Cultural Values Scale

The Cultural Values Scale is a self-report measure designed to assess individual endorsement of cultural values spanning individualism and collectivism. Developed within the cross-cultural psychology literature, the scale captures how individuals prioritize personal autonomy, achievement, and self-expression against group harmony, interdependence, and collective well-being. It has become a standard tool for understanding cultural orientation in diverse populations.

Oyserman, D., Coon, H. M., & Kemmelmeier, M. (2002). Rethinking individualism and collectivism: Evaluation of theoretical assumptions and meta-analyses. Psychological Bulletin, 128(1), 3–72.

collectivism-individualism-scale social-dominance-orientation-scale acculturation-scale modern-racism-scale


CURB-65 Pneumonia Severity Score

Confusion, Urea, Respiratory rate, Blood pressure, age ≥65 (CURB-65)

CURB-65, derived and validated by Lim et al. in 2003, is a 5-point severity of illness score for community-acquired pneumonia (CAP). It assesses confusion, urea nitrogen, respiratory rate, blood pressure, and age ≥65 years to stratify mortality risk and guide admission and treatment decisions.

Community-acquired pneumonia severity assessment

Lim, W. S., van der Eerden, M. M., Laing, R., et al. (2003). Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax, 58(5), 377-382.

Capelastegui, A., España, P. P., Quintana, J. M., et al. (2006). Validation of a community-acquired pneumonia severity score. European Respiratory Journal, 27(2), 405-413.


Mixture Cure Model

Mixture Cure Model (Cure Fraction Model)

cure fraction model
cure rate model
bounded cumulative hazard model
İyileşme Modeli (Mixture Cure Model)

The mixture cure model, first proposed by Boag in 1949 for cancer survival data, is a parametric survival model that explicitly accounts for a fraction of subjects who will never experience the event of interest — the so-called cured or immune fraction. It is the appropriate tool whenever the Kaplan-Meier curve levels off into a long, stable plateau rather than continuing to decline, indicating that a proportion of subjects are permanently event-free.

Right-censoring with a cured (immune) fraction

Boag, J. W. (1949). Maximum Likelihood Estimates of the Proportion of Patients Cured. Journal of the Royal Statistical Society B, 11(1), 15–53.

kaplan-meier cox-ph weibull-aft fine-gray-model log-rank-test


Curriculum Analysis

Curriculum Analysis in Educational Research

curriculum evaluation
curriculum review
syllabus analysis
curriculum appraisal

1949 (Tyler); 1980s–2000s (Posner's analytic framework)

George J. Posner (systematic framework); Ralph Tyler (foundational rationale)

Curriculum analysis is a systematic research method for examining the content, structure, goals, and underlying assumptions of educational curricula — including written syllabi, textbooks, lesson plans, and policy documents. By mapping what is taught, how it is sequenced, and what values are embedded, researchers and educators can evaluate alignment with learning objectives, identify gaps or biases, and guide curriculum reform across all levels of education.

Curriculum documents, syllabi, lesson plans, textbooks, policy texts

Posner, G. J. (2004). Analyzing the Curriculum (3rd ed.). McGraw-Hill.

English, F. W. (1980). Curriculum mapping. Educational Leadership, 37(7), 558–559.

content-analysis document-analysis program-evaluation educational-action-research discourse-analysis thematic-analysis


Curriculum Learning

Scheduled Training
Difficulty-Based Training
Self-Paced Learning
Müfredat Öğrenimi

Curriculum Learning is a training strategy for machine learning models, introduced by Bengio et al. in 2009, in which training examples are presented in a meaningful order—typically from easy to hard—rather than at random. Inspired by how humans and animals learn progressively, it organizes training data into a curriculum that starts with simpler, cleaner, or more representative samples and gradually introduces harder or more complex examples as the model matures.

Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. International Conference on Machine Learning (ICML), 41–48.


CushQoL

Cushing's Syndrome Quality of Life Questionnaire

Cushing's syndrome-specific quality of life

Sergio Webb, María D. Bernal, Juan M. Rivera-Caravaca

CushQoL is a disease-specific 12-item quality of life questionnaire developed to assess the multidimensional impacts of Cushing's syndrome—a severe endocrine disorder characterized by excess cortisol production. Developed by Webb and colleagues in 2008, it captures physical symptoms (fatigue, weight gain, weakness, hirsutism), psychological manifestations (depression, anxiety, cognitive impairment), and social/occupational dysfunction unique to Cushing's syndrome. It is the standard outcome measure for assessing quality of life improvement following curative therapy.

Webb, S. M., Bernal, M. D., Rivera-Caravaca, J. M., & Córdoba-Soriano, J. G. (2008). Development and validation of CushQoL, a disease-specific quality of life questionnaire in Cushing's syndrome. J Clin Endocrinol Metab, 93(5), 1751-1759.

Tiemensma, J., Kaptein, A. A., Pereira, A. M., et al. (2015). Persistent cognitive impairment in patients treated for Cushing's syndrome. J Clin Endocrinol Metab, 95(6), 2604-2609.


Customer Journey Mapping

Customer experience design and touchpoint analysis

Adaptive Path and Service Design community

Customer Journey Mapping is a service design methodology that visualizes the complete customer experience across all touchpoints and phases of a customer relationship, from awareness through advocacy. Developed through work in design and service management, journey mapping integrates behavioral data, customer emotions, pain points, and moments of truth to reveal opportunities for experience improvement and organizational alignment.

Rosenbaum, M. S., Ostrom, A. L., & Kuntze, R. (2005). Tensions in Service Quality and Service Marketing: An Introduction. Journal of Services Marketing, 19(7), 422-428.

Patrucco, A. S., Ciccullo, F., & Creazza, A. (2018). Mitigating Operational Risks in the Freight Forwarding Industry. International Journal of Logistics: Research and Applications, 21(2), 131-146.

Curedale, R. (2013). Service Design: 250 Essential Methods. Design Community College Inc.

brand-equity-measurement net-promoter-score customer-lifetime-value market-segmentation-analysis advertising-effectiveness-study


Customer Lifetime Value

Customer value quantification and retention

Customer Lifetime Value (CLV) is a financial metric that quantifies the total profit a company expects to generate from its relationship with a customer over the entire duration of that relationship. Developed through work by Blattberg, Getz, and Thomas in the 1990s-2000s, CLV integrates acquisition costs, purchase behavior, retention rates, and margin information to estimate the net present value of each customer.

Blattberg, R. C., Getz, G., & Thomas, J. S. (2001). Customer Equity: Building and Managing Relationships as Assets. Harvard Business School Press.

Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., ... & Sriram, S. (2006). Modeling Customer Lifetime Value. Journal of Service Research, 9(2), 139-155.

Kumar, V., & Pansari, A. (2016). Competitive Advantage Through Engagement. Journal of Marketing Research, 53(4), 497-514.

brand-equity-measurement net-promoter-score customer-journey-mapping market-segmentation-analysis advertising-effectiveness-study


Customer Loyalty Scale

Behavioral Loyalty Scale
Loyalty Commitment Scale

The Customer Loyalty Scale (CLS) measures customer loyalty as a combination of attitudinal commitment and behavioral intention. Developed by Dick and Basu (1994), the scale distinguishes between behavioral loyalty (repeat purchases) and attitudinal loyalty (emotional commitment), recognizing that true loyalty involves both. CLS captures multiple dimensions of loyalty including repurchase intention, word-of-mouth advocacy, and resistance to competitive offerings. The instrument is widely used in marketing research to predict customer lifetime value and identify at-risk customers.

Multi-dimensional behavioral and attitudinal loyalty scale

Dick, A. S., & Basu, K. (1994). Customer Loyalty: Toward an Integrated Conceptual Framework. Journal of the Academy of Marketing Science, 22(2), 99-113.

Hennig-Thurau, T., Langer, M. F., & Hansen, U. (2002). Modeling and Managing Student Loyalty: An Approach Based on the Concept of Relationship Quality. Journal of Service Research, 4(4), 331-344.

customer-satisfaction-index brand-equity-scale servqual servperf


American Customer Satisfaction Index

ACSI
National Customer Satisfaction Index

Claes Fornell, Michael D. Johnson, Eugene W. Anderson, Jaesung Cha, Barbara E. Bryant

The American Customer Satisfaction Index (ACSI), developed by Fornell and colleagues in 1996, is a structural equation modeling-based approach to measuring and predicting customer satisfaction across industries and over time. ACSI assesses customer expectations, perceived value, perceived quality, complaints, and loyalty in a unified framework. Since 1994, ACSI data has been collected quarterly on thousands of customers across diverse U.S. industries, making it a key economic indicator and benchmark for organizational performance.

Structural equation model for satisfaction and loyalty

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

Fornell, C., Mital, V., & Veingerl, I. (2015). Developing and Testing a Theory of Consumer Delight. Journal of the Academy of Marketing Science, 43(2), 299-315.

servqual servperf customer-loyalty-scale brand-equity-scale


CUSUM Chart

cumulative sum chart
CUSUM control chart
Page's CUSUM
kümülatif toplam kontrol kartı

The cumulative sum (CUSUM) control chart, introduced by E. S. Page in 1954, monitors a process by accumulating the deviations of observations from a target value rather than judging each point in isolation. Because small persistent shifts add up over time, the running sum makes them visible far sooner than a Shewhart chart, making CUSUM the tool of choice for detecting small, sustained changes in the process mean.

Statistical process control chart for small shifts

Cumulative deviation of the process mean from target

Fast detection of small sustained shifts

Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41(1/2), 100–115.

Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons.

shewhart-control-chart ewma-chart attributes-control-chart sequential-analysis


CUSUM Test

CUSUM / CUSUMSQ Parameter-Stability Test

Cumulative Sum Test
CUSUMSQ Test
Brown-Durbin-Evans Test
Kümülatif Toplam Testi

The CUSUM (Cumulative Sum) and CUSUMSQ (Cumulative Sum of Squares) tests, introduced by Brown, Durbin, and Evans (1975), assess whether the coefficients of a linear regression model remain constant over time. They are standard tools in econometrics for detecting structural breaks, policy shifts, or regime changes in time-series data without requiring prior knowledge of when a break occurs.

Stability of regression coefficients over time

CUSUM (mean shift) and CUSUMSQ (variance shift)

Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B, 37(2), 149–192.


Cut-off Grade (Lane)

Lane Model
Cut-off Grade Optimization
Lane's Optimization Model

Lane's Cut-off Grade Model, developed by Kenneth F. Lane and formalized in his 1988 book, provides a rigorous economic framework for determining the minimum grade at which ore should be mined and processed. It accounts for variable mining costs, metallurgical recovery, and commodity prices to optimize profit per unit processed. The model is foundational in mining economics and underpins daily operational decisions at thousands of mines worldwide.

Economic optimization framework for ore classification

Lane, K. F. (1988). The economic definition of ore: cutoff grades in theory and practice. Mining Journal Books, London.

Stewart, W. P., Michaud, D. E. (2011). Technical evaluation of mineral reserves. Society for Mining, Metallurgy & Exploration, Inc.


Cyberbullying Victimization Scale

Cyberbullying Victimization Scale (CBVS)

CBVS
Cyberbullying Victimization
Online Harassment Scale

The Cyberbullying Victimization Scale measures the frequency and nature of bullying experienced through digital channels—social media, text messages, gaming platforms, email, and online forums. Developed by Smith and colleagues (2008) and refined through meta-analytic synthesis by Kowalski and colleagues (2014), the scale captures both the prevalence of cyberbullying incidents and their psychological impact, distinguishing cyberbullying from traditional in-person bullying by its permanence, ease of viral spread, and 24/7 accessibility.

Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). Cyberbullying: its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, 49(4), 376–385.

Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among adolescents. Psychological Bulletin, 140(4), 1073–1137.


CycleGAN

CycleGAN (Cycle-Consistent Image Translation)

Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation
Cycle-GAN
Çevrimsel Tutarlı GAN

CycleGAN, introduced by Zhu et al. at ICCV 2017, learns to translate images between two visual domains without requiring paired training examples. It trains two generators and two discriminators simultaneously, enforcing a cycle-consistency constraint so that an image translated from domain X to Y and back again recovers the original. This makes it applicable whenever large aligned datasets are unavailable, such as converting photographs to artwork styles, turning summer landscapes into winter scenes, or mapping satellite imagery to map tiles.

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE International Conference on Computer Vision (ICCV), 2242–2251.


Cyclic Voltammetry

Cyclic Voltammetry (CV) is an electrochemical technique that measures redox reactions by varying the voltage applied to an electrode and monitoring the resulting current. Developed by Nicholson and Shain in 1964, CV is one of the most widely used electrochemical methods, providing rapid information about the redox potentials and electron-transfer kinetics of molecules and materials.

Nicholson, R. S., & Shain, I. (1964). Electroanalytical chemistry. Analytical Chemistry, 36(4), 706-723.

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


Cyclomatic Complexity

Cyclomatic Complexity (CC), introduced by Thomas McCabe in 1976, is a quantitative metric measuring the number of linearly independent paths through a function's control-flow graph. A function with high cyclomatic complexity is harder to understand, test, and maintain; McCabe advocated a threshold of 10 as the complexity limit for maintainability.

McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, SE-2(4), 308–320.

Campbell, G. H. (1986). Defining a good metric, a software testing perspective. ASQ Software Quality Conference.

Nagy, C., & Kriebel, K. (2001). Achieving optimal complexity and reliability. SAMS Publishing.

halstead-complexity cognitive-complexity test-coverage refactoring


D-TOPSIS

D-TOPSIS (Modified TOPSIS based on D-Numbers (Deng Evidence Theory)) is a ranking multi-criteria decision-making (MCDM) method introduced by Fei, L., Hu, Y., Xiao, F., Chen, L., Deng, Y. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

D-Number ranking — D:Ω→[0,1] with Σ_{B⊆Ω} D(B) ≤ 1; incomplete evidence allowed (Deng 2012)

Fei, L., Hu, Y., Xiao, F., Chen, L., Deng, Y. (2016). A Modified TOPSIS Method Based on D Numbers and Its Applications in Human Resources Selection. Mathematical Problems in Engineering

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


D-Value and Z-Value

decimal reduction time
thermal resistance

D-value (decimal reduction time) and Z-value characterize the thermal resistance of microorganisms in food. D-value is the time required at a specific temperature to reduce microbial population by 90% (one log unit). Z-value is the temperature change needed to reduce the D-value tenfold. Together, they enable food processors to design thermal processes ensuring microbial safety while minimizing nutrient loss.

Stumbo, C. R. (1973). Thermobacteriology in food processing (2nd ed.). Academic Press.

Betts, R. P., Everis, L., & Brock, C. (2000). Principles of thermal process validation. Campden & Chorleywood Food Research Association.


DAG Causal Identification

Causal Identification with Directed Acyclic Graphs (do-calculus)

DAG ile Nedensel Tanımlama (do-calculus)

DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths.

Backdoor / frontdoor adjustment via do-calculus

Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.

Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics: A Primer. Wiley.

propensity-score-matching inverse-probability-weighting instrumental-variables mediation-analysis sensitivity-analysis-observational


DSES

The DSES, developed by Underwood and Teresi in 2002, is a 16-item self-report measure designed to capture the frequency and depth of spiritual experiences that occur in everyday life. Unlike scales that measure religious affiliation or institutional participation, the DSES assesses whether and how often individuals report direct, lived spiritual experience—moments of connection to something transcendent, sacred, or divine. It has become widely used in health services research, chaplaincy, and gerontological studies to quantify spiritual well-being and predict psychological and health outcomes.

Underwood, L. G., & Teresi, J. A. (2002). The Daily Spiritual Experience Scale: Development, theoretical description, reliability, exploratory factor analysis, and preliminary construct validity using health-related data. Annals of Behavioral Medicine, 24(1), 22–33.


Dallas Pain Questionnaire

multidimensional low back pain assessment

The Dallas Pain Questionnaire (DPQ) is a 16-item self-report instrument developed by Lawlis and colleagues in 1989 to assess the multidimensional impact of low back pain. The DPQ captures four domains: daily activities impact, work/leisure impairment, anxiety/depression, and pain severity, providing a comprehensive profile of low back pain's functional and psychological consequences.

Self-report questionnaire measuring low back pain functional impact and psychological symptoms

Lawlis, G.F., Cuencas, R., Selby, D., & McCoy, C.E. (1989). The development of the Dallas Pain Questionnaire. An assessment of pain in patients with chronic low-back pain. Spine, 14(5), 511-516.

McCombe, P.F., Bogduk, N., & Lord, S.M. (1994). A comparison of three treatment approaches for chronic low back pain. Spine, 14(12), 1371-1377.

Schmidt, H., Shirazi-Adl, A., Galbusera, F., & Wilke, H.J. (2010). The relation between the instantaneous center of rotation and facet joint forces—A parametric study. European Spine Journal, 17(6), 865-876.

roland-morris-disability mcgill-pain-questionnaire pain-catastrophizing-scale pain-self-efficacy-questionnaire


DANP

DANP (DEMATEL-based ANP) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Tzeng, G.-H., Huang, J.-J. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Hybrid: DEMATEL total-relation + ANP supermatrix

Tzeng, G.-H., Huang, J.-J. (2010). Multiple Attribute Decision Making: Methods and Applications. Chapman & Hall/CRC

ahpsort aploco aras aroman artasi cobra cocoso codas


Dark Triad Scale

The Dark Triad Personality Scale measures three socially aversive personality traits: narcissism (entitlement and exploitativeness), Machiavellianism (manipulativeness and strategic lying), and psychopathy (callousness and thrill-seeking). Developed by Delroy Paulhus and Kevin Williams in 2002, and later operationalized in brief forms like the Short Dark Triad (SD3) by Jones and Paulhus in 2014, the Dark Triad construct has become standard in personality psychology for assessing antagonistic, self-centered, and deceitful traits. The scale enables research on personality pathology, workplace toxicity, and evolutionary psychology.

Paulhus, D. L., & Williams, K. M. (2002). The Dark Triad of personality: Narcissism, Machiavellianism, and psychopathy. Journal of Research in Personality, 36(6), 556–563.

Jones, D. N., & Paulhus, D. L. (2014). Introducing the Short Dark Triad (SD3): A brief measure of dark personality traits. PLOS ONE, 9(8), e106350.

Jonason, P. K., Li, N. P., Webster, G. D., & Schmitt, D. P. (2009). The Dark Triad: Facilitating a short-term mating strategy in humans. European Journal of Personality, 23(1), 5–25.


Disease Activity Score 28

28-Joint Disease Activity Score for Rheumatoid Arthritis

The DAS28 is a composite measure of rheumatoid arthritis (RA) disease activity, combining joint counts, inflammatory markers, and patient-reported global health. Developed in 1995 by Prevoo and colleagues, it has become the gold standard for monitoring RA activity in clinical trials and practice. It integrates objective clinical signs with laboratory and subjective assessment, providing a single numerical index of disease burden.

Prevoo ML, Hart's AM, Van Houwelingen HC, et al. Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis & Rheumatism. 1995;38(1):44-48.

cdai-rheumatoid-arthritis sdai-rheumatoid-arthritis rapid3 basdai sledai


DASH Outcome Measure

Disabilities of the Arm, Shoulder and Hand Outcome Measure

The Disabilities of the Arm, Shoulder and Hand (DASH) is a 30-item self-report questionnaire designed to measure physical disability and symptoms in patients with upper extremity disorders. Developed by Hudak, Amadio, and Bombardier in 1996, the DASH has become the most widely used patient-reported outcome measure for assessing disability and functional impact in shoulder, elbow, wrist, and hand conditions.

Hudak, P. L., Amadio, P. C., & Bombardier, C. (1996). Development of an upper extremity outcome measure: the DASH (Disabilities of the Arm, Shoulder and Hand). American Journal of Industrial Medicine, 29(6), 602–608.

Beaton, D. E., Wright, J. G., Katz, J. N., & Upper Extremity Collaborative Group. (2005). Development of the QuickDASH: comparison of three item-reduction approaches. Journal of Bone and Joint Surgery, 87(5), 1038–1046.

womac ndi-neck-disability oswestry-disability-index fugl-meyer-assessment


Depression Anxiety Stress Scales

Stephen H. Lovibond and Peter F. Lovibond

The Depression Anxiety Stress Scales-21 (DASS-21) is a 21-item self-report instrument measuring three correlated but distinct dimensions of psychological distress: depression, anxiety, and stress. Developed by Lovibond and Lovibond in 1995, the DASS-21 is a short form of the original 42-item DASS. It has become widely used in research and clinical settings for its brevity, multidimensional structure, and strong psychometric properties.

Three-dimensional mental health screening

Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales. Psychology Foundation of Australia.

Henry, J. D., & Crawford, J. R. (2005). The short-form version of the Depression Anxiety Stress Scale (DASS-21): Construct validity and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 44(2), 227-239.


Drug Abuse Screening Test

Drug Abuse Screening Test - 10 Item Version

Drug use disorder screening and severity

The Drug Abuse Screening Test (DAST) is a brief, validated self-report instrument developed by Skinner in 1982 to screen for drug abuse and dependence in medical and psychiatric populations. The 10-item DAST-10 comprises yes/no questions assessing drug use patterns, consequences, and interference with life functioning. It is widely used in primary care, emergency medicine, and substance abuse treatment settings for rapid identification of individuals requiring further substance abuse evaluation.

Ten-item substance abuse screening instrument

Skinner, H. A. (1982). The Drug Abuse Screening Test. Addictive Behaviors, 7(4), 363-371.

Bohn, M. J., Babor, T. F., & Kranzler, H. R. (1995). The Alcohol Use Disorders Identification Test (AUDIT): validation of a screening instrument for use in medical settings. Journal of Studies on Alcohol, 56(4), 423-432.

Carey, K. B., Carey, M. P., Chandra, P. S., & Shalin, V. (2003). Measuring readiness-to-change substance abuse among psychiatrically ill adults. Psychology of Addictive Behaviors, 17(3), 224-232.


Data Augmentation

Training Data Augmentation
Image Augmentation
Veri Artırma
Synthetic Data Augmentation

Data augmentation is a family of techniques that artificially expands a training dataset by applying label-preserving transformations to existing samples. Originally systematized for image classification tasks, it is now applied broadly across vision, text, audio, and tabular domains. It emerged as a practical answer to the chronic scarcity of labeled data in supervised deep learning and remains a standard preprocessing step in modern neural network pipelines.

Regularization / data preprocessing technique

Images, text, tabular, or time-series data

Reduces overfitting when labeled data is scarce

Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60.


Data Collection Methods

Data Collection Techniques and Instrument Design

Floyd Fowler (surveys); John Creswell, Robert DeVellis (qualitative and scale methodology)

Data collection methods are the specific techniques and instruments used to gather information from research participants or sources. Common quantitative methods include surveys (questionnaires, interviews), physiological measurements (blood pressure, lab assays), behavioral observations, and administrative/secondary data (e.g., medical records, national registers). Qualitative methods include in-depth interviews, focus groups, observations, and document analysis. Selection and design of data collection instruments directly affect data quality, validity, and reliability. Floyd Fowler's work on survey methodology (1980s–2010s), Robert DeVellis's scale development approach, and John Creswell's frameworks for qualitative data collection provide systematic guidance.

Fowler, F. J. (2014). Survey Research Methods (5th ed.). SAGE Publications.

Creswell, J. W. (2017). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (4th ed.). SAGE Publications.

DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). SAGE Publications.


Data-Driven MCDA

Data-Driven Multi-Criteria Decision Analysis

Data-Driven MCDA is a hybrid framework that integrates machine learning and statistical learning into traditional multi-criteria decision analysis. Instead of eliciting weights from expert judgment, it learns criteria importance from historical decision data, enabling more scalable and empirically grounded decision support.

Learning-based criteria weighting and aggregation

Греченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143.

Brans, J. P., & Vincke, P. (2013). Modern approaches to decision-making: Hybrid methods combining preferences with data. European Journal of Operational Research, 248(1), 1-12.


Data Fabrication and Falsification

Definition, Detection, and Prevention of Research Data Fabrication and Falsification

FFP Data Violations
Data Integrity Violations

U.S. Office of Research Integrity; definitions in federal policy 42 CFR 93

Data fabrication and falsification are serious forms of research misconduct involving intentional misrepresentation of research data. Fabrication means inventing data that were never actually collected; falsification means altering authentic data to change the meaning. Both undermine scientific integrity, waste research resources, and can harm research subjects and the public. Federal policy (42 CFR Part 93) formally defines these violations; detection is improving through statistical analysis tools and data transparency practices; prevention requires robust data governance and culture of accountability.

U.S. Office of Research Integrity. (2005). Public Health Service Policy on Research Misconduct. 42 CFR Part 93. Definitions of fabrication and falsification.

Carlisle, J.B. (2017). Data Fabrication and Deviation in Statistics in Anesthesia Articles. Anesthesia, 72(2), 221–237.

Nuijten, M.B., Hartgerink, C.H., van Assen, M.A., et al. (2015). The Prevalence of Statistical Reporting Errors in Psychology (1985-2013). Behavior Research Methods, 48(4), 1205–1226.


Data Federation

Federated Database Systems and Query Processing

Data federation is an approach to integrating data from heterogeneous, autonomous, distributed databases without requiring centralized storage. Formalized by Sheth and Larson in 1990, federated systems provide a unified interface to query multiple independent sources while preserving their autonomy and existing schemas.

Sheth, A. P., & Larson, P. A. (1990). Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Computing Surveys, 22(3), 183-236.

Özsu, M. T., & Valduriez, P. (2011). Distributed and parallel database systems. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1657-1668.

Garcia-Molina, H., Ullman, J. D., & Widom, J. (2009). Database Systems: The Complete Book (2nd ed.). Pearson Education.

distributed-databases query-optimization schema-integration data-virtualization wrapper-adapter


Data Fusion

Sensor Data Fusion
Information Fusion
Multi-source Data Fusion
Veri Füzyonu

Data fusion is a multi-level process that combines data and information from multiple sensors and sources to achieve improved accuracy, completeness, and confidence in estimates that cannot be obtained from any single source alone. Formally introduced as the Joint Directors of Laboratories (JDL) model by Hall and Llinas in 1997, the framework organizes fusion into hierarchical processing levels ranging from raw signal combination to higher-order situation and threat assessment.

Multi-level information integration pipeline

Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6–23.


Data Protection and Privacy in Research

Regulatory Frameworks and Practical Applications of Data Privacy and Security in Human Subjects Research

research privacy
GDPR research
data security
confidentiality
de-identification
anonymization

European Union; U.S. Department of Health and Human Services; International research ethics community

Research involving human subjects generates sensitive data: medical records, genetic information, behavioral responses, economic or social information. Regulatory frameworks—HIPAA (Health Insurance Portability and Accountability Act) in the U.S., GDPR (General Data Protection Regulation) in the European Union, and parallel regulations in other countries—establish legal obligations for data protection and privacy. Researchers must implement technical and procedural safeguards to prevent unauthorized access, maintain confidentiality, and comply with participant rights (access, rectification, deletion, data portability). Understanding data protection requirements is not optional compliance; it is foundational to ethical research.

European Union. (2018). Regulation (EU) 2016/679 of the European Parliament and of the Council: General Data Protection Regulation (GDPR). Official Journal of the European Union, L 119, 1-88.

U.S. Department of Health and Human Services. (1996). Health Insurance Portability and Accountability Act (HIPAA). Public Law 104-191.

U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Sections on Confidentiality and Privacy.

National Academies of Sciences, Engineering, and Medicine. (2015). Proposed Revisions to the Common Rule for the Protection of Human Subjects. Letter Report.

ethics-committee-application waiver-of-informed-consent vulnerable-populations-research clinical-trial-registration ethics-committee-types


Data Sharing and Open Science

Data Sharing, Reproducibility, and Open Science Practices

Open Data
Research Data Sharing
Research Reproducibility

Open science movement; Center for Open Science; funding agencies (NIH, EU, NSF)

Data sharing and open science are practices that maximize research transparency and reproducibility by making raw data, analysis code, and methods publicly available alongside publications. The replication crisis (widespread failure to reproduce published findings in psychology, medicine, and other fields) revealed that traditional publication—focusing on novel results—incentivizes selective reporting and p-hacking. Open science practices (preregistration, data sharing, code sharing, open materials) aim to reduce bias and enable independent verification. Major funders (NIH, NSF, EU) now mandate open science practices, and many journals require data availability statements or code repositories.

Open Science Framework (2023). OSF. Center for Open Science.

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR Guiding Principles for Scientific Data Management and Stewardship. Scientific Data, 3, 160018.

Cohen, S. A., Cox, R. P., Favor, T. K., & Glover, S. C. (2016). The Role of Preregistration in Psychological Research. Psychological Science Agenda (American Psychological Association).

peer-review-process preprint-servers open-access-publishing plagiarism-in-research


Data Warehousing

Data Warehousing Architecture and Design

Business Analytics & Analytics Architecture

Data warehousing is an approach to designing integrated repositories of historical business data optimized for analysis and reporting. Pioneered by William Inmon and Ralph Kimball in the early 1990s, data warehouses consolidate data from diverse operational sources into a centralized, time-stamped, non-volatile store supporting complex queries across multiple dimensions.

Inmon, W. H. (1992). Building the Data Warehouse. New York: QED Technical Publishing.

Kimball, R. (1996). The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. New York: John Wiley & Sons.

Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.

etl-process olap-cube-design dimensional-modeling star-schema fact-tables


Database Normalization

Database normalization is a systematic process for organizing relational database schemas to eliminate redundancy and enforce data integrity. Introduced by Edgar Codd in 1970-1971 as part of the relational database model, it defines a series of normal forms (1NF through BCNF) that progressively eliminate different types of data anomalies.

Codd, E. F. (1970). A relational model of data for large shared data banks. Communications of the ACM, 13(6), 377-387.

Codd, E. F. (1971). Further normalization of the data base relational model. IBM Research Report RJ892.

Boyce, R. F., Chamberlin, D. D., King, W. F., & Hammer, M. M. (1975). Specifying and enforcing integrity constraints in SQL. SIGMOD Record, 5(4), 70-77.

entity-relationship-modeling functional-dependencies data-quality referential-integrity schema-design


Database Partitioning

Database Table Partitioning and Sharding

Database partitioning is a technique for dividing large tables across multiple physical storage units or servers to improve performance and scalability. Developed in the context of distributed databases, partitioning allows individual queries to access smaller subsets of data, reducing I/O and enabling horizontal scaling as data grows.

Stonebraker, M., & Schloss, G. A. (1986). Distributed INGRES to homogeneous and heterogeneous computer systems. Proceedings of the ACM SIGMOD International Conference on Management of Data, 64-77.

Johnson, B. (2000). Distributed systems and databases (2nd ed.). New York: Morgan Kaufmann.

Garcia-Molina, H., Ullman, J. D., & Widom, J. (2009). Database Systems: The Complete Book (2nd ed.). Pearson Education.

scalability-strategies distributed-databases range-partitioning hash-partitioning query-routing


Davies-Bouldin Index

Davies-Bouldin Index for Cluster Separation

The Davies-Bouldin Index, introduced by Davies and Bouldin in 1979, is a metric for evaluating clustering quality based on the average similarity between each cluster and its most similar neighboring cluster. Lower values indicate better clustering, with a minimum of 0 representing perfectly separated, non-overlapping clusters.

Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227.

silhouette-score calinski-harabasz-index dunn-index gap-statistic adjusted-rand-index


Daylight Simulation

Daylight Simulation and Daylighting Performance Analysis

daylighting analysis
illuminance simulation
daylight availability assessment

Daylight Simulation is a computational method for predicting the availability and distribution of daylight in interior spaces and assessing visual comfort under varying sky conditions. Developed by researchers like Christoph Reinhart and John Mardaljevic in the 2000s, it has become central to designing healthy, energy-efficient buildings that maximize natural light while controlling glare.

computational daylighting assessment method

Reinhart, C. F., Mardaljevic, J., Rogers, Z. (2010). Dynamic Daylight Performance Metrics for Sustainable Building Design. Leukos, 3(1), 7-31.

Mardaljevic, J. (2006). Examples of Climate-Based Daylight Modelling. Proceedings of the Lux Europa 2005 Conference.

Wienold, J., Christoffersen, J. (2009). Evaluation of Discomfort Glare and its Dynamic Properties Using the Daylight Glare Index. Building and Environment, 44(9), 1921-1930.


DISS

Daytime insomnia consequences; functional impact

The Daytime Insomnia Symptom Scale (DISS) is a focused assessment tool measuring the daytime functional consequences and symptoms resulting from nighttime insomnia. Developed within research on sleep disturbance and daytime functioning, it captures the daytime manifestations of poor sleep: fatigue, concentration difficulty, mood disturbance, and functional impairment in work, social, and personal domains. The DISS is particularly valuable in quantifying the real-world impact of insomnia on daily activities and quality of life.

Gentili, A., Weiner, D. K., Kuchibhatla, M., & Edinger, J. D. (2007). Factors that modify the relationship between pain and depression in older adults. Journal of the American Geriatrics Society, 55(12), 1862-1873.


DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

DBSCAN Kümeleme
density-based clustering
density-based spatial clustering

DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.

Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.

kmeans-clustering hierarchical-clustering random-forest svm-classification


DCC-GARCH model

Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroscedasticity Model

DCC-GARCH
Dynamic Conditional Correlation GARCH
Engle DCC model
multivariate DCC

The DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series.

Multivariate financial time series (returns, exchange rates, asset prices)

Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.

arch-model egarch-model tgarch-model vector-autoregression granger-causality-test johansen-cointegration-test


DCC-GARCH

dynamic conditional correlation
Engle DCC
multivariate GARCH
DCC-GARCH — Dinamik Koşullu Korelasyon

DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step.

Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350.

Aielli, G. P. (2013). Dynamic Conditional Correlation: On Properties and Estimation. Journal of Business & Economic Statistics, 31(3), 282-299.

egarch arima copula-models value-at-risk extreme-value-theory


DCC-MIDAS

DCC-MIDAS combines dynamic conditional correlation (DCC) GARCH with mixed-frequency data sampling (MIDAS), enabling estimation of time-varying correlations between variables when observations arrive at different frequencies. Introduced by Engle et al. (2013), it models how correlations evolve with low-frequency macroeconomic conditions using high-frequency asset price information. This is crucial for portfolio risk management and understanding macro-finance linkages.

Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.

Colacito, R., Engle, R. F., & Ghysels, E. (2011). A component model for dynamic correlations. Journal of Econometrics, 164(1), 45-59.


De Jong Gierveld Loneliness Scale

De Jong Gierveld Loneliness Scale (DJGLS)

DJGLS
De Jong-Gierveld Scale
11-item Loneliness Scale

The De Jong Gierveld Loneliness Scale is one of the most extensively used brief instruments for measuring loneliness in population surveys, clinical research, and gerontological studies. Developed by Jenny De Jong Gierveld and Fons Kamphuis in 1985, the 11-item scale (with a shorter 6-item version available) measures emotional and social dimensions of loneliness, based on the theory that loneliness arises from a discrepancy between desired and actual social relationships. The DJGLS is valued for its brevity, ease of administration, strong psychometric properties, and widespread availability in 30+ languages.

De Jong Gierveld, J., & Kamphuis, F. (1985). The development of a Rasch-type loneliness scale. Applied Psychological Measurement, 9(4), 289-299.

De Jong Gierveld, J., & Van Tilburg, T. (2006). A 6-item scale for overall, emotional, and social loneliness. Research on Aging, 28(5), 582-598.


De Novo Transcriptome Assembly

transcriptome assembly
de novo assembly
RNA-Seq assembly

De novo transcriptome assembly reconstructs full-length messenger RNA sequences directly from sequencing reads without requiring a reference genome. Pioneered by Regev, Haas, and colleagues, this pipeline enables transcript discovery in non-model organisms and detection of novel isoforms, fusion genes, and splice variants.

Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., ... & Regev, A. (2011). Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology, 29(7), 644-652.

Haas, B. J., Papanicolaou, A., Yassour, M., Grabherr, M., Blood, P. D., Bowden, J., ... & Regev, A. (2013). De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nature Protocols, 8(8), 1494-1512.

Pertea, M., Pertea, G. M., Antonescu, C. M., Chang, T. C., Mendell, J. T., & Salzberg, S. L. (2015). StringTie enables improved assembly of novel transcripts from RNA-seq data. Nature Biotechnology, 33(3), 290-295.


DEA-BCC

DEA-BCC (Data Envelopment Analysis (BCC / VRS model)) is a dea multi-criteria decision-making (MCDM) method introduced by Banker, R. D., Charnes, A., Cooper, W. W. in 1984. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Non-parametric efficiency frontier — Variable Returns to Scale (BCC model)

Banker, R. D., Charnes, A., Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science


DEA-CROSSEFF

DEA-CROSSEFF (DEA Cross-Efficiency — peer appraisal using cross-evaluation matrix) is a dea multi-criteria decision-making (MCDM) method introduced by Sexton, T. R., Silkman, R. H., Hogan, A. J. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

DEA extension — cross-evaluation with peer appraisal averaging

Sexton, T. R., Silkman, R. H., Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation


DEA-DYNAMIC-NETWORK

DEA-DYNAMIC-NETWORK (Dynamic-Network DEA with Carryovers and Bad Outputs) is a dea multi-criteria decision-making (MCDM) method introduced by Fukuyama, H. Weber, W. L. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Non-parametric dynamic network efficiency — Multi-period two-stage production with carryover assets, bad inputs (lagged NPLs), and bad outputs (nonperforming loans); DN-directional distance function

Fukuyama, H., Weber, W. L. (2013). A dynamic network DEA model with an application to Japanese Shinkin banks. Efficiency and Productivity Growth: Modelling in the Financial Services Industry


DEA-ENV

DEA-ENV (Environmental DEA with Undesirable Outputs (EEI model)) is a dea multi-criteria decision-making (MCDM) method introduced by Färe, R. Grosskopf, S. Lovell, C. A. K. Pasurka, C. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Non-parametric environmental efficiency — Weak disposability of undesirable outputs (CRS / VRS)

Färe, R., Grosskopf, S., Lovell, C. A. K., Pasurka, C. (1989). Multilateral Productivity Comparisons When Some Outputs are Undesirable: A Nonparametric Approach. Review of Economics and Statistics


DEA Hospital Efficiency

Data Envelopment Analysis for Hospital Efficiency Measurement

Efficiency measurement, Operations management

Abraham Charnes, William Cooper, Edward Rhodes

Data Envelopment Analysis (DEA) is a linear programming technique for measuring the relative efficiency of multiple hospitals using multiple inputs and outputs. Introduced by Charnes, Cooper, and Rhodes in 1978, DEA has become the standard method for benchmarking hospital performance in healthcare systems worldwide.

Non-parametric frontier estimation technique

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

Bannick, R. R., & Ozcan, Y. A. (2008). Efficiency evaluation of long-term care facilities. Health Care Management Science, 11(2), 81–91.

Ozcan, Y. A. (2014). Health Care Management: A Quantitative Approach (4th ed.). John Wiley & Sons.

balanced-scorecard-healthcare staffing-ratio-analysis hospital-readmission-model lean-healthcare cost-effectiveness-analysis-hta


DEA-NETWORK-SBM

DEA-NETWORK-SBM (Network Slacks-Based Measure DEA with Window Analysis) is a dea multi-criteria decision-making (MCDM) method introduced by Tone, K. Tsutsui, M. in 2009. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Non-parametric network efficiency — Multi-stage slacks-based measure (SBM) with window analysis for longitudinal performance; input and output slacks aggregated across K stages and T-w+1 windows

Tone, K., Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research


DEA-NETWORK

DEA-NETWORK (Two-Stage Network DEA with Undesirable Outputs) is a dea multi-criteria decision-making (MCDM) method introduced by Fukuyama, H. Weber, W. L. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Non-parametric network efficiency — Two-stage production with intermediate products and bad outputs

Fukuyama, H., Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega


DEA-RAM

DEA-RAM (Range-Adjusted Measure of Inefficiency) is a dea multi-criteria decision-making (MCDM) method introduced by Cooper, W. W. Park, K. S. Pastor, J. T. in 1999. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Non-parametric efficiency — Non-radial, weighted slack-based (VRS)

Cooper, W. W., Park, K. S., Pastor, J. T. (1999). RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA. Journal of Productivity Analysis


DEA-SBM

DEA-SBM (Slack-Based Measure Data Envelopment Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Tone, K. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Non-radial DEA efficiency via input/output slacks (ρ ∈ [0,1])

Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research


DEA-SUPEREFF

DEA-SUPEREFF (Super-Efficiency Data Envelopment Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Andersen, P. Petersen, N. C. in 1993. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

DEA with efficiency scores >1 for frontier units by excluding target DMU

Andersen, P., Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science


DEA

DEA (Data Envelopment Analysis (CCR model) for efficiency-based ranking) is a dea multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W., Rhodes, E. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Non-parametric efficiency frontier (CCR model)

Charnes, A., Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research


Dead Reckoning

ded reckoning
inertial navigation
odometry

Dead Reckoning is a fundamental navigation method that estimates position and heading by integrating velocity and angular rate measurements from inertial sensors over time, without external references such as GPS. The term derives from maritime tradition ('deduced reckoning') and remains a cornerstone of aerospace and autonomous vehicle navigation. Dead reckoning works reliably in GPS-denied environments and is the baseline navigation method when external navigation aids are unavailable.

Savage, P. G. (2007). Strapdown Inertial Integration Technology (2nd ed.). Strapdown Associates.

Titterton, D. H., & Weston, J. L. (2004). Strapdown Inertial Navigation Technology (2nd ed.). Institution of Engineering and Technology.

Groves, P. D. (2008). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Artech House.


Debit Valuation Adjustment

Debit Valuation Adjustment (DVA) represents the value of your own credit risk to counterparties. DVA measures the gain in derivative value if you default on your obligations—a benefit for your shareholders because creditors receive less than the full derivative value. DVA is controversial but now mandatory under IFRS 13 for fair value accounting.

Gregory, J. (2009). Counterparty Credit Risk: The New Challenge for Global Financial Markets. John Wiley & Sons.

Burgard, C., & Kjaer, M. (2011). Partial differential equation representations of derivatives with counterparty risk and funding costs. Journal of Credit Risk, 7(3), 1-19.


Deception and Debriefing in Research

Ethical Framework and Procedures for Deception in Behavioral and Psychological Research

deceptive research
deception studies
debriefing
informed deception
revealing deception

American Psychological Association; International research ethics community

Deception in research—withholding information about study procedures, hypotheses, or true purpose—is ethically permissible under limited circumstances when specific criteria are met. The regulatory framework (45 CFR 46.116(a)(5) in the U.S.; APA Ethical Code Section 8.07) allows deception if: (1) it is not reasonably possible to conduct the research without deception, (2) the deception does not involve risks greater than 'minimal risk,' and (3) participants receive full disclosure and the opportunity to withdraw data after debriefing. Deception is particularly common in social and behavioral research (studying prejudice, conformity, ethical decision-making) where awareness of the true hypothesis would fundamentally alter behavior. Understanding when deception is justified and how to implement it ethically is essential for behavioral researchers.

U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Section 46.116(a)(5).

American Psychological Association. (2017). Ethical Principles of Psychologists and Code of Conduct. Section 8.07 - Deception in Research.

The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research.

Festinger, L., Riecken, H. W., & Schachter, S. (1956). When Prophecy Fails: A Social and Psychological Study of a Modern Group That Predicted the Destruction of the World. University of Minnesota Press.

ethics-committee-application participant-debrief risk-benefit-assessment vulnerable-populations-research ethics-committee-types


Decision Analytic Modeling

Decision Analytic Modeling in Health Economics

decision analysis
decision tree
decision model
health economic model

Pauker & Kassirer (medical decision analysis, Massachusetts General Hospital)

Decision analytic modeling is a systematic framework for comparing health interventions by integrating evidence on probabilities, outcomes, costs, and patient preferences into a quantitative model. Developed by Pauker and Kassirer in 1975, decision analysis structures clinical uncertainty and economic trade-offs, enabling transparent comparison of treatment options and identification of optimal strategies. Used in health technology assessment, clinical practice guideline development, and resource allocation decisions.

Pauker, S. G., & Kassirer, J. P. (1975). Therapeutic Decision Making: A Cost-Benefit Analysis. New England Journal of Medicine, 293(5), 229-234.

Briggs, A. H., Claxton, K., & Sculpher, M. J. (2006). Decision Modelling for Health Economic Evaluation. Oxford: Oxford University Press.

Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford: Oxford University Press.

cost-effectiveness-analysis markov-model-health-economics quality-adjusted-life-year cost-benefit-analysis budget-impact-analysis


Decision Tree

Decision Tree (CART — Classification and Regression Trees)

Karar Ağacı (Decision Tree)
karar ağacı
classification tree
regression tree
CART

A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.

Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.

random-forest xgboost logistic-regression naive-bayes svm-classification


Decisional Conflict Scale

The Decisional Conflict Scale (DCS) is a 16-item self-reported outcome measure that quantifies the degree of uncertainty, value ambivalence, and decision distress experienced by patients facing healthcare choices. Developed by Annette O'Connor in 1995, the DCS assesses five core domains: personal uncertainty, understanding of options and outcomes, clarity of personal values, perceived social support, and confidence in making the decision. It has become the gold standard for measuring decisional conflict in healthcare research and clinical trials of decision support interventions.

O'Connor, A. M. (1995). Validation of a decisional conflict scale. Medical Decision Making, 15(1), 25-30.

O'Connor, A. M. (2008). User Manual – Decisional Conflict Scale. University of Ottawa.

sure-test-decision-quality collaboste-scale patient-enablement-instrument control-preferences-scale


Declaration of Helsinki

The Declaration of Helsinki (1964) is the foundational international ethical code for medical research involving human subjects, established by the World Medical Association. It extended earlier principles (Nuremberg Code 1947) to include therapeutic research and formalized the physician's ethical duty to prioritize subject welfare. Updated nine times through 2013, it remains the standard adopted by major medical journals, research ethics committees, and regulatory bodies worldwide.

World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA, 310(20), 2191–2194.

World Medical Association. (1964). Declaration of Helsinki: Recommendations Guiding Physicians in Biomedical Research Involving Human Subjects. Helsinki: WMA.

belmont-report nuremberg-code informed-consent-research institutional-review-board


Deep Belief Network

DBN
Deep Generative Network
Stacked RBM Network
Derin İnanç Ağı

Geoffrey Hinton, Simon Osindero & Yee-Whye Teh

A Deep Belief Network is a generative probabilistic model composed of multiple layers of stochastic, latent variables. Introduced by Hinton, Osindero, and Teh in 2006, DBNs were among the first deep architectures to be trained efficiently. Each pair of adjacent layers forms a Restricted Boltzmann Machine, and the network is trained greedily, one layer at a time, before optional supervised fine-tuning. DBNs revived interest in deep learning and demonstrated that hierarchical feature learning from raw data is tractable.

Unsupervised pretraining + supervised fine-tuning

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.


Deep Packet Inspection

DPI
complete packet inspection
packet filtering

Deep Packet Inspection (DPI) is a network traffic analysis technique that examines the complete packet payload beyond header information to identify, classify, and potentially control data traffic. Developed in the 1990s for network monitoring and management, DPI analyzes packet contents to detect protocols, applications, and patterns, enabling security monitoring, quality of service management, and content filtering. DPI is widely used by Internet service providers, enterprises, and security organizations to monitor network traffic and enforce policies.

Leconte, M., & Thomas, A. (2009). Deep Packet Inspection (DPI) technologies. In Proceedings of the Global Telecommunications Conference (GLOBECOM), 2009, pp. 1-6.

Soro, F., & Visaggio, G. (2012). Deep packet inspection: evolution and challenges. In Proceedings of the 2012 6th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS).


Deep Reinforcement Learning

Deep Reinforcement Learning (DQN / PPO / A3C)

Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C)

Deep Reinforcement Learning combines neural networks with reinforcement learning so an agent learns by interacting with an environment, popularised by Mnih and colleagues' 2015 Nature work on human-level Atari control. Instead of learning from a fixed labelled dataset, the agent takes actions, observes rewards, and gradually shapes a policy that maximises long-run return.

Sequential decision-making (agent–environment interaction)

Prediction & control (policy/value learning)

Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533.

Schulman, J. et al. (2017). Proximal Policy Optimization Algorithms. arXiv:1707.06347.

random-forest xgboost convolutional-neural-network recurrent-neural-network neural-architecture-search


Deep Remote Sensing

Deep Learning Remote Sensing
DL-based Remote Sensing Analysis
Neural Remote Sensing Segmentation
Derin Uzaktan Algılama

Deep Learning for Remote Sensing Image Segmentation applies convolutional neural networks and encoder-decoder architectures to automatically classify and delineate objects in satellite or aerial imagery at the pixel level. Systematically reviewed by Zhu et al. (2017) in IEEE Geoscience and Remote Sensing Magazine, this paradigm unified previously fragmented approaches — scene classification, object detection, and semantic segmentation — under a single learned-feature framework capable of exploiting the spatial, spectral, and temporal richness of remote sensing data.

Input: Multi-spectral or hyperspectral satellite/aerial imagery

Output: Pixel-wise semantic segmentation maps

Zhu, X. X., et al. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8–36.


DeepAR

probabilistic autoregressive RNN forecasting

Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)

DeepAR is Amazon's industrial forecasting model, introduced by Salinas, Flunkert and Gasthaus (2017; published 2020), that uses an autoregressive recurrent neural network to estimate the parameters of a probability distribution at each step, producing a confidence interval rather than a single point forecast. It can model many related time series jointly within one model.

Autoregressive recurrent neural network (probabilistic forecasting)

Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191.

Salinas, D., Flunkert, V. & Gasthaus, J. (2017). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. arXiv:1704.04110.

nhits patchtst arima random-forest conformal-prediction-ts


DeepHit

Neural network competing risks
DL competing events

DeepHit is a deep neural network framework for survival analysis with competing risks. Introduced by Lee et al. in 2018, it extends DeepSurv to handle settings where multiple, mutually exclusive events can occur, such as disease-specific mortality versus death from other causes. DeepHit solves the challenge of personalized risk prediction when subjects can experience different types of terminal events, a common scenario in medical and reliability applications.

Lee, C., Zame, W., Yoon, J., & van der Schaar, M. (2018). DeepHit: A deep learning approach for dynamic survival analysis with competing risks. AAAI Conference on Artificial Intelligence, 32(1), 2314–2321.

Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.

Katzman, J. L., et al. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. Journal of Machine Learning Research, 40, 40–51.


DeepSurv

Neural network survival
DL survival model

DeepSurv is a deep neural network approach to survival analysis that learns personalized survival distributions directly from data. Introduced by Katzman et al. in 2018, it extends the Cox proportional hazards model using deep learning to capture complex, nonlinear relationships between covariates and survival outcomes. It solves the problem of modeling heterogeneous treatment effects and time-to-event predictions in high-dimensional settings.

Faraggi, D., & Simon, R. (1995). A neural network model for survival data. Statistics in Medicine, 14(1), 73–82.

Lee, C., Zame, W., Yoon, J., & van der Schaar, M. (2018). Deephit: A deep learning approach for dynamic survival analysis. AAAI Conference on Artificial Intelligence, 32(1).


Defect Prediction Model

Software Defect Prediction and Risk Classification

fault prediction
bug prediction
defect classification

Thomas Ostrand, Elaine Weyuker, Robert Bell

Defect prediction models forecast the likelihood of software faults in code modules using statistical or machine learning approaches. Pioneered by Ostrand, Weyuker, and Bell (2005), these models correlate code metrics (complexity, churn, coupling) with historical defect data to identify high-risk components. Organizations use predictions to allocate testing resources, guide code review, and prioritize refactoring.

Ostrand, T. J., Weyuker, E. J., & Bell, R. M. (2005). Predicting the location and number of faults in large software systems. IEEE Transactions on Software Engineering, 31(4), 340–355.

Nagappan, N., Ball, T., & Zeller, A. (2006). Mining metrics to predict component failures. In Proceedings of the 28th International Conference on Software Engineering (pp. 452–461).

Menzies, T., Greenwald, J., & Russ, P. (2007). Problems with precision: A response to comments on 'Data mining static code attributes to learn defect predictors'. IEEE Transactions on Software Engineering, 33(9), 637–640.

software-complexity-metrics code-coverage-analysis static-code-analysis agile-velocity-tracking


DEFUZZ-ALPHA-CUT

DEFUZZ-ALPHA-CUT (Alpha-Cut Defuzzification — Crisp interval or representative via α-level cut) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Zadeh, L.A. in 1965; 1985. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Defuzzification operator — alpha-cut crisp approximation

Zadeh, L.A. (1965). Fuzzy sets. Information and Control

topsis vikor waspas fuzzy-ahp fuzzy-topsis fuzzy-vikor


DEFUZZ-BISECTOR

DEFUZZ-BISECTOR (Bisector of Area Defuzzification — Vertical line dividing fuzzy set area equally) is a defuzzification multi-criteria decision-making (MCDM) method introduced by van Leekwijck, W.; Kerre, E.E. in 1999. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Defuzzification operator — bisector of area

van Leekwijck, W., Kerre, E.E. (1999). Defuzzification: criteria and classification. Fuzzy Sets and Systems


DEFUZZ-CENTROID-GAUSSIAN

DEFUZZ-CENTROID-GAUSSIAN (Gaussian Centroid Defuzzification — Crisp representative of Gaussian fuzzy number) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Zadeh, L.A. in 1965. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Defuzzification operator — Gaussian membership function

topsis vikor waspas fuzzy-ahp fuzzy-topsis


DEFUZZ-CENTROID

DEFUZZ-CENTROID (Centroid Defuzzification — Centre of gravity crisp representative) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Mamdani, E.H. in 1975. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Defuzzification operator — centroid of area

Mamdani, E.H., Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies

topsis vikor waspas mabac codas fuzzy-ahp fuzzy-topsis fuzzy-vikor


DEFUZZ-MOM

DEFUZZ-MOM (Mean of Maxima (MOM) Defuzzification — Crisp value at the plateau of maximum membership) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Lee, C.C. in 1990. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Defuzzification operator — mean of maxima

Lee, C.C. (1990). Fuzzy logic in control systems: fuzzy logic controller, Part I. IEEE Transactions on Systems, Man, and Cybernetics


DEFUZZ-SCORE-IFN

DEFUZZ-SCORE-IFN (Score Function Defuzzification — Crisp ranking score for IFN/PFN/q-ROFS families) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Chen, S.M.; Tan, J.M. in 1994; extended 2014, 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Defuzzification operator — score and accuracy functions for membership-nonmembership pairs

Chen, S.M., Tan, J.M. (1994). Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems

topsis vikor waspas mabac codas if-topsis if-vikor pf-topsis


DEFUZZ-TYPE-REDUCTION

DEFUZZ-TYPE-REDUCTION (Type Reduction Defuzzification — Karnik-Mendel algorithm for interval type-2 fuzzy sets) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Karnik, N.N.; Mendel, J.M. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Defuzzification operator — type-2 to type-1 reduction via Karnik-Mendel

Karnik, N.N., Mendel, J.M. (2001). Centroid of a type-2 fuzzy set. Information Sciences


Degradation Models

Degradation Models (Accelerated Degradation)

Accelerated Degradation Testing
Degradation Path Models
Performance Degradation Analysis
Bozunma Modelleri

Degradation models estimate product lifetime by tracking measurable performance characteristics—such as crack length, light output, or insulation resistance—over time rather than waiting for outright failure. Introduced in rigorous form by Meeker, Escobar, and Lu (1998), these models fit a stochastic degradation path to repeated measurements and define failure as the first time the characteristic crosses a predetermined threshold, enabling reliable lifetime inference from accelerated test data with very few or no observed failures.

Threshold-crossing of a measurable characteristic

Meeker, W. Q., Escobar, L. A., & Lu, C. J. (1998). Accelerated degradation tests: modeling and analysis. Technometrics, 40(2), 89–99.


Degree Centrality

Degree Centrality (Freeman Node Connectivity Measure)

node degree
degree score
DC
connectivity centrality

Degree centrality is the simplest and most intuitive measure of a node's importance in a network, defined as the number of direct ties a node has to other nodes. Normalized by dividing by the maximum possible ties, it allows comparison across networks of different sizes and is the starting point of almost every network analysis.

Adjacency matrix or edge list (binary or weighted graph)

Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.

Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.

betweenness-centrality closeness-centrality eigenvector-centrality social-network-analysis modularity-analysis weighted-degree-centrality


Degree Heating Weeks

Degree Heating Weeks (DHW) is a thermal stress metric that quantifies accumulated heat exposure above a coral bleaching threshold, computed from satellite sea surface temperature data. Developed by NOAA's Coral Reef Watch program in 2003, DHW provides a standardized index for predicting and monitoring coral bleaching stress globally. The metric combines intensity and duration of thermal anomalies to estimate cumulative physiological stress on coral colonies.

Liu, G., Strong, A. E., & Skirving, W. (2003). Remote sensing of sea surface temperatures during 2002 Great Barrier Reef coral bleaching. EOS Transactions, 84(15), 137-141.

Strong, A. E., Liu, G., Meyer, V., et al. (2016). Integrating thermal habitat monitoring and coral bleaching forecasting. Coral Reefs, 35(1), 1-7.


Delirium Observation Screening Scale

DOS
Delirium Screening Scale
Delirium Observation

The Delirium Observation Screening Scale (DOS), developed by Mieke J. Schuurmans and colleagues in 2003, is a brief clinician-rated screening instrument designed to detect delirium in hospitalized older adults. Delirium—acute onset confusion, inattention, and disorganized thinking—is a common complication in hospitals and intensive care units that increases mortality, morbidity, and length of stay. The DOS captures the hallmark features of delirium through direct observation, making it practical for rapid, repeated screening in busy clinical settings.

Clinician-rated observation screening tool

Schuurmans, M. J., Shortridge-Baggett, L. M., & Duursma, S. A. (2003). The Delirium Observation Screening Scale: a screening instrument for delirium. Res Theory Nurs Pract, 17(1), 31-50.

Schuurmans, M. J., Duursma, S. A., Shortridge-Baggett, L. M., Clevers, G. J., & van der Hoeven, J. G. (2003). Elderly patients with delirium in the hospital. Differences in patient characteristics. A comparative study. Int J Nurs Stud, 40(3), 255-263.


Delphi Method

Delphi Method (Structured Expert Consensus)

Delphi Yöntemi
Delphi technique
expert consensus method

Norman Dalkey & Olaf Helmer (RAND Corporation)

The Delphi method is a structured, iterative survey technique developed by Norman Dalkey and Olaf Helmer at the RAND Corporation in 1963 for eliciting and converging expert opinion on complex topics where empirical data are unavailable or insufficient. It collects independent judgements from a geographically dispersed expert panel over multiple anonymous rounds, feeding aggregated results back to participants after each round so they can revise their views in light of the group's collective position.

Structured iterative expert-elicitation process

Output: Consensus ratings or priority rankings with inter-rater agreement metrics

Ordinal / categorical (Likert-scale survey responses)

Dalkey, N. & Helmer, O. (1963). An Experimental Application of the Delphi Method to the Use of Experts. Management Science, 9(3), 458-467.

focus-group nominal-group-technique content-analysis action-research mixed-methods


Delphi Technique

Delphi method
Delphi survey
expert consensus method
iterative expert panel

Norman Dalkey and Olaf Helmer (RAND Corporation)

The Delphi technique is a structured, multi-round data collection method that harvests and refines expert opinion through iterative questionnaires and controlled feedback. Developed at RAND Corporation in the 1950s, it is designed to converge a dispersed expert panel toward a reliable consensus on complex, uncertain, or future-oriented questions — without the conformity pressures of face-to-face group discussion.

Structured questionnaire responses from expert panels (mixed: ratings, rankings, open text)

Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458–467.

Linstone, H. A., & Turoff, M. (Eds.). (1975). The Delphi Method: Techniques and Applications. Addison-Wesley.

survey structured-interview focus-group nominal-group-technique content-analysis triangulated-delphi-technique


DELPHI

DELPHI (Delphi Method — iterative expert consensus for criterion importance elicitation) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Dalkey, N., Helmer, O. in 1963. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight_Subjective (expert consensus, iterative Likert/ranking)

Dalkey, N., Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science

ahpsort aploco aras aroman artasi cobra cocoso codas


DEMATEL

DEMATEL (Decision Making Trial and Evaluation Laboratory) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Gabus, A., Fontela, E. in 1972. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Cause-effect influence network (total relation matrix) — produces prominence + relation for criteria weighting

Gabus, A., Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Centre, Geneva, Switzerland


Mattis Dementia Rating Scale

The Mattis Dementia Rating Scale (DRS) is a comprehensive 36-item clinician-administered neuropsychological battery designed to assess and quantify cognitive decline in dementia. Developed by Sandra Mattis in 1988, the DRS measures five major cognitive domains—attention, initiation/perseveration, construction, conceptualization, and memory—and provides both a total score and subscale scores. The DRS is particularly valued in neurodegenerative disease research and clinical settings for its sensitivity to cognitive change over time and its utility in detecting cognitive impairment across the dementia spectrum.

Clinician-administered comprehensive neuropsychological scale

Mattis, S. (1988). Dementia Rating Scale (DRS). Odessa, FL: Psychological Assessment Resources.

Monsch, A. U., Bondi, M. W., Butters, N., Salmon, D. P., Kluger, A., & Thal, L. J. (1992). Comparisons of verbal and nonverbal cognitive function in Alzheimer's disease. Neurology, 42(8), 1638-1644.

Vangel Jr, S. J., & Lichtenberg, P. A. (2008). Mattis Dementia Rating Scale: Clinical utility in older adults. Clinical Neuropsychologist, 22(1), 71-80.

mmse adas-cog addenbrookes-cognitive-examination saint-louis-mental-status trail-making-test


Democratic Support Scale

SFD
Democratic Legitimacy Scale
System Support Scale

The Democratic Support Scale measures citizen commitment to democracy as a regime type, including beliefs that democracy is the best system of government, willingness to defend democratic institutions, and rejection of non-democratic alternatives. Pioneered by Norris (1999) and Dalton (2004) in comparative research, the measure distinguishes regime support (belief in democracy's superiority) from performance support (satisfaction with current government). It addresses the paradox of 'critical citizens'—in advanced democracies, people often express dissatisfaction with current government performance while maintaining deep commitment to democratic principles.

Dalton, R. J. (2004). Democratic challenges, democratic choices: The erosion of political support in advanced industrial democracies. Oxford: Oxford University Press.

Norris, P. (1999). Critical citizens: Global support for democratic governance. Oxford: Oxford University Press.

Gibson, J. L. (2004). Constraining the Klan: Civil society and the limits of democratic tolerance. In P. B. Baltes, N. J. Smelser, & P. B. Baltes (Eds.), International encyclopedia of the social & behavioral sciences. Oxford: Elsevier.


Democratic Values Scale

Democratic Norms and Values Assessment Scale

Russell Dalton, Hans-Dieter Klingemann, Christian Welzel

The Democratic Values Scale measures commitment to core principles of democratic governance including free speech, rule of law, fair elections, protection of minorities, and transparent institutions. Rather than measuring support for democracy as a system (which is nearly universal in principle), it captures depth of commitment to democratic norms, tolerance for dissent, and willingness to protect rights of political opponents. Developed by comparative political scientists including Dalton, Klingemann, and Welzel, it reveals psychological foundations of democratic stability.

Dalton, R. J. (2004). Democratic challenges, democratic choices: The erosion of political support in advanced industrial democracies. Oxford University Press.

Klingemann, H. D. (1999). Mapping political support in the 1990s: A global analysis. In P. Norris (Ed.), Critical citizens: Global support for democratic government (pp. 31-56). Oxford University Press.

Welzel, C., & Inglehart, R. (2015). Banyan model of egalitarianism and hierarchy: The cultural evolution of political values. Journal of Cross-Cultural Psychology, 46(1), 83-112.

political-efficacy-scale institutional-trust-scale civic-engagement-scale generalized-trust-scale xenophobia-scale


Dempster-Shafer Fusion

belief function fusion
evidence combination

Dempster-Shafer fusion is an ensemble method based on evidence theory (belief functions) that combines predictions from multiple sources by assigning basic probability masses to subsets of hypotheses. Rather than requiring a probability distribution over single outcomes, it allows uncertainty over sets of outcomes, providing a richer representation of confidence and doubt. Developed by Dempster (1968) and formalized by Shafer (1976), this method is particularly useful when sources are unreliable, conflicting, or provide partial evidence.

Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society, 30(2), 205-247.

Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.

bayesian-fusion majority-voting ensemble-averaging weighted-voting


Dempster-Shafer Theory

Dempster-Shafer Theory of Evidence (Belief Functions)

evidence theory
belief functions
evidential reasoning
Dempster-Shafer kanıt teorisi

Dempster-Shafer theory is a mathematical framework for reasoning under uncertainty that generalizes Bayesian probability by representing ignorance explicitly. Instead of forcing a single probability on each hypothesis, it assigns belief mass to sets of hypotheses and derives a belief-plausibility interval, and it provides Dempster's rule for fusing evidence from multiple independent sources. Developed from Arthur Dempster's 1967 work and Glenn Shafer's 1976 monograph, it underpins evidential reasoning and sensor/decision fusion.

Uncertainty calculus for combining evidence

Belief and plausibility (ignorance explicitly)

Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.

bayesian-network case-based-reasoning naive-bayes fuzzy-cognitive-maps


Denavit-Hartenberg Parameters

Denavit-Hartenberg Convention for Robot Kinematics

DH parameters
DH convention
Robot kinematics convention

The Denavit-Hartenberg (DH) convention is a systematic mathematical method for assigning coordinate frames to the links of an articulated robot or mechanism, enabling compact representation and computation of forward and inverse kinematics. Introduced by Denavit and Hartenberg in 1955, this method uses only four parameters per joint to describe the spatial relationship between adjacent links, dramatically simplifying kinematic analysis and control of complex multi-jointed systems.

Mathematical convention for describing articulated mechanisms

Denavit, J., & Hartenberg, R. S. (1955). A kinematic notation for lower-pair mechanisms based on matrices. Journal of Applied Mechanics, 22(2), 215-221.

Craig, J. J. (2005). Introduction to Robotics: Mechanics and Control (3rd ed.). Pearson Education.

Spong, M. W., Hutchinson, S., & Vidyasagar, M. (2006). Robot Modeling and Control. John Wiley & Sons.

inverse-kinematics cnc-tool-path-generation modal-analysis design-for-manufacturing-and-assembly


Dendrochronology Method

Tree-Ring Analysis for Dating and Climate Reconstruction

Tree-ring dating
Dendrochronological analysis
Ring-width chronology

Dendrochronology is the science of dating and analyzing tree rings to reconstruct past climatic conditions, chronologies, and tree growth patterns. Pioneered by Andrew Ellicott Douglass in the early twentieth century and formalized by Fritts and colleagues, dendrochronology enables precise dating of historical wood samples and generates millennial-length climate records, becoming indispensable for paleoclimatology, archaeology, and forest ecology.

Historical and climatic inference pipeline

Fritts, H. C. (1976). Tree Rings and Climate. Academic Press.

Stokes, D. L., & Smiley, T. L. (1996). An Introduction to Tree-Ring Dating. University of Arizona Press.

Douglass, A. E. (1929). The Secret of the Southwest Solved by Talkless Rings. National Geographic Magazine, 56(6), 736–770.

Briffa, K. R. (2000). Annual Climate Variability in the Holocene: Interpreting the Message of Ancient Trees. The Quaternary Review, 9(2), 87–105.

forest-inventory-sampling allometric-biomass-equation carbon-stock-estimation-forest stand-basal-area-measurement


Dendrochronology

Dendrochronology: Tree Ring Dating and Climate Reconstruction

Tree-ring analysis
Chronology
Paleoclimatology

Dendrochronology is the science of dating and interpreting wood and climate from tree rings. Each annual ring records the tree's growth response to weather during that year: wide rings indicate favorable conditions (adequate water, warmth, light); narrow rings indicate stress (drought, cold, shade). By crossmatching ring-width patterns across trees and backward in time using dead wood, researchers construct chronologies extending centuries to millennia, providing archives of regional precipitation, temperature, and hydroclimate independent of instrumental records.

Archival and climate reconstruction method

Douglass, A. E. (1909). Weather records in the growth of giant sequoias. Monthly Weather Review, 37(1), 713-714.

Fritts, H. C. (1976). Tree rings and climate. Academic Press.

Cook, E. R., & Krusic, P. J. (2015). The North American summer PDSI: Regional reconstructions and applications. Dendrochronologia, 26(3), 155-173.


DenseNet

Densely Connected Convolutional Network (DenseNet)

Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.

DenseNet (Densely Connected Convolutional Network), introduced by Huang, Liu, van der Maaten, and Weinberger at CVPR 2017 (Best Paper Award), connects every layer to every subsequent layer within a dense block so that each layer receives the concatenated feature maps of all preceding layers — maximising feature reuse, strengthening gradient flow, and achieving competitive accuracy with substantially fewer parameters than comparable architectures such as ResNet.

Dense convolutional neural network (feed-forward dense connectivity)

Image classification, feature extraction, transfer learning

growth rate k, number of dense blocks, compression factor theta, bottleneck layers

Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700–4708.

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

resnet vgg inception efficientnet convolutional-neural-network


Density Functional Theory

Density Functional Theory (DFT) is a computational method for determining the properties of materials and molecules by modeling the ground state electron density. Developed by Walter Kohn and Lu Jeu Sham in the 1960s, DFT reduces the complexity of quantum chemistry from tracking individual electron coordinates to optimizing the total electron density, enabling efficient simulations of large molecular and condensed-matter systems.

Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138.

Hohenberg, P., Kohn, W. (1964). Inhomogeneous electron gas. Physical Review, 136, B864–B871.

Burke, K. (2012). Perspective on density functional theory. The Journal of Chemical Physics, 136, 150901.

hartree-fock-method moller-plesset-perturbation-theory time-dependent-dft quantum-monte-carlo


MDAS

MDAS
Modified Dental Anxiety Scale (MDAS)

The Modified Dental Anxiety Scale (MDAS) is a brief 5-item self-report instrument measuring anxiety anticipation and response to common dental situations. Developed by Humphris and colleagues in 1995 as a refinement of prior instruments, the MDAS has become the gold standard for rapid dental anxiety screening in clinical practice and research. Its brevity, psychometric rigor, and clinical utility have made it the most frequently used measure of dental anxiety internationally.

Humphris, G. M., Morrison, T., & Lindsay, S. J. (1995). The Modified Dental Anxiety Scale: validation and United Kingdom norms. Community Dentistry and Oral Epidemiology, 23(6), 326-330.


Corah Dental Anxiety Scale

The Corah Dental Anxiety Scale (DAS), also known as the Dental Anxiety Scale, is a brief 4-item self-report questionnaire designed to measure anxiety associated with dental treatment. Developed by Norman L. Corah in 1969, the DAS is the most widely used instrument for assessing dental anxiety in clinical practice and research, valued for its brevity, ease of administration, and strong psychometric properties.

Corah, N. L. (1969). Development of a dental anxiety scale. Journal of Dental Research, 48(4), 596.


Caries-risk Assessment Tool

John D. Featherstone and collaborative organizations (AAPD, ADA)

The Caries-risk Assessment Tool (CAT), also known as Caries Management by Risk Assessment (CAMBRA), is a systematic framework for evaluating a patient's risk of developing dental caries (cavities). Developed by Featherstone and endorsed by the American Academy of Pediatric Dentistry (AAPD), American Dental Association (ADA), and International Association of Dental Research, the CAT stratifies patients into low, moderate, or high caries-risk categories based on clinical and behavioral factors. Risk assessment guides preventive interventions, enabling individualized caries management and efficient resource allocation.

Risk assessment questionnaire and clinical examination

Featherstone, J. D. (2004). The caries balance: contributing factors and early detection. Journal of the California Dental Association, 31(2), 129-133.

American Academy of Pediatric Dentistry. (2023). Guideline on caries-risk assessment and management for infants, children, and adolescents. Pediatric Dentistry, 45(6), 341-354.


Dental Erosion Index

Tooth Surface Loss Assessment by Erosion Index

tooth wear index
erosion severity index
TSL index

Preventive dentistry and wear assessment

Multiple indices (Lussi index, BEWE, etc.)

The Dental Erosion Index is a systematic clinical assessment tool that quantifies the severity of tooth surface loss caused by non-carious erosive agents (acidic substances, mechanical abrasion, or biological factors). Multiple index systems exist (e.g., Lussi Index, Basic Erosive Wear Examination or BEWE), each scoring erosion based on the extent and depth of surface loss on coronal and cervical tooth surfaces. Erosion assessment is critical for identifying patients at risk for advanced tooth loss, determining preventive interventions, and guiding restorative management.

Lussi, A., Jaeggi, T., & Zero, D. (2004). The role of diet in the aetiology of dental erosion. Caries Research, 38(1), 34-44.

Bartlett, D. W., Lussi, A., West, N. X., Bouchard, P., Sanz, M., & Bourgeois, D. (2013). Prevalence, aetiology and consequences of erosive tooth wear. European Journal of Oral Sciences, 121(1), 1-6.

Ericson, D., & Ericson, T. (2010). Erosion of the teeth in patients with eating disorders. Acta Odontologica Scandinavica, 68(5), 297-304.

dmft-index gingival-index oral-hygiene-index periodontal-probing


Resonance Frequency Analysis for Implants

Implant Stability Assessment by Resonance Frequency Analysis

RFA
Implant Stability Quotient
ISQ
osseointegration assessment

Resonance Frequency Analysis (RFA) is a non-invasive, objective method for assessing dental implant stability and osseointegration. Introduced by Meredith and colleagues in 1996, RFA measures the stiffness of the implant-bone interface by analysing the frequency response of an implant abutment to vibration. The Implant Stability Quotient (ISQ), derived from RFA, enables quantitative monitoring of implant stability at insertion, during healing, and post-integration, facilitating clinical decision-making regarding loading timing and success prediction.

Meredith, N., Alleyne, D., & Cawley, P. (1996). Quantitative determination of the stability of the implant-tissue interface using resonance frequency analysis. Clinical Oral Implants Research, 7(3), 261-267.

Nedir, R., Bischof, M., Szmukler-Moncler, S., Bernard, J. P., & Samson, J. (2004). Predicting osseointegration by means of implant primary stability. Clinical Oral Implants Research, 15(5), 520-528.

Aparicio, C., Lang, N. P., & Rangert, B. (2006). Validity and clinical significance of biomechanical testing of implant/bone interface. Clinical Oral Implants Research, 17(2), 2-7.

bone-density-dental orthodontic-cephalometry occlusal-analysis periodontal-probing


Dental Microwear Texture Analysis

Dental Microwear Texture Analysis (DMTA)

Dental microwear texture analysis (DMTA) is a method that reconstructs diet and dietary behavior from microscopic wear patterns on the surfaces of teeth. Pioneered by Mark Teaford in the 1980s, DMTA analyzes the three-dimensional texture of wear patterns produced as food is chewed. The method reflects short-term (last few months) dietary composition, complementing longer-term dietary information obtained from stable isotope analysis. DMTA has proven powerful for distinguishing diets rich in tough/fibrous foods from those dominated by hard/brittle foods.

Ungar, P. S. (2007). Evolution of the human diet: The known, the unknown, and the unknowable. Oxford University Press.

Teaford, M. F. (1988). A review of dental microwear and diet in modern mammals. Scanning Microscopy, 2(2), 1149-1166.

Grine, F. E. (1986). Dental evidence for dietary differences in Australopithecus and Paranthropus. Journal of Human Evolution, 15(10), 783-822.

isotope-diet-reconstruction geometric-morphometrics use-wear-analysis minimum-number-of-individuals


Dependency Parsing

Dependency Parsing (Syntactic Dependency Analysis)

Bağımlılık Ayrıştırma (Dependency Parsing)

Dependency parsing is a natural-language-processing task that reveals the syntactic dependency relations between the words of a sentence as a tree structure. Surveyed in the dependency-grammar tradition by Nivre (2005) and made fast and accurate with neural networks by Chen and Manning (2014), it is commonly used as a prerequisite step for information extraction and relation detection.

Output: Dependency tree of word-to-word syntactic relations

Tree linking each word to its syntactic head with a relation label

Prerequisite for information extraction and relation detection

Nivre, J. (2005). Dependency Grammar and Dependency Parsing. MSI Report.

Chen, D. & Manning, C. D. (2014). A Fast and Accurate Dependency Parser Using Neural Networks. EMNLP.


Cambridge Depersonalisation Scale

The CDS is a 29-item self-report measure of depersonalisation and derealisation experiences, developed by Sierra and Berrios in 2000. It is the most widely used instrument for assessing dissociative symptom severity in both clinical and research settings, valuable for identifying depersonalisation disorder, monitoring treatment response, and understanding the prevalence of depersonalisation in anxiety, mood, and trauma populations.

Sierra, M., & Berrios, G. E. (2000). The Cambridge Depersonalisation Scale: a new instrument for the measurement of depersonalisation. Psychiatry Research, 93(2), 153–164.


Deployment Risk and Resilience Inventory (DRRI-2)

Deployment stressors and resilience factors

The DRRI-2 is a comprehensive self-report inventory measuring pre-deployment, deployment, and post-deployment risk and protective (resilience) factors influencing mental health outcomes in military personnel. Developed by King and colleagues in 2006 and refined in 2008, it captures contextual, behavioral, social, and psychological factors that shape post-deployment adjustment. It is used in military health surveillance, clinical formulation, and research examining how risk-resilience balance predicts PTSD and other adverse outcomes.

King, D. W., King, L. A., Vogt, D. S., Knight, J., & Samper, R. E. (2006). Deployment Risk and Resilience Inventory: A collection of empirically derived factors for stress outcomes. Journal of Behavioral Decision Making, 19(2), 87-101.

Vogt, D. S., Proctor, S. P., King, D. W., King, L. A., & Vasterling, J. J. (2008). Validation of scales from the Deployment Risk and Resilience Inventory in a large sample of Operation Enduring Freedom/Operation Iraqi Freedom veterans. Psychological Assessment, 20(2), 180-191.

pcl-military combat-exposure-scale military-identity-scale post-deployment-reintegration


Children's DLQI

The Children's Dermatology Life Quality Index (cDLQI) is a pediatric-adapted version of the adult DLQI, measuring the impact of skin disease on quality of life in children and adolescents aged 4–16 years. Developed by Lewis-Jones and Finlay in 1995, it uses child-friendly language and addresses domains relevant to childhood (school, leisure, friendships, clothing) rather than work and adult relationships. cDLQI is the standard quality-of-life measure in pediatric dermatology trials and clinical practice.

Lewis-Jones MS, Finlay AY. The Children's Dermatology Life Quality Index (cDLQI): initial validation and practical use. Br J Dermatol. 1995;132(6):942-949.

Finlay AY. Quality of life measurement in dermatology: a practical approach. Br J Dermatol. 1997;136(3):305-314.