK suite
object-oriented complexity

CK Metrics is a suite of six object-oriented design metrics introduced by Chidamber and Kemerer in 1994 to measure class complexity, cohesion, and coupling. The metrics quantify OO design quality; high coupling and low cohesion predict defects and maintenance effort.

Chidamber, S. R., & Kemerer, C. F. (1994). A metrics suite for object-oriented design. IEEE Transactions on Software Engineering, 20(6), 476–493.

Basili, V. R., Briand, L. C., & Melo, W. L. (1996). A validation of object-oriented design metrics as quality indicators. IEEE Transactions on Software Engineering, 22(10), 751–761.

Lanza, M., & Marinescu, R. (2007). Object-Oriented Metrics in Practice. Springer.


Classic Grounded Theory

Glaserian GT
CGT
original grounded theory
classic GT

Classic Grounded Theory (CGT) is a systematic qualitative methodology for generating substantive theory from empirical data. Developed by Barney Glaser and Anselm Strauss in 1967, it uses iterative cycles of data collection, constant comparison, and memo writing to produce a core category and surrounding conceptual framework that explains a social or psychological process. Unlike its later variants, Glaserian CGT insists on emergence — theory must arise from data without forcing preconceived frameworks.

Interviews, field observations, documents, memos

20–50 participants (theoretical sampling until saturation)

Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.

Glaser, B. G. (1978). Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press.

grounded-theory phenomenology ethnography case-study narrative-analysis action-research


Classroom Environment Scale

The Classroom Environment Scale is a comprehensive instrument measuring the social, emotional, and organizational climate of educational settings. Developed by Moos and Trickett in 1974, the CES assesses students' or teachers' perceptions of classroom relationships, instructional climate, and classroom management. By providing a multidimensional profile of classroom environment, the CES enables educators to identify strengths and opportunities for improvement in classroom culture, directly informing interventions to enhance student engagement, achievement, and well-being.

Moos, R. H., & Trickett, E. J. (1974). Classroom Environment Scale: A method for assessing the social climate of classrooms. Consulting Psychologists Press.

Fraser, B. J. (1994). Research on classroom and school climate. In D. L. Gabel (Ed.), Handbook of research on science teaching and learning (pp. 493–541). National Science Teachers Association.

academic-help-seeking-scale peer-learning-scale university-student-satisfaction academic-burnout-scale test-anxiety-inventory


Classroom Observation

classroom observation research
structured classroom observation
instructional observation
lesson observation

1960s (Flanders Interaction Analysis); refined through 1990s–2000s

Ned Flanders (systematic interaction analysis); Robert Pianta et al. (CLASS system)

Classroom observation is a field research method in which a trained observer systematically watches, documents, and analyzes teaching and learning events as they occur in a real classroom setting. It can be structured (using a predefined coding instrument such as Flanders Interaction Analysis or CLASS), semi-structured, or open-ended (ethnographic notes), and is used across educational research, teacher professional development, school evaluation, and curriculum studies to generate ecologically valid evidence about instructional practice.

Qualitative and quantitative observational research

Field notes, observation protocols, audio/video recordings, rating scales

Flanders, N. A. (1970). Analyzing Teaching Behavior. Addison-Wesley.

Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom Assessment Scoring System (CLASS) Manual, Pre-K. Paul H. Brookes Publishing.

educational-action-research lesson-study ethnography thematic-analysis discourse-analysis program-evaluation


Cleanroom Software Engineering

Cleanroom Software Engineering Methodology

Cleanroom Software Engineering is a software development methodology developed by Mills, Dyer, and Linger in the 1980s that emphasizes defect prevention through formal specifications, code reviews, and statistical testing rather than debugging. Inspired by pharmaceutical manufacturing cleanrooms, the approach aims for near-zero-defect delivery.

Mills, H. D., Dyer, M., & Linger, R. C. (1987). Cleanroom software engineering. IEEE Software, 4(5), 19–25.

Linger, R. C., & Mills, H. D. (1994). A case study in cleanroom software engineering: A NASA mission-critical application. Proceedings of the International Conference on Software Engineering.

Dyer, M. (1992). The Cleanroom Approach to Quality Software Development. Wiley.


CCAS

climate change beliefs and risk perception

The Climate Change Attitude Scale (CCAS) measures individuals' beliefs about climate change causation, severity, and human responsibility, as well as attitudes toward climate action and climate policy. Developed by Li and Monroe (2019) as an extension of general environmental attitude scales, the CCAS focuses specifically on climate change perceptions—whether individuals believe climate change is real, anthropogenic (human-caused), severe, and actionable. The scale is essential for tracking public opinion on climate, identifying populations skeptical of climate science, evaluating climate communication campaign effectiveness, and examining links between climate beliefs and policy support or climate action.

Li, H., & Monroe, M. C. (2019). Development and validation of the Climate Change Attitude Scale (CCAS). Climatic Change, 152(3–4), 601–613.

Leiserowitz, A., Maibach, E., & Roser-Renouf, C. (2009). Climategate, public opinion, and the loss of trust. Yale Project on Climate Change Communication. Yale University.

new-ecological-paradigm environmental-concern-scale carbon-footprint-awareness-scale pro-environmental-behavior-scale


Clinical Audit

Clinical Audit for Quality Assurance and Evidence-Based Practice

UK National Health Service and healthcare quality movements

Clinical audit is a systematic, cyclical process that measures the quality of clinical care against evidence-based standards and benchmarks, identifies gaps, and implements improvements to bring practice into alignment with current best evidence. Originating in the UK NHS, clinical audit is now a fundamental quality assurance tool in healthcare organizations worldwide.

Institute of Medicine. (2001). Crossing the Quality Chasm: A New Health System for the 21st Century. National Academies Press.

Davies, H. T., Crombie, I. K., & Tavakoli, M. (2006). When can odds ratios mislead? British Medical Journal, 316(7136), 989–991.

National Institute for Health and Clinical Excellence (NICE). (2002). Principles for best practice in clinical audit. Royal Society of Medicine Press.

balanced-scorecard-healthcare dea-hospital-efficiency lean-healthcare six-sigma-healthcare hospital-readmission-model


Clinical Frailty Scale

CFS
Frailty Scale
Clinical Frailty Assessment

The Clinical Frailty Scale (CFS), developed by Kenneth Rockwood and colleagues in 2005, is a brief, validated tool for assessing frailty in older adults. Frailty—a syndrome of diminished physiologic reserve, increased vulnerability, and reduced functional ability—is recognized as a distinct clinical state that predicts mortality, disability, and healthcare utilization independent of age and comorbidities. The CFS uses a seven-point (or nine-point in later versions) clinical judgment-based scale, making it practical and rapid for bedside use in hospitals, clinics, and long-term care.

Rockwood, K., Song, X., MacKnight, C., et al. (2005). A global clinical measure of fitness and frailty in elderly people. CMAJ, 173(5), 489-495.

Rockwood, K., Andrew, M., & Mitnitski, A. (2007). A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci, 62(7), 738-743.


Clinical Global Impressions Scale

The Clinical Global Impressions Scale is a clinician-administered two-part assessment developed by William Guy in the ECDEU Assessment Manual (1976) to provide rapid, global ratings of illness severity and treatment response. Part 1 (CGI-Severity) rates current severity; Part 2 (CGI-Improvement) rates change since treatment initiation. The CGI is among the most widely used global outcome measures in psychiatric research and clinical practice, prized for its brevity, interpretability, and ability to capture clinician expertise and nuanced clinical judgment.

Guy, W. (1976). ECDEU Assessment Manual for Psychopharmacology. Rockville, MD: National Institute of Mental Health, US Department of Health, Education, and Welfare.

Busner, J., & Targum, S. D. (2007). The Clinical Global Impressions Scale: applying a research tool in clinical practice. Psychiatry (Edgmont), 4(7), 28–37.

Kadouri, A., Corruble, E., & Ly, K. H. (2007). The CGI: assessment of its usefulness in the context of a European multicentric antidepressant drug trial. European Neuropsychopharmacology, 17(6–7), 468–472.

phq-9 hamilton-depression-rating-scale montgomery-asberg-depression patient-global-impression-change


Clinical Handover Quality Scale

Multiple researchers including Arora, Riesenberg, and colleagues, based on aviation handoff protocols and clinical error analysis

The Clinical Handover Quality Scale (CHQS) is a comprehensive framework and measurement tool for assessing the quality of clinical handovers—the critical communication process by which responsibility for a patient's care is transferred from one provider or team to another. Handovers occur multiple times daily in healthcare settings (shift changes, patient transfers between units, discharge planning, procedure-to-recovery transitions) and are recognized as high-risk moments for communication breakdown, incomplete information transfer, and consequent patient harm. The CHQS measures handover quality across dimensions including information content, clarity, timeliness, opportunity for questions, and documented understanding. It is used in hospitals, operating rooms, and intensive care units to assess handover effectiveness and to guide improvement in standardized handoff protocols such as SBAR (Situation, Background, Assessment, Recommendation).

Manser, T. (2005). Managing the risks of organizational accidents. Journal of Contingencies and Crisis Management, 12(4), 141–150.

Arora, V., Johnson, J., Lovinger, D., Humphrey, H. J., & Meltzer, D. O. (2009). Communication failures in patient sign-out and suggestions for improvement. Journal of the American Medical Association, 294(9), 1095–1102.

Riesenberg, L. A., Leitzsch, J., & Massucci, J. L. (2009). Residents' perceptions of handoff importance and effectiveness at an academic medical center. Journal of Hospital Medicine, 4(5), 340–346.

teamstepps-perceptions patient-safety-climate-scale safety-attitudes-questionnaire healthcare-teamwork-scale


CLES+T

Clinical Learning Environment Scale with Teacher Feedback Subscale

Clinical Learning Environment Supervision Scale

Marja Saarikoski, Hanne Leino-Kilpi, Tony Warne

The CLES+T is a 34-item self-report questionnaire measuring nursing students' perceptions of their clinical learning environment and the quality of supervision received from their clinical preceptor or teacher. Originally developed by Saarikoski and colleagues in 2007 and expanded in 2008 to include a specific teacher feedback dimension, the CLES+T evaluates five key domains: Ward Learning Environment, Supervisory Relationship, Feedback and Evaluation, Nurse Teacher's Pedagogical Competence, and Empowerment. The scale is widely used in nursing education to assess the quality of clinical placements and identify areas for educational improvement.

Saarikoski, M., Isoaho, H., Warne, T., & Leino-Kilpi, H. (2008). The nurse teacher in clinical practice: Developing the new sub-dimension for the Clinical Learning Environment Supervision and Nurse Teacher (CLES+T) evaluation scale. Int J Nurs Stud 45(8): 1233–1237.

Saarikoski, M., Leino-Kilpi, H., & Warne, T. (2007). Clinical learning environments as perceived by nursing students: Factor structure and relationships to learning outcomes. Nurse Educ Today 27(2): 122–128.

ripls simulation-debriefing-quality clinical-teaching-quality-scale student-clinical-placement-scale


Clinical Pathway Analysis

Clinical Pathway Analysis for Nursing Care Management

Care Pathway
Critical Pathway
Nursing Care Plan
Protocol-Based Care

Healthcare quality and process improvement specialists

Clinical Pathway Analysis is a process improvement methodology that develops and evaluates standardized, evidence-based care plans for specific diagnoses or procedures. Clinical pathways map the expected course of care, including nursing interventions, diagnostic tests, medications, and patient education activities across time. By analyzing deviations from the pathway and tracking outcomes, organizations can identify opportunities to improve quality, reduce costs, and ensure consistent care delivery.

Coffman, J. (2005). Critical pathways: An effective tool for managing care. American Nurse Today, 1(7), 22-26.

Panella, M., Marchisio, S., & Di Stanislao, F. (2003). Reducing clinical variations with clinical pathways: Do pathways work? International Journal of Quality in Health Care, 15(6), 509-521.

braden-scale nursing-sensitive-indicators care-dependency-scale medication-reconciliation


CRIB

CRIB is a neonatal illness severity scoring system designed to predict mortality risk in very low birth weight (VLBW) infants using birth weight, gestational age, gender, Apgar score, and initial blood gas parameters. Developed by Parry et al. in 1991 and refined as CRIB-II in 2005, it incorporates demographic and delivery room data along with early physiological measurements. CRIB is particularly valuable for international comparisons of neonatal outcome quality and has become a standard severity-adjustment tool in neonatal epidemiology.

Parry, G. W., Sims, D. G., Wincott, J. L., & Cockburn, F. (1991). Clinical Risk Index for Babies (CRIB): Prospective Validation. Archives of Disease in Childhood, 66(7), 717-722.

Bardell, T., Knottnerus, A., Motohashi, A., et al. (2005). CRIB II: An Update of the Clinical Risk Index for Babies Score. Archives of Disease in Childhood Fetal and Neonatal Edition, 90(4), F334-F338.


Clinical Scoring System in Veterinary Medicine

Systematic Clinical Scoring Assessment in Veterinary Medicine

clinical assessment scoring
veterinary patient scoring

Clinical scoring systems provide standardized methods for objectively assessing animal health status, pain, disease severity, and treatment outcomes. Developed progressively by veterinary organizations and research groups since the early 2000s, these systems enable consistent documentation, comparison of cases, and evidence-based clinical decision-making across species and practice settings.

Hansen, B. D., Lascelles, B. D., Keates, H., et al. (2015). Painful Osteoarthritis in Cats: Chronic Pain Assessment, Management, and Welfare Considerations. Journal of Feline Medicine and Surgery, 17(8), 637-646.

Hellyer, P., Rodan, I., Brunt, J., et al. (2007). AAFP and IAAFP Feline Acute Pain Management Guidelines. Journal of Feline Medicine and Surgery, 9(1), 13-25.

Mathews, K. A. (2014). Pain Assessment and Management in Livestock and Poultry. Journal of the American Veterinary Medical Association, 244(8), 882-893.


CTQS

Teaching Quality Assessment
Clinical Instructor Effectiveness Scale

The CTQS is a self-report questionnaire measuring students' perceptions of their clinical educator's (preceptor, clinical instructor, or mentor) teaching quality and effectiveness. Developed by Ohrling, Hallberg, and Gaberson in the early 2000s, the CTQS evaluates dimensions of clinical teaching including role modeling, knowledge transmission, feedback quality, student empowerment, and development of critical thinking. The scale is used in nursing and health professions education to provide educators with feedback on their teaching performance, identify professional development needs, and inform program quality assurance.

Ohrling, K. & Hallberg, I. R. (2001). The meaning of precepting: Nurse preceptors' lived experience of precept education. J Adv Nurs 33(1): 96–103.

Gaberson, K. B., Schroeder, S. G., & Aplin, M. C. (2003). Evaluating clinical teaching: A tool for nurse educators. Nurse Educ Pract 3(1): 48–56.

clinical-learning-environment-scale ripls student-clinical-placement-scale professional-identity-scale


Clinical Text Mining

Clinical Text Mining (Clinical NLP Information Extraction)

clinical NLP
clinical information extraction
Klinik Metin Madenciliği

2000s–2020s (established domain; BioBERT milestone 2020)

Community-driven; foundational work by i2b2/UTHealth NLP challenges and BioBERT (Lee et al., 2020)

Clinical text mining is a specialised branch of natural language processing that extracts structured clinical facts — diagnoses, symptoms, medications, treatments, and ICD codes — from unstructured healthcare documents such as discharge summaries, progress notes, and radiology reports. Grounded in biomedical NLP models like BioBERT (Lee et al., 2020) and the i2b2/UTHealth shared-task benchmarks (Stubbs & Uzuner, 2015), it converts free-text clinical narratives into machine-readable data suitable for clinical decision support and health analytics.

Clinical narratives: discharge summaries, progress notes, radiology and pathology reports

ICD codes, symptoms, diagnoses, medications, treatments, risk factors

BioBERT, ClinicalBERT, medspaCy, scispaCy

Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.

Stubbs, A. & Uzuner, Ö. (2015). Annotating risk factors for heart disease in clinical narratives for the 2014 i2b2/UTHealth shared task. Journal of the American Medical Informatics Association, 22(e1), e30–e39.

sentiment-analysis named-entity-recognition text-classification scientific-text-mining information-extraction


Clinical Trial Registration

Prospective Registration of Clinical Trials and Observational Studies

trial registration
prospective registration
ClinicalTrials.gov
trial registry
ISRCTN

World Health Organization; International Committee of Medical Journal Editors

Clinical trial registration is the prospective documentation of a trial's key information (hypothesis, design, population, outcomes) in a public registry before enrollment begins or results are known. In 2005, the World Health Organization established the requirement that all clinical trials be registered in an internationally recognized registry before participant enrollment. The International Committee of Medical Journal Editors (ICMJE) made registration a condition for publication in major medical journals in 2005, updated in 2015. Primary registries include ClinicalTrials.gov (U.S.), ISRCTN (UK), EudraCT (EU), and others operating under WHO oversight. Registration serves to prevent selective outcome reporting, reduce publication bias, and enhance research transparency.

World Health Organization. (2005). Ensuring that Studies Are Prospectively Registered. International Clinical Trials Registry Platform (ICTRP) Statement.

International Committee of Medical Journal Editors. (2015). Recommendations for the Conduct, Reporting, Editorship, and Publication of Scholarly Work in Medical Journals. JAMA, 314(20), 2142-2150.

U.S. Food and Drug Administration. (2020). Expanded Access (Compassionate Use). Electronic Code of Federal Regulations Title 21, Section 312.300.

De Angelis, C., Drazen, J. M., Frizelle, F. A., Haivas, G., Hebert, P. C., Ioannidis, J. P., & Schroeder, T. V. (2004). Clinical Trial Registration: A Statement from the International Committee of Medical Journal Editors. N Engl J Med, 351(12), 1250-1251.

ethics-committee-application ethics-committee-types risk-benefit-assessment data-protection-research vulnerable-populations-research


CLIP

CLIP
Contrastive Language-Image Pre-training
zero-shot image classifier
visual-language model

Radford, A.; Kim, J. W.; et al. (OpenAI)

CLIP (Contrastive Language-Image Pretraining) is a vision-language model introduced by Radford et al. at OpenAI in 2021 that jointly learns aligned image and text representations by training on 400 million internet-sourced image-text pairs using a contrastive objective, enabling zero-shot transfer to image classification tasks without any task-specific fine-tuning.

Contrastive vision-language pretraining model

Zero-shot image classification, image-text retrieval, visual representation learning

400 million image-text pairs (WebImageText)

Contrastive loss (InfoNCE) over image-text pairs

Image encoder (ViT or ResNet) + text encoder (Transformer)

Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 8748–8763.

Radford, A., et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. arXiv:2103.00020.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

vision-transformer bert resnet gpt dall-e stable-diffusion


Closed-Loop Supply Chain

reverse logistics
circular economy logistics

A closed-loop supply chain (CLSC) integrates forward logistics (moving products to customers) with reverse logistics (recovering products, components, or materials from customers) to optimize resource recovery, reduce waste, and minimize environmental impact. Products flow forward for customer use, then flow backward for remanufacturing, refurbishment, recycling, or proper disposal. CLSC is driven by regulatory compliance (e.g., take-back laws), cost recovery, environmental responsibility, and increasingly, customer demand for sustainable business practices.

Guide, V. D. R., & Van Wassenhove, L. N. (2003). Business aspects of closed-loop supply chains. Pittsburgh: Carnegie Mellon University Press.

Rogers, D. S., & Tibben-Lembke, R. S. (2002). Differences between forward and reverse logistics. Supply Chain Management Review, 6(5), 60-67.

scor-model inventory-routing vendor-managed-inventory aggregate-planning facility-layout


Closeness Centrality

Closeness Centrality (Bavelas-Freeman Shortest-Path Measure)

closeness
farness-based centrality
geodesic closeness
normalized closeness centrality

Bavelas, A.; formalized by Freeman, L. C.

Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts.

Graph / adjacency matrix (unweighted or weighted edges)

Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.

Bavelas, A. (1950). Communication patterns in task-oriented groups. Journal of the Acoustical Society of America, 22(6), 725–730.

degree-centrality betweenness-centrality eigenvector-centrality social-network-analysis pagerank network-diffusion-analysis


Cloud Condensation Nuclei Analysis

Cloud Condensation Nuclei (CCN) Analysis and Measurement

CCN analysis
Cloud condensation nuclei
CCN measurement

Cloud condensation nuclei (CCN) analysis examines the number and properties of aerosol particles capable of nucleating cloud droplets at various supersaturation levels. This field involves measuring CCN concentrations, characterizing their chemical composition and size, and relating aerosol properties to cloud microphysical processes.

Dusek, U., Frank, G. P., Hildebrandt, L., et al. (2006). Size matters more than chemistry for cloud-nucleating ability of aerosol particles. Science, 312(5778), 1375-1378.

Kreidenweis, S. M., Remer, L. A., Bruintjes, R., & Dubovik, O. (2001). Determining aerosol properties from satellite and ground-based measurements. Journal of Geophysical Research, 106(D12), 12325-12344.


Cluster Analysis

clustering
unsupervised classification
data clustering
numerical taxonomy

Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means

Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.

Continuous, ordinal, binary, or mixed multivariate data

Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley.

Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.

principal-component-analysis exploratory-factor-analysis discriminant-analysis multidimensional-scaling latent-class-analysis mixture-modeling


Cluster Randomized A/B Test

cluster A/B test
group-randomized A/B test
network A/B test
cluster-level split test

2010s (digital platforms); cluster RCT roots date to the 1970s–1980s

Developed from cluster randomized trial methodology; popularized in digital experimentation by researchers at Facebook, LinkedIn, and Microsoft Research (2010s)

A cluster randomized A/B test is an experimental design in which intact groups (clusters) — such as cities, schools, social network communities, or app user segments — are randomly assigned as whole units to either the treatment (A) or control (B) condition, rather than randomizing individual users or subjects. This approach is used when treatment effects would spill over between individuals if individual-level randomization were applied, or when the intervention must be delivered at the group level.

Behavioral, clickstream, or outcome data aggregated at the cluster level

Ugander, J., Karrer, B., Backstrom, L., & Kleinberg, J. (2013). Graph cluster randomization: Network exposure to multiple universes. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 329–337.

Hayes, R. J., & Moulton, L. H. (2017). Cluster Randomised Trials (2nd ed.). CRC Press.

cluster-randomized-controlled-trial multi-arm-experiment factorial-ab-test adaptive-ab-test blocked-ab-test field-experiment


Cluster Randomized Adaptive Experiment

adaptive cluster RCT
adaptive group-randomized trial
cluster adaptive design
adaptive cluster trial

Synthesised from cluster randomization methodology (Donner, 1978; Donner & Klar, 2000) and adaptive design frameworks (Bauer & Kohne, 1994; Pallmann et al., 2018)

A cluster randomized adaptive experiment combines two methodological principles: (1) intact groups such as schools, clinics, or villages are randomly assigned to treatment conditions rather than individuals, and (2) pre-specified rules allow the design to be modified during the trial based on accumulating cluster-level data. Adaptations may include dropping underperforming arms, reallocating clusters, or adjusting sample size, while maintaining statistical validity and controlling Type I error.

Continuous, binary, count, or time-to-event outcomes measured on individuals nested within clusters; interim aggregate cluster-level data used for adaptation decisions

Hayes, R. J., & Moulton, L. H. (2017). Cluster Randomised Trials (2nd ed.). CRC Press / Chapman & Hall.

Pallmann, P., Bedding, A. W., Choodari-Oskooei, B., Dimairo, M., Flight, L., Hampson, L. V., ... & Jaki, T. (2018). Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Medicine, 16(1), 29.

cluster-randomized-controlled-trial adaptive-experiment adaptive-randomized-controlled-trial blocked-randomized-controlled-trial multi-arm-experiment stepped-wedge-design


Cluster Randomized Control Group Experimental Design

CRCT with control group
group-randomized trial
cluster RCT control group design
community randomized controlled trial

Murray, D. M.; Donner, A. and Klar, N. (systematic formalization)

A cluster randomized control group experimental design randomly assigns intact groups (clusters) — such as schools, clinics, or communities — rather than individuals to treatment or control conditions. At least one cluster group receives no active intervention, serving as the control. This design is essential when individual randomization is impractical or contamination between participants in close proximity is likely.

Continuous, categorical, or count outcome data collected from individuals nested within clusters

Donner, A., & Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Arnold.

Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials: A Biomedical Research Paradigm. Oxford University Press.


Cluster Randomized Controlled Trial

cluster RCT
group-randomized trial
community randomized trial
cluster-randomized experiment

Cornfield (1978); systematised by Donner and colleagues (1980s)

A cluster randomized controlled trial (cluster RCT) is an experimental design in which intact social or organisational groups — such as schools, clinics, villages, or workplaces — are randomly assigned to treatment conditions rather than individual participants. Outcomes are still measured at the individual level, but the unit of randomization is the cluster. This design is essential when an intervention is delivered to whole groups, when there is a risk of contamination between participants in the same setting, or when individual randomization is logistically or ethically impractical.

Continuous, binary, count, or time-to-event outcomes measured on individuals nested within clusters

randomized-controlled-trial factorial-randomized-controlled-trial stepped-wedge-design blocked-randomized-controlled-trial multilevel-modeling intraclass-correlation


Cluster Randomized Factorial Experiment

cluster-randomized factorial design
group-randomized factorial trial
CRT factorial
clustered factorial experiment

1990s (formalized in group-randomized trial literature)

David M. Murray and colleagues; Allan Donner & Neil Klar

A cluster randomized factorial experiment assigns intact groups (clusters such as schools, clinics, or communities) at random to all combinations of two or more treatment factors, enabling simultaneous evaluation of multiple interventions and their interactions while respecting the natural grouping of participants. It merges the logistical and ethical advantages of cluster randomization with the efficiency of factorial design.

Continuous, binary, or count outcomes measured at the individual level within clusters

Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press.

cluster-randomized-controlled-trial factorial-experiment factorial-randomized-controlled-trial blocked-factorial-experiment fractional-factorial-experiment multi-arm-experiment


Cluster Randomized Field Experiment

CRFE
cluster-randomized trial in the field
group-randomized field experiment
community-randomized field experiment

David M. Murray (group-randomized trials framework); applied broadly in public health and education research

A cluster randomized field experiment (CRFE) assigns intact groups — schools, villages, clinics, workplaces — rather than individuals to treatment or control conditions, and the experiment is conducted in real-world settings rather than a laboratory. Randomization at the group level controls for contamination between conditions while preserving the ecological validity of the natural environment. It is the dominant design for evaluating community-level, school-based, or workplace interventions in public health, education policy, and development economics.

Continuous, binary, or count outcomes measured at individual or cluster level in real-world settings

cluster-randomized-controlled-trial field-experiment randomized-controlled-trial stepped-wedge-design blocked-field-experiment factorial-field-experiment


Cluster Randomized Fractional Factorial Experiment

Cluster-Randomized Fractional Factorial Experiment

cluster-randomized fractional factorial design

group-randomized fractional factorial trial

1950s (fractional factorial); 1980s-1990s (cluster-randomized extensions)

Box, Hunter & Hunter (fractional factorial foundations); Murray & colleagues (group-randomized trial methodology)

A cluster-randomized fractional factorial experiment combines two design principles: randomization is applied to intact groups (clusters such as schools, clinics, or communities) rather than individuals, and only a carefully chosen fraction of all possible factor-level combinations is tested. This pairing makes it practical to screen or evaluate multiple intervention components simultaneously in settings where individual randomization is infeasible, while keeping the number of required clusters manageable.

Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.

cluster-randomized-controlled-trial fractional-factorial-experiment factorial-randomized-controlled-trial blocked-fractional-factorial-experiment multi-arm-experiment adaptive-experiment


Cluster Randomized Full Factorial Experiment

Cluster-Randomized Full Factorial Experimental Design

cluster RCT full factorial
group-randomized full factorial design
CRT full factorial
cluster full factorial trial

Late 20th–early 21st century (formalized ~1998–2014)

Synthesis of cluster randomization (Murray, 1998) and factorial design traditions (Fisher, 1935; Collins et al., 2014)

A cluster-randomized full factorial experiment assigns intact groups (clusters) rather than individuals to every possible combination of two or more experimental factors. All factor-level combinations are tested simultaneously, enabling estimation of both main effects and all interaction effects, while preserving the integrity of naturally occurring social or organizational units such as schools, clinics, or communities.

Continuous, binary, or count outcomes; hierarchical (clustered) observations

Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504.

cluster-randomized-controlled-trial full-factorial-experiment factorial-randomized-controlled-trial blocked-full-factorial-experiment fractional-factorial-experiment multilevel-modeling


Cluster Randomized Laboratory Experiment

cluster-randomized lab experiment
group-randomized laboratory study
cluster RCT laboratory variant
clustered lab trial

1990s (formalized; cluster randomization principles developed in 1970s-1980s)

David M. Murray (group-randomized trial methodology); built on classical cluster sampling in experimental design

A cluster randomized laboratory experiment assigns intact groups — such as lab sections, cohorts, or naturally formed teams — rather than individual participants, to experimental conditions. All participants within a cluster receive the same treatment. The design is used when individual randomization would cause contamination between conditions, while retaining the controlled environment of a laboratory setting.

Controlled laboratory experiment with cluster-level randomization

Continuous, ordinal, or categorical outcome measures collected in a controlled lab environment

cluster-randomized-controlled-trial laboratory-experiment factorial-laboratory-experiment blocked-laboratory-experiment randomized-controlled-trial multilevel-modeling


Cluster Randomized Multi-Arm Experiment

multi-arm cluster RCT
cluster-randomized multi-group trial
multi-arm group-randomized trial
CRCT multi-arm

Building on cluster randomization (Donner & Klar) and multi-arm trial methods developed in clinical and public health research

A cluster randomized multi-arm experiment assigns intact groups — such as schools, clinics, or villages — rather than individuals to three or more experimental conditions simultaneously. Randomization occurs at the cluster level to prevent contamination between arms, while the multi-arm structure allows simultaneous evaluation of several interventions against a common control or each other, improving efficiency over a series of two-arm studies.

Hemming, K., Girling, A., Sitch, A., Marsh, J., & Lilford, R. (2017). Sample size calculations for cluster randomised controlled trials with a fixed number of clusters. BMC Medical Research Methodology, 17, 73.


Cluster Randomized Multiple Baseline Design

CR-MBD
cluster-randomized MBD
group-randomized multiple baseline
multilevel multiple baseline design

Extension of Baer, Wolf & Risley (1968) multiple baseline; cluster adaptation by Murray and colleagues (1990s)

The cluster randomized multiple baseline design combines cluster-level random assignment with the logic of the multiple baseline design. Intact groups — such as classrooms, schools, or clinics — are randomly assigned to receive an intervention at staggered time points. This preserves the within-unit repeated-measure logic of the multiple baseline while adding the causal warrant of random assignment at the cluster level.

Experimental design (single-subject / small-N with cluster randomization)

Repeated measures (observations per unit per time point), cluster-level assignment variable

Kowalski, J. T., & Shadish, W. R. (2015). Incorporating cluster randomization into multiple baseline designs. Journal of School Psychology, 53(6), 435-449.

multiple-baseline-design cluster-randomized-controlled-trial abab-design factorial-multiple-baseline-design randomized-controlled-trial single-subject-experimental-design


Cluster Randomized Solomon Four-Group Design

Cluster Randomized Solomon Four-Group Experimental Design

CR-S4GD
cluster-randomized four-group design
group-randomized Solomon design
Solomon four-group cluster trial

1949 (Solomon design); cluster extension formalized in 1990s

Richard L. Solomon (four-group logic, 1949); cluster randomization methods developed by Murray and colleagues in the 1990s

The cluster randomized Solomon four-group design combines cluster randomization — assigning intact groups such as schools, clinics, or communities to conditions — with the Solomon four-group structure that isolates the effect of pretesting. Four clusters (or sets of clusters) are created: two receive the treatment and two serve as controls, with only one treatment cluster and one control cluster receiving a pretest, while the others go straight to the posttest. This structure simultaneously controls for pretest sensitization and the logistical constraint that individual randomization is infeasible.

Continuous, ordinal, or categorical outcome data collected at cluster and individual levels

Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.

solomon-four-group-design cluster-randomized-controlled-trial blocked-solomon-four-group-design factorial-randomized-controlled-trial pretest-posttest-experimental-design multilevel-modeling


Cluster Randomized Trial

Cluster Randomized Controlled Trial (CRT)

CRT
cluster RCT
cluster trial
group randomization

A cluster randomized trial (CRT) randomizes intact groups—schools, clinics, villages, or hospital wards—rather than individuals. Developed by Campbell, Grimshaw, and colleagues in the late 1990s to address real-world settings where intervention delivery or contamination occurs at the group level, CRTs are now standard for evaluating population-level, community-based, and policy interventions.

Campbell, M. K., Grimshaw, J. M., & Elbourne, D. R. (2000). Intracluster correlation coefficients in cluster randomized trials: empirical insights into how should they be reported. BMC Medical Research Methodology, 4, 30.

Eldridge, S. M., Ashmore, S., Frenkel, S., Cryer, C., & Underwood, M. (2006). Uncertainty in analyses of safety after cluster randomization. Clinical Trials, 3(2), 152–162.

Campbell, M. K., Piaggio, G., Elbourne, D. R., & Altman, D. G. (2012). Consort 2010 statement: extension to cluster randomised trials. BMJ, 345, e5661.

randomized-controlled-trial intracluster-correlation adaptive-trial-design pragmatic-clinical-trial mixed-models-multilevel


Cluster-Robust Standard Errors

Cluster-Robust (Clustered) Standard Errors

clustered standard errors
cluster-robust inference
clustered variance estimator
Küme Robust Standart Hatalar

Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis)

Cluster-robust standard errors correct the variance of regression coefficients when observations are correlated within clusters such as schools, hospitals, or regions. The clustered sandwich estimator grew out of Liang & Zeger's (1986) generalized estimating equations and was synthesized for applied work by Cameron & Miller (2015), delivering valid inference when ordinary standard errors would be too small.

Robust variance estimation for regression

Cluster-robust sandwich (clustered Huber-White) variance

Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22.

Cameron, A. C. & Miller, D. L. (2015). A Practitioner's Guide to Cluster-Robust Inference. Journal of Human Resources, 50(2), 317-372.

ols-regression panel-fixed-effects wild-bootstrap permutation-test huber-white-robust-se


Cluster Sampling

cluster random sampling
area sampling
one-stage cluster sampling

Early-to-mid 20th century; canonical treatment 1953/1977

Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice

Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters.

Quantitative or mixed; any unit-level measurements within naturally occurring groups

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.

simple-random-sampling stratified-sampling multistage-sampling systematic-sampling proportional-cluster-sampling area-probability-sampling


CMB Anisotropy Analysis

Cosmic Microwave Background Anisotropy Analysis

CMB Power Spectrum
CMB Anisotropies
Microwave Background Analysis

The Cosmic Microwave Background is the ancient light from when the universe first became transparent, about 380,000 years after the Big Bang. Its tiny temperature variations (anisotropies) across the sky encode a wealth of information about the universe's composition, geometry, and history. First discovered by Arno Penzias and Robert Wilson in 1965, detailed measurements of CMB anisotropies have become the most powerful probe of cosmology.

Penzias, A. A., & Wilson, R. W. (1965). A measurement of excess antenna temperature at 4080 Mc/s. Astrophysical Journal, 142, 419-421.

Smoot, G. F., et al. (1992). Structure in the COBE differential microwave radiometer first-year maps. Astrophysical Journal Letters, 396(1), L1-L5.

Planck Collaboration (2018). Planck 2018 results. VI. Cosmological parameters. Astronomy & Astrophysics, 641, A6.


CNC Tool Path Generation

CNC Tool Path Generation and Optimization

NC tool path planning
Toolpath programming

CNC tool path generation is the computational process of determining the precise sequence and trajectory of tool movements required to machine a workpiece on computer numerical control (CNC) machines. Developed from the intersection of numerical control automation and computational geometry in the 1990s, this method translates CAD designs into executable machine instructions, enabling efficient and accurate manufacturing of complex parts.

Computational method for manufacturing automation

Elbestawi, M. A., Papazafiriou, T., & Du, R. (1994). In-process detection of tool wear in milling using cutting force signature. International Journal of Machine Tools and Manufacture, 34(4), 555-566.

Li, Y., Lum, L., Wang, J. X., & Kishawy, H. A. (2009). Tool life modeling in vibration-assisted machining of Inconel 718. Machining Science and Technology, 13(2), 226-245.

Sarhan, A. A., Sayuti, M., & Hamdi, M. (2011). Machinability of aerospace AL-Si-Cu alloy AA2014-T6 under dry and cryogenic cutting conditions. Journal of Materials Processing Technology, 211(3), 484-492.

additive-manufacturing-slicing inverse-kinematics denavit-hartenberg-parameters taylor-tool-life design-for-manufacturing-and-assembly


Convolutional Neural Network

Convolutional Neural Network for Classification

CNN (Evrişimli Sinir Ağı — Sınıflandırma)

A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced.

LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324.

random-forest svm-classification xgboost transformer-nlp autoencoder


CNN Image Classification

Convolutional Neural Network Image Classification (ResNet / VGG / EfficientNet)

CNN — Görüntü Sınıflandırma (ResNet / VGG / EfficientNet)

convolutional neural network image classifier

He, K. et al. (ResNet); Tan, M. & Le, Q.V. (EfficientNet)

CNN image classification uses deep convolutional architectures such as ResNet (He et al., 2016), VGG and EfficientNet (Tan & Le, 2019) to sort images into categories. Stacked convolutional layers learn a hierarchy of visual features directly from pixels, and skip (residual) connections prevent the vanishing-gradient problem in very deep networks.

Deep convolutional neural network (supervised)

He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR.

Tan, M. & Le, Q.V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML, PMLR 97, 6105–6114. arXiv:1905.11946.

cnn-text-classification dilated-cnn random-forest svm-classification xgboost


TextCNN

Convolutional Neural Network for Text Classification (TextCNN)

convolutional neural network for sentence classification

TextCNN is a convolutional neural network for text classification, introduced by Yoon Kim in 2014, that applies parallel convolution filters of different window sizes over word embeddings to capture local n-gram patterns. It is fast and effective for sentiment analysis and topic classification.

Convolutional neural network (deep learning)

Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. EMNLP.

Zhang, Y. & Wallace, B. (2015). A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification. arXiv:1510.03820.

gru bidirectional-rnn dilated-cnn xgboost random-forest


Co-Authorship Network Analysis

collaboration network
authorship network
research collaboration mapping

Co-authorship network analysis is a method that maps research collaboration patterns by treating authors as nodes and co-authored papers as edges in a network graph. The structure, density, and centrality patterns of this network reveal how researchers connect, collaborate across institutions and disciplines, and form research communities. Pioneered formally by Newman (2001), co-authorship analysis provides quantitative insights into the social fabric of science, revealing collaboration patterns, identifying scientific leaders, and detecting institutional or disciplinary boundaries.

Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.

Braun, T., Glänzel, W., & Schubert, A. (2001). Dynamic scientometric relations: Citation and collaboration patterns in selected research areas. Scientometrics, 51(3), 487–502.

science-mapping keyword-co-occurrence bibliographic-coupling co-citation-analysis


Co-Citation Analysis

co-citation mapping
historiograph
direct citation
citation pair analysis

Co-citation analysis is a method that identifies the intellectual structure of a research domain by examining how frequently pairs of documents are cited together in other publications. When two papers are frequently cited together in the literature, they are considered co-cited, indicating they are conceptually related or influential within the same research community. Developed by Henry Small in 1973, co-citation analysis maps the 'invisible colleges' of science—networks of researchers working on related problems—and reveals how knowledge domains evolve over time.

Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.

Small, H., & Griffiths, B. C. (1974). The structure of scientific literatures I: Identifying and graphing specialties. Science Studies, 4(1), 17–40.

bibliographic-coupling keyword-co-occurrence science-mapping journal-co-citation-analysis research-front-identification


Co-kriging

cokriging
co-regionalization kriging
multivariate kriging
CK

Matheron, G.; extended by Journel & Huijbregts

Co-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secondary (co-) variables. It extends ordinary kriging to multivariate settings, yielding more accurate predictions when the secondary variable is more densely sampled or spatially correlated with the primary variable of interest.

Continuous spatial variables with co-located or secondary observations

Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London.

Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press, New York.


Co-occurrence Analysis

word co-occurrence
co-occurrence network
Kelime Eş-Oluşum Analizi

Co-occurrence analysis is a text-mining technique that statistically counts the word pairs that appear together within a window or a sentence and uses their frequencies to reveal semantic maps and thematic structure. It rests on the distributional principle articulated by J.R. Firth in 1957 — that a word is characterised by the company it keeps.

Text-mining / distributional-semantics technique

Input: Tokenised text corpus

Output: Co-occurrence frequency matrix / network of word pairs

Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell.

Turney, P.D. & Pantel, P. (2010). From Frequency to Meaning: Vector Space Models of Semantics. Journal of Artificial Intelligence Research, 37, 141-188.

tf-idf topic-modeling keyword-extraction sentiment-analysis


Co-word Analysis

keyword co-occurrence analysis
co-word mapping
keyword co-word network
CWA

Michel Callon, Jean-Pierre Courtial, and colleagues

Co-word analysis is a scientometric technique that quantifies how often pairs of keywords, subject terms, or title words appear together across a corpus of publications. By treating simultaneous occurrence as a proxy for conceptual relatedness, it constructs networks and clusters that reveal the intellectual structure, dominant themes, and emerging sub-fields of a research domain.

Scientometric network analysis technique

Keywords, title terms, or abstracts from bibliographic records

Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.

He, Q. (1999). Knowledge discovery through co-word analysis. Library Trends, 48(1), 133–159.

bibliometric-analysis co-citation-analysis bibliographic-coupling science-mapping scientometric-analysis thematic-evolution-analysis


CO2SYS is a widely-used software package for calculating the speciation and equilibrium state of the marine carbonate system from measurements of two carbonate parameters. Developed by Ernie Lewis and Doug Wallace in 1998, CO2SYS enables oceanographers to compute all carbonate species (dissolved CO2, bicarbonate, carbonate), saturation states, and pH from pairs of measured parameters. The tool is essential for ocean acidification monitoring and research.

Lewis, E., & Wallace, D. W. R. (1998). Program developed for CO2 system calculations. ORNL/CDIAC-105. Oak Ridge National Laboratory.

Pelletier, G. J., Lewis, E., & Wallace, D. W. R. (2007). CO2SYS.XLS: A calculator for the CO2 system in seawater. Open-file report 2007-1047. USGS.


Coalescent Theory

Coalescent theory is a probabilistic framework that traces the genealogical history of DNA sequences backward in time to their most recent common ancestor. Developed by John Kingman in 1982, this method forms the foundation of modern population genetics, enabling researchers to understand demographic events, estimate genetic parameters, and reconstruct evolutionary histories from modern genetic data.

Kingman, J. F. C. (1982). The coalescent. Stochastic Processes and their Applications, 13(3), 235–248.

Hudson, R. R. (1983). Properties of a neutral allele model with intragenic recombination. Theoretical Population Biology, 23(2), 183–201.

Tajima, F. (1983). Evolutionary relationship of DNA sequences in finite populations. Genetics, 105(2), 437–460.

selection-sweep admixture-analysis ancestral-state-reconstruction f-statistics


Coarsened Exact Matching in Education Research

Coarsened Exact Matching for Causal Inference in Education Research

CEM in education
CEM for educational studies
exact matching education
coarsened matching educational data

Coarsened Exact Matching (CEM) is a pre-processing matching strategy that reduces imbalance between treated and comparison groups before outcome analysis. In education research it is used to create balanced comparison groups from administrative records, survey data, or quasi-experimental study designs — for example comparing students who received an intervention against comparable students who did not, without relying on randomisation.

Observational panel or cross-sectional data with categorical or coarsened continuous covariates

Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24.

Morgan, S. L., & Winship, C. (2015). Counterfactuals and Causal Inference: Methods and Principles for Social Research (2nd ed.). Cambridge University Press.


Coarsened Exact Matching

CEM
coarsened matching
monotonic imbalance bounding matching

Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.

Cross-sectional or panel; mixed continuous and categorical covariates

Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.

Iacus, S. M., King, G., & Porro, G. (2011). Multivariate matching methods that are monotonic imbalance bounding. Journal of the American Statistical Association, 106(493), 345-361.

propensity-score-matching propensity-score-weighting matching-estimator entropy-balancing difference-in-differences inverse-probability-weighting


COBRA

COBRA (COmprehensive distance Based RAnking) is a ranking multi-criteria decision-making (MCDM) method introduced by Krstić, M., Agnusdei, G. P., Tadić, S., Kovač, M., Miglietta, P. P. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Distance from PIS/NIS/AS (Euclidean × Taxicab combined)

Krstić, M., Agnusdei, G. P., Tadić, S., Kovač, M., Miglietta, P. P. (2022). A Novel Axiomatic DEA-COBRA Framework for Evaluating the Sustainable Performance of Agri-Food Systems. Sustainability

ahp anp bwm critic entropy fucom merec swara


Cochran Q Test

Cochran Q Testi
Cochran's Q
Q test for related proportions

Cochran's Q test is a nonparametric hypothesis test introduced by William G. Cochran in 1950 for comparing proportions across three or more related binary measurements. It extends McNemar's test to the multiple-condition case and is the method of choice when every participant is observed under each condition and the outcome is recorded as a simple success/failure (1/0).

Cochran, W. G. (1950). The comparison of percentages in matched samples. Biometrika, 37(3–4), 256–266.

mcnemar-test friedman-test repeated-measures-anova chi-square-test


Cochrane RoB 2.0

Jonathan Sterne, Julian Higgins (Cochrane Collaboration)

RoB 2 is the Cochrane Collaboration's updated methodology for assessing the risk of bias in randomized controlled trials (RCTs). Published in 2019, it replaced the original Cochrane RoB tool with a more structured, transparent approach using signalling questions and domain-based judgments to evaluate five critical sources of bias.

Clinician-rated / Research team assessment

Sterne, J. A., Savović, J., Page, M. J., Elbers, R. G., Blencowe, N. S., Boutron, I., ... & Higgins, J. P. (2019). RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ, 366, l4898.

casp-rct-checklist grade-evidence-profiling consort-reporting-checklist prisma-checklist


COCOSO

COCOSO (Combined Compromise Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Aggregated exponential comparison (WSM + WPM combination)

Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z. (2019). A combined compromise solution (COCOSO) method for multi-criteria decision-making problems. Management Decision

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


CODAS

CODAS (Combinative Distance-Based Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Distance from anti-ideal (Euclidean + Taxicab)

Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research


Code Coverage Analysis

coverage metrics
test coverage
instrumentation-based measurement

Code coverage analysis measures the extent to which source code is executed by a test suite, quantifying which lines, branches, or paths are exercised. Tools instrument code to track execution, reporting coverage percentages and identifying untested regions. Coverage analysis guides test creation, detects dead code, and validates test adequacy in quality assurance processes.

Zhu, H., Hall, P. A. V., & May, J. H. R. (1997). Software unit test coverage and adequacy. ACM Computing Surveys, 29(4), 366–427.

Frankl, P. G., & Weiss, S. N. (1988). An experimental comparison of the effectiveness of branch testing and data flow testing. IEEE Transactions on Software Engineering, 14(12), 1763–1773.

Corbet, J. (2008). Code coverage for the Linux kernel. Linux Weekly News article.

software-complexity-metrics software-testing-equivalence static-code-analysis defect-prediction-model


Gesine C. Feldman, Andrew M. Hayes, and colleagues at Rutgers University

The Cognitive and Affective Mindfulness Scale (CAMS) is a 12-item trait mindfulness measure designed to assess the degree to which individuals are present, aware, and non-judging toward their internal (cognitive and emotional) and external experiences. Developed by Feldman, Hayes, and colleagues at Rutgers University and published in the Journal of Clinical Psychology in 2007, the CAMS emphasizes the emotional and cognitive regulation aspects of mindfulness, particularly the capacity to observe thoughts and feelings without judgment. The CAMS-Revised (CAMS-R, 2006) is the refined version, offering strong brevity and psychometric properties that make it especially useful in clinical settings where time and assessment burden must be minimized.

Feldman, G. C., Hayes, A. M., Kumar, S. M., Greeson, J. M., & Laurenceau, J.-P. (2007). Mindfulness and emotion regulation: The development and initial validation of the Cognitive and Affective Mindfulness Scale. Journal of Clinical Psychology, 63(4), 373-385.

five-facet-mindfulness-questionnaire freiburg-mindfulness-inventory mindful-attention-awareness-scale toronto-mindfulness-scale philadelphia-mindfulness-scale


Cognitive-Behavioral Therapy Assessment

Cognitive-Behavioral Therapy Assessment Protocol

CBT assessment
functional analysis
behavioral formulation

Cognitive-Behavioral Therapy (CBT) assessment is a structured diagnostic and formulation process that identifies the relationships between situations, thoughts, feelings, and behaviors maintaining psychological distress. Rooted in the cognitive model developed by Aaron T. Beck in the 1960s, CBT assessment produces a personalized functional analysis that guides treatment planning and intervention selection.

Clark, D. A., & Beck, A. T. (2010). Cognitive therapy of anxiety disorders: Science and practice. Guilford Press.

Westbrook, D., Kennerley, H., & Kirk, J. (2011). An introduction to cognitive-behaviour therapy: Skills and applications (2nd ed.). Sage.


Cognitive Diagnosis Model

Cognitive Diagnosis Models (DINA / G-DINA)

Diagnostic Classification Model
Skills Assessment Model
Attribute Mastery Model
Bilişsel Tanı Modeli

Cognitive Diagnosis Models (CDMs) are a family of latent variable models designed to classify examinees according to their mastery of a set of discrete cognitive attributes or skills. The Generalized DINA (G-DINA) framework, introduced by Jimmy de la Torre in 2011, provides a unifying structure that encompasses many specific CDMs — including the DINA, DINO, ACDM, and LLM models — as special cases, enabling fine-grained diagnostic feedback beyond a single total score.

Latent variable diagnostic classification model

Input: Binary item responses and a Q-matrix

Output: Attribute mastery profiles for each examinee

de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199.


Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) combines computerized adaptive testing (CAT) with cognitive diagnostic models (CDMs) to efficiently assess students' specific skill profiles. Rather than producing a single overall ability score, CD-CAT adaptively selects items to quickly identify which skills a student has mastered and which need development.

Skill-adaptive testing with psychometric diagnostic classification

Choi, K. M., Lee, Y. S., & Park, Y. S. (2015). What CDM can tell about examinees' strengths and weaknesses: Cognitive diagnostic information in TIMSS. Journal of Educational Evaluation for Policy Analysis, 24(1), 79-100.

Kaplan, M., de la Torre, J., & Barrada, J. R. (2015). New item selection methods for cognitive diagnosis computerized adaptive testing. Journal of Educational Measurement, 52(4), 393-411.

Hsu, C. L., Wang, W. C., & Chen, S. Y. (2013). Variable-length computerized adaptive testing based on cognitive diagnosis models: A simulation study. Applied Psychological Measurement, 37(1), 3-23.

dina-model dino-model rule-space-methodology necessary-condition-analysis latent-transition-analysis


Cognitive Failures Questionnaire

The Cognitive Failures Questionnaire (CFQ) is a 25-item self-report instrument designed to measure the frequency of everyday cognitive lapses and failures in memory, attention, and action slips. Developed by Broadbent and colleagues at the University of Oxford in 1982, the CFQ assesses subjective cognitive complaints in the general population and across diverse clinical and occupational settings. Higher scores reflect more frequent subjective cognitive failures and are associated with stress, fatigue, mood disturbance, and, in some populations, objective cognitive impairment.

Self-report questionnaire of everyday cognitive failures

Broadbent, D. E., Cooper, P. F., FitzGerald, P., & Parkes, K. R. (1982). The Cognitive Failures Questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21(1), 1-16.

Wallace, J. C., Kass, S. J., & Stanny, C. J. (2002). The Cognitive Failures Questionnaire revisited: Dimensions and correlates. The Journal of General Psychology, 129(3), 238-256.

Mercier, L., & Desrochers, A. (2008). A cross-cultural study of the Cognitive Failures Questionnaire in younger and older adults. Journal of Psychoeducational Assessment, 26(2), 125-138.

prospective-retrospective-memory trail-making-test frontal-assessment-battery mmse saint-louis-mental-status


Cognitive Load Scale

The Cognitive Load Scale (CLS), developed by Fred Paas in 1992 and refined by Paas and colleagues in subsequent years, is a brief, single-item or multi-item self-report instrument for assessing the cognitive load (mental effort) imposed by a learning or task environment. Originating in cognitive load theory research, the CLS has become a fundamental measurement tool in educational psychology, instructional design, and human factors, used to evaluate how instructional materials, interface designs, or training methods affect learner or operator mental burden.

Paas, F. G. W. C. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429–434.

Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.

nasa-task-load-index workload-profile situational-awareness-rating user-experience-questionnaire


Cognitive Reflection Test

CRT
Frederick Reflection Test
Cognitive Reflection

The Cognitive Reflection Test (CRT) is a brief measure of cognitive reflection—the ability to override intuitive, reflexive answers in favor of deliberate, analytical reasoning. Participants answer problems that have an intuitive but incorrect answer and a correct answer requiring reflection. The CRT reveals individual differences in the tendency to engage in slow, deliberate thinking (System 2) versus relying on fast, intuitive judgments (System 1). CRT scores predict performance on reasoning tasks, economic decisions, and susceptibility to cognitive biases.

Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Literature, 43(4), 1047-1048.

Primi, C., Morsanyi, K., Chiesi, F., Donati, M. A., & Hamilton, J. (2014). The development and testing of a new version of the Cognitive Reflection Test applying Item Response Theory (IRT). Journal of Behavioral Decision Making, 27(5), 397-411.

Toplak, M. E., West, R. F., & Stanovich, K. E. (2011). The Cognitive Reflection Test as a predictor of performance on heuristics-and-biases tasks. Journal of Behavioral Decision Making, 24(2), 185-194.

rationality cognitive-bias dual-process-theory analytical-thinking


TICS

Modified Telephone Interview for Cognitive Status

The Telephone Interview for Cognitive Status (TICS) is a telephone-administered cognitive screening instrument developed by Breitner and colleagues in the late 1980s and modified (TICS-m) to assess cognitive function in older adults via remote interview. Designed for epidemiological studies and clinical research where in-person assessment is impractical or resource-intensive, the TICS combines questions assessing orientation, attention, language, memory, and reasoning in a format suitable for administration by trained interviewers without specialized clinical equipment. It has become widely used in longitudinal cohort studies, clinical trials, and telemedicine settings for cognitive screening and monitoring.

Breitner, J. C., Folstein, M. F., & Murphy, E. A. (1989). Familial aggregation in Alzheimer dementia: comparison of risk estimates. Genet Epidemiol, 6(1), 35-45.

Plassman, B. L., Welsh-Bohmer, K. A., Bigler, E. D., et al. (2007). Telephone assessment of cognitive function in older adults: the Adult Changes in Thought Study. Neuroepidemiology, 27(2), 92-100.

Breitner, J. C. S., Wyse, B. W., Anthony, J. C., et al. (1999). APOE-epsilon4 count predicts age when prevalence of AD increases, then declines: the Cache County Study. Neurology, 53(2), 321-331.

geriatric-anxiety-inventory social-engagement-scale life-space-assessment edmonton-frail-scale frail-scale


Cognitive Walkthrough

Clayton Lewis, Peter Polson, Cathleen Wharton, John Rieman

Cognitive Walkthrough is an inspection method for evaluating interface designs by simulating and analyzing how users will learn to use a system through exploration and trial. Developed by Clayton Lewis, Peter Polson, Cathleen Wharton, and John Rieman in 1990, this method is grounded in cognitive psychology and focuses specifically on learnability—whether first-time or occasional users can discover how to perform tasks without formal training. Evaluators role-play user actions, answer a set of critical questions about feedback and discovery at each step, and document usability problems.

Evaluative walkthrough examining how users learn to use an interface

Lewis, C., Polson, P. G., Wharton, C., & Rieman, J. (1990). Testing a walkthrough methodology for specifying and evaluating user interface designs. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 387–392).

Wharton, C., Rieman, J., Lewis, C., & Polson, P. (1994). The cognitive walkthrough method: A practitioner's guide. In J. Nielsen & R. L. Mack (Eds.), Usability Inspection Methods (pp. 105–140). John Wiley & Sons.

heuristic-evaluation think-aloud-protocol pluralistic-walkthrough klm-goms


Cohen's Kappa

Cohen's Kappa Coefficient of Inter-Rater Agreement

Cohen's Kappa (Değerlendiriciler Arası Uyum)

Cohen's kappa (κ) is a statistical measure of inter-rater reliability for categorical classifications, introduced by Jacob Cohen in 1960. Unlike simple percent agreement, kappa corrects for the level of agreement that would be expected purely by chance, making it the standard metric when two raters independently assign observations to the same set of mutually exclusive categories.

Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37–46.

Landis, J.R. & Koch, G.G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), 159–174.

fleiss-kappa intraclass-correlation mcnemar-test chi-square-test krippendorffs-alpha


Cohort-Component Projection

Component Method of Population Projection

Cohort-Component Projection is the standard demographic method for forecasting future population size and age-sex structure by explicitly tracking births, deaths, and migration for each age-sex cohort across discrete time steps. Systematically formalized in the textbook literature by Preston, Heuveline, and Guillot (2001), the method builds on foundational actuarial and demographic work dating to the early twentieth century and remains the workhorse technique used by national statistical offices and international organizations worldwide.

Age- and sex-specific fertility, mortality, and migration rates

Output: Future population by age, sex, and time step

Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Blackwell.


Cohort Study Design

prospective study
follow-up study
longitudinal study
cohort study

Donald Acheson, Olli Miettinen, and others in modern epidemiology

A cohort study follows a group of individuals forward in time from exposure to outcome. Exposed and unexposed participants (or participants with differing exposure levels) are enrolled at baseline, characterized, and observed prospectively until the outcome occurs or the study ends. Cohort studies are fundamental to epidemiology and are the design of choice for establishing causal associations when randomized trials are infeasible or unethical.

Miettinen, O. S. (1976). Estimability and estimation in case-referent studies. American Journal of Epidemiology, 103(2), 226–235.

Rothman, K. J., Lash, T. L., & Greenland, S. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.

Veierød, M. B., Lydersen, S., & Laake, P. (2012). Medical Statistics in Clinical and Epidemiological Research. Gyldendal Akademisk.

case-control-study-design cross-sectional-study-design relative-risk confounding-bias selection-bias


Cohort Study

longitudinal study
follow-up study
panel study
incidence study

Mid-20th century (formal epidemiological design codified ~1950s)

Doll & Hill (British Doctors Study, 1951); Snow (cholera, 1854)

A cohort study assembles a group of individuals who share a common starting point — typically freedom from the outcome of interest — and follows them over time to observe who develops the outcome. By comparing incidence rates between exposed and unexposed subgroups, researchers can estimate relative risk and absolute risk differences. Cohort studies are the gold-standard observational design for measuring disease incidence and establishing temporal relationships between exposure and outcome.

Longitudinal individual-level data; exposure and outcome measurements over time

Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.

Doll, R., & Hill, A. B. (1954). The mortality of doctors in relation to their smoking habits: a preliminary report. British Medical Journal, 1(4877), 1451–1455.

case-control-study randomized-clinical-trial cross-sectional-epidemiological-study survival-analysis nested-case-control prospective-cohort-study


Cointegration Test

Cointegration Test (Johansen / Engle-Granger)

The cointegration test examines whether non-stationary time series that each contain a unit root share a stable long-run equilibrium relationship. The single-equation residual approach was introduced by Engle and Granger (1987) and the system-based rank approach by Johansen (1988).

Johansen trace/maximum-eigenvalue (VECM) or Engle-Granger two-step residual test

Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.

Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.

vecm-model var-model arima ardl-bounds-test granger-causality


Cokriging

Cokriging (Multivariate Geostatistical Interpolation)

co-kriging
multivariate kriging
ortak kriging

Georges Matheron (geostatistics); multivariate extension

Cokriging extends kriging to use one or more correlated secondary variables to improve prediction of a primary variable. When the variable of interest is sparsely sampled but a related, cheaper-to-measure variable is densely sampled, cokriging borrows strength from the secondary variable through their cross-correlation, yielding more accurate interpolations and prediction variances than kriging the primary variable alone.

Output: Prediction + kriging variance

Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.

Cressie, N. A. C. (1993). Statistics for Spatial Data (Revised ed.). John Wiley & Sons.

kriging universal-kriging inverse-distance-weighting geographically-weighted-regression


Cold Storage Protocol

Refrigerated Storage Management for Extended Shelf Life and Quality Preservation

refrigerated storage
cold chain management
temperature-controlled storage

Cold storage protocol establishes optimal temperature, humidity, and duration guidelines for preserving fruit and vegetable quality during extended storage. By maintaining precise refrigeration conditions and monitoring produce condition, growers and distributors can extend shelf life from days to weeks or months, enabling long-distance trade and seasonal market smoothing. This foundational postharvest technique is essential to global fresh produce supply chains.

Kays, S. J., & Paull, R. E. (2004). Postharvest Biology (2nd ed.). Exon Press.

McCollum, T. G., & D'Aquino, S. (2007). Preharvest and postharvest factors influencing the quality of tropical and subtropical fruit. In Handbook of Fruits and Fruit Processing (pp. 583–600). Blackwell Publishing.

postharvest-storage-simulation brix-measurement ripeness-index fruit-color-analysis


Collaborative Filtering

Collaborative Filtering (Recommender Systems)

user-based collaborative filtering
item-based collaborative filtering
matrix factorization recommender
işbirlikçi filtreleme

GroupLens; Sarwar et al. (item-based); Koren et al. (matrix factorization)

Collaborative filtering recommends items to a user by leveraging the preferences of many users — 'people who liked what you liked also liked this'. It learns from a sparse user-item interaction matrix, either by finding similar users or items (neighbourhood methods, formalized by Sarwar et al. in 2001) or by factorizing the matrix into latent user and item factors (matrix factorization, popularized by Koren et al. after the Netflix Prize).

Recommendation from user-item interactions

Neighbourhood (user/item) and latent-factor (matrix factorization)

Input: User-item rating/interaction matrix

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, 285–295.

Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.

matrix-completion non-negative-matrix-factorization k-nearest-neighbors principal-component-analysis


The Collaborative Study Psychotherapy Rating Scale (CSPRS) is an observer-rated measure of therapist adherence to a psychotherapy protocol and general competence in delivering the intervention. Developed for the NIMH Treatment of Depression Collaborative Research Program, the CSPRS uses audiotape or videotape review to assess whether therapists follow intended treatment protocols and execute techniques skillfully. It is the gold standard instrument for fidelity and competence measurement in psychotherapy research and training.

Waltz, J., Addis, M. E., Koerner, K., & Jacobson, N. S. (1993). Testing the integrity of a psychotherapy protocol: Assessment of adherence and competence. Journal of Consulting and Clinical Psychology, 61(4), 620–630.

Shaw, B. F., Elkin, I., Yamaguchi, J. L., Olmsted, M., & Vallis, T. M. (1999). Adherence ratings and clinical outcome: A meta-analytic review. Journal of Consulting and Clinical Psychology, 67(2), 147–154.

working-alliance-inventory session-rating-scale therapeutic-alliance-scale outcome-rating-scale


CollaboRATE

CollaboRATE Shared Decision Making Scale

Collaborative Therapeutic Engagement Scale

CollaboRATE is a three-item patient-reported outcome measure designed to assess shared decision making (SDM) quality in clinical consultations. Developed by Glyn Elwyn and colleagues in 2013, it measures the degree to which clinicians involve patients in decisions about their care through simple, actionable items that are easy to administer and interpret. The scale has become a standard benchmark for evaluating SDM implementation in healthcare systems.

Elwyn, G., Barr, P. J., Grande, S. W., Thompson, R., Walsh, T., & Ozanne, E. M. (2013). Developing CollaboRATE: A fast and frugal patient-reported measure of shared decision making in clinical encounters. Patient Education and Counseling, 93(1), 102-107.

decisional-conflict-scale care-transitions-measure control-preferences-scale trust-in-physician-scale


Collectivism-Individualism Scale

Theodore M. Singelis and Hazel R. Markus

The Collectivism-Individualism Scale is a self-report measure designed to assess individual differences in independent versus interdependent self-construal and cultural orientation toward individualism and collectivism. Developed by Singelis (1994) and refined through subsequent research by Triandis and colleagues, the scale operationalizes self-concept dimensions as independent (autonomous, unique) or interdependent (connected, embedded in relationships). It has become a fundamental tool for cross-cultural psychology research.

Singelis, T. M. (1994). The measurement of independent and interdependent self-construals. Personality and Social Psychology Bulletin, 20(5), 580–591.

Triandis, H. C. (1995). Individualism and collectivism. Westview Press.

cultural-values-scale acculturation-scale social-dominance-orientation-scale social-capital-scale


Collocation Analysis

Birliktelik Analizi (Collocation Analysis)

Collocation analysis is a statistical text-mining technique that identifies word pairs or expressions that frequently occur together, using association measures rather than chance co-occurrence. Introduced in the lexicography work of Church and Hanks (1990), it is used for terminology extraction and language analysis, surfacing the multi-word units that carry meaning in a corpus.

Pointwise mutual information (PMI), frequency co-occurrence

Church, K.W. & Hanks, P. (1990). Word Association Norms, Mutual Information, and Lexicography. Computational Linguistics, 16(1), 22-29.

Manning, C.D. & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.


Color Harmony Analysis

Color Combination Analysis
Chromatic Harmony Evaluation

Color Harmony Analysis is a systematic method for evaluating the aesthetic coherence and visual appeal of color combinations in design. Rooted in foundational color theory principles established by Johannes Itten and Albert H. Munsell, this pipeline assesses how colors interact within compositions to create pleasing, balanced, and emotionally effective palettes.

Munsell, A. H. (1912). A Color Notation. Munsell Color Company.

Itten, J. (1970). The Elements of Color: A Treatise on the Color System of Johannes Itten. Van Nostrand Reinhold.

Birren, F. (1987). Selling Color to People. Citadel Press.

visual-complexity-measure color-palette-extraction image-aesthetics-assessment gestalt-principles-analysis visual-balance-measurement


Color Palette Extraction

Dominant Color Identification
Palette Mining

Color analysis and computational imaging

Color Palette Extraction is a computational method for automatically identifying the dominant and aesthetically significant colors within an image or design. By clustering and ranking color frequencies using computer vision techniques, this pipeline produces actionable color palettes suitable for design replication, brand identity development, or creative inspiration.

Hasan, M. K., & Findley, W. M. (2012). Computational Color Harmony. IEEE Transactions on Image Processing, 21(2), 827–837.

Lu, C., Shi, X., & Jia, Y. (2009). Dominant Color Extraction by Region-based Energy Minimization. IEEE Transactions on Image Processing, 18(8), 1860–1871.

O'Donovan, P., Agarwala, A., & Hertzmann, A. (2012). Color Compatibility from Large Datasets. ACM Transactions on Graphics, 30(4), 63:1–63:12.

color-harmony-analysis image-aesthetics-assessment visual-complexity-measure visual-balance-measurement gestalt-principles-analysis


Columbia Suicide Severity Rating Scale

Columbia-Suicide Severity Rating Scale (C-SSRS)

C-SSRS
Columbia Suicide Severity Rating Scale

The Columbia-Suicide Severity Rating Scale is a brief clinician-administered assessment of suicide risk developed by Kelly Posner and colleagues at Columbia University to address limitations in prior screening tools. First published in the American Journal of Psychiatry in 2011, the C-SSRS has become the FDA-endorsed standard for monitoring suicidal ideation and behavior in antidepressant, anticonvulsant, and neuropsychiatric medication trials. The scale assesses both suicidal ideation (frequency and intensity) and suicidal behavior (attempts, preparatory acts) over defined time windows, providing structured risk stratification.

Posner, K., Brown, G. K., Stanley, B., Brent, D. A., Yershova, K. V., Oquendo, M. A., & Shen, S. (2011). The Columbia-Suicide Severity Rating Scale: initial validity and internal consistency findings from three multisite studies with adolescents and adults. American Journal of Psychiatry, 168(12), 1266–1277.

Mundt, J. C., Greist, J. H., Jefferson, J. W., Federico, M., Mann, J. J., & Posner, K. (2013). Prediction of suicidal behavior in clinical trials of treatment for depression. Depression and Anxiety, 30(1), 22–29.

Stanley, B., & Brown, G. K. (2012). Safety planning intervention: a brief intervention to mitigate suicide risk. Cognitive and Behavioral Practice, 19(2), 256–264.

phq-9 bdi-ii patient-global-impression-change quick-inventory-depressive


Column Chromatography

liquid chromatography
column liquid chromatography

Column chromatography is a liquid separation technique in which a stationary phase (typically silica gel or alumina) is packed into a vertical column, and a mobile phase (solvent) percolates through it to separate mixture components. Pioneered by Mikhail Tsvet in 1903, column chromatography remains the workhorse of organic chemistry laboratories for purifying reaction products and isolating target compounds.

Skoog, D. A., Holler, F. J., & Crouch, S. R. (2017). Principles of Instrumental Analysis (7th ed.). Cengage Learning.

Still, W. C., Kahn, M., & Mitra, A. (1978). Rapid chromatographic purification based on solvent-induced density differences and easy detection. The Journal of Organic Chemistry, 43(14), 2923–2925.


Column Generation (Dantzig-Wolfe)

Column Generation (Dantzig-Wolfe Decomposition)

Dantzig-Wolfe decomposition
column generation method

Column Generation, developed by George B. Dantzig and Philip Wolfe in 1960, is a powerful optimization technique for solving large-scale linear programming problems with special structure. Also known as Dantzig-Wolfe Decomposition, it decomposes the problem into a master problem (restricted to a subset of variables/columns) and a pricing subproblem (identifying new variables), iteratively improving the solution by introducing only relevant columns.

Dantzig, G. B., & Wolfe, P. (1960). Decomposition principle for linear programs. Operations Research, 8(1), 101-111.

Gilmore, P. C., & Gomory, R. E. (1961). A linear programming approach to the cutting-stock problem. Operations Research, 9(6), 849-859.


Combat Exposure Scale

Keane, Fairbank, Caddell, Zimering, Taylor, & Mora

The CES is a 7-item self-report measure of combat exposure developed by Keane and colleagues in 1989. It quantifies the frequency and intensity of combat experiences, including direct fire, causalities witnessed, and hazardous mission environments. It is widely used in veteran research and clinical screening to characterize trauma load and risk for PTSD.

Keane, T. M., Fairbank, J. A., Caddell, J. M., Zimering, R. T., Taylor, K. L., & Mora, C. A. (1989). Clinical evaluation of a measure to assess combat exposure. Psychological Assessment, 1(1), 53-55.

Keane, T. M., Caddell, J. M., & Taylor, K. L. (1988). Mississippi Scale for Combat-Related PTSD: Three studies in reliability and validity. Journal of Consulting and Clinical Psychology, 56(1), 85-90.

pcl-military moral-injury-events-scale peritraumatic-distress-inventory deployment-risk-resilience


COMET

COMET (Characteristic Objects METhod) is a ranking multi-criteria decision-making (MCDM) method introduced by Sałabun, W. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fuzzy rule base on characteristic objects — rank-reversal free

Sałabun, W. (2015). The Characteristic Objects Method: A New Distance-based Approach to Multicriteria Decision-making Problems. Journal of Multi-Criteria Decision Analysis


Comfort Care Checklist

Comfort Care Checklist for the Last Hours of Life

Comfort Care Checklist
Last Hours Checklist

Hospice and palliative care organizations; End-of-Life Nursing Education Consortium (ELNEC)

The Comfort Care Checklist is a bedside verification tool designed to ensure comprehensive comfort and dignity in the final hours to days of life. Developed by hospice and palliative care organizations, particularly within the End-of-Life Nursing Education Consortium (ELNEC), the checklist systematically verifies that pain and other symptoms are managed, family is present and supported, spiritual needs are addressed, and documentation reflects the patient's and family's wishes—ensuring nothing essential is overlooked during the most vulnerable time.

Naylor, M. D., Bowles, K. H., & Brooten, D. A. (2002). Patients' and caregivers' perspectives on preparing for hospital discharge. Journal of Cardiovascular Nursing, 16(5), 36–48.

Reuben, D. B., & Tinetti, M. E. (2012). Goal-oriented patient care—An alternative health outcomes paradigm. New England Journal of Medicine, 366(9), 777–779.

palliative-performance-scale mcgill-quality-of-life patient-dignity-inventory support-team-assessment-schedule needs-assessment-palliative


Common Factors Questionnaire

The Common Factors Questionnaire (CFQ) is a structured client-report measure that quantifies the client's perception of therapeutic factors deemed common to effective psychotherapy across all modalities—including alliance, therapist empathy, client agency, goal clarity, and emotional expression. Based on Lambert's contextual model and Wampold's therapeutic relationship framework, the CFQ operationalizes the empirical finding that 70% or more of therapy outcome variance is attributable to common factors (relationship, expectancy, therapeutic environment) rather than specific technique. It is used in research to examine mechanisms of change and to compare common factors across therapy types.

Lambert, M. J., & Barley, D. E. (2001). Research summary on the therapeutic relationship and psychotherapy outcome. Psychotherapy: Theory, Research, Practice, Training, 38(4), 357–361.

Wampold, B. E. (2001). The great psychotherapy debate: Models, methods, and findings. Mahwah, NJ: Lawrence Erlbaum.

working-alliance-inventory session-rating-scale helpful-aspects-of-therapy therapeutic-alliance-scale


Common Spatial Pattern

Common Spatial Pattern (CSP) is a spatial filtering technique that identifies electrode combinations that maximize the variance difference between two classes of EEG activity, typically used in brain-computer interfaces to enhance motor imagery discrimination. Introduced by Ramoser and colleagues in 2000, CSP has become a standard feature extraction method in BCI research.

Spatial filtering and feature extraction

Ramoser, H., Mueller-Gerking, J., & Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4), 441-446.

Koles, Z. J., Lazar, M. S., & Zhou, S. Z. (1991). Spatial patterns underlying population differences in the background EEG. Brain Topography, 2(4), 275-284.


Commonsense Reasoning

Sağduyu Akıl Yürütme (Commonsense Reasoning)

Sap et al. (ATOMIC, 2019); Zellers et al. (HellaSwag, 2019)

Commonsense reasoning in NLP refers to the capacity of a language model or inference system to draw on implicit, world-knowledge facts that humans take for granted — facts not stated in the text — to answer questions, complete stories, or interpret dialogue. Landmark benchmarks formalising the task include ATOMIC (Sap et al., 2019), an if-then commonsense knowledge graph, and HellaSwag (Zellers et al., 2019), a sentence-completion challenge that exposed gaps in machine understanding of everyday events.

Inferred implicit knowledge or selected plausible continuation

Sap, M. et al. (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI.

Zellers, R. et al. (2019). HellaSwag: Can a Machine Really Finish Your Sentence? ACL.

question-answering neural-machine-reading knowledge-graph-nlp bert-embeddings retrieval-augmented-generation semantic-role-labeling