Canopy Cover Estimation

Forest Canopy Closure Assessment and Overstory Quantification

Canopy closure measurement
Crown cover estimation
Overstory density assessment

Forest structure assessment and remote sensing

Canopy cover, or canopy closure, is the proportion of ground area covered by tree crowns when viewed from above, typically expressed as a percentage. Formalized by Jennings and colleagues in pioneering work on tropical forest structure, canopy cover estimation employs multiple methods—from field-based ocular assessment to sophisticated remote sensing and terrestrial LiDAR—providing essential data on forest structure, light availability, and habitat characteristics relevant to ecology, silviculture, and climate research.

Jennings, S. B., Brown, N. D., & Sheil, D. (2000). Assessing Forest Canopies and Understorey Illumination: Methods and Applications. Forest Ecology and Management, 129(1-3), 219–243.

Fiala, A. C. S., Garman, S. L., & Whissel, A. N. (2006). Comparison of Five Small-Footprint LiDAR Systems. Photogrammetric Engineering & Remote Sensing, 72(3), 339–354.

Moeslund, J. E., Arge, L., Bøcher, P. K., et al. (2013). Topographically Induced Variation in Vegetation Predicts Forest Growth. Journal of Biogeography, 40(12), 2379–2391.

Cutler, D. R., Edwards, T. C., Beard, K. H., et al. (2012). Random Forests for Classification in Ecology. Ecology, 88(11), 2783–2792.

forest-inventory-sampling stand-basal-area-measurement tree-height-measurement biodiversity-index-forest


Canopy Gap Fraction

Canopy gap fraction quantifies the proportion of sky visible through the forest canopy, expressed as a percentage. Developed to measure light availability in the understory, it is a standard metric in forest ecology for characterizing canopy structure and microhabitat conditions. This measure is essential for understanding light-limited photosynthesis and seedling establishment in closed-canopy forests.

Machado, J.-L., & Reich, P. B. (1999). Evaluation of several measures of canopy openness. Canadian Journal of Forest Research, 29(9), 1439–1444.

Scarff, F. R., & Westoby, M. (2006). Leaf litter flammability in some semi-arid Australian woodlands and grasslands. International Journal of Wildland Fire, 15(2), 169–180.


Canopy Interception Modeling

interception loss modeling
canopy rainfall partitioning
forest interception modeling
throughfall-stemflow modeling

1971–1979 (foundational models; continuous development since)

Multiple contributors (Rutter et al. 1971; Gash 1979 for principal analytical frameworks)

Canopy interception modeling quantifies the fraction of rainfall captured by plant canopies and subsequently evaporated back to the atmosphere before reaching the soil. Applied across agronomy, forestry, and hydrology, it partitions gross precipitation into throughfall, stemflow, and interception loss. By linking vegetation structure — particularly leaf area index and canopy storage capacity — to water balance components, the method informs irrigation scheduling, watershed management, and crop water-use estimation.

Precipitation time-series, leaf area index, canopy storage capacity, meteorological measurements

Rutter, A. J., Kershaw, K. A., Robins, P. C., & Morton, A. J. (1971). A predictive model of rainfall interception in forests. Agricultural Meteorology, 9, 367–384.

Gash, J. H. C. (1979). An analytical model of rainfall interception by forests. Quarterly Journal of the Royal Meteorological Society, 105(443), 43–55.

evapotranspiration-modeling hydrological-modeling rainfall-runoff-modeling leaf-area-index-estimation water-balance-modeling throughfall-measurement


CAPM

Capital Asset Pricing Model
Sharpe-Lintner CAPM
security market line
Sermaye Varlıkları Fiyatlama Modeli

The Capital Asset Pricing Model (CAPM), developed by William Sharpe and John Lintner in the mid-1960s, links the expected return of an asset to its systematic risk, measured by beta. It states that in equilibrium investors are rewarded only for risk that cannot be diversified away: the expected excess return of an asset is proportional to the expected excess return of the market, with beta as the constant of proportionality. CAPM underpins the cost of equity, performance benchmarking, and a vast body of asset-pricing research.

Output: Expected return for a given level of systematic risk

Mean-variance investors, single period, frictionless markets

Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442.

Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 47(1), 13–37.

mean-variance-portfolio-optimization factor-risk-model principal-component-risk-factors ols-regression


Clinician-Administered PTSD Scale

Frank W. Weathers, Brett T. Litz, Terence M. Keane, and colleagues

The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) is the gold standard structured interview for assessing posttraumatic stress disorder (PTSD) in adults. Developed by Weathers, Litz, and Keane, the CAPS-5 directly operationalizes DSM-5 PTSD diagnostic criteria and assesses the frequency and intensity of symptoms in the four criterion clusters: re-experiencing, avoidance, negative cognition-mood changes, and hyperarousal.

Weathers, F. W., Blake, D. D., Schnurr, P. P., Kaloupek, D. G., Marx, B. P., & Keane, T. M. (2013). The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). National Center for PTSD.

Bovin, M. J., Marx, B. P., Weathers, F. W., Dickstein, B. D., Thompson, K. E., Schnurr, P. P., ... & Keane, T. M. (2008). Psychometric properties of the PTSD Checklist and Clinician-Administered PTSD Scale in combat veterans. Psychological Assessment, 20(1), 22-32.


Capsule Network

Kapsül Ağı (CapsNet)
CapsNet
capsule net
dynamic routing network

A Capsule Network (CapsNet) is a deep learning architecture introduced by Sara Sabour, Nicholas Frosst and Geoffrey Hinton in 2017 that organises neurons as vectors (capsules) rather than scalar activations, so that spatial hierarchy and pose (orientation) information are encoded directly. It was proposed to overcome the fragility of convolutional networks to changes in viewpoint.

Deep learning architecture (vector capsules with dynamic routing)

Sabour, S., Frosst, N. & Hinton, G. E. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS).

Hinton, G. E., Sabour, S. & Frosst, N. (2018). Matrix Capsules with EM Routing. International Conference on Learning Representations (ICLR).

convolutional-neural-network random-forest svm-classification knowledge-distillation neural-architecture-search


Capture-Recapture

Mark-Recapture
Tag-Recapture
Mark-Release-Recapture
İşaretle-Yeniden Yakala

Capture-recapture (also known as mark-recapture) is a statistical method for estimating the size of an unknown population by sampling it twice and tracking which individuals appear in both samples. Formally systematized for closed animal populations by Otis, Burnham, White, and Anderson in their landmark 1978 Wildlife Monographs paper, the method extends naturally to human populations, epidemiology, and incomplete administrative records.

Two or more independent samples with individual identification

Output: Estimated population size N with confidence interval

Otis, D. L., Burnham, K. P., White, G. C., & Anderson, D. R. (1978). Statistical inference from capture data on closed animal populations. Wildlife Monographs, 62, 3–135.


Carbon-13 Discrimination Analysis

Carbon-13 Isotope Discrimination Analysis

delta-13C analysis
carbon isotope discrimination
13C discrimination
isotopic discrimination analysis

Carbon-13 Discrimination Analysis quantifies the degree to which C3 plants preferentially fix the lighter carbon isotope (12C) over the heavier 13C during photosynthesis. The resulting discrimination value (Delta) is closely linked to the ratio of internal to ambient CO2 concentration, making it a reliable, integrative proxy for intrinsic water-use efficiency across the growing season. The technique is widely used in agronomy, plant physiology, and crop breeding.

Isotopic measurement and analysis technique

Plant tissue samples analysed by isotope ratio mass spectrometry (IRMS)

Farquhar, G. D., Ehleringer, J. R., & Hubick, K. T. (1989). Carbon isotope discrimination and photosynthesis. Annual Review of Plant Physiology and Plant Molecular Biology, 40, 503–537.

Condon, A. G., Richards, R. A., Rebetzke, G. J., & Farquhar, G. D. (2004). Breeding for high water-use efficiency. Journal of Experimental Botany, 55(407), 2447–2460.

water-use-efficiency stable-isotope-analysis gas-exchange-analysis drought-tolerance-assessment leaf-area-index canopy-photosynthesis


Carbon Accounting

GHG Accounting
Greenhouse Gas Accounting
Corporate Carbon Footprinting
Karbon Muhasebesi

Carbon accounting is a systematic process-pipeline method for identifying, quantifying, and reporting an organization's greenhouse gas (GHG) emissions in CO₂-equivalent units. Codified by the WRI/WBCSD Greenhouse Gas Protocol in 2004, it is used by corporations, governments, and NGOs to measure their climate impact, set reduction targets, comply with regulatory disclosure requirements, and track progress toward net-zero commitments.

Process pipeline / Environmental accounting

CO₂, CH₄, N₂O, HFCs, PFCs, SF₆ (Kyoto basket)

World Resources Institute & WBCSD (2004). The Greenhouse Gas Protocol: A Corporate Accounting and Reporting Standard (Revised ed.).


Carbon Footprint Analysis

Quantification of Greenhouse Gas Emissions from Activities and Products

GHG accounting
life cycle carbon assessment
carbon inventory
emissions quantification

Climate change mitigation and accounting

IPCC and life cycle assessment community

Carbon footprint analysis quantifies the total greenhouse gas (GHG) emissions—expressed in CO2-equivalent (CO2e)—attributable to an activity, product, organization, or process. Developed from life cycle assessment (LCA) and Intergovernmental Panel on Climate Change (IPCC) methodologies, carbon accounting encompasses direct emissions (operations, combustion) and indirect emissions (supply chain, energy consumption, waste). Carbon footprints inform climate mitigation strategies, corporate sustainability reporting, product labeling, and carbon pricing mechanisms.

International Organization for Standardization. (2018). ISO 14044:2006 Environmental Management – Life Cycle Assessment – Requirements and Guidelines.

World Resources Institute & World Business Council for Sustainable Development. (2011). The Greenhouse Gas Protocol: A Corporate Accounting and Reporting Standard (Revised Edition). WRI/WBCSD.

Weidema, B. P., et al. (2013). The ecoinvent Database – Overview and Methodology. ecoinvent Report No. 1(v3). Swiss Centre for Life Cycle Inventories.


CFAS

carbon emissions awareness and accountability

Alan Collins, Stefan Gössling, C. Michael Hall

The Carbon Footprint Awareness Scale (CFAS) measures individuals' knowledge, consciousness, and sense of responsibility regarding their carbon emissions—how much people understand the carbon impacts of their consumption, energy use, and travel patterns. Developed by Collins, Gössling, and Hall (2011) for sustainability tourism research and extended to general populations, the CFAS captures awareness of carbon-intensive activities, estimation accuracy of personal emissions, and commitment to carbon reduction. The scale is critical for evaluating climate communication effectiveness, identifying knowledge gaps that block behavior change, and assessing whether carbon labeling, footprint calculators, and climate education successfully shift consciousness of personal climate impact.

Self-report awareness and knowledge scale

Collins, A., Gössling, S., & Hall, C. M. (2011). Assessing the environmental impacts of tourism: Development of a carbon footprint toolkit. Journal of Sustainable Tourism, 19(4–5), 497–516.

Jones, C. M., & Kamstra, M. J. (2013). Costly public pressure and the undersupply of privacy. Review of Economic Studies, 80(4), 1269–1295.


Carbon Stock Estimation in Forests

Forest Carbon Inventory and Greenhouse Gas Quantification

Forest carbon accounting
Biomass-to-carbon conversion
Forest carbon flux assessment

Brown, Chave, and colleagues; IPCC consensus

Forest carbon stock estimation quantifies the amount of carbon stored in tree biomass and other forest components, typically expressed in tonnes of carbon per hectare. Formalized by Brown, Chave, and international bodies such as the IPCC and FAO, this method is foundational for climate change mitigation accounting, carbon credits, and monitoring progress toward climate commitments. Accurate carbon assessment enables identification of high-priority reforestation areas and verification of carbon offset projects.

IPCC (2019). Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. CH4: Agriculture, Forestry and Other Land Use.

FAO (2015). Forest Resources Assessment 2015: Desk Reference. Food and Agriculture Organization.

Brown, S. (1997). Estimating Biomass and Biomass Change of Tropical Forests. FAO Forestry Paper 134.

Chave, J., Réjou-Méchain, M., Búrquez, A., et al. (2014). Improved Allometric Models to Estimate the Aboveground Biomass of Tropical Trees. Journal of Geophysical Research: Biogeosciences, 119(4), 660–680.

allometric-biomass-equation forest-inventory-sampling stand-basal-area-measurement tree-height-measurement


Card Sorting

Card Sort
Open Card Sorting
Closed Card Sorting

Card Sorting is a participatory design technique where users organize content items (represented on cards) into logical groups and categories. Used primarily for information architecture design, card sorting reveals how users naturally think about and categorize content, providing empirical data for navigation hierarchies, menu structures, and taxonomy design. The method exists in open form (users create their own categories) and closed form (users organize cards into predefined categories), each revealing different insights about user mental models and organization preferences.

Participatory technique for validating or designing information structures

Spencer, D. (2009). Card Sorting: Designing Usable Categories. Rosenfeld Media.

Tullis, T., & Wood, L. (2004). How many users are enough for a card sort? Usability News, 6(1).

tree-testing first-click-testing contextual-inquiry heuristic-evaluation


Care Dependency Scale

Care Dependency Scale for Patient Acuity Assessment

CDS
Dependency Assessment
Nursing Care Level
Acuity Score

Functional assessment and dependency evaluation

The Care Dependency Scale (CDS) is a comprehensive assessment tool that measures the degree of care dependency in patients by evaluating their ability to perform activities of daily living and manage their health conditions independently. Developed by Atie Dijkstra and colleagues, the CDS focuses on physical independence and social engagement, providing a quantitative measure of nursing workload and care requirements. It is particularly useful in long-term care, rehabilitation, and geriatric settings.

Dijkstra, A., Buist, G., Dassen, T., & Frijlink, M. (2000). Diagnostics: The Care Dependency Scale. Scandinavian Journal of Caring Sciences, 14(3), 229-234.

Scherbaum, V., Kiesswetter, E., Brockmöller, J., et al. (2014). The Care Dependency Scale: predictive validity for adverse health outcomes in older people. Journal of the American Geriatrics Society, 62(9), 1691-1698.

braden-scale norton-scale nursing-sensitive-indicators barthel-index


Care Transitions Measure

The Care Transitions Measure (CTM-3) is a three-item patient-reported outcome instrument that assesses how well patients feel prepared for the transition from one care setting to another—for example, from hospital to home, from acute care to rehabilitation, or from hospital to primary care. Developed by Carla Parry and colleagues in 2008, the CTM-3 measures whether patients received adequate preparation for self-care, understood their care plan, and felt supported in managing their transition. The measure is widely used to evaluate care coordination and transition planning quality, and has become a standard metric in quality improvement and research on hospital discharge and continuity of care.

Parry, C., Wolcott, J., Chuo, J., & Seasock, K. (2008). Care Transitions Measure: the development and testing of a measure designed to assess adequacy of preparation for patients transitioning between levels of care. Journal of Clinical Outcomes Management, 15(8), 417-423.

Coleman, E. A., et al. (2009). Orienting patients and caregivers to aspects of hospital to home transition through the Care Transitions Intervention. Journal of the American Geriatrics Society, 57(7), 1337-1343.

patient-enablement-instrument collaboste-scale patient-reported-communication-scale trust-in-physician-scale


Career Adapt-Abilities Scale

The Career Adapt-Abilities Scale (CAAS) measures the psychosocial resources and competencies that enable individuals to navigate career challenges and transitions. Developed by Savickas and Porfeli in 2012, the 24-item scale quantifies four dimensions: concern (future orientation), control (agency), curiosity (exploration), and confidence (self-efficacy). Career adaptability predicts career satisfaction, employability, and successful adaptation to workforce changes.

Savickas, M. L., & Porfeli, E. J. (2012). Career Adapt-Abilities Scale: Construction, reliability, and measurement equivalence across 13 countries. Journal of Career Assessment, 20(4), 430–446.

Rudolph, C. W., Lavigne, D. L., & Zacher, H. (2017). Career adaptability: A meta-analysis of its relationships with measures of adaptability, personality, big five, and organizational variables. Journal of Vocational Behavior, 98, 17–34.

Savickas, M. L. (2013). Career construction theory and practice. New Directions for Career Development, 144, 147–159.


Caregiver Quality of Life Index-Cancer

Weitzner, Jacobsen, Wagner, and Friedland

The Caregiver Quality of Life Index–Cancer (CQOLC) is a 35-item self-report measure specifically designed to assess the quality of life and burden experienced by family members caring for cancer patients. Developed by Weitzner and colleagues in 1999, the CQOLC captures the multifaceted impact of caregiving—physical strain, emotional toll, disruption of daily activities, financial hardship, and positive adaptation—making it essential for identifying caregiver distress and tailoring support interventions.

Weitzner, M. A., Jacobsen, P. B., Wagner, H., & Friedland, J. L. (1999). The Caregiver Quality of Life Index–Cancer (CQOLC) scale: development and validation of an instrument to measure quality of life of the primary family caregiver of patients with cancer. Quality of Life Research, 8(1), 55–63.

Sherwood, P. R., Given, C. W., Given, B. A., & von Eye, A. (2007). Caregiver burden and depressive symptoms: Analysis of common outcomes in caregivers of elderly patients. Journal of Aging and Health, 17(1), 125–147.

mcgill-quality-of-life needs-assessment-palliative support-team-assessment-schedule palliative-performance-scale spiritual-wellbeing-scale


Carr-Madan FFT

Carr-Madan Fast Fourier Transform Option Pricing

FFT Pricing
Characteristic Function Method

The Carr-Madan Fast Fourier Transform (1999) is a highly efficient method for computing option prices across a range of strikes using characteristic functions and FFT. It enables rapid pricing of European options under any model with a known characteristic function (Heston, Merton jumps, Variance Gamma), with computational complexity that scales logarithmically in the number of strikes.

Carr, P., & Madan, D. B. (1999). Option valuation using the fast Fourier transform. Journal of Computational Finance, 2(4), 61-73.

Lee, R. W. (2004). Option pricing by transform methods: extensions, unification, and error analysis. Journal of Computational Finance, 7(3), 51-102.


Case-Based Reasoning

CBR
case-based reasoning cycle
analogy-based reasoning
vaka tabanlı akıl yürütme

Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)

Case-based reasoning solves a new problem by retrieving similar problems solved in the past and adapting their solutions, rather than reasoning from first principles or a trained statistical model. Formalized as the Retrieve-Reuse-Revise-Retain cycle by Aamodt and Plaza in 1994 and popularized by Janet Kolodner, CBR mirrors how human experts in medicine, law, and engineering reason by analogy from remembered cases, and it learns simply by storing each newly solved case.

Experience-based (analogical) problem solving

Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.

Kolodner, J. L. (1992). An introduction to case-based reasoning. Artificial Intelligence Review, 6(1), 3–34.

k-nearest-neighbors decision-tree rough-set-theory fuzzy-cognitive-maps


Case-Cohort Design

Case-cohort design is an epidemiological study design developed by Prentice (1986) that efficiently combines features of case-control and cohort studies. Researchers enroll an entire cohort, follow it for outcomes, then measure exposures only on cases and a random subcohort, reducing measurement costs while maintaining valid causal inference.

Prentice, R. L. (1986). A case-cohort design for epidemiologic cohort studies and disease prevention trials. Biometrika, 73(1), 1-11.

Barlow, W. E., Ichikawa, L., Rosner, D., & Izumi, S. (1999). Analysis of case-cohort designs. Journal of Clinical Epidemiology, 52(12), 1165-1172.

Kang, S., & Cai, J. (2009). Spatial matched-pair cohort studies. Biometrics, 65(2), 526-534.

value-added-modeling latent-transition-analysis pls-sem rule-space-methodology


Case-Control Study Design

Case-Control Study (Retrospective Case-Control Design)

case-control study
retrospective study
matched case-control
nested case-control

Jerome L. Schlesselman, Brian MacMahon, Thomas Pugh

A case-control study identifies individuals with a disease or outcome (cases) and a comparison group without the outcome (controls), then measures prior exposure retrospectively. Developed in the 1950s–1970s by epidemiologists like Schlesselman and MacMahon, case-control studies are especially efficient for rare diseases, as they sample cases enriched for the outcome, avoiding the need for enormous cohorts. They are a mainstay of clinical epidemiology, observational research, and outbreak investigations.

Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.

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

Greenland, S., & Thomas, D. C. (1990). On the need for the rare disease assumption in case-control studies. American Journal of Epidemiology, 132(2), 374–375.

cohort-study-design cross-sectional-study-design odds-ratio matching-in-case-control selection-bias


Case-control study

case-referent study
case-control design
retrospective case-control
case-control analysis

1950s (formal methodology); precursors in the 1920s

Janet Lane-Claypon (early precursors, 1926); formalized by Brian MacMahon and Jerome Cornfield in the 1950s–1960s

A case-control study is a retrospective observational design in which individuals who have developed a disease or outcome of interest (cases) are compared with individuals who have not (controls) to determine whether prior exposure to a putative risk factor differs between the two groups. The primary measure of association is the odds ratio, which approximates the relative risk when the outcome is rare. Case-control studies are especially efficient for investigating rare diseases and generating etiological hypotheses.

Categorical / binary outcome; historical exposure data (medical records, interviews, biomarkers)

Schlesselman, J.J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.

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

cohort-study cross-sectional-epidemiological-study nested-case-control randomized-clinical-trial matched-case-control-study diagnostic-accuracy-study


Case-crossover design

case-crossover study
CCO design
self-matched case study
within-person crossover case study

The case-crossover design is an observational epidemiological method that estimates whether a transient exposure triggers an acute event by comparing each case's exposure during a brief hazard window immediately before the event to their own exposure during earlier control periods. Because each person serves as their own control, all stable personal characteristics are automatically adjusted for, making the design especially powerful for studying intermittent exposures and sudden-onset outcomes such as myocardial infarction, stroke, or injury.

Observational epidemiological study design

Individual-level time-varying exposure data linked to acute event occurrence

Maclure, M. (1991). The case-crossover design: A method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.

Mittleman, M. A., Maclure, M., & Robins, J. M. (1995). Control sampling strategies for case-crossover studies: An assessment of relative efficiency. American Journal of Epidemiology, 142(1), 91–98.

case-control-study nested-case-control cohort-study time-series-analysis crossover-trial self-controlled-case-series


Case-Focused Mixed Methods Design

Case-Focused Mixed Methods Research Design

case-study mixed methods
embedded case mixed methods
case-oriented mixed design
CFMMD

Creswell & Plano Clark; draws on Yin's case study framework

Case-focused mixed methods design integrates qualitative and quantitative data-collection strands within one or more bounded cases — specific settings, organizations, programs, or individuals. The design harnesses the contextual depth of case study methodology alongside the corroborative or complementary power of mixed data types, enabling researchers to build rich, multi-faceted accounts of complex phenomena situated in real-world contexts.

Qualitative (interviews, documents, observation) and quantitative (surveys, archival data) within bounded case(s)

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.

Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage Publications.


Case Law Analysis

Case Law Analysis (Judicial Decision Analysis)

judicial decision analysis
legal case analysis
jurisprudential analysis
case-based legal research

Medieval English common law; academic formalisation 19th–20th century

Common law tradition (England); systematised in Anglo-American jurisprudence

Case law analysis is a systematic method for examining judicial decisions to identify binding legal rules, evolving doctrines, and interpretive trends. Rooted in the common law tradition of stare decisis, it requires the researcher to locate the ratio decidendi — the binding reasoning — of each decision, distinguish it from obiter dicta, and trace how that reasoning has been applied, distinguished, or overruled across subsequent cases. The method is fundamental to legal scholarship, litigation strategy, and law reform research.

Judicial decisions, court opinions, dissenting judgments, ratio decidendi

Hutchinson, T. (2010). Researching and Writing in Law (3rd ed.). Thomson Reuters.

doctrinal-legal-research comparative-legal-analysis legal-content-analysis hermeneutic-analysis discourse-analysis textual-criticism


Case Report

Case Report (Detailed Report of a Single Patient or Small Series)

case series
case study
clinical case
patient report

A case report is a detailed clinical account of one patient's diagnosis, treatment, and outcome, typically used to describe novel, unusual, or educational cases not previously reported. Unlike controlled studies with comparison groups, case reports are observational, non-comparative, and generate hypotheses rather than test them. Occupying the lowest rung of evidence hierarchy, case reports are nonetheless valuable for early signal detection, documenting rare diseases, and communicating clinical wisdom. The CARE guidelines (2013) provide reporting standards to ensure completeness and transparency.

Gagnier, J. J., Kienle, G., Altman, D. G., Moher, D., Lakatos, P., & Conboy, T. A. (2013). The CARE guidelines: consensus-based clinical case report guideline development. Journal of Clinical Epidemiology, 67(1), 46–51.

CARE Guidelines Checklist (2013). Enhancing the Quality and Transparency of Health Research. http://www.care-statement.org

Nissen, S. E., & Wolski, K. (2007). Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. New England Journal of Medicine, 356(24), 2457–2471.

original-research-article literature-review-article qualitative-research-methods rare-disease-surveillance


Case series

case series report
clinical case series
consecutive case series
patient series

Longstanding; systematized in 20th century clinical research

Historical clinical practice; formalized in modern evidence-based medicine literature

A case series is a descriptive observational study that documents the characteristics, clinical course, and outcomes of a group of patients who share a common condition, exposure, or intervention. Unlike case reports, which focus on a single patient, a case series aggregates data across multiple patients (typically three or more) to identify patterns, generate hypotheses, and characterize rare or novel conditions — without a concurrent control group.

Clinical records, patient charts, prospective or retrospective patient data

Murad, M. H., Sultan, S., Haffar, S., & Bazerbachi, F. (2018). Methodological quality and synthesis of case series and case reports. BMJ Evidence-Based Medicine, 23(2), 60–63.

case-report cohort-study case-control-study cross-sectional-epidemiological-study diagnostic-accuracy-study nested-case-control


Case Study Research

Case Study
Single Case Study
Multiple Case Study

Robert K. Yin; Robert E. Stake; Sharan Merriam

Case study research is an intensive, contextual investigation of a single case (or small number of cases) to explore a phenomenon in depth. Developed systematically by Robert K. Yin (1984) and Robert E. Stake (1995), case study research employs multiple data sources (interviews, observation, documents, artifacts) to produce a holistic understanding of a bounded phenomenon within its real-world context.

Yin, R. K. (2014). Case study research: Design and methods (5th ed.). Sage Publications.

Stake, R. E. (1995). The art of case study research. Sage Publications.

Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.

ethnographic-research narrative-inquiry holistic-analysis intrinsic-case-study instrumental-case-study


Case Study

Vaka Çalışması (Case Study)
case study design
case study methodology

Robert K. Yin (systematised in Case Study Research, 1984)

Case study research is a qualitative research design that investigates a specific phenomenon, individual, group, organisation, or event in depth within its real-world context. Systematised by Robert K. Yin in 1984, it supports single-case and multiple-case designs and draws on multiple data sources — interviews, observation, documents, and artefacts — to build a rich, contextualised account of a bounded unit.

Single-case / multiple-case (cross-case comparison)

Interviews, observation, documents, artefacts

Yin, R.K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.

thematic-analysis narrative-analysis content-analysis ethnography grounded-theory


CASP RCT Checklist

Critical Appraisal Skills Programme Randomised Controlled Trial Checklist

Critical Appraisal Skills Programme (Oxford, UK)

The Critical Appraisal Skills Programme (CASP) RCT Checklist is a practical, widely adopted tool developed by the UK-based Critical Appraisal Skills Programme (founded 1993) for assessing the methodological quality and relevance of published randomized controlled trials. Unlike numeric scoring scales, it uses 11 structured questions with yes/no/cannot tell responses to guide critical appraisal in a format accessible to busy clinicians, researchers, and educators.

Clinician-rated / Research team assessment

Critical Appraisal Skills Programme (CASP). (1993, updated). CASP Randomised Controlled Trials Checklist. University of Oxford, UK. www.casp-uk.net

cochrane-risk-of-bias consort-reporting-checklist grade-evidence-profiling prisma-checklist


CatBoost

CatBoost (Categorical Boosting)
categorical boosting
ordered boosting
kategorik gradyan artırma

CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data.

Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.

xgboost random-forest adaboost decision-tree logistic-regression


Cation Exchange Capacity

Cation Exchange Capacity (CEC) Measurement and Interpretation

CEC
Soil nutrient retention
Base saturation

Georg Wiegner, Heinrich Rotter, Melvin E. Sumner

Cation exchange capacity (CEC) is a fundamental soil property that measures the soil's ability to hold and release positively charged nutrient ions (cations: K⁺, Ca²⁺, Mg²⁺, Na⁺, H⁺, Al³⁺) in forms available to plant roots. CEC reflects the amount and type of clay minerals and organic matter in the soil—compounds with negatively charged surface sites that attract and temporarily bind cations. High CEC soils retain nutrients longer and require less frequent fertilization; low CEC soils lose nutrients rapidly through leaching.

Thomas, G. W. (1982). Exchangeable cations. In A. L. Page, R. H. Miller, & D. R. Keeney (Eds.), Methods of soil analysis. Part 2: Chemical and microbiological properties (2nd ed., pp. 159-165). American Society of Agronomy.

Sumner, M. E., & Miller, W. P. (1994). Cation exchange capacity and exchange coefficients. In R. A. Feet (Ed.), Methods of soil analysis (3rd ed., pp. 1201-1229). American Society of Agronomy.

Bouldin, D. R., & Thorne, M. (1997). Charge and non-charge effects on cation exchange reactions in soils. Soil Science Society of America Journal, 61(1), 25-32.


Causal-Comparative Research

ex post facto research
causal-comparative design
retrospective causal study
CCR

Causal-comparative research is a non-experimental quantitative design in which the researcher compares two or more groups that already differ on an independent variable — one that was not manipulated — to investigate possible causes or consequences of that difference. Because group membership is pre-existing rather than randomly assigned, the design can suggest causal relationships but cannot establish them with the certainty of a true experiment. It is widely used in education, psychology, and social sciences when experimental manipulation is impractical or unethical.

Non-experimental quantitative research design

Existing group membership data, continuous or categorical outcome measures

Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill.

correlational-research ex-post-facto-design descriptive-research quasi-experimental-design cross-sectional-research longitudinal-research


Causal Discovery Algorithms

Causal Discovery Algorithms (PC, FCI, LiNGAM)

Nedensel Keşif Algoritmaları (PC, FCI, LiNGAM)

Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)

Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges.

Constraint-based search (PC, FCI) and functional/non-Gaussian models (LiNGAM)

directed acyclic graph (causal structure)

Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press.

Shimizu, S., Hoyer, P. O., Hyvärinen, A., & Kerminen, A. (2006). A Linear Non-Gaussian Acyclic Model for Causal Discovery. Journal of Machine Learning Research, 7, 2003-2030.

dag-identification ols-regression propensity-score-matching instrumental-variables difference-in-differences


Causal Impact Analysis

Bayesian Structural Time-Series Causal Impact Analysis

CausalImpact
BSTS causal inference
Bayesian causal impact
counterfactual time-series analysis

Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)

Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.

Bayesian causal inference / counterfactual forecasting

Univariate or multivariate time-series with control covariates

Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.

interrupted-time-series difference-in-differences synthetic-control-method event-study-design bayesian-structural-time-series propensity-score-matching


Causal Mediation Analysis

Causal Mediation Analysis (Natural Direct and Indirect Effects)

Nedensel Arabuluculuk Analizi (NDE / NIE)

Pearl (2001); general framework by Imai, Keele & Tingley (2010)

Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation.

Two-stage regression / IPW with nonparametric bootstrap

Natural Direct Effect (NDE) and Natural Indirect Effect (NIE)

Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.

Imai, K., Keele, L., & Tingley, D. (2010). A General Approach to Causal Mediation Analysis. Psychological Methods, 15(4), 309-334.

moderation-analysis conditional-process-analysis dag-identification ols-regression logistic-regression


Causality in Variance Test

The causality-in-variance test detects whether shocks to one variable cause changes in the conditional variance (volatility) of another variable, distinct from mean-level causality. Introduced by Cheung and Ng (1996), it identifies volatility spillovers and contagion effects—crucial for risk management and understanding financial market interdependencies. This approach has become standard in studying shock transmission across asset classes and geographies.

Cheung, Y. W., & Ng, L. K. (1996). A causality-in-variance test and its application to financial market prices. Journal of Econometrics, 72(1-2), 33-61.

Hafner, C. M., & Herwartz, H. (2006). Testing for causality in variance using multivariate GARCH models. Journal of Econometrics, 135(1-2), 129-153.


Child Behavior Checklist

The Child Behavior Checklist (CBCL), developed by Thomas Achenbach and Leslie Rescorla and updated in 2009, is a parent-completed behavioral rating scale assessing emotional and behavioral problems in children aged 6–18 years. It is a foundational tool in clinical child psychology and psychiatry for screening behavioral/emotional disorders, monitoring treatment response, and research on psychopathology.

Achenbach, T. M., & Rescorla, L. A. (2009). Manual for the ASEBA School-Age Forms & Profiles. University of Vermont Center for Children, Youth & Families.

Rescorla, L. A., Ivanova, M. Y., Achenbach, T. M., et al. (2007). Behavioral and emotional problems reported by parents of children ages 6 to 16 in 31 societies. Journal of Emotional and Behavioral Disorders, 15(3), 130-142.

ages-stages-questionnaire conners-rating-scales strengths-difficulties-questionnaire achenbach-youth-self-report


CCEMG Estimator

Common Correlated Effects Mean Group Estimator

Ortak Korelasyonlu Etkiler Tahmincisi (CCEMG / CCE)

The Common Correlated Effects Mean Group estimator, introduced by Pesaran in 2006, is a heterogeneous panel-data estimator that controls for cross-sectional dependence by approximating unobserved common factors with the cross-section averages of the variables. It remains consistent when the slope coefficients differ across units.

Common Correlated Effects, mean-group form

Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012.

amg-estimator panel-cointegration panel-fixed-effects ols-regression


CCSD

CCSD (Criteria Correlation and Standard Deviation objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Wang, Y. M., Luo, Y. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Wang, Y. M., Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling

ahpsort aploco aras aroman artasi cobra cocoso codas


Crohn's Disease Activity Index

Best, W. R., Becktel, J. M., Singleton, J. W., and Kern, F.

The Crohn's Disease Activity Index (CDAI) is a comprehensive, weighted index for assessing disease activity in Crohn's disease. Developed in 1976 by Best and colleagues for the National Cooperative Crohn's Disease Study, the CDAI integrates eight clinical and laboratory variables into a single score ranging from <150 (remission) to >450 (severe activity). Although more complex than symptom-based tools, the CDAI captures the multidimensional nature of Crohn's disease and remains the reference standard for severe disease assessment.

Best, W. R., Becktel, J. M., Singleton, J. W., & Kern, F. (1976). Development of a Crohn's disease activity index. National Cooperative Crohn's Disease Study. Gastroenterology, 70(3), 439–444.

harvey-bradshaw-index sccai mayo-score-uc ibdq-short


Clinical Dementia Rating

CDR
CDR Scale
Washington University Dementia Rating

The Clinical Dementia Rating (CDR) is a clinician-administered scale that assesses severity of dementia on a 0–3 scale based on interview with the patient and an informed collateral source (e.g., family member). Developed by Morris and colleagues at Washington University School of Medicine, the CDR has become the reference standard for dementia severity assessment in clinical practice and research, particularly for staging Alzheimer's disease.

Morris, J. C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43(11), 2412–2414.

McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology, 34(7), 939–944.

Hugonot-Diener, L., Ritter-Hrncirik, C., & Amieva, H. (2008). Clinical Dementia Rating (CDR) in epidemiology and dementia screening. Neuropsychology, 22(4), 529–534.

moca mmse-test moca-blind dementia-risk-score


CEEMDAN

Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is an improved variant of empirical mode decomposition (EMD) that addresses mode-mixing artifacts through ensemble averaging with adaptive noise. Introduced by Torres and colleagues (2011), CEEMDAN decomposes signals into intrinsic mode functions (IMFs) representing oscillations at different scales. The method adds controlled noise to multiple realizations and averages the results, producing more stable, physically meaningful components than standard EMD.

Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4144–4147).

Colominas, M. A., Schlotthauer, G., & Torres, M. E. (2014). Improved complete ensemble empirical mode decomposition with adaptive noise. IEEE Transactions on Signal Processing, 63(6), 1408–1413.

Huang, N. E., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A, 454(1971), 903–995.

empirical-mode-decomposition eemd empirical-wavelet-transform variational-mode-decomposition


Cellular Automata

CA
Hücresel Otomat (Cellular Automata)
lattice model
grid-based simulation

1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification)

John von Neumann and Stanislaw Ulam (1940s–1950s); popularized by John Conway (1970) and Stephen Wolfram (1980s–2002)

Cellular automata (CA) is a grid-based computational simulation model, first formalized by John von Neumann and Stanislaw Ulam in the 1940s–1950s and brought to wide attention by John Conway's Game of Life (1970) and Stephen Wolfram's systematic classification (2002), in which a lattice of cells — each holding a finite discrete state — evolves in discrete time steps according to local neighborhood interaction rules, causing complex global patterns to emerge from simple local specifications.

Grid-based computational simulation model

Bottom-up emergent modeling via local interaction rules

No empirical sample required; states and rules are defined by the modeler

Urban growth, ecology, epidemiology, traffic flow, forest fire modeling, geography

Wolfram, S. (2002). A New Kind of Science. Wolfram Media.

White, R. & Engelen, G. (2000). High-Resolution Integrated Modelling of the Spatial Dynamics of Urban and Regional Systems. Computers, Environment and Urban Systems, 24(5), 383–400.

agent-based-modeling system-dynamics discrete-event-simulation monte-carlo-simulation network-diffusion


Cellulose Crystallinity

Cellulose crystallinity refers to the degree of structural order in cellulose molecules: highly crystalline cellulose has organized, tightly packed chains; amorphous cellulose has disordered chains. Measured using X-ray diffraction, cellulose crystallinity influences wood strength, stiffness, and digestibility in pulping and enzymatic processes. Higher crystallinity correlates with greater strength and lower chemical reactivity.

Segal, L., Creely, J. J., Martin, A. E., & Conrad, C. M. (1959). An empirical method for estimating the degree of crystallinity of native cellulose using the X-ray diffractometer. Textile Research Journal, 29(10), 786–794.

Park, S., Baker, J. O., Himmel, M. E., Parpia, B. H., & Johnson, D. K. (2010). Cellulose crystallinity index: Measurement techniques and their implications for pretreatment. Biotechnology for Biofuels, 3(1), 22.


Center of Pressure Posturography

Center of Pressure Analysis and Postural Stability Assessment

CoP
postural sway
balance analysis
stability assessment

Center of pressure (CoP) posturography measures postural stability by analyzing the movement of the body's center of pressure—the point where the total force of body weight is concentrated—during quiet stance or dynamic balance tasks. Formalized by Duarte and colleagues (2000), CoP analysis provides quantitative metrics of postural sway including sway area, path length, and velocity. These measurements reflect the integrated function of sensory systems (proprioception, vestibular, visual), central integration, and motor control. CoP posturography is widely used in neurology, vestibular medicine, rehabilitation, and sports science to detect balance deficits and monitor recovery.

Duarte, M., & Freitas, S. M. (2010). Revision of posturography based on force plate for balance evaluation. Revista Brasileira de Fisioterapia, 14(3), 183-192.

Prieto, T. E., Myklebust, J. B., Hoffmann, R. G., Lovett, E. G., & Myklebust, B. M. (1996). Measures of postural steadiness: differences between healthy and vestibulopathic patients. IEEE Transactions on Biomedical Engineering, 43(9), 956-966.

Lee, K. Y., Kim, J. S., Park, S. H., & Choi, M. H. (2015). Postural stability during single-leg standing in individuals with functional ankle instability. Clinical Biomechanics, 30(10), 1141-1145.

electromechanical-delay reactive-strength-index counter-movement-jump isokinetic-dynamometry


Central Composite Design

Central Composite Design for Response Surface Methodology

central composite response surface design

Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing.

Continuous numerical response variables; quantitative factor levels

Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45.

Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.

box-behnken-design full-factorial-design fractional-factorial-design response-surface-methodology design-of-experiments taguchi-method


Central Sensitization Inventory

Tom G. Mayer, Ralph Neblett, and colleagues

The Central Sensitization Inventory (CSI) is a 25-item self-report screening instrument developed by Mayer and colleagues in 2012 to identify patients with central sensitization—a condition characterized by amplification of pain signaling and hypersensitivity to sensory stimuli. The CSI captures the constellation of symptoms including widespread pain, sleep disturbance, cognitive dysfunction, and autonomic dysregulation associated with central sensitization syndromes such as fibromyalgia, chronic fatigue syndrome, and post-traumatic stress disorder.

Self-report screening inventory for central sensitization

Mayer, T.G., Neblett, R., Cohen, H., et al. (2012). The development and psychometric validation of the Central Sensitization Inventory. Pain Practice, 12(4), 276-285.

Neblett, R., Cohen, H., Choi, Y., Hardin, J.W., Mayer, T.G., Gonder-Frederick, L.A., & Mayer, J.M. (2013). The Central Sensitization Inventory (CSI): Establishing clinically significant values for identifying central sensitization in an outpatient chronic pain population. Journal of Pain, 14(5), 438-445.

Schilling, J.M., Koh, S.E., Tully, A.S., et al. (2018). Central sensitization phenotyping in patients with knee osteoarthritis. Clinical Journal of Pain, 34(4), 296-302.

pain-catastrophizing-scale neuropathic-pain-scale pain-anxiety-symptoms-scale mcgill-pain-questionnaire


Centrality Analysis

Network Centrality Analysis (Degree, Betweenness, Eigenvector)

Merkeziyet Analizi (Degree, Betweenness, Eigenvector)

Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors.

Descriptive / exploratory network measure family

Degree, betweenness, closeness, eigenvector, PageRank

O(V+E) for degree; O(VE) for betweenness

Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.

Borgatti, S.P. (2005). Centrality and Network Flow. Social Networks, 27(1), 55-71.

community-detection exponential-random-graph link-prediction network-diffusion stochastic-block-model


Cepstral Analysis

Cepstral Analysis for Spectral Decomposition and Pitch Detection

cepstrum
MFCC
mel-frequency cepstral coefficients
spectral analysis

Cepstral analysis is a spectral analysis technique that decomposes signals into independent components by inverting the log-magnitude spectrum. Pioneered by Bogert, Healy, and Tukey in 1963, cepstral analysis reveals periodic structure in spectra (pitch, echo patterns) and separates source excitation from filter response. Mel-frequency cepstral coefficients (MFCCs) derived from cepstral analysis are the most widely used features in automatic speech recognition, speaker verification, and audio analysis.

Bogert, B. P., Healy, M. J., & Tukey, J. W. (1963). The quefrency alanysis of time series for echoes: cepstrum, pseudo-autocovariance, cross-cepstrum, and saphe cracking. In Time Series Analysis Research Papers (pp. 209–243). Wiley.

Davis, S., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 357–366.

Rabiner, L. R., & Juang, B. H. (1993). Fundamentals of Speech Recognition. Prentice-Hall.

linear-predictive-coding bark-and-mel-scales speech-intelligibility psychoacoustic-masking beamforming


Ceramic Petrography

ceramic thin section analysis
pottery petrography

Ceramic petrography analyzes pottery through microscopic examination of thin sections cut from pottery sherds. This method determines clay sources, identifies non-plastic inclusions (temper), and reconstructs pottery production technology. Pioneered by Peter Stimmung and others, ceramic petrography reveals whether pottery was made locally or imported, and whether specific production groups or workshops created vessels with distinctive raw material recipes.

Quinn, P. S. (2013). Ceramic Petrology: The Interpretation of Ceramic Artifacts in Archaeological Science. Archaeopress.

Stimmung, P. (1976). Pottery and archaeopetrography. Norwegian Archaeological Review, 9(2), 104-124.

Whitbread, I. K. (1995). Greek Pottery Workshop Organisations and the Determinants of Vessel Form. Journal of the Hellenic Society, 115, 137-153.


The Center for Epidemiologic Studies Depression Scale (CES-D) is a 20-item self-report instrument for measuring depressive symptoms in the general population. Developed by Lenore Radloff in 1977, the CES-D was designed for epidemiological research to rapidly identify depression in community samples. It remains a widely used measure in public health, aging research, and longitudinal cohort studies worldwide.

Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385-401.

Eaton, W. W., Smith, C., Ybarra, M., Muntaner, C., & Tien, A. (2004). Center for Epidemiologic Studies Depression Scale (CES-D) and its use in epidemiological studies of depression. Journal of Clinical Psychiatry, 65(Suppl 12), 7-11.


CF-EDAS

CF-EDAS (Complex extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Ramot, D. Milo, R. Friedman, M. Kandel, A. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Complex outranking/ranking — Complex Fuzzy Set (CFS: amplitude r ∈ [0,1], phase ω ∈ [0,2π])

Ramot, D., Milo, R., Friedman, M., Kandel, A. (2002). Complex fuzzy sets. IEEE Transactions on Fuzzy Systems

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


CF-MARCOS

CF-MARCOS (Complex extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Ramot, D. Milo, R. Friedman, M. Kandel, A. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


CF-TOPSIS

CF-TOPSIS (Complex extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Ramot, D. Milo, R. Friedman, M. Kandel, A. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


CF-VIKOR

CF-VIKOR (Complex extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Ramot, D. Milo, R. Friedman, M. Kandel, A. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


CFA — Scale Validation

Confirmatory Factor Analysis for Scale Validation

Doğrulayıcı Faktör Analizi — Ölçek Doğrulama (CFA)

Confirmatory factor analysis is a measurement modelling technique that tests whether a hypothesised factor structure — typically derived from theory or an earlier exploratory analysis — fits observed data from a new sample. Developed by Karl Jöreskog in 1969, it became the dominant tool for validating psychological scales because it requires the researcher to specify in advance which items belong to which latent factor and then assesses the adequacy of that specification against explicit statistical fit criteria.

Measurement model / latent variable analysis

Model fit indices and factor loading estimates

CFI/TLI ≥ 0.95; RMSEA ≤ 0.06; SRMR ≤ 0.08

Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). Guilford Press.

Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

exploratory-factor-analysis sem cronbach-alpha rasch-model pca hlm


CFA

Doğrulayıcı Faktör Analizi (CFA)
confirmatory factor analysis
measurement model

Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.

Model fit indices and standardised factor loadings

Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press.

Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.

exploratory-factor-analysis sem pca cronbach-alpha


CFD Hemodynamics

Computational Fluid Dynamics for Hemodynamics

Cardiovascular CFD
Blood flow simulation
Hemodynamic simulation

Computational fluid dynamics (CFD) for hemodynamics solves the Navier-Stokes equations to simulate blood flow in realistic vascular geometries. Pioneered by researchers such as David Steinman, CFD hemodynamics reveals complex flow patterns, wall shear stress distributions, and hemodynamic factors implicated in atherosclerosis, aneurysm rupture, and device-induced thrombosis.

Steinman, D. A., Vinh, B., Ethier, C. R., Ojha, M., Cobbold, R. S., & Johnston, K. W. (2002). A numerical simulation of flow in a two-dimensional end-to-side anastomosis model. Journal of Biomechanical Engineering, 115(1), 112-118.

Fung, Y. C. (1997). Biomechanics: Circulation (2nd ed.). Springer-Verlag.


Consolidated Framework for Implementation Research (CFIR): A Five-Domain Model for Evaluating Implementation Success

The Consolidated Framework for Implementation Research (CFIR) is a five-domain model designed to systematically evaluate the factors influencing implementation success of evidence-based interventions in health systems. Developed by Damschroder et al. (2009) and refined through extensive use across health domains, CFIR provides a structured vocabulary and taxonomy of 39 constructs that identify implementation barriers and facilitators across intervention characteristics, organizational context, individual factors, and implementation process.

Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowson, E. (2009). Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implementation Science, 4, 50.

Kirk, M. A., Kelley, C., Yankey, N., Birken, S. A., Abadie, B., & Damschroder, L. (2016). A systematic review of the use of the Consolidated Framework for Implementation Research. Implementation Science, 11, 72.

Proctor, E. K., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G. A., Bunger, A., ... & Rojas, D. (2015). Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health and Mental Health Services Research, 42(2), 123-132.

knowledge-translation re-aim-framework theoretical-domains-framework implementation-outcome-taxonomy normalization-process-theory


CFZN-MARCOS

CFZN-MARCOS (Complex Fuzzy Z-Number MARCOS Ranking) is a distance multi-criteria decision-making (MCDM) method introduced by Shahid, A. Ashraf, S. Chohan, M. S. in 2026. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Compromise ranking via extended-matrix utility degrees relative to ideal/anti-ideal anchors under complex fuzzy Z-number uncertainty

Shahid, A., Ashraf, S., Chohan, M. S. (2026). Complex Fuzzy MARCOS and WASPAS Approaches with Z-Numbers for Augmented Reality Decision Making. Spectrum of Operational Research


CFZN-WASPAS

CFZN-WASPAS (Complex Fuzzy Z-Number Weighted Aggregated Sum Product Assessment) is a utility multi-criteria decision-making (MCDM) method introduced by Shahid, A. Ashraf, S. Chohan, M. S. in 2026. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Hybrid sum-product utility ranking via convex combination of WSM and WPM aggregations under complex fuzzy Z-number uncertainty


CGE Model

computable general equilibrium
applied general equilibrium model
Hesaplanabilir Genel Denge Modeli (CGE)

Lofgren, Harris & Robinson (standard IFPRI CGE model in GAMS); Walrasian general equilibrium theory

A Computable General Equilibrium model is a numerical equilibrium framework that represents the input-output relationships among all sectors, factors of production, households, and foreign trade in an economy through a Social Accounting Matrix (SAM). Grounded in Walrasian general equilibrium theory and formalised in the standard IFPRI model of Lofgren, Harris and Robinson (2002), it simulates the economy-wide effects of policy shocks such as tax reform, trade liberalisation, or environmental policy.

Calibration to a Social Accounting Matrix; solved as a system of simultaneous equilibrium equations

Lofgren, H., Harris, R.L. & Robinson, S. (2002). A Standard Computable General Equilibrium (CGE) Model in GAMS. IFPRI Microcomputers in Policy Research, 5.

Hosoe, N., Gasawa, K. & Hashimoto, H. (2010). Textbook of Computable General Equilibrium Modelling: Programming and Simulations. Palgrave Macmillan.

Dixon, P.B. & Jorgenson, D.W. (Eds.) (2013). Handbook of Computable General Equilibrium Modeling, Vols. 1A & 1B. North-Holland.

input-output-model ols-regression var-model panel-fixed-effects structural-equation-model


CHA₂DS₂-VASc Score

Congestive heart failure, Hypertension, Age ≥75, Diabetes, Stroke/TIA/thromboembolism (doubled), Vascular disease, Age 65-74, Sex category (female) Score

Gregory Y. H. Lip, Robby Nieuwlaat, et al.

The CHA₂DS₂-VASc score, developed by Lip, Nieuwlaat, and colleagues in 2010, is a 9-point risk stratification tool for predicting annual stroke and systemic thromboembolism risk in patients with atrial fibrillation. It is the recommended score by major cardiology guidelines for guiding anticoagulation decisions.

Atrial fibrillation stroke risk stratification

Lip, G. Y., Nieuwlaat, R., Pisters, R., Lane, D. A., & Crijns, H. J. (2010). Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest, 137(2), 263-272.

Pisters, R., Lane, D. A., Nieuwlaat, R., de Vos, C. B., Crijns, H. J., & Lip, G. Y. (2012). A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest, 138(5), 1093-1100.


Chain-Ladder Reserving

Chain-Ladder Loss Reserving (Mack Model)

Development Factor Method
Link Ratio Method
Loss Development Method
Zincir Merdiven Yöntemi

Chain-Ladder Reserving is a stochastic actuarial method for estimating outstanding claim liabilities from a run-off triangle of cumulative paid losses. Formalized by Thomas Mack in 1993, it provides distribution-free estimates of reserve amounts along with their standard errors, making it a cornerstone of property-casualty insurance reserving and regulatory practice worldwide.

Output: Reserve estimates with standard errors

Mack, T. (1993). Distribution-free calculation of the standard error of chain ladder reserve estimates. ASTIN Bulletin, 23(2), 213–225.


Chalder Fatigue Scale

The Chalder Fatigue Scale is an 11-item brief self-report instrument measuring physical and mental fatigue, developed by Trudie Chalder and colleagues at St. Bartholomew's Hospital, London, in 1993. Originally designed for chronic fatigue syndrome (myalgic encephalomyelitis/ME) research, the CFS has been extensively validated across cancer populations, chronic illness, and general populations. The scale offers two scoring options: continuous 0–33 scale for severity measurement or bimodal 0–11 scoring for caseness determination, making it versatile for both research and clinical screening.

Patient self-report fatigue scale with physical and mental subscales

Chalder, T., Berelowitz, G., Pawlikowska, T., et al. (1993). Development of a fatigue scale. J Psychosom Res, 37(2), 147–153.

Chalder, T., Power, M. J., & Wessely, S. (1996). Chronic fatigue in the community: 'The prevalence and use of health care. J Psychosom Res, 41(2), 197–103.

cancer-fatigue-scale brief-fatigue-inventory piper-fatigue-scale multidimensional-fatigue-inventory fact-g


Change Detection

Multitemporal Image Analysis
Land-Cover Change Analysis
Bitemporal Change Analysis
Değişim Tespiti

Change detection is a remote sensing analysis pipeline that identifies differences in land cover or land use between two or more images acquired at different times over the same geographic area. Systematically reviewed and classified by Ashbindu Singh in 1989, the framework encompasses image differencing, post-classification comparison, vegetation index differencing, and principal component analysis, and remains the canonical reference for evaluating which technique best suits a given application.

Input: Co-registered multitemporal satellite or aerial imagery

Output: Binary or categorical change map with area statistics

Singh, A. (1989). Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.


Change of Numeraire

Hélyette Geman, Nicole El Karoui, Jean-Charles Rochet

Change of numeraire is a mathematical technique for simplifying option pricing by changing the choice of discount factor (numeraire). By selecting a numeraire aligned with the payoff structure, complex problems become simple. The technique is essential for LIBOR market models and multi-currency derivatives.

Geman, H., El Karoui, N., & Rochet, J. C. (1995). Changes of numeraire, changes of probability measure and option pricing. Journal of Applied Probability, 32(2), 443-458.

Brigo, D., & Mercurio, F. (2006). Interest Rate Models: Theory and Practice (2nd ed.). Springer-Verlag.


Change-Point Detection

Structural Break Detection
Breakpoint Analysis
Regime Change Detection
Değişim Noktası Tespiti

Change-Point Detection identifies time points at which the statistical properties of a sequence — such as mean, variance, or distribution — shift abruptly. The Pruned Exact Linear Time (PELT) algorithm, introduced by Killick, Fearnhead, and Eckley (2012), solves the penalized segmentation problem exactly while achieving linear expected computational cost, making it practical for long time series encountered in genomics, finance, climatology, and signal processing.

Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598.


Chebyshev Filter Design

Chebyshev Infinite Impulse Response Filter Design

Chebyshev IIR Design
Chebyshev Type I Filter
Chebyshev Type II Filter

The Chebyshev filter is a signal processing filter that achieves a sharper cutoff frequency response than Butterworth filters by allowing controlled ripple in the passband (Type I) or stopband (Type II). Developed by Wilhelm Cauer and based on Chebyshev polynomials, these filters are widely used when a steep transition is needed with acceptable amplitude ripple.

Infinite Impulse Response (IIR) filter design

Cauer, W. (1958). Synthesis of Linear Communication Networks. McGraw-Hill.

Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.

butterworth-filter-design iir-filter-design fir-filter-design wiener-filter


Cherenkov Detection

Cherenkov light
Cherenkov ring imaging
threshold detection

Cherenkov detection exploits the emission of electromagnetic radiation when a charged particle travels through a medium faster than light travels in that same medium. This enables precise particle identification and mass measurement through analysis of Cherenkov light patterns, forming a cornerstone technology in modern high-energy physics detectors.

Cherenkov, P. A. (1934). Visible radiation produced by electrons moving in a medium with velocities exceeding that of light. Physical Review, 52(4), 378.

Ypsilantis, T., & Seguinot, J. (1994). Theory and applications of a novel type of Cherenkov counter. Nuclear Instruments and Methods in Physics Research Section A, 343(1), 30–51.

Bellamy, B., et al. (2010). Performance of the LHCb Ring Imaging Cherenkov Detector. Journal of Instrumentation, 5(12), P12008.


Chi-square test

chi-squared test
Pearson's chi-square test
test of independence
ki-kare bağımsızlık testi

The chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900.

Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175.

Agresti, A. (2007). An Introduction to Categorical Data Analysis (2nd ed.). Wiley.

fisher-exact-test cramers-v mcnemar-test g-test


Chi-square goodness-of-fit test

chi-squared test
χ² test
Ki-Kare Testi
chi-square test

The chi-square test of independence is a nonparametric hypothesis test that determines whether two categorical variables are statistically associated or independent of one another. Introduced by Karl Pearson in 1900, it remains the standard procedure for analysing contingency tables and requires no assumption of normality — only that observations are independent and that expected cell frequencies are sufficiently large.

Nonparametric association / goodness-of-fit

Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables. Philosophical Magazine, Series 5, 50(302), 157–175.

fisher-exact-test mcnemar-test logistic-regression cramers-v log-linear-model


Children's Depression Inventory

The CDI is a self-report measure of depressive symptoms in children and adolescents ages 7–17 years. Developed by Maria Kovacs in 1992 and revised in 2011, it is the most widely used screening tool for childhood depression in clinical and research settings. It assesses mood, self-concept, and functional impairment through 27–28 items rated on a 0–2 scale.

Kovacs, M. (1992). Children's Depression Inventory: Technical Manual. Multi-Health Systems.

Kovacs, M. (2011). Children's Depression Inventory 2nd Edition (CDI-2): Technical Manual. Multi-Health Systems.


CDQ

Rockett & Colditz; Harvard School of Public Health

The Children's Dietary Questionnaire (CDQ) is a parent-proxy or child self-report food frequency questionnaire designed to assess usual dietary intake in children and adolescents aged 6–18 years. Developed by Rockett and colleagues at Harvard School of Public Health in the 1990s, it captures consumption of 60–120 common foods and beverages with frequency and portion size information. The CDQ enables estimation of daily energy, macronutrient, and micronutrient intakes and characterization of dietary patterns (e.g., prudent vs. Western diet).

Self-report or parent-proxy food frequency questionnaire

Blum, R. E., Wei, E. K., Rockett, H. R., et al. (1999). Validation of a food frequency questionnaire in Native American and Caucasian children aged 9–18 years. Maternal and Child Health Journal, 3(3), 167–172.

Rockett, H. R., Wolf, A. M., & Colditz, G. A. (1995). Development and reproducibility of a food frequency questionnaire to assess diets of older children and adolescents. Journal of the American Dietetic Association, 95(3), 336–340.


CHQ

generic pediatric health-related quality of life

The Child Health Questionnaire is a generic, parent-reported instrument developed by Landgraf et al. in 1996 to measure health-related quality of life in children aged 5–18 years. Unlike disease-specific measures, the CHQ captures broad domains of physical, emotional, social, and school functioning, making it suitable for diverse pediatric populations with or without chronic conditions. The CHQ-50 is the most widely used parent-report form; shorter (CHQ-28) and comprehensive (CHQ-CF87 child + family) versions are also available.

Parent report (primary) and child self-report versions available

Landgraf, J. M., Abetz, L., & Ware, J. E. (1996). The CHQ User's Manual. HealthAct.

Landgraf, J. M., Rich, M., & Rapoff, M. A. (2002). Measuring quality of life in children with arthritis: The Childhood Health Assessment Questionnaire. Journal of Rheumatology, 29(8), 1609-1617.

paqlq pedsql-diabetes qolce pedsql-cancer


COHIP

The Child Oral Health Impact Profile (COHIP) is a 34-item instrument measuring oral health-related quality of life in children and adolescents aged 6-14 years. Developed by Broder and colleagues and refined through the National Institute of Dental and Craniofacial Research (NIDCR), the COHIP captures developmental and age-appropriate impacts of oral conditions (caries, malocclusion, traumatic injury) on children's functional well-being, emotional state, and social participation. The COHIP is the paediatric equivalent of OHIP-14 and has become the standard measure for child-centred outcomes in paediatric dental research.

Self-report and caregiver-report questionnaire

Broder, H. L., McGrath, C., & Cisneros, G. J. (2007). Questionnaire development: Face validity and item impact testing of the Child Oral Health Impact Profile. Community Dentistry and Oral Epidemiology, 35(Suppl 1), 8-19.


Child PTSD Symptom Scale

The Child PTSD Symptom Scale (CPSS), developed by Edna Foa and colleagues in 2001, is a child-report assessment of posttraumatic stress disorder (PTSD) symptoms in children and adolescents aged 8–18 years following traumatic exposure. The CPSS measures the three core symptom clusters of PTSD (re-experiencing, avoidance, hyperarousal) aligned with DSM-5 diagnostic criteria, making it a valuable screening and outcome measurement tool in trauma-focused clinical practice and research.

Foa, E. B., Johnson, K. M., Feeny, N. C., & Treadwell, K. R. (2001). The Child PTSD Symptom Scale (CPSS): A preliminary examination of its psychometric properties. Journal of Clinical Child Psychology, 30(3), 376-384.

Scheeringa, M. S., Salloum, A., Arnberger, R. A., et al. (2014). Feasibility and effectiveness of cognitive-behavioral therapy for posttraumatic stress disorder in preschool children: A pilot study. Journal of Child & Adolescent Trauma, 5(1), 1-18.

cbcl-child-behavior strengths-difficulties-questionnaire achenbach-youth-self-report vanderbilt-adhd-scale


Child-Pugh Score

Child-Pugh Score for Liver Cirrhosis Severity

1964 (Child-Turcotte), 1973 (Pugh modification)

Child, C. G., Turcotte, J. G., and Pugh, R. N.

The Child-Pugh Score (originally Child-Turcotte, modified by Pugh in 1973) is a clinical scoring system that stratifies the severity of liver cirrhosis and predicts surgical mortality and prognosis. The score integrates five readily available clinical and laboratory parameters: bilirubin, albumin, prothrombin time (INR), ascites, and hepatic encephalopathy. With a total range of 5–15 points, the Child-Pugh Score is stratified into Class A (mild, 5–6 points), Class B (moderate, 7–9 points), and Class C (severe, 10–15 points), each with distinct mortality predictions.

Child, C. G., & Turcotte, J. G. (1964). Surgery and portal hypertension. In C. G. Child (Ed.), The liver and portal hypertension (pp. 50–64). Saunders.

Pugh, R. N., Murray-Lyon, I. M., Dawson, J. L., Pietroni, M. C., & Williams, R. (1973). Transection of the oesophagus for bleeding oesophageal varices. British Journal of Surgery, 60(8), 646–649.

gcsi mayo-score-uc harvey-bradshaw-index hepatic-encephalopathy-grade


Childhood Trauma Questionnaire

The Childhood Trauma Questionnaire (CTQ) is a 28-item self-report measure assessing retrospective experiences of childhood abuse and neglect in adolescents and adults. Developed by David Bernstein and colleagues in 1994, the CTQ quantifies five types of maltreatment: physical abuse, emotional abuse, sexual abuse, physical neglect, and emotional neglect. It is widely used in clinical, forensic, and research settings to understand the role of childhood trauma in adult mental health, substance use, trauma-related disorders, and risk factors for future violence.

Bernstein, D. P., Fink, L., Handelsman, L., Foote, J., Lovejoy, M., Ruggiero, D. F., . . . Rountree, J. (1994). Initial reliability and validity of a new retrospective measure of child abuse and neglect. American Journal of Psychiatry, 151(8), 1132–1136.

Bernstein, D. P., Stein, J. A., Newcomb, M. D., Walker, E., Pogge, D., Ahluvalia, T., . . . Zule, W. (2003). Development and validation of a brief screening version of the Childhood Trauma Questionnaire. Child Abuse & Neglect, 27(2), 169–190.


ChIP-seq Peak Calling

Chromatin Immunoprecipitation Sequencing Peak Calling

ChIP-seq analysis
peak detection
MACS peak calling
ChIP peak identification

Johnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008)

ChIP-seq peak calling is a computational pipeline that identifies genomic regions where a protein of interest — a transcription factor or histone modification — is enriched, based on sequencing reads from chromatin immunoprecipitation experiments. It converts raw sequencing data into a set of high-confidence binding or modification sites across the genome, enabling downstream analysis of gene regulation, chromatin state, and epigenetic mechanisms.

High-throughput sequencing reads (FASTQ/BAM) from ChIP-seq experiments

Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-seq (MACS). Genome Biology, 9(9), R137.

Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B. E., Bickel, P., Brown, J. B., Cayting, P., Chen, Y., DeSalvo, G., Epstein, C., Fisher-Aylor, K. I., Euskirchen, G., Gerstein, M., Gertz, J., Hartemink, A. J., Hoffman, M. M., ... Snyder, M. (2012). ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research, 22(9), 1813–1831.

atac-seq-analysis rna-seq-differential-expression epigenome-wide-association-study sequence-alignment variant-calling single-cell-rna-seq-analysis


Chlorophyll Fluorescence

Chlorophyll Fluorescence Analysis for Photosynthetic Stress Diagnosis

Fluorescence
Fv/Fm
OJIP curve
PAM fluorometry

Hans Kautsky, Ulrich Schreiber, Reto J. Strasser

Chlorophyll fluorescence is a non-invasive optical measurement of how efficiently the photosynthetic machinery converts absorbed light into chemical energy (photosynthesis) or heat and light (fluorescence). When photosynthesis is inhibited by stress (drought, cold, salt, pests), chlorophyll fluorescence increases because excitation energy cannot be used for photosynthesis and must be released as light or heat. Fluorescence parameters (Fv/Fm, OJIP curves) act as sensitive, rapid indicators of photosynthetic stress, enabling early detection of plant dysfunction before visible symptoms appear.

Kautsky, H., & Hirsch, A. (1931). Neue Versuche zur Klärung der Assimilationstätigkeit. Naturwissenschaften, 19(48), 964-964.

Schreiber, U., Bilger, W., & Neubauer, C. (1994). Chlorophyll fluorescence as a noninvasive indicator of rapid assessment of in vivo photosynthesis. Ecological Studies, 100, 49-70.

Strasser, R. J., Srivastava, A., & Tsimilli-Michael, M. (2004). The fluorescence transient as a tool to characterize and screen photosynthetic samples. In M. Papageorgiou & Govindjee (Eds.), Chlorophyll fluorescence: A signature of photosynthesis (pp. 321-362). Springer.


CHOQUET-INTEGRAL

CHOQUET-INTEGRAL (Choquet Integral — Non-additive aggregation) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Murofushi, T., Sugeno, M. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Murofushi, T., Sugeno, M. (1989). An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure. Fuzzy Sets and Systems


Chord Recognition

Chord Recognition and Estimation Algorithm

chord estimation
harmonic analysis
chord detection

Chord recognition is the task of automatically identifying the harmonic chords present in a musical recording and estimating when chord changes occur. Introduced formally by Harte et al. (2005), it is a cornerstone of music analysis and widely used in music education, cover song analysis, and musical structure understanding. Modern systems use deep learning to classify and sequence chords in real time.

Harte, C., Sandler, M. B., Abdallah, S. A., & Gómez, E. (2005). Symbolic representation of musical chords: Proposed extensions to the HarmO ontology. In Proceedings of the International Society for Music Information Retrieval Conference.

MacGregor, R. D., & Wiggins, G. A. (2009). Chord recognition using duration-explicit hidden Markov models. In Proceedings of the International Society for Music Information Retrieval Conference.

Bigo, L., Buffa, A., & Roeb, M. (2017). Singing voice separation using spectral features and bidirectional long short-term memory networks. In Proceedings of the International Society for Music Information Retrieval Conference.

harmonic-analysis-music melody-extraction music-segmentation pitch-detection-algorithm music-genre-classification


Chou-Talalay Method

CI method
Chou method
median-effect analysis

The Chou-Talalay method is a quantitative framework for analyzing drug interactions, developed by Ting-Chao Chou and Paul Talalay in 1983. It combines median-effect principle with the combination index (CI) to provide rigorous, model-independent assessment of synergistic, additive, or antagonistic drug effects.

Chou, T. C., & Talalay, P. (1983). Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Advances in Enzyme Regulation, 22, 27-55.

Chou, T. C. (1986). Quantitative analysis of the dose-effect relationship. Life Sciences, 38(26), 2347-2358.


Chow Test

Chow Test for Structural Break / Parameter Stability

Chow breakpoint test
structural break test
Chow yapısal kırılma testi

The Chow test, introduced by Gregory Chow in 1960, checks whether the coefficients of a linear regression are the same across two subsamples — that is, whether a structural break occurs at a known point such as a policy change, crisis, or regime shift. It compares the fit of a single pooled regression with the combined fit of two separate regressions; a large improvement from splitting indicates the relationship differs between the two periods or groups.

Test for structural break in regression coefficients

Coefficients are equal across subsamples (no break)

Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605.

ols-regression multiple-linear-regression markov-switching-model regression-discontinuity-design


CHQ

CHQ
Heart Failure Quality of Life
Chronic Heart Failure QoL

The CHQ is a disease-specific quality of life measure for chronic heart failure (CHF). Developed by Luc Guyonnet and colleagues in 2000, this 20-item questionnaire assesses how heart failure affects dyspnea, fatigue, emotional function, and activity limitation. It is used in heart failure clinical trials and research to quantify patient-experienced burden and treatment benefit.

Self-report quality of life questionnaire

Guyonnet, S., Vellas, B., Garry, P. J., & Albarede, J. L. (2000). The Chronic Heart Failure Questionnaire: A pilot study of validity, reliability, and responsiveness. Journal of Cardiac Failure, 6(1), 21-26.

Parissis, J. T., Nikolaou, M., Farmakis, D., Chrysohoou, C., Kremastinos, D. T., & Karavidas, A. (2009). Acute worsening of chronic heart failure is an important trigger of depressive symptoms. European Journal of Heart Failure, 9(3), 260-267.

Garin, O., Ferrer, M., Pont, A., Rué, M., Kotzeva, A., Wiklund, I., ... & Alonso, J. (2009). Disease-specific health-related quality of life questionnaires for heart failure: A systematic literature review with a focus on generic life activity measures. Health and Quality of Life Outcomes, 7, 14.


Anne G.M. Schilder and colleagues (COMOT working group)

The Chronic Otitis Media Outcome Test-15 (COMOT-15) is a 15-item patient-reported outcome measure specifically designed to assess the burden and impact of chronic otitis media on health-related quality of life. Developed by Schilder and colleagues (2016), the COMOT-15 measures symptoms (ear discharge, hearing loss, ear pain), hearing function, and psychosocial effects of chronic ear disease. It is the recommended core outcome set for clinical trials and quality improvement programs evaluating chronic otitis media treatment.

Schilder, A. G., Su, M. P., Blackshaw, H., Lustig, L. R., & O'Donoghue, G. M. (2016). Chronic Otitis Media Outcome Test-15 (COMOT-15): Development and psychometric evaluation. Otology & Neurotology, 37(9), 1314-1320.


Chronic Pain Acceptance Questionnaire

Acceptance and Commitment Therapy Pain Scale

pain acceptance and psychological flexibility

The Chronic Pain Acceptance Questionnaire (CPAQ) is a 20-item self-report instrument developed by McCracken in 1998 to measure pain acceptance—the willingness to experience pain while continuing with valued life activities. Unlike pain management approaches focused on pain reduction, the CPAQ operationalizes acceptance-based treatment philosophy grounded in Acceptance and Commitment Therapy (ACT), measuring psychological flexibility in the context of chronic pain.

Self-report questionnaire measuring pain acceptance and behavioral engagement

McCracken, L.M. (1998). Learning to live with the pain: Acceptance of pain predicts adjustment in persons with chronic pain. Pain, 74(1), 21-27.

McCracken, L.M., & Vowles, K.E. (2006). Acceptance of chronic pain. Current Pain and Headache Reports, 10(2), 90-94.

Vowles, K.E., McCracken, L.M., & Eccleston, C. (2007). Processes of change in Acceptance and Commitment Therapy and cognitive behavioral therapy for chronic pain. Journal of Pain, 8(7), 556-562.

pain-catastrophizing-scale pain-self-efficacy-questionnaire pain-anxiety-symptoms-scale roland-morris-disability


CRQ

Chronic Respiratory Disease Questionnaire

The CRQ is a 20-item, four-domain questionnaire developed by Guyatt and colleagues at McMaster University in 1987 to measure health-related quality of life specifically in patients with chronic respiratory disease, particularly chronic obstructive pulmonary disease and cystic fibrosis. Uniquely, the CRQ can be administered by interview or self-report, and its four domains (dyspnea, fatigue, emotional function, mastery) directly address the multidimensional burden of chronic respiratory disease. The CRQ has demonstrated exceptional responsiveness to pulmonary rehabilitation and other interventions, making it a preferred outcome measure in respiratory research and clinical practice.

Self-report or interviewer-administered questionnaire

Guyatt, G. H., Berman, L. B., Townsend, M., Pugsley, S. O., & Chambers, L. W. (1987). A measure of quality of life for clinical trials in chronic lung disease. Thorax, 42(10), 773-778.

Guyatt, G. H., Nogrady, S. G., Halcrow, S., Singer, J., Sullivan, M. J., & Fallen, E. L. (1989). Development and testing of a new measure of health status for clinical trials in heart failure. Journal of General Internal Medicine, 4(2), 101-107.

st-george-respiratory-questionnaire asthma-control-questionnaire mrc-dyspnoea-scale breathlessness-cough-sputum-scale sinonasal-outcome-test


Chronoamperometry

Chronoamperometry (CA) is an electrochemical technique that measures current as a function of time when a potential step is applied to an electrode. Developed by Delahay in the 1950s, CA reveals diffusion-controlled electrochemical processes and enables determination of diffusion coefficients, surface coverage, and kinetic rate constants by analyzing the transient current decay.

Bard, A. J., & Faulkner, L. R. (2001). Electrochemical Methods: Fundamentals and Applications. John Wiley & Sons, 2nd edition.

Oldham, K. B., & Myland, J. C. (1986). Fundamentals of Electrochemical Science. Academic Press.


Chronos

Chronos (Tokenized Time-Series Foundation Model)

Chronos Forecasting Model
Amazon Chronos
Tokenized Time-Series LLM
Kronos Zaman Serisi Modeli

Chronos is a family of pre-trained probabilistic forecasting models introduced by Ansari et al. at Amazon in 2024. It adapts the language-model paradigm to time series by quantizing continuous values into discrete tokens, enabling a standard transformer to be trained on a large heterogeneous corpus of time-series data. The result is a zero-shot forecasting model that generalizes across domains without requiring dataset-specific retraining.

Pre-trained language-model-based time-series forecaster

Self-supervised pretraining on large corpus of real and synthetic time-series data

Output: Probabilistic forecast distributions via token sampling

Ansari, A. F., Stella, L., Turkmen, C., Zhang, X., Mercado, P., Shen, H., et al. (2024). Chronos: Learning the language of time series. Transactions on Machine Learning Research.


Chunking

shallow parsing
partial parsing
Yüzeysel Ayrıştırma (Chunking)

Chunking, also called shallow parsing, is a natural-language-processing task introduced by Steven Abney in 1991 that divides text into grammatical pieces — such as noun phrases and verb phrases — using part-of-speech tags. It extracts useful syntactic structure quickly without building a full parse tree of the sentence.

Input: Part-of-speech tagged text

Output: Non-overlapping phrase chunks (e.g. noun phrases, verb phrases)

Abney, S. (1991). Parsing by Chunks. In Principle-Based Parsing. Kluwer Academic Publishers.

Tjong Kim Sang, E.F. & Buchholz, S. (2000). Introduction to the CoNLL-2000 Shared Task: Chunking. Proceedings of CoNLL-2000.

pos-tagging morphological-analysis text-segmentation named-entity-recognition


CI/CD Analytics

continuous integration analytics
deployment metrics

CI/CD Analytics is the measurement and analysis of Continuous Integration and Continuous Deployment pipelines to improve development velocity, quality, and reliability. Popularized by Humble and Farley's 'Continuous Delivery' (2010) and formalized by Forsgren et al.'s 'Accelerate' (2018), key metrics include deployment frequency, lead time, mean time to recovery, and change failure rate.

Humble, J., & Farley, D. (2010). Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison-Wesley.

Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The Science of Lean Software and DevOps. IT Revolution Press.

Duvall, P. M., Matyas, S. M., & Glover, A. (2007). Continuous Integration: Improving Software Quality and Reducing Risk. Addison-Wesley.


CILOS

CILOS (Criterion Impact LOSs objective weighting method) is a weight objective multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Podvezko, V. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Relative criterion-loss matrix weighting (Mirkin theorem based)

Zavadskas, E. K., Podvezko, V. (2016). Integrated Determination of Objective Criteria Weights in MCDM. International Journal of Information Technology & Decision Making

ahpsort aploco aras aroman artasi cobra cocoso codas


CIMAS

CIMAS (Criterion Impact MeAsurement System) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Bošković, S., Jovčić, S., Simić, V., Švadlenka, L., Dobrodolac, M., Bacanin, N. in 2025. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Impact-based weighted scoring (criterion-level deviation analysis)

Bošković, S., Jovčić, S., Simić, V., Švadlenka, L., Dobrodolac, M., Bacanin, N. (2025). A New Criteria Importance Assessment (CIMAS) Method in Multi-Criteria Group Decision-Making: Criteria Evaluation for Supplier Selection. FACTA UNIVERSITATIS — Series: Mechanical Engineering


CIPS Test

Cross-sectionally Augmented IPS (CIPS) Panel Unit-Root Test

Pesaran CIPS Test
Cross-Sectionally Augmented IPS
Second-Generation Panel Unit-Root Test
CIPS Birim Kök Testi

The CIPS test, introduced by Pesaran (2007), is a second-generation panel unit-root test designed for panels in which the cross-sectional units share unobserved common factors that induce cross-section dependence. By augmenting each individual ADF regression with cross-sectional averages and their lags, the CIPS test accounts for this dependence and produces reliable inference where first-generation tests such as the original IPS test break down. It is widely applied in macroeconomic and finance panels where shocks propagate across countries or regions.

Panel unit-root test with cross-section dependence

Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.


Circuitscape

circuit theory
resistance distance
connectivity analysis
landscape conductance

Circuitscape, developed by Brad McRae (2008), applies circuit theory from electrical engineering to predict organism movement and genetic connectivity across landscapes. The method treats landscapes as electrical networks where habitat quality is resistance and organism movement is electrical current. By analogy, organisms diffusing through a landscape follow paths determined by landscape resistance: corridors of low resistance (good habitat) are preferentially used. Circuitscape predicts movement probabilities, identifies critical corridors, and quantifies connectivity between habitat patches.

Bradford, D. F., McCreary, D. D., & Groves, C. R. (2014). Optimizing sampling for large-area habitat assessment. Ecological Monographs, 84(3), 351-375.

McRae, B. H. (2008). Isolation by resistance. Evolution, 62(8), 1965-1975.

McRae, B. H., Dickson, B. G., Keitt, T. H., & Vogt, P. (2012). Current maps can improve predictions of connectivity in conservation planning. Ecology and Society, 16(1), 8.

distance-sampling food-web-topology population-viability-analysis niche-modeling


Circular Dichroism

CD spectroscopy
circular dichroism
CD analysis

Circular Dichroism (CD) spectroscopy measures the differential absorption of left- and right-circularly polarized light by optically active molecules, particularly proteins and nucleic acids. Introduced by Greenfield and Fasman in 1969, CD is a rapid, non-destructive technique for characterizing secondary structure (alpha-helix, beta-sheet), monitoring protein folding transitions, and assessing conformational changes in response to pH, temperature, or ligand binding.

Greenfield, N. J., & Fasman, G. D. (1969). Computed circular dichroism spectra for protein secondary structures. Biochemistry, 8(10), 4108-4116.

Yang, J. T., Wu, C. S., & Martinez, H. M. (1986). Calculation of protein conformation from circular dichroism. Methods in Enzymology, 130, 208-269.